None:\\n best = self.get_highest_accuracy()\\n self.logger.info(f\\\"\\\\n\\\\nThe model with the highest accuracy {best[0]} has the following characteristics: \\\\n\\\")\\n for k, v in best[1].items():\\n if k != 'best_performer':\\n self.logger.info(f\\\"{k} : {v}\\\")\\n else:\\n self.logger.info(f\\\"Best Accuracy: {v['Accuracy']}\\\")\\n self.logger.info(\\\"Features used: \\\")\\n for f in v['Labels']:\\n self.logger.info(f)\\n for nw, w in v['Vars'].items():\\n self.logger.info(f\\\"{nw}: {w}\\\")\",\n \"def measure_test(self):\\n return self.execute(Sgp40I2cCmdExecuteSelfTest())\",\n \"def __holdout_test(self):\\n\\t\\tprint(\\\"\\\\t\\\\tHoldout Validation Results:\\\")\\n\\n\\t\\tprint(\\\"\\\\t\\\\t\\\\tAccuracy: \\\", analysis.accuracy(self.y_pred, self.y_test))\\n\\t\\tprint(\\\"\\\\t\\\\t\\\\tPrecision: \\\", analysis.precision(self.y_pred, self.y_test))\\n\\t\\tprint(\\\"\\\\t\\\\t\\\\tSpecificity: \\\", analysis.specificity(self.y_pred, self.y_test))\\n\\t\\tprint(\\\"\\\\t\\\\t\\\\tRecall: \\\", analysis.recall(self.y_pred, self.y_test))\",\n \"def print_stats(session, feature_batch, label_batch, cost, accuracy):\\n # TODO: Implement Function\\n cst = session.run(cost, feed_dict={x:feature_batch, y:label_batch, keep_prob:1.0})\\n acc = session.run(accuracy, feed_dict={x:valid_features, y:valid_labels, keep_prob:1.0}) \\n print(\\\"cost:{:.3f};accuracy:{:.3f} <br>\\\".format(cst,acc))\",\n \"def summary(self, test_type='t-test'):\\n summary = f'Results for running {self.cv_method} evaluation for {self.method} '\\n summary += f'on {self.n_model} models:\\\\n\\\\n'\\n name_length = max([max(len(m.name) for m in self.models) + 1, 6])\\n means = self.get_means()\\n sems = self.get_sem()\\n if means is None:\\n means = np.nan * np.ones(self.n_model)\\n if sems is None:\\n sems = np.nan * np.ones(self.n_model)\\n try:\\n p_zero = self.test_zero(test_type=test_type)\\n p_noise = self.test_noise(test_type=test_type)\\n except ValueError:\\n p_zero = np.nan * np.ones(self.n_model)\\n p_noise = np.nan * np.ones(self.n_model)\\n # header of the results table\\n summary += 'Model' + (' ' * (name_length - 5))\\n summary += '| Eval \\\\u00B1 SEM |'\\n summary += ' p (against 0) |'\\n summary += ' p (against NC) |\\\\n'\\n summary += '-' * (name_length + 51)\\n summary += '\\\\n'\\n for i, m in enumerate(self.models):\\n summary += m.name + (' ' * (name_length - len(m.name)))\\n summary += f'| {means[i]: 5.3f} \\\\u00B1 {sems[i]:4.3f} |'\\n if p_zero[i] < 0.001:\\n summary += ' < 0.001 |'\\n else:\\n summary += f'{p_zero[i]:>13.3f} |'\\n if p_noise[i] < 0.001:\\n summary += ' < 0.001 |'\\n else:\\n summary += f'{p_noise[i]:>14.3f} |'\\n summary += '\\\\n'\\n summary += '\\\\n'\\n if self.cv_method == 'crossvalidation':\\n summary += 'No p-values available as crossvalidation provides no variance estimate'\\n elif test_type == 't-test':\\n summary += 'p-values are based on uncorrected t-tests'\\n elif test_type == 'bootstrap':\\n summary += 'p-values are based on percentiles of the bootstrap samples'\\n elif test_type == 'ranksum':\\n summary += 'p-values are based on ranksum tests'\\n return summary\",\n \"def _generate_report(self):\\n total_duration = 0.0\\n total_nb_tests = 0\\n total_nb_success = 0\\n nb_modules = 0\\n payload = []\\n\\n res_table = prettytable.PrettyTable(\\n padding_width=2,\\n field_names=['Module', 'Duration', 'nb. Test Run', 'Success'])\\n res_table.align['Module'] = \\\"l\\\"\\n res_table.align['Duration'] = \\\"r\\\"\\n res_table.align['Success'] = \\\"r\\\"\\n\\n # for each scenario we draw a row for the table\\n for item in self.summary:\\n if item['task_status'] is True:\\n nb_modules += 1\\n total_duration += item['overall_duration']\\n total_nb_tests += item['nb_tests']\\n total_nb_success += item['nb_success']\\n try:\\n success_avg = 100 * item['nb_success'] / item['nb_tests']\\n except ZeroDivisionError:\\n success_avg = 0\\n success_str = f\\\"{success_avg:0.2f}%\\\"\\n duration_str = time.strftime(\\\"%H:%M:%S\\\",\\n time.gmtime(item['overall_duration']))\\n res_table.add_row([item['test_name'], duration_str,\\n item['nb_tests'], success_str])\\n payload.append({'module': item['test_name'],\\n 'details': {'duration': item['overall_duration'],\\n 'nb tests': item['nb_tests'],\\n 'success rate': success_str,\\n 'success': item['success'],\\n 'failures': item['failures']}})\\n\\n total_duration_str = time.strftime(\\\"%H:%M:%S\\\",\\n time.gmtime(total_duration))\\n try:\\n self.result = 100 * total_nb_success / total_nb_tests\\n except ZeroDivisionError:\\n self.result = 100\\n success_rate = f\\\"{self.result:0.2f}\\\"\\n success_rate_str = str(success_rate) + '%'\\n res_table.add_row([\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"])\\n res_table.add_row([\\\"TOTAL:\\\", total_duration_str, total_nb_tests,\\n success_rate_str])\\n\\n LOGGER.info(\\\"Rally Summary Report:\\\\n\\\\n%s\\\\n\\\", res_table.get_string())\\n LOGGER.info(\\\"Rally '%s' success_rate is %s%% in %s/%s modules\\\",\\n self.case_name, success_rate, nb_modules,\\n len(self.summary))\\n self.details['summary'] = {'duration': total_duration,\\n 'nb tests': total_nb_tests,\\n 'nb success': success_rate}\\n self.details[\\\"modules\\\"] = payload\",\n \"def log_inference(tester, name, description):\\r\\n\\tfor dataset, output in tester.preds.items():\\r\\n\\t\\tresults = pandas.DataFrame.from_dict(output)\\r\\n\\t\\tpath = os.path.join(\\r\\n\\t\\t\\tEXPERIMENT_PATH, tester.config[\\\"name\\\"] + '-' + dataset)\\r\\n\\t\\twith open(path + \\\".csv\\\", \\\"w\\\") as f:\\r\\n\\t\\t\\tresults.to_csv(f, sep=\\\"\\\\t\\\", encoding='utf-8', \\r\\n\\t\\t\\t\\tfloat_format='%.3f', index=False)\\r\\n\\r\\n\\t\\twith open(path + \\\"-predictions.csv\\\", \\\"w\\\") as f:\\r\\n\\t\\t\\tresults[[\\\"tag\\\", \\\"y_hat\\\"]].to_csv(\\r\\n\\t\\t\\t\\tf, index=False, float_format='%.3f', header=False)\",\n \"def show_all(self):\\n self.explained_variance_score()\\n self.max_error()\\n self.mean_absolute_error()\\n self.mean_squared_error()\\n self.median_absolute_error()\\n self.r2_score()\\n self.mean_poisson_deviance()\\n self.mean_gamma_deviance()\\n self.feature_importance()\\n self.learning_curve()\",\n \"def print_results(self):\\n pass\",\n \"def dump(self):\\n fmt='%20s:%10.4fs%6.1f%%'\\n print('\\\\n----------------TIME MANAGER PROFILE----------------\\\\n\\\\n')\\n total_t=time.time()-self.tic0\\n for rec in self.record:\\n print(fmt % (rec[0],rec[1],100.0*rec[1]/total_t))\\n print(fmt % ('TOTAL ELAPSED TIME', total_t, 100.0))\\n print('\\\\n----------------TIME MANAGER PROFILE----------------\\\\n\\\\n')\",\n \"def write_output(self) -> None:\\n self.home.round(2).to_csv(var.indicators_base_cumsum + \\\"home_\\\" + str(self.year) + \\\".csv\\\")\\n self.away.round(2).to_csv(var.indicators_base_cumsum + \\\"away_\\\" + str(self.year) + \\\".csv\\\")\",\n \"def eval_perf_total(model, X_train, y_train, X_test, y_test):\\n\\n y_hat_train = model.predict(X_train)\\n y_hat_test = model.predict(X_test)\\n \\n train_mae = metrics.mean_absolute_error(y_train, y_hat_train)\\n train_mse = metrics.mean_squared_error(y_train, y_hat_train)\\n train_rmse = np.sqrt(metrics.mean_squared_error(y_train, y_hat_train))\\n train_r = metrics.r2_score(y_train, y_hat_train)\\n\\n print('Evaluating Performance on Training Data:\\\\n')\\n print(f' Train Mean Absolute Error: {train_mae:,.2f}')\\n print(f' Train Mean Squared Error: {train_mse:,.2f}\\\\n')\\n print(f'Train Root Mean Squared Error: {train_rmse:,.2f}')\\n print(f'Train R-Square Value: {round(train_r,2)}')\\n\\n print('\\\\n'+'---'*25+'\\\\n')\\n\\n test_mae = metrics.mean_absolute_error(y_test, y_hat_test)\\n test_mse = metrics.mean_squared_error(y_test, y_hat_test)\\n test_rmse = np.sqrt(metrics.mean_squared_error(y_test, y_hat_test))\\n test_r = metrics.r2_score(y_test, y_hat_test)\\n\\n print('Evaluating Performance on Testing Data:\\\\n')\\n print(f' Test Mean Absolute Error: {test_mae:,.2f}')\\n print(f' Test Mean Squared Error: {test_mse:,.2f}\\\\n')\\n print(f'Test Root Mean Squared Error: {test_rmse:,.2f}')\\n print(f'Test R-Square Value: {round(test_r,2)}')\",\n \"def print_metrics(result):\\n logging.log(LOG_LEVEL_OUTPUT_INFO,\\n '------------------------------------------------')\\n logging.log(LOG_LEVEL_OUTPUT_INFO, ' KEY METRICS: ')\\n logging.log(LOG_LEVEL_OUTPUT_INFO,\\n '------------------------------------------------')\\n logging.log(LOG_LEVEL_OUTPUT_INFO, '* pages_count: %d',\\n get_counter_metric(result, 'pages_count'))\\n logging.log(LOG_LEVEL_OUTPUT_INFO, '* revisions_count: %d',\\n get_counter_metric(result, 'revisions_count'))\\n logging.log(LOG_LEVEL_OUTPUT_INFO, '* very_long_page_histories_count: %d',\\n get_counter_metric(result, 'very_long_page_histories_count'))\\n revisions_per_page_distr = get_distributions_metric(\\n result, 'revisions_per_page_distr')\\n logging.log(LOG_LEVEL_OUTPUT_INFO, '* revisions_per_page_distr.mean: %d',\\n revisions_per_page_distr.mean)\\n logging.log(LOG_LEVEL_OUTPUT_INFO, '* revisions_per_page_distr.sum: %d',\\n revisions_per_page_distr.sum)\\n cumulative_page_rev_size_distr = get_distributions_metric(\\n result, 'cumulative_page_rev_size_distr')\\n logging.log(LOG_LEVEL_OUTPUT_INFO,\\n '* cumulative_page_rev_size_distr.mean: %d',\\n cumulative_page_rev_size_distr.mean)\\n logging.log(LOG_LEVEL_OUTPUT_INFO, '* cumulative_page_rev_size_distr.sum: %d',\\n cumulative_page_rev_size_distr.sum)\",\n \"def printReport(self):\\n\\t\\tself.app.printflush('Fetched: ' + str(self.fetched_count), self.app.IGNORE_EXIT_FLAG)\\n\\t\\tself.app.printflush('Processes: ' + str(self.processes), self.app.IGNORE_EXIT_FLAG)\\n\\t\\tself.app.printflush('Updated: ' + str(self.updated_count), self.app.IGNORE_EXIT_FLAG)\\n\\t\\tself.app.printflush('Average page load time: ' + str(self.average_time), self.app.IGNORE_EXIT_FLAG)\\n\\t\\tself.app.printflush('Returned with code: ' + repr(self.code_statistics), self.app.IGNORE_EXIT_FLAG)\\n\\t\\tself.app.printflush('Closing Processes... ', self.app.IGNORE_EXIT_FLAG)\",\n \"def _log_performance(i, train_perf, mean_pol_perf, best_perf, results_path,\\n tb_logger):\\n # tb_logger.add_scalars('performance', {\\n # 'train_perf': train_perf,\\n # 'eval_perf': mean_pol_perf,\\n # 'best_train_perf': best_perf,\\n # }, global_step=i)\\n\\n with open(results_path, 'a') as results_file:\\n if i == 0:\\n headings = ['Iter', 'Sampled Pol', 'Mean Pol', 'Best (Sampled)']\\n headings = ' | '.join(headings)\\n results_file.write(headings + '\\\\n')\\n print('Timestamp' + (' ' * 17) + ' | ' + headings)\\n\\n log = ['{:<4d}', '{:>11.2f}', '{:>8.2f}', '{:>14.2f}']\\n log = ' '.join(log).format(i, train_perf, mean_pol_perf, best_perf)\\n results_file.write(log + '\\\\n')\\n print('{:<26s} {}'.format(\\n timer.asctime(timer.localtime(timer.time())), log))\",\n \"def dataStats(self):\\n print (\\\"Performing statistical analysis of the data\\\")\\n # stuff to do\",\n \"def _compute_statistics(self):\\n # log to file\\n output_dir = self.params['output_dir']\\n if not os.path.exists(output_dir):\\n os.mkdir(output_dir)\\n output_dir = os.path.join(output_dir, 'statistics.log')\\n log = logging.getLogger('tensorflow')\\n handle = logging.FileHandler(output_dir)\\n log.addHandler(handle)\\n\\n # FLOPS\\n encoder_flops, decoder_flops = 0, 0\\n encoder_count, decoder_count = 0, 0\\n graph = tf.get_default_graph()\\n for operation in graph.get_operations():\\n flops = ops.get_stats_for_node_def(graph, operation.node_def,\\n 'flops').value\\n if flops is None:\\n continue\\n if operation.name.startswith('model/encoder'):\\n # encoder\\n encoder_flops += flops\\n encoder_count += 1\\n tf.logging.info('encoder operation %s : %d', operation.name, flops)\\n elif operation.name.startswith('model/decoder'):\\n # decoder\\n decoder_flops += flops\\n decoder_count += 1\\n tf.logging.info('decoder operation %s : %d', operation.name, flops)\\n else:\\n # gradient\\n pass\\n tf.logging.info('flops of %d encoder tensor: %d',\\n encoder_count, encoder_flops)\\n tf.logging.info('flops of %d decoder tensor: %d',\\n decoder_count, decoder_flops)\\n tf.logging.info('flops of total %d tensor: %d',\\n encoder_count + decoder_count,\\n encoder_flops + decoder_flops)\\n # parameters\\n encoder_parameters, decoder_parameters = 0, 0\\n encoder_count, decoder_count = 0, 0\\n for var in tf.trainable_variables():\\n parameters = np.prod(var.get_shape().as_list())\\n if var.name.startswith('model/encoder'):\\n # encoder\\n encoder_parameters += parameters\\n encoder_count += 1\\n tf.logging.info('encoder variable %s : %d', var.name, parameters)\\n elif var.name.startswith('model/decoder'):\\n # decoder\\n decoder_parameters += parameters\\n decoder_count += 1\\n tf.logging.info('decoder variable %s : %d', var.name, parameters)\\n\\n tf.logging.info('parameters of %d encoder tensor: %d',\\n encoder_count, encoder_parameters)\\n tf.logging.info('parameters of %d decoder tensor: %d',\\n decoder_count, decoder_parameters)\\n tf.logging.info('parameters of total %d tensor: %d',\\n encoder_count + decoder_count,\\n encoder_parameters + decoder_parameters)\\n # disable log to file\\n log.removeHandler(handle)\",\n \"def print_testvals(self):\\n print(self.use_planck)\",\n \"def print_stats(self, session, x, y, keep_prob, feature_batch, label_batch, valid_features, valid_labels,\\n cost, accuracy):\\n cost = session.run(cost, feed_dict={x: feature_batch, y: label_batch, keep_prob: 1.0})\\n train_accuracy = session.run(accuracy, feed_dict={x: feature_batch, y: label_batch, keep_prob: 1.0})\\n valid_accuracy = session.run(accuracy, feed_dict={x: valid_features, y: valid_labels, keep_prob: 1.0})\\n logger.info(\\\"loss:{}\\\".format(cost))\\n logger.info(\\\"Train Accuracy:{}\\\".format(train_accuracy))\\n logger.info(\\\"Validation Accuracy:{}\\\".format(valid_accuracy))\",\n \"def logging(self, function):\\n avg_nms_time_per_step = sum(self.nms_times)/len(self.nms_times)\\n avg_total_time_per_step = sum(self.total_times)/len(self.total_times)\\n\\n avg_min_latency = [x[0] for x in self.inference_times]\\n avg_max_latency = [x[1] for x in self.inference_times]\\n avg_latency = [x[2] for x in self.inference_times]\\n\\n function(\\\"Inference stats: image size {}x{}, batches per step {}, batch size {}, {} steps\\\".format(\\n self.cfg.model.image_size, self.cfg.model.image_size, self.cfg.ipuopts.batches_per_step, self.cfg.model.micro_batch_size, len(self.total_times)\\n ))\\n function(\\\"--------------------------------------------------\\\")\\n function(\\\"Inference\\\")\\n function(\\\"Average Min Latency per Batch: {:.3f} ms\\\".format(1000 * sum(avg_min_latency)/len(self.inference_times)))\\n function(\\\"Average Max Latency per Batch: {:.3f} ms\\\".format(1000 * sum(avg_max_latency)/len(self.inference_times)))\\n function(\\\"Average Latency per Batch: {:.3f} ms\\\".format(1000 * sum(avg_latency)/len(self.inference_times)))\\n function(\\\"Average Inference Throughput: {:.3f} img/s\\\".format(sum(self.inference_throughputs)/len(self.inference_throughputs)))\\n function(\\\"--------------------------------------------------\\\")\\n # TODO remove the NMS and end-to-end time report once NMS is on device\\n function(\\\"End-to-end\\\")\\n function(\\\"Average NMS Latency per Batch: {:.3f} ms\\\".format(1000 * avg_nms_time_per_step/self.cfg.ipuopts.batches_per_step))\\n function(\\\"Average End-to-end Latency per Batch: {:.3f} ms\\\".format(1000 * avg_total_time_per_step/self.cfg.ipuopts.batches_per_step))\\n function(\\\"End-to-end Throughput: {:.3f} img/s\\\".format(sum(self.total_throughputs)/len(self.total_throughputs)))\\n function(\\\"==================================================\\\")\\n\\n if self.cfg.eval.metrics:\\n self.compute_and_print_eval_metrics()\",\n \"def display_results(self):\\n print(f'AI player results - Wins: {self._wins}, Draws: {self._draws}, Loss: {self._losses}')\\n print(f'Total win percentage {(self._wins / self._count) * 100}', end='\\\\n\\\\n')\\n\\n with open(os.path.join(os.path.dirname(__file__), 'ai-results.json'), 'w') as f:\\n output_dict = {str(k): v for k, v in self._state_values.items()}\\n json.dump(output_dict, f, indent=4, sort_keys=True)\",\n \"def generate_report(self, output_path):\\n generated_on = str(datetime.datetime.now())\\n checkpoint_name = self._get_checkpoint_name()\\n dataset_name = Path(self._train_dataset_path).resolve().name\\n train_features, train_labels = self.get_features_and_labels(\\n self._read_dataset(self._train_dataset_path)\\n )\\n test_features, test_lables = self.get_features_and_labels(\\n self._read_dataset(self._test_dataset_path)\\n )\\n training_accuracy = self.get_model_accuracy(\\n train_features,\\n train_labels\\n )\\n test_accuracy = self.get_model_accuracy(\\n test_features,\\n test_lables,\\n )\\n with open(output_path, 'a') as f:\\n report = (\\n \\\"*****************************************************\\\\n\\\"\\n \\\"Report generated on: {}\\\\n\\\"\\n \\\"Training dataset: {}\\\\n\\\"\\n \\\"Model checkpoint: {}\\\\n\\\"\\n \\\"---\\\\n\\\"\\n \\\"Accuracy on training data: {}\\\\n\\\"\\n \\\"Accuracy on testing data: {}\\\\n\\\"\\n \\\"\\\\n\\\"\\n ).format(\\n generated_on,\\n dataset_name,\\n checkpoint_name,\\n training_accuracy,\\n test_accuracy,\\n )\\n f.writelines(report)\",\n \"def print_brief_summary(self):\\n print (\\\"Model {}\\\".format(self.modelName))\\n print (\\\"Precision {}\\\".format(self.precision))\\n print (\\\"Recall {}\\\".format(self.recall))\\n print (\\\"f1 score {}\\\".format(self.f1))\\n \\n # work here\\n print (\\\"\\\\nGold NER label counts:\\\")\\n for ner in self.gold_cts.keys():\\n print (\\\"{} : {} (tag{})\\\".format(self.gold_cts[ner], self.nerTags.ids_to_words([ner]), ner))\\n print (\\\"\\\\nPredicted NER label counts:\\\")\\n for ner in self.pred_cts.keys():\\n print (\\\"{} : {} (tag{})\\\".format(self.pred_cts[ner], self.nerTags.ids_to_words([ner]), ner))\",\n \"def create_summary_statistics(forward_accuracy, backward_accuracy, merged_accuracy):\\n summary_statistics = open(f'summary_statistics.txt', 'a')\\n summary_statistics.write(f'The forward model has an accuracy of: {forward_accuracy}\\\\n')\\n summary_statistics.write(f'The backward model has an accuracy of: {backward_accuracy}\\\\n')\\n summary_statistics.write(f'The merged model has an accuracy of: {merged_accuracy}\\\\n')\\n summary_statistics.close()\",\n \"def save(self,filename):\\n f = open(filename,'w')\\n f.write('Test results for %s v%s\\\\n' % (self.description,self.version))\\n f.write('Series ran by %s\\\\n\\\\n' % self.person_name)\\n for result in self.values():\\n f.write('%-70s : %s\\\\n' % (result.id,result.outcome))\\n if result.outcome != Result.PASS:\\n for (kind, annotation) in result.annotations.items():\\n f.write('%s:\\\\n%s\\\\n' % (kind, as_utf8(annotation)))\\n f.write('\\\\n')\\n f.write('\\\\n\\\\nPasses: %i\\\\n' % self.get_pass_count())\\n f.write('Fails: %i\\\\n' % self.get_fail_count())\\n f.write('Errors: %i\\\\n' % self.get_error_count())\\n f.write('Untested: %i\\\\n' % self.get_untested_count())\\n f.write('Skipped: %i\\\\n' % self.get_skipped_count())\\n f.close()\",\n \"def report(self, X, y):\\n pred = self.model.predict(X)\\n skplt.estimators.plot_feature_importances(\\n self.model, x_tick_rotation=90)\\n plt.show()\\n skplt.metrics.plot_confusion_matrix(y, pred, normalize=True)\\n plt.show()\\n print(\\\"Classification report\\\")\\n print(classification_report(y, pred))\\n skplt.metrics.plot_precision_recall(y, self.model.predict_proba(X))\\n plt.show()\\n pd.DataFrame({\\n \\\"predict_probability\\\": self.model.predict_proba(X)[:, 1],\\n \\\"observed\\\": y\\n }).hist(\\n \\\"predict_probability\\\", by=\\\"observed\\\")\\n plt.suptitle(\\\"Probability distribution by class\\\")\\n plt.show()\",\n \"def compute_and_print_eval_metrics(self):\\n s = ('%20s' + '%12s' * 6) % ('Class', 'Images', 'Targets', 'P', 'R', 'mAP@.5', 'mAP@.5:.95')\\n precision, recall, f1, mean_precision, mean_recall, map50, map = 0., 0., 0., 0., 0., 0., 0.\\n ap = []\\n eval_stats = [np.concatenate(x, 0) for x in zip(*self.eval_stats)]\\n if len(eval_stats) and eval_stats[0].any():\\n precision, recall, ap, f1, ap_class = ap_per_class(*eval_stats)\\n precision, recall, ap50, ap = precision[:, 0], recall[:, 0], ap[:, 0], ap.mean(1)\\n mean_precision, mean_recall, map50, map = precision.mean(), recall.mean(), ap50.mean(), ap.mean()\\n nt = np.bincount(eval_stats[3].astype(np.int64), minlength=len(self.class_names)) # number of targets per class\\n else:\\n nt = np.zeros(1)\\n\\n pf = '%20s' + '%12.5g' * 6 # print format\\n print(\\\"\\\\n EVALUTAION \\\\n\\\")\\n print(s)\\n print(pf % ('all', self.seen, nt.sum(), mean_precision, mean_recall, map50, map))\\n if self.cfg.eval.verbose:\\n for indx, cls in enumerate(ap_class):\\n print(pf % (self.class_names[cls], self.seen, nt[cls], precision[indx], recall[indx], ap50[indx], ap[indx]))\",\n \"def metrics(_):\\r\\n collector = BuildsCollector()\\r\\n build_metrics, headers = collector.get_metrics_table()\\r\\n print(tabulate(build_metrics, headers=headers))\",\n \"def report(self):\\n if isinstance(self.steps[-1][1], Regressor):\\n return self.steps[-1][1].regression_report()\\n else:\\n return self.steps[-1][1].classification_report()\",\n \"def final_eval(self):\\n # Test performance - Load best model\\n self.load_model(self.best_model_path, model_type='best')\\n logging.info(\\\"Loading best model after epoch: %d\\\" %\\n self.train_info['epoch'])\\n\\n perf_file = path.join(self.model_dir, \\\"perf.json\\\")\\n if self.slurm_id:\\n parent_dir = path.dirname(path.normpath(self.model_dir))\\n perf_dir = path.join(parent_dir, \\\"perf\\\")\\n if not path.exists(perf_dir):\\n os.makedirs(perf_dir)\\n perf_file = path.join(perf_dir, self.slurm_id + \\\".json\\\")\\n\\n output_dict = {'model_dir': self.model_dir}\\n for key, val in vars(self.args).items():\\n output_dict[key] = val\\n\\n for split in ['dev', 'test']:\\n logging.info('\\\\n')\\n logging.info('%s' % split.capitalize())\\n result_dict = self.eval_model(split, final_eval=True)\\n if split != 'test':\\n logging.info('Calculated F1: %.3f' % result_dict['fscore'])\\n\\n output_dict[split] = result_dict\\n\\n json.dump(output_dict, open(perf_file, 'w'), indent=2)\\n\\n logging.info(\\\"Final performance summary at %s\\\" % perf_file)\\n sys.stdout.flush()\",\n \"def output_summary_stats(self):\\n total_return = self.equity_curve['equity_curve'][-1]\\n returns = self.equity_curve['returns']\\n pnl = self.equity_curve['equity_curve']\\n\\n sharpe_ratio = create_sharpe_ratio(returns)\\n drawdown, max_dd, dd_duration = create_drawdowns(pnl)\\n self.equity_curve['drawdown'] = drawdown\\n if len(dd_duration) == 1:\\n dd_duration = dd_duration[0]\\n\\n stats = [(\\\"Total Return\\\", \\\"%0.2f%%\\\" % ((total_return - 1.0) * 100.0)),\\n (\\\"Sharpe Ratio\\\", \\\"%0.2f\\\" % sharpe_ratio),\\n (\\\"Max Drawdown\\\", \\\"%0.2f%%\\\" % (max_dd * 100.0)),\\n (\\\"Drawdown Duration\\\", \\\"%s\\\" % dd_duration)]\\n\\n self.equity_curve.to_csv('equity.csv')\\n self.positions.to_csv('positions.csv')\\n self.prices.to_csv('prices.csv')\\n\\n return stats\",\n \"def print_metrics(mva, df_train, df_test,\\n y_train, y_test,\\n mva_response_train=None, mva_response_test=None,\\n w_train=None, w_test=None):\\n\\n train_prediction = mva.predict(df_train)\\n test_prediction = mva.predict(df_test)\\n\\n if mva_response_train is None:\\n mva_response_train = classifiers.evaluate_mva(df_train, mva)\\n if mva_response_test is None:\\n mva_response_test = classifiers.evaluate_mva(df_test, mva)\\n\\n print(\\\"\\\\nClassification Reports:\\\")\\n print(\\\"Test sample:\\\")\\n print(classification_report(y_test, test_prediction,\\n target_names=[\\\"background\\\", \\\"signal\\\"]))\\n print(\\\"Training sample:\\\")\\n print(classification_report(y_train, train_prediction,\\n target_names=[\\\"background\\\", \\\"signal\\\"]))\\n\\n print(\\\"Confusion matrix:\\\")\\n print(\\\"Test sample:\\\")\\n print(confusion_matrix(y_test, test_prediction))\\n print(\\\"Training sample:\\\")\\n print(confusion_matrix(y_train, train_prediction))\\n print()\\n\\n print(\\\"KS Test p-value:\\\")\\n print(\\\"Signal:\\\")\\n print(ks_2samp(mva_response_train[y_train == 1],\\n mva_response_test[y_test == 1],\\n None if w_train is None else w_train[y_train == 1],\\n None if w_test is None else w_test[y_test == 1])[1])\\n print(\\\"Background:\\\")\\n print(ks_2samp(mva_response_train[y_train == 0],\\n mva_response_test[y_test == 0],\\n None if w_train is None else w_train[y_train == 0],\\n None if w_test is None else w_test[y_test == 0])[1])\\n print()\\n\\n # Try really hard to get the feature importances\\n feature_importances = []\\n try:\\n feature_importances = mva.feature_importances_\\n except AttributeError:\\n pass\\n try: # last step of a pipeline?\\n feature_importances = mva.steps[-1][1].feature_importances_\\n except AttributeError:\\n pass\\n try: # grid search?\\n feature_importances = mva.best_estimator_.feature_importances_\\n except AttributeError:\\n pass\\n try: # grid search last step of pipeline?\\n feature_importances = \\\\\\n mva.steps[-1][1].best_estimator_.feature_importances_\\n except AttributeError:\\n pass\\n try: # grid search of a pipeline?\\n feature_importances = \\\\\\n mva.best_estimator_.steps[-1][0].feature_importances_\\n except AttributeError:\\n pass\\n\\n if len(feature_importances):\\n print(\\\"Feature importance:\\\")\\n for var, importance in sorted(\\n zip(list(df_train), feature_importances),\\n key=lambda x: x[1],\\n reverse=True):\\n print(\\\"{0:15} {1:.3E}\\\".format(var, importance))\\n else:\\n pass\\n print()\",\n \"def print_result(self):\\n print(\\\"Final results: \\\")\\n for i in range(1, len(self.agents) + 1):\\n agent = self.agents[i-1]\\n print(agent.name + \\\": {} wins\\\".format(self.results[agent.name]))\",\n \"def output_summary_stats(self):\\n total_return = self.equity_curve['equity_curve'][-1]\\n returns = self.equity_curve['returns']\\n pnl = self.equity_curve['equity_curve']\\n \\n sharpe_ratio = create_sharpe_ratio(returns, periods=252*6.5*60)\\n drawdown, max_dd, dd_duration = create_drawdowns(pnl)\\n self.equity_curve['drawdown'] = drawdown\\n \\n stats = [(\\\"Total Return\\\", \\\"%0.2f%%\\\" % ((total_return - 1.0) * 100.0)), \\n (\\\"Sharpe Ratio\\\", \\\"%0.2f\\\" % sharpe_ratio), \\n (\\\"Max Drawdown\\\", \\\"%0.2f%%\\\" % (max_dd * 100.0)), \\n (\\\"Drawdown Duration\\\", \\\"%d\\\" % dd_duration)]\\n \\n self.equity_curve.to_csv('equity.csv')\\n \\n return stats\",\n \"def _to_string(self):\\n self.results.print_results()\\n self.results.print_comparison()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.63392967","0.63388044","0.62344825","0.62191135","0.61377233","0.61345345","0.5990634","0.597709","0.59057546","0.5875184","0.58620185","0.5844489","0.5844022","0.5822778","0.5800217","0.58001125","0.57988024","0.57855344","0.57675505","0.5763641","0.5757206","0.5753155","0.5734435","0.57340693","0.5715338","0.57051563","0.5705033","0.5691088","0.5685489","0.5661358","0.5652667","0.5649887","0.5647671","0.56398696","0.5634195","0.5628173","0.56151265","0.56109834","0.5606967","0.56011254","0.55909884","0.558674","0.5585981","0.5581149","0.55793434","0.557807","0.556885","0.5554471","0.554375","0.5535822","0.5534701","0.55311704","0.55269706","0.55253404","0.552293","0.55169725","0.5506406","0.5505374","0.549348","0.54928225","0.5473037","0.546896","0.5453779","0.544389","0.54435986","0.54429764","0.54381037","0.5435993","0.54307544","0.54212964","0.5411925","0.5411679","0.54015857","0.5400754","0.5395652","0.5394564","0.5390419","0.53893507","0.5385113","0.537628","0.5375971","0.53666437","0.53665483","0.5364676","0.53640497","0.5363705","0.5361083","0.5358629","0.53477365","0.5344487","0.5344369","0.53432256","0.534123","0.5337885","0.53342307","0.53330016","0.5331818","0.5331081","0.5318058","0.5313299"],"string":"[\n \"0.63392967\",\n \"0.63388044\",\n \"0.62344825\",\n \"0.62191135\",\n \"0.61377233\",\n \"0.61345345\",\n \"0.5990634\",\n \"0.597709\",\n \"0.59057546\",\n \"0.5875184\",\n \"0.58620185\",\n \"0.5844489\",\n \"0.5844022\",\n \"0.5822778\",\n \"0.5800217\",\n \"0.58001125\",\n \"0.57988024\",\n \"0.57855344\",\n \"0.57675505\",\n \"0.5763641\",\n \"0.5757206\",\n \"0.5753155\",\n \"0.5734435\",\n \"0.57340693\",\n \"0.5715338\",\n \"0.57051563\",\n \"0.5705033\",\n \"0.5691088\",\n \"0.5685489\",\n \"0.5661358\",\n \"0.5652667\",\n \"0.5649887\",\n \"0.5647671\",\n \"0.56398696\",\n \"0.5634195\",\n \"0.5628173\",\n \"0.56151265\",\n \"0.56109834\",\n \"0.5606967\",\n \"0.56011254\",\n \"0.55909884\",\n \"0.558674\",\n \"0.5585981\",\n \"0.5581149\",\n \"0.55793434\",\n \"0.557807\",\n \"0.556885\",\n \"0.5554471\",\n \"0.554375\",\n \"0.5535822\",\n \"0.5534701\",\n \"0.55311704\",\n \"0.55269706\",\n \"0.55253404\",\n \"0.552293\",\n \"0.55169725\",\n \"0.5506406\",\n \"0.5505374\",\n \"0.549348\",\n \"0.54928225\",\n \"0.5473037\",\n \"0.546896\",\n \"0.5453779\",\n \"0.544389\",\n \"0.54435986\",\n \"0.54429764\",\n \"0.54381037\",\n \"0.5435993\",\n \"0.54307544\",\n \"0.54212964\",\n \"0.5411925\",\n \"0.5411679\",\n \"0.54015857\",\n \"0.5400754\",\n \"0.5395652\",\n \"0.5394564\",\n \"0.5390419\",\n \"0.53893507\",\n \"0.5385113\",\n \"0.537628\",\n \"0.5375971\",\n \"0.53666437\",\n \"0.53665483\",\n \"0.5364676\",\n \"0.53640497\",\n \"0.5363705\",\n \"0.5361083\",\n \"0.5358629\",\n \"0.53477365\",\n \"0.5344487\",\n \"0.5344369\",\n \"0.53432256\",\n \"0.534123\",\n \"0.5337885\",\n \"0.53342307\",\n \"0.53330016\",\n \"0.5331818\",\n \"0.5331081\",\n \"0.5318058\",\n \"0.5313299\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6249993"},"document_rank":{"kind":"string","value":"2"}}},{"rowIdx":14365,"cells":{"query":{"kind":"string","value":"Simulates the backtest and outputs portfolio performance."},"document":{"kind":"string","value":"def simulate_trading(self):\n self._run_backtest()\n self._output_performance()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def backtest_portfolio(self):\n self.rank=dict()\n self.accuracy=dict()\n portfolio = dict()\n \n for algo in self.algos:\n portfolio[algo]=pd.DataFrame(index=self.positions.index)\n self.pos_diff=dict()\n self.pos_diff[algo] = self.positions[algo].diff()\n \n portfolio[algo]['price_diff'] = self.bars['Close']-self.bars['Open']\n #portfolio['price_diff'][0:5] = 0.0\n portfolio[algo]['profit'] = self.positions[algo] * portfolio[algo]['price_diff']\n portfolio[algo]['total'] = self.initial_capital + portfolio[algo]['profit'].cumsum()\n portfolio[algo]['returns'] = portfolio[algo]['total'].pct_change()\n d=np.array(portfolio[algo]['profit']).copy()\n d[d>0]=1\n d[d<0]=0\n d[np.array(self.positions[algo])==0]=1\n for i in np.arange(1,len(d)+1):\n c=float(sum(d[0:i]))/(i)\n d[i-1]=c\n portfolio[algo]['accuracy']=d\n self.rank[algo]=float(portfolio[algo]['total'][-1] - portfolio[algo]['total'][0])\n self.returns=portfolio\n c=np.array(self.returns[algo]['profit'])\n c[c>0]=1\n c[c<0]=0\n c[np.array(self.positions[algo])==0]=1\n accuracy=round(float(c.sum())/len(c),2)*self.rank[algo]\n self.accuracy[algo]=accuracy\n #self.ranking= sorted(self.rank.items(), key=operator.itemgetter(1), reverse=True)\n self.ranking= sorted(self.accuracy.items(), key=operator.itemgetter(1))\n self.ready=True\n return (portfolio, self.rank, self.ranking)","def simulate_trading(self):\n self._generate_trading_instances()\n self._run_backtest()\n self.portfolio.output_equity()\n res=self.portfolio.get_statistics()\n self.plot.plot_equity()\n return res","def backtest_portfolio(self):\n raise NotImplementedError(\"Should implement backtest_portfolio()!\")","def target_portfolio_simulation(num_of_years=30, trials=100, method='normal'):\n print(\"Running method target_portfolio_simulation()\")\n\n sim_fia_cv = pd.DataFrame(index=range(num_of_years + 1))\n\n sim_base_total = pd.DataFrame(index=range(num_of_years + 1))\n sim_base_income = pd.DataFrame(index=range(num_of_years + 1))\n\n sim_port_total = pd.DataFrame(index=range(num_of_years + 1))\n sim_port_income = pd.DataFrame(index=range(num_of_years + 1))\n\n read_income_inputs = pd.read_excel(src + \"portfolio_information.xlsx\", sheet_name='income_model_inputs',\n index_col=[0])\n\n read_returns_est = pd.read_excel(src + \"portfolio_information.xlsx\", sheet_name='income_assets_returns_estimates',\n index_col=[0])\n\n read_asset_weights = pd.read_excel(src + \"portfolio_information.xlsx\", sheet_name='asset_weights',\n index_col=[0])\n\n # read_asset_weights.drop(read_asset_weights.index[-1], axis=0, inplace=True)\n\n # read random returns for simulation\n read_normal = pd.read_csv(src + 'median_returns_unsorted.csv', index_col=[0], parse_dates=True)\n cols = [read_normal.columns[c].split('_')[1] for c in np.arange(len(read_normal.columns))]\n read_normal.rename(columns=dict(zip(list(read_normal.columns), cols)), inplace=True)\n\n read_small = pd.read_csv(src + 'median_returns_smallest.csv', index_col=[0], parse_dates=True)\n read_small.rename(columns=dict(zip(list(read_small.columns), cols)), inplace=True)\n\n read_large = pd.read_csv(src + 'median_returns_largest.csv', index_col=[0], parse_dates=True)\n read_large.rename(columns=dict(zip(list(read_large.columns), cols)), inplace=True)\n\n assets_col_names = list(read_normal.columns)\n\n tickers = list(read_asset_weights.index)\n wts = np.array(read_asset_weights.loc[:, 'base'])\n\n def asset_median_returns(data, ticker):\n return data.filter(regex=ticker).median(axis=1)\n\n # dataframe for unsorted returns (normal)\n median_returns_normal = read_normal.copy()\n median_returns_normal.loc[:, 'portfolio_return'] = median_returns_normal.dot(wts)\n median_normal_fia = pd.DataFrame({'FIA': asset_median_returns(read_normal, 'FIA')})\n\n # dataframe for smallest to largest returns\n median_returns_smallest = read_small.copy()\n median_returns_smallest.loc[:, 'portfolio_return'] = median_returns_smallest.dot(wts)\n median_smallest_fia = pd.DataFrame({'FIA': asset_median_returns(read_small, 'FIA')})\n\n # dataframe for largest to smallest returns\n median_returns_largest = read_large.copy()\n median_returns_largest.loc[:, 'portfolio_return'] = median_returns_largest.dot(wts)\n median_largest_fia = pd.DataFrame({'FIA': asset_median_returns(read_large, 'FIA')})\n\n years = list(range(0, num_of_years + 1))\n income_cols = ['year', 'strategy_term', 'index_returns', 'term_ret', 'term_ret_with_par', 'term_annualize',\n 'ann_net_spread', 'term_ret_netspr', 'high_inc_benefit_base', 'rider_fee', 'eoy_income',\n 'contract_value']\n\n term = int(read_income_inputs.loc['term', 'inputs'])\n fia_ret = read_returns_est.loc[read_returns_est.index[-1], 'Annualized Returns']\n fia_risk = read_returns_est.loc[read_returns_est.index[-1], 'Annualized Risk']\n par_rate = float(read_income_inputs.loc['par_rate', 'inputs'])\n spread = float(read_income_inputs.loc['spread', 'inputs'])\n bonus_term = int(read_income_inputs.loc['bonus_term', 'inputs'])\n premium = float(read_income_inputs.loc['premium', 'inputs'])\n income_bonus = float(read_income_inputs.loc['income_bonus', 'inputs'])\n\n income_starts = int(read_income_inputs.loc['start_income_years', 'inputs'])\n income_growth = float(read_income_inputs.loc['income_growth', 'inputs'])\n rider_fee = float(read_income_inputs.loc['rider_fee', 'inputs'])\n inc_payout_factor = float(read_income_inputs.loc['income_payout_factor', 'inputs'])\n contract_bonus = float(read_income_inputs.loc['contract_bonus', 'inputs'])\n social = float(read_income_inputs.loc['social', 'inputs'])\n inflation = float(read_income_inputs.loc['inflation', 'inputs'])\n wtd_cpn_yield = float(read_income_inputs.loc['wtd_coupon_yld', 'inputs'])\n life_expectancy = int(read_income_inputs.loc['life_expectancy_age', 'inputs'])\n clients_age = int(read_income_inputs.loc['clients_age', 'inputs'])\n\n # ---------------INCOME MODEL--------------------------------------------\n runs = 0\n returns_dict = {}\n asset_dict = {}\n fia_dict = {}\n\n income_df = pd.DataFrame(index=years, columns=income_cols)\n income_df.loc[:, 'year'] = years\n income_df.loc[:, 'strategy_term'] = income_df.loc[:, 'year'] % term\n income_df.loc[:, 'strategy_term'] = income_df['strategy_term'].apply(lambda x: 1 if x == 0 else 0)\n\n if method == 'normal':\n income_df.loc[:, 'index_returns'] = read_normal.loc[:, 'FIA']\n\n elif method == 'smallest':\n income_df.loc[:, 'index_returns'] = read_small.loc[:, 'FIA']\n\n else:\n income_df.loc[:, 'index_returns'] = read_large.loc[:, 'FIA']\n\n # income_df.loc[:, 'index_returns'] = np.random.normal(fia_ret, fia_risk, size=(len(years), 1))\n\n cumprod = (1. + income_df['index_returns']).rolling(window=term).agg(lambda x: x.prod()) - 1\n income_df.loc[:, 'term_ret'] = np.where(income_df.loc[:, 'strategy_term'] == 1, cumprod, 0)\n income_df.loc[:, 'term_ret_with_par'] = income_df.loc[:, 'term_ret'] * par_rate\n income_df.loc[:, 'term_annualize'] = income_df.loc[:, 'term_ret_with_par'].apply(\n lambda x: (1 + x) ** (1 / term) - 1)\n income_df.loc[:, 'ann_net_spread'] = income_df.loc[:, 'term_annualize'] - spread\n income_df.loc[:, 'ann_net_spread'] = np.where(income_df.loc[:, 'strategy_term'] == 1,\n income_df.loc[:, 'ann_net_spread'], 0)\n income_df.loc[:, 'term_ret_netspr'] = income_df.loc[:, 'ann_net_spread'].apply(lambda x: (1 + x) ** term - 1)\n\n for counter in years:\n if counter == 0:\n income_df.loc[counter, 'high_inc_benefit_base'] = premium * (1 + income_bonus)\n\n elif counter <= min(bonus_term, income_starts):\n income_df.loc[counter, 'high_inc_benefit_base'] = income_df.loc[counter - 1, 'high_inc_benefit_base'] * \\\n (1 + income_growth)\n else:\n income_df.loc[counter, 'high_inc_benefit_base'] = income_df.loc[counter - 1, 'high_inc_benefit_base']\n\n income_df.loc[:, 'rider_fee'] = income_df.loc[:, 'high_inc_benefit_base'] * rider_fee\n income_df.loc[:, 'eoy_income'] = np.where(income_df.loc[:, 'year'] > income_starts,\n income_df.loc[:, 'high_inc_benefit_base'] * inc_payout_factor, 0)\n\n for counter in years:\n if counter == 0:\n income_df.loc[counter, 'contract_value'] = premium * (1 + contract_bonus)\n\n elif income_df.loc[counter, 'strategy_term'] == 1:\n x1 = income_df.loc[counter - 1, 'contract_value'] - income_df.loc[counter, 'rider_fee']\n x2 = (x1 * (1 + income_df.loc[counter, 'term_ret_netspr'])) - income_df.loc[counter, 'eoy_income']\n income_df.loc[counter, 'contract_value'] = x2\n\n else:\n x1 = income_df.loc[counter - 1, 'contract_value'] - income_df.loc[counter, 'rider_fee'] - \\\n income_df.loc[counter, 'eoy_income']\n\n income_df.loc[counter, 'contract_value'] = x1\n\n # variable stores the income number that is used in the base and fia portfolio calcs.\n\n income_from_fia = income_df.loc[income_df.index[-1], 'eoy_income']\n\n income_df.loc[:, 'contract_value'] = income_df.loc[:, 'contract_value'].apply(lambda x: 0 if x <= 0 else x)\n\n sim_fia_cv.loc[:, str(runs)] = income_df.loc[:, 'contract_value']\n\n # --------------------BASE MODEL---------------------------------------------\n base_wts = read_asset_weights.loc[:, 'base']\n base_assets = list(base_wts.index)\n base_weights = list(base_wts.values)\n base_returns = list(read_returns_est.loc[:, 'Annualized Returns'].values)\n base_std = list(read_returns_est.loc[:, 'Annualized Risk'].values)\n\n base_investment = float(read_income_inputs.loc['risky_assets', 'Base'])\n adv_fees = float(read_income_inputs.loc['advisor_fees', 'Base'])\n\n # -------------------required income----------------------------------\n req_annual_income = float(read_income_inputs.loc['annual_income', 'inputs'])\n income_needed = req_annual_income - social\n income_net_fia_income = max(0, income_needed - income_from_fia)\n cpn_income_base = base_investment * wtd_cpn_yield\n\n # ----------------------RANDOM RETURNS--------------------------\n r_cols = base_assets\n boy_value = ['bv_{}'.format(name) for name in base_assets]\n eoy_value = ['ev_{}'.format(name) for name in base_assets]\n\n random_returns = pd.DataFrame(index=income_df.index, columns=r_cols)\n\n for c in range(len(r_cols)):\n ret = np.random.normal(base_returns[c], base_std[c], size=(len(random_returns.index), 1))\n\n if method == 'smallest':\n random_returns = read_small.copy()\n\n elif method == 'largest':\n random_returns = read_large.copy()\n\n else:\n random_returns = read_normal.copy()\n\n base_df = random_returns.copy()\n fia_portfolio_df = random_returns.copy()\n port_investment = float(read_income_inputs.loc['risky_assets', 'FIA'])\n cpn_income_port = port_investment * wtd_cpn_yield\n\n # -------------BASE PORTFOLIO----------------------------\n for name in boy_value:\n base_df.loc[:, name] = 0.0\n\n for counter in years:\n period_returns = list(random_returns.loc[counter, :])\n if counter == 0:\n\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment for c in range(len(boy_value))]\n\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\n base_df.loc[counter, 'total_net_fees'] = 0.0\n base_df.loc[counter, 'income'] = 0.0\n base_investment = base_df.loc[counter, boy_value].sum()\n\n elif (counter > 0) and (counter < income_starts):\n\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment * (1 + period_returns[c])\n for c in range(len(boy_value))]\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\n base_df.loc[counter, 'adv_fees'] = base_df.loc[counter, 'total'] * adv_fees\n base_df.loc[counter, 'total_net_fees'] = base_df.loc[counter, 'total'] - base_df.loc[\n counter, 'adv_fees']\n\n # --coupon payment is invested back into the risky portfolio until the income is withdrawn----\n base_investment = base_df.loc[counter, 'total_net_fees'] + cpn_income_base\n\n else:\n\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment * (1 + period_returns[c])\n for c in range(len(boy_value))]\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\n base_df.loc[counter, 'adv_fees'] = base_df.loc[counter, 'total'] * adv_fees\n\n # ---req. income is adjusted for inflation from the second year of withdrawal. Reinvestment of coupon\n # stops from the year income starts. Req. income is reduced by the coupon payments\n\n if counter == income_starts:\n\n income_needed = req_annual_income - social\n base_df.loc[counter, 'income'] = income_needed - cpn_income_base\n income_needed = req_annual_income\n\n else:\n income_needed = income_needed * (1 + inflation) - social\n base_df.loc[counter, 'income'] = income_needed - cpn_income_base\n income_needed = income_needed + social\n\n base_df.loc[counter, 'total_net_fees'] = base_df.loc[counter, 'total'] - \\\n base_df.loc[counter, 'adv_fees'] - \\\n base_df.loc[counter, 'income']\n\n base_investment = base_df.loc[counter, 'total_net_fees']\n\n base_df.loc[:, 'adj_total'] = base_df.loc[:, 'total_net_fees'].apply(lambda x: x if x > 0 else 0)\n sim_base_total.loc[:, 's_{}'.format(str(runs))] = base_df.loc[:, 'total_net_fees']\n sim_base_income.loc[:, 's_{}'.format(str(runs))] = base_df.loc[:, 'income']\n\n # ----------------------------FIA PORTFOLIO----------------------------------------------\n for name in boy_value:\n fia_portfolio_df.loc[:, name] = 0.0\n\n for counter in years:\n period_returns = list(random_returns.loc[counter, :])\n if counter == 0:\n\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment\n for c in range(len(boy_value))]\n fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\n fia_portfolio_df.loc[counter, 'total_net_fees'] = 0.0\n fia_portfolio_df.loc[counter, 'income'] = 0.0\n port_investment = fia_portfolio_df.loc[counter, boy_value].sum()\n\n elif (counter > 0) and (counter < income_starts):\n\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment * (1 + period_returns[c])\n for c in range(len(boy_value))]\n fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\n fia_portfolio_df.loc[counter, 'adv_fees'] = fia_portfolio_df.loc[counter, 'total'] * adv_fees\n fia_portfolio_df.loc[counter, 'total_net_fees'] = fia_portfolio_df.loc[counter, 'total'] - \\\n fia_portfolio_df.loc[counter, 'adv_fees']\n\n port_investment = fia_portfolio_df.loc[counter, 'total_net_fees'] + cpn_income_port\n\n else:\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment * (1 + period_returns[c])\n for c in range(len(boy_value))]\n fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\n fia_portfolio_df.loc[counter, 'adv_fees'] = fia_portfolio_df.loc[counter, 'total'] * adv_fees\n\n # ---req. income is adjusted for inflation from the second year of withdrawal. Reinvestment of coupon\n # stops from the year income starts. Req. income is reduced by the coupon payments\n\n if counter == income_starts:\n\n income_needed = req_annual_income - social\n income_net_fia_income = max(0, income_needed - income_from_fia)\n fia_portfolio_df.loc[counter, 'income'] = max(0, income_net_fia_income - cpn_income_port)\n income_needed = req_annual_income\n\n else:\n income_needed = income_needed * (1 + inflation) - social\n income_net_fia_income = max(0, income_needed - income_from_fia)\n fia_portfolio_df.loc[counter, 'income'] = max(0, income_net_fia_income - cpn_income_port)\n income_needed = income_needed + social\n\n fia_portfolio_df.loc[counter, 'total_net_fees'] = fia_portfolio_df.loc[counter, 'total'] - \\\n fia_portfolio_df.loc[counter, 'adv_fees'] - \\\n fia_portfolio_df.loc[counter, 'income']\n\n port_investment = fia_portfolio_df.loc[counter, 'total_net_fees']\n\n sim_port_total.loc[:, 's_{}'.format(str(runs))] = fia_portfolio_df.loc[:, 'total_net_fees'] + \\\n income_df.loc[:, 'contract_value']\n\n sim_port_income.loc[:, 's_{}'.format(str(runs))] = fia_portfolio_df.loc[:, 'income']\n\n fia_portfolio_df.loc[:, 'adj_total'] = fia_portfolio_df.loc[:, 'total_net_fees'].apply(\n lambda x: x if x > 0 else 0)\n\n # ---------income breakdown for Base portfolio----------------------------------\n base_df.to_csv(src + 'base_port_detail.csv')\n sim_base_total.to_csv(src + 'base_ending_values.csv')\n income_breakdown_base = pd.DataFrame(sim_base_total.quantile(0.5, axis=1))\n income_breakdown_base.loc[:, 'income_from_portfolio'] = sim_base_income.quantile(0.5, axis=1)\n income_breakdown_base.loc[:, 'fia_income'] = 0.0\n income_breakdown_base.loc[:, 'social_security_income'] = social\n income_breakdown_base.loc[:, 'coupon_income'] = cpn_income_base\n\n income_breakdown_base.rename(columns={income_breakdown_base.columns[0]: 'portfolio_ending_value'}, inplace=True)\n income_breakdown_base.loc[:, 'income_from_portfolio'][\n income_breakdown_base.loc[:, 'portfolio_ending_value'] <= 0] = 0\n income_breakdown_base.loc[:, 'total_income'] = income_breakdown_base.loc[:, income_breakdown_base.columns[1:]].sum(\n axis=1)\n\n # ------------Block Ends-------------------------------------------------------------\n\n # ---------income breakdown for FIA portfolio----------------------------------\n fia_portfolio_df.to_csv(src + 'fia_port_detail.csv')\n sim_port_total.to_csv(src + 'fiaport_ending_values.csv')\n\n income_breakdown_port = pd.DataFrame(sim_port_total.quantile(0.5, axis=1))\n income_breakdown_port.loc[:, 'income_from_portfolio'] = sim_port_income.quantile(0.5, axis=1)\n income_breakdown_port.loc[:, 'fia_income'] = income_from_fia\n income_breakdown_port.loc[:, 'social_security_income'] = social\n income_breakdown_port.loc[:, 'coupon_income'] = cpn_income_port\n\n income_breakdown_port.rename(columns={income_breakdown_port.columns[0]: 'portfolio_ending_value'}, inplace=True)\n income_breakdown_port.loc[:, 'income_from_portfolio'][\n income_breakdown_port.loc[:, 'portfolio_ending_value'] <= 0] = 0\n income_breakdown_port.loc[:, 'total_income'] = income_breakdown_port.loc[:, income_breakdown_port.columns[1:]].sum(\n axis=1)\n\n # ------------Block Ends-------------------------------------------------------------\n q_cut = [0.0, 0.05, 0.25, 0.5, 0.75, 0.95, 1.0]\n sim_base_income[sim_base_total < income_needed] = 0.0\n\n sim_port_income[sim_port_total < income_net_fia_income] = 0\n\n sim_port_income = sim_port_income + income_from_fia\n\n # base_quantile = sim_base_total.loc[sim_base_total.index[-1]].quantile([0.05, 0.25, 0.50, 0.75, 0.90])\n\n # port_quantile = sim_port_total.loc[sim_port_total.index[-1]].quantile([0.05, 0.25, 0.50, 0.75, 0.90])\n\n base_quantile = sim_base_total.loc[sim_base_total.index[-1]].quantile(q_cut)\n\n port_quantile = sim_port_total.loc[sim_port_total.index[-1]].quantile(q_cut)\n\n # q_cut = [0.0, .05, 0.25, 0.5, 0.75, 0.95, 1.0]\n cols = ['Min', '5th', '25th', '50th', '75th', '90th', 'Max']\n\n # ----drop year 0--------\n sim_base_total = sim_base_total[1:]\n sim_port_total = sim_port_total[1:]\n\n # ----------------quantile analysis for base terminal value--------------------------\n base_qcut = pd.DataFrame(index=sim_base_total.index, columns=cols)\n for c in range(len(cols)):\n base_qcut.loc[:, cols[c]] = sim_base_total.quantile(q_cut[c], axis=1)\n\n base_qcut.clip(lower=0, inplace=True)\n\n # ----------------------quantile analysis for base income----------------------------\n base_income_qcut = pd.DataFrame(index=sim_base_income.index, columns=cols)\n for c in range(len(cols)):\n base_income_qcut.loc[:, cols[c]] = sim_base_income.quantile(q_cut[c], axis=1)\n\n # ----Remove NaN's prior to the income start years------------\n # base_income_qcut = base_income_qcut.loc[income_starts:]\n\n # -------------quantile analysis for portfolio terminal value ----------------\n port_qcut = pd.DataFrame(index=sim_port_total.index, columns=cols)\n for c in range(len(cols)):\n port_qcut.loc[:, cols[c]] = sim_port_total.quantile(q_cut[c], axis=1)\n\n port_qcut.clip(lower=0, inplace=True)\n\n # ---------------quantile analysis for portfolio income----------------------------\n port_income_qcut = pd.DataFrame(index=sim_port_income.index, columns=cols)\n for c in range(len(cols)):\n port_income_qcut.loc[:, cols[c]] = sim_port_income.quantile(q_cut[c], axis=1)\n\n # ----Remove NaN's prior to the income start years------------\n # port_income_qcut = port_income_qcut.loc[income_starts:]\n\n # ----------probability ending value will be less than 0 at the end of the horizon -----------------------\n base_legacy_risk = (sim_base_total.loc[sim_base_total.index[life_expectancy - clients_age]] < 0).sum() / (\n trials + 1)\n port_legacy_risk = (sim_port_total.loc[sim_port_total.index[life_expectancy - clients_age]] < 0).sum() / (\n trials + 1)\n\n legacy_risk = pd.DataFrame([base_legacy_risk, port_legacy_risk,\n 'Prob. of portfolio value less than 0 at the end of the expected life'],\n index=['base', 'fia_portfolio', 'Notes'],\n columns=['Ruin Probability'])\n\n # -----------Year-wise probability of ending value greater than 0 -----------------\n base_psuccess = sim_base_total.apply(lambda x: x > 0).sum(axis=1) / (trials + 1)\n port_psuccess = sim_port_total.apply(lambda x: x > 0).sum(axis=1) / (trials + 1)\n\n # -----------------------WRITING FILES TO EXCEL ---------------------------\n col_names = ['50th', 'age', 'comment']\n writer = pd.ExcelWriter(src + method + '_simulated_income_summary.xlsx', engine='xlsxwriter')\n read_income_inputs.to_excel(writer, sheet_name='inputs_for_income')\n\n read_returns_est.to_excel(writer, sheet_name='asset_returns_estimates')\n\n age_index = list(range(clients_age + 1, clients_age + len(base_qcut) + 1))\n base_qcut.loc[:, 'age'] = age_index\n base_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n base_qcut.loc[income_starts:, col_names].to_excel(writer, sheet_name='base_ending_value_quantiles')\n\n base_income_qcut = base_income_qcut.loc[1:, :]\n base_income_qcut.loc[:, 'age'] = age_index\n base_income_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n base_income_qcut.loc[income_starts:, col_names].to_excel(writer, sheet_name='base_income_quantiles')\n\n port_qcut.loc[:, 'age'] = age_index\n port_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n port_qcut.loc[income_starts:, col_names].to_excel(writer, sheet_name='fia_port_ending_value_quantiles')\n\n port_income_qcut = port_income_qcut.loc[1:, :]\n port_income_qcut.loc[:, 'age'] = age_index\n port_income_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n # port_income_qcut.loc[:, 'ending_contract_value'] = sim_fia_cv\n port_income_qcut.loc[income_starts:, col_names].to_excel(writer, sheet_name='fia_port_income_quantiles')\n\n # prob_success_df = pd.concat([base_psuccess, port_psuccess], axis=1)\n # prob_success_df.rename(columns={prob_success_df.columns[0]: 'prob(ending_value>0)_base',\n # prob_success_df.columns[1]: 'prob(ending_value>0)_port'}, inplace=True)\n\n # prob_success_df.loc[:, 'age'] = age_index\n # prob_success_df.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n # prob_success_df.to_excel(writer, sheet_name='success_probability')\n\n income_breakdown_base = income_breakdown_base.loc[1:, :]\n income_breakdown_base.loc[:, 'age'] = age_index\n income_breakdown_base.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n income_breakdown_base.loc[income_starts:, :].to_excel(writer, sheet_name='base_income_breakdown_median')\n\n income_breakdown_port = income_breakdown_port.loc[1:, :]\n income_breakdown_port.loc[:, 'age'] = age_index\n income_breakdown_port.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n income_breakdown_port.loc[:, 'ending_contract_value'] = income_df.loc[:, 'contract_value']\n income_breakdown_port.loc[income_starts:, :].to_excel(writer, sheet_name='fia_income_breakdown_median')\n\n if method == 'normal':\n # median_returns_normal.loc[:, 'fia_median_returns'] = median_normal_fia\n median_returns_normal.to_excel(writer, sheet_name='gr_port_median_normal')\n\n elif method == 'smallest':\n # median_returns_smallest.loc[:, 'fia_median_returns'] = median_smallest_fia\n median_returns_smallest.to_excel(writer, sheet_name='gr_port_median_asc')\n\n else:\n # median_returns_largest.loc[:, 'fia_median_returns'] = median_largest_fia\n median_returns_largest.to_excel(writer, sheet_name='gr_port_median_desc')\n\n terminal_val = pd.read_csv(src + 'terminal_values.csv', index_col=[0])\n ending_val = pd.read_csv(src + 'ending_values.csv', index_col=[0])\n ending_val_ror = pd.read_csv(src + 'ending_values_ror.csv', index_col=[0])\n\n terminal_val.to_excel(writer, sheet_name='terminal_values')\n ending_val.to_excel(writer, sheet_name='port_ending_values')\n ending_val_ror.to_excel(writer, sheet_name='port_annual_growth')\n\n writer.save()\n\n # -----------------Plotting charts--------------------------------------------\n base_qcut.loc[income_starts:].plot(grid=True, title='Quantile Terminal Value - Base Portfolio')\n plt.savefig(src + \"quantile_terminal_base.png\")\n plt.close('all')\n\n base_income_qcut.plot(grid=True, title='Quantile Income - Base Portfolio')\n plt.savefig(src + \"quantile_income_base.png\")\n plt.close('all')\n\n base_psuccess.plot(grid=True, title='Probability of Success (Portfolio Ending Value > 0) - Base Portfolio')\n plt.savefig(src + \"success_probabilty_base.png\")\n plt.close('all')\n\n (1 - base_psuccess).plot(grid=True, title='Probability of Ruin (Portfolio Ending Value < 0) - Base Portfolio')\n plt.savefig(src + \"ruin_probability_base.png\")\n plt.close('all')\n\n port_qcut.loc[income_starts:].plot(grid=True, title='Quantile Terminal Value - FIA Portfolio')\n plt.savefig(src + \"quantile_terminal_fia.png\")\n plt.close('all')\n\n port_income_qcut.plot(grid=True, title='Quantile Income - FIA Portfolio')\n plt.savefig(src + \"quantile_income_fia.png\")\n plt.close('all')\n\n port_psuccess.plot(grid=True, title='Probability of Success (Portfolio Ending Value > 0) - FIA Portfolio')\n plt.savefig(src + \"success_probabilty_fia.png\")\n plt.close('all')\n\n (1 - port_psuccess).plot(grid=True, title='Probability of Ruin (Portfolio Ending Value < 0) - FIA Portfolio')\n plt.savefig(src + \"ruin_probability_fia.png\")\n plt.close('all')\n\n print(\"simulation completed for {}\".format(method))","def simulation_using_historical_returns(num_of_years=30, trials=100, method='normal'):\n\n print(\"Running method simulation_using_historical_returns()\")\n\n sim_fia_cv = pd.DataFrame(index=range(num_of_years + 1))\n\n sim_base_total = pd.DataFrame(index=range(num_of_years + 1))\n sim_base_income = pd.DataFrame(index=range(num_of_years + 1))\n\n sim_port_total = pd.DataFrame(index=range(num_of_years + 1))\n sim_port_income = pd.DataFrame(index=range(num_of_years + 1))\n\n read_income_inputs = pd.read_excel(src + \"portfolio_information.xlsx\", sheet_name='income_model_inputs',\n index_col=[0])\n\n read_returns_est = pd.read_excel(src + \"portfolio_information.xlsx\", sheet_name='income_assets_returns_estimates',\n index_col=[0])\n\n read_asset_weights = pd.read_excel(src + \"portfolio_information.xlsx\", sheet_name='asset_weights',\n index_col=[0])\n\n # read the base portfolio from accumulation model\n fia_name = read_income_inputs.loc['fia_name', 'inputs']\n path = src + fia_name + '/'\n file_name = fia_name + '_portfolio.csv'\n\n # base_port = pd.read_csv(path + 'base_portfolio.csv', index_col=[0], parse_dates=True)\n\n base_port = pd.read_csv(path + file_name, index_col=[0], parse_dates=True)\n\n ls1 = list(read_asset_weights.index[:-2])\n ls2 = ['Cash', fia_name, 'Total']\n combined_ls = ls1 + ls2\n base_port = base_port.loc[:, combined_ls]\n base_port.loc[:, 'port_return'] = base_port['Total'].pct_change().fillna(0)\n base_port.drop('Total', axis=1, inplace=True)\n yearly_base_port = base_port.groupby(by=base_port.index.year).apply(sum)\n\n # --Simulate only for first 20 years of returns--------\n first_n_years = int(read_income_inputs.loc['simulate_for_first_n_years', 'inputs'])\n yearly_base_port = yearly_base_port.loc[yearly_base_port.index[-(first_n_years + 1):], :]\n\n # --------Unsorted returns ---------------------\n read_normal = yearly_base_port.copy()\n read_normal.iloc[0] = 0.0\n read_normal.to_csv(src + 'base_port_ann_ret_unsorted.csv')\n read_normal.reset_index(drop=True, inplace=True)\n\n # ---make a copy to add to the consolidated summary file----\n save_normal = read_normal.copy()\n read_normal.drop('port_return', axis=1, inplace=True)\n read_normal.rename(columns={fia_name: 'FIA'}, inplace=True)\n\n # - Shift returns by one year and assign 0 returns to all assets in teh year 0, the year of initial investment.\n # read_normal = read_normal.shift().fillna(0)\n read_normal.to_csv(src + 'historical_port_return_unsorted.csv')\n\n # ----Best to worst returns----\n read_large = yearly_base_port.copy()\n\n # --Logic to sort the last 20 years of returns and assign 0 to first year, the year of investment\n read_large.iloc[0] = 1000.0\n read_large = read_large.sort_values(by=['port_return'], ascending=False)\n # --assign 0 to first year, the year of investment\n read_large.iloc[0] = 0.0\n read_large.to_csv(src + 'base_port_ann_ret_desc.csv')\n read_large.reset_index(drop=True, inplace=True)\n # read_large = read_large.shift().fillna(0)\n\n # ---make a copy to add to the consolidated summary file---\n save_desc = read_large.copy()\n read_large.drop('port_return', axis=1, inplace=True)\n read_large.rename(columns={fia_name: 'FIA'}, inplace=True)\n read_large.to_csv(src + 'historical_port_return_desc.csv')\n\n # ------Worst to Best Returns------------\n read_small = yearly_base_port.copy()\n read_small.iloc[0] = -1000.0\n read_small = read_small.sort_values(by=['port_return'], ascending=True)\n read_small.iloc[0] = 0.0\n read_small.to_csv(src + 'base_port_ann_ret_asc.csv')\n read_small.reset_index(drop=True, inplace=True)\n # read_small = read_small.shift().fillna(0)\n\n # ---make a copy to add to the consolidated summary file---\n save_asc = read_small.copy()\n read_small.drop('port_return', axis=1, inplace=True)\n read_small.rename(columns={fia_name: 'FIA'}, inplace=True)\n read_small.to_csv(src + 'historical_port_return_asc.csv')\n\n # read_asset_weights.drop(read_asset_weights.index[-1], axis=0, inplace=True)\n\n # read random returns for simulation\n # read_normal = pd.read_csv(src + 'median_returns_unsorted.csv', index_col=[0], parse_dates=True)\n # cols = [read_normal.columns[c].split('_')[1] for c in np.arange(len(read_normal.columns))]\n # read_normal.rename(columns=dict(zip(list(read_normal.columns), cols)), inplace=True)\n #\n # read_small = pd.read_csv(src + 'median_returns_smallest.csv', index_col=[0], parse_dates=True)\n # read_small.rename(columns=dict(zip(list(read_small.columns), cols)), inplace=True)\n #\n # read_large = pd.read_csv(src + 'median_returns_largest.csv', index_col=[0], parse_dates=True)\n # read_large.rename(columns=dict(zip(list(read_large.columns), cols)), inplace=True)\n\n assets_col_names = list(read_normal.columns)\n\n tickers = list(read_asset_weights.index)\n wts = np.array(read_asset_weights.loc[:, 'base'])\n\n def asset_median_returns(data, ticker):\n return data.filter(regex=ticker).median(axis=1)\n\n # dataframe for unsorted returns (normal)\n median_returns_normal = read_normal.copy()\n median_returns_normal.loc[:, 'portfolio_return'] = median_returns_normal.dot(wts)\n # median_returns_normal.rename(columns={fia_name: 'FIA'}, inplace=True)\n # median_normal_fia = pd.DataFrame({'FIA': asset_median_returns(read_normal, 'FIA')})\n\n # dataframe for smallest to largest returns\n median_returns_smallest = read_small.copy()\n median_returns_smallest.loc[:, 'portfolio_return'] = median_returns_smallest.dot(wts)\n # median_returns_smallest.rename(columns={fia_name: 'FIA'}, inplace=True)\n # median_smallest_fia = pd.DataFrame({'FIA': asset_median_returns(read_small, 'FIA')})\n\n # dataframe for largest to smallest returns\n median_returns_largest = read_large.copy()\n median_returns_largest.loc[:, 'portfolio_return'] = median_returns_largest.dot(wts)\n # median_returns_largest.rename(columns={fia_name: 'FIA'}, inplace=True)\n # median_largest_fia = pd.DataFrame({'FIA': asset_median_returns(read_large, 'FIA')})\n\n # years = list(range(0, num_of_years + 1))\n years = np.arange(len(read_normal))\n income_cols = ['year', 'strategy_term', 'index_returns', 'term_ret', 'term_ret_with_par', 'term_annualize',\n 'ann_net_spread', 'term_ret_netspr', 'high_inc_benefit_base', 'rider_fee', 'eoy_income',\n 'contract_value']\n\n term = int(read_income_inputs.loc['term', 'inputs'])\n fia_ret = read_returns_est.loc[read_returns_est.index[-1], 'Annualized Returns']\n fia_risk = read_returns_est.loc[read_returns_est.index[-1], 'Annualized Risk']\n par_rate = float(read_income_inputs.loc['par_rate', 'inputs'])\n spread = float(read_income_inputs.loc['spread', 'inputs'])\n bonus_term = int(read_income_inputs.loc['bonus_term', 'inputs'])\n premium = float(read_income_inputs.loc['premium', 'inputs'])\n income_bonus = float(read_income_inputs.loc['income_bonus', 'inputs'])\n\n income_starts = int(read_income_inputs.loc['start_income_years', 'inputs'])\n # --Income start right away, not deferred ----\n income_starts = 0\n income_growth = float(read_income_inputs.loc['income_growth', 'inputs'])\n rider_fee = float(read_income_inputs.loc['rider_fee', 'inputs'])\n inc_payout_factor = float(read_income_inputs.loc['income_payout_factor', 'inputs'])\n contract_bonus = float(read_income_inputs.loc['contract_bonus', 'inputs'])\n social = float(read_income_inputs.loc['social', 'inputs'])\n inflation = float(read_income_inputs.loc['inflation', 'inputs'])\n wtd_cpn_yield = float(read_income_inputs.loc['wtd_coupon_yld', 'inputs'])\n life_expectancy = int(read_income_inputs.loc['life_expectancy_age', 'inputs'])\n clients_age = int(read_income_inputs.loc['clients_age', 'inputs'])\n\n # --------------------------------------INCOME MODEL--------------------------------------------\n runs = 0\n returns_dict = {}\n asset_dict = {}\n fia_dict = {}\n\n income_df = pd.DataFrame(index=years, columns=income_cols)\n income_df.loc[:, 'year'] = years\n income_df.loc[:, 'strategy_term'] = income_df.loc[:, 'year'] % term\n income_df.loc[:, 'strategy_term'] = income_df['strategy_term'].apply(lambda x: 1 if x == 0 else 0)\n\n if method == 'normal':\n income_df.loc[:, 'index_returns'] = read_normal.loc[:, 'FIA']\n\n elif method == 'smallest':\n income_df.loc[:, 'index_returns'] = read_small.loc[:, 'FIA']\n\n else:\n income_df.loc[:, 'index_returns'] = read_large.loc[:, 'FIA']\n\n # income_df.loc[:, 'index_returns'] = np.random.normal(fia_ret, fia_risk, size=(len(years), 1))\n\n cumprod = (1. + income_df['index_returns']).rolling(window=term).agg(lambda x: x.prod()) - 1\n income_df.loc[:, 'term_ret'] = np.where(income_df.loc[:, 'strategy_term'] == 1, cumprod, 0)\n income_df.loc[:, 'term_ret_with_par'] = income_df.loc[:, 'term_ret'] * par_rate\n income_df.loc[:, 'term_annualize'] = income_df.loc[:, 'term_ret_with_par'].apply(\n lambda x: (1 + x) ** (1 / term) - 1)\n income_df.loc[:, 'ann_net_spread'] = income_df.loc[:, 'term_annualize'] - spread\n income_df.loc[:, 'ann_net_spread'] = np.where(income_df.loc[:, 'strategy_term'] == 1,\n income_df.loc[:, 'ann_net_spread'], 0)\n income_df.loc[:, 'term_ret_netspr'] = income_df.loc[:, 'ann_net_spread'].apply(lambda x: (1 + x) ** term - 1)\n\n for counter in years:\n if counter == 0:\n income_df.loc[counter, 'high_inc_benefit_base'] = premium * (1 + income_bonus)\n\n elif counter <= min(bonus_term, income_starts):\n income_df.loc[counter, 'high_inc_benefit_base'] = income_df.loc[counter - 1, 'high_inc_benefit_base'] * \\\n (1 + income_growth)\n else:\n income_df.loc[counter, 'high_inc_benefit_base'] = income_df.loc[counter - 1, 'high_inc_benefit_base']\n\n income_df.loc[:, 'rider_fee'] = income_df.loc[:, 'high_inc_benefit_base'] * rider_fee\n income_df.loc[:, 'eoy_income'] = np.where(income_df.loc[:, 'year'] > income_starts,\n income_df.loc[:, 'high_inc_benefit_base'] * inc_payout_factor, 0)\n\n for counter in years:\n if counter == 0:\n income_df.loc[counter, 'contract_value'] = premium * (1 + contract_bonus)\n\n elif income_df.loc[counter, 'strategy_term'] == 1:\n x1 = income_df.loc[counter - 1, 'contract_value'] - income_df.loc[counter, 'rider_fee']\n x2 = (x1 * (1 + income_df.loc[counter, 'term_ret_netspr'])) - income_df.loc[counter, 'eoy_income']\n income_df.loc[counter, 'contract_value'] = x2\n\n else:\n x1 = income_df.loc[counter - 1, 'contract_value'] - income_df.loc[counter, 'rider_fee'] - \\\n income_df.loc[counter, 'eoy_income']\n\n income_df.loc[counter, 'contract_value'] = x1\n\n # variable stores the income number that is used in the base and fia portfolio calcs.\n\n income_from_fia = income_df.loc[income_df.index[-1], 'eoy_income']\n\n income_df.loc[:, 'contract_value'] = income_df.loc[:, 'contract_value'].apply(lambda x: 0 if x <= 0 else x)\n\n sim_fia_cv.loc[:, str(runs)] = income_df.loc[:, 'contract_value']\n\n # ---------------------------------BASE MODEL---------------------------------------------\n\n base_wts = read_asset_weights.loc[:, 'base']\n base_assets = list(base_wts.index)\n base_weights = list(base_wts.values)\n base_returns = list(read_returns_est.loc[:, 'Annualized Returns'].values)\n base_std = list(read_returns_est.loc[:, 'Annualized Risk'].values)\n\n base_investment = float(read_income_inputs.loc['risky_assets', 'Base'])\n adv_fees = float(read_income_inputs.loc['advisor_fees', 'Base'])\n\n # -------------------------------------required income--------------------------------\n req_annual_income = float(read_income_inputs.loc['annual_income', 'inputs'])\n income_needed = req_annual_income - social\n income_net_fia_income = max(0, income_needed - income_from_fia)\n cpn_income_base = base_investment * wtd_cpn_yield\n\n # --------------------------------------------RANDOM RETURNS-----------------------------------\n r_cols = base_assets\n boy_value = ['bv_{}'.format(name) for name in base_assets]\n eoy_value = ['ev_{}'.format(name) for name in base_assets]\n\n random_returns = pd.DataFrame(index=income_df.index, columns=r_cols)\n\n for c in range(len(r_cols)):\n ret = np.random.normal(base_returns[c], base_std[c], size=(len(random_returns.index), 1))\n\n if method == 'smallest':\n random_returns = read_small.copy()\n\n elif method == 'largest':\n random_returns = read_large.copy()\n\n else:\n random_returns = read_normal.copy()\n\n base_df = random_returns.copy()\n fia_portfolio_df = random_returns.copy()\n port_investment = float(read_income_inputs.loc['risky_assets', 'FIA'])\n cpn_income_port = port_investment * wtd_cpn_yield\n\n # -------------------------------------BASE PORTFOLIO----------------------------\n for name in boy_value:\n base_df.loc[:, name] = 0.0\n\n for counter in years:\n period_returns = list(random_returns.loc[counter, :])\n if counter == 0:\n\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment for c in range(len(boy_value))]\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\n base_df.loc[counter, 'total_net_fees'] = 0.0\n base_df.loc[counter, 'income'] = 0.0\n base_investment = base_df.loc[counter, boy_value].sum()\n\n elif (counter > 0) and (counter < income_starts):\n\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment * (1 + period_returns[c])\n for c in range(len(boy_value))]\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\n base_df.loc[counter, 'adv_fees'] = base_df.loc[counter, 'total'] * adv_fees\n base_df.loc[counter, 'total_net_fees'] = base_df.loc[counter, 'total'] - base_df.loc[\n counter, 'adv_fees']\n\n # --coupon payment is invested back into the risky portfolio until the income is withdrawn----\n base_investment = base_df.loc[counter, 'total_net_fees'] + cpn_income_base\n\n else:\n\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment * (1 + period_returns[c])\n for c in range(len(boy_value))]\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\n base_df.loc[counter, 'adv_fees'] = base_df.loc[counter, 'total'] * adv_fees\n\n # ---req. income is adjusted for inflation from the second year of withdrawal. Reinvestment of coupon\n # stops from the year income starts. Req. income is reduced by the coupon payments\n\n if counter == income_starts:\n\n income_needed = req_annual_income - social\n base_df.loc[counter, 'income'] = income_needed - cpn_income_base\n income_needed = req_annual_income\n\n else:\n income_needed = income_needed * (1 + inflation) - social\n base_df.loc[counter, 'income'] = income_needed - cpn_income_base\n income_needed = income_needed + social\n\n base_df.loc[counter, 'total_net_fees'] = base_df.loc[counter, 'total'] - \\\n base_df.loc[counter, 'adv_fees'] - \\\n base_df.loc[counter, 'income']\n\n base_investment = base_df.loc[counter, 'total_net_fees']\n\n base_df.loc[:, 'adj_total'] = base_df.loc[:, 'total_net_fees'].apply(lambda x: x if x > 0 else 0)\n sim_base_total.loc[:, 's_{}'.format(str(runs))] = base_df.loc[:, 'total_net_fees']\n sim_base_income.loc[:, 's_{}'.format(str(runs))] = base_df.loc[:, 'income']\n\n # -------------------required income----------------------------------\n req_annual_income = float(read_income_inputs.loc['annual_income', 'inputs'])\n income_needed = req_annual_income - social\n # income_net_fia_income = max(0, income_needed - income_from_fia)\n cpn_income_base = base_investment * wtd_cpn_yield\n\n # ----------------------------FIA PORTFOLIO----------------------------------------------\n for name in boy_value:\n fia_portfolio_df.loc[:, name] = 0.0\n\n for counter in years:\n period_returns = list(random_returns.loc[counter, :])\n if counter == 0:\n\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment\n for c in range(len(boy_value))]\n fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\n fia_portfolio_df.loc[counter, 'total_net_fees'] = 0.0\n fia_portfolio_df.loc[counter, 'income'] = 0.0\n port_investment = fia_portfolio_df.loc[counter, boy_value].sum()\n\n elif (counter > 0) and (counter < income_starts):\n\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment * (1 + period_returns[c])\n for c in range(len(boy_value))]\n fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\n fia_portfolio_df.loc[counter, 'adv_fees'] = fia_portfolio_df.loc[counter, 'total'] * adv_fees\n fia_portfolio_df.loc[counter, 'total_net_fees'] = fia_portfolio_df.loc[counter, 'total'] - \\\n fia_portfolio_df.loc[counter, 'adv_fees']\n\n port_investment = fia_portfolio_df.loc[counter, 'total_net_fees'] + cpn_income_port\n\n else:\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment * (1 + period_returns[c])\n for c in range(len(boy_value))]\n fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\n fia_portfolio_df.loc[counter, 'adv_fees'] = fia_portfolio_df.loc[counter, 'total'] * adv_fees\n\n # ---req. income is adjusted for inflation from the second year of withdrawal. Reinvestment of coupon\n # stops from the year income starts. Req. income is reduced by the coupon payments\n\n if counter == income_starts:\n\n income_needed = req_annual_income - social\n income_net_fia_income = max(0, income_needed - income_from_fia)\n fia_portfolio_df.loc[counter, 'income'] = max(0, income_net_fia_income - cpn_income_port)\n income_needed = req_annual_income\n\n else:\n income_needed = income_needed * (1 + inflation) - social\n income_net_fia_income = max(0, income_needed - income_from_fia)\n fia_portfolio_df.loc[counter, 'income'] = max(0, income_net_fia_income - cpn_income_port)\n income_needed = income_needed + social\n\n fia_portfolio_df.loc[counter, 'total_net_fees'] = fia_portfolio_df.loc[counter, 'total'] - \\\n fia_portfolio_df.loc[counter, 'adv_fees'] - \\\n fia_portfolio_df.loc[counter, 'income']\n\n port_investment = fia_portfolio_df.loc[counter, 'total_net_fees']\n\n sim_port_total.loc[:, 's_{}'.format(str(runs))] = fia_portfolio_df.loc[:, 'total_net_fees'] + \\\n income_df.loc[:, 'contract_value']\n\n sim_port_income.loc[:, 's_{}'.format(str(runs))] = fia_portfolio_df.loc[:, 'income']\n\n fia_portfolio_df.loc[:, 'adj_total'] = fia_portfolio_df.loc[:, 'total_net_fees'].apply(\n lambda x: x if x > 0 else 0)\n\n # -------------------------------------income breakdown for Base portfolio----------------------------------\n base_df.to_csv(src + method + '_base_port_detail.csv')\n sim_base_total.to_csv(src + 'base_ending_values.csv')\n income_breakdown_base = pd.DataFrame(sim_base_total.quantile(0.5, axis=1))\n income_breakdown_base.loc[:, 'income_from_portfolio'] = sim_base_income.quantile(0.5, axis=1)\n income_breakdown_base.loc[:, 'fia_income'] = 0.0\n income_breakdown_base.loc[:, 'social_security_income'] = social\n income_breakdown_base.loc[:, 'coupon_income'] = cpn_income_base\n\n income_breakdown_base.rename(columns={income_breakdown_base.columns[0]: 'portfolio_ending_value'}, inplace=True)\n income_breakdown_base.loc[:, 'income_from_portfolio'][\n income_breakdown_base.loc[:, 'portfolio_ending_value'] <= 0] = 0\n income_breakdown_base.loc[:, 'total_income'] = income_breakdown_base.loc[:, income_breakdown_base.columns[1:]].sum(\n axis=1)\n\n # ------------------------------------Block Ends-------------------------------------------------------------\n\n # ------------------------------income breakdown for FIA portfolio----------------------------------\n fia_portfolio_df.loc[:, 'contract_value'] = income_df.loc[:, 'contract_value']\n fia_portfolio_df.to_csv(src + method + '_fia_port_detail.csv')\n sim_port_total.to_csv(src + 'fiaport_ending_values.csv')\n\n income_breakdown_port = pd.DataFrame(sim_port_total.quantile(0.5, axis=1))\n income_breakdown_port.loc[:, 'income_from_portfolio'] = sim_port_income.quantile(0.5, axis=1)\n income_breakdown_port.loc[:, 'fia_income'] = income_from_fia\n income_breakdown_port.loc[:, 'social_security_income'] = social\n income_breakdown_port.loc[:, 'coupon_income'] = cpn_income_port\n\n income_breakdown_port.rename(columns={income_breakdown_port.columns[0]: 'portfolio_ending_value'}, inplace=True)\n income_breakdown_port.loc[:, 'income_from_portfolio'][\n income_breakdown_port.loc[:, 'portfolio_ending_value'] <= 0] = 0\n income_breakdown_port.loc[:, 'total_income'] = income_breakdown_port.loc[:, income_breakdown_port.columns[1:]].sum(\n axis=1)\n\n # ------------------------------------Block Ends-------------------------------------------------------------\n q_cut = [0.0, .05, 0.25, 0.5, 0.75, 0.95, 1.0]\n sim_base_income[sim_base_total < income_needed] = 0.0\n\n sim_port_income[sim_port_total < income_net_fia_income] = 0\n\n sim_port_income = sim_port_income + income_from_fia\n\n base_quantile = sim_base_total.loc[sim_base_total.index[-1]].quantile(q_cut)\n\n port_quantile = sim_port_total.loc[sim_port_total.index[-1]].quantile(q_cut)\n\n cols = ['Min', '5th', '25th', '50th', '75th', '90th', 'Max']\n\n # ----------------------------------drop year 0---------------------------------\n sim_base_total = sim_base_total[1:]\n sim_port_total = sim_port_total[1:]\n\n # --------------------------------quantile analysis for base terminal value-----------------------------\n base_qcut = pd.DataFrame(index=sim_base_total.index, columns=cols)\n for c in range(len(cols)):\n base_qcut.loc[:, cols[c]] = sim_base_total.quantile(q_cut[c], axis=1)\n\n base_qcut.clip(lower=0, inplace=True)\n\n # ----------------------------------quantile analysis for base income----------------------------\n base_income_qcut = pd.DataFrame(index=sim_base_income.index, columns=cols)\n for c in range(len(cols)):\n base_income_qcut.loc[:, cols[c]] = sim_base_income.quantile(q_cut[c], axis=1)\n\n # ----Remove NaN's prior to the income start years------------\n # base_income_qcut = base_income_qcut.loc[income_starts:]\n\n # -------------quantile analysis for portfolio terminal value ---------------\n\n port_qcut = pd.DataFrame(index=sim_port_total.index, columns=cols)\n for c in range(len(cols)):\n port_qcut.loc[:, cols[c]] = sim_port_total.quantile(q_cut[c], axis=1)\n\n port_qcut.clip(lower=0, inplace=True)\n\n # ---------------quantile analysis for portfolio income----------------------------\n port_income_qcut = pd.DataFrame(index=sim_port_income.index, columns=cols)\n for c in range(len(cols)):\n port_income_qcut.loc[:, cols[c]] = sim_port_income.quantile(q_cut[c], axis=1)\n\n # ----Remove NaN's prior to the income start years------------\n # port_income_qcut = port_income_qcut.loc[income_starts:]\n\n # ----------probability ending value will be less than 0 at the end of the horizon -------------------------\n\n base_legacy_risk = (sim_base_total.loc[sim_base_total.index[life_expectancy - clients_age]] < 0).sum() / (\n trials + 1)\n port_legacy_risk = (sim_port_total.loc[sim_port_total.index[life_expectancy - clients_age]] < 0).sum() / (\n trials + 1)\n\n legacy_risk = pd.DataFrame([base_legacy_risk, port_legacy_risk,\n 'Prob. of portfolio value less than 0 at the end of the expected life'],\n index=['base', 'fia_portfolio', 'Notes'],\n columns=['Ruin Probability'])\n\n # -----------Year-wise probability of ending value greater than 0 -----------------\n base_psuccess = sim_base_total.apply(lambda x: x > 0).sum(axis=1) / (trials + 1)\n port_psuccess = sim_port_total.apply(lambda x: x > 0).sum(axis=1) / (trials + 1)\n\n # -----------------------WRITING FILES TO EXCEL ---------------------------\n col_names = ['50th', 'age', 'comment']\n writer = pd.ExcelWriter(src + method + '_historical_simulated_income_summary.xlsx', engine='xlsxwriter')\n read_income_inputs.to_excel(writer, sheet_name='inputs_for_income')\n\n read_returns_est.to_excel(writer, sheet_name='asset_returns_estimates')\n\n age_index = list(range(clients_age + 1, clients_age + len(base_qcut) + 1))\n base_qcut.loc[:, 'age'] = age_index\n base_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n base_qcut.loc[income_starts:, col_names].to_excel(writer, sheet_name='base_ending_value_quantiles')\n\n base_income_qcut = base_income_qcut.loc[1:, :]\n base_income_qcut.loc[:, 'age'] = age_index\n base_income_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n base_income_qcut.loc[income_starts:, col_names].to_excel(writer, sheet_name='base_income_quantiles')\n\n port_qcut.loc[:, 'age'] = age_index\n port_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n port_qcut.loc[income_starts:, col_names].to_excel(writer, sheet_name='fia_port_ending_value_quantiles')\n\n port_income_qcut = port_income_qcut.loc[1:, :]\n port_income_qcut.loc[:, 'age'] = age_index\n port_income_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n port_income_qcut.loc[income_starts:, col_names].to_excel(writer, sheet_name='fia_port_income_quantiles')\n\n # prob_success_df = pd.concat([base_psuccess, port_psuccess], axis=1)\n # prob_success_df.rename(columns={prob_success_df.columns[0]: 'prob(ending_value>0)_base',\n # prob_success_df.columns[1]: 'prob(ending_value>0)_port'}, inplace=True)\n\n # prob_success_df.loc[:, 'age'] = age_index\n # prob_success_df.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n # prob_success_df.to_excel(writer, sheet_name='success_probability')\n\n income_breakdown_base = income_breakdown_base.loc[1:, :]\n income_breakdown_base.loc[:, 'age'] = age_index\n income_breakdown_base.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n income_breakdown_base.loc[income_starts:, :].to_excel(writer, sheet_name='base_income_breakdown_median')\n\n income_breakdown_port = income_breakdown_port.loc[1:, :]\n income_breakdown_port.loc[:, 'age'] = age_index\n income_breakdown_port.loc[:, 'ending contract value'] = income_df.loc[:, 'contract_value'].fillna(0)\n income_breakdown_port.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n\n income_breakdown_port.loc[income_starts:, :].to_excel(writer, sheet_name='fia_income_breakdown_median')\n\n if method == 'normal':\n # median_returns_normal.loc[:, 'fia_median_returns'] = median_normal_fia\n # median_returns_normal.to_excel(writer, sheet_name='unsorted_port_return')\n save_normal.to_excel(writer, sheet_name='port_return_unsorted')\n\n elif method == 'smallest':\n # median_returns_smallest.loc[:, 'fia_median_returns'] = median_smallest_fia\n # median_returns_smallest.to_excel(writer, sheet_name='port_return_asc')\n save_asc.to_excel(writer, sheet_name='port_return_asc')\n\n else:\n # median_returns_largest.loc[:, 'fia_median_returns'] = median_largest_fia\n # median_returns_largest.to_excel(writer, sheet_name='port_return_desc')\n save_desc.to_excel(writer, sheet_name='port_return_desc')\n\n # terminal_val = pd.read_csv(src + 'terminal_values.csv', index_col=[0])\n # ending_val = pd.read_csv(src + 'ending_values.csv', index_col=[0])\n # ending_val_ror = pd.read_csv(src + 'ending_values_ror.csv', index_col=[0])\n\n # terminal_val.to_excel(writer, sheet_name='terminal_values')\n # ending_val.to_excel(writer, sheet_name='port_ending_values')\n # ending_val_ror.to_excel(writer, sheet_name='port_annual_growth')\n\n writer.save()\n\n # -----------------Plotting charts--------------------------------------------\n # base_qcut.loc[income_starts:].plot(grid=True, title='Quantile Terminal Value - Base Portfolio')\n # plt.savefig(src + \"quantile_terminal_base.png\")\n # plt.close('all')\n #\n # base_income_qcut.plot(grid=True, title='Quantile Income - Base Portfolio')\n # plt.savefig(src + \"quantile_income_base.png\")\n # plt.close('all')\n #\n # base_psuccess.plot(grid=True, title='Probability of Success (Portfolio Ending Value > 0) - Base Portfolio')\n # plt.savefig(src + \"success_probabilty_base.png\")\n # plt.close('all')\n #\n # (1 - base_psuccess).plot(grid=True, title='Probability of Ruin (Portfolio Ending Value < 0) - Base Portfolio')\n # plt.savefig(src + \"ruin_probability_base.png\")\n # plt.close('all')\n #\n # port_qcut.loc[income_starts:].plot(grid=True, title='Quantile Terminal Value - FIA Portfolio')\n # plt.savefig(src + \"quantile_terminal_fia.png\")\n # plt.close('all')\n #\n # port_income_qcut.plot(grid=True, title='Quantile Income - FIA Portfolio')\n # plt.savefig(src + \"quantile_income_fia.png\")\n # plt.close('all')\n #\n # port_psuccess.plot(grid=True, title='Probability of Success (Portfolio Ending Value > 0) - FIA Portfolio')\n # plt.savefig(src + \"success_probabilty_fia.png\")\n # plt.close('all')\n #\n # (1 - port_psuccess).plot(grid=True, title='Probability of Ruin (Portfolio Ending Value < 0) - FIA Portfolio')\n # plt.savefig(src + \"ruin_probability_fia.png\")\n # plt.close('all')\n\n print(\"simulation completed for {}\".format(method))","def test_interest_vs_stockprice(self):\n stock_prices = np.array([[5, 10, 20, 40]], dtype=float)\n interest_rate = 2.0 # 200%\n test_case = StockMarket(5, stock_prices, interest_rate)\n test_case.dynamic_programming_bottom_up()\n for portfolio in set(test_case.backtracing_portfolio()):\n self.assertEqual(0, portfolio)","def portfolio_simulations_using_target_returns(num_of_years=30, trials=100):\n print(\"Running portfolio_simulations_using_target_returns() method\")\n sim_fia_cv = pd.DataFrame(index=range(num_of_years + 1))\n\n sim_base_total = pd.DataFrame(index=range(num_of_years + 1))\n sim_base_income = pd.DataFrame(index=range(num_of_years + 1))\n\n sim_port_total = pd.DataFrame(index=range(num_of_years + 1))\n sim_port_income = pd.DataFrame(index=range(num_of_years + 1))\n\n read_income_inputs = pd.read_excel(src + \"portfolio_information.xlsx\", sheet_name='income_model_inputs',\n index_col=[0])\n\n read_returns_est = pd.read_excel(src + \"portfolio_information.xlsx\", sheet_name='income_assets_returns_estimates',\n index_col=[0])\n\n read_asset_weights = pd.read_excel(src + \"portfolio_information.xlsx\", sheet_name='asset_weights',\n index_col=[0])\n\n # read random returns for simulation\n read_normal = pd.read_csv(src + 'median_returns_unsorted.csv', index_col=[0], parse_dates=True)\n cols = [read_normal.columns[c].split('_')[1] for c in np.arange(len(read_normal.columns))]\n read_normal.rename(columns=dict(zip(list(read_normal.columns), cols)), inplace=True)\n idx = list(read_normal.index)\n\n runs = 0\n while runs <= trials:\n # --------Shuffling the path of random returns from the median portfolio\n np.random.shuffle(idx)\n read_normal = read_normal.set_index([idx])\n read_normal = read_normal.sort_index()\n\n # assets_col_names = list(read_normal.columns)\n # tickers = list(read_asset_weights.index)\n wts = np.array(read_asset_weights.loc[:, 'base'])\n\n def asset_median_returns(data, ticker):\n return data.filter(regex=ticker).median(axis=1)\n\n # dataframe for unsorted returns (normal)\n median_returns_normal = read_normal.copy()\n median_returns_normal.loc[:, 'portfolio_return'] = median_returns_normal.dot(wts)\n median_normal_fia = pd.DataFrame({'FIA': asset_median_returns(read_normal, 'FIA')})\n\n years = list(range(0, num_of_years + 1))\n income_cols = ['year', 'strategy_term', 'index_returns', 'term_ret', 'term_ret_with_par', 'term_annualize',\n 'ann_net_spread', 'term_ret_netspr', 'high_inc_benefit_base', 'rider_fee', 'eoy_income',\n 'contract_value']\n\n term = int(read_income_inputs.loc['term', 'inputs'])\n fia_ret = read_returns_est.loc[read_returns_est.index[-1], 'Annualized Returns']\n fia_risk = read_returns_est.loc[read_returns_est.index[-1], 'Annualized Risk']\n par_rate = float(read_income_inputs.loc['par_rate', 'inputs'])\n spread = float(read_income_inputs.loc['spread', 'inputs'])\n bonus_term = int(read_income_inputs.loc['bonus_term', 'inputs'])\n premium = float(read_income_inputs.loc['premium', 'inputs'])\n income_bonus = float(read_income_inputs.loc['income_bonus', 'inputs'])\n\n income_starts = int(read_income_inputs.loc['start_income_years', 'inputs'])\n income_growth = float(read_income_inputs.loc['income_growth', 'inputs'])\n rider_fee = float(read_income_inputs.loc['rider_fee', 'inputs'])\n inc_payout_factor = float(read_income_inputs.loc['income_payout_factor', 'inputs'])\n contract_bonus = float(read_income_inputs.loc['contract_bonus', 'inputs'])\n social = float(read_income_inputs.loc['social', 'inputs'])\n inflation = float(read_income_inputs.loc['inflation', 'inputs'])\n wtd_cpn_yield = float(read_income_inputs.loc['wtd_coupon_yld', 'inputs'])\n life_expectancy = int(read_income_inputs.loc['life_expectancy_age', 'inputs'])\n clients_age = int(read_income_inputs.loc['clients_age', 'inputs'])\n\n # ---------------INCOME MODEL--------------------------------------------\n # runs = 0\n returns_dict = {}\n asset_dict = {}\n fia_dict = {}\n\n income_df = pd.DataFrame(index=years, columns=income_cols)\n income_df.loc[:, 'year'] = years\n income_df.loc[:, 'strategy_term'] = income_df.loc[:, 'year'] % term\n income_df.loc[:, 'strategy_term'] = income_df['strategy_term'].apply(lambda x: 1 if x == 0 else 0)\n\n income_df.loc[:, 'index_returns'] = read_normal.loc[:, 'FIA']\n\n cumprod = (1. + income_df['index_returns']).rolling(window=term).agg(lambda x: x.prod()) - 1\n income_df.loc[:, 'term_ret'] = np.where(income_df.loc[:, 'strategy_term'] == 1, cumprod, 0)\n income_df.loc[:, 'term_ret_with_par'] = income_df.loc[:, 'term_ret'] * par_rate\n income_df.loc[:, 'term_annualize'] = income_df.loc[:, 'term_ret_with_par'].apply(\n lambda x: (1 + x) ** (1 / term) - 1)\n income_df.loc[:, 'ann_net_spread'] = income_df.loc[:, 'term_annualize'] - spread\n income_df.loc[:, 'ann_net_spread'] = np.where(income_df.loc[:, 'strategy_term'] == 1,\n income_df.loc[:, 'ann_net_spread'], 0)\n income_df.loc[:, 'term_ret_netspr'] = income_df.loc[:, 'ann_net_spread'].apply(lambda x: (1 + x) ** term - 1)\n\n for counter in years:\n if counter == 0:\n income_df.loc[counter, 'high_inc_benefit_base'] = premium * (1 + income_bonus)\n\n elif counter <= min(bonus_term, income_starts):\n income_df.loc[counter, 'high_inc_benefit_base'] = income_df.loc[counter - 1, 'high_inc_benefit_base'] * \\\n (1 + income_growth)\n else:\n income_df.loc[counter, 'high_inc_benefit_base'] = income_df.loc[counter - 1, 'high_inc_benefit_base']\n\n income_df.loc[:, 'rider_fee'] = income_df.loc[:, 'high_inc_benefit_base'] * rider_fee\n income_df.loc[:, 'eoy_income'] = np.where(income_df.loc[:, 'year'] > income_starts,\n income_df.loc[:, 'high_inc_benefit_base'] * inc_payout_factor, 0)\n\n for counter in years:\n if counter == 0:\n income_df.loc[counter, 'contract_value'] = premium * (1 + contract_bonus)\n\n elif income_df.loc[counter, 'strategy_term'] == 1:\n x1 = income_df.loc[counter - 1, 'contract_value'] - income_df.loc[counter, 'rider_fee']\n x2 = (x1 * (1 + income_df.loc[counter, 'term_ret_netspr'])) - income_df.loc[counter, 'eoy_income']\n income_df.loc[counter, 'contract_value'] = x2\n\n else:\n x1 = income_df.loc[counter - 1, 'contract_value'] - income_df.loc[counter, 'rider_fee'] - \\\n income_df.loc[counter, 'eoy_income']\n\n income_df.loc[counter, 'contract_value'] = x1\n\n # variable stores the income number that is used in the base and fia portfolio calcs.\n\n income_from_fia = income_df.loc[income_df.index[-1], 'eoy_income']\n\n income_df.loc[:, 'contract_value'] = income_df.loc[:, 'contract_value'].apply(lambda x: 0 if x <= 0 else x)\n\n sim_fia_cv.loc[:, str(runs)] = income_df.loc[:, 'contract_value']\n\n # --------------------BASE MODEL---------------------------------------------\n\n base_wts = read_asset_weights.loc[:, 'base']\n base_assets = list(base_wts.index)\n base_weights = list(base_wts.values)\n base_returns = list(read_returns_est.loc[:, 'Annualized Returns'].values)\n base_std = list(read_returns_est.loc[:, 'Annualized Risk'].values)\n\n base_investment = float(read_income_inputs.loc['risky_assets', 'Base'])\n adv_fees = float(read_income_inputs.loc['advisor_fees', 'Base'])\n\n # -------------------required income----------------------------------\n req_annual_income = float(read_income_inputs.loc['annual_income', 'inputs'])\n income_needed = req_annual_income - social\n income_net_fia_income = max(0, income_needed - income_from_fia)\n cpn_income_base = base_investment * wtd_cpn_yield\n\n # ----------------------RANDOM RETURNS--------------------------\n r_cols = base_assets\n boy_value = ['bv_{}'.format(name) for name in base_assets]\n eoy_value = ['ev_{}'.format(name) for name in base_assets]\n\n random_returns = pd.DataFrame(index=income_df.index, columns=r_cols)\n\n random_returns = read_normal.copy()\n\n base_df = random_returns.copy()\n fia_portfolio_df = random_returns.copy()\n port_investment = float(read_income_inputs.loc['risky_assets', 'FIA'])\n cpn_income_port = port_investment * wtd_cpn_yield\n\n # -------------BASE PORTFOLIO----------------------------\n for name in boy_value:\n base_df.loc[:, name] = 0.0\n\n for counter in years:\n period_returns = list(random_returns.loc[counter, :])\n if counter == 0:\n\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment for c in range(len(boy_value))]\n\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\n base_df.loc[counter, 'total_net_fees'] = 0.0\n base_df.loc[counter, 'income'] = 0.0\n base_investment = base_df.loc[counter, boy_value].sum()\n\n elif (counter > 0) and (counter < income_starts):\n\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment * (1 + period_returns[c])\n for c in range(len(boy_value))]\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\n base_df.loc[counter, 'adv_fees'] = base_df.loc[counter, 'total'] * adv_fees\n base_df.loc[counter, 'total_net_fees'] = base_df.loc[counter, 'total'] - base_df.loc[\n counter, 'adv_fees']\n\n # --coupon payment is invested back into the risky portfolio until the income is withdrawn----\n base_investment = base_df.loc[counter, 'total_net_fees'] + cpn_income_base\n\n else:\n\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment * (1 + period_returns[c])\n for c in range(len(boy_value))]\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\n base_df.loc[counter, 'adv_fees'] = base_df.loc[counter, 'total'] * adv_fees\n\n # ---req. income is adjusted for inflation from the second year of withdrawal. Reinvestment of coupon\n # stops from the year income starts. Req. income is reduced by the coupon payments\n\n if counter == income_starts:\n\n income_needed = req_annual_income - social\n base_df.loc[counter, 'income'] = income_needed - cpn_income_base\n income_needed = req_annual_income\n\n else:\n income_needed = income_needed * (1 + inflation) - social\n base_df.loc[counter, 'income'] = income_needed - cpn_income_base\n income_needed = income_needed + social\n\n base_df.loc[counter, 'total_net_fees'] = base_df.loc[counter, 'total'] - \\\n base_df.loc[counter, 'adv_fees'] - \\\n base_df.loc[counter, 'income']\n\n base_investment = base_df.loc[counter, 'total_net_fees']\n\n base_df.loc[:, 'adj_total'] = base_df.loc[:, 'total_net_fees'].apply(lambda x: x if x > 0 else 0)\n sim_base_total.loc[:, 's_{}'.format(str(runs))] = base_df.loc[:, 'total_net_fees']\n sim_base_income.loc[:, 's_{}'.format(str(runs))] = base_df.loc[:, 'income']\n\n # ----------------------------FIA PORTFOLIO----------------------------------------------\n for name in boy_value:\n fia_portfolio_df.loc[:, name] = 0.0\n\n for counter in years:\n period_returns = list(random_returns.loc[counter, :])\n if counter == 0:\n\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment\n for c in range(len(boy_value))]\n fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\n fia_portfolio_df.loc[counter, 'total_net_fees'] = 0.0\n fia_portfolio_df.loc[counter, 'income'] = 0.0\n port_investment = fia_portfolio_df.loc[counter, boy_value].sum()\n\n elif (counter > 0) and (counter < income_starts):\n\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment * (1 + period_returns[c])\n for c in range(len(boy_value))]\n fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\n fia_portfolio_df.loc[counter, 'adv_fees'] = fia_portfolio_df.loc[counter, 'total'] * adv_fees\n fia_portfolio_df.loc[counter, 'total_net_fees'] = fia_portfolio_df.loc[counter, 'total'] - \\\n fia_portfolio_df.loc[counter, 'adv_fees']\n\n port_investment = fia_portfolio_df.loc[counter, 'total_net_fees'] + cpn_income_port\n\n else:\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment * (1 + period_returns[c])\n for c in range(len(boy_value))]\n fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\n fia_portfolio_df.loc[counter, 'adv_fees'] = fia_portfolio_df.loc[counter, 'total'] * adv_fees\n\n # ---req. income is adjusted for inflation from the second year of withdrawal. Reinvestment of coupon\n # stops from the year income starts. Req. income is reduced by the coupon payments\n\n if counter == income_starts:\n\n income_needed = req_annual_income - social\n income_net_fia_income = max(0, income_needed - income_from_fia)\n fia_portfolio_df.loc[counter, 'income'] = max(0, income_net_fia_income - cpn_income_port)\n income_needed = req_annual_income\n\n else:\n income_needed = income_needed * (1 + inflation) - social\n income_net_fia_income = max(0, income_needed - income_from_fia)\n fia_portfolio_df.loc[counter, 'income'] = max(0, income_net_fia_income - cpn_income_port)\n income_needed = income_needed + social\n\n fia_portfolio_df.loc[counter, 'total_net_fees'] = fia_portfolio_df.loc[counter, 'total'] - \\\n fia_portfolio_df.loc[counter, 'adv_fees'] - \\\n fia_portfolio_df.loc[counter, 'income']\n\n port_investment = fia_portfolio_df.loc[counter, 'total_net_fees']\n\n sim_port_total.loc[:, 's_{}'.format(str(runs))] = fia_portfolio_df.loc[:, 'total_net_fees'] + \\\n income_df.loc[:, 'contract_value']\n\n sim_port_income.loc[:, 's_{}'.format(str(runs))] = fia_portfolio_df.loc[:, 'income']\n\n fia_portfolio_df.loc[:, 'adj_total'] = fia_portfolio_df.loc[:, 'total_net_fees'].apply(\n lambda x: x if x > 0 else 0)\n\n runs = runs + 1\n\n # ----% of trials ending value at expected life is less than 0\n inflation_factor = (1 + annual_inflation) ** (life_expectancy - clients_age - income_starts)\n required_income_horizon = income_needed * inflation_factor\n required_income_horizon_net_fia = required_income_horizon - income_from_fia\n\n # prob_success_base = (sim_base_total.iloc[life_expectancy - clients_age] < 0).sum() / trials\n # prob_success_fia_port = (sim_port_total.iloc[life_expectancy - clients_age] < 0).sum() / trials\n\n prob_failure_base = (sim_base_total.iloc[life_expectancy - clients_age] < required_income_horizon).sum() / trials\n prob_failure_fia_port = (sim_port_total.iloc[life_expectancy - clients_age] < required_income_horizon_net_fia) \\\n .sum() / trials\n\n # ----Calculate at total average lifetime income for base portfolio----\n base_inc = sim_base_income.copy()\n base_inc = base_inc.cumsum()\n avg_income_base = base_inc.iloc[life_expectancy - clients_age].mean()\n\n # ----Calculate at total average lifetime income for FIA portfolio----\n port_inc = sim_port_income.copy()\n port_inc = port_inc.cumsum()\n avg_income_port = port_inc.iloc[life_expectancy - clients_age].mean()\n avg_income_port = avg_income_port + (income_from_fia * (life_expectancy - clients_age))\n\n # ---------income breakdown for Base portfolio----------------------------------\n base_df.to_csv(src + 'base_port_detail.csv')\n sim_base_total.to_csv(src + 'base_ending_values.csv')\n income_breakdown_base = pd.DataFrame(sim_base_total.quantile(0.5, axis=1))\n income_breakdown_base.loc[:, 'income_from_portfolio'] = sim_base_income.quantile(0.5, axis=1)\n income_breakdown_base.loc[:, 'fia_income'] = 0.0\n income_breakdown_base.loc[:, 'social_security_income'] = social\n income_breakdown_base.loc[:, 'coupon_income'] = cpn_income_base\n\n income_breakdown_base.rename(columns={income_breakdown_base.columns[0]: 'portfolio_ending_value'}, inplace=True)\n income_breakdown_base.loc[:, 'income_from_portfolio'][\n income_breakdown_base.loc[:, 'portfolio_ending_value'] <= 0] = 0\n income_breakdown_base.loc[:, 'total_income'] = income_breakdown_base.loc[:, income_breakdown_base.columns[1:]].sum(\n axis=1)\n\n # ------------Block Ends-------------------------------------------------------------\n\n # ---------income breakdown for FIA portfolio----------------------------------\n fia_portfolio_df.to_csv(src + 'fia_port_detail.csv')\n sim_port_total.to_csv(src + 'fiaport_ending_values.csv')\n\n income_breakdown_port = pd.DataFrame(sim_port_total.quantile(0.5, axis=1))\n income_breakdown_port.loc[:, 'income_from_portfolio'] = sim_port_income.quantile(0.5, axis=1)\n income_breakdown_port.loc[:, 'fia_income'] = income_from_fia\n income_breakdown_port.loc[:, 'social_security_income'] = social\n income_breakdown_port.loc[:, 'coupon_income'] = cpn_income_port\n\n income_breakdown_port.rename(columns={income_breakdown_port.columns[0]: 'portfolio_ending_value'}, inplace=True)\n income_breakdown_port.loc[:, 'income_from_portfolio'][\n income_breakdown_port.loc[:, 'portfolio_ending_value'] <= 0] = 0\n income_breakdown_port.loc[:, 'total_income'] = income_breakdown_port.loc[:, income_breakdown_port.columns[1:]].sum(\n axis=1)\n\n # ------------Block Ends-------------------------------------------------------------\n q_cut = [0.0, 0.05, 0.25, 0.5, 0.75, 0.95, 1.0]\n sim_base_income[sim_base_total < income_needed] = 0.0\n\n sim_port_income[sim_port_total < income_net_fia_income] = 0\n\n sim_port_income = sim_port_income + income_from_fia\n\n # base_quantile = sim_base_total.loc[sim_base_total.index[-1]].quantile([0.05, 0.25, 0.50, 0.75, 0.90])\n #\n # port_quantile = sim_port_total.loc[sim_port_total.index[-1]].quantile([0.05, 0.25, 0.50, 0.75, 0.90])\n\n base_quantile = sim_base_total.loc[sim_base_total.index[-1]].quantile(q_cut)\n\n port_quantile = sim_port_total.loc[sim_port_total.index[-1]].quantile(q_cut)\n\n # q_cut = [0.0, .05, 0.25, 0.5, 0.75, 0.95, 1.0]\n cols = ['Min', '5th', '25th', '50th', '75th', '90th', 'Max']\n\n # ----------------------------drop year 0--------------------------------\n sim_base_total = sim_base_total[1:]\n sim_port_total = sim_port_total[1:]\n\n # ---------quantile analysis for base terminal value-----------------------------\n base_qcut = pd.DataFrame(index=sim_base_total.index, columns=cols)\n for c in range(len(cols)):\n base_qcut.loc[:, cols[c]] = sim_base_total.quantile(q_cut[c], axis=1)\n\n base_qcut.clip(lower=0, inplace=True)\n\n # ---------quantile analysis for base income----------------------------\n base_income_qcut = pd.DataFrame(index=sim_base_income.index, columns=cols)\n for c in range(len(cols)):\n base_income_qcut.loc[:, cols[c]] = sim_base_income.quantile(q_cut[c], axis=1)\n\n # ----Remove NaN's prior to the income start years------------\n # base_income_qcut = base_income_qcut.loc[income_starts:]\n\n # -------------quantile analysis for portfolio terminal value ---------------\n\n port_qcut = pd.DataFrame(index=sim_port_total.index, columns=cols)\n for c in range(len(cols)):\n port_qcut.loc[:, cols[c]] = sim_port_total.quantile(q_cut[c], axis=1)\n\n port_qcut.clip(lower=0, inplace=True)\n\n # ---------------quantile analysis for portfolio income----------------------------\n port_income_qcut = pd.DataFrame(index=sim_port_income.index, columns=cols)\n for c in range(len(cols)):\n port_income_qcut.loc[:, cols[c]] = sim_port_income.quantile(q_cut[c], axis=1)\n\n # ----Remove NaN's prior to the income start years------------\n # port_income_qcut = port_income_qcut.loc[income_starts:]\n\n # ----------probability ending value will be less than 0 at the end of the horizon -------------------------\n\n base_legacy_risk = (sim_base_total.loc[sim_base_total.index[life_expectancy - clients_age]] < 0).sum() / (\n trials + 1)\n port_legacy_risk = (sim_port_total.loc[sim_port_total.index[life_expectancy - clients_age]] < 0).sum() / (\n trials + 1)\n\n legacy_risk = pd.DataFrame([base_legacy_risk, port_legacy_risk,\n 'Prob. of portfolio value less than 0 at the end of the expected life'],\n index=['base', 'fia_portfolio', 'Notes'],\n columns=['Ruin Probability'])\n\n # -----------Year-wise probability of ending value greater than 0 -----------------\n base_psuccess = sim_base_total.apply(lambda x: x > 0).sum(axis=1) / (trials + 1)\n port_psuccess = sim_port_total.apply(lambda x: x > 0).sum(axis=1) / (trials + 1)\n\n # -----------------------WRITING FILES TO EXCEL ---------------------------\n writer = pd.ExcelWriter(src + 'simulated_portfolios_summary.xlsx', engine='xlsxwriter')\n sim_base_total.to_excel(writer, sheet_name='base_ending_value')\n sim_port_total.to_excel(writer, sheet_name='fiaport_ending_value')\n base_qcut.to_excel(writer, sheet_name='base_quantile_ending')\n base_income_qcut.to_excel(writer, sheet_name='base_quantile_income')\n port_qcut.to_excel(writer, sheet_name='port_quantile_ending')\n port_income_qcut.to_excel(writer, sheet_name='port_quantile_income')\n\n sucess_df = pd.concat([base_psuccess, port_psuccess], axis=1)\n sucess_df.rename(columns={sucess_df.columns[0]: 'Base', sucess_df.columns[1]: 'Fia_Port'}, inplace=True)\n\n base_mean = sim_base_total[sim_base_total <= 0].isnull().sum().mean()\n port_mean = sim_port_total[sim_port_total <= 0].isnull().sum().mean()\n\n base_median = sim_base_total[sim_base_total <= 0].isnull().sum().median()\n port_median = sim_port_total[sim_port_total <= 0].isnull().sum().median()\n\n stats_df = pd.DataFrame([[base_mean, port_mean, 'Average years portfolio ending value > 0, out of N trials'],\n [base_median, port_median, 'Ending Value >0, 50% of the time']],\n index=['Mean years', 'Median years'], columns=['Base', 'fia_port', 'Comment'])\n\n # ---Average of terminal values at the end of horizon from N Trials\n stats_df.loc['Average Portfolio', 'Base'] = sim_base_total.iloc[-1].mean() + clients_age\n stats_df.loc['Average Portfolio', 'fia_port'] = sim_port_total.iloc[-1].mean() + clients_age\n stats_df.loc['Average Portfolio', 'Comment'] = \"Average of terminal values at the end of analysis period\" \\\n \" from N Trials\"\n\n # ----Median of terminal values at the end of horizon from N Trials\n stats_df.loc['Median Portfolio', 'Base'] = sim_base_total.iloc[-1].median() + clients_age\n stats_df.loc['Median Portfolio', 'fia_port'] = sim_port_total.iloc[-1].median() + clients_age\n stats_df.loc['Median Portfolio', 'Comment'] = \"Median of terminal values at the end of analysis period \" \\\n \"from N Trials\"\n\n # ---Average of terminal values at the end of Actuarial life from N Trials Base Portfolio\n stats_df.loc['Average Portfolio (end of expected_life)', 'Base'] = sim_base_total.iloc[\n life_expectancy - clients_age].mean()\n\n # ----Median of terminal values at the end of horizon from N Trials Base Portfolio\n stats_df.loc['Median Portfolio (end of expected_life)', 'Base'] = sim_base_total.iloc[\n life_expectancy - clients_age].median()\n\n # ---Average of terminal values at the end of Actuarial life from N Trials - FIA portfolio\n stats_df.loc['Average Portfolio (end of expected_life)', 'fia_port'] = sim_port_total.iloc[\n life_expectancy - clients_age].mean()\n stats_df.loc['Average Portfolio (end of expected_life)', 'Comment'] = \"Average of terminal values at the end of \" \\\n \"Actuarial life from N Trials\"\n\n # ----Median of terminal values at the end of horizon from N Trials - FIA Portfolio\n stats_df.loc['Median Portfolio (end of expected_life)', 'fia_port'] = sim_port_total.iloc[\n life_expectancy - clients_age].median()\n stats_df.loc['Median Portfolio (end of expected_life)', 'Comment'] = \"Median of terminal values at the end of \" \\\n \"horizon from N Trials\"\n\n stats_df.loc['% Success(value>0 at the end of expected_life)', 'Base'] = 1 - prob_failure_base\n stats_df.loc['% Success(value>0 at the end of expected_life)', 'fia_port'] = 1 - prob_failure_fia_port\n stats_df.loc['% Success(value>0 at the end of expected_life)', 'Comment'] = \"% of N trials yearly ending value \" \\\n \"greater than 0\"\n\n # -----Mininum of N portfolios terminal value at the end of Actuarial Age\n stats_df.loc['Minimum Value', 'Base'] = sim_base_total.iloc[life_expectancy - clients_age].min()\n stats_df.loc['Minimum Value', 'fia_port'] = sim_port_total.iloc[life_expectancy - clients_age].min()\n stats_df.loc['Minimum Value', 'Comment'] = \"Mininum of N portfolios terminal value at the end of Actuarial Age\"\n\n # -----Maxinum of N portfolios terminal value at the end of Actuarial Age\n stats_df.loc['Maximum Value', 'Base'] = sim_base_total.iloc[life_expectancy - clients_age].max()\n stats_df.loc['Maximum Value', 'fia_port'] = sim_port_total.iloc[life_expectancy - clients_age].max()\n stats_df.loc['Maximum Value', 'Comment'] = \"Maxinum of N portfolios terminal value at the end of Actuarial Age\"\n\n # -----Average income from N portfolios at the ned of Actuarial Age\n stats_df.loc['Avg Income (lifetime)', 'Base'] = avg_income_base\n stats_df.loc['Avg Income (lifetime)', 'fia_port'] = avg_income_port\n stats_df.loc['Avg Income (lifetime)', 'Comment'] = \"Average income from N portfolios at the end of Actuarial Age\"\n\n sucess_df.to_excel(writer, sheet_name='yearly_success_rates')\n stats_df.to_excel(writer, sheet_name='Stats')\n\n writer.save()\n\n print(\"simulation completed.\")","def realtest(r,stds,mean_returns,covar,start2,end2,tickers):\n \n # Get allocation weights of minimum variance portfolio for r during model time period\n weights = calc_min_variance_portfolio(mean_returns,covar,r)\n \n # Get data during backtesting time period\n from find_optimized_portfolio import process_data\n stds_new,mean_returns_new,covar_new,tickers,start2,end2=process_data(start2,end2,tickers)\n \n # Dot backtesting period's returns with model's weights to get the portfolio's return\n # during backtest period\n realtest_r = weights @ mean_returns_new\n # Get stdev during backtest period\n realtest_std=calc_portfolio_stdev(covar_new,weights)\n\n return realtest_r,realtest_std, weights, tickers, start2, end2","def portfolio_performance(returns,weights):\r\n print('Calculating Portfolio Performance')\r\n # returns=target_asset_port_data_attributes['component_returns']\r\n # weights =target_asset_port_data_attributes['effective_weights']\r\n\r\n component_returns= returns\r\n compnent_weights = pd.DataFrame(data=np.nan,index= component_returns.index,columns=component_returns.columns)\r\n compnent_weights.loc[weights.index,:] = weights\r\n\r\n portfolio_dates = component_returns.index\r\n components = component_returns.columns\r\n\r\n # pre-allocate\r\n BoP_df = pd.DataFrame(data=np.nan,index=portfolio_dates,columns=components)\r\n EoP_df = pd.DataFrame(data=np.nan,index=portfolio_dates,columns=components)\r\n PnL_df = pd.DataFrame(data=np.nan,index=portfolio_dates,columns=components)\r\n portfolio_BoP = pd.DataFrame(data=np.nan,index=portfolio_dates,columns=['Portfolio BoP'])\r\n portfolio_EoP = pd.DataFrame(data=np.nan,index=portfolio_dates,columns=['Portfolio EoP'])\r\n portfolio_PnL = pd.DataFrame(data=np.nan,index=portfolio_dates,columns=['Portfolio PnL'])\r\n \r\n portfolio_index = pd.DataFrame(data=np.nan,index=portfolio_dates,columns=['Index'])\r\n previous_index_value = np.int64(1)\r\n\r\n pre_date = portfolio_dates[0]\r\n # set BoP to start weights\r\n for date,row in component_returns.iterrows():\r\n # print(date)\r\n # 1st date\r\n if date == portfolio_dates[0]:\r\n BoP_df.loc[date] = compnent_weights.iloc[0,:]\r\n EoP_df.loc[date] = BoP_df.loc[date] * (1+component_returns.loc[date])\r\n PnL_df.loc[date] = EoP_df.loc[date].subtract(BoP_df.loc[date])\r\n\r\n portfolio_BoP.loc[date] = BoP_df.loc[date].sum()\r\n portfolio_EoP.loc[date] = EoP_df.loc[date].sum()\r\n portfolio_PnL.loc[date] = PnL_df.loc[date].sum()\r\n\r\n portfolio_index.loc[date] = np.nansum([previous_index_value,portfolio_PnL.loc[date].values])\r\n previous_index_value = portfolio_index.loc[date]\r\n pre_date = date\r\n\r\n # after first date\r\n else:\r\n BoP_df.loc[date] = EoP_df.loc[pre_date]\r\n # weights override\r\n if date in compnent_weights.index:\r\n none_NaN_index = ~compnent_weights.loc[date].isnull()\r\n if not compnent_weights.loc[date][none_NaN_index].empty:\r\n tmp_sum = BoP_df.loc[date].sum()\r\n BoP_df.loc[date][none_NaN_index.values] = (compnent_weights.loc[date][none_NaN_index.values].values)*tmp_sum\r\n\r\n \r\n EoP_df.loc[date] = BoP_df.loc[date] * (1+component_returns.loc[date])\r\n PnL_df.loc[date] = EoP_df.loc[date].subtract(BoP_df.loc[date])\r\n\r\n portfolio_BoP.loc[date] = BoP_df.loc[date].sum()\r\n portfolio_EoP.loc[date] = EoP_df.loc[date].sum()\r\n portfolio_PnL.loc[date] = PnL_df.loc[date].sum()\r\n \r\n portfolio_index.loc[date] = np.nansum([previous_index_value,portfolio_PnL.loc[date].values])\r\n previous_index_value = portfolio_index.loc[date]\r\n pre_date = date\r\n\r\n\r\n portfolio_returns = portfolio_index.pct_change(1) \r\n portfolio_returns.columns = ['Returns']\r\n\r\n portfolio_index\r\n perf = portfolio_index.calc_stats()\r\n \r\n output = pd.Series(data = [perf,PnL_df,portfolio_index,portfolio_BoP,portfolio_EoP,BoP_df], index=['Portfolio Perf','Component PnL','portfolio_index','portfolio_BoP','portfolio_EoP','BoP_df'])\r\n return output","def _run_backtest(self):\n i = 0\n \n while True:\n i += 1\n print(i)\n \n # Update the market bars\n if self.data_handler.continue_backtest == True:\n self.data_handler.update_bars()\n else:\n break\n \n # Handle the Events\n while True:\n try:\n event = self.events.get(False)\n except queue.Empty:\n break\n else:\n # The inner-loop acts on the events by calling the appropriate method of the appropriate object\n if event is not None:\n if event.type == 'MARKET':\n self.strategy.calculate_signals(event)\n self.portfolio.update_timeindex(event)\n \n elif event.type == 'SIGNAL':\n self.signals += 1\n self.portfolio.update_signal(event)\n \n elif event.type == 'ORDER':\n self.orders += 1\n self.execution_handler.execute_order(event)\n \n elif event.type == 'FILL':\n self.fills += 1\n self.portfolio.update_fill(event)\n \n # Pauses for a duration of self.heartbeat seconds\n time.sleep(self.heartbeat)","def after_run(self):\n # Calculate the performance of the strategy and portfolio\n self.portfolio.calc_stats()\n self.calc_performance()\n\n return self","def _process_results(self):\n self.portfolio.create_backtest_result_dataframe()\n stats = self._show_stats()\n return stats","def _output_performance(self):\n self.portfolio.create_equity_curve_dataframe()\n \n print(\"Creating summary statistics...\")\n stats = self.portfolio.output_summary_stats()\n \n print(\"Creating equity curve...\")\n print(self.portfolio.equity_curve.tail(10))\n pprint.pprint(stats)\n \n print(\"Signals: %s\" % self.signals)\n print(\"Orders: %s\" % self.orders)\n print(\"Fills: %s\" % self.fills)","def _output_performance(self):\n self.portfolio.create_equity_curve_dataframe()\n\n print(\"Creating summary statistics...\")\n stats = self.portfolio.output_summary_stats()\n\n print(\"Creating equity curve...\")\n print(self.portfolio.equity_curve.tail(10))\n pprint.pprint(stats)\n\n print(\"Signals: %s\" % self.signals)\n print(\"Orders: %s\" % self.orders)\n print(\"Fills: %s\" % self.fills)","def backtesting_engine(symbol, strategy, fromdate, todate, args=None):\n\n # Backtesting Engine\n cerebro = bt.Cerebro()\n\n # Add a Strategy if no Data Required for the model\n if args is None:\n cerebro.addstrategy(strategy)\n # If the Strategy requires a Model and therefore data\n elif args is not None:\n cerebro.addstrategy(strategy, args)\n\n # Retrieve Data from Alpaca\n data = bt.feeds.YahooFinanceData(\n dataname=symbol,\n fromdate=fromdate, # datetime.date(2015, 1, 1)\n todate=todate, # datetime.datetime(2016, 1, 1)\n reverse=False\n )\n\n # Add Data to Backtesting Engine\n cerebro.adddata(data)\n\n # Set Initial Portfolio Value\n cerebro.broker.setcash(100000.0)\n\n # Add Analysis Tools\n cerebro.addanalyzer(bt.analyzers.SharpeRatio, _name='sharpe')\n cerebro.addanalyzer(bt.analyzers.Returns, _name='returns')\n cerebro.addanalyzer(bt.analyzers.SQN, _name='sqn')\n cerebro.addanalyzer(bt.analyzers.DrawDown, _name='drawdown')\n cerebro.addanalyzer(bt.analyzers.PositionsValue, _name='posval')\n cerebro.addanalyzer(bt.analyzers.PyFolio, _name='pyfolio')\n\n # Starting Portfolio Value\n print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())\n\n # Run the Backtesting Engine\n backtest = cerebro.run()\n\n # Print Analysis and Final Portfolio Value\n print(\n 'Final Portfolio Value: %.2f' % cerebro.broker.getvalue()\n )\n print(\n 'Return: ', backtest[0].analyzers.returns.get_analysis()\n )\n print(\n 'Sharpe Ratio: ', backtest[0].analyzers.sharpe.get_analysis()\n )\n print(\n 'System Quality Number: ', backtest[0].analyzers.sqn.get_analysis()\n )\n print(\n 'Drawdown: ', backtest[0].analyzers.drawdown.get_analysis()\n )\n print(\n 'Active Position Value: ', backtest[0].analyzers.posval.get_analysis()\n )\n print(\n 'Pyfolio: ', backtest[0].analyzers.pyfolio.get_analysis()\n )\n\n # Print Analysis and Final Portfolio Value\n pyfoliozer = backtest[0].analyzers.getbyname('pyfolio')\n returns, positions, transactions, gross_lev = pyfoliozer.get_pf_items()\n\n # See if we can add regular FB data to compare against returns of algo\n pf.create_full_tear_sheet(\n returns, positions=positions, transactions=transactions\n )","def portfolio_analytics(port_returns, market_returns):\n\n # add the intercept to the model\n x2 = sm.add_constant(market_returns)\n\n # train the model\n estimator = sm.OLS(port_returns, x2)\n model = estimator.fit()\n\n # get portfolio analytics\n alpha, beta = model.params\n r_squared = model.rsquared\n regression = model.predict()\n\n return alpha, beta, r_squared, regression","def portfolio_analytics(port_returns, market_returns):\n\n # add the intercept to the model\n x2 = sm.add_constant(market_returns)\n\n # train the model\n estimator = sm.OLS(port_returns, x2)\n model = estimator.fit()\n\n # get portfolio analytics\n alpha, beta = model.params\n r_squared = model.rsquared\n regression = model.predict()\n\n return alpha, beta, r_squared, regression","def income_model_constant_portfolio_return(num_of_years=30, trials=100, method='normal'):\n\n sim_fia_cv = pd.DataFrame(index=range(num_of_years + 1))\n\n sim_base_total = pd.DataFrame(index=range(num_of_years + 1))\n sim_base_income = pd.DataFrame(index=range(num_of_years + 1))\n\n sim_port_total = pd.DataFrame(index=range(num_of_years + 1))\n sim_port_income = pd.DataFrame(index=range(num_of_years + 1))\n\n sim_base_total_pre_income = pd.DataFrame(index=range(num_of_years + 1))\n sim_port_total_pre_income = pd.DataFrame(index=range(num_of_years + 1))\n\n sim_base_total_preincome = pd.DataFrame(index=range(num_of_years + 1))\n sim_port_total_preincome = pd.DataFrame(index=range(num_of_years + 1))\n\n # read_income_inputs = pd.read_csv(src + \"income_model_inputs.csv\", index_col='Items')\n read_income_inputs = pd.read_excel(src + \"portfolio_information.xlsx\", sheet_name='income_model_inputs',\n index_col=[0])\n\n # read_returns_est = pd.read_csv(src + \"income_assets_returns_estimates.csv\", index_col='Symbol')\n read_returns_est = pd.read_excel(src + \"portfolio_information.xlsx\", sheet_name='income_assets_returns_estimates',\n index_col=[0])\n\n # read_returns_est.drop(['BM', read_returns_est.index[-1]], axis=0, inplace=True)\n # read_portfolio_inputs = pd.read_csv(src + \"income_portfolio_inputs.csv\", index_col='Items')\n\n # read_asset_weights = pd.read_csv(src + \"asset_weights.csv\", index_col='Asset')\n read_asset_weights = pd.read_excel(src + \"portfolio_information.xlsx\", sheet_name='asset_weights',\n index_col=[0])\n\n read_asset_weights.drop(read_asset_weights.index[-1], axis=0, inplace=True)\n\n # read random returns for simulation\n read_normal = pd.read_csv(src + 'sort_normal.csv', index_col=[0], parse_dates=True)\n read_small = pd.read_csv(src + 'sort_small_to_large.csv', index_col=[0], parse_dates=True)\n read_large = pd.read_csv(src + 'sort_large_to_small.csv', index_col=[0], parse_dates=True)\n assets_col_names = list(read_normal.columns)\n\n tickers = list(read_asset_weights.index)\n wts = np.array(read_asset_weights.loc[:, 'base'])\n\n def asset_median_returns(data, ticker):\n return data.filter(regex=ticker).median(axis=1)\n\n # # dataframe for unsorted returns (normal)\n # median_returns_normal = pd.DataFrame({t: asset_median_returns(read_normal, t) for t in tickers})\n # median_returns_normal.loc[:, 'portfolio_return'] = median_returns_normal.dot(wts)\n # median_normal_fia = pd.DataFrame({'FIA': asset_median_returns(read_normal, 'r_FIA')})\n #\n # # dataframe for smallest to largest returns\n # median_returns_smallest = pd.DataFrame({t: asset_median_returns(read_small, t) for t in tickers})\n # median_returns_smallest.loc[:, 'portfolio_return'] = median_returns_smallest.dot(wts)\n # median_smallest_fia = pd.DataFrame({'FIA': asset_median_returns(read_small, 'r_FIA')})\n #\n # # dataframe for unsorted returns (normal)\n # median_returns_largest = pd.DataFrame({t: asset_median_returns(read_large, t) for t in tickers})\n # median_returns_largest.loc[:, 'portfolio_return'] = median_returns_largest.dot(wts)\n # median_largest_fia = pd.DataFrame({'FIA': asset_median_returns(read_large, 'r_FIA')})\n\n years = list(range(0, num_of_years + 1))\n income_cols = ['year', 'strategy_term', 'index_returns', 'term_ret', 'term_ret_with_par', 'term_annualize',\n 'ann_net_spread', 'term_ret_netspr', 'high_inc_benefit_base', 'rider_fee', 'eoy_income',\n 'contract_value']\n\n term = int(read_income_inputs.loc['term', 'inputs'])\n fia_ret = read_returns_est.loc[read_returns_est.index[-1], 'Annualized Returns']\n fia_risk = read_returns_est.loc[read_returns_est.index[-1], 'Annualized Risk']\n par_rate = float(read_income_inputs.loc['par_rate', 'inputs'])\n spread = float(read_income_inputs.loc['spread', 'inputs'])\n bonus_term = int(read_income_inputs.loc['bonus_term', 'inputs'])\n premium = float(read_income_inputs.loc['premium', 'inputs'])\n income_bonus = float(read_income_inputs.loc['income_bonus', 'inputs'])\n\n income_starts = int(read_income_inputs.loc['start_income_years', 'inputs'])\n income_growth = float(read_income_inputs.loc['income_growth', 'inputs'])\n rider_fee = float(read_income_inputs.loc['rider_fee', 'inputs'])\n inc_payout_factor = float(read_income_inputs.loc['income_payout_factor', 'inputs'])\n contract_bonus = float(read_income_inputs.loc['contract_bonus', 'inputs'])\n social = float(read_income_inputs.loc['social', 'inputs'])\n inflation = float(read_income_inputs.loc['inflation', 'inputs'])\n wtd_cpn_yield = float(read_income_inputs.loc['wtd_coupon_yld', 'inputs'])\n life_expectancy = int(read_income_inputs.loc['life_expectancy_age', 'inputs'])\n clients_age = int(read_income_inputs.loc['clients_age', 'inputs'])\n \n # -------------For Constant Growth Rates------------------------\n const_fia_index_ret = float(read_income_inputs.loc['const_fia_index_ret', 'inputs'])\n const_risky_port_ret = float(read_income_inputs.loc['const_risky_port_ret', 'inputs'])\n\n # ---------------INCOME MODEL--------------------------------------------\n runs = 0\n returns_dict = {}\n asset_dict = {}\n fia_dict = {}\n\n income_df = pd.DataFrame(index=years, columns=income_cols)\n income_df.loc[:, 'year'] = years\n income_df.loc[:, 'strategy_term'] = income_df.loc[:, 'year'] % term\n income_df.loc[:, 'strategy_term'] = income_df['strategy_term'].apply(lambda x: 1 if x == 0 else 0)\n\n if method == 'normal':\n # income_df.loc[:, 'index_returns'] = read_normal.loc[:, '{}_{}'.format('r_FIA', str(runs))]\n # ----------CONSTANT FIA INDEX GROWTH RATE-------------------\n income_df.loc[:, 'index_returns'] = const_fia_index_ret\n\n elif method == 'smallest':\n income_df.loc[:, 'index_returns'] = read_small.loc[:, '{}_{}'.format('r_FIA', str(runs))]\n\n else:\n income_df.loc[:, 'index_returns'] = read_large.loc[:, '{}_{}'.format('r_FIA', str(runs))]\n\n # income_df.loc[:, 'index_returns'] = np.random.normal(fia_ret, fia_risk, size=(len(years), 1))\n\n cumprod = (1. + income_df['index_returns']).rolling(window=term).agg(lambda x: x.prod()) - 1\n income_df.loc[:, 'term_ret'] = np.where(income_df.loc[:, 'strategy_term'] == 1, cumprod, 0)\n income_df.loc[:, 'term_ret_with_par'] = income_df.loc[:, 'term_ret'] * par_rate\n income_df.loc[:, 'term_annualize'] = income_df.loc[:, 'term_ret_with_par'].apply(\n lambda x: (1 + x) ** (1 / term) - 1)\n income_df.loc[:, 'ann_net_spread'] = income_df.loc[:, 'term_annualize'] - spread\n income_df.loc[:, 'ann_net_spread'] = np.where(income_df.loc[:, 'strategy_term'] == 1,\n income_df.loc[:, 'ann_net_spread'], 0)\n income_df.loc[:, 'term_ret_netspr'] = income_df.loc[:, 'ann_net_spread'].apply(lambda x: (1 + x) ** term - 1)\n\n for counter in years:\n if counter == 0:\n income_df.loc[counter, 'high_inc_benefit_base'] = premium * (1 + income_bonus)\n\n elif counter <= min(bonus_term, income_starts):\n income_df.loc[counter, 'high_inc_benefit_base'] = income_df.loc[counter - 1, 'high_inc_benefit_base'] * \\\n (1 + income_growth)\n else:\n income_df.loc[counter, 'high_inc_benefit_base'] = income_df.loc[counter - 1, 'high_inc_benefit_base']\n\n income_df.loc[:, 'rider_fee'] = income_df.loc[:, 'high_inc_benefit_base'] * rider_fee\n income_df.loc[:, 'eoy_income'] = np.where(income_df.loc[:, 'year'] > income_starts,\n income_df.loc[:, 'high_inc_benefit_base'] * inc_payout_factor, 0)\n\n for counter in years:\n if counter == 0:\n income_df.loc[counter, 'contract_value'] = premium * (1 + contract_bonus)\n\n elif income_df.loc[counter, 'strategy_term'] == 1:\n x1 = income_df.loc[counter - 1, 'contract_value'] - income_df.loc[counter, 'rider_fee']\n x2 = (x1 * (1 + income_df.loc[counter, 'term_ret_netspr'])) - income_df.loc[counter, 'eoy_income']\n income_df.loc[counter, 'contract_value'] = x2\n\n else:\n x1 = income_df.loc[counter - 1, 'contract_value'] - income_df.loc[counter, 'rider_fee'] - \\\n income_df.loc[counter, 'eoy_income']\n\n income_df.loc[counter, 'contract_value'] = x1\n\n # variable stores the income number that is used in the base and fia portfolio calcs.\n\n income_from_fia = income_df.loc[income_df.index[-1], 'eoy_income']\n\n income_df.loc[:, 'contract_value'] = income_df.loc[:, 'contract_value'].apply(lambda x: 0 if x <= 0 else x)\n\n sim_fia_cv.loc[:, str(runs)] = income_df.loc[:, 'contract_value']\n\n # -------------------------------------BASE MODEL---------------------------------------------\n\n base_wts = read_asset_weights.loc[:, 'base']\n base_assets = list(base_wts.index)\n base_weights = list(base_wts.values)\n base_returns = list(read_returns_est.loc[:, 'Annualized Returns'].values)\n base_std = list(read_returns_est.loc[:, 'Annualized Risk'].values)\n\n base_investment = float(read_income_inputs.loc['risky_assets', 'Base'])\n adv_fees = float(read_income_inputs.loc['advisor_fees', 'Base'])\n\n # -------------------required income----------------------------------\n req_annual_income = float(read_income_inputs.loc['annual_income', 'inputs'])\n income_needed = req_annual_income - social\n income_net_fia_income = max(0, income_needed - income_from_fia)\n cpn_income_base = base_investment * wtd_cpn_yield\n\n # ----------------------RANDOM RETURNS--------------------------\n r_cols = ['r_{}'.format(name) for name in base_assets]\n boy_value = ['bv_{}'.format(name) for name in base_assets]\n eoy_value = ['ev_{}'.format(name) for name in base_assets]\n\n random_returns = pd.DataFrame(index=income_df.index, columns=r_cols)\n\n # for c in range(len(r_cols)):\n # ret = np.random.normal(base_returns[c], base_std[c], size=(len(random_returns.index), 1))\n\n if method == 'smallest':\n this_run_cols = ['{}_{}'.format(cname, str(runs)) for cname in r_cols]\n random_returns = read_small.loc[:, this_run_cols]\n\n # random_returns.loc[:, r_cols[c]] = np.sort(ret.flatten())\n # asset_dict.update({'{}_{}'.format(r_cols[c], str(runs)): np.sort(ret.flatten())})\n\n elif method == 'largest':\n this_run_cols = ['{}_{}'.format(cname, str(runs)) for cname in r_cols]\n random_returns = read_large.loc[:, this_run_cols]\n\n # random_returns.loc[:, r_cols[c]] = np.flip(np.sort(ret.flatten()))\n # asset_dict.update({'{}_{}'.format(r_cols[c], str(runs)): np.flip(np.sort(ret.flatten()))})\n\n else:\n this_run_cols = ['{}_{}'.format(cname, str(runs)) for cname in r_cols]\n random_returns = read_normal.loc[:, this_run_cols]\n\n # random_returns.loc[:, r_cols[c]] = ret.flatten()\n # asset_dict.update({'{}_{}'.format(r_cols[c], str(runs)): ret.flatten()})\n\n # store the simulated assets returns in one dictionary\n # returns_dict.update({str(runs): random_returns})\n\n # collect the asset based returns from all simulation and calculate the median returns.\n # def get_median_returns(sym):\n # cols = [sym + '_' + str(c) for c in np.arange(trials)]\n # asset_df = pd.DataFrame({c: asset_dict.get(c) for c in cols})\n # return asset_df.median(axis=1)\n #\n # asset_median_returns = pd.DataFrame({symbol: get_median_returns(symbol) for symbol in r_cols})\n #\n # asset_median_returns.loc[:, 'simulated_portfolio_median_returns'] = asset_median_returns.dot(base_weights)\n\n base_df = random_returns.copy()\n pre_income_base_df = random_returns.copy()\n\n # base_investment = float(read_portfolio_inputs.loc['risky_assets', 'Base'])\n\n fia_portfolio_df = random_returns.copy()\n pre_income_port_df = random_returns.copy()\n port_investment = float(read_income_inputs.loc['risky_assets', 'FIA'])\n cpn_income_port = port_investment * wtd_cpn_yield\n\n # ---------Initial Investments for pre-income account values---------------------\n pre_income_base_inv = base_investment\n pre_income_port_inv = port_investment\n # ----------------------------------------BASE PORTFOLIO----------------------------\n for name in boy_value:\n base_df.loc[:, name] = 0.0\n pre_income_base_df.loc[:, name] = 0.0\n\n for counter in years:\n period_returns = list(random_returns.loc[counter, :])\n if counter == 0:\n # ---------------For year 0, the year of investment------------\n\n # ------------Calculate the annual portfolio returns - Gross Returns--------------------\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment for c in range(len(boy_value))]\n\n # -------------Record the Pre Income Base Portfolio-----------------------------\n\n pre_income_base_df.loc[counter, boy_value] = [base_weights[c] *\n pre_income_base_inv for c in range(len(boy_value))]\n pre_income_base_df.loc[counter, 'total'] = pre_income_base_df.loc[counter, boy_value].sum()\n pre_income_base_inv = pre_income_base_df.loc[counter, boy_value].sum()\n\n # ------------------Pre Income Block Ends------------------------\n\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\n\n base_df.loc[counter, 'total_net_fees'] = 0.0\n base_df.loc[counter, 'income'] = 0.0\n # base_investment = base_df.loc[counter, boy_value].sum()\n base_df.loc[counter, 'total_pre_income'] = base_investment\n\n elif (counter > 0) and (counter < income_starts):\n\n # ----For years between the start of the investment and start if the income---------------\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment * (1 + period_returns[c])\n for c in range(len(boy_value))]\n\n # -------------Record the Pre Income Base Portfolio-----------------------------\n pre_income_base_df.loc[counter, boy_value] = [\n base_weights[c] * pre_income_base_inv * (1 + period_returns[c])\n for c in range(len(boy_value))]\n\n # pre_income_base_df.loc[counter, 'total'] = pre_income_base_df.loc[counter, boy_value].sum()\n pre_income_base_df.loc[counter, 'total'] = base_investment * (1 + const_risky_port_ret)\n pre_income_base_df.loc[counter, 'adv_fees'] = pre_income_base_df.loc[counter, 'total'] * adv_fees\n pre_income_base_df.loc[counter, 'total_net_fees'] = pre_income_base_df.loc[counter, 'total'] - \\\n pre_income_base_df.loc[counter, 'adv_fees']\n pre_income_base_inv = pre_income_base_df.loc[counter, 'total_net_fees'] + cpn_income_base\n\n # ------------------Pre Income Block Ends------------------------\n\n # base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\n base_df.loc[counter, 'total'] = base_investment * (1 + 0.06)\n base_df.loc[counter, 'adv_fees'] = base_df.loc[counter, 'total'] * adv_fees\n base_df.loc[counter, 'total_net_fees'] = base_df.loc[counter, 'total'] - base_df.loc[\n counter, 'adv_fees']\n\n # --coupon payment is invested back into the risky portfolio until the income is withdrawn----\n base_investment = base_df.loc[counter, 'total_net_fees'] + cpn_income_base\n base_df.loc[counter, 'total_pre_income'] = base_df.loc[counter, 'total_net_fees']\n\n else:\n\n # -------------For Years after the income started----------------------\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment * (1 + period_returns[c])\n for c in range(len(boy_value))]\n\n # -------------Record the Pre Income Base Portfolio-----------------------------\n pre_income_base_df.loc[counter, boy_value] = [\n base_weights[c] * pre_income_base_inv * (1 + period_returns[c])\n for c in range(len(boy_value))]\n\n # pre_income_base_df.loc[counter, 'total'] = pre_income_base_df.loc[counter, boy_value].sum()\n pre_income_base_df.loc[counter, 'total'] = base_investment * (1 + const_risky_port_ret)\n pre_income_base_df.loc[counter, 'adv_fees'] = pre_income_base_df.loc[counter, 'total'] * adv_fees\n pre_income_base_df.loc[counter, 'total_net_fees'] = pre_income_base_df.loc[counter, 'total'] - \\\n pre_income_base_df.loc[counter, 'adv_fees']\n pre_income_base_inv = pre_income_base_df.loc[counter, 'total_net_fees'] + cpn_income_base\n\n # ------------------Pre Income Block Ends------------------------\n\n # base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\n base_df.loc[counter, 'total'] = base_investment * (1 + const_risky_port_ret)\n base_df.loc[counter, 'adv_fees'] = base_df.loc[counter, 'total'] * adv_fees\n\n # ---req. income is adjusted for inflation from the second year of withdrawal. Reinvestment of coupon\n # stops from the year income starts. Req. income is reduced by the coupon payments\n\n if counter == income_starts:\n\n income_needed = req_annual_income - social\n base_df.loc[counter, 'income'] = income_needed - cpn_income_base\n income_needed = req_annual_income\n\n else:\n income_needed = income_needed * (1 + inflation) - social\n base_df.loc[counter, 'income'] = income_needed - cpn_income_base\n income_needed = income_needed + social\n\n base_df.loc[counter, 'total_net_fees'] = base_df.loc[counter, 'total'] - \\\n base_df.loc[counter, 'adv_fees'] - \\\n base_df.loc[counter, 'income']\n\n base_df.loc[counter, 'total_pre_income'] = base_df.loc[counter, 'total'] - \\\n base_df.loc[counter, 'adv_fees']\n\n base_investment = base_df.loc[counter, 'total_net_fees']\n\n # -------------------Portfolio with PreIncome Values----------------------------\n sim_base_total_preincome.loc[:, 's_{}'.format(str(runs))] = pre_income_base_df.loc[:, 'total_net_fees']\n sim_base_total_preincome.fillna(float(read_income_inputs.loc['risky_assets', 'Base']), inplace=True)\n # --------------------------------PreIncome Block Ends----------------------------\n\n base_df.loc[:, 'adj_total'] = base_df.loc[:, 'total_net_fees'].apply(lambda x: x if x > 0 else 0)\n sim_base_total.loc[:, 's_{}'.format(str(runs))] = base_df.loc[:, 'total_net_fees']\n sim_base_income.loc[:, 's_{}'.format(str(runs))] = base_df.loc[:, 'income']\n sim_base_total_pre_income.loc[:, 's_{}'.format(str(runs))] = base_df.loc[:, 'total_pre_income']\n\n # ----------------------------FIA PORTFOLIO----------------------------------------------\n for name in boy_value:\n fia_portfolio_df.loc[:, name] = 0.0\n pre_income_port_df.loc[:, name] = 0.0\n\n for counter in years:\n period_returns = list(random_returns.loc[counter, :])\n if counter == 0:\n\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment\n for c in range(len(boy_value))]\n\n # -------------Record the Pre Income Base Portfolio-----------------------------\n\n pre_income_port_df.loc[counter, boy_value] = [base_weights[c] *\n pre_income_port_inv for c in range(len(boy_value))]\n pre_income_port_df.loc[counter, 'total'] = pre_income_port_df.loc[counter, boy_value].sum()\n pre_income_port_inv = pre_income_port_df.loc[counter, boy_value].sum()\n\n # ------------------Pre Income Block Ends------------------------\n\n fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\n fia_portfolio_df.loc[counter, 'total_net_fees'] = 0.0\n fia_portfolio_df.loc[counter, 'income'] = 0.0\n # port_investment = fia_portfolio_df.loc[counter, boy_value].sum()\n fia_portfolio_df.loc[counter, 'total_pre_income'] = port_investment\n\n elif (counter > 0) and (counter < income_starts):\n\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment * (1 + period_returns[c])\n for c in range(len(boy_value))]\n\n # ------------------Record the Pre Income Base Portfolio-----------------------------\n pre_income_port_df.loc[counter, boy_value] = [\n base_weights[c] * pre_income_port_inv * (1 + period_returns[c])\n for c in range(len(boy_value))]\n\n # pre_income_port_df.loc[counter, 'total'] = pre_income_port_df.loc[counter, boy_value].sum()\n \n # -----------------------CONSTANT GROWTH RATE-----------------\n pre_income_port_df.loc[counter, 'total'] = port_investment * (1 + const_risky_port_ret)\n\n pre_income_port_df.loc[counter, 'adv_fees'] = pre_income_port_df.loc[counter, 'total'] * adv_fees\n pre_income_port_df.loc[counter, 'total_net_fees'] = pre_income_port_df.loc[counter, 'total'] - \\\n pre_income_port_df.loc[counter, 'adv_fees']\n pre_income_port_inv = pre_income_port_df.loc[counter, 'total_net_fees'] + cpn_income_base\n\n # ------------------Pre Income Block Ends------------------------\n\n # fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\n \n # -------CONSTANT GROWTH RATE-----------------\n fia_portfolio_df.loc[counter, 'total'] = port_investment * (1 + const_risky_port_ret)\n\n fia_portfolio_df.loc[counter, 'adv_fees'] = fia_portfolio_df.loc[counter, 'total'] * adv_fees\n fia_portfolio_df.loc[counter, 'total_net_fees'] = fia_portfolio_df.loc[counter, 'total'] - \\\n fia_portfolio_df.loc[counter, 'adv_fees']\n fia_portfolio_df.loc[counter, 'total_pre_income'] = fia_portfolio_df.loc[counter, 'total_net_fees']\n\n port_investment = fia_portfolio_df.loc[counter, 'total_net_fees'] + cpn_income_port\n\n else:\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment * (1 + period_returns[c])\n for c in range(len(boy_value))]\n\n # -------------Record the Pre Income Base Portfolio-----------------------------\n pre_income_port_df.loc[counter, boy_value] = [\n base_weights[c] * pre_income_port_inv * (1 + period_returns[c])\n for c in range(len(boy_value))]\n\n # pre_income_port_df.loc[counter, 'total'] = pre_income_port_df.loc[counter, boy_value].sum()\n pre_income_port_df.loc[counter, 'total'] = port_investment * (1 + const_risky_port_ret)\n pre_income_port_df.loc[counter, 'adv_fees'] = pre_income_port_df.loc[counter, 'total'] * adv_fees\n pre_income_port_df.loc[counter, 'total_net_fees'] = pre_income_port_df.loc[counter, 'total'] - \\\n pre_income_port_df.loc[counter, 'adv_fees']\n pre_income_port_inv = pre_income_port_df.loc[counter, 'total_net_fees'] + cpn_income_base\n\n # ------------------Pre Income Block Ends------------------------\n\n # fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\n fia_portfolio_df.loc[counter, 'total'] = port_investment * (1 + const_risky_port_ret)\n fia_portfolio_df.loc[counter, 'adv_fees'] = fia_portfolio_df.loc[counter, 'total'] * adv_fees\n\n # ---req. income is adjusted for inflation from the second year of withdrawal. Reinvestment of coupon\n # stops from the year income starts. Req. income is reduced by the coupon payments\n\n if counter == income_starts:\n\n income_needed = req_annual_income - social\n income_net_fia_income = max(0, income_needed - income_from_fia)\n fia_portfolio_df.loc[counter, 'income'] = max(0, income_net_fia_income - cpn_income_port)\n income_needed = req_annual_income\n\n else:\n income_needed = income_needed * (1 + inflation) - social\n income_net_fia_income = max(0, income_needed - income_from_fia)\n fia_portfolio_df.loc[counter, 'income'] = max(0, income_net_fia_income - cpn_income_port)\n income_needed = income_needed + social\n\n fia_portfolio_df.loc[counter, 'total_net_fees'] = fia_portfolio_df.loc[counter, 'total'] - \\\n fia_portfolio_df.loc[counter, 'adv_fees'] - \\\n fia_portfolio_df.loc[counter, 'income']\n\n fia_portfolio_df.loc[counter, 'total_pre_income'] = fia_portfolio_df.loc[counter, 'total'] - \\\n fia_portfolio_df.loc[counter, 'adv_fees']\n\n port_investment = fia_portfolio_df.loc[counter, 'total_net_fees']\n\n sim_port_total.loc[:, 's_{}'.format(str(runs))] = fia_portfolio_df.loc[:, 'total_net_fees'] + \\\n income_df.loc[:, 'contract_value']\n\n sim_port_income.loc[:, 's_{}'.format(str(runs))] = fia_portfolio_df.loc[:, 'income']\n\n fia_portfolio_df.loc[:, 'adj_total'] = fia_portfolio_df.loc[:, 'total_net_fees'].apply(\n lambda x: x if x > 0 else 0)\n\n sim_port_total_pre_income.loc[:, 's_{}'.format(str(runs))] = fia_portfolio_df.loc[:, 'total_pre_income']\n\n # -------------------Portfolio with PreIncome Values----------------------------\n sim_port_total_preincome.loc[:, 's_{}'.format(str(runs))] = pre_income_port_df.loc[:, 'total_net_fees'] + \\\n income_df.loc[:, 'contract_value']\n\n sim_port_total_preincome.fillna(float(read_income_inputs.loc['risky_assets', 'FIA']), inplace=True)\n \n # --------------------------------PreIncome Block Ends----------------------------\n\n # ------------------Calculate % of portfolios ending value greater than required LIFETIME cumm. income---------\n total_income_by_age = sim_base_income.loc[:, sim_base_income.columns[0]].cumsum()\n total_income_by_acturial_age = total_income_by_age.loc[life_expectancy - clients_age]\n total_income_by_age.fillna(0, inplace=True)\n income_dataframe = pd.DataFrame(total_income_by_age)\n income_dataframe.loc[:, 'remaining_income_by_acturial_age'] = total_income_by_age.apply(\n lambda x: total_income_by_acturial_age - x)\n\n s = income_dataframe.loc[:, 'remaining_income_by_acturial_age']\n base_prob_of_success = sim_base_total.gt(s, axis=0).sum(axis=1)\n port_prob_of_success = sim_port_total.gt(s, axis=0).sum(axis=1)\n\n # ----------------------------Portfolio sufficient for NEXT YEARS income needs-------------------\n next_year_income = sim_base_income.loc[:, sim_base_income.columns[0]].shift(-1).fillna(0) # Yearly Income Reqd.\n base_success_next_year = sim_base_total.gt(next_year_income, axis=0).sum(axis=1)\n\n base_for_next_year_need = sim_base_total[sim_base_total.gt(next_year_income, axis=0)]\n\n port_success_next_year = sim_port_total.gt(next_year_income, axis=0).sum(axis=1)\n\n port_for_next_year_need = sim_port_total[sim_port_total.gt(next_year_income, axis=0)]\n\n # ---------------Portfolio for 45 years of simulation---------------------------------------\n base_success_portfolio = sim_base_total[sim_base_total.gt(next_year_income, axis=0)]\n port_success_portfolio = sim_port_total[sim_port_total.gt(next_year_income, axis=0)]\n\n # ----------------Portfolio Simulation until the acturial age------------------------------\n acturial_years = life_expectancy - clients_age\n base_success_portfolio_act_age = base_success_portfolio.loc[acturial_years, :]\n port_success_portfolio_act_age = port_success_portfolio.loc[acturial_years, :]\n\n # -------------------------Base Portfolio TS with max Terminal Value ----------------------------\n if base_success_portfolio_act_age.isnull().sum() == trials:\n base_max_portfolio = 0.0\n else:\n base_max_portfolio = base_success_portfolio.loc[:, base_success_portfolio_act_age.idxmax()]\n\n # -------------------------FIA Portfolio TS with max Terminal Value ----------------------------\n if port_success_portfolio_act_age.isnull().sum() == trials:\n port_max_portfolio = 0.0\n else:\n port_max_portfolio = port_success_portfolio.loc[:, port_success_portfolio_act_age.idxmax()]\n\n # ------------------------------Average age with full income------------------------------\n base_mean_age = ((life_expectancy - clients_age) - base_success_portfolio.loc[1:life_expectancy - clients_age, :]\n .isnull().sum()).mean()\n\n port_mean_age = ((life_expectancy - clients_age) - port_success_portfolio.loc[1:life_expectancy - clients_age, :]\n .isnull().sum()).mean()\n\n # ----------------------------Median Age with full Income------------------------------------------\n base_median_age = ((life_expectancy - clients_age) - base_success_portfolio.loc[1:life_expectancy - clients_age, :]\n .isnull().sum()).median()\n\n port_median_age = ((life_expectancy - clients_age) - port_success_portfolio.loc[1:life_expectancy - clients_age, :]\n .isnull().sum()).median()\n\n # --------------Mean Value for all the portfolios at end of the acturial age--------------------\n base_act_avg_porfolio = base_success_portfolio.loc[life_expectancy - clients_age, :].fillna(0).mean()\n port_act_avg_porfolio = port_success_portfolio.loc[life_expectancy - clients_age, :].fillna(0).mean()\n\n # --------------Median Value for all the portfolios at end of the acturial age--------------------\n base_act_median_porfolio = base_success_portfolio.loc[life_expectancy - clients_age, :].fillna(0).median()\n port_act_median_porfolio = port_success_portfolio.loc[life_expectancy - clients_age, :].fillna(0).median()\n\n # # --------------Mean Value for all the portfolios in the simulation--------------------\n # base_sim_mean = base_success_portfolio.loc[1:life_expectancy - clients_age, :].mean().mean()\n # port_sim_mean = port_success_portfolio.loc[1:life_expectancy - clients_age, :].mean().mean()\n #\n # # --------------Median Value for all the portfolios in the simulation--------------------\n # base_sim_mean = base_success_portfolio.loc[1:life_expectancy - clients_age, :].mean().median()\n # port_sim_mean = port_success_portfolio.loc[1:life_expectancy - clients_age, :].mean().median()\n\n # -------Max Portfolio value at the end of acturial age----------------------------------------\n base_act_max = base_success_portfolio.loc[life_expectancy - clients_age, :].max()\n port_act_max = port_success_portfolio.loc[life_expectancy - clients_age, :].max()\n\n # -------Min Portfolio value at the end of acturial age----------------------------------------\n base_act_min = base_success_portfolio.loc[life_expectancy - clients_age, :].min()\n port_act_min = port_success_portfolio.loc[life_expectancy - clients_age, :].min()\n\n # ---------------------Lifetime Average Income----------------------------------\n base_total_income = sim_base_income.cumsum().loc[acturial_years, :].mean()\n port_total_income = income_from_fia + sim_port_income\n port_total_income = port_total_income.cumsum().loc[acturial_years, :].mean()\n\n simulation_stats = pd.DataFrame(index=['Average Years', 'Median Years', 'Average Age', 'Median Age',\n 'Average Portfolio (act.age)', 'Median Portfolio (act.age)',\n 'Max Portfolio Value', 'Min Portfolio Value',\n 'Average Lifetime Income'], columns=['Base', 'FIA'])\n\n simulation_stats.loc['Average Years', :] = [base_mean_age, base_mean_age]\n simulation_stats.loc['Median Years', :] = [base_median_age, base_median_age]\n simulation_stats.loc['Average Age', :] = [base_mean_age + clients_age, base_mean_age + clients_age]\n simulation_stats.loc['Median Age', :] = [base_median_age + clients_age, base_median_age + clients_age]\n simulation_stats.loc['Average Portfolio (act.age)', :] = [base_act_avg_porfolio, port_act_avg_porfolio]\n simulation_stats.loc['Median Portfolio (act.age)', :] = [base_act_median_porfolio, port_act_median_porfolio]\n simulation_stats.loc['Max Portfolio Value', :] = [base_act_max, port_act_max]\n simulation_stats.loc['Min Portfolio Value', :] = [base_act_min, port_act_min]\n simulation_stats.loc['Average Lifetime Income', :] = [base_total_income, port_total_income]\n comments = ['Average years of portfolios that meet the next years income needs for the lifetime',\n 'Median years of portfolios that meet the next years income needs for the lifetime',\n 'Average Clients Age',\n 'Median Clients Age',\n 'Average of terminal values for the portfolios at the end of the acturial life',\n 'Median of terminal values for the portfolios at the end of the acturial life',\n 'Maximum of terminal values for the portfolios at the end of the acturial life',\n 'Minimum of terminal values for the portfolios at the end of the acturial life',\n 'Average of total income generated by all portfolios at the end of the acturial life']\n\n simulation_stats.loc[:, 'Notes'] = comments\n\n # --------------------------------------------------------------------------------\n\n # # -----------------------------------income breakdown for Base portfolio----------------------------------\n # base_df.to_csv(src + 'base_port_detail.csv')\n # sim_base_total.to_csv(src + 'base_ending_values.csv')\n # income_breakdown_base = pd.DataFrame(sim_base_total.quantile(0.5, axis=1))\n # income_breakdown_base.loc[:, 'income_from_portfolio'] = sim_base_income.quantile(0.5, axis=1)\n # income_breakdown_base.loc[:, 'fia_income'] = 0.0\n # income_breakdown_base.loc[:, 'social_security_income'] = social\n # income_breakdown_base.loc[:, 'coupon_income'] = cpn_income_base\n #\n # income_breakdown_base.rename(columns={income_breakdown_base.columns[0]: 'portfolio_ending_value'}, inplace=True)\n # income_breakdown_base.loc[:, 'income_from_portfolio'][\n # income_breakdown_base.loc[:, 'portfolio_ending_value'] <= 0] = 0\n # income_breakdown_base.loc[:, 'total_income'] = income_breakdown_base.loc[:, income_breakdown_base.columns[1:]].sum(\n # axis=1)\n #\n # # --------------------------------------Block Ends-----------------------------------------------------------\n #\n # # ---------------------------------------income breakdown for FIA portfolio----------------------------------\n # fia_portfolio_df.to_csv(src + 'fia_port_detail.csv')\n # sim_port_total.to_csv(src + 'fiaport_ending_values.csv')\n #\n # income_breakdown_port = pd.DataFrame(sim_port_total.quantile(0.5, axis=1))\n # income_breakdown_port.loc[:, 'income_from_portfolio'] = sim_port_income.quantile(0.5, axis=1)\n # income_breakdown_port.loc[:, 'fia_income'] = income_from_fia\n # income_breakdown_port.loc[:, 'social_security_income'] = social\n # income_breakdown_port.loc[:, 'coupon_income'] = cpn_income_port\n #\n # income_breakdown_port.rename(columns={income_breakdown_port.columns[0]: 'portfolio_ending_value'}, inplace=True)\n # income_breakdown_port.loc[:, 'income_from_portfolio'][\n # income_breakdown_port.loc[:, 'portfolio_ending_value'] <= 0] = 0\n # income_breakdown_port.loc[:, 'total_income'] = income_breakdown_port.loc[:, income_breakdown_port.columns[1:]].sum(\n # axis=1)\n #\n # # ----------------------------------Block Ends-------------------------------------------------------------\n q_cut = [0.0, 0.1, 0.25, 0.5, 0.75, 0.95, 1.0]\n sim_base_income[sim_base_total < income_needed] = 0.0\n\n sim_port_income[sim_port_total < income_net_fia_income] = 0\n\n sim_port_income = sim_port_income + income_from_fia\n\n # base_quantile = sim_base_total.loc[sim_base_total.index[-1]].quantile([0.05, 0.25, 0.50, 0.75, 0.90])\n #\n # port_quantile = sim_port_total.loc[sim_port_total.index[-1]].quantile([0.05, 0.25, 0.50, 0.75, 0.90])\n\n base_quantile = sim_base_total.loc[sim_base_total.index[-1]].quantile(q_cut)\n\n port_quantile = sim_port_total.loc[sim_port_total.index[-1]].quantile(q_cut)\n\n # q_cut = [0.0, .05, 0.25, 0.5, 0.75, 0.95, 1.0]\n cols = ['Min', '10th', '25th', '50th', '75th', '90th', 'Max']\n\n # ------------------------------------------drop year 0-----------------------------------------\n sim_base_total = sim_base_total[1:]\n sim_port_total = sim_port_total[1:]\n\n # ---------------------------------plot for histogram for porfolios--------------------------------------\n # base_term_value = sim_base_total.loc[sim_base_total.index[:life_expectancy - clients_age], :]\n # fact = 1 / len(base_term_value)\n # base_ann_ret = (base_term_value.iloc[-1] / base_term_value.iloc[0]) ** fact - 1\n # counts, bins, bars = plt.hist(base_ann_ret)\n\n # ------------------------quantile analysis for base terminal value-----------------------------\n base_qcut = pd.DataFrame(index=sim_base_total.index, columns=cols)\n for c in range(len(cols)):\n base_qcut.loc[:, cols[c]] = sim_base_total.quantile(q_cut[c], axis=1)\n\n base_qcut.clip(lower=0, inplace=True)\n\n sim_base_total.clip(lower=0, inplace=True)\n\n # -------------------------------------quantile analysis for base income----------------------------\n base_income_qcut = pd.DataFrame(index=sim_base_income.index, columns=cols)\n for c in range(len(cols)):\n base_income_qcut.loc[:, cols[c]] = sim_base_income.quantile(q_cut[c], axis=1)\n\n # ----Remove NaN's prior to the income start years------------\n # base_income_qcut = base_income_qcut.loc[income_starts:]\n\n # ---------------------------------quantile analysis for portfolio terminal value ---------------\n\n port_qcut = pd.DataFrame(index=sim_port_total.index, columns=cols)\n for c in range(len(cols)):\n port_qcut.loc[:, cols[c]] = sim_port_total.quantile(q_cut[c], axis=1)\n\n port_qcut.clip(lower=0, inplace=True)\n\n # ----------------------------------quantile analysis for portfolio income----------------------------\n port_income_qcut = pd.DataFrame(index=sim_port_income.index, columns=cols)\n for c in range(len(cols)):\n port_income_qcut.loc[:, cols[c]] = sim_port_income.quantile(q_cut[c], axis=1)\n\n # ----Remove NaN's prior to the income start years------------\n # port_income_qcut = port_income_qcut.loc[income_starts:]\n\n # ----------probability ending value will be less than 0 at the end of the horizon -----------------------\n # base_legacy_risk = (sim_base_total.loc[sim_base_total.index[-1]] < 0).sum() / (trials)\n\n base_legacy_risk = (sim_base_total.loc[sim_base_total.index[life_expectancy - clients_age]] < 0).sum() / trials\n port_legacy_risk = (sim_port_total.loc[sim_port_total.index[life_expectancy - clients_age]] < 0).sum() / trials\n\n # port_legacy_risk = (sim_port_total.loc[sim_port_total.index[-1]] <= 0).sum() / (trials)\n\n legacy_risk = pd.DataFrame([base_legacy_risk, port_legacy_risk,\n 'Prob. of portfolio value less than 0 at the end of the expected life'],\n index=['base', 'fia_portfolio', 'Notes'],\n columns=['Ruin Probability'])\n\n # -----------Year-wise probability of ending value greater than 0 -----------------\n base_psuccess = sim_base_total.apply(lambda x: x > 0).sum(axis=1) / trials\n port_psuccess = sim_port_total.apply(lambda x: x > 0).sum(axis=1) / trials\n\n # -----------------------WRITING FILES TO EXCEL ---------------------------\n\n writer = pd.ExcelWriter(dest_simulation + method + '_leveled_growth_simulation.xlsx', engine='xlsxwriter')\n read_income_inputs.to_excel(writer, sheet_name='inputs_for_income')\n\n read_returns_est.to_excel(writer, sheet_name='asset_returns_estimates')\n # read_portfolio_inputs.to_excel(writer, sheet_name='portfolio_inputs')\n\n age_index = list(range(clients_age + 1, clients_age + len(base_qcut) + 1))\n # base_qcut.loc[:, 'clients_age'] = age_index\n # base_qcut.loc[:, 'comment'] = ''\n # base_qcut.loc[:, 'comment'] = np.where(base_qcut.clients_age == life_expectancy, 'expected_life', \"\")\n base_inv = float(read_income_inputs.loc['risky_assets', 'Base'])\n base_qcut.loc[:, 'age'] = age_index\n base_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n\n # -----------------------To start with year 0---------------------------------\n insert_col = [base_inv, base_inv, base_inv, base_inv, base_inv, base_inv,\n base_inv, clients_age, np.nan]\n base_qcut.loc[len(base_qcut) + 1, :] = 0.0\n base_qcut = base_qcut.shift(1)\n base_qcut.iloc[0] = insert_col\n base_qcut.reset_index(drop=True, inplace=True)\n base_qcut.to_excel(writer, sheet_name='base_ending_value_quantiles')\n # base_qcut.loc[income_starts:, :].to_excel(writer, sheet_name='base_ending_value_quantiles')\n\n # base_income_qcut = base_income_qcut[1:] base_income_qcut.loc[:, 'clients_age'] = age_index\n # base_income_qcut.loc[:, 'comment'] = '' base_income_qcut.loc[:, 'comment'] = np.where(\n # base_income_qcut.clients_age == life_expectancy, 'expected_life', \"\")\n\n base_income_qcut = base_income_qcut.loc[1:, :]\n base_income_qcut.loc[:, 'age'] = age_index\n base_income_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n base_income_qcut.loc[income_starts:, :].to_excel(writer, sheet_name='base_income_quantiles')\n\n # age_index = list(range(clients_age+1, clients_age + len(port_qcut)+1))\n # port_qcut.loc[:, 'clients_age'] = age_index\n # port_qcut.loc[:, 'comment'] = ''\n # port_qcut.loc[:, 'comment'] = np.where(port_qcut.clients_age == life_expectancy, 'expected_life', \"\")\n\n port_qcut.loc[:, 'age'] = age_index\n port_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n port_qcut.loc[len(port_qcut) + 1, :] = 0.0\n port_qcut = port_qcut.shift(1)\n port_qcut.iloc[0] = insert_col\n port_qcut.reset_index(drop=True, inplace=True)\n port_qcut.to_excel(writer, sheet_name='fia_port_ending_value_quantiles')\n # port_qcut.loc[income_starts:, :].to_excel(writer, sheet_name='fia_port_ending_value_quantiles')\n\n # port_income_qcut = port_income_qcut[1:] port_income_qcut.loc[:, 'clients_age'] = age_index\n # port_income_qcut.loc[:, 'comment'] = '' port_income_qcut.loc[:, 'comment'] = np.where(\n # port_income_qcut.clients_age == life_expectancy, 'expected_life', \"\")\n\n port_income_qcut = port_income_qcut.loc[1:, :]\n port_income_qcut.loc[:, 'age'] = age_index\n port_income_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n port_income_qcut.loc[income_starts:, :].to_excel(writer, sheet_name='fia_port_income_quantiles')\n\n prob_success_df = pd.concat([base_psuccess, port_psuccess], axis=1)\n prob_success_df.rename(columns={prob_success_df.columns[0]: 'prob(ending_value>0)_base',\n prob_success_df.columns[1]: 'prob(ending_value>0)_port'}, inplace=True)\n\n # prob_success_df.loc[:, 'clients_age'] = age_index\n # prob_success_df.loc[:, 'comment'] = ''\n # prob_success_df.loc[:, 'comment'] = np.where(prob_success_df.clients_age == life_expectancy, 'expected_life', \"\")\n\n prob_success_df.loc[:, 'age'] = age_index\n prob_success_df.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n prob_success_df.loc[:, 'prob(ending_value>lifetime_req income)_base'] = base_prob_of_success / trials\n prob_success_df.loc[:, 'prob(ending_value>lifetime_req income)_port'] = port_prob_of_success / trials\n prob_success_df.loc[:, 'prob(ending_value>next_year_req_income)_base'] = base_success_next_year / trials\n prob_success_df.loc[:, 'prob(ending_value>next_year_req_income)_port'] = port_success_next_year / trials\n prob_success_df.loc[:, 'base_max_portfolio_at_acturial_age'] = base_max_portfolio\n prob_success_df.loc[:, 'port_max_portfolio_at_acturial_age'] = port_max_portfolio\n\n # --------------------Percentile Portfolio's based on Acturial Life------------------------\n base_success = prob_success_df.loc[life_expectancy - clients_age, 'prob(ending_value>next_year_req_income)_base']\n port_success = prob_success_df.loc[life_expectancy - clients_age, 'prob(ending_value>next_year_req_income)_port']\n\n # acturial_age_base_tv = sim_base_total.loc[:life_expectancy - clients_age, ]\n # percentile_base_tv = sim_base_total.apply(lambda x: np.nanpercentile(x, base_success), axis=1)\n\n # ----------------Year wise percentile portfolio to meet next year income. Based on the success at acturial age.\n # Yearly portfolio values that can provide the next year income below the success rate at end of life (Percentile)-\n\n # acturial_age_base = base_for_next_year_need.loc[:life_expectancy - clients_age, ]\n # acturial_age_base = base_for_next_year_need.copy().fillna(0)\n percentile_base = base_for_next_year_need.apply(lambda x: np.nanpercentile(x, base_success), axis=1)\n\n # ----Pre Income Portfolio based on the Probab. of Success to meet next year's income at the end on the Act. Age\n base_pre_income_success = sim_base_total_preincome.apply(lambda x: np.nanpercentile(x, base_success), axis=1)\n base_ann_ret_pre_income = base_pre_income_success.pct_change().fillna(0)\n\n # acturial_age_port_tv = sim_port_total.loc[:life_expectancy - clients_age, ]\n # percentile_port_tv = sim_port_total.apply(lambda x: np.nanpercentile(x, port_success), axis=1)\n\n # ----------------Year wise percentile portfolio to meet next year income. Based on the success at acturial age.\n # Yearly portfolio values that can provide the next year income below the success rate at end of life (Percentile)-\n\n # acturial_age_port = port_for_next_year_need.loc[:life_expectancy - clients_age, ]\n # acturial_age_base = port_for_next_year_need.copy().fillna(0)\n percentile_port = port_for_next_year_need.apply(lambda x: np.nanpercentile(x, port_success), axis=1)\n\n # ----Pre Income Portfolio based on the Probab. of Success to meet next year's income at the end on the Act. Age\n port_pre_income_success = sim_port_total_preincome.apply(lambda x: np.nanpercentile(x, port_success), axis=1)\n port_ann_ret_pre_income = port_pre_income_success.pct_change().fillna(0)\n\n prob_success_df.loc[:, 'acturial_success_percentile_base_portfolio'] = percentile_base\n prob_success_df.loc[:, 'acturial_success_percentile_port_portfolio'] = percentile_port\n\n prob_success_df.loc[:, 'base_pre_income_ann_ret'] = base_ann_ret_pre_income\n prob_success_df.loc[:, 'port_pre_income_ann_ret'] = port_ann_ret_pre_income\n\n # prob_success_df.loc[:, 'terminalVal_success_percentile_base_portfolio'] = percentile_base_tv\n # prob_success_df.loc[:, 'terminalVal_success_percentile_port_portfolio'] = percentile_port_tv\n\n sim_base_total_preincome.to_excel(writer, sheet_name='base_preincome_portfolios')\n # -------Add premium to year 0 value to get total portfolio value---------\n sim_port_total_preincome.iloc[0] = sim_port_total_preincome.iloc[0] + premium\n sim_port_total_preincome.to_excel(writer, sheet_name='port_preincome_portfolios')\n\n # -------------For Simulation slide - BASE Portfolio - Can Delete --------------------\n # base_qcut_preinc = pd.DataFrame(index=sim_base_total_preincome.index, columns=cols)\n # for c in range(len(cols)):\n # base_qcut_preinc.loc[:, cols[c]] = sim_base_total_preincome.quantile(q_cut[c], axis=1)\n #\n # # -------------For Simulation slide - Proposed Portfolio --------------------\n # port_qcut_preinc = pd.DataFrame(index=sim_port_total_preincome.index, columns=cols)\n # for c in range(len(cols)):\n # port_qcut_preinc.loc[:, cols[c]] = sim_port_total_preincome.quantile(q_cut[c], axis=1)\n #\n # base_qcut_preinc.to_excel(writer, sheet_name='base_preincome_quantiles')\n # port_qcut_preinc.to_excel(writer, sheet_name='port_preincome_quantiles')\n\n prob_success_df.to_excel(writer, sheet_name='success_probability')\n\n # --------------BASE - Accumulation and Income Breakdown based on the success percentile portfolio---------------\n base_df.to_csv(src + 'base_port_detail.csv')\n sim_base_total.to_csv(src + 'base_ending_values.csv')\n income_breakdown_base = pd.DataFrame(sim_base_total.quantile(base_success, axis=1))\n income_breakdown_base.loc[:, 'income_from_risky_assets'] = sim_base_income.quantile(base_success, axis=1) \\\n - social - cpn_income_port\n income_breakdown_base.loc[:, 'guaranteed_income'] = 0.0\n income_breakdown_base.loc[:, 'social_security_income'] = social\n income_breakdown_base.loc[:, 'coupon_income'] = cpn_income_base\n\n income_breakdown_base.rename(columns={income_breakdown_base.columns[0]: 'portfolio_ending_value'}, inplace=True)\n income_breakdown_base.loc[:, 'income_from_risky_assets'][\n income_breakdown_base.loc[:, 'portfolio_ending_value'] <= 0] = 0\n income_breakdown_base.loc[:, 'total_income'] = income_breakdown_base.loc[:, income_breakdown_base.columns[1:]].sum(\n axis=1)\n\n # ----------FIA PORTFOLIO - Accumulation and Income Breakdown based on the success percentile portfolio-----------\n fia_portfolio_df.to_csv(src + 'fia_port_detail.csv')\n sim_port_total.to_csv(src + 'fiaport_ending_values.csv')\n\n income_breakdown_port = pd.DataFrame(sim_port_total.quantile(port_success, axis=1))\n income_breakdown_port.loc[:, 'income_from_risky_assets'] = sim_port_income.quantile(port_success, axis=1) \\\n - income_from_fia - social - cpn_income_port\n income_breakdown_port.loc[:, 'guaranteed_income'] = income_from_fia\n income_breakdown_port.loc[:, 'social_security_income'] = social\n income_breakdown_port.loc[:, 'coupon_income'] = cpn_income_port\n\n income_breakdown_port.rename(columns={income_breakdown_port.columns[0]: 'portfolio_ending_value'}, inplace=True)\n income_breakdown_port.loc[:, 'income_from_risky_assets'][\n income_breakdown_port.loc[:, 'portfolio_ending_value'] <= 0] = 0\n income_breakdown_port.loc[:, 'total_income'] = income_breakdown_port.loc[:, income_breakdown_port.columns[1:]].sum(\n axis=1)\n\n # -------------------Write simulation Statistics-------------------------------------\n simulation_stats.to_excel(writer, sheet_name='simulation_statistics')\n\n # port_psuccess.to_excel(writer, sheet_name='fia_port_success_probability')\n\n income_breakdown_base = income_breakdown_base.loc[1:, :]\n income_breakdown_base.loc[:, 'age'] = age_index\n income_breakdown_base.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n income_breakdown_base.loc[income_starts:, :].to_excel(writer, sheet_name='base_income_breakdown_median')\n\n income_breakdown_port = income_breakdown_port.loc[1:, :]\n income_breakdown_port.loc[:, 'age'] = age_index\n income_breakdown_port.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n income_breakdown_port.loc[income_starts:, :].to_excel(writer, sheet_name='fia_income_breakdown_median')\n\n legacy_risk.to_excel(writer, sheet_name='ruin_probability')\n\n # if method == 'normal':\n # median_returns_normal.loc[:, 'fia_median_returns'] = median_normal_fia\n # median_returns_normal.to_excel(writer, sheet_name='gr_port_median_normal')\n #\n # elif method == 'smallest':\n # median_returns_smallest.loc[:, 'fia_median_returns'] = median_smallest_fia\n # median_returns_smallest.to_excel(writer, sheet_name='gr_port_median_asc')\n #\n # else:\n # median_returns_largest.loc[:, 'fia_median_returns'] = median_largest_fia\n # median_returns_largest.to_excel(writer, sheet_name='gr_port_median_desc')\n\n # ---------------------Histogram for S&P Forecast---------------------------------------\n sp_returns = read_returns_est.loc['SPXT Index', 'Annualized Returns']\n sp_risk = read_returns_est.loc['SPXT Index', 'Annualized Risk']\n sp_random_ret = np.random.normal(loc=sp_returns, scale=sp_risk, size=10000)\n bins, data = np.histogram(sp_random_ret, bins=20)\n df_ret = pd.DataFrame(data, columns=['Return_range'])\n df_bins = pd.DataFrame(bins, columns=['Count'])\n df_hist = df_ret.join(df_bins)\n\n df_hist.to_excel(writer, sheet_name='sp500_histogram')\n writer.save()\n\n print(\"simulation completed....\")","def _run_backtest(self):\n i = 0\n\n while True:\n i += 1\n print(i)\n\n # Update the market bars\n if self.data_handler.continue_backtest == True:\n self.data_handler.update_bars()\n else:\n break\n\n # Handle the Events\n while True:\n try:\n event = self.events.get(False)\n except queue.Empty:\n break\n else:\n # The inner-loop acts on the events by calling the appropriate method of the appropriate object\n if event is not None:\n if event.type == 'MARKET':\n self.strategy.calculate_signals(event)\n self.portfolio.update_timeindex(event)\n\n elif event.type == 'SIGNAL':\n self.signals += 1\n self.portfolio.update_signal(event)\n\n elif event.type == 'ORDER':\n self.orders += 1\n self.execution_handler.execute_order(event)\n\n elif event.type == 'FILL':\n self.fills += 1\n self.portfolio.update_fill(event)\n\n # Pauses for a duration of self.heartbeat seconds\n time.sleep(self.heartbeat)","def income_model_asset_based_portfolio_custom(num_of_years=30, trials=100, method='normal', income=True):\n\n sim_fia_cv = pd.DataFrame(index=range(num_of_years + 1))\n\n sim_base_total = pd.DataFrame(index=range(num_of_years + 1))\n sim_base_income = pd.DataFrame(index=range(num_of_years + 1))\n\n sim_port_total = pd.DataFrame(index=range(num_of_years + 1))\n sim_port_income = pd.DataFrame(index=range(num_of_years + 1))\n\n sim_base_total_pre_income = pd.DataFrame(index=range(num_of_years + 1))\n sim_port_total_pre_income = pd.DataFrame(index=range(num_of_years + 1))\n\n # read_income_inputs = pd.read_csv(src + \"income_model_inputs.csv\", index_col='Items')\n read_income_inputs = pd.read_excel(src + \"portfolio_information.xlsx\", sheet_name='income_model_inputs',\n index_col=[0])\n\n # read_returns_est = pd.read_csv(src + \"income_assets_returns_estimates.csv\", index_col='Symbol')\n read_returns_est = pd.read_excel(src + \"portfolio_information.xlsx\", sheet_name='income_assets_returns_estimates',\n index_col=[0])\n\n clean_names = list(read_returns_est.index)\n clean_names = [s.split(' ')[0] for s in clean_names]\n read_returns_est.loc[:, 'names'] = clean_names\n read_returns_est.set_index('names', drop=True, inplace=True)\n read_returns_est = read_returns_est[:-1]\n read_returns_est.rename(index={'SBMMTB3': 'Cash', read_returns_est.index[-1]: 'FIA'}, inplace=True)\n\n # ---------------Returns DataFrame based on the use input------------------------------------\n ann_ret = np.full((num_of_years + 1, len(read_returns_est)), read_returns_est.loc[:, 'Annualized Returns'])\n read_normal = pd.DataFrame(ann_ret, index=np.arange(num_of_years + 1), columns=read_returns_est.index)\n # read_normal.rename(columns={read_normal.columns[-1]: 'FIA'}, inplace=True)\n user_est_fia_return = float(read_income_inputs.loc['fia_forecast', 'inputs'])\n read_normal.loc[:, 'FIA'] = user_est_fia_return\n\n read_returns_est.loc['FIA', 'Annualized Returns'] = user_est_fia_return\n\n # read_returns_est.drop(['BM', read_returns_est.index[-1]], axis=0, inplace=True)\n # read_portfolio_inputs = pd.read_csv(src + \"income_portfolio_inputs.csv\", index_col='Items')\n\n # read_asset_weights = pd.read_csv(src + \"asset_weights.csv\", index_col='Asset')\n read_asset_weights = pd.read_excel(src + \"portfolio_information.xlsx\", sheet_name='asset_weights',\n index_col=[0])\n\n read_asset_weights.drop(read_asset_weights.index[-1], axis=0, inplace=True)\n\n # read random returns for simulation\n # read_normal = pd.read_csv(src + 'sort_normal.csv', index_col=[0], parse_dates=True)\n # read_small = pd.read_csv(src + 'sort_small_to_large.csv', index_col=[0], parse_dates=True)\n # read_large = pd.read_csv(src + 'sort_large_to_small.csv', index_col=[0], parse_dates=True)\n assets_col_names = list(read_normal.columns)\n\n tickers = list(read_asset_weights.index)\n wts = np.array(read_asset_weights.loc[:, 'base'])\n\n def asset_median_returns(data, ticker):\n return data.filter(regex=ticker).median(axis=1)\n\n # dataframe for unsorted returns (normal)\n median_returns_normal = pd.DataFrame({t: asset_median_returns(read_normal, t) for t in tickers})\n median_returns_normal.loc[:, 'portfolio_return'] = median_returns_normal.dot(wts)\n median_normal_fia = pd.DataFrame({'FIA': asset_median_returns(read_normal, 'FIA')})\n #\n # # dataframe for smallest to largest returns\n # median_returns_smallest = pd.DataFrame({t: asset_median_returns(read_small, t) for t in tickers})\n # median_returns_smallest.loc[:, 'portfolio_return'] = median_returns_smallest.dot(wts)\n # median_smallest_fia = pd.DataFrame({'FIA': asset_median_returns(read_small, 'r_FIA')})\n #\n # # dataframe for unsorted returns (normal)\n # median_returns_largest = pd.DataFrame({t: asset_median_returns(read_large, t) for t in tickers})\n # median_returns_largest.loc[:, 'portfolio_return'] = median_returns_largest.dot(wts)\n # median_largest_fia = pd.DataFrame({'FIA': asset_median_returns(read_large, 'r_FIA')})\n\n years = list(range(0, num_of_years + 1))\n income_cols = ['year', 'strategy_term', 'index_returns', 'term_ret', 'term_ret_with_par', 'term_annualize',\n 'ann_net_spread', 'term_ret_netspr', 'high_inc_benefit_base', 'rider_fee', 'eoy_income',\n 'contract_value']\n\n term = int(read_income_inputs.loc['term', 'inputs'])\n fia_ret = read_returns_est.loc[read_returns_est.index[-1], 'Annualized Returns']\n fia_risk = read_returns_est.loc[read_returns_est.index[-1], 'Annualized Risk']\n par_rate = float(read_income_inputs.loc['par_rate', 'inputs'])\n spread = float(read_income_inputs.loc['spread', 'inputs'])\n bonus_term = int(read_income_inputs.loc['bonus_term', 'inputs'])\n premium = float(read_income_inputs.loc['premium', 'inputs'])\n income_bonus = float(read_income_inputs.loc['income_bonus', 'inputs'])\n\n income_starts = int(read_income_inputs.loc['start_income_years', 'inputs'])\n income_growth = float(read_income_inputs.loc['income_growth', 'inputs'])\n rider_fee = float(read_income_inputs.loc['rider_fee', 'inputs'])\n inc_payout_factor = float(read_income_inputs.loc['income_payout_factor', 'inputs'])\n contract_bonus = float(read_income_inputs.loc['contract_bonus', 'inputs'])\n social = float(read_income_inputs.loc['social', 'inputs'])\n inflation = float(read_income_inputs.loc['inflation', 'inputs'])\n wtd_cpn_yield = float(read_income_inputs.loc['wtd_coupon_yld', 'inputs'])\n life_expectancy = int(read_income_inputs.loc['life_expectancy_age', 'inputs'])\n clients_age = int(read_income_inputs.loc['clients_age', 'inputs'])\n\n # ---------------INCOME MODEL--------------------------------------------\n runs = 0\n returns_dict = {}\n asset_dict = {}\n fia_dict = {}\n while runs < trials:\n print(runs)\n\n income_df = pd.DataFrame(index=years, columns=income_cols)\n income_df.loc[:, 'year'] = years\n income_df.loc[:, 'strategy_term'] = income_df.loc[:, 'year'] % term\n income_df.loc[:, 'strategy_term'] = income_df['strategy_term'].apply(lambda x: 1 if x == 0 else 0)\n\n income_df.loc[:, 'index_returns'] = read_normal.loc[:, 'FIA']\n # income_df.loc[:, 'index_returns'] = np.random.normal(fia_ret, fia_risk, size=(len(years), 1))\n\n cumprod = (1. + income_df['index_returns']).rolling(window=term).agg(lambda x: x.prod()) - 1\n income_df.loc[:, 'term_ret'] = np.where(income_df.loc[:, 'strategy_term'] == 1, cumprod, 0)\n income_df.loc[:, 'term_ret_with_par'] = income_df.loc[:, 'term_ret'] * par_rate\n income_df.loc[:, 'term_annualize'] = income_df.loc[:, 'term_ret_with_par'].apply(\n lambda x: (1 + x) ** (1 / term) - 1)\n income_df.loc[:, 'ann_net_spread'] = income_df.loc[:, 'term_annualize'] - spread\n income_df.loc[:, 'ann_net_spread'] = np.where(income_df.loc[:, 'strategy_term'] == 1,\n income_df.loc[:, 'ann_net_spread'], 0)\n income_df.loc[:, 'term_ret_netspr'] = income_df.loc[:, 'ann_net_spread'].apply(lambda x: (1 + x) ** term - 1)\n\n for counter in years:\n if counter == 0:\n income_df.loc[counter, 'high_inc_benefit_base'] = premium * (1 + income_bonus)\n\n elif counter <= min(bonus_term, income_starts):\n income_df.loc[counter, 'high_inc_benefit_base'] = income_df.loc[counter - 1, 'high_inc_benefit_base'] * \\\n (1 + income_growth)\n else:\n income_df.loc[counter, 'high_inc_benefit_base'] = income_df.loc[counter - 1, 'high_inc_benefit_base']\n\n income_df.loc[:, 'rider_fee'] = income_df.loc[:, 'high_inc_benefit_base'] * rider_fee\n income_df.loc[:, 'eoy_income'] = np.where(income_df.loc[:, 'year'] > income_starts,\n income_df.loc[:, 'high_inc_benefit_base'] * inc_payout_factor, 0)\n\n for counter in years:\n if counter == 0:\n income_df.loc[counter, 'contract_value'] = premium * (1 + contract_bonus)\n\n elif income_df.loc[counter, 'strategy_term'] == 1:\n x1 = income_df.loc[counter - 1, 'contract_value'] - income_df.loc[counter, 'rider_fee']\n x2 = (x1 * (1 + income_df.loc[counter, 'term_ret_netspr'])) - income_df.loc[counter, 'eoy_income']\n income_df.loc[counter, 'contract_value'] = x2\n\n else:\n x1 = income_df.loc[counter - 1, 'contract_value'] - income_df.loc[counter, 'rider_fee'] - \\\n income_df.loc[counter, 'eoy_income']\n\n income_df.loc[counter, 'contract_value'] = x1\n\n # variable stores the income number that is used in the base and fia portfolio calcs.\n\n income_from_fia = income_df.loc[income_df.index[-1], 'eoy_income']\n\n income_df.loc[:, 'contract_value'] = income_df.loc[:, 'contract_value'].apply(lambda x: 0 if x <= 0 else x)\n\n sim_fia_cv.loc[:, str(runs)] = income_df.loc[:, 'contract_value']\n\n # -------------------------------------BASE MODEL---------------------------------------------\n\n base_wts = read_asset_weights.loc[:, 'base']\n base_assets = list(base_wts.index)\n base_weights = list(base_wts.values)\n base_returns = list(read_returns_est.loc[:, 'Annualized Returns'].values)\n base_std = list(read_returns_est.loc[:, 'Annualized Risk'].values)\n\n base_investment = float(read_income_inputs.loc['risky_assets', 'Base'])\n adv_fees = float(read_income_inputs.loc['advisor_fees', 'Base'])\n\n # -------------------required income----------------------------------\n if income:\n req_annual_income = float(read_income_inputs.loc['annual_income', 'inputs'])\n income_needed = req_annual_income - social\n income_net_fia_income = max(0, income_needed - income_from_fia)\n cpn_income_base = base_investment * wtd_cpn_yield\n else:\n req_annual_income = 0.0\n income_needed = 0.0\n income_net_fia_income = 0.0\n cpn_income_base = base_investment * wtd_cpn_yield\n\n # ----------------------RANDOM RETURNS--------------------------\n r_cols = ['r_{}'.format(name) for name in base_assets]\n boy_value = ['bv_{}'.format(name) for name in base_assets]\n eoy_value = ['ev_{}'.format(name) for name in base_assets]\n\n random_returns = pd.DataFrame(index=income_df.index, columns=r_cols)\n\n # for c in range(len(r_cols)):\n # ret = np.random.normal(base_returns[c], base_std[c], size=(len(random_returns.index), 1))\n\n # this_run_cols = ['{}_{}'.format(cname, str(runs)) for cname in r_cols]\n random_returns = read_normal.loc[:, base_assets]\n\n # random_returns.loc[:, r_cols[c]] = ret.flatten()\n # asset_dict.update({'{}_{}'.format(r_cols[c], str(runs)): ret.flatten()})\n\n # store the simulated assets returns in one dictionary\n # returns_dict.update({str(runs): random_returns})\n\n # collect the asset based returns from all simulation and calculate the median returns.\n # def get_median_returns(sym):\n # cols = [sym + '_' + str(c) for c in np.arange(trials)]\n # asset_df = pd.DataFrame({c: asset_dict.get(c) for c in cols})\n # return asset_df.median(axis=1)\n #\n # asset_median_returns = pd.DataFrame({symbol: get_median_returns(symbol) for symbol in r_cols})\n #\n # asset_median_returns.loc[:, 'simulated_portfolio_median_returns'] = asset_median_returns.dot(base_weights)\n\n base_df = random_returns.copy()\n\n # base_investment = float(read_portfolio_inputs.loc['risky_assets', 'Base'])\n\n fia_portfolio_df = random_returns.copy()\n port_investment = float(read_income_inputs.loc['risky_assets', 'FIA'])\n cpn_income_port = port_investment * wtd_cpn_yield\n\n # ----------------------------------------BASE PORTFOLIO----------------------------\n for name in boy_value:\n base_df.loc[:, name] = 0.0\n\n for counter in years:\n period_returns = list(random_returns.loc[counter, :])\n if counter == 0:\n\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment for c in range(len(boy_value))]\n\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\n base_df.loc[counter, 'total_net_fees'] = 0.0\n base_df.loc[counter, 'income'] = 0.0\n base_investment = base_df.loc[counter, boy_value].sum()\n\n elif (counter > 0) and (counter < income_starts):\n\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment * (1 + period_returns[c])\n for c in range(len(boy_value))]\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\n base_df.loc[counter, 'adv_fees'] = base_df.loc[counter, 'total'] * adv_fees\n base_df.loc[counter, 'total_net_fees'] = base_df.loc[counter, 'total'] - base_df.loc[\n counter, 'adv_fees']\n\n # --coupon payment is invested back into the risky portfolio until the income is withdrawn----\n base_investment = base_df.loc[counter, 'total_net_fees'] + cpn_income_base\n\n else:\n\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment * (1 + period_returns[c])\n for c in range(len(boy_value))]\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\n base_df.loc[counter, 'adv_fees'] = base_df.loc[counter, 'total'] * adv_fees\n\n # ---req. income is adjusted for inflation from the second year of withdrawal. Reinvestment of coupon\n # stops from the year income starts. Req. income is reduced by the coupon payments\n\n if counter == income_starts:\n\n income_needed = req_annual_income - social\n base_df.loc[counter, 'income'] = income_needed - cpn_income_base\n income_needed = req_annual_income\n\n else:\n income_needed = income_needed * (1 + inflation) - social\n base_df.loc[counter, 'income'] = income_needed - cpn_income_base\n income_needed = income_needed + social\n\n base_df.loc[counter, 'total_net_fees'] = base_df.loc[counter, 'total'] - \\\n base_df.loc[counter, 'adv_fees'] - \\\n base_df.loc[counter, 'income']\n\n base_df.loc[counter, 'total_pre_income'] = base_df.loc[counter, 'total'] - \\\n base_df.loc[counter, 'adv_fees']\n\n base_investment = base_df.loc[counter, 'total_net_fees']\n\n base_df.loc[:, 'adj_total'] = base_df.loc[:, 'total_net_fees'].apply(lambda x: x if x > 0 else 0)\n sim_base_total.loc[:, 's_{}'.format(str(runs))] = base_df.loc[:, 'total_net_fees']\n sim_base_income.loc[:, 's_{}'.format(str(runs))] = base_df.loc[:, 'income']\n sim_base_total_pre_income.loc[:, 's_{}'.format(str(runs))] = base_df.loc[:, 'total_pre_income']\n\n # ----------------------------FIA PORTFOLIO----------------------------------------------\n for name in boy_value:\n fia_portfolio_df.loc[:, name] = 0.0\n\n for counter in years:\n period_returns = list(random_returns.loc[counter, :])\n if counter == 0:\n\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment\n for c in range(len(boy_value))]\n fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\n fia_portfolio_df.loc[counter, 'total_net_fees'] = 0.0\n fia_portfolio_df.loc[counter, 'income'] = 0.0\n port_investment = fia_portfolio_df.loc[counter, boy_value].sum()\n\n elif (counter > 0) and (counter < income_starts):\n\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment * (1 + period_returns[c])\n for c in range(len(boy_value))]\n fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\n fia_portfolio_df.loc[counter, 'adv_fees'] = fia_portfolio_df.loc[counter, 'total'] * adv_fees\n fia_portfolio_df.loc[counter, 'total_net_fees'] = fia_portfolio_df.loc[counter, 'total'] - \\\n fia_portfolio_df.loc[counter, 'adv_fees']\n\n port_investment = fia_portfolio_df.loc[counter, 'total_net_fees'] + cpn_income_port\n\n else:\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment * (1 + period_returns[c])\n for c in range(len(boy_value))]\n fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\n fia_portfolio_df.loc[counter, 'adv_fees'] = fia_portfolio_df.loc[counter, 'total'] * adv_fees\n\n # ---req. income is adjusted for inflation from the second year of withdrawal. Reinvestment of coupon\n # stops from the year income starts. Req. income is reduced by the coupon payments\n\n if counter == income_starts:\n\n income_needed = req_annual_income - social\n income_net_fia_income = max(0, income_needed - income_from_fia)\n fia_portfolio_df.loc[counter, 'income'] = max(0, income_net_fia_income - cpn_income_port)\n income_needed = req_annual_income\n\n else:\n income_needed = income_needed * (1 + inflation) - social\n income_net_fia_income = max(0, income_needed - income_from_fia)\n fia_portfolio_df.loc[counter, 'income'] = max(0, income_net_fia_income - cpn_income_port)\n income_needed = income_needed + social\n\n if income:\n fia_portfolio_df.loc[counter, 'total_net_fees'] = fia_portfolio_df.loc[counter, 'total'] - \\\n fia_portfolio_df.loc[counter, 'adv_fees'] - \\\n fia_portfolio_df.loc[counter, 'income']\n else:\n fia_portfolio_df.loc[counter, 'total_net_fees'] = fia_portfolio_df.loc[counter, 'total'] - \\\n fia_portfolio_df.loc[counter, 'adv_fees'] + \\\n income_from_fia\n\n fia_portfolio_df.loc[counter, 'total_pre_income'] = fia_portfolio_df.loc[counter, 'total'] - \\\n fia_portfolio_df.loc[counter, 'adv_fees']\n\n port_investment = fia_portfolio_df.loc[counter, 'total_net_fees']\n\n sim_port_total.loc[:, 's_{}'.format(str(runs))] = fia_portfolio_df.loc[:, 'total_net_fees'] + \\\n income_df.loc[:, 'contract_value']\n\n sim_port_income.loc[:, 's_{}'.format(str(runs))] = fia_portfolio_df.loc[:, 'income']\n\n fia_portfolio_df.loc[:, 'adj_total'] = fia_portfolio_df.loc[:, 'total_net_fees'].apply(\n lambda x: x if x > 0 else 0)\n\n sim_port_total_pre_income.loc[:, 's_{}'.format(str(runs))] = fia_portfolio_df.loc[:, 'total_pre_income']\n\n runs += 1\n\n # ------------------Calculate % of portfolios ending value greater than required LIFETIME cumm. income---------\n total_income_by_age = sim_base_income.loc[:, sim_base_income.columns[0]].cumsum()\n total_income_by_acturial_age = total_income_by_age.loc[life_expectancy - clients_age]\n total_income_by_age.fillna(0, inplace=True)\n income_dataframe = pd.DataFrame(total_income_by_age)\n income_dataframe.loc[:, 'remaining_income_by_acturial_age'] = total_income_by_age.apply(\n lambda x: total_income_by_acturial_age - x)\n\n s = income_dataframe.loc[:, 'remaining_income_by_acturial_age']\n base_prob_of_success = sim_base_total.gt(s, axis=0).sum(axis=1)\n port_prob_of_success = sim_port_total.gt(s, axis=0).sum(axis=1)\n\n # ----------------------------Portfolio sufficient for NEXT YEARS income needs-------------------\n next_year_income = sim_base_income.loc[:, sim_base_income.columns[0]].shift(-1).fillna(0) # Yearly Income Reqd.\n base_success_next_year = sim_base_total.gt(next_year_income, axis=0).sum(axis=1)\n\n base_for_next_year_need = sim_base_total[sim_base_total.gt(next_year_income, axis=0)]\n\n port_success_next_year = sim_port_total.gt(next_year_income, axis=0).sum(axis=1)\n\n port_for_next_year_need = sim_port_total[sim_port_total.gt(next_year_income, axis=0)]\n\n # ---------------Portfolio for 45 years of simulation---------------------------------------\n base_success_portfolio = sim_base_total[sim_base_total.gt(next_year_income, axis=0)]\n port_success_portfolio = sim_port_total[sim_port_total.gt(next_year_income, axis=0)]\n\n # ----------------Portfolio Simulation until the acturial age------------------------------\n acturial_years = life_expectancy - clients_age\n base_success_portfolio_act_age = base_success_portfolio.loc[acturial_years, :]\n port_success_portfolio_act_age = port_success_portfolio.loc[acturial_years, :]\n\n # -------------------------Base Portfolio TS with max Terminal Value ----------------------------\n if base_success_portfolio_act_age.isnull().sum() == trials:\n base_max_portfolio = 0.0\n else:\n base_max_portfolio = base_success_portfolio.loc[:, base_success_portfolio_act_age.idxmax()]\n\n # -------------------------FIA Portfolio TS with max Terminal Value ----------------------------\n if port_success_portfolio_act_age.isnull().sum() == trials:\n port_max_portfolio = 0.0\n else:\n port_max_portfolio = port_success_portfolio.loc[:, port_success_portfolio_act_age.idxmax()]\n # ------------------------------Average age with full income------------------------------\n base_mean_age = ((life_expectancy - clients_age) - base_success_portfolio.loc[1:life_expectancy - clients_age, :]\n .isnull().sum()).mean()\n\n port_mean_age = ((life_expectancy - clients_age) - port_success_portfolio.loc[1:life_expectancy - clients_age, :]\n .isnull().sum()).mean()\n\n # ----------------------------Median Age with full Income------------------------------------------\n base_median_age = ((life_expectancy - clients_age) - base_success_portfolio.loc[1:life_expectancy - clients_age, :]\n .isnull().sum()).median()\n\n port_median_age = ((life_expectancy - clients_age) - port_success_portfolio.loc[1:life_expectancy - clients_age, :]\n .isnull().sum()).median()\n\n # --------------Mean Value for all the portfolios at end of the acturial age--------------------\n base_act_avg_porfolio = base_success_portfolio.loc[life_expectancy - clients_age, :].fillna(0).mean()\n port_act_avg_porfolio = port_success_portfolio.loc[life_expectancy - clients_age, :].fillna(0).mean()\n\n # --------------Median Value for all the portfolios at end of the acturial age--------------------\n base_act_median_porfolio = base_success_portfolio.loc[life_expectancy - clients_age, :].fillna(0).median()\n port_act_median_porfolio = port_success_portfolio.loc[life_expectancy - clients_age, :].fillna(0).median()\n\n # # --------------Mean Value for all the portfolios in the simulation--------------------\n # base_sim_mean = base_success_portfolio.loc[1:life_expectancy - clients_age, :].mean().mean()\n # port_sim_mean = port_success_portfolio.loc[1:life_expectancy - clients_age, :].mean().mean()\n #\n # # --------------Median Value for all the portfolios in the simulation--------------------\n # base_sim_mean = base_success_portfolio.loc[1:life_expectancy - clients_age, :].mean().median()\n # port_sim_mean = port_success_portfolio.loc[1:life_expectancy - clients_age, :].mean().median()\n\n # -------Max Portfolio value at the end of acturial age----------------------------------------\n base_act_max = base_success_portfolio.loc[life_expectancy - clients_age, :].max()\n port_act_max = port_success_portfolio.loc[life_expectancy - clients_age, :].max()\n\n # -------Min Portfolio value at the end of acturial age----------------------------------------\n base_act_min = base_success_portfolio.loc[life_expectancy - clients_age, :].min()\n port_act_min = port_success_portfolio.loc[life_expectancy - clients_age, :].min()\n\n # ---------------------Lifetime Average Income----------------------------------\n base_total_income = sim_base_income.cumsum().loc[acturial_years, :].mean()\n port_total_income = income_from_fia + sim_port_income\n port_total_income = port_total_income.cumsum().loc[acturial_years, :].mean()\n\n simulation_stats = pd.DataFrame(index=['Average Years', 'Median Years', 'Average Age', 'Median Age',\n 'Average Portfolio (act.age)', 'Median Portfolio (act.age)',\n 'Max Portfolio Value', 'Min Portfolio Value',\n 'Average Lifetime Income'], columns=['Base', 'FIA'])\n\n simulation_stats.loc['Average Years', :] = [base_mean_age, base_mean_age]\n simulation_stats.loc['Median Years', :] = [base_median_age, base_median_age]\n simulation_stats.loc['Average Age', :] = [base_mean_age + clients_age, base_mean_age + clients_age]\n simulation_stats.loc['Median Age', :] = [base_median_age + clients_age, base_median_age + clients_age]\n simulation_stats.loc['Average Portfolio (act.age)', :] = [base_act_avg_porfolio, port_act_avg_porfolio]\n simulation_stats.loc['Median Portfolio (act.age)', :] = [base_act_median_porfolio, port_act_median_porfolio]\n simulation_stats.loc['Max Portfolio Value', :] = [base_act_max, port_act_max]\n simulation_stats.loc['Min Portfolio Value', :] = [base_act_min, port_act_min]\n simulation_stats.loc['Average Lifetime Income', :] = [base_total_income, port_total_income]\n comments = ['Average years of portfolios that meet the next years income needs for the lifetime',\n 'Median years of portfolios that meet the next years income needs for the lifetime',\n 'Average Clients Age',\n 'Median Clients Age',\n 'Average of terminal values for the portfolios at the end of the acturial life',\n 'Median of terminal values for the portfolios at the end of the acturial life',\n 'Maximum of terminal values for the portfolios at the end of the acturial life',\n 'Minimum of terminal values for the portfolios at the end of the acturial life',\n 'Average of total income generated by all portfolios at the end of the acturial life']\n\n simulation_stats.loc[:, 'Notes'] = comments\n\n # --------------------------------------------------------------------------------\n\n # # -----------------------------------income breakdown for Base portfolio----------------------------------\n # base_df.to_csv(src + 'base_port_detail.csv')\n # sim_base_total.to_csv(src + 'base_ending_values.csv')\n # income_breakdown_base = pd.DataFrame(sim_base_total.quantile(0.5, axis=1))\n # income_breakdown_base.loc[:, 'income_from_portfolio'] = sim_base_income.quantile(0.5, axis=1)\n # income_breakdown_base.loc[:, 'fia_income'] = 0.0\n # income_breakdown_base.loc[:, 'social_security_income'] = social\n # income_breakdown_base.loc[:, 'coupon_income'] = cpn_income_base\n #\n # income_breakdown_base.rename(columns={income_breakdown_base.columns[0]: 'portfolio_ending_value'}, inplace=True)\n # income_breakdown_base.loc[:, 'income_from_portfolio'][\n # income_breakdown_base.loc[:, 'portfolio_ending_value'] <= 0] = 0\n # income_breakdown_base.loc[:, 'total_income'] = income_breakdown_base.loc[:, income_breakdown_base.columns[1:]].sum(\n # axis=1)\n #\n # # --------------------------------------Block Ends-----------------------------------------------------------\n #\n # # ---------------------------------------income breakdown for FIA portfolio----------------------------------\n # fia_portfolio_df.to_csv(src + 'fia_port_detail.csv')\n # sim_port_total.to_csv(src + 'fiaport_ending_values.csv')\n #\n # income_breakdown_port = pd.DataFrame(sim_port_total.quantile(0.5, axis=1))\n # income_breakdown_port.loc[:, 'income_from_portfolio'] = sim_port_income.quantile(0.5, axis=1)\n # income_breakdown_port.loc[:, 'fia_income'] = income_from_fia\n # income_breakdown_port.loc[:, 'social_security_income'] = social\n # income_breakdown_port.loc[:, 'coupon_income'] = cpn_income_port\n #\n # income_breakdown_port.rename(columns={income_breakdown_port.columns[0]: 'portfolio_ending_value'}, inplace=True)\n # income_breakdown_port.loc[:, 'income_from_portfolio'][\n # income_breakdown_port.loc[:, 'portfolio_ending_value'] <= 0] = 0\n # income_breakdown_port.loc[:, 'total_income'] = income_breakdown_port.loc[:, income_breakdown_port.columns[1:]].sum(\n # axis=1)\n #\n # # ----------------------------------Block Ends-------------------------------------------------------------\n q_cut = [0.0, 0.05, 0.25, 0.5, 0.75, 0.95, 1.0]\n sim_base_income[sim_base_total < income_needed] = 0.0\n\n sim_port_income[sim_port_total < income_net_fia_income] = 0\n\n sim_port_income = sim_port_income + income_from_fia\n\n # base_quantile = sim_base_total.loc[sim_base_total.index[-1]].quantile([0.05, 0.25, 0.50, 0.75, 0.90])\n #\n # port_quantile = sim_port_total.loc[sim_port_total.index[-1]].quantile([0.05, 0.25, 0.50, 0.75, 0.90])\n\n base_quantile = sim_base_total.loc[sim_base_total.index[-1]].quantile(q_cut)\n\n port_quantile = sim_port_total.loc[sim_port_total.index[-1]].quantile(q_cut)\n\n # q_cut = [0.0, .05, 0.25, 0.5, 0.75, 0.95, 1.0]\n cols = ['Min', '5th', '25th', '50th', '75th', '90th', 'Max']\n\n # ------------------------------------------drop year 0-----------------------------------------\n sim_base_total = sim_base_total[1:]\n sim_port_total = sim_port_total[1:]\n\n # ------------------------quantile analysis for base terminal value-----------------------------\n base_qcut = pd.DataFrame(index=sim_base_total.index, columns=cols)\n for c in range(len(cols)):\n base_qcut.loc[:, cols[c]] = sim_base_total.quantile(q_cut[c], axis=1)\n\n base_qcut.clip(lower=0, inplace=True)\n\n sim_base_total.clip(lower=0, inplace=True)\n\n # -------------------------------------quantile analysis for base income----------------------------\n base_income_qcut = pd.DataFrame(index=sim_base_income.index, columns=cols)\n for c in range(len(cols)):\n base_income_qcut.loc[:, cols[c]] = sim_base_income.quantile(q_cut[c], axis=1)\n\n # ----Remove NaN's prior to the income start years------------\n # base_income_qcut = base_income_qcut.loc[income_starts:]\n\n # ---------------------------------quantile analysis for portfolio terminal value ---------------\n\n port_qcut = pd.DataFrame(index=sim_port_total.index, columns=cols)\n for c in range(len(cols)):\n port_qcut.loc[:, cols[c]] = sim_port_total.quantile(q_cut[c], axis=1)\n\n port_qcut.clip(lower=0, inplace=True)\n\n # ----------------------------------quantile analysis for portfolio income----------------------------\n port_income_qcut = pd.DataFrame(index=sim_port_income.index, columns=cols)\n for c in range(len(cols)):\n port_income_qcut.loc[:, cols[c]] = sim_port_income.quantile(q_cut[c], axis=1)\n\n # ----Remove NaN's prior to the income start years------------\n # port_income_qcut = port_income_qcut.loc[income_starts:]\n\n # ----------probability ending value will be less than 0 at the end of the horizon -----------------------\n # base_legacy_risk = (sim_base_total.loc[sim_base_total.index[-1]] < 0).sum() / (trials)\n\n base_legacy_risk = (sim_base_total.loc[sim_base_total.index[life_expectancy - clients_age]] < 0).sum() / trials\n port_legacy_risk = (sim_port_total.loc[sim_port_total.index[life_expectancy - clients_age]] < 0).sum() / trials\n\n # port_legacy_risk = (sim_port_total.loc[sim_port_total.index[-1]] <= 0).sum() / (trials)\n\n legacy_risk = pd.DataFrame([base_legacy_risk, port_legacy_risk,\n 'Prob. of portfolio value less than 0 at the end of the expected life'],\n index=['base', 'fia_portfolio', 'Notes'],\n columns=['Ruin Probability'])\n\n # -----------Year-wise probability of ending value greater than 0 -----------------\n base_psuccess = sim_base_total.apply(lambda x: x > 0).sum(axis=1) / trials\n port_psuccess = sim_port_total.apply(lambda x: x > 0).sum(axis=1) / trials\n\n # -----------------------WRITING FILES TO EXCEL ---------------------------\n\n writer = pd.ExcelWriter(src + method + '_simulated_income_summary_custom.xlsx', engine='xlsxwriter')\n read_income_inputs.to_excel(writer, sheet_name='inputs_for_income')\n\n read_returns_est.to_excel(writer, sheet_name='asset_returns_estimates')\n # read_portfolio_inputs.to_excel(writer, sheet_name='portfolio_inputs')\n\n age_index = list(range(clients_age + 1, clients_age + len(base_qcut) + 1))\n # base_qcut.loc[:, 'clients_age'] = age_index\n # base_qcut.loc[:, 'comment'] = ''\n # base_qcut.loc[:, 'comment'] = np.where(base_qcut.clients_age == life_expectancy, 'expected_life', \"\")\n\n base_inv = float(read_income_inputs.loc['risky_assets', 'Base'])\n base_qcut.loc[:, 'age'] = age_index\n base_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n\n # --------To start with year 0---------------------------------\n insert_col = [base_inv, base_inv, base_inv, base_inv, base_inv, base_inv,\n base_inv, clients_age, np.nan]\n base_qcut.loc[len(base_qcut) + 1, :] = 0.0\n base_qcut = base_qcut.shift(1)\n base_qcut.iloc[0] = insert_col\n base_qcut.reset_index(drop=True, inplace=True)\n base_qcut.loc[:, 'Annual Return'] = base_qcut.loc[:, '50th'].pct_change().fillna(0)\n base_qcut.to_excel(writer, sheet_name='base_ending_value_quantiles')\n # base_qcut.loc[income_starts:, :].to_excel(writer, sheet_name='base_ending_value_quantiles')\n\n # base_income_qcut = base_income_qcut[1:] base_income_qcut.loc[:, 'clients_age'] = age_index\n # base_income_qcut.loc[:, 'comment'] = '' base_income_qcut.loc[:, 'comment'] = np.where(\n # base_income_qcut.clients_age == life_expectancy, 'expected_life', \"\")\n\n base_income_qcut = base_income_qcut.loc[1:, :]\n base_income_qcut.loc[:, 'age'] = age_index\n base_income_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n\n base_income_qcut.loc[income_starts:, :].to_excel(writer, sheet_name='base_income_quantiles')\n\n # age_index = list(range(clients_age+1, clients_age + len(port_qcut)+1))\n # port_qcut.loc[:, 'clients_age'] = age_index\n # port_qcut.loc[:, 'comment'] = ''\n # port_qcut.loc[:, 'comment'] = np.where(port_qcut.clients_age == life_expectancy, 'expected_life', \"\")\n\n port_qcut.loc[:, 'age'] = age_index\n port_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n port_qcut.loc[len(port_qcut) + 1, :] = 0.0\n port_qcut = port_qcut.shift(1)\n port_qcut.iloc[0] = insert_col\n port_qcut.reset_index(drop=True, inplace=True)\n port_qcut.loc[:, 'Annual Return'] = port_qcut.loc[:, '50th'].pct_change().fillna(0)\n port_qcut.to_excel(writer, sheet_name='fia_port_ending_value_quantiles')\n # port_qcut.loc[income_starts:, :].to_excel(writer, sheet_name='fia_port_ending_value_quantiles')\n\n # port_income_qcut = port_income_qcut[1:] port_income_qcut.loc[:, 'clients_age'] = age_index\n # port_income_qcut.loc[:, 'comment'] = '' port_income_qcut.loc[:, 'comment'] = np.where(\n # port_income_qcut.clients_age == life_expectancy, 'expected_life', \"\")\n\n port_income_qcut = port_income_qcut.loc[1:, :]\n port_income_qcut.loc[:, 'age'] = age_index\n port_income_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n port_income_qcut.loc[income_starts:, :].to_excel(writer, sheet_name='fia_port_income_quantiles')\n\n prob_success_df = pd.concat([base_psuccess, port_psuccess], axis=1)\n prob_success_df.rename(columns={prob_success_df.columns[0]: 'prob(ending_value>0)_base',\n prob_success_df.columns[1]: 'prob(ending_value>0)_port'}, inplace=True)\n\n # prob_success_df.loc[:, 'clients_age'] = age_index\n # prob_success_df.loc[:, 'comment'] = ''\n # prob_success_df.loc[:, 'comment'] = np.where(prob_success_df.clients_age == life_expectancy, 'expected_life', \"\")\n\n prob_success_df.loc[:, 'age'] = age_index\n prob_success_df.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n prob_success_df.loc[:, 'prob(ending_value>lifetime_req income)_base'] = base_prob_of_success / trials\n prob_success_df.loc[:, 'prob(ending_value>lifetime_req income)_port'] = port_prob_of_success / trials\n prob_success_df.loc[:, 'prob(ending_value>next_year_req_income)_base'] = base_success_next_year / trials\n prob_success_df.loc[:, 'prob(ending_value>next_year_req_income)_port'] = port_success_next_year / trials\n prob_success_df.loc[:, 'base_max_portfolio_at_acturial_age'] = base_max_portfolio\n prob_success_df.loc[:, 'port_max_portfolio_at_acturial_age'] = port_max_portfolio\n\n # --------------------Percentile Portfolio's based on Acturial Life------------------------\n base_success = prob_success_df.loc[life_expectancy - clients_age, 'prob(ending_value>next_year_req_income)_base']\n port_success = prob_success_df.loc[life_expectancy - clients_age, 'prob(ending_value>next_year_req_income)_port']\n\n # acturial_age_base_tv = sim_base_total.loc[:life_expectancy - clients_age, ]\n # percentile_base_tv = sim_base_total.apply(lambda x: np.nanpercentile(x, base_success), axis=1)\n\n # ----------------Year wise percentile portfolio to meet next year income. Based on the success at acturial age.\n # Yearly portfolio values that can provide the next year income below the success rate at end of life (Percentile)-\n\n # acturial_age_base = base_for_next_year_need.loc[:life_expectancy - clients_age, ]\n # acturial_age_base = base_for_next_year_need.copy().fillna(0)\n percentile_base = base_for_next_year_need.apply(lambda x: np.nanpercentile(x, base_success), axis=1)\n\n # acturial_age_port_tv = sim_port_total.loc[:life_expectancy - clients_age, ]\n # percentile_port_tv = sim_port_total.apply(lambda x: np.nanpercentile(x, port_success), axis=1)\n\n # ----------------Year wise percentile portfolio to meet next year income. Based on the success at acturial age.\n # Yearly portfolio values that can provide the next year income below the success rate at end of life (Percentile)-\n\n # acturial_age_port = port_for_next_year_need.loc[:life_expectancy - clients_age, ]\n # acturial_age_base = port_for_next_year_need.copy().fillna(0)\n percentile_port = port_for_next_year_need.apply(lambda x: np.nanpercentile(x, port_success), axis=1)\n\n prob_success_df.loc[:, 'acturial_success_percentile_base_portfolio'] = percentile_base\n prob_success_df.loc[:, 'acturial_success_percentile_port_portfolio'] = percentile_port\n\n # prob_success_df.loc[:, 'terminalVal_success_percentile_base_portfolio'] = percentile_base_tv\n # prob_success_df.loc[:, 'terminalVal_success_percentile_port_portfolio'] = percentile_port_tv\n\n prob_success_df.to_excel(writer, sheet_name='success_probability')\n\n # --------------BASE - Accumulation and Income Breakdown based on the success percentile portfolio---------------\n base_df.to_csv(src + 'base_port_detail.csv')\n sim_base_total.to_csv(src + 'base_ending_values.csv')\n income_breakdown_base = pd.DataFrame(sim_base_total.quantile(base_success, axis=1))\n income_breakdown_base.loc[:, 'income_from_risky_assets'] = sim_base_income.quantile(base_success, axis=1) \\\n - social - cpn_income_port\n income_breakdown_base.loc[:, 'guaranteed_income'] = 0.0\n income_breakdown_base.loc[:, 'social_security_income'] = social\n income_breakdown_base.loc[:, 'coupon_income'] = cpn_income_base\n\n income_breakdown_base.rename(columns={income_breakdown_base.columns[0]: 'portfolio_ending_value'}, inplace=True)\n income_breakdown_base.loc[:, 'income_from_risky_assets'][\n income_breakdown_base.loc[:, 'portfolio_ending_value'] <= 0] = 0\n income_breakdown_base.loc[:, 'total_income'] = income_breakdown_base.loc[:, income_breakdown_base.columns[1:]].sum(\n axis=1)\n\n # ----------FIA PORTFOLIO - Accumulation and Income Breakdown based on the success percentile portfolio-----------\n fia_portfolio_df.to_csv(src + 'fia_port_detail.csv')\n sim_port_total.to_csv(src + 'fiaport_ending_values.csv')\n\n income_breakdown_port = pd.DataFrame(sim_port_total.quantile(port_success, axis=1))\n income_breakdown_port.loc[:, 'income_from_risky_assets'] = sim_port_income.quantile(port_success, axis=1) \\\n - income_from_fia - social - cpn_income_port\n income_breakdown_port.loc[:, 'guaranteed_income'] = income_from_fia\n income_breakdown_port.loc[:, 'social_security_income'] = social\n income_breakdown_port.loc[:, 'coupon_income'] = cpn_income_port\n\n income_breakdown_port.rename(columns={income_breakdown_port.columns[0]: 'portfolio_ending_value'}, inplace=True)\n income_breakdown_port.loc[:, 'income_from_risky_assets'][\n income_breakdown_port.loc[:, 'portfolio_ending_value'] <= 0] = 0\n income_breakdown_port.loc[:, 'total_income'] = income_breakdown_port.loc[:, income_breakdown_port.columns[1:]].sum(\n axis=1)\n\n # -------------------Write simulation Statistics-------------------------------------\n simulation_stats.to_excel(writer, sheet_name='simulation_statistics')\n\n # port_psuccess.to_excel(writer, sheet_name='fia_port_success_probability')\n\n income_breakdown_base = income_breakdown_base.loc[1:, :]\n income_breakdown_base.loc[:, 'age'] = age_index\n income_breakdown_base.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n income_breakdown_base.loc[income_starts:, :].to_excel(writer, sheet_name='base_income_breakdown_median')\n\n income_breakdown_port = income_breakdown_port.loc[1:, :]\n income_breakdown_port.loc[:, 'age'] = age_index\n income_breakdown_port.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n income_breakdown_port.loc[income_starts:, :].to_excel(writer, sheet_name='fia_income_breakdown_median')\n\n legacy_risk.to_excel(writer, sheet_name='ruin_probability')\n\n median_returns_normal.loc[:, 'fia_median_returns'] = median_normal_fia\n median_returns_normal.to_excel(writer, sheet_name='gr_port_median_normal')\n\n writer.save()\n\n # -----------------Plotting charts--------------------------------------------\n base_qcut.loc[income_starts:].plot(grid=True, title='Quantile Terminal Value - Base Portfolio')\n plt.savefig(src + \"quantile_terminal_base.png\")\n plt.close('all')\n\n base_income_qcut.plot(grid=True, title='Quantile Income - Base Portfolio')\n plt.savefig(src + \"quantile_income_base.png\")\n plt.close('all')\n\n base_psuccess.plot(grid=True, title='Probability of Success (Portfolio Ending Value > 0) - Base Portfolio')\n plt.savefig(src + \"success_probabilty_base.png\")\n plt.close('all')\n\n (1 - base_psuccess).plot(grid=True, title='Probability of Ruin (Portfolio Ending Value < 0) - Base Portfolio')\n plt.savefig(src + \"ruin_probability_base.png\")\n plt.close('all')\n\n port_qcut.loc[income_starts:].plot(grid=True, title='Quantile Terminal Value - FIA Portfolio')\n plt.savefig(src + \"quantile_terminal_fia.png\")\n plt.close('all')\n\n port_income_qcut.plot(grid=True, title='Quantile Income - FIA Portfolio')\n plt.savefig(src + \"quantile_income_fia.png\")\n plt.close('all')\n\n port_psuccess.plot(grid=True, title='Probability of Success (Portfolio Ending Value > 0) - FIA Portfolio')\n plt.savefig(src + \"success_probabilty_fia.png\")\n plt.close('all')\n\n (1 - port_psuccess).plot(grid=True, title='Probability of Ruin (Portfolio Ending Value < 0) - FIA Portfolio')\n plt.savefig(src + \"ruin_probability_fia.png\")\n plt.close('all')\n\n print(\"simulation completed....\")","def simulate_trading(self):\n # Create the file output stream\n posix_now = datetime.datetime.timestamp(datetime.datetime.now())\n out_path = os.getcwd() + \"/OutputResults/backtest_{}\".format(posix_now)[:-7:] + \".csv\"\n\n out = open(out_path, \"w+\")\n\n spl = len(self.strat_params_list)\n for i, sp in enumerate(self.strat_params_list): # http://book.pythontips.com/en/latest/enumerate.html\n print(\"Strategy %s out of %s...\" % (i + 1, spl))\n self._generate_trading_instances(sp)\n self._run_backtest()\n stats = self._output_performance()\n pprint.pprint(stats)\n\n tot_ret = float(stats[0][1].replace(\"%\", \"\"))\n cagr = float(stats[1][1].replace(\"%\", \"\"))\n sharpe = float(stats[2][1])\n max_dd = float(stats[3][1].replace(\"%\", \"\"))\n dd_dur = int(stats[4][1])\n\n # This should be more general in future implementations...\n out.write(\n \"%s,%s,%s,%s,%s,%s,%s,%s\\n\" % (sp[\"ols_window\"], sp[\"zscore_high\"], sp[\"zscore_low\"],\n tot_ret, cagr, sharpe, max_dd, dd_dur)\n )\n\n out.close()","def _run_backtest(self):\n i = 0\n while True:\n i += 1\n if self.data_handler.continue_backtest == True:\n self.data_handler.update_bars()\n #print(self.data_handler.get_latest_bar_datetime(self.symbol_list[0]))\n else:\n break\n while self.backtest:\n try:\n event = self.events.get(False)\n except Empty:\n break\n else:\n if event is not None:\n if event.type == EventType.MARKET:\n try:\n self.strategy.On_Bars(event)\n self.portfolio.update_balance(event)\n self.portfolio.order_check(event)\n except EquityError:\n print('Not Engough Equity,Backtest Will be Stop...')\n self.backtest=False\n break\n elif event.type == EventType.ORDER_SEND:\n self.portfolio.update_order(event)\n elif event.type == EventType.ORDER_CLOSE:\n try:\n self.portfolio.update_order(event)\n self.portfolio.update_euity(event)\n except EquityError:\n print ('Not Engough Equity,Backtest Will be Stop...')\n self.backtest=False\n break\n elif event.type == EventType.ORDER_MODIFY:\n self.portfolio.update_order(event)\n time.sleep(self.heartbeat)","def before_trading_start(context, data):\n factors = pipeline_output('ff_example')\n\n # get the data we're going to use\n returns = factors['returns']\n mkt_cap = factors.sort_values(['market_cap'], ascending=True)\n be_me = factors.sort_values(['be_me'], ascending=True)\n\n # to compose the six portfolios, split our universe into portions\n half = int(len(mkt_cap)*0.5)\n small_caps = mkt_cap[:half]\n big_caps = mkt_cap[half:]\n \n thirty = int(len(be_me)*0.3)\n seventy = int(len(be_me)*0.7)\n growth = be_me[:thirty]\n neutral = be_me[thirty:seventy]\n value = be_me[seventy:]\n\n # now use the portions to construct the portfolios.\n # note: these portfolios are just lists (indices) of equities\n small_value = small_caps.index.intersection(value.index)\n small_neutral = small_caps.index.intersection(neutral.index)\n small_growth = small_caps.index.intersection(growth.index)\n \n big_value = big_caps.index.intersection(value.index)\n big_neutral = big_caps.index.intersection(neutral.index)\n big_growth = big_caps.index.intersection(growth.index)\n\n # take the mean to get the portfolio return, assuming uniform\n # allocation to its constituent equities.\n sv = returns[small_value].mean()\n sn = returns[small_neutral].mean()\n sg = returns[small_growth].mean()\n \n bv = returns[big_value].mean()\n bn = returns[big_neutral].mean()\n bg = returns[big_growth].mean()\n\n # computing SMB\n context.smb = (sv + sn + sg)/3 - (bv + bn + bg)/3\n\n # computing HML\n context.hml = (sv + bv)/2 - (sg + bg)/2","def generate_portfolio_data(self):\n self.__load_portfolio_historical_prices()\n self.__populate_historical_trade_data()\n self.__calculate_portfolio_returns()\n self.__calculate_portfolio_performance()","def back_testing(self, func=None):\n # preallocate real statistics dict\n ism_label = []\n \n if not self.moving_average:\n # iterate on all ism packages\n for _i, ism_pack in enumerate(self.ism_data):\n ism_code = ism_pack[0][0]\n interval = float(ism_pack[0][-5])\n df = self.back_testing_data[ism_code]\n # using close price only now, check data is/not empty\n if df.empty:\n res = np.nan\n continue\n else:\n run_price = df['ClosePrice_run'].sum()\n stop_price = df['ClosePrice_stop'].sum()\n res = ((stop_price-run_price)/run_price)*243/interval # 243 market days/year\n ism_label.append([ism_code, res])\n \n else: # using moving_average\n # iterate on all ism packages\n # for _i, ism_pack in enumerate(self.ism_data):\n # ism_code = ism_pack[0][0]\n # interval = int(ism_pack[0][-5])\n # df = self.back_testing_data[ism_code]\n # \n # # using close price only now\n # dim = int(len(df)/(L_shift+R_shift-1)) \n # close_price = np.array_split(df['ClosePrice'], dim)\n # f1 = lambda x: float(x[-1]-x[0]) # real return for single stock\n # f2 = lambda x: float(x[0]) # initial value of single stock\n # if L_shift+R_shift >= interval:\n # ls1 = np.zeros((L_shift+R_shift-interval,), dtype=np.float32)\n # ls2 = np.zeros_like(ls1)\n # for _j, par in enumerate(close_price):\n # # sum over different stocks in one ism_pack\n # par_rolling = par.rolling(interval)\n # ls1 += np.asarray(par_rolling.apply(f1).dropna())\n # ls2 += np.asarray(par_rolling.apply(f2).dropna())\n # ism_label[ism_code] = {}\n # ism_label[ism_code]['real_return'] = (ls1/ls2/interval)\n # else:\n # print(\"L_shift+R_shift < interval\")\n pass \n \n return ism_label","def test_low_stockprice_high_interest(self):\n stock_prices = np.array([[5, 4, 4, 2],\n [5, 3, 3, 3],\n [5, 4, 2, 2],\n [5, 3, 3, 1]], dtype=float)\n interest_rate = 2.0 # 200%\n test_case = StockMarket(5, stock_prices, interest_rate)\n test_case.dynamic_programming_bottom_up()\n for portfolio in set(test_case.backtracing_portfolio()):\n self.assertEqual(0, portfolio)","def backtest(self, strategy): \n \n # load data\n data = self.import_data(currency_ids = np.unique(self.signals.currency_id),\n start_date = self.start_date,\n end_date = self.end_date)\n \n # add cash and columns for currencies\n data[\"capital\"] = 0\n data[\"actions\"] = \"\" # should become a dictW \n data[\"description\"] = \"\"\n data[\"capital\"].iloc[0] = self.initial_capital\n data[\"worth_usd\"] = 0\n \n for currency_id in np.unique(self.signals.currency_id):\n data[currency_id + '_position'] = 0\n \n # a sell or buy can influence subsequent positions, so calculate iteratively\n for observation in range(1, len(data.index)):\n \n date = data.index[observation]\n \n # investment this period is zero\n investment_capital_period = 0\n \n # amount of currency_ids initially same as last period\n for currency_id in np.unique(self.signals.currency_id):\n data[currency_id + '_position'].iloc[observation] = data[currency_id + '_position'].iloc[observation-1] \n \n # at each point, compute size of each position (cash and currencies), and record actions\n if(data.index[observation] in self.signals.index):\n \n action_df = pd.DataFrame(columns=list([\"Currency\",\"NominalAmount\", \"CapitalAmount\"]))\n \n # could be multiple actions\n for index, action in self.signals.loc[date].iterrows(): \n currency_id = action['currency_id']\n signal = action['signal']\n \n # Buy\n if signal == 1:\n \n # buy for 10% currency_id\n investment_capital = data[\"capital\"].iloc[observation-1] * 0.10 \n\n # estimate how many coins\n investment_nominal = round(investment_capital / data[currency_id].iloc[observation])\n \n # calculate exact capital needed\n investment_capital_exact = investment_nominal * data[currency_id].iloc[observation]\n investment_capital_period = investment_capital_period + investment_capital_exact \n \n # change the amount of currency hold\n data[currency_id + '_position'].iloc[observation] = data[currency_id + '_position'].iloc[observation-1] + investment_nominal\n \n # report action by appending a Series to the (empty) dataframe\n action_df = action_df.append(pd.Series({\"Currency\": currency_id, \n \"NominalAmount\": investment_nominal, \n \"CapitalAmount\": investment_capital_exact}),ignore_index=True)\n \n # report description\n data[\"description\"].iloc[observation] = (data[\"actions\"].iloc[observation] + \"\\n Buy \" + \n str(investment_nominal) + \" \" + str(currency_id) + \n \" for \" + str(investment_capital_exact))\n \n # Sell\n if signal == -1:\n \n # sell currency_id for 10% of total capital\n investment_capital = data[\"capital\"].iloc[observation-1] * 0.10 \n \n # estimate how many coins\n investment_nominal = round(investment_capital / data[currency_id].iloc[observation])\n \n # calculate exact capital needed\n investment_capital_exact = investment_nominal * data[currency_id].iloc[observation]\n investment_capital_period = investment_capital_period - investment_capital_exact\n \n # change the amount of currency hold\n data[currency_id + '_position'].iloc[observation] = data[currency_id + '_position'].iloc[observation-1] - investment_nominal\n \n # report action\n action_df = action_df.append(pd.Series({\"Currency\": currency_id, \n \"NominalAmount\": investment_nominal, \n \"CapitalAmount\": investment_capital_exact}),ignore_index=True)\n \n # report description\n data[\"description\"].iloc[observation] = data[\"actions\"].iloc[observation] + \"Sell \" + str(investment_nominal) + \" \" + str(currency_id) + \" for \" + str(investment_capital_exact)\n \n # report actions\n data[\"actions\"].iloc[observation] = action_df.to_json()\n \n # calculate resulting cash capital\n data[\"capital\"].iloc[observation] = data[\"capital\"].iloc[observation-1] - investment_capital_period\n \n # calculate worth by capital (usd) and each currency * price\n data[\"worth_usd\"].iloc[observation] = data[\"capital\"].iloc[observation] \n return data","def run_back_test(self, algorithm):\n for date in self.dates:\n info = dict()\n info['cash'] = self.cash['cash'][date]\n info['date'] = date\n original_order = Order()\n for stock in self.stocks:\n original_order.order(stock, self.stock_data[stock].position['Position'][date])\n make_order = algorithm(self.get_back_dataframe(date), original_order, info)\n\n for stock in make_order.orders:\n self.order_stock(stock, make_order.orders[stock], date)","def income_model_asset_based_portfolio_quantile(num_of_years=30, trials=100, method='normal'):\n\n sim_fia_cv = pd.DataFrame(index=range(num_of_years + 1))\n\n sim_base_total = pd.DataFrame(index=range(num_of_years + 1))\n sim_base_income = pd.DataFrame(index=range(num_of_years + 1))\n\n sim_port_total = pd.DataFrame(index=range(num_of_years + 1))\n sim_port_income = pd.DataFrame(index=range(num_of_years + 1))\n\n sim_base_total_pre_income = pd.DataFrame(index=range(num_of_years + 1))\n sim_port_total_pre_income = pd.DataFrame(index=range(num_of_years + 1))\n\n sim_base_total_preincome = pd.DataFrame(index=range(num_of_years + 1))\n sim_port_total_preincome = pd.DataFrame(index=range(num_of_years + 1))\n\n # read_income_inputs = pd.read_csv(src + \"income_model_inputs.csv\", index_col='Items')\n read_income_inputs = pd.read_excel(src + \"portfolio_information.xlsx\", sheet_name='income_model_inputs',\n index_col=[0])\n\n # read_returns_est = pd.read_csv(src + \"income_assets_returns_estimates.csv\", index_col='Symbol')\n read_returns_est = pd.read_excel(src + \"portfolio_information.xlsx\", sheet_name='income_assets_returns_estimates',\n index_col=[0])\n\n # read_returns_est.drop(['BM', read_returns_est.index[-1]], axis=0, inplace=True)\n # read_portfolio_inputs = pd.read_csv(src + \"income_portfolio_inputs.csv\", index_col='Items')\n\n # read_asset_weights = pd.read_csv(src + \"asset_weights.csv\", index_col='Asset')\n read_asset_weights = pd.read_excel(src + \"portfolio_information.xlsx\", sheet_name='asset_weights',\n index_col=[0])\n\n read_asset_weights.drop(read_asset_weights.index[-1], axis=0, inplace=True)\n\n # read random returns for simulation\n read_normal = pd.read_csv(src + 'sort_normal.csv', index_col=[0], parse_dates=True)\n read_small = pd.read_csv(src + 'sort_small_to_large.csv', index_col=[0], parse_dates=True)\n read_large = pd.read_csv(src + 'sort_large_to_small.csv', index_col=[0], parse_dates=True)\n assets_col_names = list(read_normal.columns)\n\n tickers = list(read_asset_weights.index)\n wts = np.array(read_asset_weights.loc[:, 'base'])\n\n def asset_median_returns(data, ticker):\n return data.filter(regex=ticker).median(axis=1)\n\n # dataframe for unsorted returns (normal)\n median_returns_normal = pd.DataFrame({t: asset_median_returns(read_normal, t) for t in tickers})\n median_returns_normal.loc[:, 'portfolio_return'] = median_returns_normal.dot(wts)\n median_normal_fia = pd.DataFrame({'FIA': asset_median_returns(read_normal, 'r_FIA')})\n\n # dataframe for smallest to largest returns\n median_returns_smallest = pd.DataFrame({t: asset_median_returns(read_small, t) for t in tickers})\n median_returns_smallest.loc[:, 'portfolio_return'] = median_returns_smallest.dot(wts)\n median_smallest_fia = pd.DataFrame({'FIA': asset_median_returns(read_small, 'r_FIA')})\n\n # dataframe for unsorted returns (normal)\n median_returns_largest = pd.DataFrame({t: asset_median_returns(read_large, t) for t in tickers})\n median_returns_largest.loc[:, 'portfolio_return'] = median_returns_largest.dot(wts)\n median_largest_fia = pd.DataFrame({'FIA': asset_median_returns(read_large, 'r_FIA')})\n\n years = list(range(0, num_of_years + 1))\n income_cols = ['year', 'strategy_term', 'index_returns', 'term_ret', 'term_ret_with_par', 'term_annualize',\n 'ann_net_spread', 'term_ret_netspr', 'high_inc_benefit_base', 'rider_fee', 'eoy_income',\n 'contract_value']\n\n term = int(read_income_inputs.loc['term', 'inputs'])\n fia_ret = read_returns_est.loc[read_returns_est.index[-1], 'Annualized Returns']\n fia_risk = read_returns_est.loc[read_returns_est.index[-1], 'Annualized Risk']\n par_rate = float(read_income_inputs.loc['par_rate', 'inputs'])\n spread = float(read_income_inputs.loc['spread', 'inputs'])\n bonus_term = int(read_income_inputs.loc['bonus_term', 'inputs'])\n premium = float(read_income_inputs.loc['premium', 'inputs'])\n income_bonus = float(read_income_inputs.loc['income_bonus', 'inputs'])\n\n income_starts = int(read_income_inputs.loc['start_income_years', 'inputs'])\n income_growth = float(read_income_inputs.loc['income_growth', 'inputs'])\n rider_fee = float(read_income_inputs.loc['rider_fee', 'inputs'])\n inc_payout_factor = float(read_income_inputs.loc['income_payout_factor', 'inputs'])\n contract_bonus = float(read_income_inputs.loc['contract_bonus', 'inputs'])\n social = float(read_income_inputs.loc['social', 'inputs'])\n inflation = float(read_income_inputs.loc['inflation', 'inputs'])\n wtd_cpn_yield = float(read_income_inputs.loc['wtd_coupon_yld', 'inputs'])\n life_expectancy = int(read_income_inputs.loc['life_expectancy_age', 'inputs'])\n clients_age = int(read_income_inputs.loc['clients_age', 'inputs'])\n\n # ---------------INCOME MODEL--------------------------------------------\n runs = 0\n returns_dict = {}\n asset_dict = {}\n fia_dict = {}\n while runs < trials:\n print(runs)\n\n income_df = pd.DataFrame(index=years, columns=income_cols)\n income_df.loc[:, 'year'] = years\n income_df.loc[:, 'strategy_term'] = income_df.loc[:, 'year'] % term\n income_df.loc[:, 'strategy_term'] = income_df['strategy_term'].apply(lambda x: 1 if x == 0 else 0)\n\n if method == 'normal':\n income_df.loc[:, 'index_returns'] = read_normal.loc[:, '{}_{}'.format('r_FIA', str(runs))]\n\n elif method == 'smallest':\n income_df.loc[:, 'index_returns'] = read_small.loc[:, '{}_{}'.format('r_FIA', str(runs))]\n\n else:\n income_df.loc[:, 'index_returns'] = read_large.loc[:, '{}_{}'.format('r_FIA', str(runs))]\n\n # income_df.loc[:, 'index_returns'] = np.random.normal(fia_ret, fia_risk, size=(len(years), 1))\n\n cumprod = (1. + income_df['index_returns']).rolling(window=term).agg(lambda x: x.prod()) - 1\n income_df.loc[:, 'term_ret'] = np.where(income_df.loc[:, 'strategy_term'] == 1, cumprod, 0)\n income_df.loc[:, 'term_ret_with_par'] = income_df.loc[:, 'term_ret'] * par_rate\n income_df.loc[:, 'term_annualize'] = income_df.loc[:, 'term_ret_with_par'].apply(\n lambda x: (1 + x) ** (1 / term) - 1)\n income_df.loc[:, 'ann_net_spread'] = income_df.loc[:, 'term_annualize'] - spread\n income_df.loc[:, 'ann_net_spread'] = np.where(income_df.loc[:, 'strategy_term'] == 1,\n income_df.loc[:, 'ann_net_spread'], 0)\n income_df.loc[:, 'term_ret_netspr'] = income_df.loc[:, 'ann_net_spread'].apply(lambda x: (1 + x) ** term - 1)\n\n for counter in years:\n if counter == 0:\n income_df.loc[counter, 'high_inc_benefit_base'] = premium * (1 + income_bonus)\n\n elif counter <= min(bonus_term, income_starts):\n income_df.loc[counter, 'high_inc_benefit_base'] = income_df.loc[counter - 1, 'high_inc_benefit_base'] * \\\n (1 + income_growth)\n else:\n income_df.loc[counter, 'high_inc_benefit_base'] = income_df.loc[counter - 1, 'high_inc_benefit_base']\n\n income_df.loc[:, 'rider_fee'] = income_df.loc[:, 'high_inc_benefit_base'] * rider_fee\n income_df.loc[:, 'eoy_income'] = np.where(income_df.loc[:, 'year'] > income_starts,\n income_df.loc[:, 'high_inc_benefit_base'] * inc_payout_factor, 0)\n\n for counter in years:\n if counter == 0:\n income_df.loc[counter, 'contract_value'] = premium * (1 + contract_bonus)\n\n elif income_df.loc[counter, 'strategy_term'] == 1:\n x1 = income_df.loc[counter - 1, 'contract_value'] - income_df.loc[counter, 'rider_fee']\n x2 = (x1 * (1 + income_df.loc[counter, 'term_ret_netspr'])) - income_df.loc[counter, 'eoy_income']\n income_df.loc[counter, 'contract_value'] = x2\n\n else:\n x1 = income_df.loc[counter - 1, 'contract_value'] - income_df.loc[counter, 'rider_fee'] - \\\n income_df.loc[counter, 'eoy_income']\n\n income_df.loc[counter, 'contract_value'] = x1\n\n # variable stores the income number that is used in the base and fia portfolio calcs.\n\n income_from_fia = income_df.loc[income_df.index[-1], 'eoy_income']\n\n income_df.loc[:, 'contract_value'] = income_df.loc[:, 'contract_value'].apply(lambda x: 0 if x <= 0 else x)\n\n sim_fia_cv.loc[:, str(runs)] = income_df.loc[:, 'contract_value']\n\n # -------------------------------------BASE MODEL---------------------------------------------\n\n base_wts = read_asset_weights.loc[:, 'base']\n base_assets = list(base_wts.index)\n base_weights = list(base_wts.values)\n base_returns = list(read_returns_est.loc[:, 'Annualized Returns'].values)\n base_std = list(read_returns_est.loc[:, 'Annualized Risk'].values)\n\n base_investment = float(read_income_inputs.loc['risky_assets', 'Base'])\n adv_fees = float(read_income_inputs.loc['advisor_fees', 'Base'])\n\n # -------------------required income----------------------------------\n req_annual_income = float(read_income_inputs.loc['annual_income', 'inputs'])\n income_needed = req_annual_income - social\n income_net_fia_income = max(0, income_needed - income_from_fia)\n cpn_income_base = base_investment * wtd_cpn_yield\n\n # ----------------------RANDOM RETURNS--------------------------\n r_cols = ['r_{}'.format(name) for name in base_assets]\n boy_value = ['bv_{}'.format(name) for name in base_assets]\n eoy_value = ['ev_{}'.format(name) for name in base_assets]\n\n random_returns = pd.DataFrame(index=income_df.index, columns=r_cols)\n\n for c in range(len(r_cols)):\n ret = np.random.normal(base_returns[c], base_std[c], size=(len(random_returns.index), 1))\n\n if method == 'smallest':\n this_run_cols = ['{}_{}'.format(cname, str(runs)) for cname in r_cols]\n random_returns = read_small.loc[:, this_run_cols]\n\n # random_returns.loc[:, r_cols[c]] = np.sort(ret.flatten())\n # asset_dict.update({'{}_{}'.format(r_cols[c], str(runs)): np.sort(ret.flatten())})\n\n elif method == 'largest':\n this_run_cols = ['{}_{}'.format(cname, str(runs)) for cname in r_cols]\n random_returns = read_large.loc[:, this_run_cols]\n\n # random_returns.loc[:, r_cols[c]] = np.flip(np.sort(ret.flatten()))\n # asset_dict.update({'{}_{}'.format(r_cols[c], str(runs)): np.flip(np.sort(ret.flatten()))})\n\n else:\n this_run_cols = ['{}_{}'.format(cname, str(runs)) for cname in r_cols]\n random_returns = read_normal.loc[:, this_run_cols]\n\n # random_returns.loc[:, r_cols[c]] = ret.flatten()\n # asset_dict.update({'{}_{}'.format(r_cols[c], str(runs)): ret.flatten()})\n\n # store the simulated assets returns in one dictionary\n # returns_dict.update({str(runs): random_returns})\n\n # collect the asset based returns from all simulation and calculate the median returns.\n # def get_median_returns(sym):\n # cols = [sym + '_' + str(c) for c in np.arange(trials)]\n # asset_df = pd.DataFrame({c: asset_dict.get(c) for c in cols})\n # return asset_df.median(axis=1)\n #\n # asset_median_returns = pd.DataFrame({symbol: get_median_returns(symbol) for symbol in r_cols})\n #\n # asset_median_returns.loc[:, 'simulated_portfolio_median_returns'] = asset_median_returns.dot(base_weights)\n\n base_df = random_returns.copy()\n pre_income_base_df = random_returns.copy()\n\n # base_investment = float(read_portfolio_inputs.loc['risky_assets', 'Base'])\n\n fia_portfolio_df = random_returns.copy()\n pre_income_port_df = random_returns.copy()\n port_investment = float(read_income_inputs.loc['risky_assets', 'FIA'])\n cpn_income_port = port_investment * wtd_cpn_yield\n\n # ---------Initial Investments for pre-income account values---------------------\n pre_income_base_inv = base_investment\n pre_income_port_inv = port_investment\n # ----------------------------------------BASE PORTFOLIO----------------------------\n for name in boy_value:\n base_df.loc[:, name] = 0.0\n pre_income_base_df.loc[:, name] = 0.0\n\n for counter in years:\n period_returns = list(random_returns.loc[counter, :])\n if counter == 0:\n # ---------------For year 0, the year of investment------------\n\n # ------------Calculate the annual portfolio returns - Gross Returns--------------------\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment for c in range(len(boy_value))]\n\n # -------------Record the Pre Income Base Portfolio-----------------------------\n\n pre_income_base_df.loc[counter, boy_value] = [base_weights[c] *\n pre_income_base_inv for c in range(len(boy_value))]\n pre_income_base_df.loc[counter, 'total'] = pre_income_base_df.loc[counter, boy_value].sum()\n pre_income_base_inv = pre_income_base_df.loc[counter, boy_value].sum()\n\n # ------------------Pre Income Block Ends------------------------\n\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\n\n base_df.loc[counter, 'total_net_fees'] = 0.0\n base_df.loc[counter, 'income'] = 0.0\n base_investment = base_df.loc[counter, boy_value].sum()\n base_df.loc[counter, 'total_pre_income'] = base_investment\n\n elif (counter > 0) and (counter < income_starts):\n\n # ----For years between the start of the investment and start if the income---------------\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment * (1 + period_returns[c])\n for c in range(len(boy_value))]\n\n # -------------Record the Pre Income Base Portfolio-----------------------------\n pre_income_base_df.loc[counter, boy_value] = [\n base_weights[c] * pre_income_base_inv * (1 + period_returns[c])\n for c in range(len(boy_value))]\n\n pre_income_base_df.loc[counter, 'total'] = pre_income_base_df.loc[counter, boy_value].sum()\n pre_income_base_df.loc[counter, 'adv_fees'] = pre_income_base_df.loc[counter, 'total'] * adv_fees\n pre_income_base_df.loc[counter, 'total_net_fees'] = pre_income_base_df.loc[counter, 'total'] - \\\n pre_income_base_df.loc[counter, 'adv_fees']\n pre_income_base_inv = pre_income_base_df.loc[counter, 'total_net_fees'] + cpn_income_base\n\n # ------------------Pre Income Block Ends------------------------\n\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\n base_df.loc[counter, 'adv_fees'] = base_df.loc[counter, 'total'] * adv_fees\n base_df.loc[counter, 'total_net_fees'] = base_df.loc[counter, 'total'] - base_df.loc[\n counter, 'adv_fees']\n\n # --coupon payment is invested back into the risky portfolio until the income is withdrawn----\n base_investment = base_df.loc[counter, 'total_net_fees'] + cpn_income_base\n base_df.loc[counter, 'total_pre_income'] = base_df.loc[counter, 'total_net_fees']\n\n else:\n\n # -------------For Years after the income started----------------------\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment * (1 + period_returns[c])\n for c in range(len(boy_value))]\n\n # -------------Record the Pre Income Base Portfolio-----------------------------\n pre_income_base_df.loc[counter, boy_value] = [\n base_weights[c] * pre_income_base_inv * (1 + period_returns[c])\n for c in range(len(boy_value))]\n\n pre_income_base_df.loc[counter, 'total'] = pre_income_base_df.loc[counter, boy_value].sum()\n pre_income_base_df.loc[counter, 'adv_fees'] = pre_income_base_df.loc[counter, 'total'] * adv_fees\n pre_income_base_df.loc[counter, 'total_net_fees'] = pre_income_base_df.loc[counter, 'total'] - \\\n pre_income_base_df.loc[counter, 'adv_fees']\n pre_income_base_inv = pre_income_base_df.loc[counter, 'total_net_fees'] + cpn_income_base\n\n # ------------------Pre Income Block Ends------------------------\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\n base_df.loc[counter, 'adv_fees'] = base_df.loc[counter, 'total'] * adv_fees\n\n # ---req. income is adjusted for inflation from the second year of withdrawal. Reinvestment of coupon\n # stops from the year income starts. Req. income is reduced by the coupon payments\n\n if counter == income_starts:\n\n income_needed = req_annual_income - social\n base_df.loc[counter, 'income'] = income_needed - cpn_income_base\n income_needed = req_annual_income\n\n else:\n income_needed = income_needed * (1 + inflation) - social\n base_df.loc[counter, 'income'] = income_needed - cpn_income_base\n income_needed = income_needed + social\n\n base_df.loc[counter, 'total_net_fees'] = base_df.loc[counter, 'total'] - \\\n base_df.loc[counter, 'adv_fees'] - \\\n base_df.loc[counter, 'income']\n\n base_df.loc[counter, 'total_pre_income'] = base_df.loc[counter, 'total'] - \\\n base_df.loc[counter, 'adv_fees']\n\n base_investment = base_df.loc[counter, 'total_net_fees']\n\n # ------------------------Portfolio with PreIncome Values----------------------------\n sim_base_total_preincome.loc[:, 's_{}'.format(str(runs))] = pre_income_base_df.loc[:, 'total_net_fees']\n sim_base_total_preincome.fillna(float(read_income_inputs.loc['risky_assets', 'Base']), inplace=True)\n # --------------------------------PreIncome Block Ends----------------------------\n\n base_df.loc[:, 'adj_total'] = base_df.loc[:, 'total_net_fees'].apply(lambda x: x if x > 0 else 0)\n sim_base_total.loc[:, 's_{}'.format(str(runs))] = base_df.loc[:, 'total_net_fees']\n sim_base_income.loc[:, 's_{}'.format(str(runs))] = base_df.loc[:, 'income']\n sim_base_total_pre_income.loc[:, 's_{}'.format(str(runs))] = base_df.loc[:, 'total_pre_income']\n\n # ----------------------------FIA PORTFOLIO----------------------------------------------\n for name in boy_value:\n fia_portfolio_df.loc[:, name] = 0.0\n pre_income_port_df.loc[:, name] = 0.0\n\n for counter in years:\n period_returns = list(random_returns.loc[counter, :])\n if counter == 0:\n\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment\n for c in range(len(boy_value))]\n\n # -------------Record the Pre Income Base Portfolio-----------------------------\n\n pre_income_port_df.loc[counter, boy_value] = [base_weights[c] *\n pre_income_port_inv for c in range(len(boy_value))]\n pre_income_port_df.loc[counter, 'total'] = pre_income_port_df.loc[counter, boy_value].sum()\n pre_income_port_inv = pre_income_port_df.loc[counter, boy_value].sum()\n\n # ------------------Pre Income Block Ends------------------------\n\n fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\n fia_portfolio_df.loc[counter, 'total_net_fees'] = 0.0\n fia_portfolio_df.loc[counter, 'income'] = 0.0\n port_investment = fia_portfolio_df.loc[counter, boy_value].sum()\n fia_portfolio_df.loc[counter, 'total_pre_income'] = port_investment\n\n elif (counter > 0) and (counter < income_starts):\n\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment * (1 + period_returns[c])\n for c in range(len(boy_value))]\n\n # ------------------Record the Pre Income Base Portfolio-----------------------------\n pre_income_port_df.loc[counter, boy_value] = [\n base_weights[c] * pre_income_port_inv * (1 + period_returns[c])\n for c in range(len(boy_value))]\n\n pre_income_port_df.loc[counter, 'total'] = pre_income_port_df.loc[counter, boy_value].sum()\n pre_income_port_df.loc[counter, 'adv_fees'] = pre_income_port_df.loc[counter, 'total'] * adv_fees\n pre_income_port_df.loc[counter, 'total_net_fees'] = pre_income_port_df.loc[counter, 'total'] - \\\n pre_income_port_df.loc[counter, 'adv_fees']\n pre_income_port_inv = pre_income_port_df.loc[counter, 'total_net_fees'] + cpn_income_base\n\n # ------------------Pre Income Block Ends------------------------\n\n fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\n fia_portfolio_df.loc[counter, 'adv_fees'] = fia_portfolio_df.loc[counter, 'total'] * adv_fees\n fia_portfolio_df.loc[counter, 'total_net_fees'] = fia_portfolio_df.loc[counter, 'total'] - \\\n fia_portfolio_df.loc[counter, 'adv_fees']\n fia_portfolio_df.loc[counter, 'total_pre_income'] = fia_portfolio_df.loc[counter, 'total_net_fees']\n\n port_investment = fia_portfolio_df.loc[counter, 'total_net_fees'] + cpn_income_port\n\n else:\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment * (1 + period_returns[c])\n for c in range(len(boy_value))]\n\n # -------------Record the Pre Income Base Portfolio-----------------------------\n pre_income_port_df.loc[counter, boy_value] = [\n base_weights[c] * pre_income_port_inv * (1 + period_returns[c])\n for c in range(len(boy_value))]\n\n pre_income_port_df.loc[counter, 'total'] = pre_income_port_df.loc[counter, boy_value].sum()\n pre_income_port_df.loc[counter, 'adv_fees'] = pre_income_port_df.loc[counter, 'total'] * adv_fees\n pre_income_port_df.loc[counter, 'total_net_fees'] = pre_income_port_df.loc[counter, 'total'] - \\\n pre_income_port_df.loc[counter, 'adv_fees']\n pre_income_port_inv = pre_income_port_df.loc[counter, 'total_net_fees'] + cpn_income_base\n\n # ------------------Pre Income Block Ends------------------------\n\n fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\n fia_portfolio_df.loc[counter, 'adv_fees'] = fia_portfolio_df.loc[counter, 'total'] * adv_fees\n\n # ---req. income is adjusted for inflation from the second year of withdrawal. Reinvestment of coupon\n # stops from the year income starts. Req. income is reduced by the coupon payments\n\n if counter == income_starts:\n\n income_needed = req_annual_income - social\n income_net_fia_income = max(0, income_needed - income_from_fia)\n fia_portfolio_df.loc[counter, 'income'] = max(0, income_net_fia_income - cpn_income_port)\n income_needed = req_annual_income\n\n else:\n income_needed = income_needed * (1 + inflation) - social\n income_net_fia_income = max(0, income_needed - income_from_fia)\n fia_portfolio_df.loc[counter, 'income'] = max(0, income_net_fia_income - cpn_income_port)\n income_needed = income_needed + social\n\n fia_portfolio_df.loc[counter, 'total_net_fees'] = fia_portfolio_df.loc[counter, 'total'] - \\\n fia_portfolio_df.loc[counter, 'adv_fees'] - \\\n fia_portfolio_df.loc[counter, 'income']\n\n fia_portfolio_df.loc[counter, 'total_pre_income'] = fia_portfolio_df.loc[counter, 'total'] - \\\n fia_portfolio_df.loc[counter, 'adv_fees']\n\n port_investment = fia_portfolio_df.loc[counter, 'total_net_fees']\n\n sim_port_total.loc[:, 's_{}'.format(str(runs))] = fia_portfolio_df.loc[:, 'total_net_fees'] + \\\n income_df.loc[:, 'contract_value']\n\n sim_port_income.loc[:, 's_{}'.format(str(runs))] = fia_portfolio_df.loc[:, 'income']\n\n fia_portfolio_df.loc[:, 'adj_total'] = fia_portfolio_df.loc[:, 'total_net_fees'].apply(\n lambda x: x if x > 0 else 0)\n\n sim_port_total_pre_income.loc[:, 's_{}'.format(str(runs))] = fia_portfolio_df.loc[:, 'total_pre_income']\n\n # -------------------Portfolio with PreIncome Values----------------------------\n sim_port_total_preincome.loc[:, 's_{}'.format(str(runs))] = pre_income_port_df.loc[:, 'total_net_fees'] + \\\n income_df.loc[:, 'contract_value']\n\n sim_port_total_preincome.fillna(float(read_income_inputs.loc['risky_assets', 'FIA']), inplace=True)\n # --------------------------------PreIncome Block Ends----------------------------\n\n runs += 1\n\n # ------------------Calculate % of portfolios ending value greater than required LIFETIME cumm. income---------\n total_income_by_age = sim_base_income.loc[:, sim_base_income.columns[0]].cumsum()\n total_income_by_acturial_age = total_income_by_age.loc[life_expectancy - clients_age]\n total_income_by_age.fillna(0, inplace=True)\n income_dataframe = pd.DataFrame(total_income_by_age)\n income_dataframe.loc[:, 'remaining_income_by_acturial_age'] = total_income_by_age.apply(\n lambda x: total_income_by_acturial_age - x)\n\n s = income_dataframe.loc[:, 'remaining_income_by_acturial_age']\n base_prob_of_success = sim_base_total.gt(s, axis=0).sum(axis=1)\n port_prob_of_success = sim_port_total.gt(s, axis=0).sum(axis=1)\n\n # ----------------------------Portfolio sufficient for NEXT YEARS income needs-------------------\n next_year_income = sim_base_income.loc[:, sim_base_income.columns[0]].shift(-1).fillna(0) # Yearly Income Reqd.\n base_success_next_year = sim_base_total.gt(next_year_income, axis=0).sum(axis=1)\n\n base_for_next_year_need = sim_base_total[sim_base_total.gt(next_year_income, axis=0)]\n\n port_success_next_year = sim_port_total.gt(next_year_income, axis=0).sum(axis=1)\n\n port_for_next_year_need = sim_port_total[sim_port_total.gt(next_year_income, axis=0)]\n\n # ---------------Portfolio for 45 years of simulation---------------------------------------\n base_success_portfolio = sim_base_total[sim_base_total.gt(next_year_income, axis=0)]\n port_success_portfolio = sim_port_total[sim_port_total.gt(next_year_income, axis=0)]\n\n # ----------------Portfolio Simulation until the acturial age------------------------------\n acturial_years = life_expectancy - clients_age\n base_success_portfolio_act_age = base_success_portfolio.loc[acturial_years, :]\n port_success_portfolio_act_age = port_success_portfolio.loc[acturial_years, :]\n\n # -------------------------Base Portfolio TS with max Terminal Value ----------------------------\n if base_success_portfolio_act_age.isnull().sum() == trials:\n base_max_portfolio = 0.0\n else:\n base_max_portfolio = base_success_portfolio.loc[:, base_success_portfolio_act_age.idxmax()]\n\n # -------------------------FIA Portfolio TS with max Terminal Value ----------------------------\n if port_success_portfolio_act_age.isnull().sum() == trials:\n port_max_portfolio = 0.0\n else:\n port_max_portfolio = port_success_portfolio.loc[:, port_success_portfolio_act_age.idxmax()]\n\n # ------------------------------Average age with full income------------------------------\n base_mean_age = ((life_expectancy - clients_age) - base_success_portfolio.loc[1:life_expectancy - clients_age, :]\n .isnull().sum()).mean()\n\n port_mean_age = ((life_expectancy - clients_age) - port_success_portfolio.loc[1:life_expectancy - clients_age, :]\n .isnull().sum()).mean()\n\n # ----------------------------Median Age with full Income------------------------------------------\n base_median_age = ((life_expectancy - clients_age) - base_success_portfolio.loc[1:life_expectancy - clients_age, :]\n .isnull().sum()).median()\n\n port_median_age = ((life_expectancy - clients_age) - port_success_portfolio.loc[1:life_expectancy - clients_age, :]\n .isnull().sum()).median()\n\n # --------------Mean Value for all the portfolios at end of the acturial age--------------------\n base_act_avg_porfolio = base_success_portfolio.loc[life_expectancy - clients_age, :].fillna(0).mean()\n port_act_avg_porfolio = port_success_portfolio.loc[life_expectancy - clients_age, :].fillna(0).mean()\n\n # ---------------Median Value for all the portfolios at end of the acturial age--------------------\n base_act_median_porfolio = base_success_portfolio.loc[life_expectancy - clients_age, :].fillna(0).median()\n port_act_median_porfolio = port_success_portfolio.loc[life_expectancy - clients_age, :].fillna(0).median()\n\n # # --------------Mean Value for all the portfolios in the simulation--------------------\n # base_sim_mean = base_success_portfolio.loc[1:life_expectancy - clients_age, :].mean().mean()\n # port_sim_mean = port_success_portfolio.loc[1:life_expectancy - clients_age, :].mean().mean()\n #\n # # --------------Median Value for all the portfolios in the simulation--------------------\n # base_sim_mean = base_success_portfolio.loc[1:life_expectancy - clients_age, :].mean().median()\n # port_sim_mean = port_success_portfolio.loc[1:life_expectancy - clients_age, :].mean().median()\n\n # -------Max Portfolio value at the end of acturial age----------------------------------------\n base_act_max = base_success_portfolio.loc[life_expectancy - clients_age, :].max()\n port_act_max = port_success_portfolio.loc[life_expectancy - clients_age, :].max()\n\n # ----------------------Min Portfolio value at the end of acturial age----------------------------------------\n base_act_min = base_success_portfolio.loc[life_expectancy - clients_age, :].min()\n port_act_min = port_success_portfolio.loc[life_expectancy - clients_age, :].min()\n\n # -----------------------------------------Lifetime Average Income-----------------------------------------\n base_total_income = sim_base_income.cumsum().loc[acturial_years, :].mean()\n port_total_income = income_from_fia + sim_port_income\n port_total_income = port_total_income.cumsum().loc[acturial_years, :].mean()\n\n simulation_stats = pd.DataFrame(index=['Average Years', 'Median Years', 'Average Age', 'Median Age',\n 'Average Portfolio (act.age)', 'Median Portfolio (act.age)',\n 'Max Portfolio Value', 'Min Portfolio Value',\n 'Average Lifetime Income'], columns=['Base', 'FIA'])\n\n simulation_stats.loc['Average Years', :] = [base_mean_age, base_mean_age]\n simulation_stats.loc['Median Years', :] = [base_median_age, base_median_age]\n simulation_stats.loc['Average Age', :] = [base_mean_age + clients_age, base_mean_age + clients_age]\n simulation_stats.loc['Median Age', :] = [base_median_age + clients_age, base_median_age + clients_age]\n simulation_stats.loc['Average Portfolio (act.age)', :] = [base_act_avg_porfolio, port_act_avg_porfolio]\n simulation_stats.loc['Median Portfolio (act.age)', :] = [base_act_median_porfolio, port_act_median_porfolio]\n simulation_stats.loc['Max Portfolio Value', :] = [base_act_max, port_act_max]\n simulation_stats.loc['Min Portfolio Value', :] = [base_act_min, port_act_min]\n simulation_stats.loc['Average Lifetime Income', :] = [base_total_income, port_total_income]\n comments = ['Average years of portfolios that meet the next years income needs for the lifetime',\n 'Median years of portfolios that meet the next years income needs for the lifetime',\n 'Average Clients Age',\n 'Median Clients Age',\n 'Average of terminal values for the portfolios at the end of the acturial life',\n 'Median of terminal values for the portfolios at the end of the acturial life',\n 'Maximum of terminal values for the portfolios at the end of the acturial life',\n 'Minimum of terminal values for the portfolios at the end of the acturial life',\n 'Average of total income generated by all portfolios at the end of the acturial life']\n\n simulation_stats.loc[:, 'Notes'] = comments\n\n # --------------------------------------------------------------------------------\n\n # # -----------------------------------income breakdown for Base portfolio----------------------------------\n # base_df.to_csv(src + 'base_port_detail.csv')\n # sim_base_total.to_csv(src + 'base_ending_values.csv')\n # income_breakdown_base = pd.DataFrame(sim_base_total.quantile(0.5, axis=1))\n # income_breakdown_base.loc[:, 'income_from_portfolio'] = sim_base_income.quantile(0.5, axis=1)\n # income_breakdown_base.loc[:, 'fia_income'] = 0.0\n # income_breakdown_base.loc[:, 'social_security_income'] = social\n # income_breakdown_base.loc[:, 'coupon_income'] = cpn_income_base\n #\n # income_breakdown_base.rename(columns={income_breakdown_base.columns[0]: 'portfolio_ending_value'}, inplace=True)\n # income_breakdown_base.loc[:, 'income_from_portfolio'][\n # income_breakdown_base.loc[:, 'portfolio_ending_value'] <= 0] = 0\n # income_breakdown_base.loc[:, 'total_income'] = income_breakdown_base.loc[:, income_breakdown_base.columns[1:]].sum(\n # axis=1)\n #\n # # --------------------------------------Block Ends-----------------------------------------------------------\n #\n # # ---------------------------------------income breakdown for FIA portfolio----------------------------------\n # fia_portfolio_df.to_csv(src + 'fia_port_detail.csv')\n # sim_port_total.to_csv(src + 'fiaport_ending_values.csv')\n #\n # income_breakdown_port = pd.DataFrame(sim_port_total.quantile(0.5, axis=1))\n # income_breakdown_port.loc[:, 'income_from_portfolio'] = sim_port_income.quantile(0.5, axis=1)\n # income_breakdown_port.loc[:, 'fia_income'] = income_from_fia\n # income_breakdown_port.loc[:, 'social_security_income'] = social\n # income_breakdown_port.loc[:, 'coupon_income'] = cpn_income_port\n #\n # income_breakdown_port.rename(columns={income_breakdown_port.columns[0]: 'portfolio_ending_value'}, inplace=True)\n # income_breakdown_port.loc[:, 'income_from_portfolio'][\n # income_breakdown_port.loc[:, 'portfolio_ending_value'] <= 0] = 0\n # income_breakdown_port.loc[:, 'total_income'] = income_breakdown_port.loc[:, income_breakdown_port.columns[1:]].sum(\n # axis=1)\n #\n # # ----------------------------------Block Ends-------------------------------------------------------------\n q_cut = [0.0, 0.1, 0.25, 0.5, 0.75, 0.95, 1.0]\n sim_base_income[sim_base_total < income_needed] = 0.0\n\n sim_port_income[sim_port_total < income_net_fia_income] = 0\n\n sim_port_income = sim_port_income + income_from_fia\n\n # base_quantile = sim_base_total.loc[sim_base_total.index[-1]].quantile([0.05, 0.25, 0.50, 0.75, 0.90])\n #\n # port_quantile = sim_port_total.loc[sim_port_total.index[-1]].quantile([0.05, 0.25, 0.50, 0.75, 0.90])\n\n base_quantile = sim_base_total.loc[sim_base_total.index[-1]].quantile(q_cut)\n\n port_quantile = sim_port_total.loc[sim_port_total.index[-1]].quantile(q_cut)\n\n # q_cut = [0.0, .05, 0.25, 0.5, 0.75, 0.95, 1.0]\n cols = ['Min', '10th', '25th', '50th', '75th', '90th', 'Max']\n\n # ------------------------------------------drop year 0-----------------------------------------\n sim_base_total = sim_base_total[1:]\n sim_port_total = sim_port_total[1:]\n\n # ---------------------------------plot for histogram for porfolios--------------------------------------\n # base_term_value = sim_base_total.loc[sim_base_total.index[:life_expectancy - clients_age], :]\n # fact = 1 / len(base_term_value)\n # base_ann_ret = (base_term_value.iloc[-1] / base_term_value.iloc[0]) ** fact - 1\n # counts, bins, bars = plt.hist(base_ann_ret)\n\n # ------------------------quantile analysis for base terminal value-----------------------------\n base_qcut = pd.DataFrame(index=sim_base_total.index, columns=cols)\n for c in range(len(cols)):\n base_qcut.loc[:, cols[c]] = sim_base_total.quantile(q_cut[c], axis=1)\n\n base_qcut.clip(lower=0, inplace=True)\n\n sim_base_total.clip(lower=0, inplace=True)\n\n # -------------------------------------quantile analysis for base income----------------------------\n base_income_qcut = pd.DataFrame(index=sim_base_income.index, columns=cols)\n for c in range(len(cols)):\n base_income_qcut.loc[:, cols[c]] = sim_base_income.quantile(q_cut[c], axis=1)\n\n # ----Remove NaN's prior to the income start years------------\n # base_income_qcut = base_income_qcut.loc[income_starts:]\n\n # ---------------------------------quantile analysis for portfolio terminal value ---------------\n\n port_qcut = pd.DataFrame(index=sim_port_total.index, columns=cols)\n for c in range(len(cols)):\n port_qcut.loc[:, cols[c]] = sim_port_total.quantile(q_cut[c], axis=1)\n\n port_qcut.clip(lower=0, inplace=True)\n\n # ----------------------------------quantile analysis for portfolio income----------------------------\n port_income_qcut = pd.DataFrame(index=sim_port_income.index, columns=cols)\n for c in range(len(cols)):\n port_income_qcut.loc[:, cols[c]] = sim_port_income.quantile(q_cut[c], axis=1)\n\n # ----Remove NaN's prior to the income start years------------\n # port_income_qcut = port_income_qcut.loc[income_starts:]\n\n # ----------probability ending value will be less than 0 at the end of the horizon -----------------------\n # base_legacy_risk = (sim_base_total.loc[sim_base_total.index[-1]] < 0).sum() / (trials)\n\n base_legacy_risk = (sim_base_total.loc[sim_base_total.index[life_expectancy - clients_age]] < 0).sum() / trials\n port_legacy_risk = (sim_port_total.loc[sim_port_total.index[life_expectancy - clients_age]] < 0).sum() / trials\n\n # port_legacy_risk = (sim_port_total.loc[sim_port_total.index[-1]] <= 0).sum() / (trials)\n\n legacy_risk = pd.DataFrame([base_legacy_risk, port_legacy_risk,\n 'Prob. of portfolio value less than 0 at the end of the expected life'],\n index=['base', 'fia_portfolio', 'Notes'],\n columns=['Ruin Probability'])\n\n # -----------Year-wise probability of ending value greater than 0 -----------------\n base_psuccess = sim_base_total.apply(lambda x: x > 0).sum(axis=1) / trials\n port_psuccess = sim_port_total.apply(lambda x: x > 0).sum(axis=1) / trials\n\n # ------------------------------------WRITING FILES TO EXCEL ---------------------------\n\n writer = pd.ExcelWriter(dest_simulation + method + '_montecarlo_income_summary.xlsx', engine='xlsxwriter')\n read_income_inputs.to_excel(writer, sheet_name='inputs_for_income')\n\n read_returns_est.to_excel(writer, sheet_name='asset_returns_estimates')\n # read_portfolio_inputs.to_excel(writer, sheet_name='portfolio_inputs')\n\n age_index = list(range(clients_age + 1, clients_age + len(base_qcut) + 1))\n # base_qcut.loc[:, 'clients_age'] = age_index\n # base_qcut.loc[:, 'comment'] = ''\n # base_qcut.loc[:, 'comment'] = np.where(base_qcut.clients_age == life_expectancy, 'expected_life', \"\")\n base_inv = float(read_income_inputs.loc['risky_assets', 'Base'])\n base_qcut.loc[:, 'age'] = age_index\n base_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n\n # -----------------------To start with year 0---------------------------------\n insert_col = [base_inv, base_inv, base_inv, base_inv, base_inv, base_inv,\n base_inv, clients_age, np.nan]\n base_qcut.loc[len(base_qcut) + 1, :] = 0.0\n base_qcut = base_qcut.shift(1)\n base_qcut.iloc[0] = insert_col\n base_qcut.reset_index(drop=True, inplace=True)\n base_qcut.to_excel(writer, sheet_name='base_ending_value_quantiles')\n # base_qcut.loc[income_starts:, :].to_excel(writer, sheet_name='base_ending_value_quantiles')\n\n # base_income_qcut = base_income_qcut[1:] base_income_qcut.loc[:, 'clients_age'] = age_index\n # base_income_qcut.loc[:, 'comment'] = '' base_income_qcut.loc[:, 'comment'] = np.where(\n # base_income_qcut.clients_age == life_expectancy, 'expected_life', \"\")\n\n base_income_qcut = base_income_qcut.loc[1:, :]\n base_income_qcut.loc[:, 'age'] = age_index\n base_income_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n base_income_qcut.loc[income_starts:, :].to_excel(writer, sheet_name='base_income_quantiles')\n\n # age_index = list(range(clients_age+1, clients_age + len(port_qcut)+1))\n # port_qcut.loc[:, 'clients_age'] = age_index\n # port_qcut.loc[:, 'comment'] = ''\n # port_qcut.loc[:, 'comment'] = np.where(port_qcut.clients_age == life_expectancy, 'expected_life', \"\")\n\n port_qcut.loc[:, 'age'] = age_index\n port_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n port_qcut.loc[len(port_qcut) + 1, :] = 0.0\n port_qcut = port_qcut.shift(1)\n port_qcut.iloc[0] = insert_col\n port_qcut.reset_index(drop=True, inplace=True)\n port_qcut.to_excel(writer, sheet_name='fia_port_ending_value_quantiles')\n # port_qcut.loc[income_starts:, :].to_excel(writer, sheet_name='fia_port_ending_value_quantiles')\n\n # port_income_qcut = port_income_qcut[1:] port_income_qcut.loc[:, 'clients_age'] = age_index\n # port_income_qcut.loc[:, 'comment'] = '' port_income_qcut.loc[:, 'comment'] = np.where(\n # port_income_qcut.clients_age == life_expectancy, 'expected_life', \"\")\n\n port_income_qcut = port_income_qcut.loc[1:, :]\n port_income_qcut.loc[:, 'age'] = age_index\n port_income_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n port_income_qcut.loc[income_starts:, :].to_excel(writer, sheet_name='fia_port_income_quantiles')\n\n prob_success_df = pd.concat([base_psuccess, port_psuccess], axis=1)\n prob_success_df.rename(columns={prob_success_df.columns[0]: 'prob(ending_value>0)_base',\n prob_success_df.columns[1]: 'prob(ending_value>0)_port'}, inplace=True)\n\n # prob_success_df.loc[:, 'clients_age'] = age_index\n # prob_success_df.loc[:, 'comment'] = ''\n # prob_success_df.loc[:, 'comment'] = np.where(prob_success_df.clients_age == life_expectancy, 'expected_life', \"\")\n\n prob_success_df.loc[:, 'age'] = age_index\n prob_success_df.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n prob_success_df.loc[:, 'prob(ending_value>lifetime_req income)_base'] = base_prob_of_success / trials\n prob_success_df.loc[:, 'prob(ending_value>lifetime_req income)_port'] = port_prob_of_success / trials\n prob_success_df.loc[:, 'prob(ending_value>next_year_req_income)_base'] = base_success_next_year / trials\n prob_success_df.loc[:, 'prob(ending_value>next_year_req_income)_port'] = port_success_next_year / trials\n prob_success_df.loc[:, 'base_max_portfolio_at_acturial_age'] = base_max_portfolio\n prob_success_df.loc[:, 'port_max_portfolio_at_acturial_age'] = port_max_portfolio\n\n # --------------------Percentile Portfolio's based on Acturial Life------------------------\n base_success = prob_success_df.loc[life_expectancy - clients_age, 'prob(ending_value>next_year_req_income)_base']\n port_success = prob_success_df.loc[life_expectancy - clients_age, 'prob(ending_value>next_year_req_income)_port']\n\n # acturial_age_base_tv = sim_base_total.loc[:life_expectancy - clients_age, ]\n # percentile_base_tv = sim_base_total.apply(lambda x: np.nanpercentile(x, base_success), axis=1)\n\n # ----------------Year wise percentile portfolio to meet next year income. Based on the success at acturial age.\n # Yearly portfolio values that can provide the next year income below the success rate at end of life (Percentile)-\n\n # acturial_age_base = base_for_next_year_need.loc[:life_expectancy - clients_age, ]\n # acturial_age_base = base_for_next_year_need.copy().fillna(0)\n percentile_base = base_for_next_year_need.apply(lambda x: np.nanpercentile(x, base_success), axis=1)\n\n # ----Pre Income Portfolio based on the Probab. of Success to meet next year's income at the end on the Act. Age\n base_pre_income_success = sim_base_total_preincome.apply(lambda x: np.nanpercentile(x, base_success), axis=1)\n base_ann_ret_pre_income = base_pre_income_success.pct_change().fillna(0)\n\n # acturial_age_port_tv = sim_port_total.loc[:life_expectancy - clients_age, ]\n # percentile_port_tv = sim_port_total.apply(lambda x: np.nanpercentile(x, port_success), axis=1)\n\n # ----------------Year wise percentile portfolio to meet next year income. Based on the success at acturial age.\n # Yearly portfolio values that can provide the next year income below the success rate at end of life (Percentile)-\n\n # acturial_age_port = port_for_next_year_need.loc[:life_expectancy - clients_age, ]\n # acturial_age_base = port_for_next_year_need.copy().fillna(0)\n percentile_port = port_for_next_year_need.apply(lambda x: np.nanpercentile(x, port_success), axis=1)\n\n # ----Pre Income Portfolio based on the Probab. of Success to meet next year's income at the end on the Act. Age\n port_pre_income_success = sim_port_total_preincome.apply(lambda x: np.nanpercentile(x, port_success), axis=1)\n port_ann_ret_pre_income = port_pre_income_success.pct_change().fillna(0)\n\n prob_success_df.loc[:, 'acturial_success_percentile_base_portfolio'] = percentile_base\n prob_success_df.loc[:, 'acturial_success_percentile_port_portfolio'] = percentile_port\n\n prob_success_df.loc[:, 'base_pre_income_ann_ret'] = base_ann_ret_pre_income\n prob_success_df.loc[:, 'port_pre_income_ann_ret'] = port_ann_ret_pre_income\n\n # prob_success_df.loc[:, 'terminalVal_success_percentile_base_portfolio'] = percentile_base_tv\n # prob_success_df.loc[:, 'terminalVal_success_percentile_port_portfolio'] = percentile_port_tv\n\n sim_base_total_preincome.to_excel(writer, sheet_name='base_preincome_portfolios')\n # -------------Adding Premium to calculate the total initial investment--------------\n sim_port_total_preincome.iloc[0] = sim_port_total_preincome.iloc[0] + premium\n sim_port_total_preincome.to_excel(writer, sheet_name='port_preincome_portfolios')\n\n # -------------For Simulation slide - BASE Portfolio - Can Delete --------------------\n # base_qcut_preinc = pd.DataFrame(index=sim_base_total_preincome.index, columns=cols)\n # for c in range(len(cols)):\n # base_qcut_preinc.loc[:, cols[c]] = sim_base_total_preincome.quantile(q_cut[c], axis=1)\n #\n # # -------------For Simulation slide - Proposed Portfolio --------------------\n # port_qcut_preinc = pd.DataFrame(index=sim_port_total_preincome.index, columns=cols)\n # for c in range(len(cols)):\n # port_qcut_preinc.loc[:, cols[c]] = sim_port_total_preincome.quantile(q_cut[c], axis=1)\n #\n # base_qcut_preinc.to_excel(writer, sheet_name='base_preincome_quantiles')\n # port_qcut_preinc.to_excel(writer, sheet_name='port_preincome_quantiles')\n\n prob_success_df.to_excel(writer, sheet_name='success_probability')\n\n # --------------BASE - Accumulation and Income Breakdown based on the success percentile portfolio---------------\n base_df.to_csv(src + 'base_port_detail.csv')\n sim_base_total.to_csv(src + 'base_ending_values.csv')\n income_breakdown_base = pd.DataFrame(sim_base_total.quantile(base_success, axis=1))\n income_breakdown_base.loc[:, 'income_from_risky_assets'] = sim_base_income.quantile(base_success, axis=1) \\\n - social - cpn_income_port\n income_breakdown_base.loc[:, 'guaranteed_income'] = 0.0\n income_breakdown_base.loc[:, 'social_security_income'] = social\n income_breakdown_base.loc[:, 'coupon_income'] = cpn_income_base\n\n income_breakdown_base.rename(columns={income_breakdown_base.columns[0]: 'portfolio_ending_value'}, inplace=True)\n income_breakdown_base.loc[:, 'income_from_risky_assets'][\n income_breakdown_base.loc[:, 'portfolio_ending_value'] <= 0] = 0\n income_breakdown_base.loc[:, 'total_income'] = income_breakdown_base.loc[:, income_breakdown_base.columns[1:]].sum(\n axis=1)\n\n # ----------FIA PORTFOLIO - Accumulation and Income Breakdown based on the success percentile portfolio-----------\n fia_portfolio_df.to_csv(src + 'fia_port_detail.csv')\n sim_port_total.to_csv(src + 'fiaport_ending_values.csv')\n\n income_breakdown_port = pd.DataFrame(sim_port_total.quantile(port_success, axis=1))\n income_breakdown_port.loc[:, 'income_from_risky_assets'] = sim_port_income.quantile(port_success, axis=1) \\\n - income_from_fia - social - cpn_income_port\n income_breakdown_port.loc[:, 'guaranteed_income'] = income_from_fia\n income_breakdown_port.loc[:, 'social_security_income'] = social\n income_breakdown_port.loc[:, 'coupon_income'] = cpn_income_port\n\n income_breakdown_port.rename(columns={income_breakdown_port.columns[0]: 'portfolio_ending_value'}, inplace=True)\n income_breakdown_port.loc[:, 'income_from_risky_assets'][\n income_breakdown_port.loc[:, 'portfolio_ending_value'] <= 0] = 0\n income_breakdown_port.loc[:, 'total_income'] = income_breakdown_port.loc[:, income_breakdown_port.columns[1:]].sum(\n axis=1)\n\n # -------------------Write simulation Statistics-------------------------------------\n simulation_stats.to_excel(writer, sheet_name='simulation_statistics')\n\n # port_psuccess.to_excel(writer, sheet_name='fia_port_success_probability')\n\n income_breakdown_base = income_breakdown_base.loc[1:, :]\n income_breakdown_base.loc[:, 'age'] = age_index\n income_breakdown_base.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n income_breakdown_base.loc[income_starts:, :].to_excel(writer, sheet_name='base_income_breakdown_median')\n\n income_breakdown_port = income_breakdown_port.loc[1:, :]\n income_breakdown_port.loc[:, 'age'] = age_index\n income_breakdown_port.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n income_breakdown_port.loc[income_starts:, :].to_excel(writer, sheet_name='fia_income_breakdown_median')\n\n legacy_risk.to_excel(writer, sheet_name='ruin_probability')\n\n if method == 'normal':\n median_returns_normal.loc[:, 'fia_median_returns'] = median_normal_fia\n median_returns_normal.to_excel(writer, sheet_name='gr_port_median_normal')\n\n elif method == 'smallest':\n median_returns_smallest.loc[:, 'fia_median_returns'] = median_smallest_fia\n median_returns_smallest.to_excel(writer, sheet_name='gr_port_median_asc')\n\n else:\n median_returns_largest.loc[:, 'fia_median_returns'] = median_largest_fia\n median_returns_largest.to_excel(writer, sheet_name='gr_port_median_desc')\n\n # ---------------------Histogram for S&P Forecast---------------------------------------\n sp_returns = read_returns_est.loc['SPXT Index', 'Annualized Returns']\n sp_risk = read_returns_est.loc['SPXT Index', 'Annualized Risk']\n sp_random_ret = np.random.normal(loc=sp_returns, scale=sp_risk, size=10000)\n bins, data = np.histogram(sp_random_ret, bins=20)\n df_ret = pd.DataFrame(data, columns=['Return_range'])\n df_bins = pd.DataFrame(bins, columns=['Count'])\n df_hist = df_ret.join(df_bins)\n df_hist.to_excel(writer, sheet_name='sp500_histogram')\n\n # ---------------------Histogram for FIA Portfolios TV>0 at the acturial age---------------------------------------\n tval_at_horizon = sim_port_total.loc[acturial_years, :]\n fact = 1 / acturial_years\n arr_returns = np.array((tval_at_horizon / 1000000) ** fact - 1)\n clean_ann_ret = arr_returns[~np.isnan(arr_returns)]\n p_bins, p_data = np.histogram(clean_ann_ret, bins=20)\n df_ret = pd.DataFrame(p_data, columns=['Return_range'])\n df_bins = pd.DataFrame(p_bins, columns=['Count'])\n df_hist = df_ret.join(df_bins)\n df_hist.to_excel(writer, sheet_name='fia_portfolio_histogram')\n\n tval_df = pd.DataFrame(sim_port_total.loc[acturial_years, :])\n tval_df.rename(columns={tval_df.columns[0]:'Terminal Values'}, inplace=True)\n tval_df.to_excel(writer, sheet_name='fia_ending_values_hist')\n writer.save()\n\n # -----------------Plotting charts--------------------------------------------\n base_qcut.loc[income_starts:].plot(grid=True, title='Quantile Terminal Value - Base Portfolio')\n plt.savefig(src + \"quantile_terminal_base.png\")\n plt.close('all')\n\n base_income_qcut.plot(grid=True, title='Quantile Income - Base Portfolio')\n plt.savefig(src + \"quantile_income_base.png\")\n plt.close('all')\n\n base_psuccess.plot(grid=True, title='Probability of Success (Portfolio Ending Value > 0) - Base Portfolio')\n plt.savefig(src + \"success_probabilty_base.png\")\n plt.close('all')\n\n (1 - base_psuccess).plot(grid=True, title='Probability of Ruin (Portfolio Ending Value < 0) - Base Portfolio')\n plt.savefig(src + \"ruin_probability_base.png\")\n plt.close('all')\n\n port_qcut.loc[income_starts:].plot(grid=True, title='Quantile Terminal Value - FIA Portfolio')\n plt.savefig(src + \"quantile_terminal_fia.png\")\n plt.close('all')\n\n port_income_qcut.plot(grid=True, title='Quantile Income - FIA Portfolio')\n plt.savefig(src + \"quantile_income_fia.png\")\n plt.close('all')\n\n port_psuccess.plot(grid=True, title='Probability of Success (Portfolio Ending Value > 0) - FIA Portfolio')\n plt.savefig(src + \"success_probabilty_fia.png\")\n plt.close('all')\n\n (1 - port_psuccess).plot(grid=True, title='Probability of Ruin (Portfolio Ending Value < 0) - FIA Portfolio')\n plt.savefig(src + \"ruin_probability_fia.png\")\n plt.close('all')\n\n print(\"simulation completed....\")","def perform_backtests(self):\r\n \r\n for test_name in self.testing_dates:\r\n print('\\t|--Test #{}'.format(test_name))\r\n test_dates = self.testing_dates[test_name]\r\n print('\\t\\t|--Performing Nested Cross-Validation')\r\n cross_validation = CrossValidate()\r\n cross_validation.output_names = self.output_names\r\n cross_validation.feature_names = self.feature_names\r\n cross_validation.feature_dict = self.feature_dict\r\n cross_validation.full_df = self.final_df_output\r\n cross_validation.cv_params = self.testing_dates\r\n cross_validation.test_name = test_name\r\n cross_validation.walk_forward_cv()\r\n self.optimal_params['Test #{}'.format(test_name)] = cross_validation.optimal_params_by_output\r\n self.cv_model_metadata['Test #{}'.format(test_name)] = cross_validation.cv_metadata_by_output\r\n \r\n print('\\t\\t|--Performing Out-Of-Sample Testing')\r\n prediction = Predict()\r\n prediction.output_names = self.output_names\r\n prediction.feature_names = self.feature_names\r\n prediction.feature_dict = self.feature_dict\r\n prediction.optimal_params_by_output = cross_validation.optimal_params_by_output\r\n prediction.cv_predictions_by_output = cross_validation.cv_predictions_by_output\r\n prediction.full_df = self.final_df_output\r\n prediction.pred_start = test_dates['pred_start']\r\n prediction.pred_end = test_dates['pred_end']\r\n prediction.run_prediction()\r\n self.full_predictions['Test #{}'.format(test_name)] = prediction.predictions_by_output\r\n self.pred_model_metadata['Test #{}'.format(test_name)] = prediction.pred_metadata_by_output\r\n \r\n print('\\nSaving model metadata...')\r\n with open(path.deployment_cv_results, 'w') as file:\r\n json.dump(self.optimal_params, file)\r\n with open(path.deployment_cv_metadata, 'w') as file:\r\n json.dump(self.cv_model_metadata, file)\r\n with open(path.deployment_pred_model_metadata, 'w') as file:\r\n json.dump(self.pred_model_metadata, file)\r\n with open(path.deployment_full_predictions, 'w') as file:\r\n json.dump(self.full_predictions, file)","def run(self, max_risk=0, min_return=0, num=0, init_holdings=None):\n if not self.dates:\n self.dates = ['2010-01-01', '2012-12-31']\n self.load_data()\n\n num_months = len(self.df_all)\n first_purchase = True\n result = {}\n baseline_result = {}\n self.baseline_values = [0]\n self.update_values = [0]\n months = []\n\n # Define dataframe to save output data \n headers = ['Date', 'Value'] + self.stocks + ['Variance', 'Returns']\n self.opt_results_df = pd.DataFrame(columns=headers)\n row = []\n\n self.price_df = pd.DataFrame(columns=self.stocks)\n\n # Initialize the plot\n plt.ylim(ymax = 1.5*self.budget, ymin = -1.5*self.budget)\n plt.xticks(list(range(0, num_months, 2)), \n self.df_baseline.index.strftime('%b')[::2], rotation='vertical')\n plt.locator_params(axis='x', nbins=num_months/2)\n plt.plot(list(range(0, num_months)), [0]*(num_months), \n color='red', label=\"Break-even\", linewidth=0.5)\n\n for i in range(3, num_months):\n\n # Look at just the data up to the current month\n df = self.df_all.iloc[0:i+1,:].copy()\n baseline_df_current = self.df_baseline.iloc[0:i+1,:]\n print(\"\\nDate:\", df.last_valid_index())\n months.append(df.last_valid_index().date()) \n\n if first_purchase:\n budget = self.budget\n initial_budget = self.budget\n baseline_shares = (budget / baseline_df_current.iloc[-1])\n baseline_result = {self.baseline[0]: baseline_shares} \n else:\n # Compute profit of current portfolio\n budget = sum([df.iloc[-1][s]*result['stocks'][s] for s in self.stocks]) \n self.update_values.append(budget - initial_budget)\n\n # Compute profit of fund portfolio\n fund_value = sum([baseline_df_current.iloc[-1][s]*baseline_result[s] \n for s in self.baseline]) \n self.baseline_values.append(fund_value - initial_budget)\n\n self.budget = budget \n\n self.load_data(df=df)\n\n self.price_df.loc[i-2] = list(self.price.values)\n\n # Output for user on command-line and plot\n update_values = np.array(self.update_values, dtype=object)\n baseline_values = np.array(self.baseline_values, dtype=object)\n plt.plot(range(3, i+1), update_values, \n color='blue', label=\"Optimized portfolio\")\n plt.plot(range(3, i+1), baseline_values, \n color='gray', label=\"Fund portfolio\", linewidth=0.5)\n \n if first_purchase:\n plt.legend(loc=\"lower left\")\n plt.title(\"Start: {start}, End: {end}\".format\\\n (start=self.df_all.first_valid_index().date(), \n end=self.df_all.last_valid_index().date()))\n\n plt.savefig(\"portfolio.png\")\n plt.pause(0.05)\n \n # Making solve run\n if self.model_type == 'DQM':\n print(f\"\\nMulti-Period DQM Run...\")\n \n self.build_dqm()\n self.solution['DQM'] = self.solve_dqm()\n result = self.solution['DQM']\n else:\n print(f\"\\nMulti-Period CQM Run...\")\n\n # Set budget to 0 to enforce that portfolio is self-financing \n if self.t_cost and not first_purchase:\n self.budget = 0 \n\n self.solution['CQM'] = self.solve_cqm(max_risk=max_risk, \n min_return=min_return,\n init_holdings=init_holdings)\n result = self.solution['CQM']\n init_holdings = result['stocks']\n\n # Print results to command-line\n value = sum([self.price[s]*result['stocks'][s] for s in self.stocks])\n returns = result['return']\n variance = result['risk'] \n\n row = [months[-1].strftime('%Y-%m-%d'), value] + \\\n [result['stocks'][s] for s in self.stocks] + \\\n [variance, returns] \n self.opt_results_df.loc[i-2] = row \n \n first_purchase = False\n\n print(self.opt_results_df)\n print(f'\\nRun completed.\\n')\n\n plt.savefig(\"portfolio.png\")\n plt.show(block=False)","def _run_backtest(self):\n i = 0\n while True:\n i += 1\n if self.data_handler.continue_backtest == True:\n self.data_handler.update_bars()\n else:\n break\n while True:\n try:\n event = self.events.get(False)\n except Empty:\n break\n else:\n if event is not None:\n if event.type == EventType.MARKET:\n self.strategy.On_Bars(event)\n self.portfolio.update_balance(event)\n self.portfolio.order_check(event)\n elif event.type == EventType.ORDER_SEND:\n self.portfolio.update_order(event)\n elif event.type == EventType.ORDER_CLOSE:\n self.portfolio.update_order(event)\n self.portfolio.update_euity(event)\n elif event.type == EventType.ORDER_MODIFY:\n self.portfolio.update_order(event)\n time.sleep(self.heartbeat)","def rebalance(context, data):\n logger.debug('rebalancing on: %s', algo.get_datetime())\n\n context.trend_filter = False\n\n # new_portfolio = algo.pipeline_output('pipeline').dropna(subset=['overall_rank']).sort_values('momentum', ascending=False)\n\n new_portfolio = algo.pipeline_output('pipeline').dropna(subset=['overall_rank']).sort_values('momentum', ascending=False)\n\n for equity, row in new_portfolio.iterrows():\n logger.debug('new portfolio (before filtering) - equity: %s', equity)\n\n # print(new_portfolio)\n\n # new_portfolio = new_portfolio[new_portfolio['overall_rank'].notna() & new_portfolio['momentum'] > 40][:20]\n \n # new_portfolio = new_portfolio[(new_portfolio['momentum_decile'] > 8)][:20]\n\n new_portfolio = new_portfolio.nlargest(20, ['overall_rank', 'momentum']) #<- $600K PL in 10 years\n\n # new_portfolio = new_portfolio.nlargest(20, ['momentum', 'overall_rank']) #<- 1M PL in 10 years\n\n if logger.level is logging.DEBUG:\n for equity, row in new_portfolio.iterrows():\n logger.debug('new portfolio - (after filtering) equity: %s', equity)\n \n\n # print(len(new_portfolio.index))\n\n # volatility driven weights\n # new_portfolio['inverse_volatility'] = new_portfolio['volatility'].apply(lambda x: 1 / x)\n # inv_vola_sum = new_portfolio['inverse_volatility'].sum()\n # new_portfolio['target_weight'] = new_portfolio['inverse_volatility'].apply(lambda x: x / inv_vola_sum)\n\n # portfolio size driven weights\n # num_equities = len(new_portfolio.index)\n # new_portfolio['target_weight'] = 1 / num_equities\\\n\n # logger.info('len existing portfolio: %s', len(context.portfolio.positions))\n\n if logger.level is logging.DEBUG:\n for equity, values in context.portfolio.positions.items():\n logger.debug('context.portfolio.positions - equity: %s, amount: %s, cost_basis: %s, sold_on: %s, sold_at_price: %s', equity, values.amount, values.cost_basis, values.last_sale_date, values.last_sale_price)\n\n \n order_target(algo.sid('FIBBG000NTFYM5'), 0)\n logger.debug('selling all bonds')\n\n for equity in context.portfolio.positions:\n if equity is algo.sid('FIBBG000NTFYM5'): \n continue\n if equity not in set(new_portfolio.index.tolist()):\n # logger.info('selling %s', equity)\n order_target_percent(equity, 0)\n\n stock_weights = 1.0 / max(len(context.portfolio.positions), len(new_portfolio.index))\n\n logger.debug('len existing portfolio (afer ejection): %s', len(context.portfolio.positions))\n logger.debug('len new portfolio: %s', len(new_portfolio.index))\n logger.debug('stock_weights: %s', stock_weights)\n\n # print(context.portfolio.positions.get(algo.sid('FIBBG000NTFYM5')))\n\n # spy = context.portfolio.positions.get(algo.sid('FIBBG000NTFYM5'))\n\n # if (spy is not None) and (spy.amount > 0):\n # order_target_percent(algo.sid('FIBBG000NTFYM5'), 0)\n\n for equity, row in new_portfolio.iterrows():\n if row.trend_filter is True:\n # logger.info('buying %s', equity)\n context.trend_filter = True\n order_target_percent(equity, stock_weights)\n else:\n context.trend_filter = False\n \n logger.debug('cash: %s', context.portfolio.cash)\n logger.debug('portfolio_value: %s', context.portfolio.portfolio_value)\n logger.debug('num_positions: %s', len(context.portfolio.positions))\n logger.debug('positions: %s', context.portfolio.positions)","def evaluate(self):\n # define start index test set\n start_test_set = int(len(self.X) * 2 / 3)\n\n # Different methods for cummulativa vs day-ahead forecasting\n if self.forecast_horizon == 1:\n # In sample\n lin_residuals_in_sample = self.y - (self.betas[0] + np.dot(self.X, self.betas[1]))\n self.rmse_in_sample = np.mean(lin_residuals_in_sample ** 2) ** 0.5\n self.var_in_sample = np.var(self.y)\n\n # Out of sample\n # Calculate MSE of wls-ev prediction\n self.mse_wlsev = np.mean((self.y[start_test_set:] - self.ols_predict()) ** 2)\n # Calculate MSE of benchmark prediction\n self.mse_benchmark = np.mean((self.y[start_test_set:] - self.benchmark_predict()) ** 2)\n else:\n # In Sample with betas estimated on full time series\n lin_residuals_in_sample = rolling_sum(self.y, self.forecast_horizon) - (\n self.betas[0] + np.dot(self.X[:-(self.forecast_horizon-1)], self.betas[1]))\n self.rmse_in_sample = np.mean(lin_residuals_in_sample ** 2) ** 0.5\n self.var_in_sample = np.var(rolling_sum(self.y, self.forecast_horizon))\n\n # Out of sample\n # calculate realized cummulative returns over forecast horizon sequences\n cum_rets_realized = rolling_sum(self.y[start_test_set:], self.forecast_horizon)\n # Calculate MSE of wls-ev prediction, only where realized values are available\n self.mse_wlsev = np.mean((cum_rets_realized - self.ols_predict()[:-(self.forecast_horizon-1)]) ** 2)\n # Calculate MSE of benchmark prediction, only where realized values are available\n self.mse_benchmark = np.mean(\n (cum_rets_realized - self.benchmark_predict()[:-(self.forecast_horizon-1)]) ** 2)\n\n # Calculate out of sample r-squared\n self.oos_r_squared = 1 - (self.mse_wlsev / self.mse_benchmark)\n # Calculate in sample r-squared\n self.in_sample_r_squared = 1.0 - (self.rmse_in_sample ** 2) / self.var_in_sample","def run_simulator(simulation_parameters, spectrum_portfolio,\n ant_heights, site_radii, modulation_and_coding_lut, costs):\n\n unprojected_point = {\n 'type': 'Feature',\n 'geometry': {\n 'type': 'Point',\n 'coordinates': (-0.07496, 51.42411),\n },\n 'properties': {\n 'site_id': 'Crystal Palace Radio Tower'\n }\n }\n\n unprojected_crs = 'epsg:4326'\n projected_crs = 'epsg:3857'\n\n environments =[\n 'urban',\n 'suburban',\n 'rural'\n ]\n\n for environment in environments:\n for site_radius in site_radii[environment]:\n\n print('--working on {}: {}'.format(environment, site_radius))\n\n transmitter, interfering_transmitters, site_area, interfering_site_areas = \\\n produce_sites_and_site_areas(\n unprojected_point['geometry']['coordinates'],\n site_radius,\n unprojected_crs,\n projected_crs\n )\n\n receivers = generate_receivers(site_area, SIMULATION_PARAMETERS, 1)\n\n for frequency, bandwidth, generation in spectrum_portfolio:\n for ant_height in ant_heights:\n\n MANAGER = SimulationManager(\n transmitter, interfering_transmitters, receivers,\n site_area, SIMULATION_PARAMETERS\n )\n\n results = MANAGER.estimate_link_budget(\n frequency, bandwidth, generation, ant_height,\n environment,\n MODULATION_AND_CODING_LUT,\n SIMULATION_PARAMETERS\n )\n\n folder = os.path.join(BASE_PATH, '..', 'results', 'full_tables')\n filename = 'full_capacity_lut_{}_{}_{}_{}.csv'.format(\n environment, site_radius, frequency, ant_height)\n\n write_full_results(results, environment, site_radius,\n frequency, bandwidth, generation, ant_height,\n folder, filename, simulation_parameters)\n\n average_site_results = obtain_average_values(\n results, simulation_parameters\n )\n\n results_directory = os.path.join(BASE_PATH, '..', 'results')\n\n write_frequency_lookup_table(average_site_results, environment,\n site_radius, frequency, bandwidth, generation,\n ant_height, results_directory,\n 'average_capacity_lut.csv',\n simulation_parameters\n )\n\n if frequency == spectrum_portfolio[0][0]:\n\n percentile_site_results = obtain_percentile_values(\n results, simulation_parameters\n )\n\n percentile_site_results = calculate_costs(\n percentile_site_results, costs, simulation_parameters,\n site_radius, environment\n )\n\n write_cost_lookup_table(percentile_site_results, environment,\n site_radius, frequency, bandwidth, generation,\n ant_height, results_directory,\n 'percentile_{}_capacity_lut.csv'.format(\n simulation_parameters['percentile']),\n simulation_parameters\n )\n\n ## write out as shapes, if desired, for debugging purposes\n geojson_receivers = convert_results_geojson(results)\n\n write_shapefile(\n geojson_receivers, os.path.join(results_directory, 'shapes'),\n 'receivers_{}.shp'.format(site_radius),\n projected_crs\n )\n\n write_shapefile(\n transmitter, os.path.join(results_directory, 'shapes'),\n 'transmitter_{}.shp'.format(site_radius),\n projected_crs\n )\n\n write_shapefile(\n site_area, os.path.join(results_directory, 'shapes'),\n 'site_area_{}.shp'.format(site_radius),\n projected_crs\n )\n\n write_shapefile(\n interfering_transmitters, os.path.join(results_directory, 'shapes'),\n 'interfering_transmitters_{}.shp'.format(site_radius),\n projected_crs\n )\n\n write_shapefile(\n interfering_site_areas, os.path.join(results_directory, 'shapes'),\n 'interfering_site_areas_{}.shp'.format(site_radius),\n projected_crs\n )","def run_test_procedures(self):\r\n print('\\nDeploying prediction model...\\n')\r\n self.final_df_output = pd.read_json(path.data_final)\r\n self.final_df_output.sort_index(inplace=True)\r\n self.fill_testing_dates()\r\n self.perform_backtests()\r\n self.create_full_predictions_dataframe()\r\n print('\\nDeployment complete!')","def test_TPt(self):\n\n test_value = self.portfolio.calculate_total_performance(\n *self.boarder)[self.test_row_number]\n calculated_value = self.manual_cumprod(\n self.portfolio._get_total_portfolio)\n self.assertAlmostEqual(test_value, calculated_value)","def copy_generate_median_portfolio_from_simulation(num_of_years=30, trials=100, method='normal'):\n sim_fia_cv = pd.DataFrame(index=range(num_of_years + 1))\n\n sim_base_total = pd.DataFrame(index=range(num_of_years + 1))\n sim_base_income = pd.DataFrame(index=range(num_of_years + 1))\n\n sim_port_total = pd.DataFrame(index=range(num_of_years + 1))\n sim_port_income = pd.DataFrame(index=range(num_of_years + 1))\n\n # read_income_inputs = pd.read_csv(src + \"income_model_inputs.csv\", index_col='Items')\n read_income_inputs = pd.read_excel(src + \"portfolio_information.xlsx\", sheet_name='income_model_inputs',\n index_col=[0])\n\n # read_returns_est = pd.read_csv(src + \"income_assets_returns_estimates.csv\", index_col='Symbol')\n read_returns_est = pd.read_excel(src + \"portfolio_information.xlsx\", sheet_name='income_assets_returns_estimates',\n index_col=[0])\n\n # read_returns_est.drop(['BM', read_returns_est.index[-1]], axis=0, inplace=True)\n # read_portfolio_inputs = pd.read_csv(src + \"income_portfolio_inputs.csv\", index_col='Items')\n\n # read_asset_weights = pd.read_csv(src + \"asset_weights.csv\", index_col='Asset')\n read_asset_weights = pd.read_excel(src + \"portfolio_information.xlsx\", sheet_name='asset_weights',\n index_col=[0])\n\n all_assets = list(read_asset_weights.index)\n read_asset_weights.drop(read_asset_weights.index[-1], axis=0, inplace=True)\n\n # read random returns for simulation\n read_normal = pd.read_csv(src + 'sort_normal.csv', index_col=[0], parse_dates=True)\n\n assets_col_names = list(read_normal.columns)\n\n tickers = list(read_asset_weights.index)\n wts = np.array(read_asset_weights.loc[:, 'base'])\n\n def asset_median_returns(data, ticker):\n return data.filter(regex=ticker).median(axis=1)\n\n # dataframe for unsorted returns (normal)\n median_returns_normal = pd.DataFrame({t: asset_median_returns(read_normal, t) for t in tickers})\n median_returns_normal.loc[:, 'portfolio_return'] = median_returns_normal.dot(wts)\n median_normal_fia = pd.DataFrame({'FIA': asset_median_returns(read_normal, 'r_FIA')})\n\n years = list(range(0, num_of_years + 1))\n income_cols = ['year', 'strategy_term', 'index_returns', 'term_ret', 'term_ret_with_par', 'term_annualize',\n 'ann_net_spread', 'term_ret_netspr', 'high_inc_benefit_base', 'rider_fee', 'eoy_income',\n 'contract_value']\n\n term = int(read_income_inputs.loc['term', 'inputs'])\n fia_ret = read_returns_est.loc[read_returns_est.index[-1], 'Annualized Returns']\n fia_risk = read_returns_est.loc[read_returns_est.index[-1], 'Annualized Risk']\n par_rate = float(read_income_inputs.loc['par_rate', 'inputs'])\n spread = float(read_income_inputs.loc['spread', 'inputs'])\n bonus_term = int(read_income_inputs.loc['bonus_term', 'inputs'])\n premium = float(read_income_inputs.loc['premium', 'inputs'])\n income_bonus = float(read_income_inputs.loc['income_bonus', 'inputs'])\n\n income_starts = int(read_income_inputs.loc['start_income_years', 'inputs'])\n income_growth = float(read_income_inputs.loc['income_growth', 'inputs'])\n rider_fee = float(read_income_inputs.loc['rider_fee', 'inputs'])\n inc_payout_factor = float(read_income_inputs.loc['income_payout_factor', 'inputs'])\n contract_bonus = float(read_income_inputs.loc['contract_bonus', 'inputs'])\n social = float(read_income_inputs.loc['social', 'inputs'])\n inflation = float(read_income_inputs.loc['inflation', 'inputs'])\n wtd_cpn_yield = float(read_income_inputs.loc['wtd_coupon_yld', 'inputs'])\n life_expectancy = int(read_income_inputs.loc['life_expectancy_age', 'inputs'])\n clients_age = int(read_income_inputs.loc['clients_age', 'inputs'])\n\n # # ---------------INCOME MODEL--------------------------------------------\n runs = 0\n returns_dict = {}\n asset_dict = {}\n fia_dict = {}\n while runs <= trials:\n print(runs)\n\n # --------------------BASE MODEL---------------------------------------------\n\n base_wts = read_asset_weights.loc[:, 'base']\n base_assets = list(base_wts.index)\n base_weights = list(base_wts.values)\n base_returns = list(read_returns_est.loc[:, 'Annualized Returns'].values)\n base_std = list(read_returns_est.loc[:, 'Annualized Risk'].values)\n\n base_investment = float(read_income_inputs.loc['risky_assets', 'Base'])\n adv_fees = float(read_income_inputs.loc['advisor_fees', 'Base'])\n\n # ----------------------RANDOM RETURNS--------------------------\n r_cols = ['r_{}'.format(name) for name in base_assets]\n boy_value = ['bv_{}'.format(name) for name in base_assets]\n eoy_value = ['ev_{}'.format(name) for name in base_assets]\n\n random_returns = pd.DataFrame(index=years, columns=r_cols)\n #\n # for c in range(len(r_cols)):\n # ret = np.random.normal(base_returns[c], base_std[c], size=(len(random_returns.index), 1))\n\n this_run_cols = ['{}_{}'.format(cname, str(runs)) for cname in r_cols]\n random_returns = read_normal.loc[:, this_run_cols]\n base_df = random_returns.copy()\n\n # -------------BASE PORTFOLIO----------------------------\n for name in boy_value:\n base_df.loc[:, name] = 0.0\n\n for counter in years:\n period_returns = list(random_returns.loc[counter, :])\n if counter == 0:\n\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment for c in range(len(boy_value))]\n\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\n\n base_investment = base_df.loc[counter, boy_value].sum()\n\n elif (counter > 0) and (counter < income_starts):\n\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment * (1 + period_returns[c])\n for c in range(len(boy_value))]\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\n\n # base_df.loc[counter, 'total_net_fees'] = base_df.loc[counter, 'total']\n\n # --coupon payment is invested back into the risky portfolio until the income is withdrawn----\n base_investment = base_df.loc[counter, 'total']\n\n else:\n\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment * (1 + period_returns[c])\n for c in range(len(boy_value))]\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\n\n base_investment = base_df.loc[counter, 'total']\n\n base_df.loc[:, 'adj_total'] = base_df.loc[:, 'total'].apply(lambda x: x if x > 0 else 0)\n\n asset_dict.update({'{}_{}'.format('s', str(runs)): base_df.loc[:, 'total']})\n # asset_dict.update({'{}_{}'.format('s', str(runs)): ret.flatten()})\n runs += 1\n\n asset_values = pd.DataFrame(asset_dict)\n assets_ror = asset_values.pct_change().fillna(0)\n terminal_values = asset_values.iloc[-1]\n terminal_df = pd.DataFrame(columns=['Terminal Value'])\n terminal_df['Terminal Value'] = terminal_values\n\n median_index = np.where(asset_values == np.median(np.array(terminal_values)))\n median_index = median_index[1][0]\n m_cols = ['{}_{}_{}'.format('r', r, str(median_index)) for r in all_assets]\n median_portfolio_asset_returns = read_normal.loc[:, m_cols]\n median_portfolio_asset_returns.loc[:, 'port_ending_values'] = asset_values.loc[:, '{}_{}'.format('s', median_index)]\n median_portfolio_asset_returns.loc[:, 'ror'] = median_portfolio_asset_returns[\n 'port_ending_values'].pct_change().fillna(0)\n desc_sort = median_portfolio_asset_returns.sort_values(by=median_portfolio_asset_returns.columns[-1],\n ascending=False)\n asc_sort = median_portfolio_asset_returns.sort_values(by=median_portfolio_asset_returns.columns[-1],\n ascending=True)\n unsorted_df = median_portfolio_asset_returns\n unsorted_df.drop(unsorted_df.columns[-2:], axis=1, inplace=True)\n desc_sort.drop(desc_sort.columns[-2:], axis=1, inplace=True)\n asc_sort.drop(asc_sort.columns[-2:], axis=1, inplace=True)\n\n # reset index and drop existing index\n desc_sort.reset_index(drop=True, inplace=True)\n asc_sort.reset_index(drop=True, inplace=True)\n\n unsorted_df.to_csv(src + 'median_returns_unsorted.csv')\n desc_sort.to_csv(src + 'median_returns_largest.csv')\n asc_sort.to_csv(src + 'median_returns_smallest.csv')\n asset_values.to_csv(src + 'ending_values.csv')\n assets_ror.to_csv(src + 'ending_values_ror.csv')\n terminal_df.to_csv(src + 'terminal_values.csv')\n print(\"simulation completed....\")","def solve_model(self): \n \n t0 = time.time() #start the clock\n \n # a. benchmark case\n \n #i. joint pdf of productivity state and tau \n self.make_joint_pdf(1)\n \n #ii. set policy. in RR08 the benchmark economy has no taxes nor subsidies\n self.tau_benchmark = np.array([0, 0, 0]) #subsidy rate, excempt rate, tax rate \n self.set_tax_system(self.tau_benchmark) #set tax system\n \n #iii. benchmark equilibrium\n self.Yss_b, self.Kss_b, self.TFPss_b, self.average_firm_size_b, self.E_star_b, _, \\\n _, self.N_ss_b, self.w_ss_b, self.cdf_stationary_b, self.cdf_emp_b = self.solve_stationary_equilibrium()\n \n print(\"\\n-----------------------------------------\")\n print(\"Benchmark Stationary Equilibrium\")\n print(\"-----------------------------------------\")\n print(f\"ss output = {self.Yss_b:.2f}\")\n print(f\"ss capital = {self.Kss_b:.2f}\")\n print(f\"ss tfp = {self.TFPss_b:.2f}\")\n print(f\"ss wage = {self.w_ss_b:.2f}\")\n print(f\"entry mass = {self.E_star_b:.3f}\")\n print(f\"avg. firm size = {self.average_firm_size_b:.2f}\")\n \n #b. plot (note that the distributions plotted here are unaffected by the distortionary policies)\n \n if self.plott:\n #i. initialize\n employed = [4.99, 49.99]\n firm_size_by_employee = np.zeros(len(employed)+1)\n share_employment = np.zeros(len(employed)+1)\n \n \n #i. percentage of firms that employ employed\n \n for i_e in range(len(employed)):\n summ = np.sum(firm_size_by_employee)\n interpolate = self.interpol(self.labor_demand_rel, self.cdf_stationary_b, employed[i_e])[0] #labor_demand_rel is labor demand with the lowest value normalized to 1\n firm_size_by_employee[i_e] = interpolate - summ\n firm_size_by_employee[-1] = 1 - np.sum(firm_size_by_employee)\n \n plt.pie(firm_size_by_employee, labels=['<5','5<50','50 =<'], autopct=\"%.1f%%\")\n plt.title('Size of Firms by Firm Size (Number of Employees)')\n plt.savefig('firm_size_rr08.pdf')\n plt.show()\n \n \n #ii. employment percentage by firm size\n for i_e in range(len(employed)):\n summ = np.sum(share_employment)\n interpolate = self.interpol(self.labor_demand_rel, self.cdf_emp_b , employed[i_e])[0]\n share_employment[i_e] = interpolate - summ\n share_employment[-1] = 1 - np.sum(share_employment)\n \n plt.pie(share_employment, labels=['<5','5<50','50 =<'], autopct=\"%.1f%%\")\n plt.title('Employment Share by Firm Size (Number of Employees)')\n plt.savefig('employment_by_firm_size_rr08.pdf')\n plt.show()\n \n #iii. productivity cdf and employment cdf\n plt.plot(self.grid_s, self.cdf_stationary_b)\n plt.plot(self.grid_s, self.cdf_emp_b)\n plt.title('Stationary CDF' )\n plt.xlabel('Productivity level')\n plt.ylabel('Cumulative Sum')\n plt.legend(['Firms by Productivity Level','Share of Employment'])\n plt.savefig('cdf_rr08.pdf')\n plt.show()\n \n \n \n #c. distortion case\n \n #i. joint pdf of productivity state and tau \n self.make_joint_pdf(0)\n \n #ii. compute stationary economy for each tau\n \n for idx, tau in enumerate(self.tau_vector):\n \n #iii. find the subsidy rate that generates the same capital stock as in benchmark economy\n self.tau_s[idx] = self.find_subsidy_rate(tau)\n \n # set tax system with newly found tau_s and given tau\n tauv = np.array([-self.tau_s[idx], self.excempt_frac, tau]) #subsidy rate, excempt rate, tax rate \n self.set_tax_system(tauv) #set tax system\n \n #v. distorted stationary equilibrium\n self.Yss_d[idx], self.Kss_d[idx], self.TFPss_d[idx], self.average_firm_size_d[idx], self.E_star_d[idx], \\\n self.Y_set_d[idx,:], self.subsidy_size_d[idx], self.N_ss_d[idx], self.w_ss_d[idx],\\\n _, _ = self.solve_stationary_equilibrium()\n \n print(\"\\n-----------------------------------------\")\n print(\"Distorted Stationary Equilibrium\")\n print(\"-----------------------------------------\\n\")\n if self.distortion_case == 1:\n print(\"Tax/Subidy Uncorrelated with Firm Level Producitivity\\n\")\n elif self.distortion_case == 2:\n print(\"Tax/Subidy Negatively Correlated with Firm Level Producitivity\")\n print(\"(low productivity firms recieve subsidy, high productivity taxed)\\n\")\n elif self.distortion_case == 2:\n print(\"Tax/Subidy Positively Correlated with Firm Level Producitivity\")\n print(\"(high productivity firms recieve subsidy, low productivity taxed)\\n\")\n if self.policy_type == 1 :\n print(\"Tax Type: Tax on output\\n\")\n elif self.policy_type == 2 :\n print(\"Tax Type: Tax on capital\\n\")\n elif self.policy_type == 3 :\n print(\"Tax Type: Tax on labor\\n\")\n print(f\"fraction of firms recieving subsidy = {self.subsidy_frac:.2f}\")\n print(f\"fraction of firms taxed = {1-self.subsidy_frac-self.excempt_frac:.2f}\")\n print(f\"fraction of firms excempt = {self.excempt_frac:.2f}\")\n print(\"-----------------------------------------\\n\")\n \n print(tabulate([['relative Yss', round(self.Yss_d[0]/self.Yss_b, 2), round(self.Yss_d[1]/self.Yss_b, 2), round(self.Yss_d[2]/self.Yss_b, 2), round(self.Yss_d[3]/self.Yss_b, 2)],\n ['relative TFPss', round(self.TFPss_d[0]/self.TFPss_b, 2), round(self.TFPss_d[1]/self.TFPss_b, 2), round(self.TFPss_d[2]/self.TFPss_b, 2), round(self.TFPss_d[3]/self.TFPss_b, 2)], \n ['relative entry mass', round(self.E_star_d[0]/self.E_star_b, 2), round(self.E_star_d[1]/self.E_star_b, 2), round(self.E_star_d[2]/self.E_star_b, 2), round(self.E_star_d[3]/self.E_star_b, 2)],\n ['share of subsidized output', round(self.Y_set_d[0,0], 2), round(self.Y_set_d[1,0], 2), round(self.Y_set_d[2,0], 2), round(self.Y_set_d[3,0], 2)],\n ['total subsidy paid of output', round(self.subsidy_size_d[0], 2), round(self.subsidy_size_d[1], 2), round(self.subsidy_size_d[2], 2), round(self.subsidy_size_d[3], 2)],\n ['subsidy rate (tau_s)', round(self.tau_s[0], 2), round(self.tau_s[1], 2), round(self.tau_s[2], 2), round(self.tau_s[3], 2)],\n [], \n ['relative Kss', round(self.Kss_d[0]/self.Kss_b, 2), round(self.Kss_d[1]/self.Kss_b, 2), round(self.Kss_d[2]/self.Kss_b, 2), round(self.Kss_d[3]/self.Kss_b, 2)], \n ['relative wss', round(self.w_ss_d[0]/self.w_ss_b, 2), round(self.w_ss_d[1]/self.w_ss_b, 2), round(self.w_ss_d[2]/self.w_ss_b, 2), round(self.w_ss_d[3]/self.w_ss_b, 2)], \n ['relative Nss', round(self.N_ss_d[0]/self.N_ss_b, 2), round(self.N_ss_d[1]/self.N_ss_b, 2), round(self.N_ss_d[2]/self.N_ss_b, 2), round(self.N_ss_d[3]/self.N_ss_b, 2)], \n ['relative avg. firm size', round(self.average_firm_size_d[0]/self.average_firm_size_b, 2), round(self.average_firm_size_d[1]/self.average_firm_size_b, 2), round(self.average_firm_size_d[2]/self.average_firm_size_b, 2), round(self.average_firm_size_d[3]/self.average_firm_size_b, 2)]],\n headers=['Variable', 'Tax = '+str(self.tau_vector[0]), \"Tax = \"+str(self.tau_vector[1]), 'Tax = '+str(self.tau_vector[2]), 'Tax = '+str(self.tau_vector[3])]))\n \n\n t1 = time.time()\n print(f'\\nTotal Run Time: {t1-t0:.2f} seconds')","def __calculate_portfolio_returns(self):\n\n p_bar = tqdm(range(1), desc=\" Calculating returns\", leave=False)\n\n trade_data = self.historical_trade_data\n\n # Helper functions to calculate cash inflow and outflow\n def f_min(x):\n return x.apply(lambda x: min(x, 0))\n\n def f_max(x):\n return x.apply(lambda x: max(x, 0))\n\n # Calculate cash inflow and outflow\n trade_data[\n pd.MultiIndex.from_product(\n [[\"Period cash inflow\"], self.tickers_list + [\"Total\"]]\n )\n ] = -1 * trade_data[\"Investment delta\"][:].apply(lambda x: f_min(x), axis=0)\n\n trade_data[\n pd.MultiIndex.from_product(\n [[\"Period cash outflow\"], self.tickers_list + [\"Total\"]]\n )\n ] = trade_data[\"Investment delta\"][:].apply(lambda x: f_max(x), axis=1)\n\n # Calculate period return\n\n trade_data[\n pd.MultiIndex.from_product(\n [[\"Period absolute return\"], self.tickers_list + [\"Total\"]]\n )\n ] = (trade_data[\"End Value\"] + trade_data[\"Period cash inflow\"]) - (\n trade_data[\"End Value\"].shift(1).fillna(0)\n + trade_data[\"Period cash outflow\"]\n )\n\n trade_data[\n pd.MultiIndex.from_product(\n [[\"Period percentage return\"], self.tickers_list + [\"Total\"]]\n )\n ] = (trade_data[\"End Value\"] + trade_data[\"Period cash inflow\"]) / (\n trade_data[\"End Value\"].shift(1).fillna(0)\n + trade_data[\"Period cash outflow\"]\n ) - 1\n\n trade_data[\"Period percentage return\"].fillna(0, inplace=True)\n\n self.historical_trade_data = trade_data\n\n self.portfolio_returns = self.historical_trade_data[\"Period percentage return\"][\n \"Total\"\n ]\n\n p_bar.n += 1\n p_bar.refresh()","def calculatedResults(meanReturns, covMatrix, riskFreeRate=0, constraintSet=(0,1)):\n # Max Sharpe Ratio Portfolio\n maxSR_Portfolio = maxSR(meanReturns, covMatrix)\n maxSR_returns, maxSR_std = portfolioPerformance(maxSR_Portfolio['x'], meanReturns, covMatrix)\n \n maxSR_allocation = pd.DataFrame(maxSR_Portfolio['x'], index=meanReturns.index, columns=['allocation'])\n maxSR_allocation.allocation = [round(i*100,0) for i in maxSR_allocation.allocation]\n \n # Min Volatility Portfolio\n minVol_Portfolio = minimizeVariance(meanReturns, covMatrix)\n minVol_returns, minVol_std = portfolioPerformance(minVol_Portfolio['x'], meanReturns, covMatrix)\n \n minVol_allocation = pd.DataFrame(minVol_Portfolio['x'], index=meanReturns.index, columns=['allocation'])\n minVol_allocation.allocation = [round(i*100,0) for i in minVol_allocation.allocation]\n\n # Efficient Frontier\n efficientList = []\n targetReturns = np.linspace(minVol_returns, maxSR_returns, 20)\n for target in targetReturns:\n efficientList.append(efficientOpt(meanReturns, covMatrix, target)['fun'])\n \n maxSR_returns, maxSR_std = round(maxSR_returns*100,2), round(maxSR_std*100,2)\n minVol_returns, minVol_std = round(minVol_returns*100,2), round(minVol_std*100,2)\n \n return maxSR_returns, maxSR_std, maxSR_allocation, minVol_returns, minVol_std, minVol_allocation, efficientList, targetReturns","def main():\n\n # Hypothesis:\n # The `impact` encapsulates the volatility, stability and overall\n # fluctuation of the market; in particular, movements that would\n # affect one's portfolio, e.g. unexpected (i.e. not predicted)\n # increases or drops in prices.\n # For the StrategyLearner should directly affect the learned\n # policy, particularly, in terms of willingness to take risks by\n # betting on the behavior of the market.\n # This can be translated into three metrics:\n # - Number of entries:\n # These should be reduced as market impact increases which\n # shows the learning agent being more cautious about its bets\n # - Cumulative return:\n # Directly related to the point mentioned above, as market\n # impact increases and the agent's willingness to take risks\n # decreaes, so is the overall performance of the strategy\n # - Training episodes:\n # This applies specifically to the Q-Learning agent, but it\n # is interesting to see how as the market impact increases,\n # the number of complete training episodes (i.e. a complete\n # pass on the trading data) is not affected. One would think\n # that the agent would converge faster when the impact is\n # large as it would quickly realize that the most optimal\n # strategy is to not do anything. However, impact does not\n # affect the rate of convergence, but rather the strategy\n # that the agent converges to\n\n # Set the seed for reproducibility\n random.seed(1481090000)\n\n # Experiment parameters\n symbol = 'JPM'\n # In-sample: January 1, 2008 to December 31 2009\n start_date = dt.datetime(2008, 1, 1)\n end_date = dt.datetime(2009, 12, 31)\n starting_value = 100000\n commission = 0.0\n # Values to use to evaluate the effect of the impact\n impact_values = [0.0, 0.005, 0.01, 0.05, 0.1, 0.25, 0.5, 1.0]\n\n all_entries = []\n all_returns = []\n all_episodes = []\n\n for impact in impact_values:\n log.info(\"Evaluating the effect of impact=%s\", impact)\n strategy_learner = StrategyLearner(verbose=False, impact=impact)\n\n log.info(\"Training StrategyLearner\")\n strategy_learner.addEvidence(\n symbol=symbol,\n sd=start_date,\n ed=end_date,\n sv=starting_value\n )\n\n log.info(\"Querying StrategyLearner to generate trades\")\n trades = strategy_learner.testPolicy(\n symbol=symbol,\n sd=start_date,\n ed=end_date,\n sv=starting_value\n )\n\n log.info(\"Transforming StrategyLearner trades into marketsim orders\")\n orders = _convert_trades_to_marketisim_orders(symbol, trades)\n\n log.info(\"Computing portfolio values for %d orders\", orders.shape[0])\n port_vals = compute_portvals(\n orders,\n start_val=starting_value,\n commission=commission,\n impact=impact\n )\n\n cumulative_return = _compute_cumulative_return(port_vals)\n\n all_entries.append(strategy_learner.metadata['entries'])\n all_returns.append(cumulative_return)\n all_episodes.append(strategy_learner.metadata['training_episodes'])\n\n _plot_and_save_number_of_entries_per_impact_value(impact_values, all_entries)\n _plot_and_save_number_of_episodes_per_impact_value(impact_values, all_episodes)\n _plot_and_save_cumulative_return_per_impact_value(impact_values, all_returns)","def test(self, num_decisions=1000):\n all_grid_states = []\n all_grid_rewards = []\n\n all_grid_states_cluster = []\n all_surrounding_states_cluster = []\n\n all_system_rewards = []\n all_system_states = []\n all_system_states_cluster = []\n\n all_actions = []\n all_dissipation = []\n\n os.system(\"mkdir \" + self.folder_name + \"Test/\")\n filename = self.folder_name + \"Test/\" + \"TEST.h5\"\n self.system.reset_context(filename)\n tag = \"_test_init\"\n self.system.run_decorrelation(20)\n\n grid_dist, surrounding_dist, _, _, _, _ = self.system.get_state_reward()\n state = self._get_state(grid_dist, surrounding_dist)\n\n all_dissipation.append(self.system.get_dissipation())\n for i in range(num_decisions):\n action_index = self._get_action(state, episode=10000)\n tag = \"_test_\" + str(i)\n actions = [self.all_actions[i] for i in action_index]\n all_actions.append(actions)\n self.system.update_action(actions)\n system_states, system_rewards, system_states_cluster = self.system.run_step(\n is_detailed=True, tag=tag)\n\n grid_dist, surrounding_dist, grid_reward, surrounding_reward, grid_states_cluster, surrounding_states_cluster = self.system.get_state_reward()\n\n # The \"grid states\" and dissipation are recorded at the end of a decision\n # Dissipation here is total entropy production (not epr)\n # Actions are recorded at the beginning of the decision\n\n state = self._get_state(grid_dist, surrounding_dist)\n reward = self._get_reward(grid_reward, surrounding_reward)\n\n all_grid_states.append(state)\n all_grid_rewards.append(reward)\n\n all_grid_states_cluster.append(grid_states_cluster)\n all_surrounding_states_cluster.append(surrounding_states_cluster)\n\n all_dissipation.append(self.system.get_dissipation())\n\n # The \"System States\" are recorded every 0.25 seconds. Excludes 0th second\n all_system_states.append(system_states)\n # Just to have a 1D array use extend\n all_system_rewards.append(system_rewards)\n all_system_states_cluster.append(system_states_cluster)\n\n if (i % 100 == 99):\n np.save(self.folder_name + \"grid_rewards\",\n np.array(all_grid_rewards))\n np.save(self.folder_name + \"grid_states\",\n np.array(all_grid_states, dtype=object))\n np.save(self.folder_name + \"grid_states_cluster\",\n np.array(all_grid_states_cluster, dtype=object))\n\n np.save(self.folder_name + \"surrounding_states_cluster\",\n np.array(all_surrounding_states_cluster))\n\n np.save(self.folder_name + \"actions\", np.array(all_actions))\n\n np.save(self.folder_name + \"dissipation\",\n np.array(all_dissipation))\n np.save(self.folder_name + \"system_states\",\n np.array(all_system_states))\n np.save(self.folder_name + \"system_states_cluster\",\n np.array(all_system_states_cluster))\n np.save(self.folder_name + \"system_rewards\",\n np.array(all_system_rewards))","def __init__(\n self,\n portfolio,\n market=None,\n commission_min=5.00,\n commission_pct=0.0,\n buy_percent=1.0,\n sell_percent=1.0,\n pm_threshold=0.0,\n pm_order=1.0,\n risk_free_return=1.0,\n name=None\n ):\n\n # Assumptions\n self.name = name if name else portfolio.name\n self.commission_min = commission_min\n self.commission_pct = commission_pct\n self.buy_percent = buy_percent\n self.sell_percent = sell_percent\n self.pm_threshold = pm_threshold\n self.pm_order = pm_order\n self.risk_free_return = risk_free_return\n self.performance = {}\n\n # Inputs\n self.portfolio = portfolio\n self.market = copy.deepcopy(market) if market else Asset(np.ones(len(self.portfolio.dates)))\n\n # Trading states\n self.long_open = {symbol:False for symbol in portfolio.assets.keys()}\n self.short_open = {symbol:False for symbol in portfolio.assets.keys()}\n\n # Keep track of intermidiate results for performance\n self.trade_data = []\n recordings = [\n 'buy price', 'buy shares', 'buy fees', 'buy date',\n 'sell price', 'sell shares', 'sell fees', 'sell date',\n 'gain', 'profit', 'loss', 'return', 'win/loose',\n 'min balance', 'min date', 'max balance', 'max date',\n 'drawdown', 'drawdown days',\n 'volatility', 'expected_return', 'beta', 'lpm', 'hpm',\n 'max', 'mean', 'min'\n ]\n self.record = {symbol:pd.DataFrame(columns=recordings) for symbol in portfolio.assets.keys()}\n self.max = {symbol:[portfolio.assets[symbol].c.iloc[0], None] for symbol in portfolio.assets.keys()}\n self.min = {symbol:[999999999999999, None] for symbol in portfolio.assets.keys()}\n self.drawdown = {symbol:[999999999999999, None] for symbol in portfolio.assets.keys()}","def first_stage_test(solver, sub_u_rate):\n bins_num = 100\n\n classifier = joblib.load(join(join(_result_path, solver), str(sub_u_rate)) + '/logistic.pkl')\n\n # evaluate positive set, which contains spies\n positive = np.load(\"./processed_data/train/raw/train_p.npy\")\n positive_x = positive[:, : -1]\n result_p = np.array(classifier.predict_proba(positive_x)[:, 1])\n plt.hist(result_p, bins=bins_num)\n plt.savefig(join(join(_result_path, solver), str(sub_u_rate)) + '/positive.png')\n plt.show()\n print(\"\\npositive set results: average: \" + str(np.mean(result_p)) + \" variance:\" + str(np.var(result_p)))\n print(\"max: \" + str(result_p.max()) + \" min: \" + str(result_p.min()))\n\n # evaluate spy set\n spy = np.load(_spy_path)\n spy_x = spy[:, : -1]\n result_spy = np.array(classifier.predict_proba(spy_x)[:, 1])\n plt.hist(result_spy, bins=bins_num)\n plt.savefig(join(join(_result_path, solver), str(sub_u_rate)) + '/spy.png')\n plt.show()\n print(\"\\nspy set results: average: \" + str(np.mean(result_spy)) + \" variance:\" + str(np.var(result_spy)))\n print(\"max: \" + str(result_spy.max()) + \" min: \" + str(result_spy.min()))\n\n # evaluate sub-unlabeled set\n sub_u = np.load(\"./processed_data/train/sub_u_\" + str(sub_u_rate) + \".npy\")\n sub_u_x = sub_u[:, :-1]\n result_sub_u = np.array(classifier.predict_proba(sub_u_x)[:, 1])\n plt.hist(result_sub_u, bins=bins_num)\n plt.savefig(join(join(_result_path, solver), str(sub_u_rate)) + '/sub-u.png')\n plt.show()\n print(\"\\nsub-unlabeled set results: average: \" + str(np.mean(result_sub_u)) + \" variance:\" + str(np.var(result_sub_u)))\n print(\"max: \" + str(result_sub_u.max()) + \" min: \" + str(result_sub_u.min()))\n\n # evaluate the whole unlabeled set\n unlabeled = np.load(\"./processed_data/train/raw/train_u.npy\")\n unlabeled_x = unlabeled[:, :-1]\n result_unlabeled = np.array(classifier.predict_proba(unlabeled_x)[:, 1])\n plt.hist(result_unlabeled, bins=bins_num)\n plt.savefig(join(join(_result_path, solver), str(sub_u_rate)) + '/unlabeled.png')\n plt.show()\n print(\"\\nunlabeled set results: average: \" + str(np.mean(result_unlabeled)) + \" variance:\" + str(\n np.var(result_unlabeled)))\n print(\"max: \" + str(result_unlabeled.max()) + \" min: \" + str(result_unlabeled.min()))","def __init__(\n self,\n meta_data: pd.DataFrame,\n factor_data: pd.DataFrame,\n signal_data: pd.DataFrame,\n settings: dict,\n ):\n logger.info(\"Initiating Backtest.\")\n df = pd.concat([meta_data, factor_data, signal_data], axis=1)\n self.settings = settings\n\n self.timeframe = settings.get('timeframe')\n self.mq_name = settings.get('mq', 'default')\n self.friction_loss = settings.get('friction_loss', 0)\n self.return_base = settings.get('return_base', 'pre_close')\n self.pos_ = settings.get('position_pctg', 1)\n self.stop_loss = settings.get('stop_loss', 100)\n self.action_on_0 = settings.get('sig_0_action', 'close')\n day_close = settings.get('day_close', None)\n if day_close is not None:\n self.trade_flag = [time.fromisoformat(t) for t in day_close.split(' ')]\n else:\n self.trade_flag = 0\n\n self.period = settings.get(\"period\", None)\n\n df.sort_index(ascending=True, inplace=True)\n df['date'] = df.index.date if self.timeframe[0] == 'T' else df.index\n\n if self.period is not None:\n (self.start_dt, self.end_dt) = (datetime.strptime(i, \"%Y-%m-%d\") for i in self.period.split(' '))\n df = df[(df['date'] >= self.start_dt.date()) & (df['date'] <= self.end_dt.date())].copy()\n\n if df.empty:\n raise ValueError('Empty DataFrame.')\n\n if self.return_base == 'pre_close':\n df['return'] = df['close'].diff()\n elif self.return_base == 'open':\n df['return'] = df['close'] - df['open']\n else:\n raise AttributeError(\n f'Wrong parameter passed: return_base. current: {self.return_base}, '\n f'must be pre_close or open.'\n )\n\n self.df = df\n\n # initiate some params\n self.collect_perfs = {}\n\n # initiate redis_mq\n self.rds_handler = rh.RedisHandle(self.mq_name)","def calc_performance(self):\n for symbol in self.portfolio.assets.keys():\n\n # Total the Performance of all the trades\n start = self.portfolio.trades[symbol].index[0]\n end = self.portfolio.trades[symbol].index[-1]\n trades = len(self.record[symbol])\n profit = self.record[symbol]['profit'].sum()\n loss = self.record[symbol]['loss'].sum()\n # Total or average the trade info for all the trades\n try:\n wins = len(self.record[symbol].groupby('win/loose').groups['w'])\n except (ValueError, KeyError):\n wins = 0\n try:\n losses = len(self.record[symbol].groupby('win/loose').groups['l'])\n except (ValueError, KeyError):\n losses = 0\n try:\n washes = len(self.record[symbol].groupby('win/loose').groups['-'])\n except (ValueError, KeyError):\n washes = 0\n max_drawdown = self.record[symbol]['drawdown'].max()\n average_drawdown = self.record[symbol]['drawdown'].mean()\n max_drawdown_time = self.record[symbol]['drawdown days'].max()\n average_drawdown_time = self.record[symbol]['drawdown days'].mean()\n # Average the risk and market comparisons for all trades\n vol_risk = self.record[symbol]['volatility'].mean()\n beta = self.record[symbol]['beta'].mean()\n lpm_risk = self.record[symbol]['lpm'].mean()\n e_r = self.record[symbol]['expected_return'].mean()\n # Calculate Risk measures\n treynor_ratio = (e_r - self.risk_free_return) / beta\n sharpe_ratio = (e_r - self.risk_free_return) / vol_risk\n # Package up the data for each symbol\n self.performance[symbol] = {\n 'start': start,\n 'end': end,\n 'trades': trades,\n 'wins': wins,\n 'losses': losses,\n 'washes': washes,\n 'profit': profit,\n 'loss': loss,\n 'net_profit': profit - loss,\n 'profit_factor': profit / loss if loss != 0 else 1.0,\n 'percent_profitable': wins / trades if trades != 0 else 0.0,\n 'average_trade_net_profit' : (profit - loss) / trades if trades != 0 else 0.0,\n 'max_drawdown' : max_drawdown,\n 'average_drawdown' : average_drawdown,\n 'max_drawdown_days' : max_drawdown_time,\n 'average_drawdown_days' : average_drawdown_time,\n 'volatility_risk' : vol_risk,\n 'beta' : beta,\n 'lower_partial_moment_risk' : lpm_risk,\n 't_r' : treynor_ratio,\n 's_r' : sharpe_ratio\n }\n\n return self","def test_Rt(self):\n\n test_value = self.portfolio._get_asset_portfolio[self.test_row_number]\n\n test_prices = self.portfolio._df_asset.iloc(axis=0)[\n self.test_row_number].values\n test_weights = self.weights.iloc(axis=0)[\n self.test_row_number].values\n calculated_value = sum(self.list_multiplication(test_prices,\n test_weights))\n self.assertAlmostEqual(test_value, calculated_value)","def generate_portfolio(S_0, params):\n\tpublic_client = gdax.PublicClient()\n\tallvar = []\n\tsumvar = 0\n\tfor coin in params:\n\t\ttheta = coin[0]\n\t\tv = theta[0]\n\t\tT = coin[1]\n\t\tprod_id = coin[2]\n\t\t# Get the current value of the coin, i.e. how much you bought\n\t\tname = prod_id + '-USD'\n\t\tstats = public_client.get_product_24hr_stats(name)\n\t\tvalue = (float(stats['high']) + float(stats['low']))/2\n\t\tallvar.append([prod_id, value, v])\n\t\tsumvar += v\n\tpriority = sorted(allvar, key=lambda i: i[2])\n\tportfolio = []\n\tfor i in priority:\n\t\tinvestment = S_0*i[2]/sumvar\n\t\tcurrency = investment/i[1]\n\t\tportfolio.append((i[0], currency, investment)) # id, investment, currency\n\tprint(\"\\nYour suggested investments are: \\n\")\n\tfor coin in portfolio:\n\t\tprint(\"%s: %s for %s USD\" % (coin[0], coin[1], coin[2]))\n\t# Prompt to save the portfolio\n\tdone = False\n\twhile done != True:\n\t\tinp = input(\"\\nWould you like to save this portfolio? (y/n)\t\")\n\t\ttry:\n\t\t\tif inp.lower() == 'y':\n\t\t\t\tpublic_client = gdax.PublicClient()\n\t\t\t\tcurrent_date = np.datetime64(public_client.get_time().get(\"iso\").split('T')[0])\n\t\t\t\t# Save the file\n\t\t\t\twith open(\"portfolios/%s.txt\" % (current_date), \"w\") as f:\n\t\t\t\t\tfor coin in portfolio:\n\t\t\t\t\t\tf.write(str(coin[0]) + ', ' + str(coin[1]) + ', ' + str(coin[2]) + '\\n')\n\t\t\t\tprint(\"Portfolio saved. Exiting.\\n\")\n\t\t\t\tdone = True\n\t\t\tif inp.lower() == 'n':\n\t\t\t\tprint(\"Program complete. Exiting.\\n\")\n\t\t\t\tdone = True\n\t\texcept ValueError:\n\t\t\tprint(\"Your input could not be interpreted.\")","def run():\n\n # Set up environment and agent\n e = Environment() # create environment (also adds some dummy traffic)\n a = e.create_agent(LearningAgent) # create agent\n e.set_primary_agent(a, enforce_deadline=False) # set agent to track\n\n # Now simulate it\n sim = Simulator(e, update_delay=0.0) # reduce update_delay to speed up simulation\n sim.run(n_trials=num_of_experiments) # press Esc or close pygame window to quit\n \n pd.Series(a.success).to_pickle('success_' + exp_id + '.pickle')\n a.Q_table.to_pickle('qtable_' + exp_id + '.pickle')\n pd.Series(a.q_delta_avg).to_pickle('convergence_' + exp_id + '.pickle')\n pd.Series(a.t_total).to_pickle('steps_' + exp_id + '.pickle')","def income_model_user_defined_returns(num_of_years=30, trials=100, method='normal'):\n\n print(\"Running method income_model_user_defined_returns()\")\n\n sim_fia_cv = pd.DataFrame(index=range(num_of_years + 1))\n\n sim_base_total = pd.DataFrame(index=range(num_of_years + 1))\n sim_base_income = pd.DataFrame(index=range(num_of_years + 1))\n\n sim_port_total = pd.DataFrame(index=range(num_of_years + 1))\n sim_port_income = pd.DataFrame(index=range(num_of_years + 1))\n\n read_income_inputs = pd.read_excel(src + \"portfolio_information.xlsx\", sheet_name='income_model_inputs',\n index_col=[0])\n\n read_returns_est = pd.read_excel(src + \"portfolio_information.xlsx\", sheet_name='income_assets_returns_estimates',\n index_col=[0])\n clean_names = list(read_returns_est.index)\n clean_names = [s.split(' ')[0] for s in clean_names]\n read_returns_est.loc[:, 'names'] = clean_names\n read_returns_est.set_index('names', drop=True, inplace=True)\n read_returns_est = read_returns_est[:-1]\n read_returns_est.rename(index={'SBMMTB3': 'Cash', read_returns_est.index[-1]: 'FIA'}, inplace=True)\n\n # ---------------Returns DataFrame based on the user input------------------------------------\n ann_ret = np.full((num_of_years + 1, len(read_returns_est)), read_returns_est.loc[:, 'Annualized Returns'])\n read_normal = pd.DataFrame(ann_ret, index=np.arange(num_of_years + 1), columns=read_returns_est.index)\n # read_normal.rename(columns={read_normal.columns[-1]: 'FIA'}, inplace=True)\n user_est_fia_return = float(read_income_inputs.loc['fia_forecast', 'inputs'])\n read_normal.loc[:, 'FIA'] = user_est_fia_return\n\n read_asset_weights = pd.read_excel(src + \"portfolio_information.xlsx\", sheet_name='asset_weights',\n index_col=[0])\n\n # read_asset_weights.drop(read_asset_weights.index[-1], axis=0, inplace=True)\n\n # read random returns for simulation\n read_normal = pd.read_csv(src + 'median_returns_unsorted.csv', index_col=[0], parse_dates=True)\n cols = [read_normal.columns[c].split('_')[1] for c in np.arange(len(read_normal.columns))]\n read_normal.rename(columns=dict(zip(list(read_normal.columns), cols)), inplace=True)\n\n # ann_ret = np.full((num_of_years+1, len(read_returns_est)), read_returns_est.loc[:, 'Annualized Returns'])\n # read_normal = pd.DataFrame(ann_ret, index=np.arange(num_of_years+1), columns=read_returns_est.index)\n # # read_normal.rename(columns={read_normal.columns[-1]: 'FIA'}, inplace=True)\n # user_est_fia_return = float(read_income_inputs.loc['fia_forecast', 'inputs'])\n # read_normal.loc[:, 'FIA'] = user_est_fia_return\n\n assets_col_names = list(read_normal.columns)\n\n tickers = list(read_asset_weights.index)\n wts = np.array(read_asset_weights.loc[:, 'base'])\n\n def asset_median_returns(data, ticker):\n return data.filter(regex=ticker).median(axis=1)\n\n # dataframe for unsorted returns (normal)\n median_returns_normal = read_normal.copy()\n median_returns_normal.loc[:, 'portfolio_return'] = median_returns_normal.dot(wts)\n median_normal_fia = pd.DataFrame({'FIA': asset_median_returns(read_normal, 'FIA')})\n\n years = list(range(0, num_of_years + 1))\n income_cols = ['year', 'strategy_term', 'index_returns', 'term_ret', 'term_ret_with_par', 'term_annualize',\n 'ann_net_spread', 'term_ret_netspr', 'high_inc_benefit_base', 'rider_fee', 'eoy_income',\n 'contract_value']\n\n term = int(read_income_inputs.loc['term', 'inputs'])\n fia_ret = read_returns_est.loc[read_returns_est.index[-1], 'Annualized Returns']\n fia_risk = read_returns_est.loc[read_returns_est.index[-1], 'Annualized Risk']\n par_rate = float(read_income_inputs.loc['par_rate', 'inputs'])\n spread = float(read_income_inputs.loc['spread', 'inputs'])\n bonus_term = int(read_income_inputs.loc['bonus_term', 'inputs'])\n premium = float(read_income_inputs.loc['premium', 'inputs'])\n income_bonus = float(read_income_inputs.loc['income_bonus', 'inputs'])\n\n income_starts = int(read_income_inputs.loc['start_income_years', 'inputs'])\n income_growth = float(read_income_inputs.loc['income_growth', 'inputs'])\n rider_fee = float(read_income_inputs.loc['rider_fee', 'inputs'])\n inc_payout_factor = float(read_income_inputs.loc['income_payout_factor', 'inputs'])\n contract_bonus = float(read_income_inputs.loc['contract_bonus', 'inputs'])\n social = float(read_income_inputs.loc['social', 'inputs'])\n inflation = float(read_income_inputs.loc['inflation', 'inputs'])\n wtd_cpn_yield = float(read_income_inputs.loc['wtd_coupon_yld', 'inputs'])\n life_expectancy = int(read_income_inputs.loc['life_expectancy_age', 'inputs'])\n clients_age = int(read_income_inputs.loc['clients_age', 'inputs'])\n\n # ---------------INCOME MODEL--------------------------------------------\n runs = 0\n returns_dict = {}\n asset_dict = {}\n fia_dict = {}\n\n income_df = pd.DataFrame(index=years, columns=income_cols)\n income_df.loc[:, 'year'] = years\n income_df.loc[:, 'strategy_term'] = income_df.loc[:, 'year'] % term\n income_df.loc[:, 'strategy_term'] = income_df['strategy_term'].apply(lambda x: 1 if x == 0 else 0)\n income_df.loc[:, 'index_returns'] = read_normal.loc[:, 'FIA']\n\n # income_df.loc[:, 'index_returns'] = np.random.normal(fia_ret, fia_risk, size=(len(years), 1))\n cumprod = (1. + income_df['index_returns']).rolling(window=term).agg(lambda x: x.prod()) - 1\n income_df.loc[:, 'term_ret'] = np.where(income_df.loc[:, 'strategy_term'] == 1, cumprod, 0)\n income_df.loc[:, 'term_ret_with_par'] = income_df.loc[:, 'term_ret'] * par_rate\n income_df.loc[:, 'term_annualize'] = income_df.loc[:, 'term_ret_with_par'].apply(\n lambda x: (1 + x) ** (1 / term) - 1)\n income_df.loc[:, 'ann_net_spread'] = income_df.loc[:, 'term_annualize'] - spread\n income_df.loc[:, 'ann_net_spread'] = np.where(income_df.loc[:, 'strategy_term'] == 1,\n income_df.loc[:, 'ann_net_spread'], 0)\n income_df.loc[:, 'term_ret_netspr'] = income_df.loc[:, 'ann_net_spread'].apply(lambda x: (1 + x) ** term - 1)\n\n for counter in years:\n if counter == 0:\n income_df.loc[counter, 'high_inc_benefit_base'] = premium * (1 + income_bonus)\n\n elif counter <= min(bonus_term, income_starts):\n income_df.loc[counter, 'high_inc_benefit_base'] = income_df.loc[counter - 1, 'high_inc_benefit_base'] * \\\n (1 + income_growth)\n else:\n income_df.loc[counter, 'high_inc_benefit_base'] = income_df.loc[counter - 1, 'high_inc_benefit_base']\n\n income_df.loc[:, 'rider_fee'] = income_df.loc[:, 'high_inc_benefit_base'] * rider_fee\n income_df.loc[:, 'eoy_income'] = np.where(income_df.loc[:, 'year'] > income_starts,\n income_df.loc[:, 'high_inc_benefit_base'] * inc_payout_factor, 0)\n\n for counter in years:\n if counter == 0:\n income_df.loc[counter, 'contract_value'] = premium * (1 + contract_bonus)\n\n elif income_df.loc[counter, 'strategy_term'] == 1:\n x1 = income_df.loc[counter - 1, 'contract_value'] - income_df.loc[counter, 'rider_fee']\n x2 = (x1 * (1 + income_df.loc[counter, 'term_ret_netspr'])) - income_df.loc[counter, 'eoy_income']\n income_df.loc[counter, 'contract_value'] = x2\n\n else:\n x1 = income_df.loc[counter - 1, 'contract_value'] - income_df.loc[counter, 'rider_fee'] - \\\n income_df.loc[counter, 'eoy_income']\n\n income_df.loc[counter, 'contract_value'] = x1\n\n # variable stores the income number that is used in the base and fia portfolio calcs.\n\n income_from_fia = income_df.loc[income_df.index[-1], 'eoy_income']\n\n income_df.loc[:, 'contract_value'] = income_df.loc[:, 'contract_value'].apply(lambda x: 0 if x <= 0 else x)\n\n sim_fia_cv.loc[:, str(runs)] = income_df.loc[:, 'contract_value']\n\n # --------------------BASE MODEL---------------------------------------------\n base_wts = read_asset_weights.loc[:, 'base']\n base_assets = list(base_wts.index)\n base_weights = list(base_wts.values)\n base_returns = list(read_returns_est.loc[:, 'Annualized Returns'].values)\n base_std = list(read_returns_est.loc[:, 'Annualized Risk'].values)\n\n base_investment = float(read_income_inputs.loc['risky_assets', 'Base'])\n adv_fees = float(read_income_inputs.loc['advisor_fees', 'Base'])\n\n # -------------------required income----------------------------------\n req_annual_income = float(read_income_inputs.loc['annual_income', 'inputs'])\n income_needed = req_annual_income - social\n income_net_fia_income = max(0, income_needed - income_from_fia)\n cpn_income_base = base_investment * wtd_cpn_yield\n\n # ----------------------RANDOM RETURNS--------------------------\n r_cols = base_assets\n boy_value = ['bv_{}'.format(name) for name in base_assets]\n eoy_value = ['ev_{}'.format(name) for name in base_assets]\n\n random_returns = pd.DataFrame(index=income_df.index, columns=r_cols)\n\n for c in range(len(r_cols)):\n ret = np.random.normal(base_returns[c], base_std[c], size=(len(random_returns.index), 1))\n\n random_returns = read_normal.copy()\n\n base_df = random_returns.copy()\n fia_portfolio_df = random_returns.copy()\n port_investment = float(read_income_inputs.loc['risky_assets', 'FIA'])\n cpn_income_port = port_investment * wtd_cpn_yield\n\n # -------------BASE PORTFOLIO----------------------------\n for name in boy_value:\n base_df.loc[:, name] = 0.0\n\n for counter in years:\n period_returns = list(random_returns.loc[counter, :])\n if counter == 0:\n\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment for c in range(len(boy_value))]\n\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\n base_df.loc[counter, 'total_net_fees'] = 0.0\n base_df.loc[counter, 'income'] = 0.0\n base_investment = base_df.loc[counter, boy_value].sum()\n\n elif (counter > 0) and (counter < income_starts):\n\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment * (1 + period_returns[c])\n for c in range(len(boy_value))]\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\n base_df.loc[counter, 'adv_fees'] = base_df.loc[counter, 'total'] * adv_fees\n base_df.loc[counter, 'total_net_fees'] = base_df.loc[counter, 'total'] - base_df.loc[\n counter, 'adv_fees']\n\n # --coupon payment is invested back into the risky portfolio until the income is withdrawn----\n base_investment = base_df.loc[counter, 'total_net_fees'] + cpn_income_base\n\n else:\n\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment * (1 + period_returns[c])\n for c in range(len(boy_value))]\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\n base_df.loc[counter, 'adv_fees'] = base_df.loc[counter, 'total'] * adv_fees\n\n # ---req. income is adjusted for inflation from the second year of withdrawal. Reinvestment of coupon\n # stops from the year income starts. Req. income is reduced by the coupon payments\n\n if counter == income_starts:\n\n income_needed = req_annual_income - social\n base_df.loc[counter, 'income'] = income_needed - cpn_income_base\n income_needed = req_annual_income\n\n else:\n income_needed = income_needed * (1 + inflation) - social\n base_df.loc[counter, 'income'] = income_needed - cpn_income_base\n income_needed = income_needed + social\n\n base_df.loc[counter, 'total_net_fees'] = base_df.loc[counter, 'total'] - \\\n base_df.loc[counter, 'adv_fees'] - \\\n base_df.loc[counter, 'income']\n\n base_investment = base_df.loc[counter, 'total_net_fees']\n\n base_df.loc[:, 'adj_total'] = base_df.loc[:, 'total_net_fees'].apply(lambda x: x if x > 0 else 0)\n sim_base_total.loc[:, 's_{}'.format(str(runs))] = base_df.loc[:, 'total_net_fees']\n sim_base_income.loc[:, 's_{}'.format(str(runs))] = base_df.loc[:, 'income']\n\n # ----------------------------FIA PORTFOLIO----------------------------------------------\n for name in boy_value:\n fia_portfolio_df.loc[:, name] = 0.0\n\n for counter in years:\n period_returns = list(random_returns.loc[counter, :])\n if counter == 0:\n\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment\n for c in range(len(boy_value))]\n fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\n fia_portfolio_df.loc[counter, 'total_net_fees'] = 0.0\n fia_portfolio_df.loc[counter, 'income'] = 0.0\n port_investment = fia_portfolio_df.loc[counter, boy_value].sum()\n\n elif (counter > 0) and (counter < income_starts):\n\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment * (1 + period_returns[c])\n for c in range(len(boy_value))]\n fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\n fia_portfolio_df.loc[counter, 'adv_fees'] = fia_portfolio_df.loc[counter, 'total'] * adv_fees\n fia_portfolio_df.loc[counter, 'total_net_fees'] = fia_portfolio_df.loc[counter, 'total'] - \\\n fia_portfolio_df.loc[counter, 'adv_fees']\n\n port_investment = fia_portfolio_df.loc[counter, 'total_net_fees'] + cpn_income_port\n\n else:\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment * (1 + period_returns[c])\n for c in range(len(boy_value))]\n fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\n fia_portfolio_df.loc[counter, 'adv_fees'] = fia_portfolio_df.loc[counter, 'total'] * adv_fees\n\n # ---req. income is adjusted for inflation from the second year of withdrawal. Reinvestment of coupon\n # stops from the year income starts. Req. income is reduced by the coupon payments\n\n if counter == income_starts:\n\n income_needed = req_annual_income - social\n income_net_fia_income = max(0, income_needed - income_from_fia)\n fia_portfolio_df.loc[counter, 'income'] = max(0, income_net_fia_income - cpn_income_port)\n income_needed = req_annual_income\n\n else:\n income_needed = income_needed * (1 + inflation) - social\n income_net_fia_income = max(0, income_needed - income_from_fia)\n fia_portfolio_df.loc[counter, 'income'] = max(0, income_net_fia_income - cpn_income_port)\n income_needed = income_needed + social\n\n fia_portfolio_df.loc[counter, 'total_net_fees'] = fia_portfolio_df.loc[counter, 'total'] - \\\n fia_portfolio_df.loc[counter, 'adv_fees'] - \\\n fia_portfolio_df.loc[counter, 'income']\n\n port_investment = fia_portfolio_df.loc[counter, 'total_net_fees']\n\n sim_port_total.loc[:, 's_{}'.format(str(runs))] = fia_portfolio_df.loc[:, 'total_net_fees'] + \\\n income_df.loc[:, 'contract_value']\n\n sim_port_income.loc[:, 's_{}'.format(str(runs))] = fia_portfolio_df.loc[:, 'income']\n\n fia_portfolio_df.loc[:, 'adj_total'] = fia_portfolio_df.loc[:, 'total_net_fees'].apply(\n lambda x: x if x > 0 else 0)\n\n # ---------income breakdown for Base portfolio----------------------------------\n base_df.to_csv(src + 'base_port_detail.csv')\n sim_base_total.to_csv(src + 'base_ending_values.csv')\n income_breakdown_base = pd.DataFrame(sim_base_total.quantile(0.5, axis=1))\n income_breakdown_base.loc[:, 'income_from_portfolio'] = sim_base_income.quantile(0.5, axis=1)\n income_breakdown_base.loc[:, 'fia_income'] = 0.0\n income_breakdown_base.loc[:, 'social_security_income'] = social\n income_breakdown_base.loc[:, 'coupon_income'] = cpn_income_base\n\n income_breakdown_base.rename(columns={income_breakdown_base.columns[0]: 'portfolio_ending_value'}, inplace=True)\n income_breakdown_base.loc[:, 'income_from_portfolio'][\n income_breakdown_base.loc[:, 'portfolio_ending_value'] <= 0] = 0\n income_breakdown_base.loc[:, 'total_income'] = income_breakdown_base.loc[:, income_breakdown_base.columns[1:]].sum(\n axis=1)\n\n # ------------Block Ends-------------------------------------------------------------\n\n # ---------income breakdown for FIA portfolio----------------------------------\n fia_portfolio_df.to_csv(src + 'fia_port_detail.csv')\n sim_port_total.to_csv(src + 'fiaport_ending_values.csv')\n\n income_breakdown_port = pd.DataFrame(sim_port_total.quantile(0.5, axis=1))\n income_breakdown_port.loc[:, 'income_from_portfolio'] = sim_port_income.quantile(0.5, axis=1)\n income_breakdown_port.loc[:, 'fia_income'] = income_from_fia\n income_breakdown_port.loc[:, 'social_security_income'] = social\n income_breakdown_port.loc[:, 'coupon_income'] = cpn_income_port\n\n income_breakdown_port.rename(columns={income_breakdown_port.columns[0]: 'portfolio_ending_value'}, inplace=True)\n income_breakdown_port.loc[:, 'income_from_portfolio'][\n income_breakdown_port.loc[:, 'portfolio_ending_value'] <= 0] = 0\n income_breakdown_port.loc[:, 'total_income'] = income_breakdown_port.loc[:, income_breakdown_port.columns[1:]].sum(\n axis=1)\n\n # ------------Block Ends-------------------------------------------------------------\n q_cut = [0.0, 0.05, 0.25, 0.5, 0.75, 0.95, 1.0]\n sim_base_income[sim_base_total < income_needed] = 0.0\n\n sim_port_income[sim_port_total < income_net_fia_income] = 0\n\n sim_port_income = sim_port_income + income_from_fia\n\n # base_quantile = sim_base_total.loc[sim_base_total.index[-1]].quantile([0.05, 0.25, 0.50, 0.75, 0.90])\n\n # port_quantile = sim_port_total.loc[sim_port_total.index[-1]].quantile([0.05, 0.25, 0.50, 0.75, 0.90])\n\n base_quantile = sim_base_total.loc[sim_base_total.index[-1]].quantile(q_cut)\n\n port_quantile = sim_port_total.loc[sim_port_total.index[-1]].quantile(q_cut)\n\n # q_cut = [0.0, .05, 0.25, 0.5, 0.75, 0.95, 1.0]\n cols = ['Min', '5th', '25th', '50th', '75th', '90th', 'Max']\n\n # ----drop year 0--------\n sim_base_total = sim_base_total[1:]\n sim_port_total = sim_port_total[1:]\n\n # ----------------quantile analysis for base terminal value--------------------------\n base_qcut = pd.DataFrame(index=sim_base_total.index, columns=cols)\n for c in range(len(cols)):\n base_qcut.loc[:, cols[c]] = sim_base_total.quantile(q_cut[c], axis=1)\n\n base_qcut.clip(lower=0, inplace=True)\n\n # ----------------------quantile analysis for base income----------------------------\n base_income_qcut = pd.DataFrame(index=sim_base_income.index, columns=cols)\n for c in range(len(cols)):\n base_income_qcut.loc[:, cols[c]] = sim_base_income.quantile(q_cut[c], axis=1)\n\n # ----Remove NaN's prior to the income start years------------\n # base_income_qcut = base_income_qcut.loc[income_starts:]\n\n # -------------quantile analysis for portfolio terminal value ----------------\n port_qcut = pd.DataFrame(index=sim_port_total.index, columns=cols)\n for c in range(len(cols)):\n port_qcut.loc[:, cols[c]] = sim_port_total.quantile(q_cut[c], axis=1)\n\n port_qcut.clip(lower=0, inplace=True)\n\n # ---------------quantile analysis for portfolio income----------------------------\n port_income_qcut = pd.DataFrame(index=sim_port_income.index, columns=cols)\n for c in range(len(cols)):\n port_income_qcut.loc[:, cols[c]] = sim_port_income.quantile(q_cut[c], axis=1)\n\n # ----Remove NaN's prior to the income start years------------\n # port_income_qcut = port_income_qcut.loc[income_starts:]\n\n # ----------probability ending value will be less than 0 at the end of the horizon -----------------------\n base_legacy_risk = (sim_base_total.loc[sim_base_total.index[life_expectancy - clients_age]] < 0).sum() / (\n trials + 1)\n port_legacy_risk = (sim_port_total.loc[sim_port_total.index[life_expectancy - clients_age]] < 0).sum() / (\n trials + 1)\n\n legacy_risk = pd.DataFrame([base_legacy_risk, port_legacy_risk,\n 'Prob. of portfolio value less than 0 at the end of the expected life'],\n index=['base', 'fia_portfolio', 'Notes'],\n columns=['Ruin Probability'])\n\n # -----------Year-wise probability of ending value greater than 0 -----------------\n base_psuccess = sim_base_total.apply(lambda x: x > 0).sum(axis=1) / (trials + 1)\n port_psuccess = sim_port_total.apply(lambda x: x > 0).sum(axis=1) / (trials + 1)\n\n # -----------------------WRITING FILES TO EXCEL ---------------------------\n col_names = ['50th', 'age', 'comment']\n writer = pd.ExcelWriter(src + method + '_simulated_income_summary_custom.xlsx', engine='xlsxwriter')\n read_income_inputs.to_excel(writer, sheet_name='inputs_for_income')\n\n read_returns_est.to_excel(writer, sheet_name='asset_returns_estimates')\n\n age_index = list(range(clients_age + 1, clients_age + len(base_qcut) + 1))\n base_qcut.loc[:, 'age'] = age_index\n base_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n base_qcut.loc[income_starts:, col_names].to_excel(writer, sheet_name='base_ending_value_quantiles')\n\n base_income_qcut = base_income_qcut.loc[1:, :]\n base_income_qcut.loc[:, 'age'] = age_index\n base_income_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n base_income_qcut.loc[income_starts:, col_names].to_excel(writer, sheet_name='base_income_quantiles')\n\n port_qcut.loc[:, 'age'] = age_index\n port_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n port_qcut.loc[income_starts:, col_names].to_excel(writer, sheet_name='fia_port_ending_value_quantiles')\n\n port_income_qcut = port_income_qcut.loc[1:, :]\n port_income_qcut.loc[:, 'age'] = age_index\n port_income_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n port_income_qcut.loc[income_starts:, col_names].to_excel(writer, sheet_name='fia_port_income_quantiles')\n\n income_breakdown_base = income_breakdown_base.loc[1:, :]\n income_breakdown_base.loc[:, 'age'] = age_index\n income_breakdown_base.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n income_breakdown_base.loc[income_starts:, :].to_excel(writer, sheet_name='base_income_breakdown_median')\n\n income_breakdown_port = income_breakdown_port.loc[1:, :]\n income_breakdown_port.loc[:, 'age'] = age_index\n income_breakdown_port.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n income_breakdown_port.loc[:, 'ending_contract_value'] = income_df.loc[:, 'contract_value']\n income_breakdown_port.loc[income_starts:, :].to_excel(writer, sheet_name='fia_income_breakdown_median')\n\n median_returns_normal.to_excel(writer, sheet_name='gr_port_median_normal')\n\n terminal_val = pd.read_csv(src + 'terminal_values.csv', index_col=[0])\n ending_val = pd.read_csv(src + 'ending_values.csv', index_col=[0])\n ending_val_ror = pd.read_csv(src + 'ending_values_ror.csv', index_col=[0])\n\n terminal_val.to_excel(writer, sheet_name='terminal_values')\n ending_val.to_excel(writer, sheet_name='port_ending_values')\n ending_val_ror.to_excel(writer, sheet_name='port_annual_growth')\n\n writer.save()\n\n # -----------------Plotting charts--------------------------------------------\n base_qcut.loc[income_starts:].plot(grid=True, title='Quantile Terminal Value - Base Portfolio')\n plt.savefig(src + \"quantile_terminal_base.png\")\n plt.close('all')\n\n base_income_qcut.plot(grid=True, title='Quantile Income - Base Portfolio')\n plt.savefig(src + \"quantile_income_base.png\")\n plt.close('all')\n\n base_psuccess.plot(grid=True, title='Probability of Success (Portfolio Ending Value > 0) - Base Portfolio')\n plt.savefig(src + \"success_probabilty_base.png\")\n plt.close('all')\n\n (1 - base_psuccess).plot(grid=True, title='Probability of Ruin (Portfolio Ending Value < 0) - Base Portfolio')\n plt.savefig(src + \"ruin_probability_base.png\")\n plt.close('all')\n\n port_qcut.loc[income_starts:].plot(grid=True, title='Quantile Terminal Value - FIA Portfolio')\n plt.savefig(src + \"quantile_terminal_fia.png\")\n plt.close('all')\n\n port_income_qcut.plot(grid=True, title='Quantile Income - FIA Portfolio')\n plt.savefig(src + \"quantile_income_fia.png\")\n plt.close('all')\n\n port_psuccess.plot(grid=True, title='Probability of Success (Portfolio Ending Value > 0) - FIA Portfolio')\n plt.savefig(src + \"success_probabilty_fia.png\")\n plt.close('all')\n\n (1 - port_psuccess).plot(grid=True, title='Probability of Ruin (Portfolio Ending Value < 0) - FIA Portfolio')\n plt.savefig(src + \"ruin_probability_fia.png\")\n plt.close('all')\n\n print(\"simulation completed for {}\".format(method))","def get_market_metrics(market_portfolio: pd.DataFrame, t_costs: float, index_id: str, index_name: str,\n test_data_start_date: datetime.date, test_data_end_date: datetime.date, market_logs=False) -> \\\n Tuple[pd.Series, pd.Series, pd.Series]:\n\n market_portfolio_metrics = pd.Series([]).rename('Market')\n market_portfolio_metrics.index.name = 'Metrics'\n\n excess_return_series = calc_excess_returns(\n market_portfolio.loc[:, 'daily_return'].groupby(level=['datadate']).mean()).rename('daily_excess_return')\n excess_return_series = excess_return_series.reset_index()\n excess_return_series.loc[:, 'datadate'] = excess_return_series['datadate'].dt.strftime(\n '%Y-%m-%d')\n excess_return_series.set_index('datadate', inplace=True)\n cumulative_excess_return = (excess_return_series.get('daily_excess_return') + 1).cumprod().rename(\n 'Cumulative Market Return')\n cumulative_excess_return.index.name = 'Time'\n # cumulative_return.plot(title='Cumulative Market Performance')\n # plt.legend(loc='best')\n # plt.show()\n\n # JOB: Calculate metrics\n # noinspection DuplicatedCode\n annualized_sharpe = calc_sharpe(market_portfolio.loc[:, ['daily_return']].groupby(level=['datadate']).mean(),\n annualize=True)\n annualized_sharpe_atc = calc_sharpe(\n market_portfolio.loc[:, ['daily_return']].groupby(level=['datadate']).mean() - 4 * t_costs,\n annualize=True)\n\n annualized_sortino = calc_sortino(market_portfolio.loc[:, ['daily_return']].groupby(level=['datadate']).mean(),\n annualize=True)\n annualized_sortino_atc = calc_sortino(\n market_portfolio.loc[:, ['daily_return']].groupby(level=['datadate']).mean() - 4 * t_costs,\n annualize=True)\n\n mean_daily_return = market_portfolio.groupby(level=['datadate'])['daily_return'].mean().mean()\n mean_daily_excess_return = calc_excess_returns(\n market_portfolio.groupby(level=['datadate'])['daily_return'].mean().rename('daily_return')).mean()\n\n market_portfolio_metrics.loc['Mean Daily Return'] = mean_daily_return\n market_portfolio_metrics.loc['Annualized Return'] = annualize_metric(mean_daily_return)\n market_portfolio_metrics.loc['Mean Daily Excess Return'] = mean_daily_excess_return\n market_portfolio_metrics.loc['Annualized Excess Return'] = annualize_metric(mean_daily_excess_return)\n market_portfolio_metrics.loc['Annualized Sharpe'] = annualized_sharpe\n market_portfolio_metrics.loc['Annualized Sortino'] = annualized_sortino\n\n # JOB: Add metrics incl. transaction costs of 5 bps per half-turn\n market_portfolio_metrics.loc['Mean Daily Return_atc'] = mean_daily_return - 4 * t_costs\n market_portfolio_metrics.loc['Annualized Return_atc'] = annualize_metric(mean_daily_return - 4 * t_costs)\n market_portfolio_metrics.loc['Mean Daily Excess Return_atc'] = mean_daily_excess_return - 4 * t_costs\n market_portfolio_metrics.loc['Annualized Excess Return_atc'] = annualize_metric(\n mean_daily_excess_return - 4 * t_costs)\n market_portfolio_metrics.loc['Annualized Sharpe_atc'] = annualized_sharpe_atc\n market_portfolio_metrics.loc['Annualized Sortino_atc'] = annualized_sortino_atc\n\n data_record = {\n 'ID': config.run_id,\n 'Experiment Run End': datetime.datetime.now().isoformat(),\n 'Parent Model Type': 'Market',\n 'Model Type': 'Market',\n 'Index ID': index_id,\n 'Index Name': index_name,\n 'Study Period ID': config.study_period_id,\n 'Study Period Length': None,\n 'Period Range': None,\n 'Study Period Start Date': None,\n 'Study Period End Date': None,\n 'Test Set Size': None,\n 'Days Test Set': None,\n 'Constituent Number': None,\n 'Average Cross Section Size': None,\n 'Test Set Start Date': test_data_start_date.isoformat(),\n 'Test Set End Date': test_data_end_date.isoformat(),\n 'Total Accuracy': None,\n\n 'Top-k Accuracy Scores': None,\n 'Top-k Mean Daily Return': market_portfolio_metrics['Mean Daily Return'],\n 'Top-k Mean Daily Excess Return': market_portfolio_metrics['Mean Daily Excess Return'],\n 'Top-k Annualized Excess Return': market_portfolio_metrics['Annualized Excess Return'],\n 'Top-k Annualized Return': market_portfolio_metrics['Annualized Return'],\n 'Top-k Annualized Sharpe': market_portfolio_metrics['Annualized Sharpe'],\n 'Top-k Annualized Sortino': market_portfolio_metrics['Annualized Sortino'],\n 'Mean Daily Return (Short)': None,\n 'Mean Daily Return (Long)': None,\n\n 'Top-k Mean Daily Return_atc': market_portfolio_metrics['Mean Daily Return_atc'],\n 'Top-k Annualized Return_atc': market_portfolio_metrics['Annualized Return_atc'],\n 'Top-k Mean Daily Excess Return_atc': market_portfolio_metrics['Mean Daily Excess Return_atc'],\n 'Top-k Annualized Excess Return_atc': market_portfolio_metrics['Annualized Excess Return_atc'],\n 'Top-k Annualized Sharpe_atc': market_portfolio_metrics['Annualized Sharpe_atc'],\n 'Top-k Annualized Sortino_atc': market_portfolio_metrics['Annualized Sortino_atc'],\n 'Top-k Mean Daily Return (Short)_atc': None,\n 'Top-k Mean Daily Return (Long)_atc': None,\n\n 'Model Configs': None,\n 'Total Epochs': None,\n\n 'Return Series': excess_return_series['daily_excess_return'].to_dict(),\n 'Prediction Error': None\n }\n\n if market_logs:\n write_to_logs(data_record)\n\n return market_portfolio_metrics, excess_return_series, cumulative_excess_return","def test_portfolio_balance(\n session, account_checking, account_savings, account_sp500, asset_krw, asset_sp500\n):\n portfolio = Portfolio()\n portfolio.base_asset = asset_krw\n portfolio.add_accounts(account_checking, account_savings, account_sp500)\n\n assert portfolio.balance(parse_date(\"2016-05-20\")) == {}\n\n deposit(account_checking, asset_krw, 1500, parse_date(\"2016-05-01\"))\n deposit(account_savings, asset_krw, 3000, parse_date(\"2016-05-01\"))\n deposit(account_sp500, asset_sp500, 120, parse_date(\"2016-05-01\"))\n\n assert portfolio.balance(parse_date(\"2016-05-20\")) == {\n asset_krw: 4500,\n asset_sp500: 120,\n }\n\n deposit(account_savings, asset_krw, 4000, parse_date(\"2016-05-02\"))\n deposit(account_savings, asset_krw, 5000, parse_date(\"2016-05-03\"))\n\n assert portfolio.balance(parse_date(\"2016-05-20\")) == {\n asset_krw: 13500,\n asset_sp500: 120,\n }\n\n balance_adjustment(account_savings, asset_krw, 10000, parse_date(\"2016-05-04\"))\n\n assert portfolio.balance(parse_date(\"2016-05-20\")) == {\n asset_krw: 11500,\n asset_sp500: 120,\n }\n\n session.delete(portfolio)\n session.commit()","def test_Pt(self):\n\n test_value = self.portfolio.calculate_asset_performance(\n *self.boarder)[self.test_row_number]\n calculated_value = self.manual_cumprod(\n self.portfolio._get_asset_portfolio)\n self.assertAlmostEqual(test_value, calculated_value)","def test_Rit(self):\n\n manual_calculated = list(self._manual_calculate_formal(self.prices))\n\n self.portfolio._generate_asset()\n test_column = self._convert_df_to_list(self.portfolio._df_asset)\n\n self.assertAlmostEqual(self._clear_column(manual_calculated),\n self._clear_column(test_column))","def run():\n\n for simulation in range(0, N_SIMULATIONS):\n\n # Set up environment and agent\n e = Environment() # create environment (also adds some dummy traffic)\n a = e.create_agent(LearningAgent) # create agent\n e.set_primary_agent(a, enforce_deadline=True) # specify agent to track\n # NOTE: You can set enforce_deadline=False while debugging to allow longer trials\n # TODO: Change later enforce_deadline=True\n\n # Now simulate it\n sim = Simulator(e, update_delay=0.001, display=False) # create simulator (uses pygame when display=True, if available)\n # NOTE: To speed up simulation, reduce update_delay and/or set display=False\n\n sim.run(n_trials=N_TRIALS) # run for a specified number of trials\n # NOTE: To quit midway, press Esc or close pygame window, or hit Ctrl+C on the command-line\n\n if simulation == N_SIMULATIONS - 1:\n\n with open('results.csv', 'a') as csvfile:\n fieldnames = ['alpha', 'gamma', 'epsilon', 'success_rate', 'last_failure']\n writer = csv.DictWriter(csvfile, fieldnames=fieldnames)\n\n for index in range(0,len(simulation_rates)):\n writer.writerow({\n 'alpha': get_simulation_params(0)[0],\n 'gamma': get_simulation_params(0)[1],\n 'epsilon': get_simulation_params(0)[2],\n 'success_rate': simulation_rates[index],\n 'last_failure': last_errors[index]})\n\n\n if N_SIMULATIONS > 1: #multiple simulation AND last simulation\n\n plt.figure(1)\n\n plt.subplot(211)\n plt.plot(simulation_rates)\n plt.title('Success Rate/Simulation')\n plt.xlabel('# Simulation')\n plt.ylabel('Success Rate')\n\n plt.subplot(212)\n plt.plot(last_errors)\n plt.title('Last failed trial per simulation')\n plt.xlabel('# Simulation')\n plt.ylabel('Last failed trial')\n\n plt.show()","def backtest(cache):\n if not cache:\n history = getBars(MARKET, TF)\n else:\n cachefile = \"cache/{}-{}.csv\".format(MARKET, TF)\n try:\n history = pd.read_csv(\n cachefile, index_col=\"datetime\", parse_dates=True)\n except:\n history = getBars(MARKET, TF)\n history.to_csv(cachefile)\n history['ma'] = history['close'].rolling(MEAN).mean()\n weAreLong = False\n PRICE_DIPPED = False\n\n entry = 0.0\n exit = None\n pl = 0.0\n history['pandl'] = 0.0\n trades = 1\n\n ### BEGIN STRATEGY DEFINITION ###\n count = 1\n\n for i in history.index:\n candle_close_rate = history['ma'][i]\n ma = history['close'][i]\n if count > MEAN:\n # playing revert to mean (RTM)\n if not weAreLong and buySignaled(candle_close_rate, ma, PRICE_DIPPED):\n entry = candle_close_rate\n exit = candle_close_rate * (1.0 + (EXIT_PERCENT / 100.0))\n stop = candle_close_rate * (1.0 - (STOP_PERC / 100.0))\n weAreLong = True\n history['pandl'][i] = pl\n elif weAreLong and history['high'][i] >= exit:\n weAreLong = False\n pl += (((exit - entry) / entry) * 100.0 ) - (2.0 * FEE)\n history['pandl'][i] = pl\n trades += 1\n elif weAreLong and candle_close_rate <= stop:\n weAreLong = False\n pl += (((candle_close_rate - entry) / entry) * 100.0 ) - (2.0 * FEE)\n history['pandl'][i] = pl\n trades += 1\n else:\n if weAreLong:\n # fpl = ((history['close'][i] * (1.0 - FEE)) - (entry * (1.0 + FEE)))\n # fpl = ( (history['close'][i] * (100.0 - FEE)) - (entry * (100.0 + FEE)) ) / 100.0\n fpl = ((candle_close_rate - entry) / entry) * 100.0\n history['pandl'][i] = pl + fpl\n else:\n fpl = 0.0\n history['pandl'][i] = pl + fpl\n count += 1\n if candle_close_rate <= ma:\n PRICE_DIPPED = True\n else:\n PRICE_DIPPED = False\n ### END STRATEGY DEFINITION ###\n\n days = (len(history) * TF) / (60 * 24)\n sharpe_ratio = getSharpe(list(history['pandl']), days)\n fig, ax = plt.subplots(1)\n plt.plot(history['pandl'])\n fig.autofmt_xdate()\n plt.ylabel('cumulative %')\n props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)\n # place a text box in upper left in axes coords\n textstr = \"\"\"{}\n Days: {}\n Trades: {}\n Settings:\n TF = {}\n MEAN = {}\n BREAKOUT = {}\n RTM = {}\n RTM_PERCENT = {}\n BO_PERCENT = {}\n EXIT_PERCENT = {},\n STOP = {}\n Sharpe = {}\n \"\"\".format(MARKET,\n days,\n trades,\n TF,\n MEAN,\n BREAKOUT,\n RTM,\n RTM_PERCENT,\n BO_PERCENT,\n EXIT_PERCENT,\n STOP_PERC,\n sharpe_ratio)\n ax.text(0.05, 0.95, textstr, transform=ax.transAxes, fontsize=14,\n verticalalignment='top', bbox=props)\n plt.title(\"BACKTEST {}\".format(MARKET))\n plt.savefig(BACKTESTFILE)\n print(\"{},{},{},{},{},{},{},{},{},{},{},{}\".format(days,\n trades,\n MARKET,\n TF,\n MEAN,\n BREAKOUT,\n RTM,\n RTM_PERCENT,\n BO_PERCENT,\n EXIT_PERCENT,\n STOP_PERC,\n sharpe_ratio))\n # plt.show()","def start_trading(self):\n backtest_result_dir = os.path.join(self.result_dir, str(self.backtest_date))\n os.mkdir(backtest_result_dir)\n backtest_scores_path = os.path.join(backtest_result_dir, 'scores.csv')\n # all_backtest_scores_exists = os.path.isfile(self.all_backtest_scores_path)\n\n last_backtest_scores = open(self.last_backtest_scores_path, \"w\")\n backtest_scores = open(backtest_scores_path, \"w\")\n\n\n fieldnames = [ 'Backtest Name', 'Backtest Date', 'Strategy', 'Start Date', 'End Date', 'Instrument(s)', 'Params'] + \\\n ['Number of signals', 'Number of orders', 'Number of trades', 'Total USD Return', 'Total BTC Return',\n 'Sharpe Ratio', 'BTC Sharpe Ratio', 'Max Drawdown', 'BTC Max Drawdown', 'Drawdown Duration', 'BTC Drawdown Duration',\n 'Monthly BTC Return', 'Yearly BTC Return', 'Avg. winning trade', 'Median duration', 'Avg. losing trade', 'Median returns winning', 'Largest losing trade',\n 'Gross loss', 'Largest winning trade', 'Avg duration', 'Avg returns losing', 'Median returns losing', 'Profit factor',\n 'Winning round trips', 'Percent profitable', 'Total profit', 'Shortest duration', 'Median returns all round trips',\n 'Losing round trips', 'Longest duration', 'Avg returns all round trips', 'Gross profit', 'Avg returns winning',\n 'Total number of round trips', 'Ratio Avg. Win:Avg. Loss', 'Avg. trade net profit', 'Even round trips',\n 'Configuration Filename', 'Leverage']\n\n try:\n with last_backtest_scores as a, backtest_scores as b:\n writer_a = csv.DictWriter(a, fieldnames=fieldnames)\n writer_b = csv.DictWriter(b, fieldnames=fieldnames)\n writer_a.writeheader()\n writer_b.writeheader()\n\n # if not all_backtest_scores_exists:\n # writer_c.writeheader()\n\n for i, (start, end) in enumerate(zip(self.start_dates, self.end_dates)):\n for j, instruments in enumerate(self.instrument_list):\n for k, params in enumerate(self.strategy_params):\n num_backtest = i * (self.num_instruments * self.num_params + 1) + j * (self.num_params + 1) + k + 1\n print(\"Strategy %s out of %s\" % (num_backtest, self.num_backtests))\n self._generate_trading_instances(start, end, instruments, params)\n self._run()\n stats = self._process_results()\n\n general_stats = stats['general']\n pnl_stats = stats['pnl']['All trades'].to_dict()\n summary_stats = stats['summary']['All trades'].to_dict()\n duration_stats = stats['duration']['All trades'].to_dict()\n return_stats = stats['returns']['All trades'].to_dict()\n params_value = '/'.join([ '{}:{}'.format(item[0], item[1]) for item in params.items() ])\n\n row = {\n 'Backtest Name': self.backtest_name,\n 'Backtest Date': self.backtest_date,\n 'Strategy': self.strategy.strategy_name,\n 'Start Date': start,\n 'End Date': end,\n 'Instrument(s)': format_instrument_list(instruments),\n 'Params': params_value,\n 'Number of signals': self.signals,\n 'Number of orders': self.orders,\n 'Number of trades': self.fills,\n **general_stats,\n **pnl_stats,\n **summary_stats,\n **duration_stats,\n **return_stats,\n 'Configuration Filename': self.configuration_filename,\n 'Leverage': self.default_leverage\n }\n\n writer_a.writerow(row)\n writer_b.writerow(row)\n\n except IOError:\n print('I/O Error')\n\n all_scores_csv = pd.concat([ pd.read_csv(self.all_backtest_scores_path), pd.read_csv(backtest_scores_path)])\n all_scores_csv.to_csv(self.all_backtest_scores_path, columns=fieldnames, index=False, encoding='utf-8-sig')\n\n self._open_results_in_excel()","def summary(self):\n print '%s Portfolio\\'s %s Strategy' % (self.portfolio.name, self.name)\n print '-' * COL_DASH_WIDTH\n\n self.display_trades()\n\n for symbol in self.portfolio.assets.keys():\n perf = self.performance[symbol]\n\n print '\\nSummary for %s from %s (first trade) to %s (last trade)' % (symbol, perf['start'], perf['end'])\n print '.' * COL_DASH_WIDTH\n print 'Summary:'\n data = [[fmtn(perf['trades']), fmtn(perf['wins']), fmtn(perf['losses']), fmtn(perf['washes'])]]\n print tabulate.tabulate(data, headers=['Total Trades', '# Wins', '# Losses', '# Washes'])\n\n print '\\nPerformance:'\n data = [[\n fmtn(perf['profit']), fmtn(perf['loss']), fmtn(perf['net_profit']),\n fmtp(perf['profit_factor']), fmtp(perf['percent_profitable']), fmtn(perf['average_trade_net_profit'])\n ]]\n print tabulate.tabulate(data, headers=['Profit', 'Loss', 'Net Profit', 'Profit Factor', 'Percent Profitable', 'Average Net Profit per Trade'])\n\n print '\\nDrawdown:'\n data = [[fmtn(perf['max_drawdown']), fmtn(perf['average_drawdown']), fmtn(perf['max_drawdown_days']), fmtn(perf['average_drawdown_days'])]]\n print tabulate.tabulate(data, headers=['Max', 'Average', 'Max Days', 'Average Days'])\n\n print '\\nRisk:'\n data = [[fmtn(perf['volatility_risk']), fmtn(perf['beta']), fmtn(perf['lower_partial_moment_risk']), fmtn(perf['t_r']), fmtn(perf['s_r'])]]\n print tabulate.tabulate(data, headers=['Volatility', 'Beta', 'Lower Partial Moment', 'Treynor Ratio', 'Sharpe Ratio'])","def run_grid_search():\n\n best_score = 0\n best_learning_rate = 0\n best_discount_rate = 0\n best_initial_q_hat = 0\n trial_results = []\n number_of_trials = 30\n # TODO These ought to be done with numpy.arange but I don't have that package installed at the moment\n for learning_rate_raw in range(5, 50, 5):\n for discount_rate_raw in range(5, 20, 5):\n for initial_q_hat in range(0, 10, 1):\n learning_rate = learning_rate_raw * 0.01\n discount_rate = discount_rate_raw * 0.05\n # Set up environment and agent\n e = Environment() # create environment (also adds some dummy traffic)\n a = e.create_agent(LearningAgent, learning_rate, discount_rate, initial_q_hat) # create agent\n e.set_primary_agent(a, enforce_deadline=True) # specify agent to track\n # NOTE: You can set enforce_deadline=False while debugging to allow longer trials\n\n # Now simulate it\n sim = Simulator(e, update_delay=0, display=False) # create simulator (uses pygame when display=True, if available)\n # NOTE: To speed up simulation, reduce update_delay and/or set display=False\n\n sim.run(n_trials=number_of_trials) # run for a specified number of trials\n # NOTE: To quit midway, press Esc or close pygame window, or hit Ctrl+C on the command-line\n score = a.get_score()\n if score > best_score:\n best_score = score\n best_learning_rate = learning_rate\n best_discount_rate = discount_rate\n best_initial_q_hat = initial_q_hat\n trial_results.append((learning_rate, discount_rate, initial_q_hat, score, a.get_proportion_of_states_visited(), len(a.get_failed_trials())/ float(number_of_trials)))\n print \"Gridsearch finished, best learning rate: %.2f, best discount rate: %.2f, best initial q hat %i\" % (best_learning_rate, best_discount_rate, best_initial_q_hat)\n\n with open('gridsearch_results.csv', 'wb') as csvfile:\n spamwriter = csv.writer(csvfile, delimiter=';', quotechar='|', quoting=csv.QUOTE_MINIMAL)\n spamwriter.writerow(('learning rate', 'discount factor', 'initial q-hat value', 'score', 'states visited', 'failed trials'))\n for result in trial_results:\n spamwriter.writerow(result)","def test(args):\n brain_name = env.brain_names[0]\n brain = env.brains[brain_name]\n env_info = env.reset(train_mode=True)[brain_name]\n\n num_agents = len(env_info.agents)\n print('Number of agents:', num_agents)\n\n # dim of each action\n action_size = brain.vector_action_space_size\n print('Size of each action:', action_size)\n\n # dim of the state space\n states = env_info.vector_observations\n state_size = states.shape[1]\n\n agent = MADDPG(state_size, action_size, actor_layer_dim_1=args.actor_layer_dim_1,\n actor_layer_dim_2=args.actor_layer_dim_2,\n actor_layer_dim_3=args.actor_layer_dim_3,\n critic_layer_dim_1=args.critic_layer_dim_1,\n critic_layer_dim_2=args.critic_layer_dim_2,\n critic_layer_dim_3=args.critic_layer_dim_3)\n\n agent.load(\n \"chkpts/{}/{:02d}_best_model.checkpoint\".format(args.model_path, args.loop_counter))\n\n test_scores = []\n for i_episode in tqdm(range(1, 1+args.test_n_run)):\n # initialize the scores\n scores = np.zeros(num_agents)\n env_info = env.reset(train_mode=True)[\n brain_name] # reset the environment\n states = env_info.vector_observations # get the current states\n dones = [False]*num_agents\n while not np.any(dones):\n actions = agent.act(states) # select actions\n # send the actions to the environment\n env_info = env.step(actions)[brain_name]\n next_states = env_info.vector_observations # get the next states\n rewards = env_info.rewards # get the rewards\n dones = env_info.local_done # see if episode has finished\n scores += rewards # update the scores\n # roll over the states to next time step\n states = next_states\n\n test_scores.append(np.max(scores))\n\n avg_score = sum(test_scores)/len(test_scores)\n print(\"Test Score: {}\".format(avg_score))\n\n return avg_score","def main():\n # Retrieve csv_file name an max_investment from argument passed in console:\n arg_csv_file, arg_max_investment = set_arg()\n if arg_csv_file:\n csv_file = arg_csv_file\n else:\n csv_file = 'dataset1_Python+P7.csv'\n if arg_max_investment:\n max_investment = float(arg_max_investment)\n else:\n max_investment = 500.00\n\n # Retrieve dataset:\n base_dataset = open_convert_and_clean_csv(csv_file)\n\n # Retrieve solution:\n start = time.perf_counter()\n print()\n print(f\"Processing with file '{csv_file}' containing {len(base_dataset)} shares...\")\n print(f\"Maximum investment: {max_investment}€\")\n print(\"Please wait...\")\n computable_dataset = add_roi_to_dataset(convert_dataset_to_cents(base_dataset))\n best_roi, combination = best_combination_dynamic(computable_dataset, max_investment)\n\n # Formatting results:\n combination.sort(key=lambda x: x[2], reverse=True)\n combination_in_euros = convert_dataset_to_euros(combination)\n best_roi /= 100\n # Following calculus is done on cent prices (combination) to avoid approximations with floats\n shares_cost = calculate_shares_cost_sum(combination) / 100\n\n # Printing results:\n print()\n print(f\"Length of dataset: {len(computable_dataset)}\")\n print(f\"Duration of Analysis: {elapsed_time_formatted(start)}\")\n print()\n print(f\"Best Return on investment after 2 years: {round(best_roi, 2)}€\")\n print(f\"Number of shares to buy : {len(combination_in_euros)}\")\n print(f\"Total cost: {round(shares_cost, 2)}€\")\n print()\n print(f\"Best combination of shares ordered by performance: \")\n for share in combination_in_euros:\n print(f\"{share[0]} | Price: {share[1]}€ | profit: {share[2]}%\")\n print()","def test_marketsim(description, group, inputs, outputs, grader):\n\n points_earned = 0.0 # initialize points for this test case\n try:\n # Try to import student code (only once)\n if not main_code in globals():\n import importlib\n # * Import module\n mod = importlib.import_module(main_code)\n globals()[main_code] = mod\n # * Import methods to test\n for m in ['compute_portvals']:\n globals()[m] = getattr(mod, m)\n\n incorrect = False\n msgs = []\n\n if group == 'author':\n try:\n # globals()['author'] = getattr(marketsim, 'author')\n auth_string = run_with_timeout(marketsim.author,seconds_per_test_case,(),{})\n if auth_string == 'tb34':\n incorrect = True\n msgs.append(\" Incorrect author name (tb34)\")\n points_earned = -20\n elif auth_string == '':\n incorrect = True\n msgs.append(\" Empty author name\")\n points_earned = -20\n except Exception as e:\n incorrect = True\n msgs.append(\" Exception occured when calling author() method: {}\".format(e))\n points_earned = -20\n else:\n # Unpack test case\n orders_file = inputs['orders_file']\n start_val = inputs['start_val']\n\n portvals = None\n fullpath_orders_file = orders_file#get_orders_data_file(orders_file)\n portvals = run_with_timeout(compute_portvals,seconds_per_test_case,(fullpath_orders_file,start_val),{})\n # Verify against expected outputs and assign points\n\n # * Check return type is correct, coax into Series\n assert (type(portvals) == pd.Series) or (type(portvals) == pd.DataFrame and len(portvals.columns) == 1), \"You must return a Series or single-column DataFrame!\"\n if type(portvals) == pd.DataFrame:\n portvals = portvals[portvals.columns[0]] # convert single-column DataFrame to Series\n if group == 'basic':\n if len(portvals) != outputs['num_days']:\n incorrect=True\n msgs.append(\" Incorrect number of days: {}, expected {}\".format(len(portvals), outputs['num_days']))\n else:\n points_earned += 2.0\n if abs(portvals[-1]-outputs['last_day_portval']) > (0.001*outputs['last_day_portval']):\n incorrect=True\n msgs.append(\" Incorrect final value: {}, expected {}\".format(portvals[-1],outputs['last_day_portval']))\n else:\n points_earned += 5.0\n adr,sr = get_stats(portvals)\n if abs(sr-outputs['sharpe_ratio']) > abs(0.001*outputs['sharpe_ratio']):\n incorrect=True\n msgs.append(\" Incorrect sharpe ratio: {}, expected {}\".format(sr,outputs['sharpe_ratio']))\n else:\n points_earned += 1.5\n if abs(adr-outputs['avg_daily_ret']) > abs(0.001*outputs['avg_daily_ret']):\n incorrect=True\n msgs.append(\" Incorrect avg daily return: {}, expected {}\".format(adr,outputs['avg_daily_ret']))\n else:\n points_earned += 1.0\n elif group=='leverage':\n if abs(portvals[-1]-outputs['last_day_portval']) > (0.001*outputs['last_day_portval']):\n incorrect = True\n msgs.append(\" Incorrect final value: {}, expected {}\".format(portvals[-1],outputs['last_day_portval']))\n else:\n points_earned += 1.0\n if incorrect:\n # inputs_str = \" orders_file: {}\\n\" \\\n # \" start_val: {}\\n\".format(orders_file, start_val)\n raise IncorrectOutput, \"Test failed on one or more output criteria.\\n Inputs:\\n{}\\n Failures:\\n{}\".format(inputs, \"\\n\".join(msgs))\n except Exception as e:\n # Test result: failed\n msg = \"Test case description: {}\\n\".format(description)\n \n # Generate a filtered stacktrace, only showing erroneous lines in student file(s)\n \n tb_list = tb.extract_tb(sys.exc_info()[2])\n if 'grading_traceback' in dir(e):\n tb_list = e.grading_traceback\n for i in xrange(len(tb_list)):\n row = tb_list[i]\n tb_list[i] = (os.path.basename(row[0]), row[1], row[2], row[3]) # show only filename instead of long absolute path\n tb_list = [row for row in tb_list if row[0] == 'marketsim.py']\n if tb_list:\n msg += \"Traceback:\\n\"\n msg += ''.join(tb.format_list(tb_list)) # contains newlines\n msg += \"{}: {}\".format(e.__class__.__name__, e.message)\n\n # Report failure result to grader, with stacktrace\n grader.add_result(GradeResult(outcome='failed', points=points_earned, msg=msg))\n raise\n else:\n # Test result: passed (no exceptions)\n grader.add_result(GradeResult(outcome='passed', points=points_earned, msg=None))","def rtest_predictoutcome():\n\n #define cohort size\n npatients = 2\n\n #init healthy patients\n simulator = AbbcEnvironment(patients=npatients)\n\n #simulate healthy patients for long term in short term increments\n nstep = int(long_term/short_term)\n\n #define action taken : -1 means patients will be simulated as healthy\n action = np.repeat(-1, npatients)\n\n #init episode list\n episode = [simulator.state]\n\n #main simulation loop to generate episodes\n for step in range(nstep):\n episode += simulator.take_action(action=action, simtime=short_term)\n\n #episode length is 1+2*nstep consisting of intit state (5xnpat) followed by\n # next state and reward (1xnpat) repeating each time step.\n #print(episode)\n #print(len(episode))\n\n #---semi gradient temporal difference (0) algorithm ---\n #init hyperparameters\n alpha = .1 #learning rate\n #init Value function model\n agent = AbbcAgent(discount=1.0)\n #loop over episodes\n for patient in range(npatients):\n #state = [nstep]\n #state += episode[0][:,patient] #get inital state\n state = np.append(episode[0][:,patient],nstep).reshape((6,1)) #get inital state\n\n print(state)\n #loop over time steps in episode\n for k in range(1,nstep+1):\n #get next state and reward\n #nextstate = [nstep-k]\n #nextstate = episode[k*2-1][:,patient]\n nextstate = np.append(episode[k*2-1][:,patient],nstep-k).reshape((6,1))\n\n reward = episode[k*2][patient]\n\n #get magnitude for forces\n magnitude = alpha * (reward + agent.discount * agent.get_value(nextstate)\n - agent.get_value(state))\n #compute forces\n forces = computeforces(agent.prednet, state, 0, \"iden\")\n\n #update model\n for layer in forces:\n index = layer[\"layer\"]\n agent.prednet[index][\"weight\"] += magnitude * layer[\"fweight\"]\n agent.prednet[index][\"bias\"] += magnitude * layer[\"fbias\"]\n\n state = np.copy(nextstate)\n\n\n #make predictions\n state = np.append(episode[0][:,patient],nstep).reshape((6,1)) #get inital state\n print(agent.get_value(state))\n\n #Value function approximates outcome return at time horizon.\n assert(False)\n\n ##define action taken\n #action = np.repeat(2, npatients)\n ##main simulation loop\n #for step in range(nstep):\n # _, drugy_reward[step,:] = simulator.take_action(action=action, simtime=short_term)","def MonteCarlo(data, iterations, strike, maturity):\n\n log_returns = np.log(1 + data.pct_change())\n\n rf = 0.025 # risk-free rate\n\n stdv = log_returns.std() * np.sqrt(250) # Annualized standard deviation\n\n T = maturity # set the maturity\n t_intervals = 250 \n delta_t = T / t_intervals\n\n iterations = iterations \n\n Z = np.random.standard_normal((t_intervals+1, iterations))\n S = np.zeros_like(Z) # price matrix\n\n # set the first row of the price matrix to be the last price in the dataframe\n S[0] = data.iloc[-1]\n\n # Perform simulations\n for t in range(1, t_intervals + 1):\n S[t] = S[t - 1] * np.exp((rf - 0.5 * stdv**2)\n * delta_t + stdv*np.sqrt(delta_t)*Z[t])\n\n # Calculate Payoff\n strike = strike\n pyff = np.maximum(S[-1] - strike, 0)\n\n C = np.exp(-rf * T) * np.sum(pyff) / iterations # calculate call oprice\n print(\"The price of the call option on %s is:\" % data.name, C)\n\n # Visulize Monte Carlo\n fig = plt.figure(figsize=(9, 6))\n plt.xlim(0, t_intervals)\n plt.grid(True)\n plt.xlabel('Days')\n plt.ylabel('Price')\n plt.title('Monte Carlo Simulation')\n plt.plot(S[:,:])\n plt.show()\n\n return C","def evaluate_benchmarks(self):\n\n # iterate over replicates\n results = {}\n for replicate_id, replicate in self.replicates:\n\n # evaluate benchmark for current replicate\n bmark = SimulationBenchmark(replicate.copy(),\n graph=self.graphs[replicate_id],\n **self.params)\n\n # store results\n results[replicate_id] = dict(\n\n labels_MAE=bmark.scores['labels'].MAE,\n level_only_MAE=bmark.scores['level_only'].MAE,\n spatial_only_MAE=bmark.scores['spatial_only'].MAE,\n community_MAE=bmark.scores['labels_comm'].MAE,\n\n labels_PCT=bmark.scores['labels'].percent_correct,\n level_only_PCT=bmark.scores['level_only'].percent_correct,\n spatial_only_PCT=bmark.scores['spatial_only'].percent_correct,\n community_PCT=bmark.scores['labels_comm'].percent_correct)\n\n # compile dataframe\n results = pd.DataFrame.from_dict(results, orient='index')\n results.index.set_names(self.multiindex, inplace=True)\n\n return results","def performance_vs_index(self, index='SPY', dateIni='Ini', dateFin='Fin'):\n if dateFin == 'Fin':\n dateFin = self.data.index[-1]\n if dateIni == 'Ini':\n dateIni = self.data.index[0]\n portfolioGains = round(self.data.loc[self.data.index[-1], 'Profit/Loss%'], 2)\n else:\n pData = self.data.loc[dateIni:dateFin]\n pData.loc[:,'Profit/Loss'] = pData['Gains'].cumsum()\n pData.loc[:,'Profit/Loss%'] = pData['Profit/Loss'] / pData['Invested'] * 100\n portfolioGains = round(pData.loc[pData.index[-1], 'Profit/Loss%'], 2)\n indexData = yf.Ticker(index).history(start=dateIni, end=dateFin)\n indexData['Var%'] = (indexData.Close - indexData.Close[0]) / indexData.Close[0] * 100\n indexGains = round(indexData.loc[indexData.index[-1], 'Var%'], 2)\n return portfolioGains, indexGains, portfolioGains - indexGains","def portfolio_returns(self,weights,returns):\n \n return weights.T @ returns","def optimize(\n target_name: str,\n setting: dict,\n start: datetime,\n end: datetime,\n rate: float,\n slippage: float,\n size: float,\n pricetick: float,\n capital: int,\n):\n backtest_me = BacktestMainEngine()\n backtest_me.set_parameters(\n start=start,\n end=end,\n rate=rate,\n slippage=slippage,\n size=size,\n pricetick=pricetick,\n capital=capital)\n backtest_me.setting = setting\n # 启动价差引擎\n backtest_me.engines['St'].start()\n # 显示回测结果\n df = backtest_me.calculate_result()\n statistics = backtest_me.calculate_statistics()\n backtest_me.save_chart(setting)\n for k, v in setting.items():\n statistics[k] = v\n # statistics['start'] = start.date()\n # statistics['end'] = end.date()\n # statistics['rate'] = rate\n # statistics['slippage'] = slippage\n # statistics['size'] = size\n # statistics['pricetick'] = pricetick\n statistics['capital'] = capital\n statistics['capital'] = capital\n if statistics['max_ddpercent']:\n statistics['收益回撤比'] = statistics['total_return'] / statistics['max_ddpercent']\n if statistics['profit_days'] + statistics['loss_days']:\n statistics['胜率'] = statistics['profit_days'] / (statistics['profit_days'] + statistics['loss_days'])\n return statistics","def calculate_performance(controls_f, df, pop_type, neg, output):\n\n print('\\n Calculating performance...')\n log_write(output['log'], '\\n\\nPerformance based on results per %s\\n\\n' % pop_type)\n\n # Remove strains with missing penetrance and few cell count\n df = df.iloc[df['Penetrance'].isna().values == 0, :]\n df = df[df['Num_cells'] >= 15].reset_index(drop=True)\n\n # Plot penetrance distribution\n plt.figure(figsize=(6, 6))\n sns.set(font_scale=1.25)\n sns.set_style('white')\n sns.kdeplot(df['Penetrance'].values, color='mediumblue', shade=True)\n plt.xlabel('Penetrance (%)')\n mean_penetrance = df['Penetrance'].mean()\n plt.title('Mean penetrance: %.4f' % mean_penetrance)\n fig = plt.gcf()\n fig.savefig(output['Penetrance'].replace('.', '_%s.' % pop_type), dpi=150, bbox_inches='tight')\n fig.clf()\n plt.close(fig)\n log_write(output['log'], 'Mean penetrance: %.2f\\n' % mean_penetrance)\n log_write(output['log'], 'Mean WT penetrance: %.2f\\n' % df[df['Strain ID'].isin(neg)]['Penetrance'].mean())\n\n # Plot WT percentile distribution\n plt.figure(figsize=(6, 6))\n sns.set(font_scale=1.25)\n sns.set_style('white')\n sns.kdeplot(df['WT_percentile_at_threshold'].values, color='mediumblue', shade=True)\n plt.xlabel('WT Percentile at the score of maximum difference')\n mean_percentile = df['WT_percentile_at_threshold'].mean()\n plt.title('Mean percentile: %.4f' % mean_percentile)\n fig = plt.gcf()\n fig.savefig(output['WT_Percentile'].replace('.', '_%s.' % pop_type), dpi=150, bbox_inches='tight')\n fig.clf()\n plt.close(fig)\n log_write(output['log'], 'Mean WT percentile at threshold: %.2f\\n' % mean_percentile)\n\n # Plot correlation\n plt.figure(figsize=(6, 6))\n sns.set(font_scale=1.25)\n sns.set_style('white')\n limits = [-0.1, 100.1]\n ticks = [0, 20, 40, 60, 80, 100]\n sns.lineplot(x=limits, y=limits, color='k', dashes=True, linewidth=1)\n sns.scatterplot(x='Penetrance', y='KS_Penetrance', color='mediumblue', data=df, s=60, alpha=0.5, linewidth=0)\n plt.xlim(limits)\n plt.xticks(ticks)\n plt.ylim(limits)\n plt.yticks(ticks)\n plt.xlabel('Penetrance')\n plt.ylabel('KS Penetrance')\n correlation = stats.pearsonr(df['Penetrance'], df['KS_Penetrance'])[0]\n plt.title('Correlation: %.4f' % correlation)\n fig = plt.gcf()\n fig.savefig(output['KS_Correlation'].replace('.', '_%s.' % pop_type), dpi=150, bbox_inches='tight')\n fig.clf()\n plt.close(fig)\n log_write(output['log'], 'KS correlation: %.4f\\n' % correlation)\n\n # Get positive controls\n if controls_f:\n df_cont = pd.read_csv(controls_f, low_memory=False)\n pos = df_cont['Strain ID'].values\n\n # Calculate performance with maximum difference\n aupr, aupr_b, auroc = calculate_auc(df, neg, pos)\n correlation = plot_penetrance_agreement(df, df_cont, neg,\n output['PeneAgreement'].replace('.', '_%s.' % pop_type))\n log_write(output['log'], 'AUPR: %.4f\\n' % aupr)\n log_write(output['log'], 'AUPR balanced: %.4f\\n' % aupr_b)\n log_write(output['log'], 'AUROC: %.4f\\n' % auroc)\n log_write(output['log'], 'Correlation: %.4f\\n' % correlation)\n\n # Plot penetrance of controls\n plt.figure(figsize=(12, 4))\n sns.set(font_scale=1.25)\n sns.set_style('white')\n plt.scatter(x=df[df['Strain ID'].isin(neg)].index.values,\n y=df[df['Strain ID'].isin(neg)]['Penetrance'].values,\n color='dodgerblue', alpha=0.3, label='Negative control', s=20)\n plt.scatter(x=df[df['Strain ID'].isin(pos)].index.values,\n y=df[df['Strain ID'].isin(pos)]['Penetrance'].values,\n color='red', alpha=0.7, label='Positive control', s=20)\n plt.xticks([])\n plt.yticks([0, 25, 50, 75, 100])\n plt.xlabel('Genes')\n plt.ylabel('Penetrance (%)')\n plt.legend(loc='upper right')\n plt.savefig(output['PenetranceControls'].replace('.', '_%s.' % pop_type),\n dpi=150, bbox_inches='tight')","def compare(numberOfSimulations, initialValue, sigma, T, strike, r = 0):\n \n numberOfTests = 100\n \n \n #the two lists that will contain our average errors for the different tests\n errorsStandardMonteCarlo = []\n errorsMonteCarloWithAV = []\n \n #our benchmark: the analytic price of the call option\n analyticPriceBS = blackScholesPriceCall(initialValue, r, sigma, T, initialValue)\n\n #look at how lambda functions are defined in Python \n payoff = lambda x : max(x - initialValue, 0)\n \n #alternatively:\n #def payoff(x):\n # return max(x - initialValue, 0)\n \n #note how to construct an object of a class\n \n blackScholesGenerator = GenerateBlackScholes(numberOfSimulations, T, initialValue, sigma, r)\n #k=0,..,numberOfTests - 1\n for k in range(numberOfTests):\n \n \n\n #first, the valuation with the standard Monte-Carlo:\n realizationsOfTheProcessWithStandardMC = blackScholesGenerator.generateRealizations()\n \n priceCalculatorWithStandardMC = SimpleEuropeanOption(realizationsOfTheProcessWithStandardMC, r)\n priceStandardMC = priceCalculatorWithStandardMC.getPrice(payoff, T) \n errorWithStandardMC= abs(priceStandardMC - analyticPriceBS)/analyticPriceBS\n #note how to append new values to a list\n errorsStandardMonteCarlo.append(errorWithStandardMC)\n \n #then, the one with Antithetic Variables:\n realizationsOfTheProcessWithAV = blackScholesGenerator.generateRealizationsAntitheticVariables()\n \n priceCalculatorWithAV = SimpleEuropeanOption(realizationsOfTheProcessWithAV, r)\n priceWithAV = priceCalculatorWithAV.getPrice(payoff, T) \n errorWithAV= abs(priceWithAV - analyticPriceBS)/analyticPriceBS\n #note how to append new values to a list\n errorsMonteCarloWithAV.append(errorWithAV)\n\n #we get and return the respective average errors: mean is imported from\n #statistics\n averageErrorStandardMC = mean(errorsStandardMonteCarlo)\n #you can also use numpy\n averageErrorAV = np.mean(errorsMonteCarloWithAV)\n \n return averageErrorStandardMC, averageErrorAV","def backtest(start=None, end=None,\n capital=100000, leverage=1, commission=0,\n slippage=0, price='open', stop_loss=0, order='B',\n universe='all', limit=5, columns=None, conditions=None,\n sort_by='price', sort_mode=True,\n connection=None, tablename=None,\n where_clause=None, data=None,\n strategy=None, output=None): \n if (data is None) and ((tablename is None) or (connection is None)):\n raise ValueError('No proper data source.\\nEither specify a database connection and tablename or provide a dataframe')\n\n if data is None:\n data = fetch_data(universe=universe, start=start, end=end,\n connection=connection, tablename=tablename,\n where_clause=where_clause)\n\n # Check whether any data is available\n isNotEmpty = lambda x: True if len(data) > 0 else False\n\n if isNotEmpty(data):\n data = prepare_data(data, columns) \n final = apply_prices(data, conditions, price, stop_loss, order) \n else:\n raise ValueError('No data fetched from database')\n\n if isNotEmpty(final): \n result = run_strategy(final, sort_by, sort_mode, limit, strategy)\n else:\n raise ValueError('No data after filtering all conditions')\n\n if output:\n return output(result)\n else:\n return get_output(result, capital, leverage, commission, slippage)","def update_portfolio(self, portfolio: PortfolioController):\n now = portfolio.get_history(seconds_back=0)\n future = portfolio.get_history(seconds_back=-self.update_interval)\n\n for fund in portfolio.funds:\n best_currency = max(portfolio.currencies, key=lambda currency: future_value(fund, currency, now, future))\n if best_currency != fund.currency:\n portfolio.request_transfer(fund, best_currency)","def tracking_error(benchmark_returns: pd.Series,\n portfolio_returns: pd.Series) -> float:\n return (benchmark_returns-portfolio_returns).std(ddof=1)","def hist_data(self):\n # database address\n jydb_address = \"117.122.223.35\"\n jydb_user_id = \"zpy\"\n jydb_user_pwd = \"Z1pe1y1@zpy\"\n jydb_db_name = \"jydb02\"\n # preallocate data set\n back_testing_data = {}\n \n # on-line mode\n conn = pymysql.connect(jydb_address, jydb_user_id, jydb_user_pwd, jydb_db_name)\n # not using moving average\n if not self.moving_average: \n # iterate on all ism packages\n for _i, ism_pack in enumerate(tqdm(self.ism_data)):\n ismcode = ism_pack[0][0]\n ProRunDate = ism_pack[0][5]\n ProStopDate = ism_pack[0][6]\n MaxStoreSum = ism_pack[0][7]\n MinInvestShare = ism_pack[0][8]\n InnerCode_ls = flatten(list(map(lambda x: [y[0] for y in x[1:]], ism_pack[1:])))\n SecuCode_ls = flatten(list(map(lambda x: [y[1] for y in x[1:]], ism_pack[1:])))\n PriorType_ls = flatten(list(map(lambda x: [y[2] for y in x[1:]], ism_pack[1:])))\n # collect data from source conn\n flag_run = \"SELECT InnerCode, OpenPrice, ClosePrice FROM QT_DailyQuote WHERE \" + \\\n \"InnerCode IN (\" + \",\".join(InnerCode_ls) + \") AND \" + \\\n \"TradingDay=\\'\" + ProRunDate + \"\\'\"\n flag_stop = \"SELECT InnerCode, OpenPrice, ClosePrice FROM QT_DailyQuote WHERE \" + \\\n \"InnerCode IN (\" + \",\".join(InnerCode_ls) + \") AND \" + \\\n \"TradingDay=\\'\" + ProStopDate + \"\\'\"\n run_price = pd.read_sql(flag_run, conn)\n stop_price = pd.read_sql(flag_stop, conn)\n back_testing_data[ismcode] = pd.merge(run_price, stop_price, on='InnerCode', \n suffixes=('_run', '_stop'))\n else: # using moving average \n # iterate on all ism packages\n for _i, ism_pack in enumerate(self.ism_data):\n ismcode = ism_pack[0][0]\n ProRunDate = ism_pack[0][5]\n TradingDay_begin = former_market_date(ProRunDate, self.L_shift, conn)\n TradingDay_end = future_market_date(ProRunDate, self.R_shift, conn)\n InnerCode_ls = flatten(list(map(lambda x: [y[0] for y in x[1:]], ism_pack[1:])))\n SecuCode_ls = flatten(list(map(lambda x: [y[1] for y in x[1:]], ism_pack[1:])))\n PriorType_ls = flatten(list(map(lambda x: [y[2] for y in x[1:]], ism_pack[1:])))\n flag = \"SELECT InnerCode, TradingDay, OpenPrice, ClosePrice FROM QT_DailyQuote WHERE \" + \\\n \"InnerCode IN (\" + \",\".join(InnerCode_ls) + \") AND \" + \\\n \"TradingDay BETWEEN \\'\" + TradingDay_begin + \"\\' AND \\'\" + TradingDay_end + \"\\'\"\n back_testing_data[ismcode] = pd.read_sql(flag, conn)\n \n # close sql connection\n conn.close()\n \n return back_testing_data","def update_portfolio(self):\n if not self._dirty_portfolio:\n return\n\n portfolio = self._portfolio\n pt = self.position_tracker\n\n portfolio.positions = pt.get_positions()\n position_stats = pt.stats\n\n portfolio.positions_value = position_value = (\n position_stats.net_value\n )\n portfolio.positions_exposure = position_stats.net_exposure\n self._cash_flow(self._get_payout_total(pt.positions))\n\n start_value = portfolio.portfolio_value\n\n # update the new starting value\n portfolio.portfolio_value = end_value = portfolio.cash + position_value\n\n pnl = end_value - start_value\n if start_value != 0:\n returns = pnl / start_value\n else:\n returns = 0.0\n\n portfolio.pnl += pnl\n portfolio.returns = (\n (1 + portfolio.returns) *\n (1 + returns) -\n 1\n )\n\n # the portfolio has been fully synced\n self._dirty_portfolio = False","def test_stock_quantity_own_all(self):\n self.portfolio = './app/test/payloads/port_own_all.json'\n self.stock = ['ETH', 'BTC']\n self.stock_url = 'https://min-api.cryptocompare.com/data/pricemultifull?fsyms=ETH&tsyms=USD'\n\n expected_data = {'ETH': 1.01, 'BTC': 0.098}\n\n #Run stock_quantity on test data\n self.add_perf = PortfolioAddPerf(self.portfolio, self.stock, self.stock_url)\n return_data = self.add_perf.stock_quantity()\n\n self.assertEqual(expected_data, return_data)","def runDTBacktest(df, \n testname=None,\n input_calculators=input_calculators,\n output_calculators=output_calculators,\n trainLearner=trainDecisionTree,\n make_trades=stdConfidenceTrades,\n calc_gains=getBuySellGains):\n df_inds = runCalculators(df.close, input_calculators)\n df_outs = runCalculators(df.close, output_calculators)\n df = pd.concat([df, df_inds, df_outs], axis=1, join_axes=[df.index])\n df = df.dropna()\n df_inds = df[df_inds.columns]\n df_outs = df[df_outs.columns]\n \n train_split = 0.9\n train_in, test_in = splitData(df_inds, train_split)\n train_out, test_out = splitData(df_outs, train_split)\n \n decTree = trainLearner(train_in, train_out)\n preds = decTree.predict(test_in)\n test_out['preds'] = preds\n trades = make_trades(preds)\n \n test_df = pd.concat([df, test_in, test_out], axis=1, join_axes=[test_in.index])\n totalGain = calc_gains(test_df['close'], trades)\n \n return {\n 'testname':testname,\n 'gain' : totalGain,\n 'dt' : decTree,\n 'df':df,\n 'preds':preds,\n 'trades':trades,\n 'test_df':test_df\n }","def BayesPaperStats(maxIters, numRuns):\n\n comm = MPI.COMM_WORLD\n rank = comm.Get_rank()\n\n assert comm.Get_size() == numRuns, \"Please ensure there is one process running per run i.e \" + str(numRuns) + \" processes.\"\n \n problemBounds = {\"Bfield\": choco.uniform(10, 1300), \"T\": choco.uniform(50, 230), \"Btheta\": choco.uniform(0, 90), \"Etheta\": choco.uniform(0, 90)}\n\n # The target for each algorithm. This was determined by using the values in the literature, so there is clearly some deviation either due to the detuning or computation.\n globalFoM = 1.033\n\n if rank == 0:\n timeList = []\n iterationList = []\n\n # Set up the database for the chocolate optimiser.\n connection = choco.SQLiteConnection(\"sqlite:///bayes_paper_\" + str(rank) + \"_db.db\")\n\n # Define which solver will be used.\n solver = choco.Bayes(connection, problemBounds, utility_function = \"ei\", n_bootstrap = int(np.ceil(maxIters/10)), clear_db = True)\n\n # Clear the database. TODO: To do this?\n connection.clear()\n\n # Start timing.\n startTime = time.time()\n timeElapsed = None\n iterationSuccess = None\n\n # Start optimisation.\n for iteration in range(maxIters):\n\n # Make one suggestion.\n try:\n token, nextParams = solver.next()\n except:\n print(\"Error suggesting a new point. Here are the last set of parameters sampled:\")\n print(str(nextParams))\n print(\"Iteration number: \" + str(iteration))\n continue\n\n # Check what FoM this gives. Go negative as this is a minimisation routine.\n fEval = abs(FitnessPaper(**nextParams))\n\n # Update best FoM.\n if fEval >= globalFoM:\n # The algorithm has managed to surpass or equal the paper value.\n iterationSuccess = iteration\n timeElapsed = time.time() - startTime\n \n if rank == 0:\n iterationList.append(iterationSuccess)\n timeList.append(timeElapsed)\n\n break\n \n # Tell the optimiser about the result.\n solver.update(token, fEval)\n\n # Run complete. Send results to main process. Tags are unique identifiers.\n if rank != 0:\n comm.send(timeElapsed, dest = 0, tag = 1)\n comm.send(iterationSuccess, dest = 0, tag = 2)\n\n # Wait for all the processes to end.\n comm.Barrier()\n\n if rank == 0:\n # Aggregate the data.\n for process in range(comm.Get_size() - 1):\n # Get the data.\n individualTime = None\n individualTime = comm.recv(individualTime, source = process + 1, tag = 1)\n\n individualIter = None\n individualIter = comm.recv(individualIter, source = process + 1, tag = 2)\n\n if individualIter is not None:\n # Both values must therefore be non-null.\n iterationList.append(individualIter)\n timeList.append(individualTime)\n\n avgRuntime = np.average(timeList)\n avgIters = np.average(iterationList)\n try:\n\n fastestTime = np.min(timeList)\n\n except ValueError:\n \n # List is empty.\n fastestTime = float('NaN')\n\n numSuccess = len(iterationList)\n successRate = numSuccess/numRuns\n\n print(\"Bayesian optimisation paper testing complete! Here are the stats:\")\n print(\"Number of successful runs: \" + str(numSuccess) + \" (Success rate of \" + str(successRate) + \")\")\n print(\"Average iterations required for success: \" + str(avgIters))\n print(\"Average time required for success: \" + str(avgRuntime))\n print(\"Fastest convergence time: \" + str(fastestTime))\n print(\"------------------------------------------------------------------------------------------------------------------\")\n \n return","def run():\n return estimate(0,1,0)","def main():\n\n # choose number of data-points and sample a pair of vectors: the input\n # values and the corresponding target values\n N = 500\n inputs, targets = sample_data(N, arbitrary_function_2, seed=1)\n\n # specify the centres and scale of some rbf basis functions\n default_centres = np.linspace(0,1,21)\n default_scale = 0.03\n default_reg_param = 0.08\n\n # get the cross-validation folds\n num_folds = 4\n folds = create_cv_folds(N, num_folds)\n\n # evaluate then plot the performance of different reg params\n evaluate_reg_param(inputs, targets, folds, default_centres, default_scale)\n # evaluate then plot the performance of different scales\n evaluate_scale(inputs, targets, folds, default_centres, default_reg_param)\n # evaluate then plot the performance of different numbers of basis\n # function centres.\n evaluate_num_centres(\n inputs, targets, folds, default_scale, default_reg_param)\n\n plt.show()","def main() -> None:\n\n download_data_tools.initial_message()\n\n # S&P 500 companies, initial year and time step\n stocks: List[str] = download_data_tools.get_stocks(['all'])\n dates: List[str] = ['1992-01', '2012-12']\n time_step: str = '1d'\n\n # Basic folders\n download_data_tools.start_folders()\n\n # Run analysis\n # Download data\n portfolio_download_data(stocks, dates, time_step)\n\n print('Ay vamos!!!')","def main(args):\n\n # Compose the model list\n modellist = []\n if args['model']:\n modellist.append(bmark.ModelInfo(args['model'], os.getcwd(), args['classname']))\n\n # Load the benchmark settings\n benchmark = None\n benchmark = bmark.load_benchmark(args['benchmark'])\n corresponding_data = False\n if 'corresponding_data' in benchmark:\n corresponding_data = benchmark['corresponding_data']\n\n # Only extend if not cached\n cache_df = None\n if not args['cache']:\n modellist.extend(benchmark['models'])\n else:\n cache_df = pd.read_csv(args['cache'])\n\n # Extract comparator settings from benchmark description\n eval_comparator = comparator.EqualityComparator()\n if 'comparator' in benchmark:\n if benchmark['comparator'] == 'nvc':\n eval_comparator = comparator.NVCComparator()\n\n # Run the model evaluation\n is_silent = (args['output'] in ['html', 'server'])\n eva = None\n if benchmark['type'] == 'adaption':\n eva = evaluator.AdaptionEvaluator(\n modellist,\n eval_comparator,\n benchmark['data.test'],\n train_datafile=benchmark['data.train'],\n train_data_person=benchmark['data.train_person'],\n silent=is_silent,\n corresponding_data=corresponding_data,\n domain_encoders=benchmark['domain_encoders'],\n cache_df=cache_df\n )\n elif benchmark['type'] == 'coverage':\n # Check for benchmark validity\n if benchmark['data.train'] or benchmark['data.train_person']:\n print('WARNING: Ignoring specified training and train_person data ' \\\n + 'for coverage evaluation...')\n\n eva = evaluator.CoverageEvaluator(\n modellist,\n eval_comparator,\n benchmark['data.test'],\n train_datafile=benchmark['data.train'],\n train_data_person=benchmark['data.train_person'],\n silent=is_silent,\n corresponding_data=corresponding_data,\n domain_encoders=benchmark['domain_encoders'],\n cache_df=cache_df\n )\n else:\n raise ValueError('Unknown benchmark type: {}'.format(benchmark['type']))\n\n with silence_stdout(is_silent):\n res_df = eva.evaluate()\n\n if 'save' in args:\n res_df.to_csv(args['save'], index=False)\n\n # Run the metric visualizer\n htmlcrtr = html_creator.HTMLCreator([\n viz_plot.AccuracyVisualizer(),\n viz_plot.BoxplotVisualizer(),\n viz_plot.TableVisualizer()\n ])\n\n # Prepare the benchmark output information and visualize the evaluation results\n benchmark_info = {\n 'name': os.path.basename(args['benchmark']),\n 'data.train': os.path.basename(\n benchmark['data.train']) if benchmark['data.train'] else '',\n 'data.train_person': os.path.basename(\n benchmark['data.train_person']) if benchmark['data.train_person'] else '',\n 'data.test': os.path.basename(benchmark['data.test']),\n 'type': benchmark['type'],\n 'corresponding_data': benchmark['corresponding_data'],\n 'domains': list(res_df['domain'].unique()),\n 'response_types': list(res_df['response_type'].unique()),\n }\n\n if args['output'] == 'browser':\n html = htmlcrtr.to_html(res_df, benchmark_info, embedded=False)\n server.load_in_default_browser(html.encode('utf8'))\n elif args['output'] == 'server':\n html = htmlcrtr.to_html(res_df, benchmark_info, embedded=True)\n sys.stdout.buffer.write(html.encode('utf-8'))\n elif args['output'] == 'html':\n html = htmlcrtr.to_html(res_df, benchmark_info, embedded=False)\n print(html)","def optimizeForReturn(required_return, stock_db, use_genetic):\n print('Optimizing portfolio for %f' % required_return)\n pf = PortfolioFactory(stock_db, required_return, use_genetic=use_genetic)\n desired_portfolio = pf.desired_portfolio\n print('Required Return: %f' % required_return)\n print('Expected Return: %f' % math.pow(\n desired_portfolio.average_return, Config.DAYS_IN_YEAR))\n print('Downside Risk: %f' % desired_portfolio.downside_risk)\n print('Downside Correl: %f' % desired_portfolio.downside_correl)\n print('Score: %f' % desired_portfolio.score)\n\n # Write desired portfolio.\n DataIO.writeDesiredPortfolio(\n desired_portfolio, stock_db,\n 'output/DesiredPortfolio_%.0f_%.4f_%s.csv' % (\n Config.MINIMUM_AMOUNT_DATA, required_return, Config.TODAY.date()))\n\n print('Finished for %f' % required_return)\n\n return desired_portfolio","def ttest():\n # open test results and perform regression analysis\n alphas = []\n betas = []\n iterations = {}\n with open(f\"Results/conclusion2.csv\") as f:\n csv_reader = csv.reader(f, delimiter=',')\n\n for run in csv_reader:\n max, max_i = get_max_run(run)\n if int(run[0]) not in iterations:\n iterations[int(run[0])] = {100 - int(run[1])-1: int(max)}\n else:\n iterations[int(run[0])][100 - int(run[1])-1] = int(max)\n\n for iteration in iterations:\n mono_levels = list(iterations[iteration].keys())\n pop_sizes = [iterations[iteration][i] for i in mono_levels]\n\n regress_result = regress(pop_sizes, mono_levels)\n alphas += [regress_result[1]]\n betas += [regress_result[0]]\n\n # plot scatter and regression line\n avg_alpha = sum(alphas)/len(alphas)\n avg_beta = sum(betas)/len(betas)\n stddev_beta = np.std(betas)\n vis.scatter_mono(iterations, avg_alpha, avg_beta)\n\n # perform t-test\n ttest_result = stats.ttest_ind(betas, [0 for i in betas], equal_var=True)\n t_stat = ttest_result[0]\n p_value = ttest_result[1]\n print(f'Results from t-test:')\n print(f'Avg beta: {avg_beta}, stddev beta: {stddev_beta}.')\n print(f't-stat: {t_stat}, p-value: {p_value}.')","def print_portfolio_info(returns, avg_rets, weights):\n ret = (weights * avg_rets).sum()\n std = (weights * returns).sum(1).std()\n sharpe = ret / std\n print(\"Optimal weights:\\n{}\\n\".format(weights))\n print(\"Expected return: {}\".format(ret))\n print(\"Expected variance: {}\".format(std ** 2))\n print(\"Expected Sharpe: {}\".format(sharpe))","def main(args):\n\n print(now(), \"benchmark_test.py running.\")\n out_dir = args.output\n if not args.from_scratch:\n print(\n f\"Testing poisons from {args.poisons_path}, in the transfer learning setting...\\n\".format()\n )\n\n ####################################################\n # Frozen Feature Extractor (ffe)\n print(\"Frozen Feature Extractor test:\")\n args.num_poisons = 25\n args.trainset_size = 2500\n args.val_period = 20\n args.optimizer = \"SGD\"\n args.lr = 0.01\n args.lr_schedule = [30]\n args.epochs = 40\n\n args.end2end = False\n\n # white-box attack\n args.output = os.path.join(out_dir, \"ffe-wb\")\n args.model = \"resnet18\"\n args.model_path = whitebox_modelpath\n poison_test.main(args)\n\n # grey box attack\n args.model = \"resnet18\"\n args.model_path = greybox_modelpath\n args.output = os.path.join(out_dir, \"ffe-gb\")\n poison_test.main(args)\n\n # black box attacks\n args.output = os.path.join(out_dir, \"ffe-bb\")\n\n args.model = \"MobileNetV2\"\n args.model_path = blackbox_modelpath[0]\n poison_test.main(args)\n\n args.model_path = blackbox_modelpath[1]\n args.model = \"VGG11\"\n poison_test.main(args)\n ####################################################\n\n ####################################################\n # End-To-End Fine Tuning (e2e)\n print(\"End-To-End Fine Tuning test:\")\n args.num_poisons = 25\n args.trainset_size = 2500\n args.val_period = 20\n args.optimizer = \"SGD\"\n args.lr = 0.01\n args.lr_schedule = [30]\n args.epochs = 40\n\n args.end2end = True\n\n # white-box attack\n args.output = os.path.join(out_dir, \"e2e-wb\")\n args.model = \"resnet18\"\n args.model_path = whitebox_modelpath\n poison_test.main(args)\n\n # grey box attack\n args.model = \"resnet18\"\n args.model_path = greybox_modelpath\n args.output = os.path.join(out_dir, \"e2e-gb\")\n poison_test.main(args)\n\n # black box attacks\n args.output = os.path.join(out_dir, \"e2e-bb\")\n\n args.model = \"MobileNetV2\"\n args.model_path = blackbox_modelpath[0]\n poison_test.main(args)\n\n args.model = \"VGG11\"\n args.model_path = blackbox_modelpath[1]\n poison_test.main(args)\n ####################################################\n\n else:\n print(\n f\"Testing poisons from {args.poisons_path}, in the from scratch training setting...\\n\".format()\n )\n\n ####################################################\n # From Scratch Training (fst)\n args.num_poisons = 500\n args.trainset_size = 50000\n args.val_period = 20\n args.optimizer = \"SGD\"\n args.lr = 0.1\n args.lr_schedule = [100, 150]\n args.epochs = 200\n args.model_path = \"\"\n args.output = os.path.join(out_dir, \"fst\")\n\n args.model = \"resnet18\"\n poison_test.main(args)\n\n args.model = \"MobileNetV2\"\n poison_test.main(args)\n\n args.model = \"VGG11\"\n poison_test.main(args)\n ####################################################","def test_zero_lookback_window(self, mock_get_prices):\n class BuyBelow10(Moonshot):\n \"\"\"\n A basic test strategy that buys below 10.\n \"\"\"\n DB = 'test-db'\n LOOKBACK_WINDOW = 0\n\n def prices_to_signals(self, prices):\n signals = prices.loc[\"Close\"] < 10\n return signals.astype(int)\n\n def _mock_get_prices():\n\n dt_idx = pd.DatetimeIndex([\"2018-05-01\",\"2018-05-02\",\"2018-05-03\", \"2018-05-04\"])\n fields = [\"Close\",\"Volume\"]\n idx = pd.MultiIndex.from_product([fields, dt_idx], names=[\"Field\", \"Date\"])\n\n prices = pd.DataFrame(\n {\n \"FI12345\": [\n #Close\n 9,\n 11,\n 10.50,\n 9.99,\n # Volume\n 5000,\n 16000,\n 8800,\n 9900\n ],\n \"FI23456\": [\n # Close\n 9.89,\n 11,\n 8.50,\n 10.50,\n # Volume\n 15000,\n 14000,\n 28800,\n 17000\n\n ],\n },\n index=idx\n )\n\n return prices\n\n def mock_download_master_file(f, *args, **kwargs):\n\n master_fields = [\"Timezone\", \"Symbol\", \"SecType\", \"Currency\", \"PriceMagnifier\", \"Multiplier\"]\n securities = pd.DataFrame(\n {\n \"FI12345\": [\n \"America/New_York\",\n \"ABC\",\n \"STK\",\n \"USD\",\n None,\n None\n ],\n \"FI23456\": [\n \"America/New_York\",\n \"DEF\",\n \"STK\",\n \"USD\",\n None,\n None,\n ]\n },\n index=master_fields\n )\n securities.columns.name = \"Sid\"\n securities.T.to_csv(f, index=True, header=True)\n f.seek(0)\n\n mock_get_prices.return_value = _mock_get_prices()\n\n with patch(\"moonshot.strategies.base.download_master_file\", new=mock_download_master_file):\n results = BuyBelow10().backtest(start_date=\"2018-05-01\", end_date=\"2018-05-04\")\n\n get_prices_call = mock_get_prices.mock_calls[0]\n _, args, kwargs = get_prices_call\n self.assertListEqual(kwargs[\"codes\"], [\"test-db\"])\n self.assertEqual(kwargs[\"start_date\"], \"2018-05-01\")\n self.assertEqual(kwargs[\"end_date\"], \"2018-05-04\")\n self.assertEqual(kwargs[\"fields\"], ['Open', 'Close', 'Volume'])\n self.assertIsNone(kwargs[\"timezone\"])\n self.assertTrue(kwargs[\"infer_timezone\"])","def cAPM_Constant(df_train, df_test, MaxRPT_r1, MaxRPT, no_rounds):\n\n # number of periods in samples\n T = df_train.shape[0]\n T_test = df_test.shape[0]\n\n # number of agents\n K = df_test.shape[1]\n\n # individual forecasts\n F = df_train.iloc[:, 1:].values\n F_test = df_test.iloc[:, :].values\n\n # outcomes\n outcomes = df_train.iloc[:, 0].values\n\n # initialize matrices containing all predictions and bets\n pred_mat = np.full((no_rounds, K), np.nan, dtype=float)\n bet_mat = np.full((no_rounds, K), np.nan, dtype=float)\n\n # budget initialization\n budgets = np.full(K, 1, dtype=float)\n\n # training the market\n for i in range(T):\n\n # first round betting\n pred_mat[0, :] = F[i, :]\n bet_mat[0, :] = budgets * MaxRPT_r1\n budgets = budgets * (1-MaxRPT_r1)\n\n # rest of rounds betting\n for j in range(1, no_rounds):\n\n pred_mat[j, :] = F[i, :]\n bet_mat[j, :] = budgets * MaxRPT\n budgets = budgets * (1-MaxRPT)\n\n # use IQR to determine outlier error threshold\n abs_errors = np.abs(outcomes[i] - pred_mat)\n Q3, Q1 = np.percentile(abs_errors, [75, 25])\n IQR = Q3 - Q1\n oet = Q3 + (IQR*1.5)\n\n # reward the agents based on accuracy of their predictions\n accuracy = np.maximum(100 * (1 - abs_errors/oet), 1)\n score = np.log(accuracy)\n revenue = np.multiply(score, bet_mat)\n budgets = budgets + np.sum(revenue, axis=0)\n\n # rescaling the budget (only for computational reasons)\n budgets = budgets/np.sum(budgets)\n\n # using the market for the out-of-sample predictions\n pred = np.full(T_test, np.nan, dtype=float)\n for i in range(T_test):\n\n # reset test budget\n budgets_test = np.copy(budgets)\n\n # first round betting\n pred_mat[0, :] = F_test[i, :]\n bet_mat[0, :] = budgets_test * MaxRPT_r1\n budgets_test = budgets_test * (1-MaxRPT_r1)\n\n # rest of rounds betting\n for j in range(1, no_rounds):\n\n pred_mat[j, :] = F_test[i, :]\n bet_mat[j, :] = budgets_test * MaxRPT\n budgets_test = budgets_test * (1-MaxRPT)\n\n # final round market prediction is the c-APM prediction\n pred[i] = np.dot(\n pred_mat[no_rounds-1, :],\n bet_mat[no_rounds-1, :])/np.sum(bet_mat[no_rounds-1, :])\n\n # output\n df_pred = pd.DataFrame(\n {\"c-APM (Constant)\": pred},\n index=df_test.index\n )\n\n return df_pred","def test_class():\n riskfree = .03\n maturity = 30/365\n moneyness = np.linspace(-.04, .04, 10)\n premium = np.ones_like(moneyness) * .05\n call = True\n data = {'riskfree': riskfree, 'maturity': maturity,\n 'moneyness': moneyness, 'call': call, 'premium': premium}\n\n sigma = .13\n bsm = BSmodel(sigma, data)\n\n weights = [.63]\n means = [-.01, .09]\n stds = [.16, .05]\n param = weights + means + stds\n mbs = MBSmodel(param, data)\n\n param_a, param_p, param_c = 4, 1.5, -.05\n gb2 = GB2model([param_a, param_p, param_c], data)\n print(gb2.get_pnames())\n\n plt.figure()\n for model in [bsm, mbs, gb2]:\n plt.plot(moneyness, model.density(moneyness), label=model.get_name())\n plt.legend()\n plt.show()\n\n plt.figure()\n for model in [bsm, mbs, gb2]:\n plt.plot(moneyness, model.premium(), label=model.get_name())\n plt.legend()\n plt.show()\n\n plt.figure()\n for model in [bsm, mbs, gb2]:\n plt.plot(moneyness, model.impvol(), label=model.get_name())\n plt.legend()\n plt.show()\n\n print('BS objective function = %.4f' % bsm.objective(sigma))\n print('GB2 objective function = %.4f'\n % gb2.objective([param_a, param_p, param_c]))","def full_screen(self,ploton=False):\n self._screener_init() # initialise the model with a single expensive test\n\n for i in range(self.nthreads): # at the start, give the workers a job to do each\n self._select_and_run_experiment(i)\n\n while self.model.b >= self.cy: # spend budget till cant afford any more expensive tests\n i = self._record_experiment(final=False)\n self._select_and_run_experiment(i)\n if ploton:\n self.model.plot(self.model.x,self.y,self.z)\n\n for i in range(self.nthreads): # finish up any remaining jobs and record their results\n self._record_experiment(final=True)\n\n return self.history","def run_simulation(n, experiments, iterations, budget, recovery_count, performance_factor, current_top,\r\n precomputed=True, dataset=None):\r\n T = 0\r\n k = 5\r\n delta = 0.1\r\n range_m = 1*np.arange(1, budget+1)\r\n scores, true_top = init(n, precomputed=precomputed, dataset=dataset)\r\n true_ranks = get_ranks(scores)\r\n\r\n for exp in tqdm.tqdm(range(experiments), desc=\"experiments\"):\r\n for itr in tqdm.tqdm(range(iterations), desc=\"iterations\"):\r\n t = 1\r\n count = 0\r\n A = np.arange(n)\r\n P = np.zeros((n, n))\r\n S = np.zeros(n)\r\n r_count = 0\r\n data = []\r\n for b in range(budget):\r\n m = range_m[b]\r\n\r\n est, t, A, P, S, count, r_count, data = sparse_borda(n, t, T, k, A, P, S, delta, scores, m*n, count, r_count, data)\r\n\r\n ranking, ranks, top = get_ranking(n, est)\r\n if(top == true_top):\r\n recovery_count[b][exp] += 1\r\n performance_factor[b][exp] += ranks[true_top]\r\n current_top[b][exp] += true_ranks[top]\r\n\r\n current_top /= iterations\r\n performance_factor /= iterations\r\n\r\n return ranking, ranks, data, scores, true_top, est, recovery_count, performance_factor, current_top","def run(self):\n results = self._optimization.run()\n self._optimization.sim.fdtd.close()\n \n # plot optimization recap figure\n plt.show()\n \n return [results[0], np.array(results[1])]","def main():\n trades = get_trades()\n _print_trades(trades)\n\n print(\"\\n# Cost basis per asset\")\n _cost_basis_per_asset(trades)\n\n for year in range(2015, 2019):\n trades_for_year = _filter_trades_by_time(trades, year)\n _print_balances(trades_for_year, year)\n _print_agg_trades(trades_for_year, year)","def test_run_beta_diversity_through_plots_parallel(self):\r\n run_beta_diversity_through_plots(\r\n self.test_data['biom'][0],\r\n self.test_data['map'][0],\r\n self.test_out,\r\n call_commands_serially,\r\n self.params,\r\n self.qiime_config,\r\n tree_fp=self.test_data['tree'][0],\r\n parallel=True,\r\n status_update_callback=no_status_updates)\r\n\r\n unweighted_unifrac_dm_fp = join(\r\n self.test_out,\r\n 'unweighted_unifrac_dm.txt')\r\n weighted_unifrac_dm_fp = join(self.test_out, 'weighted_unifrac_dm.txt')\r\n unweighted_unifrac_pc_fp = join(\r\n self.test_out,\r\n 'unweighted_unifrac_pc.txt')\r\n weighted_unifrac_pc_fp = join(self.test_out, 'weighted_unifrac_pc.txt')\r\n weighted_unifrac_html_fp = join(self.test_out,\r\n 'weighted_unifrac_emperor_pcoa_plot', 'index.html')\r\n\r\n # check for expected relations between values in the unweighted unifrac\r\n # distance matrix\r\n dm = parse_distmat_to_dict(open(unweighted_unifrac_dm_fp))\r\n self.assertTrue(dm['f1']['f2'] < dm['f1']['p1'],\r\n \"Distance between pair of fecal samples is larger than distance\"\r\n \" between fecal and palm sample (unweighted unifrac).\")\r\n self.assertEqual(dm['f1']['f1'], 0)\r\n # check for expected relations between values in the weighted unifrac\r\n # distance matrix\r\n dm = parse_distmat_to_dict(open(weighted_unifrac_dm_fp))\r\n self.assertTrue(dm['f1']['f2'] < dm['f1']['p1'],\r\n \"Distance between pair of fecal samples is larger than distance\"\r\n \" between fecal and palm sample (unweighted unifrac).\")\r\n self.assertEqual(dm['f1']['f1'], 0)\r\n\r\n # check that final output files have non-zero size\r\n self.assertTrue(getsize(unweighted_unifrac_pc_fp) > 0)\r\n self.assertTrue(getsize(weighted_unifrac_pc_fp) > 0)\r\n self.assertTrue(getsize(weighted_unifrac_html_fp) > 0)\r\n\r\n # Check that the log file is created and has size > 0\r\n log_fp = glob(join(self.test_out, 'log*.txt'))[0]\r\n self.assertTrue(getsize(log_fp) > 0)","def test_run_beta_diversity_through_plots(self):\r\n run_beta_diversity_through_plots(\r\n self.test_data['biom'][0],\r\n self.test_data['map'][0],\r\n self.test_out,\r\n call_commands_serially,\r\n self.params,\r\n self.qiime_config,\r\n tree_fp=self.test_data['tree'][0],\r\n parallel=False,\r\n status_update_callback=no_status_updates)\r\n\r\n unweighted_unifrac_dm_fp = join(\r\n self.test_out,\r\n 'unweighted_unifrac_dm.txt')\r\n weighted_unifrac_dm_fp = join(self.test_out, 'weighted_unifrac_dm.txt')\r\n unweighted_unifrac_pc_fp = join(\r\n self.test_out,\r\n 'unweighted_unifrac_pc.txt')\r\n weighted_unifrac_pc_fp = join(self.test_out, 'weighted_unifrac_pc.txt')\r\n weighted_unifrac_html_fp = join(self.test_out,\r\n 'weighted_unifrac_emperor_pcoa_plot', 'index.html')\r\n\r\n # check for expected relations between values in the unweighted unifrac\r\n # distance matrix\r\n dm = parse_distmat_to_dict(open(unweighted_unifrac_dm_fp))\r\n self.assertTrue(dm['f1']['f2'] < dm['f1']['p1'],\r\n \"Distance between pair of fecal samples is larger than distance\"\r\n \" between fecal and palm sample (unweighted unifrac).\")\r\n self.assertEqual(dm['f1']['f1'], 0)\r\n # check for expected relations between values in the weighted unifrac\r\n # distance matrix\r\n dm = parse_distmat_to_dict(open(weighted_unifrac_dm_fp))\r\n self.assertTrue(dm['f1']['f2'] < dm['f1']['p1'],\r\n \"Distance between pair of fecal samples is larger than distance\"\r\n \" between fecal and palm sample (unweighted unifrac).\")\r\n self.assertEqual(dm['f1']['f1'], 0)\r\n\r\n # check that final output files have non-zero size\r\n self.assertTrue(getsize(unweighted_unifrac_pc_fp) > 0)\r\n self.assertTrue(getsize(weighted_unifrac_pc_fp) > 0)\r\n self.assertTrue(getsize(weighted_unifrac_html_fp) > 0)\r\n\r\n # Check that the log file is created and has size > 0\r\n log_fp = glob(join(self.test_out, 'log*.txt'))[0]\r\n self.assertTrue(getsize(log_fp) > 0)","def parameter_optimization(self):\n out = open(self.csv_dir + self.strategy_id + '_gridsearch.csv', \"w\")\n spl = len(self.para_list)\n for i, sp in enumerate(self.para_list):\n print(\"Strategy %s out of %s...\" % (i + 1, spl))\n self._generate_trading_instances(sp)\n self._run_backtest()\n stats = self.portfolio.get_statistics()\n tot_profit = float(stats[0][1])\n sharpe = float(stats[1][1])\n max_dd = float(stats[2][1])\n win_rate = float(stats[7][1].replace(\"%\", \"\"))\n profit_factor = float(stats[8][1])\n\n out.write(\n \"%s,%s,%s,%s,%s,%s,%s\\n\" %\n (sp[\"takeprofit\"], sp[\"period\"], tot_profit, sharpe, max_dd, win_rate, profit_factor)\n )\n out.close()","def main():\n\n convergence_rates_e1 = pd.DataFrame(index=['Mean', 'St.D'])\n convergence_rates_e4 = pd.DataFrame(index=['Mean', 'St.D'])\n success_rates = pd.DataFrame(index=['1e-1', '1e-2', '1e-3', '1e-4', '1e-5'])\n peak_ratios = pd.DataFrame(index=['1e-1', '1e-2', '1e-3', '1e-4', '1e-5'])\n\n for function in range(1, 21):\n results = pickle.load(open('results/benchmarking_result_{}.pkl'.format(function), 'rb'))\n\n col_name = 'F{}'.format(function)\n index = function-1\n\n convergence_rates_e1.insert(index, col_name, results.ConvergenceRates[0])\n convergence_rates_e4.insert(index, col_name, results.ConvergenceRates[3])\n\n success_rates.insert(index, col_name, results.SuccessRate)\n peak_ratios.insert(index, col_name, results.PeakRatio)\n\n for i in results.SimulationSwarms:\n\n x_axis = [k for k, _ in i.items()]\n y_axis = [v for _, v in i.items()]\n\n plt.subplot(4, 5, function) # subplot indexes from 1\n plt.plot(x_axis, y_axis, 'k-')\n\n plt.savefig('results/nmmso_benchmark.png')\n plt.show()\n\n pd.set_option('display.max_columns', None) # make sure all columns are printed below\n print(convergence_rates_e1)\n print(convergence_rates_e4)\n print(success_rates)\n print(peak_ratios)\n\n # Table V from Fieldsend et al.\n table4 = pd.Series([\n peak_ratios.stack().median(),\n peak_ratios.stack().mean(),\n peak_ratios.stack().std()\n ])\n print(table4)\n\n # do we want to reproduce Fig. 3","def run_performance():\n # Create a Struct data instance from config\n inputs = Struct(config)\n inputs.throttle = throttle\n # Get oxidizer properties at the given temperature\n n2o = n2o_properties(inputs.ox.T_tank)\n # Our integration variables are oxidizer mass and liquid oxidizer volume\n Mox = n2o.rho_l*(inputs.ox.liquid_V) + n2o.rho_g*(inputs.ox.tank_V-inputs.ox.liquid_V)\n if inputs.options.output_on:\n print(\"Initial oxidizer mass: {} kg.\".format(Mox))\n\n start = time.perf_counter() # Start timer for integration\n\n time, record = integration(inputs) # Time = time for integration, record = output data\n F_thrust = record.F_thrust\n p_cc = record.p_cc\n p_oxtank = record.p_oxtank\n p_oxpresstank = record.p_oxpresstank\n p_fueltank = record.p_fueltank\n p_fuelpresstank = record.p_fuelpresstank\n p_oxmanifold = record.p_oxmanifold\n T_oxtank = record.T_oxtank\n T_cc = record.T_cc\n area_core = record.area_core\n OF = record.OF_i\n gamma_ex = record.gamma_ex\n m_dot_ox = record.m_dot_ox\n m_dot_fuel = record.m_dot_fuel\n p_crit = record.p_crit\n m_dot_ox_crit = record.m_dot_ox_crit\n M_e = record.M_e\n p_exit = record.p_exit\n p_shock = record.p_shock\n\n time_elapsed = start-time.perf_counter() # Stop the timer and print elapsed time\n if inputs.options.output_on:\n print(\"Time elapsed for this timestep: {} sec.\".format(time_elapsed))","def test_TRit(self):\n\n prices = self._convert_df_to_list(self.prices)\n currency = self._convert_df_to_list(self.currency)\n\n manual_total = self.list_multiplication(prices, currency)\n manual_calculated = list(self._manual_calculate_formal(manual_total))\n\n self.portfolio._generate_total()\n test_column = self._convert_df_to_list(self.portfolio._df_total)\n\n self.assertAlmostEqual(self._clear_column(manual_calculated),\n self._clear_column(test_column))"],"string":"[\n \"def backtest_portfolio(self):\\n self.rank=dict()\\n self.accuracy=dict()\\n portfolio = dict()\\n \\n for algo in self.algos:\\n portfolio[algo]=pd.DataFrame(index=self.positions.index)\\n self.pos_diff=dict()\\n self.pos_diff[algo] = self.positions[algo].diff()\\n \\n portfolio[algo]['price_diff'] = self.bars['Close']-self.bars['Open']\\n #portfolio['price_diff'][0:5] = 0.0\\n portfolio[algo]['profit'] = self.positions[algo] * portfolio[algo]['price_diff']\\n portfolio[algo]['total'] = self.initial_capital + portfolio[algo]['profit'].cumsum()\\n portfolio[algo]['returns'] = portfolio[algo]['total'].pct_change()\\n d=np.array(portfolio[algo]['profit']).copy()\\n d[d>0]=1\\n d[d<0]=0\\n d[np.array(self.positions[algo])==0]=1\\n for i in np.arange(1,len(d)+1):\\n c=float(sum(d[0:i]))/(i)\\n d[i-1]=c\\n portfolio[algo]['accuracy']=d\\n self.rank[algo]=float(portfolio[algo]['total'][-1] - portfolio[algo]['total'][0])\\n self.returns=portfolio\\n c=np.array(self.returns[algo]['profit'])\\n c[c>0]=1\\n c[c<0]=0\\n c[np.array(self.positions[algo])==0]=1\\n accuracy=round(float(c.sum())/len(c),2)*self.rank[algo]\\n self.accuracy[algo]=accuracy\\n #self.ranking= sorted(self.rank.items(), key=operator.itemgetter(1), reverse=True)\\n self.ranking= sorted(self.accuracy.items(), key=operator.itemgetter(1))\\n self.ready=True\\n return (portfolio, self.rank, self.ranking)\",\n \"def simulate_trading(self):\\n self._generate_trading_instances()\\n self._run_backtest()\\n self.portfolio.output_equity()\\n res=self.portfolio.get_statistics()\\n self.plot.plot_equity()\\n return res\",\n \"def backtest_portfolio(self):\\n raise NotImplementedError(\\\"Should implement backtest_portfolio()!\\\")\",\n \"def target_portfolio_simulation(num_of_years=30, trials=100, method='normal'):\\n print(\\\"Running method target_portfolio_simulation()\\\")\\n\\n sim_fia_cv = pd.DataFrame(index=range(num_of_years + 1))\\n\\n sim_base_total = pd.DataFrame(index=range(num_of_years + 1))\\n sim_base_income = pd.DataFrame(index=range(num_of_years + 1))\\n\\n sim_port_total = pd.DataFrame(index=range(num_of_years + 1))\\n sim_port_income = pd.DataFrame(index=range(num_of_years + 1))\\n\\n read_income_inputs = pd.read_excel(src + \\\"portfolio_information.xlsx\\\", sheet_name='income_model_inputs',\\n index_col=[0])\\n\\n read_returns_est = pd.read_excel(src + \\\"portfolio_information.xlsx\\\", sheet_name='income_assets_returns_estimates',\\n index_col=[0])\\n\\n read_asset_weights = pd.read_excel(src + \\\"portfolio_information.xlsx\\\", sheet_name='asset_weights',\\n index_col=[0])\\n\\n # read_asset_weights.drop(read_asset_weights.index[-1], axis=0, inplace=True)\\n\\n # read random returns for simulation\\n read_normal = pd.read_csv(src + 'median_returns_unsorted.csv', index_col=[0], parse_dates=True)\\n cols = [read_normal.columns[c].split('_')[1] for c in np.arange(len(read_normal.columns))]\\n read_normal.rename(columns=dict(zip(list(read_normal.columns), cols)), inplace=True)\\n\\n read_small = pd.read_csv(src + 'median_returns_smallest.csv', index_col=[0], parse_dates=True)\\n read_small.rename(columns=dict(zip(list(read_small.columns), cols)), inplace=True)\\n\\n read_large = pd.read_csv(src + 'median_returns_largest.csv', index_col=[0], parse_dates=True)\\n read_large.rename(columns=dict(zip(list(read_large.columns), cols)), inplace=True)\\n\\n assets_col_names = list(read_normal.columns)\\n\\n tickers = list(read_asset_weights.index)\\n wts = np.array(read_asset_weights.loc[:, 'base'])\\n\\n def asset_median_returns(data, ticker):\\n return data.filter(regex=ticker).median(axis=1)\\n\\n # dataframe for unsorted returns (normal)\\n median_returns_normal = read_normal.copy()\\n median_returns_normal.loc[:, 'portfolio_return'] = median_returns_normal.dot(wts)\\n median_normal_fia = pd.DataFrame({'FIA': asset_median_returns(read_normal, 'FIA')})\\n\\n # dataframe for smallest to largest returns\\n median_returns_smallest = read_small.copy()\\n median_returns_smallest.loc[:, 'portfolio_return'] = median_returns_smallest.dot(wts)\\n median_smallest_fia = pd.DataFrame({'FIA': asset_median_returns(read_small, 'FIA')})\\n\\n # dataframe for largest to smallest returns\\n median_returns_largest = read_large.copy()\\n median_returns_largest.loc[:, 'portfolio_return'] = median_returns_largest.dot(wts)\\n median_largest_fia = pd.DataFrame({'FIA': asset_median_returns(read_large, 'FIA')})\\n\\n years = list(range(0, num_of_years + 1))\\n income_cols = ['year', 'strategy_term', 'index_returns', 'term_ret', 'term_ret_with_par', 'term_annualize',\\n 'ann_net_spread', 'term_ret_netspr', 'high_inc_benefit_base', 'rider_fee', 'eoy_income',\\n 'contract_value']\\n\\n term = int(read_income_inputs.loc['term', 'inputs'])\\n fia_ret = read_returns_est.loc[read_returns_est.index[-1], 'Annualized Returns']\\n fia_risk = read_returns_est.loc[read_returns_est.index[-1], 'Annualized Risk']\\n par_rate = float(read_income_inputs.loc['par_rate', 'inputs'])\\n spread = float(read_income_inputs.loc['spread', 'inputs'])\\n bonus_term = int(read_income_inputs.loc['bonus_term', 'inputs'])\\n premium = float(read_income_inputs.loc['premium', 'inputs'])\\n income_bonus = float(read_income_inputs.loc['income_bonus', 'inputs'])\\n\\n income_starts = int(read_income_inputs.loc['start_income_years', 'inputs'])\\n income_growth = float(read_income_inputs.loc['income_growth', 'inputs'])\\n rider_fee = float(read_income_inputs.loc['rider_fee', 'inputs'])\\n inc_payout_factor = float(read_income_inputs.loc['income_payout_factor', 'inputs'])\\n contract_bonus = float(read_income_inputs.loc['contract_bonus', 'inputs'])\\n social = float(read_income_inputs.loc['social', 'inputs'])\\n inflation = float(read_income_inputs.loc['inflation', 'inputs'])\\n wtd_cpn_yield = float(read_income_inputs.loc['wtd_coupon_yld', 'inputs'])\\n life_expectancy = int(read_income_inputs.loc['life_expectancy_age', 'inputs'])\\n clients_age = int(read_income_inputs.loc['clients_age', 'inputs'])\\n\\n # ---------------INCOME MODEL--------------------------------------------\\n runs = 0\\n returns_dict = {}\\n asset_dict = {}\\n fia_dict = {}\\n\\n income_df = pd.DataFrame(index=years, columns=income_cols)\\n income_df.loc[:, 'year'] = years\\n income_df.loc[:, 'strategy_term'] = income_df.loc[:, 'year'] % term\\n income_df.loc[:, 'strategy_term'] = income_df['strategy_term'].apply(lambda x: 1 if x == 0 else 0)\\n\\n if method == 'normal':\\n income_df.loc[:, 'index_returns'] = read_normal.loc[:, 'FIA']\\n\\n elif method == 'smallest':\\n income_df.loc[:, 'index_returns'] = read_small.loc[:, 'FIA']\\n\\n else:\\n income_df.loc[:, 'index_returns'] = read_large.loc[:, 'FIA']\\n\\n # income_df.loc[:, 'index_returns'] = np.random.normal(fia_ret, fia_risk, size=(len(years), 1))\\n\\n cumprod = (1. + income_df['index_returns']).rolling(window=term).agg(lambda x: x.prod()) - 1\\n income_df.loc[:, 'term_ret'] = np.where(income_df.loc[:, 'strategy_term'] == 1, cumprod, 0)\\n income_df.loc[:, 'term_ret_with_par'] = income_df.loc[:, 'term_ret'] * par_rate\\n income_df.loc[:, 'term_annualize'] = income_df.loc[:, 'term_ret_with_par'].apply(\\n lambda x: (1 + x) ** (1 / term) - 1)\\n income_df.loc[:, 'ann_net_spread'] = income_df.loc[:, 'term_annualize'] - spread\\n income_df.loc[:, 'ann_net_spread'] = np.where(income_df.loc[:, 'strategy_term'] == 1,\\n income_df.loc[:, 'ann_net_spread'], 0)\\n income_df.loc[:, 'term_ret_netspr'] = income_df.loc[:, 'ann_net_spread'].apply(lambda x: (1 + x) ** term - 1)\\n\\n for counter in years:\\n if counter == 0:\\n income_df.loc[counter, 'high_inc_benefit_base'] = premium * (1 + income_bonus)\\n\\n elif counter <= min(bonus_term, income_starts):\\n income_df.loc[counter, 'high_inc_benefit_base'] = income_df.loc[counter - 1, 'high_inc_benefit_base'] * \\\\\\n (1 + income_growth)\\n else:\\n income_df.loc[counter, 'high_inc_benefit_base'] = income_df.loc[counter - 1, 'high_inc_benefit_base']\\n\\n income_df.loc[:, 'rider_fee'] = income_df.loc[:, 'high_inc_benefit_base'] * rider_fee\\n income_df.loc[:, 'eoy_income'] = np.where(income_df.loc[:, 'year'] > income_starts,\\n income_df.loc[:, 'high_inc_benefit_base'] * inc_payout_factor, 0)\\n\\n for counter in years:\\n if counter == 0:\\n income_df.loc[counter, 'contract_value'] = premium * (1 + contract_bonus)\\n\\n elif income_df.loc[counter, 'strategy_term'] == 1:\\n x1 = income_df.loc[counter - 1, 'contract_value'] - income_df.loc[counter, 'rider_fee']\\n x2 = (x1 * (1 + income_df.loc[counter, 'term_ret_netspr'])) - income_df.loc[counter, 'eoy_income']\\n income_df.loc[counter, 'contract_value'] = x2\\n\\n else:\\n x1 = income_df.loc[counter - 1, 'contract_value'] - income_df.loc[counter, 'rider_fee'] - \\\\\\n income_df.loc[counter, 'eoy_income']\\n\\n income_df.loc[counter, 'contract_value'] = x1\\n\\n # variable stores the income number that is used in the base and fia portfolio calcs.\\n\\n income_from_fia = income_df.loc[income_df.index[-1], 'eoy_income']\\n\\n income_df.loc[:, 'contract_value'] = income_df.loc[:, 'contract_value'].apply(lambda x: 0 if x <= 0 else x)\\n\\n sim_fia_cv.loc[:, str(runs)] = income_df.loc[:, 'contract_value']\\n\\n # --------------------BASE MODEL---------------------------------------------\\n base_wts = read_asset_weights.loc[:, 'base']\\n base_assets = list(base_wts.index)\\n base_weights = list(base_wts.values)\\n base_returns = list(read_returns_est.loc[:, 'Annualized Returns'].values)\\n base_std = list(read_returns_est.loc[:, 'Annualized Risk'].values)\\n\\n base_investment = float(read_income_inputs.loc['risky_assets', 'Base'])\\n adv_fees = float(read_income_inputs.loc['advisor_fees', 'Base'])\\n\\n # -------------------required income----------------------------------\\n req_annual_income = float(read_income_inputs.loc['annual_income', 'inputs'])\\n income_needed = req_annual_income - social\\n income_net_fia_income = max(0, income_needed - income_from_fia)\\n cpn_income_base = base_investment * wtd_cpn_yield\\n\\n # ----------------------RANDOM RETURNS--------------------------\\n r_cols = base_assets\\n boy_value = ['bv_{}'.format(name) for name in base_assets]\\n eoy_value = ['ev_{}'.format(name) for name in base_assets]\\n\\n random_returns = pd.DataFrame(index=income_df.index, columns=r_cols)\\n\\n for c in range(len(r_cols)):\\n ret = np.random.normal(base_returns[c], base_std[c], size=(len(random_returns.index), 1))\\n\\n if method == 'smallest':\\n random_returns = read_small.copy()\\n\\n elif method == 'largest':\\n random_returns = read_large.copy()\\n\\n else:\\n random_returns = read_normal.copy()\\n\\n base_df = random_returns.copy()\\n fia_portfolio_df = random_returns.copy()\\n port_investment = float(read_income_inputs.loc['risky_assets', 'FIA'])\\n cpn_income_port = port_investment * wtd_cpn_yield\\n\\n # -------------BASE PORTFOLIO----------------------------\\n for name in boy_value:\\n base_df.loc[:, name] = 0.0\\n\\n for counter in years:\\n period_returns = list(random_returns.loc[counter, :])\\n if counter == 0:\\n\\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment for c in range(len(boy_value))]\\n\\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\\n base_df.loc[counter, 'total_net_fees'] = 0.0\\n base_df.loc[counter, 'income'] = 0.0\\n base_investment = base_df.loc[counter, boy_value].sum()\\n\\n elif (counter > 0) and (counter < income_starts):\\n\\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment * (1 + period_returns[c])\\n for c in range(len(boy_value))]\\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\\n base_df.loc[counter, 'adv_fees'] = base_df.loc[counter, 'total'] * adv_fees\\n base_df.loc[counter, 'total_net_fees'] = base_df.loc[counter, 'total'] - base_df.loc[\\n counter, 'adv_fees']\\n\\n # --coupon payment is invested back into the risky portfolio until the income is withdrawn----\\n base_investment = base_df.loc[counter, 'total_net_fees'] + cpn_income_base\\n\\n else:\\n\\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment * (1 + period_returns[c])\\n for c in range(len(boy_value))]\\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\\n base_df.loc[counter, 'adv_fees'] = base_df.loc[counter, 'total'] * adv_fees\\n\\n # ---req. income is adjusted for inflation from the second year of withdrawal. Reinvestment of coupon\\n # stops from the year income starts. Req. income is reduced by the coupon payments\\n\\n if counter == income_starts:\\n\\n income_needed = req_annual_income - social\\n base_df.loc[counter, 'income'] = income_needed - cpn_income_base\\n income_needed = req_annual_income\\n\\n else:\\n income_needed = income_needed * (1 + inflation) - social\\n base_df.loc[counter, 'income'] = income_needed - cpn_income_base\\n income_needed = income_needed + social\\n\\n base_df.loc[counter, 'total_net_fees'] = base_df.loc[counter, 'total'] - \\\\\\n base_df.loc[counter, 'adv_fees'] - \\\\\\n base_df.loc[counter, 'income']\\n\\n base_investment = base_df.loc[counter, 'total_net_fees']\\n\\n base_df.loc[:, 'adj_total'] = base_df.loc[:, 'total_net_fees'].apply(lambda x: x if x > 0 else 0)\\n sim_base_total.loc[:, 's_{}'.format(str(runs))] = base_df.loc[:, 'total_net_fees']\\n sim_base_income.loc[:, 's_{}'.format(str(runs))] = base_df.loc[:, 'income']\\n\\n # ----------------------------FIA PORTFOLIO----------------------------------------------\\n for name in boy_value:\\n fia_portfolio_df.loc[:, name] = 0.0\\n\\n for counter in years:\\n period_returns = list(random_returns.loc[counter, :])\\n if counter == 0:\\n\\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment\\n for c in range(len(boy_value))]\\n fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\\n fia_portfolio_df.loc[counter, 'total_net_fees'] = 0.0\\n fia_portfolio_df.loc[counter, 'income'] = 0.0\\n port_investment = fia_portfolio_df.loc[counter, boy_value].sum()\\n\\n elif (counter > 0) and (counter < income_starts):\\n\\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment * (1 + period_returns[c])\\n for c in range(len(boy_value))]\\n fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\\n fia_portfolio_df.loc[counter, 'adv_fees'] = fia_portfolio_df.loc[counter, 'total'] * adv_fees\\n fia_portfolio_df.loc[counter, 'total_net_fees'] = fia_portfolio_df.loc[counter, 'total'] - \\\\\\n fia_portfolio_df.loc[counter, 'adv_fees']\\n\\n port_investment = fia_portfolio_df.loc[counter, 'total_net_fees'] + cpn_income_port\\n\\n else:\\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment * (1 + period_returns[c])\\n for c in range(len(boy_value))]\\n fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\\n fia_portfolio_df.loc[counter, 'adv_fees'] = fia_portfolio_df.loc[counter, 'total'] * adv_fees\\n\\n # ---req. income is adjusted for inflation from the second year of withdrawal. Reinvestment of coupon\\n # stops from the year income starts. Req. income is reduced by the coupon payments\\n\\n if counter == income_starts:\\n\\n income_needed = req_annual_income - social\\n income_net_fia_income = max(0, income_needed - income_from_fia)\\n fia_portfolio_df.loc[counter, 'income'] = max(0, income_net_fia_income - cpn_income_port)\\n income_needed = req_annual_income\\n\\n else:\\n income_needed = income_needed * (1 + inflation) - social\\n income_net_fia_income = max(0, income_needed - income_from_fia)\\n fia_portfolio_df.loc[counter, 'income'] = max(0, income_net_fia_income - cpn_income_port)\\n income_needed = income_needed + social\\n\\n fia_portfolio_df.loc[counter, 'total_net_fees'] = fia_portfolio_df.loc[counter, 'total'] - \\\\\\n fia_portfolio_df.loc[counter, 'adv_fees'] - \\\\\\n fia_portfolio_df.loc[counter, 'income']\\n\\n port_investment = fia_portfolio_df.loc[counter, 'total_net_fees']\\n\\n sim_port_total.loc[:, 's_{}'.format(str(runs))] = fia_portfolio_df.loc[:, 'total_net_fees'] + \\\\\\n income_df.loc[:, 'contract_value']\\n\\n sim_port_income.loc[:, 's_{}'.format(str(runs))] = fia_portfolio_df.loc[:, 'income']\\n\\n fia_portfolio_df.loc[:, 'adj_total'] = fia_portfolio_df.loc[:, 'total_net_fees'].apply(\\n lambda x: x if x > 0 else 0)\\n\\n # ---------income breakdown for Base portfolio----------------------------------\\n base_df.to_csv(src + 'base_port_detail.csv')\\n sim_base_total.to_csv(src + 'base_ending_values.csv')\\n income_breakdown_base = pd.DataFrame(sim_base_total.quantile(0.5, axis=1))\\n income_breakdown_base.loc[:, 'income_from_portfolio'] = sim_base_income.quantile(0.5, axis=1)\\n income_breakdown_base.loc[:, 'fia_income'] = 0.0\\n income_breakdown_base.loc[:, 'social_security_income'] = social\\n income_breakdown_base.loc[:, 'coupon_income'] = cpn_income_base\\n\\n income_breakdown_base.rename(columns={income_breakdown_base.columns[0]: 'portfolio_ending_value'}, inplace=True)\\n income_breakdown_base.loc[:, 'income_from_portfolio'][\\n income_breakdown_base.loc[:, 'portfolio_ending_value'] <= 0] = 0\\n income_breakdown_base.loc[:, 'total_income'] = income_breakdown_base.loc[:, income_breakdown_base.columns[1:]].sum(\\n axis=1)\\n\\n # ------------Block Ends-------------------------------------------------------------\\n\\n # ---------income breakdown for FIA portfolio----------------------------------\\n fia_portfolio_df.to_csv(src + 'fia_port_detail.csv')\\n sim_port_total.to_csv(src + 'fiaport_ending_values.csv')\\n\\n income_breakdown_port = pd.DataFrame(sim_port_total.quantile(0.5, axis=1))\\n income_breakdown_port.loc[:, 'income_from_portfolio'] = sim_port_income.quantile(0.5, axis=1)\\n income_breakdown_port.loc[:, 'fia_income'] = income_from_fia\\n income_breakdown_port.loc[:, 'social_security_income'] = social\\n income_breakdown_port.loc[:, 'coupon_income'] = cpn_income_port\\n\\n income_breakdown_port.rename(columns={income_breakdown_port.columns[0]: 'portfolio_ending_value'}, inplace=True)\\n income_breakdown_port.loc[:, 'income_from_portfolio'][\\n income_breakdown_port.loc[:, 'portfolio_ending_value'] <= 0] = 0\\n income_breakdown_port.loc[:, 'total_income'] = income_breakdown_port.loc[:, income_breakdown_port.columns[1:]].sum(\\n axis=1)\\n\\n # ------------Block Ends-------------------------------------------------------------\\n q_cut = [0.0, 0.05, 0.25, 0.5, 0.75, 0.95, 1.0]\\n sim_base_income[sim_base_total < income_needed] = 0.0\\n\\n sim_port_income[sim_port_total < income_net_fia_income] = 0\\n\\n sim_port_income = sim_port_income + income_from_fia\\n\\n # base_quantile = sim_base_total.loc[sim_base_total.index[-1]].quantile([0.05, 0.25, 0.50, 0.75, 0.90])\\n\\n # port_quantile = sim_port_total.loc[sim_port_total.index[-1]].quantile([0.05, 0.25, 0.50, 0.75, 0.90])\\n\\n base_quantile = sim_base_total.loc[sim_base_total.index[-1]].quantile(q_cut)\\n\\n port_quantile = sim_port_total.loc[sim_port_total.index[-1]].quantile(q_cut)\\n\\n # q_cut = [0.0, .05, 0.25, 0.5, 0.75, 0.95, 1.0]\\n cols = ['Min', '5th', '25th', '50th', '75th', '90th', 'Max']\\n\\n # ----drop year 0--------\\n sim_base_total = sim_base_total[1:]\\n sim_port_total = sim_port_total[1:]\\n\\n # ----------------quantile analysis for base terminal value--------------------------\\n base_qcut = pd.DataFrame(index=sim_base_total.index, columns=cols)\\n for c in range(len(cols)):\\n base_qcut.loc[:, cols[c]] = sim_base_total.quantile(q_cut[c], axis=1)\\n\\n base_qcut.clip(lower=0, inplace=True)\\n\\n # ----------------------quantile analysis for base income----------------------------\\n base_income_qcut = pd.DataFrame(index=sim_base_income.index, columns=cols)\\n for c in range(len(cols)):\\n base_income_qcut.loc[:, cols[c]] = sim_base_income.quantile(q_cut[c], axis=1)\\n\\n # ----Remove NaN's prior to the income start years------------\\n # base_income_qcut = base_income_qcut.loc[income_starts:]\\n\\n # -------------quantile analysis for portfolio terminal value ----------------\\n port_qcut = pd.DataFrame(index=sim_port_total.index, columns=cols)\\n for c in range(len(cols)):\\n port_qcut.loc[:, cols[c]] = sim_port_total.quantile(q_cut[c], axis=1)\\n\\n port_qcut.clip(lower=0, inplace=True)\\n\\n # ---------------quantile analysis for portfolio income----------------------------\\n port_income_qcut = pd.DataFrame(index=sim_port_income.index, columns=cols)\\n for c in range(len(cols)):\\n port_income_qcut.loc[:, cols[c]] = sim_port_income.quantile(q_cut[c], axis=1)\\n\\n # ----Remove NaN's prior to the income start years------------\\n # port_income_qcut = port_income_qcut.loc[income_starts:]\\n\\n # ----------probability ending value will be less than 0 at the end of the horizon -----------------------\\n base_legacy_risk = (sim_base_total.loc[sim_base_total.index[life_expectancy - clients_age]] < 0).sum() / (\\n trials + 1)\\n port_legacy_risk = (sim_port_total.loc[sim_port_total.index[life_expectancy - clients_age]] < 0).sum() / (\\n trials + 1)\\n\\n legacy_risk = pd.DataFrame([base_legacy_risk, port_legacy_risk,\\n 'Prob. of portfolio value less than 0 at the end of the expected life'],\\n index=['base', 'fia_portfolio', 'Notes'],\\n columns=['Ruin Probability'])\\n\\n # -----------Year-wise probability of ending value greater than 0 -----------------\\n base_psuccess = sim_base_total.apply(lambda x: x > 0).sum(axis=1) / (trials + 1)\\n port_psuccess = sim_port_total.apply(lambda x: x > 0).sum(axis=1) / (trials + 1)\\n\\n # -----------------------WRITING FILES TO EXCEL ---------------------------\\n col_names = ['50th', 'age', 'comment']\\n writer = pd.ExcelWriter(src + method + '_simulated_income_summary.xlsx', engine='xlsxwriter')\\n read_income_inputs.to_excel(writer, sheet_name='inputs_for_income')\\n\\n read_returns_est.to_excel(writer, sheet_name='asset_returns_estimates')\\n\\n age_index = list(range(clients_age + 1, clients_age + len(base_qcut) + 1))\\n base_qcut.loc[:, 'age'] = age_index\\n base_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n base_qcut.loc[income_starts:, col_names].to_excel(writer, sheet_name='base_ending_value_quantiles')\\n\\n base_income_qcut = base_income_qcut.loc[1:, :]\\n base_income_qcut.loc[:, 'age'] = age_index\\n base_income_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n base_income_qcut.loc[income_starts:, col_names].to_excel(writer, sheet_name='base_income_quantiles')\\n\\n port_qcut.loc[:, 'age'] = age_index\\n port_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n port_qcut.loc[income_starts:, col_names].to_excel(writer, sheet_name='fia_port_ending_value_quantiles')\\n\\n port_income_qcut = port_income_qcut.loc[1:, :]\\n port_income_qcut.loc[:, 'age'] = age_index\\n port_income_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n # port_income_qcut.loc[:, 'ending_contract_value'] = sim_fia_cv\\n port_income_qcut.loc[income_starts:, col_names].to_excel(writer, sheet_name='fia_port_income_quantiles')\\n\\n # prob_success_df = pd.concat([base_psuccess, port_psuccess], axis=1)\\n # prob_success_df.rename(columns={prob_success_df.columns[0]: 'prob(ending_value>0)_base',\\n # prob_success_df.columns[1]: 'prob(ending_value>0)_port'}, inplace=True)\\n\\n # prob_success_df.loc[:, 'age'] = age_index\\n # prob_success_df.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n # prob_success_df.to_excel(writer, sheet_name='success_probability')\\n\\n income_breakdown_base = income_breakdown_base.loc[1:, :]\\n income_breakdown_base.loc[:, 'age'] = age_index\\n income_breakdown_base.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n income_breakdown_base.loc[income_starts:, :].to_excel(writer, sheet_name='base_income_breakdown_median')\\n\\n income_breakdown_port = income_breakdown_port.loc[1:, :]\\n income_breakdown_port.loc[:, 'age'] = age_index\\n income_breakdown_port.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n income_breakdown_port.loc[:, 'ending_contract_value'] = income_df.loc[:, 'contract_value']\\n income_breakdown_port.loc[income_starts:, :].to_excel(writer, sheet_name='fia_income_breakdown_median')\\n\\n if method == 'normal':\\n # median_returns_normal.loc[:, 'fia_median_returns'] = median_normal_fia\\n median_returns_normal.to_excel(writer, sheet_name='gr_port_median_normal')\\n\\n elif method == 'smallest':\\n # median_returns_smallest.loc[:, 'fia_median_returns'] = median_smallest_fia\\n median_returns_smallest.to_excel(writer, sheet_name='gr_port_median_asc')\\n\\n else:\\n # median_returns_largest.loc[:, 'fia_median_returns'] = median_largest_fia\\n median_returns_largest.to_excel(writer, sheet_name='gr_port_median_desc')\\n\\n terminal_val = pd.read_csv(src + 'terminal_values.csv', index_col=[0])\\n ending_val = pd.read_csv(src + 'ending_values.csv', index_col=[0])\\n ending_val_ror = pd.read_csv(src + 'ending_values_ror.csv', index_col=[0])\\n\\n terminal_val.to_excel(writer, sheet_name='terminal_values')\\n ending_val.to_excel(writer, sheet_name='port_ending_values')\\n ending_val_ror.to_excel(writer, sheet_name='port_annual_growth')\\n\\n writer.save()\\n\\n # -----------------Plotting charts--------------------------------------------\\n base_qcut.loc[income_starts:].plot(grid=True, title='Quantile Terminal Value - Base Portfolio')\\n plt.savefig(src + \\\"quantile_terminal_base.png\\\")\\n plt.close('all')\\n\\n base_income_qcut.plot(grid=True, title='Quantile Income - Base Portfolio')\\n plt.savefig(src + \\\"quantile_income_base.png\\\")\\n plt.close('all')\\n\\n base_psuccess.plot(grid=True, title='Probability of Success (Portfolio Ending Value > 0) - Base Portfolio')\\n plt.savefig(src + \\\"success_probabilty_base.png\\\")\\n plt.close('all')\\n\\n (1 - base_psuccess).plot(grid=True, title='Probability of Ruin (Portfolio Ending Value < 0) - Base Portfolio')\\n plt.savefig(src + \\\"ruin_probability_base.png\\\")\\n plt.close('all')\\n\\n port_qcut.loc[income_starts:].plot(grid=True, title='Quantile Terminal Value - FIA Portfolio')\\n plt.savefig(src + \\\"quantile_terminal_fia.png\\\")\\n plt.close('all')\\n\\n port_income_qcut.plot(grid=True, title='Quantile Income - FIA Portfolio')\\n plt.savefig(src + \\\"quantile_income_fia.png\\\")\\n plt.close('all')\\n\\n port_psuccess.plot(grid=True, title='Probability of Success (Portfolio Ending Value > 0) - FIA Portfolio')\\n plt.savefig(src + \\\"success_probabilty_fia.png\\\")\\n plt.close('all')\\n\\n (1 - port_psuccess).plot(grid=True, title='Probability of Ruin (Portfolio Ending Value < 0) - FIA Portfolio')\\n plt.savefig(src + \\\"ruin_probability_fia.png\\\")\\n plt.close('all')\\n\\n print(\\\"simulation completed for {}\\\".format(method))\",\n \"def simulation_using_historical_returns(num_of_years=30, trials=100, method='normal'):\\n\\n print(\\\"Running method simulation_using_historical_returns()\\\")\\n\\n sim_fia_cv = pd.DataFrame(index=range(num_of_years + 1))\\n\\n sim_base_total = pd.DataFrame(index=range(num_of_years + 1))\\n sim_base_income = pd.DataFrame(index=range(num_of_years + 1))\\n\\n sim_port_total = pd.DataFrame(index=range(num_of_years + 1))\\n sim_port_income = pd.DataFrame(index=range(num_of_years + 1))\\n\\n read_income_inputs = pd.read_excel(src + \\\"portfolio_information.xlsx\\\", sheet_name='income_model_inputs',\\n index_col=[0])\\n\\n read_returns_est = pd.read_excel(src + \\\"portfolio_information.xlsx\\\", sheet_name='income_assets_returns_estimates',\\n index_col=[0])\\n\\n read_asset_weights = pd.read_excel(src + \\\"portfolio_information.xlsx\\\", sheet_name='asset_weights',\\n index_col=[0])\\n\\n # read the base portfolio from accumulation model\\n fia_name = read_income_inputs.loc['fia_name', 'inputs']\\n path = src + fia_name + '/'\\n file_name = fia_name + '_portfolio.csv'\\n\\n # base_port = pd.read_csv(path + 'base_portfolio.csv', index_col=[0], parse_dates=True)\\n\\n base_port = pd.read_csv(path + file_name, index_col=[0], parse_dates=True)\\n\\n ls1 = list(read_asset_weights.index[:-2])\\n ls2 = ['Cash', fia_name, 'Total']\\n combined_ls = ls1 + ls2\\n base_port = base_port.loc[:, combined_ls]\\n base_port.loc[:, 'port_return'] = base_port['Total'].pct_change().fillna(0)\\n base_port.drop('Total', axis=1, inplace=True)\\n yearly_base_port = base_port.groupby(by=base_port.index.year).apply(sum)\\n\\n # --Simulate only for first 20 years of returns--------\\n first_n_years = int(read_income_inputs.loc['simulate_for_first_n_years', 'inputs'])\\n yearly_base_port = yearly_base_port.loc[yearly_base_port.index[-(first_n_years + 1):], :]\\n\\n # --------Unsorted returns ---------------------\\n read_normal = yearly_base_port.copy()\\n read_normal.iloc[0] = 0.0\\n read_normal.to_csv(src + 'base_port_ann_ret_unsorted.csv')\\n read_normal.reset_index(drop=True, inplace=True)\\n\\n # ---make a copy to add to the consolidated summary file----\\n save_normal = read_normal.copy()\\n read_normal.drop('port_return', axis=1, inplace=True)\\n read_normal.rename(columns={fia_name: 'FIA'}, inplace=True)\\n\\n # - Shift returns by one year and assign 0 returns to all assets in teh year 0, the year of initial investment.\\n # read_normal = read_normal.shift().fillna(0)\\n read_normal.to_csv(src + 'historical_port_return_unsorted.csv')\\n\\n # ----Best to worst returns----\\n read_large = yearly_base_port.copy()\\n\\n # --Logic to sort the last 20 years of returns and assign 0 to first year, the year of investment\\n read_large.iloc[0] = 1000.0\\n read_large = read_large.sort_values(by=['port_return'], ascending=False)\\n # --assign 0 to first year, the year of investment\\n read_large.iloc[0] = 0.0\\n read_large.to_csv(src + 'base_port_ann_ret_desc.csv')\\n read_large.reset_index(drop=True, inplace=True)\\n # read_large = read_large.shift().fillna(0)\\n\\n # ---make a copy to add to the consolidated summary file---\\n save_desc = read_large.copy()\\n read_large.drop('port_return', axis=1, inplace=True)\\n read_large.rename(columns={fia_name: 'FIA'}, inplace=True)\\n read_large.to_csv(src + 'historical_port_return_desc.csv')\\n\\n # ------Worst to Best Returns------------\\n read_small = yearly_base_port.copy()\\n read_small.iloc[0] = -1000.0\\n read_small = read_small.sort_values(by=['port_return'], ascending=True)\\n read_small.iloc[0] = 0.0\\n read_small.to_csv(src + 'base_port_ann_ret_asc.csv')\\n read_small.reset_index(drop=True, inplace=True)\\n # read_small = read_small.shift().fillna(0)\\n\\n # ---make a copy to add to the consolidated summary file---\\n save_asc = read_small.copy()\\n read_small.drop('port_return', axis=1, inplace=True)\\n read_small.rename(columns={fia_name: 'FIA'}, inplace=True)\\n read_small.to_csv(src + 'historical_port_return_asc.csv')\\n\\n # read_asset_weights.drop(read_asset_weights.index[-1], axis=0, inplace=True)\\n\\n # read random returns for simulation\\n # read_normal = pd.read_csv(src + 'median_returns_unsorted.csv', index_col=[0], parse_dates=True)\\n # cols = [read_normal.columns[c].split('_')[1] for c in np.arange(len(read_normal.columns))]\\n # read_normal.rename(columns=dict(zip(list(read_normal.columns), cols)), inplace=True)\\n #\\n # read_small = pd.read_csv(src + 'median_returns_smallest.csv', index_col=[0], parse_dates=True)\\n # read_small.rename(columns=dict(zip(list(read_small.columns), cols)), inplace=True)\\n #\\n # read_large = pd.read_csv(src + 'median_returns_largest.csv', index_col=[0], parse_dates=True)\\n # read_large.rename(columns=dict(zip(list(read_large.columns), cols)), inplace=True)\\n\\n assets_col_names = list(read_normal.columns)\\n\\n tickers = list(read_asset_weights.index)\\n wts = np.array(read_asset_weights.loc[:, 'base'])\\n\\n def asset_median_returns(data, ticker):\\n return data.filter(regex=ticker).median(axis=1)\\n\\n # dataframe for unsorted returns (normal)\\n median_returns_normal = read_normal.copy()\\n median_returns_normal.loc[:, 'portfolio_return'] = median_returns_normal.dot(wts)\\n # median_returns_normal.rename(columns={fia_name: 'FIA'}, inplace=True)\\n # median_normal_fia = pd.DataFrame({'FIA': asset_median_returns(read_normal, 'FIA')})\\n\\n # dataframe for smallest to largest returns\\n median_returns_smallest = read_small.copy()\\n median_returns_smallest.loc[:, 'portfolio_return'] = median_returns_smallest.dot(wts)\\n # median_returns_smallest.rename(columns={fia_name: 'FIA'}, inplace=True)\\n # median_smallest_fia = pd.DataFrame({'FIA': asset_median_returns(read_small, 'FIA')})\\n\\n # dataframe for largest to smallest returns\\n median_returns_largest = read_large.copy()\\n median_returns_largest.loc[:, 'portfolio_return'] = median_returns_largest.dot(wts)\\n # median_returns_largest.rename(columns={fia_name: 'FIA'}, inplace=True)\\n # median_largest_fia = pd.DataFrame({'FIA': asset_median_returns(read_large, 'FIA')})\\n\\n # years = list(range(0, num_of_years + 1))\\n years = np.arange(len(read_normal))\\n income_cols = ['year', 'strategy_term', 'index_returns', 'term_ret', 'term_ret_with_par', 'term_annualize',\\n 'ann_net_spread', 'term_ret_netspr', 'high_inc_benefit_base', 'rider_fee', 'eoy_income',\\n 'contract_value']\\n\\n term = int(read_income_inputs.loc['term', 'inputs'])\\n fia_ret = read_returns_est.loc[read_returns_est.index[-1], 'Annualized Returns']\\n fia_risk = read_returns_est.loc[read_returns_est.index[-1], 'Annualized Risk']\\n par_rate = float(read_income_inputs.loc['par_rate', 'inputs'])\\n spread = float(read_income_inputs.loc['spread', 'inputs'])\\n bonus_term = int(read_income_inputs.loc['bonus_term', 'inputs'])\\n premium = float(read_income_inputs.loc['premium', 'inputs'])\\n income_bonus = float(read_income_inputs.loc['income_bonus', 'inputs'])\\n\\n income_starts = int(read_income_inputs.loc['start_income_years', 'inputs'])\\n # --Income start right away, not deferred ----\\n income_starts = 0\\n income_growth = float(read_income_inputs.loc['income_growth', 'inputs'])\\n rider_fee = float(read_income_inputs.loc['rider_fee', 'inputs'])\\n inc_payout_factor = float(read_income_inputs.loc['income_payout_factor', 'inputs'])\\n contract_bonus = float(read_income_inputs.loc['contract_bonus', 'inputs'])\\n social = float(read_income_inputs.loc['social', 'inputs'])\\n inflation = float(read_income_inputs.loc['inflation', 'inputs'])\\n wtd_cpn_yield = float(read_income_inputs.loc['wtd_coupon_yld', 'inputs'])\\n life_expectancy = int(read_income_inputs.loc['life_expectancy_age', 'inputs'])\\n clients_age = int(read_income_inputs.loc['clients_age', 'inputs'])\\n\\n # --------------------------------------INCOME MODEL--------------------------------------------\\n runs = 0\\n returns_dict = {}\\n asset_dict = {}\\n fia_dict = {}\\n\\n income_df = pd.DataFrame(index=years, columns=income_cols)\\n income_df.loc[:, 'year'] = years\\n income_df.loc[:, 'strategy_term'] = income_df.loc[:, 'year'] % term\\n income_df.loc[:, 'strategy_term'] = income_df['strategy_term'].apply(lambda x: 1 if x == 0 else 0)\\n\\n if method == 'normal':\\n income_df.loc[:, 'index_returns'] = read_normal.loc[:, 'FIA']\\n\\n elif method == 'smallest':\\n income_df.loc[:, 'index_returns'] = read_small.loc[:, 'FIA']\\n\\n else:\\n income_df.loc[:, 'index_returns'] = read_large.loc[:, 'FIA']\\n\\n # income_df.loc[:, 'index_returns'] = np.random.normal(fia_ret, fia_risk, size=(len(years), 1))\\n\\n cumprod = (1. + income_df['index_returns']).rolling(window=term).agg(lambda x: x.prod()) - 1\\n income_df.loc[:, 'term_ret'] = np.where(income_df.loc[:, 'strategy_term'] == 1, cumprod, 0)\\n income_df.loc[:, 'term_ret_with_par'] = income_df.loc[:, 'term_ret'] * par_rate\\n income_df.loc[:, 'term_annualize'] = income_df.loc[:, 'term_ret_with_par'].apply(\\n lambda x: (1 + x) ** (1 / term) - 1)\\n income_df.loc[:, 'ann_net_spread'] = income_df.loc[:, 'term_annualize'] - spread\\n income_df.loc[:, 'ann_net_spread'] = np.where(income_df.loc[:, 'strategy_term'] == 1,\\n income_df.loc[:, 'ann_net_spread'], 0)\\n income_df.loc[:, 'term_ret_netspr'] = income_df.loc[:, 'ann_net_spread'].apply(lambda x: (1 + x) ** term - 1)\\n\\n for counter in years:\\n if counter == 0:\\n income_df.loc[counter, 'high_inc_benefit_base'] = premium * (1 + income_bonus)\\n\\n elif counter <= min(bonus_term, income_starts):\\n income_df.loc[counter, 'high_inc_benefit_base'] = income_df.loc[counter - 1, 'high_inc_benefit_base'] * \\\\\\n (1 + income_growth)\\n else:\\n income_df.loc[counter, 'high_inc_benefit_base'] = income_df.loc[counter - 1, 'high_inc_benefit_base']\\n\\n income_df.loc[:, 'rider_fee'] = income_df.loc[:, 'high_inc_benefit_base'] * rider_fee\\n income_df.loc[:, 'eoy_income'] = np.where(income_df.loc[:, 'year'] > income_starts,\\n income_df.loc[:, 'high_inc_benefit_base'] * inc_payout_factor, 0)\\n\\n for counter in years:\\n if counter == 0:\\n income_df.loc[counter, 'contract_value'] = premium * (1 + contract_bonus)\\n\\n elif income_df.loc[counter, 'strategy_term'] == 1:\\n x1 = income_df.loc[counter - 1, 'contract_value'] - income_df.loc[counter, 'rider_fee']\\n x2 = (x1 * (1 + income_df.loc[counter, 'term_ret_netspr'])) - income_df.loc[counter, 'eoy_income']\\n income_df.loc[counter, 'contract_value'] = x2\\n\\n else:\\n x1 = income_df.loc[counter - 1, 'contract_value'] - income_df.loc[counter, 'rider_fee'] - \\\\\\n income_df.loc[counter, 'eoy_income']\\n\\n income_df.loc[counter, 'contract_value'] = x1\\n\\n # variable stores the income number that is used in the base and fia portfolio calcs.\\n\\n income_from_fia = income_df.loc[income_df.index[-1], 'eoy_income']\\n\\n income_df.loc[:, 'contract_value'] = income_df.loc[:, 'contract_value'].apply(lambda x: 0 if x <= 0 else x)\\n\\n sim_fia_cv.loc[:, str(runs)] = income_df.loc[:, 'contract_value']\\n\\n # ---------------------------------BASE MODEL---------------------------------------------\\n\\n base_wts = read_asset_weights.loc[:, 'base']\\n base_assets = list(base_wts.index)\\n base_weights = list(base_wts.values)\\n base_returns = list(read_returns_est.loc[:, 'Annualized Returns'].values)\\n base_std = list(read_returns_est.loc[:, 'Annualized Risk'].values)\\n\\n base_investment = float(read_income_inputs.loc['risky_assets', 'Base'])\\n adv_fees = float(read_income_inputs.loc['advisor_fees', 'Base'])\\n\\n # -------------------------------------required income--------------------------------\\n req_annual_income = float(read_income_inputs.loc['annual_income', 'inputs'])\\n income_needed = req_annual_income - social\\n income_net_fia_income = max(0, income_needed - income_from_fia)\\n cpn_income_base = base_investment * wtd_cpn_yield\\n\\n # --------------------------------------------RANDOM RETURNS-----------------------------------\\n r_cols = base_assets\\n boy_value = ['bv_{}'.format(name) for name in base_assets]\\n eoy_value = ['ev_{}'.format(name) for name in base_assets]\\n\\n random_returns = pd.DataFrame(index=income_df.index, columns=r_cols)\\n\\n for c in range(len(r_cols)):\\n ret = np.random.normal(base_returns[c], base_std[c], size=(len(random_returns.index), 1))\\n\\n if method == 'smallest':\\n random_returns = read_small.copy()\\n\\n elif method == 'largest':\\n random_returns = read_large.copy()\\n\\n else:\\n random_returns = read_normal.copy()\\n\\n base_df = random_returns.copy()\\n fia_portfolio_df = random_returns.copy()\\n port_investment = float(read_income_inputs.loc['risky_assets', 'FIA'])\\n cpn_income_port = port_investment * wtd_cpn_yield\\n\\n # -------------------------------------BASE PORTFOLIO----------------------------\\n for name in boy_value:\\n base_df.loc[:, name] = 0.0\\n\\n for counter in years:\\n period_returns = list(random_returns.loc[counter, :])\\n if counter == 0:\\n\\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment for c in range(len(boy_value))]\\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\\n base_df.loc[counter, 'total_net_fees'] = 0.0\\n base_df.loc[counter, 'income'] = 0.0\\n base_investment = base_df.loc[counter, boy_value].sum()\\n\\n elif (counter > 0) and (counter < income_starts):\\n\\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment * (1 + period_returns[c])\\n for c in range(len(boy_value))]\\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\\n base_df.loc[counter, 'adv_fees'] = base_df.loc[counter, 'total'] * adv_fees\\n base_df.loc[counter, 'total_net_fees'] = base_df.loc[counter, 'total'] - base_df.loc[\\n counter, 'adv_fees']\\n\\n # --coupon payment is invested back into the risky portfolio until the income is withdrawn----\\n base_investment = base_df.loc[counter, 'total_net_fees'] + cpn_income_base\\n\\n else:\\n\\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment * (1 + period_returns[c])\\n for c in range(len(boy_value))]\\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\\n base_df.loc[counter, 'adv_fees'] = base_df.loc[counter, 'total'] * adv_fees\\n\\n # ---req. income is adjusted for inflation from the second year of withdrawal. Reinvestment of coupon\\n # stops from the year income starts. Req. income is reduced by the coupon payments\\n\\n if counter == income_starts:\\n\\n income_needed = req_annual_income - social\\n base_df.loc[counter, 'income'] = income_needed - cpn_income_base\\n income_needed = req_annual_income\\n\\n else:\\n income_needed = income_needed * (1 + inflation) - social\\n base_df.loc[counter, 'income'] = income_needed - cpn_income_base\\n income_needed = income_needed + social\\n\\n base_df.loc[counter, 'total_net_fees'] = base_df.loc[counter, 'total'] - \\\\\\n base_df.loc[counter, 'adv_fees'] - \\\\\\n base_df.loc[counter, 'income']\\n\\n base_investment = base_df.loc[counter, 'total_net_fees']\\n\\n base_df.loc[:, 'adj_total'] = base_df.loc[:, 'total_net_fees'].apply(lambda x: x if x > 0 else 0)\\n sim_base_total.loc[:, 's_{}'.format(str(runs))] = base_df.loc[:, 'total_net_fees']\\n sim_base_income.loc[:, 's_{}'.format(str(runs))] = base_df.loc[:, 'income']\\n\\n # -------------------required income----------------------------------\\n req_annual_income = float(read_income_inputs.loc['annual_income', 'inputs'])\\n income_needed = req_annual_income - social\\n # income_net_fia_income = max(0, income_needed - income_from_fia)\\n cpn_income_base = base_investment * wtd_cpn_yield\\n\\n # ----------------------------FIA PORTFOLIO----------------------------------------------\\n for name in boy_value:\\n fia_portfolio_df.loc[:, name] = 0.0\\n\\n for counter in years:\\n period_returns = list(random_returns.loc[counter, :])\\n if counter == 0:\\n\\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment\\n for c in range(len(boy_value))]\\n fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\\n fia_portfolio_df.loc[counter, 'total_net_fees'] = 0.0\\n fia_portfolio_df.loc[counter, 'income'] = 0.0\\n port_investment = fia_portfolio_df.loc[counter, boy_value].sum()\\n\\n elif (counter > 0) and (counter < income_starts):\\n\\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment * (1 + period_returns[c])\\n for c in range(len(boy_value))]\\n fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\\n fia_portfolio_df.loc[counter, 'adv_fees'] = fia_portfolio_df.loc[counter, 'total'] * adv_fees\\n fia_portfolio_df.loc[counter, 'total_net_fees'] = fia_portfolio_df.loc[counter, 'total'] - \\\\\\n fia_portfolio_df.loc[counter, 'adv_fees']\\n\\n port_investment = fia_portfolio_df.loc[counter, 'total_net_fees'] + cpn_income_port\\n\\n else:\\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment * (1 + period_returns[c])\\n for c in range(len(boy_value))]\\n fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\\n fia_portfolio_df.loc[counter, 'adv_fees'] = fia_portfolio_df.loc[counter, 'total'] * adv_fees\\n\\n # ---req. income is adjusted for inflation from the second year of withdrawal. Reinvestment of coupon\\n # stops from the year income starts. Req. income is reduced by the coupon payments\\n\\n if counter == income_starts:\\n\\n income_needed = req_annual_income - social\\n income_net_fia_income = max(0, income_needed - income_from_fia)\\n fia_portfolio_df.loc[counter, 'income'] = max(0, income_net_fia_income - cpn_income_port)\\n income_needed = req_annual_income\\n\\n else:\\n income_needed = income_needed * (1 + inflation) - social\\n income_net_fia_income = max(0, income_needed - income_from_fia)\\n fia_portfolio_df.loc[counter, 'income'] = max(0, income_net_fia_income - cpn_income_port)\\n income_needed = income_needed + social\\n\\n fia_portfolio_df.loc[counter, 'total_net_fees'] = fia_portfolio_df.loc[counter, 'total'] - \\\\\\n fia_portfolio_df.loc[counter, 'adv_fees'] - \\\\\\n fia_portfolio_df.loc[counter, 'income']\\n\\n port_investment = fia_portfolio_df.loc[counter, 'total_net_fees']\\n\\n sim_port_total.loc[:, 's_{}'.format(str(runs))] = fia_portfolio_df.loc[:, 'total_net_fees'] + \\\\\\n income_df.loc[:, 'contract_value']\\n\\n sim_port_income.loc[:, 's_{}'.format(str(runs))] = fia_portfolio_df.loc[:, 'income']\\n\\n fia_portfolio_df.loc[:, 'adj_total'] = fia_portfolio_df.loc[:, 'total_net_fees'].apply(\\n lambda x: x if x > 0 else 0)\\n\\n # -------------------------------------income breakdown for Base portfolio----------------------------------\\n base_df.to_csv(src + method + '_base_port_detail.csv')\\n sim_base_total.to_csv(src + 'base_ending_values.csv')\\n income_breakdown_base = pd.DataFrame(sim_base_total.quantile(0.5, axis=1))\\n income_breakdown_base.loc[:, 'income_from_portfolio'] = sim_base_income.quantile(0.5, axis=1)\\n income_breakdown_base.loc[:, 'fia_income'] = 0.0\\n income_breakdown_base.loc[:, 'social_security_income'] = social\\n income_breakdown_base.loc[:, 'coupon_income'] = cpn_income_base\\n\\n income_breakdown_base.rename(columns={income_breakdown_base.columns[0]: 'portfolio_ending_value'}, inplace=True)\\n income_breakdown_base.loc[:, 'income_from_portfolio'][\\n income_breakdown_base.loc[:, 'portfolio_ending_value'] <= 0] = 0\\n income_breakdown_base.loc[:, 'total_income'] = income_breakdown_base.loc[:, income_breakdown_base.columns[1:]].sum(\\n axis=1)\\n\\n # ------------------------------------Block Ends-------------------------------------------------------------\\n\\n # ------------------------------income breakdown for FIA portfolio----------------------------------\\n fia_portfolio_df.loc[:, 'contract_value'] = income_df.loc[:, 'contract_value']\\n fia_portfolio_df.to_csv(src + method + '_fia_port_detail.csv')\\n sim_port_total.to_csv(src + 'fiaport_ending_values.csv')\\n\\n income_breakdown_port = pd.DataFrame(sim_port_total.quantile(0.5, axis=1))\\n income_breakdown_port.loc[:, 'income_from_portfolio'] = sim_port_income.quantile(0.5, axis=1)\\n income_breakdown_port.loc[:, 'fia_income'] = income_from_fia\\n income_breakdown_port.loc[:, 'social_security_income'] = social\\n income_breakdown_port.loc[:, 'coupon_income'] = cpn_income_port\\n\\n income_breakdown_port.rename(columns={income_breakdown_port.columns[0]: 'portfolio_ending_value'}, inplace=True)\\n income_breakdown_port.loc[:, 'income_from_portfolio'][\\n income_breakdown_port.loc[:, 'portfolio_ending_value'] <= 0] = 0\\n income_breakdown_port.loc[:, 'total_income'] = income_breakdown_port.loc[:, income_breakdown_port.columns[1:]].sum(\\n axis=1)\\n\\n # ------------------------------------Block Ends-------------------------------------------------------------\\n q_cut = [0.0, .05, 0.25, 0.5, 0.75, 0.95, 1.0]\\n sim_base_income[sim_base_total < income_needed] = 0.0\\n\\n sim_port_income[sim_port_total < income_net_fia_income] = 0\\n\\n sim_port_income = sim_port_income + income_from_fia\\n\\n base_quantile = sim_base_total.loc[sim_base_total.index[-1]].quantile(q_cut)\\n\\n port_quantile = sim_port_total.loc[sim_port_total.index[-1]].quantile(q_cut)\\n\\n cols = ['Min', '5th', '25th', '50th', '75th', '90th', 'Max']\\n\\n # ----------------------------------drop year 0---------------------------------\\n sim_base_total = sim_base_total[1:]\\n sim_port_total = sim_port_total[1:]\\n\\n # --------------------------------quantile analysis for base terminal value-----------------------------\\n base_qcut = pd.DataFrame(index=sim_base_total.index, columns=cols)\\n for c in range(len(cols)):\\n base_qcut.loc[:, cols[c]] = sim_base_total.quantile(q_cut[c], axis=1)\\n\\n base_qcut.clip(lower=0, inplace=True)\\n\\n # ----------------------------------quantile analysis for base income----------------------------\\n base_income_qcut = pd.DataFrame(index=sim_base_income.index, columns=cols)\\n for c in range(len(cols)):\\n base_income_qcut.loc[:, cols[c]] = sim_base_income.quantile(q_cut[c], axis=1)\\n\\n # ----Remove NaN's prior to the income start years------------\\n # base_income_qcut = base_income_qcut.loc[income_starts:]\\n\\n # -------------quantile analysis for portfolio terminal value ---------------\\n\\n port_qcut = pd.DataFrame(index=sim_port_total.index, columns=cols)\\n for c in range(len(cols)):\\n port_qcut.loc[:, cols[c]] = sim_port_total.quantile(q_cut[c], axis=1)\\n\\n port_qcut.clip(lower=0, inplace=True)\\n\\n # ---------------quantile analysis for portfolio income----------------------------\\n port_income_qcut = pd.DataFrame(index=sim_port_income.index, columns=cols)\\n for c in range(len(cols)):\\n port_income_qcut.loc[:, cols[c]] = sim_port_income.quantile(q_cut[c], axis=1)\\n\\n # ----Remove NaN's prior to the income start years------------\\n # port_income_qcut = port_income_qcut.loc[income_starts:]\\n\\n # ----------probability ending value will be less than 0 at the end of the horizon -------------------------\\n\\n base_legacy_risk = (sim_base_total.loc[sim_base_total.index[life_expectancy - clients_age]] < 0).sum() / (\\n trials + 1)\\n port_legacy_risk = (sim_port_total.loc[sim_port_total.index[life_expectancy - clients_age]] < 0).sum() / (\\n trials + 1)\\n\\n legacy_risk = pd.DataFrame([base_legacy_risk, port_legacy_risk,\\n 'Prob. of portfolio value less than 0 at the end of the expected life'],\\n index=['base', 'fia_portfolio', 'Notes'],\\n columns=['Ruin Probability'])\\n\\n # -----------Year-wise probability of ending value greater than 0 -----------------\\n base_psuccess = sim_base_total.apply(lambda x: x > 0).sum(axis=1) / (trials + 1)\\n port_psuccess = sim_port_total.apply(lambda x: x > 0).sum(axis=1) / (trials + 1)\\n\\n # -----------------------WRITING FILES TO EXCEL ---------------------------\\n col_names = ['50th', 'age', 'comment']\\n writer = pd.ExcelWriter(src + method + '_historical_simulated_income_summary.xlsx', engine='xlsxwriter')\\n read_income_inputs.to_excel(writer, sheet_name='inputs_for_income')\\n\\n read_returns_est.to_excel(writer, sheet_name='asset_returns_estimates')\\n\\n age_index = list(range(clients_age + 1, clients_age + len(base_qcut) + 1))\\n base_qcut.loc[:, 'age'] = age_index\\n base_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n base_qcut.loc[income_starts:, col_names].to_excel(writer, sheet_name='base_ending_value_quantiles')\\n\\n base_income_qcut = base_income_qcut.loc[1:, :]\\n base_income_qcut.loc[:, 'age'] = age_index\\n base_income_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n base_income_qcut.loc[income_starts:, col_names].to_excel(writer, sheet_name='base_income_quantiles')\\n\\n port_qcut.loc[:, 'age'] = age_index\\n port_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n port_qcut.loc[income_starts:, col_names].to_excel(writer, sheet_name='fia_port_ending_value_quantiles')\\n\\n port_income_qcut = port_income_qcut.loc[1:, :]\\n port_income_qcut.loc[:, 'age'] = age_index\\n port_income_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n port_income_qcut.loc[income_starts:, col_names].to_excel(writer, sheet_name='fia_port_income_quantiles')\\n\\n # prob_success_df = pd.concat([base_psuccess, port_psuccess], axis=1)\\n # prob_success_df.rename(columns={prob_success_df.columns[0]: 'prob(ending_value>0)_base',\\n # prob_success_df.columns[1]: 'prob(ending_value>0)_port'}, inplace=True)\\n\\n # prob_success_df.loc[:, 'age'] = age_index\\n # prob_success_df.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n # prob_success_df.to_excel(writer, sheet_name='success_probability')\\n\\n income_breakdown_base = income_breakdown_base.loc[1:, :]\\n income_breakdown_base.loc[:, 'age'] = age_index\\n income_breakdown_base.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n income_breakdown_base.loc[income_starts:, :].to_excel(writer, sheet_name='base_income_breakdown_median')\\n\\n income_breakdown_port = income_breakdown_port.loc[1:, :]\\n income_breakdown_port.loc[:, 'age'] = age_index\\n income_breakdown_port.loc[:, 'ending contract value'] = income_df.loc[:, 'contract_value'].fillna(0)\\n income_breakdown_port.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n\\n income_breakdown_port.loc[income_starts:, :].to_excel(writer, sheet_name='fia_income_breakdown_median')\\n\\n if method == 'normal':\\n # median_returns_normal.loc[:, 'fia_median_returns'] = median_normal_fia\\n # median_returns_normal.to_excel(writer, sheet_name='unsorted_port_return')\\n save_normal.to_excel(writer, sheet_name='port_return_unsorted')\\n\\n elif method == 'smallest':\\n # median_returns_smallest.loc[:, 'fia_median_returns'] = median_smallest_fia\\n # median_returns_smallest.to_excel(writer, sheet_name='port_return_asc')\\n save_asc.to_excel(writer, sheet_name='port_return_asc')\\n\\n else:\\n # median_returns_largest.loc[:, 'fia_median_returns'] = median_largest_fia\\n # median_returns_largest.to_excel(writer, sheet_name='port_return_desc')\\n save_desc.to_excel(writer, sheet_name='port_return_desc')\\n\\n # terminal_val = pd.read_csv(src + 'terminal_values.csv', index_col=[0])\\n # ending_val = pd.read_csv(src + 'ending_values.csv', index_col=[0])\\n # ending_val_ror = pd.read_csv(src + 'ending_values_ror.csv', index_col=[0])\\n\\n # terminal_val.to_excel(writer, sheet_name='terminal_values')\\n # ending_val.to_excel(writer, sheet_name='port_ending_values')\\n # ending_val_ror.to_excel(writer, sheet_name='port_annual_growth')\\n\\n writer.save()\\n\\n # -----------------Plotting charts--------------------------------------------\\n # base_qcut.loc[income_starts:].plot(grid=True, title='Quantile Terminal Value - Base Portfolio')\\n # plt.savefig(src + \\\"quantile_terminal_base.png\\\")\\n # plt.close('all')\\n #\\n # base_income_qcut.plot(grid=True, title='Quantile Income - Base Portfolio')\\n # plt.savefig(src + \\\"quantile_income_base.png\\\")\\n # plt.close('all')\\n #\\n # base_psuccess.plot(grid=True, title='Probability of Success (Portfolio Ending Value > 0) - Base Portfolio')\\n # plt.savefig(src + \\\"success_probabilty_base.png\\\")\\n # plt.close('all')\\n #\\n # (1 - base_psuccess).plot(grid=True, title='Probability of Ruin (Portfolio Ending Value < 0) - Base Portfolio')\\n # plt.savefig(src + \\\"ruin_probability_base.png\\\")\\n # plt.close('all')\\n #\\n # port_qcut.loc[income_starts:].plot(grid=True, title='Quantile Terminal Value - FIA Portfolio')\\n # plt.savefig(src + \\\"quantile_terminal_fia.png\\\")\\n # plt.close('all')\\n #\\n # port_income_qcut.plot(grid=True, title='Quantile Income - FIA Portfolio')\\n # plt.savefig(src + \\\"quantile_income_fia.png\\\")\\n # plt.close('all')\\n #\\n # port_psuccess.plot(grid=True, title='Probability of Success (Portfolio Ending Value > 0) - FIA Portfolio')\\n # plt.savefig(src + \\\"success_probabilty_fia.png\\\")\\n # plt.close('all')\\n #\\n # (1 - port_psuccess).plot(grid=True, title='Probability of Ruin (Portfolio Ending Value < 0) - FIA Portfolio')\\n # plt.savefig(src + \\\"ruin_probability_fia.png\\\")\\n # plt.close('all')\\n\\n print(\\\"simulation completed for {}\\\".format(method))\",\n \"def test_interest_vs_stockprice(self):\\n stock_prices = np.array([[5, 10, 20, 40]], dtype=float)\\n interest_rate = 2.0 # 200%\\n test_case = StockMarket(5, stock_prices, interest_rate)\\n test_case.dynamic_programming_bottom_up()\\n for portfolio in set(test_case.backtracing_portfolio()):\\n self.assertEqual(0, portfolio)\",\n \"def portfolio_simulations_using_target_returns(num_of_years=30, trials=100):\\n print(\\\"Running portfolio_simulations_using_target_returns() method\\\")\\n sim_fia_cv = pd.DataFrame(index=range(num_of_years + 1))\\n\\n sim_base_total = pd.DataFrame(index=range(num_of_years + 1))\\n sim_base_income = pd.DataFrame(index=range(num_of_years + 1))\\n\\n sim_port_total = pd.DataFrame(index=range(num_of_years + 1))\\n sim_port_income = pd.DataFrame(index=range(num_of_years + 1))\\n\\n read_income_inputs = pd.read_excel(src + \\\"portfolio_information.xlsx\\\", sheet_name='income_model_inputs',\\n index_col=[0])\\n\\n read_returns_est = pd.read_excel(src + \\\"portfolio_information.xlsx\\\", sheet_name='income_assets_returns_estimates',\\n index_col=[0])\\n\\n read_asset_weights = pd.read_excel(src + \\\"portfolio_information.xlsx\\\", sheet_name='asset_weights',\\n index_col=[0])\\n\\n # read random returns for simulation\\n read_normal = pd.read_csv(src + 'median_returns_unsorted.csv', index_col=[0], parse_dates=True)\\n cols = [read_normal.columns[c].split('_')[1] for c in np.arange(len(read_normal.columns))]\\n read_normal.rename(columns=dict(zip(list(read_normal.columns), cols)), inplace=True)\\n idx = list(read_normal.index)\\n\\n runs = 0\\n while runs <= trials:\\n # --------Shuffling the path of random returns from the median portfolio\\n np.random.shuffle(idx)\\n read_normal = read_normal.set_index([idx])\\n read_normal = read_normal.sort_index()\\n\\n # assets_col_names = list(read_normal.columns)\\n # tickers = list(read_asset_weights.index)\\n wts = np.array(read_asset_weights.loc[:, 'base'])\\n\\n def asset_median_returns(data, ticker):\\n return data.filter(regex=ticker).median(axis=1)\\n\\n # dataframe for unsorted returns (normal)\\n median_returns_normal = read_normal.copy()\\n median_returns_normal.loc[:, 'portfolio_return'] = median_returns_normal.dot(wts)\\n median_normal_fia = pd.DataFrame({'FIA': asset_median_returns(read_normal, 'FIA')})\\n\\n years = list(range(0, num_of_years + 1))\\n income_cols = ['year', 'strategy_term', 'index_returns', 'term_ret', 'term_ret_with_par', 'term_annualize',\\n 'ann_net_spread', 'term_ret_netspr', 'high_inc_benefit_base', 'rider_fee', 'eoy_income',\\n 'contract_value']\\n\\n term = int(read_income_inputs.loc['term', 'inputs'])\\n fia_ret = read_returns_est.loc[read_returns_est.index[-1], 'Annualized Returns']\\n fia_risk = read_returns_est.loc[read_returns_est.index[-1], 'Annualized Risk']\\n par_rate = float(read_income_inputs.loc['par_rate', 'inputs'])\\n spread = float(read_income_inputs.loc['spread', 'inputs'])\\n bonus_term = int(read_income_inputs.loc['bonus_term', 'inputs'])\\n premium = float(read_income_inputs.loc['premium', 'inputs'])\\n income_bonus = float(read_income_inputs.loc['income_bonus', 'inputs'])\\n\\n income_starts = int(read_income_inputs.loc['start_income_years', 'inputs'])\\n income_growth = float(read_income_inputs.loc['income_growth', 'inputs'])\\n rider_fee = float(read_income_inputs.loc['rider_fee', 'inputs'])\\n inc_payout_factor = float(read_income_inputs.loc['income_payout_factor', 'inputs'])\\n contract_bonus = float(read_income_inputs.loc['contract_bonus', 'inputs'])\\n social = float(read_income_inputs.loc['social', 'inputs'])\\n inflation = float(read_income_inputs.loc['inflation', 'inputs'])\\n wtd_cpn_yield = float(read_income_inputs.loc['wtd_coupon_yld', 'inputs'])\\n life_expectancy = int(read_income_inputs.loc['life_expectancy_age', 'inputs'])\\n clients_age = int(read_income_inputs.loc['clients_age', 'inputs'])\\n\\n # ---------------INCOME MODEL--------------------------------------------\\n # runs = 0\\n returns_dict = {}\\n asset_dict = {}\\n fia_dict = {}\\n\\n income_df = pd.DataFrame(index=years, columns=income_cols)\\n income_df.loc[:, 'year'] = years\\n income_df.loc[:, 'strategy_term'] = income_df.loc[:, 'year'] % term\\n income_df.loc[:, 'strategy_term'] = income_df['strategy_term'].apply(lambda x: 1 if x == 0 else 0)\\n\\n income_df.loc[:, 'index_returns'] = read_normal.loc[:, 'FIA']\\n\\n cumprod = (1. + income_df['index_returns']).rolling(window=term).agg(lambda x: x.prod()) - 1\\n income_df.loc[:, 'term_ret'] = np.where(income_df.loc[:, 'strategy_term'] == 1, cumprod, 0)\\n income_df.loc[:, 'term_ret_with_par'] = income_df.loc[:, 'term_ret'] * par_rate\\n income_df.loc[:, 'term_annualize'] = income_df.loc[:, 'term_ret_with_par'].apply(\\n lambda x: (1 + x) ** (1 / term) - 1)\\n income_df.loc[:, 'ann_net_spread'] = income_df.loc[:, 'term_annualize'] - spread\\n income_df.loc[:, 'ann_net_spread'] = np.where(income_df.loc[:, 'strategy_term'] == 1,\\n income_df.loc[:, 'ann_net_spread'], 0)\\n income_df.loc[:, 'term_ret_netspr'] = income_df.loc[:, 'ann_net_spread'].apply(lambda x: (1 + x) ** term - 1)\\n\\n for counter in years:\\n if counter == 0:\\n income_df.loc[counter, 'high_inc_benefit_base'] = premium * (1 + income_bonus)\\n\\n elif counter <= min(bonus_term, income_starts):\\n income_df.loc[counter, 'high_inc_benefit_base'] = income_df.loc[counter - 1, 'high_inc_benefit_base'] * \\\\\\n (1 + income_growth)\\n else:\\n income_df.loc[counter, 'high_inc_benefit_base'] = income_df.loc[counter - 1, 'high_inc_benefit_base']\\n\\n income_df.loc[:, 'rider_fee'] = income_df.loc[:, 'high_inc_benefit_base'] * rider_fee\\n income_df.loc[:, 'eoy_income'] = np.where(income_df.loc[:, 'year'] > income_starts,\\n income_df.loc[:, 'high_inc_benefit_base'] * inc_payout_factor, 0)\\n\\n for counter in years:\\n if counter == 0:\\n income_df.loc[counter, 'contract_value'] = premium * (1 + contract_bonus)\\n\\n elif income_df.loc[counter, 'strategy_term'] == 1:\\n x1 = income_df.loc[counter - 1, 'contract_value'] - income_df.loc[counter, 'rider_fee']\\n x2 = (x1 * (1 + income_df.loc[counter, 'term_ret_netspr'])) - income_df.loc[counter, 'eoy_income']\\n income_df.loc[counter, 'contract_value'] = x2\\n\\n else:\\n x1 = income_df.loc[counter - 1, 'contract_value'] - income_df.loc[counter, 'rider_fee'] - \\\\\\n income_df.loc[counter, 'eoy_income']\\n\\n income_df.loc[counter, 'contract_value'] = x1\\n\\n # variable stores the income number that is used in the base and fia portfolio calcs.\\n\\n income_from_fia = income_df.loc[income_df.index[-1], 'eoy_income']\\n\\n income_df.loc[:, 'contract_value'] = income_df.loc[:, 'contract_value'].apply(lambda x: 0 if x <= 0 else x)\\n\\n sim_fia_cv.loc[:, str(runs)] = income_df.loc[:, 'contract_value']\\n\\n # --------------------BASE MODEL---------------------------------------------\\n\\n base_wts = read_asset_weights.loc[:, 'base']\\n base_assets = list(base_wts.index)\\n base_weights = list(base_wts.values)\\n base_returns = list(read_returns_est.loc[:, 'Annualized Returns'].values)\\n base_std = list(read_returns_est.loc[:, 'Annualized Risk'].values)\\n\\n base_investment = float(read_income_inputs.loc['risky_assets', 'Base'])\\n adv_fees = float(read_income_inputs.loc['advisor_fees', 'Base'])\\n\\n # -------------------required income----------------------------------\\n req_annual_income = float(read_income_inputs.loc['annual_income', 'inputs'])\\n income_needed = req_annual_income - social\\n income_net_fia_income = max(0, income_needed - income_from_fia)\\n cpn_income_base = base_investment * wtd_cpn_yield\\n\\n # ----------------------RANDOM RETURNS--------------------------\\n r_cols = base_assets\\n boy_value = ['bv_{}'.format(name) for name in base_assets]\\n eoy_value = ['ev_{}'.format(name) for name in base_assets]\\n\\n random_returns = pd.DataFrame(index=income_df.index, columns=r_cols)\\n\\n random_returns = read_normal.copy()\\n\\n base_df = random_returns.copy()\\n fia_portfolio_df = random_returns.copy()\\n port_investment = float(read_income_inputs.loc['risky_assets', 'FIA'])\\n cpn_income_port = port_investment * wtd_cpn_yield\\n\\n # -------------BASE PORTFOLIO----------------------------\\n for name in boy_value:\\n base_df.loc[:, name] = 0.0\\n\\n for counter in years:\\n period_returns = list(random_returns.loc[counter, :])\\n if counter == 0:\\n\\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment for c in range(len(boy_value))]\\n\\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\\n base_df.loc[counter, 'total_net_fees'] = 0.0\\n base_df.loc[counter, 'income'] = 0.0\\n base_investment = base_df.loc[counter, boy_value].sum()\\n\\n elif (counter > 0) and (counter < income_starts):\\n\\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment * (1 + period_returns[c])\\n for c in range(len(boy_value))]\\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\\n base_df.loc[counter, 'adv_fees'] = base_df.loc[counter, 'total'] * adv_fees\\n base_df.loc[counter, 'total_net_fees'] = base_df.loc[counter, 'total'] - base_df.loc[\\n counter, 'adv_fees']\\n\\n # --coupon payment is invested back into the risky portfolio until the income is withdrawn----\\n base_investment = base_df.loc[counter, 'total_net_fees'] + cpn_income_base\\n\\n else:\\n\\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment * (1 + period_returns[c])\\n for c in range(len(boy_value))]\\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\\n base_df.loc[counter, 'adv_fees'] = base_df.loc[counter, 'total'] * adv_fees\\n\\n # ---req. income is adjusted for inflation from the second year of withdrawal. Reinvestment of coupon\\n # stops from the year income starts. Req. income is reduced by the coupon payments\\n\\n if counter == income_starts:\\n\\n income_needed = req_annual_income - social\\n base_df.loc[counter, 'income'] = income_needed - cpn_income_base\\n income_needed = req_annual_income\\n\\n else:\\n income_needed = income_needed * (1 + inflation) - social\\n base_df.loc[counter, 'income'] = income_needed - cpn_income_base\\n income_needed = income_needed + social\\n\\n base_df.loc[counter, 'total_net_fees'] = base_df.loc[counter, 'total'] - \\\\\\n base_df.loc[counter, 'adv_fees'] - \\\\\\n base_df.loc[counter, 'income']\\n\\n base_investment = base_df.loc[counter, 'total_net_fees']\\n\\n base_df.loc[:, 'adj_total'] = base_df.loc[:, 'total_net_fees'].apply(lambda x: x if x > 0 else 0)\\n sim_base_total.loc[:, 's_{}'.format(str(runs))] = base_df.loc[:, 'total_net_fees']\\n sim_base_income.loc[:, 's_{}'.format(str(runs))] = base_df.loc[:, 'income']\\n\\n # ----------------------------FIA PORTFOLIO----------------------------------------------\\n for name in boy_value:\\n fia_portfolio_df.loc[:, name] = 0.0\\n\\n for counter in years:\\n period_returns = list(random_returns.loc[counter, :])\\n if counter == 0:\\n\\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment\\n for c in range(len(boy_value))]\\n fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\\n fia_portfolio_df.loc[counter, 'total_net_fees'] = 0.0\\n fia_portfolio_df.loc[counter, 'income'] = 0.0\\n port_investment = fia_portfolio_df.loc[counter, boy_value].sum()\\n\\n elif (counter > 0) and (counter < income_starts):\\n\\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment * (1 + period_returns[c])\\n for c in range(len(boy_value))]\\n fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\\n fia_portfolio_df.loc[counter, 'adv_fees'] = fia_portfolio_df.loc[counter, 'total'] * adv_fees\\n fia_portfolio_df.loc[counter, 'total_net_fees'] = fia_portfolio_df.loc[counter, 'total'] - \\\\\\n fia_portfolio_df.loc[counter, 'adv_fees']\\n\\n port_investment = fia_portfolio_df.loc[counter, 'total_net_fees'] + cpn_income_port\\n\\n else:\\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment * (1 + period_returns[c])\\n for c in range(len(boy_value))]\\n fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\\n fia_portfolio_df.loc[counter, 'adv_fees'] = fia_portfolio_df.loc[counter, 'total'] * adv_fees\\n\\n # ---req. income is adjusted for inflation from the second year of withdrawal. Reinvestment of coupon\\n # stops from the year income starts. Req. income is reduced by the coupon payments\\n\\n if counter == income_starts:\\n\\n income_needed = req_annual_income - social\\n income_net_fia_income = max(0, income_needed - income_from_fia)\\n fia_portfolio_df.loc[counter, 'income'] = max(0, income_net_fia_income - cpn_income_port)\\n income_needed = req_annual_income\\n\\n else:\\n income_needed = income_needed * (1 + inflation) - social\\n income_net_fia_income = max(0, income_needed - income_from_fia)\\n fia_portfolio_df.loc[counter, 'income'] = max(0, income_net_fia_income - cpn_income_port)\\n income_needed = income_needed + social\\n\\n fia_portfolio_df.loc[counter, 'total_net_fees'] = fia_portfolio_df.loc[counter, 'total'] - \\\\\\n fia_portfolio_df.loc[counter, 'adv_fees'] - \\\\\\n fia_portfolio_df.loc[counter, 'income']\\n\\n port_investment = fia_portfolio_df.loc[counter, 'total_net_fees']\\n\\n sim_port_total.loc[:, 's_{}'.format(str(runs))] = fia_portfolio_df.loc[:, 'total_net_fees'] + \\\\\\n income_df.loc[:, 'contract_value']\\n\\n sim_port_income.loc[:, 's_{}'.format(str(runs))] = fia_portfolio_df.loc[:, 'income']\\n\\n fia_portfolio_df.loc[:, 'adj_total'] = fia_portfolio_df.loc[:, 'total_net_fees'].apply(\\n lambda x: x if x > 0 else 0)\\n\\n runs = runs + 1\\n\\n # ----% of trials ending value at expected life is less than 0\\n inflation_factor = (1 + annual_inflation) ** (life_expectancy - clients_age - income_starts)\\n required_income_horizon = income_needed * inflation_factor\\n required_income_horizon_net_fia = required_income_horizon - income_from_fia\\n\\n # prob_success_base = (sim_base_total.iloc[life_expectancy - clients_age] < 0).sum() / trials\\n # prob_success_fia_port = (sim_port_total.iloc[life_expectancy - clients_age] < 0).sum() / trials\\n\\n prob_failure_base = (sim_base_total.iloc[life_expectancy - clients_age] < required_income_horizon).sum() / trials\\n prob_failure_fia_port = (sim_port_total.iloc[life_expectancy - clients_age] < required_income_horizon_net_fia) \\\\\\n .sum() / trials\\n\\n # ----Calculate at total average lifetime income for base portfolio----\\n base_inc = sim_base_income.copy()\\n base_inc = base_inc.cumsum()\\n avg_income_base = base_inc.iloc[life_expectancy - clients_age].mean()\\n\\n # ----Calculate at total average lifetime income for FIA portfolio----\\n port_inc = sim_port_income.copy()\\n port_inc = port_inc.cumsum()\\n avg_income_port = port_inc.iloc[life_expectancy - clients_age].mean()\\n avg_income_port = avg_income_port + (income_from_fia * (life_expectancy - clients_age))\\n\\n # ---------income breakdown for Base portfolio----------------------------------\\n base_df.to_csv(src + 'base_port_detail.csv')\\n sim_base_total.to_csv(src + 'base_ending_values.csv')\\n income_breakdown_base = pd.DataFrame(sim_base_total.quantile(0.5, axis=1))\\n income_breakdown_base.loc[:, 'income_from_portfolio'] = sim_base_income.quantile(0.5, axis=1)\\n income_breakdown_base.loc[:, 'fia_income'] = 0.0\\n income_breakdown_base.loc[:, 'social_security_income'] = social\\n income_breakdown_base.loc[:, 'coupon_income'] = cpn_income_base\\n\\n income_breakdown_base.rename(columns={income_breakdown_base.columns[0]: 'portfolio_ending_value'}, inplace=True)\\n income_breakdown_base.loc[:, 'income_from_portfolio'][\\n income_breakdown_base.loc[:, 'portfolio_ending_value'] <= 0] = 0\\n income_breakdown_base.loc[:, 'total_income'] = income_breakdown_base.loc[:, income_breakdown_base.columns[1:]].sum(\\n axis=1)\\n\\n # ------------Block Ends-------------------------------------------------------------\\n\\n # ---------income breakdown for FIA portfolio----------------------------------\\n fia_portfolio_df.to_csv(src + 'fia_port_detail.csv')\\n sim_port_total.to_csv(src + 'fiaport_ending_values.csv')\\n\\n income_breakdown_port = pd.DataFrame(sim_port_total.quantile(0.5, axis=1))\\n income_breakdown_port.loc[:, 'income_from_portfolio'] = sim_port_income.quantile(0.5, axis=1)\\n income_breakdown_port.loc[:, 'fia_income'] = income_from_fia\\n income_breakdown_port.loc[:, 'social_security_income'] = social\\n income_breakdown_port.loc[:, 'coupon_income'] = cpn_income_port\\n\\n income_breakdown_port.rename(columns={income_breakdown_port.columns[0]: 'portfolio_ending_value'}, inplace=True)\\n income_breakdown_port.loc[:, 'income_from_portfolio'][\\n income_breakdown_port.loc[:, 'portfolio_ending_value'] <= 0] = 0\\n income_breakdown_port.loc[:, 'total_income'] = income_breakdown_port.loc[:, income_breakdown_port.columns[1:]].sum(\\n axis=1)\\n\\n # ------------Block Ends-------------------------------------------------------------\\n q_cut = [0.0, 0.05, 0.25, 0.5, 0.75, 0.95, 1.0]\\n sim_base_income[sim_base_total < income_needed] = 0.0\\n\\n sim_port_income[sim_port_total < income_net_fia_income] = 0\\n\\n sim_port_income = sim_port_income + income_from_fia\\n\\n # base_quantile = sim_base_total.loc[sim_base_total.index[-1]].quantile([0.05, 0.25, 0.50, 0.75, 0.90])\\n #\\n # port_quantile = sim_port_total.loc[sim_port_total.index[-1]].quantile([0.05, 0.25, 0.50, 0.75, 0.90])\\n\\n base_quantile = sim_base_total.loc[sim_base_total.index[-1]].quantile(q_cut)\\n\\n port_quantile = sim_port_total.loc[sim_port_total.index[-1]].quantile(q_cut)\\n\\n # q_cut = [0.0, .05, 0.25, 0.5, 0.75, 0.95, 1.0]\\n cols = ['Min', '5th', '25th', '50th', '75th', '90th', 'Max']\\n\\n # ----------------------------drop year 0--------------------------------\\n sim_base_total = sim_base_total[1:]\\n sim_port_total = sim_port_total[1:]\\n\\n # ---------quantile analysis for base terminal value-----------------------------\\n base_qcut = pd.DataFrame(index=sim_base_total.index, columns=cols)\\n for c in range(len(cols)):\\n base_qcut.loc[:, cols[c]] = sim_base_total.quantile(q_cut[c], axis=1)\\n\\n base_qcut.clip(lower=0, inplace=True)\\n\\n # ---------quantile analysis for base income----------------------------\\n base_income_qcut = pd.DataFrame(index=sim_base_income.index, columns=cols)\\n for c in range(len(cols)):\\n base_income_qcut.loc[:, cols[c]] = sim_base_income.quantile(q_cut[c], axis=1)\\n\\n # ----Remove NaN's prior to the income start years------------\\n # base_income_qcut = base_income_qcut.loc[income_starts:]\\n\\n # -------------quantile analysis for portfolio terminal value ---------------\\n\\n port_qcut = pd.DataFrame(index=sim_port_total.index, columns=cols)\\n for c in range(len(cols)):\\n port_qcut.loc[:, cols[c]] = sim_port_total.quantile(q_cut[c], axis=1)\\n\\n port_qcut.clip(lower=0, inplace=True)\\n\\n # ---------------quantile analysis for portfolio income----------------------------\\n port_income_qcut = pd.DataFrame(index=sim_port_income.index, columns=cols)\\n for c in range(len(cols)):\\n port_income_qcut.loc[:, cols[c]] = sim_port_income.quantile(q_cut[c], axis=1)\\n\\n # ----Remove NaN's prior to the income start years------------\\n # port_income_qcut = port_income_qcut.loc[income_starts:]\\n\\n # ----------probability ending value will be less than 0 at the end of the horizon -------------------------\\n\\n base_legacy_risk = (sim_base_total.loc[sim_base_total.index[life_expectancy - clients_age]] < 0).sum() / (\\n trials + 1)\\n port_legacy_risk = (sim_port_total.loc[sim_port_total.index[life_expectancy - clients_age]] < 0).sum() / (\\n trials + 1)\\n\\n legacy_risk = pd.DataFrame([base_legacy_risk, port_legacy_risk,\\n 'Prob. of portfolio value less than 0 at the end of the expected life'],\\n index=['base', 'fia_portfolio', 'Notes'],\\n columns=['Ruin Probability'])\\n\\n # -----------Year-wise probability of ending value greater than 0 -----------------\\n base_psuccess = sim_base_total.apply(lambda x: x > 0).sum(axis=1) / (trials + 1)\\n port_psuccess = sim_port_total.apply(lambda x: x > 0).sum(axis=1) / (trials + 1)\\n\\n # -----------------------WRITING FILES TO EXCEL ---------------------------\\n writer = pd.ExcelWriter(src + 'simulated_portfolios_summary.xlsx', engine='xlsxwriter')\\n sim_base_total.to_excel(writer, sheet_name='base_ending_value')\\n sim_port_total.to_excel(writer, sheet_name='fiaport_ending_value')\\n base_qcut.to_excel(writer, sheet_name='base_quantile_ending')\\n base_income_qcut.to_excel(writer, sheet_name='base_quantile_income')\\n port_qcut.to_excel(writer, sheet_name='port_quantile_ending')\\n port_income_qcut.to_excel(writer, sheet_name='port_quantile_income')\\n\\n sucess_df = pd.concat([base_psuccess, port_psuccess], axis=1)\\n sucess_df.rename(columns={sucess_df.columns[0]: 'Base', sucess_df.columns[1]: 'Fia_Port'}, inplace=True)\\n\\n base_mean = sim_base_total[sim_base_total <= 0].isnull().sum().mean()\\n port_mean = sim_port_total[sim_port_total <= 0].isnull().sum().mean()\\n\\n base_median = sim_base_total[sim_base_total <= 0].isnull().sum().median()\\n port_median = sim_port_total[sim_port_total <= 0].isnull().sum().median()\\n\\n stats_df = pd.DataFrame([[base_mean, port_mean, 'Average years portfolio ending value > 0, out of N trials'],\\n [base_median, port_median, 'Ending Value >0, 50% of the time']],\\n index=['Mean years', 'Median years'], columns=['Base', 'fia_port', 'Comment'])\\n\\n # ---Average of terminal values at the end of horizon from N Trials\\n stats_df.loc['Average Portfolio', 'Base'] = sim_base_total.iloc[-1].mean() + clients_age\\n stats_df.loc['Average Portfolio', 'fia_port'] = sim_port_total.iloc[-1].mean() + clients_age\\n stats_df.loc['Average Portfolio', 'Comment'] = \\\"Average of terminal values at the end of analysis period\\\" \\\\\\n \\\" from N Trials\\\"\\n\\n # ----Median of terminal values at the end of horizon from N Trials\\n stats_df.loc['Median Portfolio', 'Base'] = sim_base_total.iloc[-1].median() + clients_age\\n stats_df.loc['Median Portfolio', 'fia_port'] = sim_port_total.iloc[-1].median() + clients_age\\n stats_df.loc['Median Portfolio', 'Comment'] = \\\"Median of terminal values at the end of analysis period \\\" \\\\\\n \\\"from N Trials\\\"\\n\\n # ---Average of terminal values at the end of Actuarial life from N Trials Base Portfolio\\n stats_df.loc['Average Portfolio (end of expected_life)', 'Base'] = sim_base_total.iloc[\\n life_expectancy - clients_age].mean()\\n\\n # ----Median of terminal values at the end of horizon from N Trials Base Portfolio\\n stats_df.loc['Median Portfolio (end of expected_life)', 'Base'] = sim_base_total.iloc[\\n life_expectancy - clients_age].median()\\n\\n # ---Average of terminal values at the end of Actuarial life from N Trials - FIA portfolio\\n stats_df.loc['Average Portfolio (end of expected_life)', 'fia_port'] = sim_port_total.iloc[\\n life_expectancy - clients_age].mean()\\n stats_df.loc['Average Portfolio (end of expected_life)', 'Comment'] = \\\"Average of terminal values at the end of \\\" \\\\\\n \\\"Actuarial life from N Trials\\\"\\n\\n # ----Median of terminal values at the end of horizon from N Trials - FIA Portfolio\\n stats_df.loc['Median Portfolio (end of expected_life)', 'fia_port'] = sim_port_total.iloc[\\n life_expectancy - clients_age].median()\\n stats_df.loc['Median Portfolio (end of expected_life)', 'Comment'] = \\\"Median of terminal values at the end of \\\" \\\\\\n \\\"horizon from N Trials\\\"\\n\\n stats_df.loc['% Success(value>0 at the end of expected_life)', 'Base'] = 1 - prob_failure_base\\n stats_df.loc['% Success(value>0 at the end of expected_life)', 'fia_port'] = 1 - prob_failure_fia_port\\n stats_df.loc['% Success(value>0 at the end of expected_life)', 'Comment'] = \\\"% of N trials yearly ending value \\\" \\\\\\n \\\"greater than 0\\\"\\n\\n # -----Mininum of N portfolios terminal value at the end of Actuarial Age\\n stats_df.loc['Minimum Value', 'Base'] = sim_base_total.iloc[life_expectancy - clients_age].min()\\n stats_df.loc['Minimum Value', 'fia_port'] = sim_port_total.iloc[life_expectancy - clients_age].min()\\n stats_df.loc['Minimum Value', 'Comment'] = \\\"Mininum of N portfolios terminal value at the end of Actuarial Age\\\"\\n\\n # -----Maxinum of N portfolios terminal value at the end of Actuarial Age\\n stats_df.loc['Maximum Value', 'Base'] = sim_base_total.iloc[life_expectancy - clients_age].max()\\n stats_df.loc['Maximum Value', 'fia_port'] = sim_port_total.iloc[life_expectancy - clients_age].max()\\n stats_df.loc['Maximum Value', 'Comment'] = \\\"Maxinum of N portfolios terminal value at the end of Actuarial Age\\\"\\n\\n # -----Average income from N portfolios at the ned of Actuarial Age\\n stats_df.loc['Avg Income (lifetime)', 'Base'] = avg_income_base\\n stats_df.loc['Avg Income (lifetime)', 'fia_port'] = avg_income_port\\n stats_df.loc['Avg Income (lifetime)', 'Comment'] = \\\"Average income from N portfolios at the end of Actuarial Age\\\"\\n\\n sucess_df.to_excel(writer, sheet_name='yearly_success_rates')\\n stats_df.to_excel(writer, sheet_name='Stats')\\n\\n writer.save()\\n\\n print(\\\"simulation completed.\\\")\",\n \"def realtest(r,stds,mean_returns,covar,start2,end2,tickers):\\n \\n # Get allocation weights of minimum variance portfolio for r during model time period\\n weights = calc_min_variance_portfolio(mean_returns,covar,r)\\n \\n # Get data during backtesting time period\\n from find_optimized_portfolio import process_data\\n stds_new,mean_returns_new,covar_new,tickers,start2,end2=process_data(start2,end2,tickers)\\n \\n # Dot backtesting period's returns with model's weights to get the portfolio's return\\n # during backtest period\\n realtest_r = weights @ mean_returns_new\\n # Get stdev during backtest period\\n realtest_std=calc_portfolio_stdev(covar_new,weights)\\n\\n return realtest_r,realtest_std, weights, tickers, start2, end2\",\n \"def portfolio_performance(returns,weights):\\r\\n print('Calculating Portfolio Performance')\\r\\n # returns=target_asset_port_data_attributes['component_returns']\\r\\n # weights =target_asset_port_data_attributes['effective_weights']\\r\\n\\r\\n component_returns= returns\\r\\n compnent_weights = pd.DataFrame(data=np.nan,index= component_returns.index,columns=component_returns.columns)\\r\\n compnent_weights.loc[weights.index,:] = weights\\r\\n\\r\\n portfolio_dates = component_returns.index\\r\\n components = component_returns.columns\\r\\n\\r\\n # pre-allocate\\r\\n BoP_df = pd.DataFrame(data=np.nan,index=portfolio_dates,columns=components)\\r\\n EoP_df = pd.DataFrame(data=np.nan,index=portfolio_dates,columns=components)\\r\\n PnL_df = pd.DataFrame(data=np.nan,index=portfolio_dates,columns=components)\\r\\n portfolio_BoP = pd.DataFrame(data=np.nan,index=portfolio_dates,columns=['Portfolio BoP'])\\r\\n portfolio_EoP = pd.DataFrame(data=np.nan,index=portfolio_dates,columns=['Portfolio EoP'])\\r\\n portfolio_PnL = pd.DataFrame(data=np.nan,index=portfolio_dates,columns=['Portfolio PnL'])\\r\\n \\r\\n portfolio_index = pd.DataFrame(data=np.nan,index=portfolio_dates,columns=['Index'])\\r\\n previous_index_value = np.int64(1)\\r\\n\\r\\n pre_date = portfolio_dates[0]\\r\\n # set BoP to start weights\\r\\n for date,row in component_returns.iterrows():\\r\\n # print(date)\\r\\n # 1st date\\r\\n if date == portfolio_dates[0]:\\r\\n BoP_df.loc[date] = compnent_weights.iloc[0,:]\\r\\n EoP_df.loc[date] = BoP_df.loc[date] * (1+component_returns.loc[date])\\r\\n PnL_df.loc[date] = EoP_df.loc[date].subtract(BoP_df.loc[date])\\r\\n\\r\\n portfolio_BoP.loc[date] = BoP_df.loc[date].sum()\\r\\n portfolio_EoP.loc[date] = EoP_df.loc[date].sum()\\r\\n portfolio_PnL.loc[date] = PnL_df.loc[date].sum()\\r\\n\\r\\n portfolio_index.loc[date] = np.nansum([previous_index_value,portfolio_PnL.loc[date].values])\\r\\n previous_index_value = portfolio_index.loc[date]\\r\\n pre_date = date\\r\\n\\r\\n # after first date\\r\\n else:\\r\\n BoP_df.loc[date] = EoP_df.loc[pre_date]\\r\\n # weights override\\r\\n if date in compnent_weights.index:\\r\\n none_NaN_index = ~compnent_weights.loc[date].isnull()\\r\\n if not compnent_weights.loc[date][none_NaN_index].empty:\\r\\n tmp_sum = BoP_df.loc[date].sum()\\r\\n BoP_df.loc[date][none_NaN_index.values] = (compnent_weights.loc[date][none_NaN_index.values].values)*tmp_sum\\r\\n\\r\\n \\r\\n EoP_df.loc[date] = BoP_df.loc[date] * (1+component_returns.loc[date])\\r\\n PnL_df.loc[date] = EoP_df.loc[date].subtract(BoP_df.loc[date])\\r\\n\\r\\n portfolio_BoP.loc[date] = BoP_df.loc[date].sum()\\r\\n portfolio_EoP.loc[date] = EoP_df.loc[date].sum()\\r\\n portfolio_PnL.loc[date] = PnL_df.loc[date].sum()\\r\\n \\r\\n portfolio_index.loc[date] = np.nansum([previous_index_value,portfolio_PnL.loc[date].values])\\r\\n previous_index_value = portfolio_index.loc[date]\\r\\n pre_date = date\\r\\n\\r\\n\\r\\n portfolio_returns = portfolio_index.pct_change(1) \\r\\n portfolio_returns.columns = ['Returns']\\r\\n\\r\\n portfolio_index\\r\\n perf = portfolio_index.calc_stats()\\r\\n \\r\\n output = pd.Series(data = [perf,PnL_df,portfolio_index,portfolio_BoP,portfolio_EoP,BoP_df], index=['Portfolio Perf','Component PnL','portfolio_index','portfolio_BoP','portfolio_EoP','BoP_df'])\\r\\n return output\",\n \"def _run_backtest(self):\\n i = 0\\n \\n while True:\\n i += 1\\n print(i)\\n \\n # Update the market bars\\n if self.data_handler.continue_backtest == True:\\n self.data_handler.update_bars()\\n else:\\n break\\n \\n # Handle the Events\\n while True:\\n try:\\n event = self.events.get(False)\\n except queue.Empty:\\n break\\n else:\\n # The inner-loop acts on the events by calling the appropriate method of the appropriate object\\n if event is not None:\\n if event.type == 'MARKET':\\n self.strategy.calculate_signals(event)\\n self.portfolio.update_timeindex(event)\\n \\n elif event.type == 'SIGNAL':\\n self.signals += 1\\n self.portfolio.update_signal(event)\\n \\n elif event.type == 'ORDER':\\n self.orders += 1\\n self.execution_handler.execute_order(event)\\n \\n elif event.type == 'FILL':\\n self.fills += 1\\n self.portfolio.update_fill(event)\\n \\n # Pauses for a duration of self.heartbeat seconds\\n time.sleep(self.heartbeat)\",\n \"def after_run(self):\\n # Calculate the performance of the strategy and portfolio\\n self.portfolio.calc_stats()\\n self.calc_performance()\\n\\n return self\",\n \"def _process_results(self):\\n self.portfolio.create_backtest_result_dataframe()\\n stats = self._show_stats()\\n return stats\",\n \"def _output_performance(self):\\n self.portfolio.create_equity_curve_dataframe()\\n \\n print(\\\"Creating summary statistics...\\\")\\n stats = self.portfolio.output_summary_stats()\\n \\n print(\\\"Creating equity curve...\\\")\\n print(self.portfolio.equity_curve.tail(10))\\n pprint.pprint(stats)\\n \\n print(\\\"Signals: %s\\\" % self.signals)\\n print(\\\"Orders: %s\\\" % self.orders)\\n print(\\\"Fills: %s\\\" % self.fills)\",\n \"def _output_performance(self):\\n self.portfolio.create_equity_curve_dataframe()\\n\\n print(\\\"Creating summary statistics...\\\")\\n stats = self.portfolio.output_summary_stats()\\n\\n print(\\\"Creating equity curve...\\\")\\n print(self.portfolio.equity_curve.tail(10))\\n pprint.pprint(stats)\\n\\n print(\\\"Signals: %s\\\" % self.signals)\\n print(\\\"Orders: %s\\\" % self.orders)\\n print(\\\"Fills: %s\\\" % self.fills)\",\n \"def backtesting_engine(symbol, strategy, fromdate, todate, args=None):\\n\\n # Backtesting Engine\\n cerebro = bt.Cerebro()\\n\\n # Add a Strategy if no Data Required for the model\\n if args is None:\\n cerebro.addstrategy(strategy)\\n # If the Strategy requires a Model and therefore data\\n elif args is not None:\\n cerebro.addstrategy(strategy, args)\\n\\n # Retrieve Data from Alpaca\\n data = bt.feeds.YahooFinanceData(\\n dataname=symbol,\\n fromdate=fromdate, # datetime.date(2015, 1, 1)\\n todate=todate, # datetime.datetime(2016, 1, 1)\\n reverse=False\\n )\\n\\n # Add Data to Backtesting Engine\\n cerebro.adddata(data)\\n\\n # Set Initial Portfolio Value\\n cerebro.broker.setcash(100000.0)\\n\\n # Add Analysis Tools\\n cerebro.addanalyzer(bt.analyzers.SharpeRatio, _name='sharpe')\\n cerebro.addanalyzer(bt.analyzers.Returns, _name='returns')\\n cerebro.addanalyzer(bt.analyzers.SQN, _name='sqn')\\n cerebro.addanalyzer(bt.analyzers.DrawDown, _name='drawdown')\\n cerebro.addanalyzer(bt.analyzers.PositionsValue, _name='posval')\\n cerebro.addanalyzer(bt.analyzers.PyFolio, _name='pyfolio')\\n\\n # Starting Portfolio Value\\n print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())\\n\\n # Run the Backtesting Engine\\n backtest = cerebro.run()\\n\\n # Print Analysis and Final Portfolio Value\\n print(\\n 'Final Portfolio Value: %.2f' % cerebro.broker.getvalue()\\n )\\n print(\\n 'Return: ', backtest[0].analyzers.returns.get_analysis()\\n )\\n print(\\n 'Sharpe Ratio: ', backtest[0].analyzers.sharpe.get_analysis()\\n )\\n print(\\n 'System Quality Number: ', backtest[0].analyzers.sqn.get_analysis()\\n )\\n print(\\n 'Drawdown: ', backtest[0].analyzers.drawdown.get_analysis()\\n )\\n print(\\n 'Active Position Value: ', backtest[0].analyzers.posval.get_analysis()\\n )\\n print(\\n 'Pyfolio: ', backtest[0].analyzers.pyfolio.get_analysis()\\n )\\n\\n # Print Analysis and Final Portfolio Value\\n pyfoliozer = backtest[0].analyzers.getbyname('pyfolio')\\n returns, positions, transactions, gross_lev = pyfoliozer.get_pf_items()\\n\\n # See if we can add regular FB data to compare against returns of algo\\n pf.create_full_tear_sheet(\\n returns, positions=positions, transactions=transactions\\n )\",\n \"def portfolio_analytics(port_returns, market_returns):\\n\\n # add the intercept to the model\\n x2 = sm.add_constant(market_returns)\\n\\n # train the model\\n estimator = sm.OLS(port_returns, x2)\\n model = estimator.fit()\\n\\n # get portfolio analytics\\n alpha, beta = model.params\\n r_squared = model.rsquared\\n regression = model.predict()\\n\\n return alpha, beta, r_squared, regression\",\n \"def portfolio_analytics(port_returns, market_returns):\\n\\n # add the intercept to the model\\n x2 = sm.add_constant(market_returns)\\n\\n # train the model\\n estimator = sm.OLS(port_returns, x2)\\n model = estimator.fit()\\n\\n # get portfolio analytics\\n alpha, beta = model.params\\n r_squared = model.rsquared\\n regression = model.predict()\\n\\n return alpha, beta, r_squared, regression\",\n \"def income_model_constant_portfolio_return(num_of_years=30, trials=100, method='normal'):\\n\\n sim_fia_cv = pd.DataFrame(index=range(num_of_years + 1))\\n\\n sim_base_total = pd.DataFrame(index=range(num_of_years + 1))\\n sim_base_income = pd.DataFrame(index=range(num_of_years + 1))\\n\\n sim_port_total = pd.DataFrame(index=range(num_of_years + 1))\\n sim_port_income = pd.DataFrame(index=range(num_of_years + 1))\\n\\n sim_base_total_pre_income = pd.DataFrame(index=range(num_of_years + 1))\\n sim_port_total_pre_income = pd.DataFrame(index=range(num_of_years + 1))\\n\\n sim_base_total_preincome = pd.DataFrame(index=range(num_of_years + 1))\\n sim_port_total_preincome = pd.DataFrame(index=range(num_of_years + 1))\\n\\n # read_income_inputs = pd.read_csv(src + \\\"income_model_inputs.csv\\\", index_col='Items')\\n read_income_inputs = pd.read_excel(src + \\\"portfolio_information.xlsx\\\", sheet_name='income_model_inputs',\\n index_col=[0])\\n\\n # read_returns_est = pd.read_csv(src + \\\"income_assets_returns_estimates.csv\\\", index_col='Symbol')\\n read_returns_est = pd.read_excel(src + \\\"portfolio_information.xlsx\\\", sheet_name='income_assets_returns_estimates',\\n index_col=[0])\\n\\n # read_returns_est.drop(['BM', read_returns_est.index[-1]], axis=0, inplace=True)\\n # read_portfolio_inputs = pd.read_csv(src + \\\"income_portfolio_inputs.csv\\\", index_col='Items')\\n\\n # read_asset_weights = pd.read_csv(src + \\\"asset_weights.csv\\\", index_col='Asset')\\n read_asset_weights = pd.read_excel(src + \\\"portfolio_information.xlsx\\\", sheet_name='asset_weights',\\n index_col=[0])\\n\\n read_asset_weights.drop(read_asset_weights.index[-1], axis=0, inplace=True)\\n\\n # read random returns for simulation\\n read_normal = pd.read_csv(src + 'sort_normal.csv', index_col=[0], parse_dates=True)\\n read_small = pd.read_csv(src + 'sort_small_to_large.csv', index_col=[0], parse_dates=True)\\n read_large = pd.read_csv(src + 'sort_large_to_small.csv', index_col=[0], parse_dates=True)\\n assets_col_names = list(read_normal.columns)\\n\\n tickers = list(read_asset_weights.index)\\n wts = np.array(read_asset_weights.loc[:, 'base'])\\n\\n def asset_median_returns(data, ticker):\\n return data.filter(regex=ticker).median(axis=1)\\n\\n # # dataframe for unsorted returns (normal)\\n # median_returns_normal = pd.DataFrame({t: asset_median_returns(read_normal, t) for t in tickers})\\n # median_returns_normal.loc[:, 'portfolio_return'] = median_returns_normal.dot(wts)\\n # median_normal_fia = pd.DataFrame({'FIA': asset_median_returns(read_normal, 'r_FIA')})\\n #\\n # # dataframe for smallest to largest returns\\n # median_returns_smallest = pd.DataFrame({t: asset_median_returns(read_small, t) for t in tickers})\\n # median_returns_smallest.loc[:, 'portfolio_return'] = median_returns_smallest.dot(wts)\\n # median_smallest_fia = pd.DataFrame({'FIA': asset_median_returns(read_small, 'r_FIA')})\\n #\\n # # dataframe for unsorted returns (normal)\\n # median_returns_largest = pd.DataFrame({t: asset_median_returns(read_large, t) for t in tickers})\\n # median_returns_largest.loc[:, 'portfolio_return'] = median_returns_largest.dot(wts)\\n # median_largest_fia = pd.DataFrame({'FIA': asset_median_returns(read_large, 'r_FIA')})\\n\\n years = list(range(0, num_of_years + 1))\\n income_cols = ['year', 'strategy_term', 'index_returns', 'term_ret', 'term_ret_with_par', 'term_annualize',\\n 'ann_net_spread', 'term_ret_netspr', 'high_inc_benefit_base', 'rider_fee', 'eoy_income',\\n 'contract_value']\\n\\n term = int(read_income_inputs.loc['term', 'inputs'])\\n fia_ret = read_returns_est.loc[read_returns_est.index[-1], 'Annualized Returns']\\n fia_risk = read_returns_est.loc[read_returns_est.index[-1], 'Annualized Risk']\\n par_rate = float(read_income_inputs.loc['par_rate', 'inputs'])\\n spread = float(read_income_inputs.loc['spread', 'inputs'])\\n bonus_term = int(read_income_inputs.loc['bonus_term', 'inputs'])\\n premium = float(read_income_inputs.loc['premium', 'inputs'])\\n income_bonus = float(read_income_inputs.loc['income_bonus', 'inputs'])\\n\\n income_starts = int(read_income_inputs.loc['start_income_years', 'inputs'])\\n income_growth = float(read_income_inputs.loc['income_growth', 'inputs'])\\n rider_fee = float(read_income_inputs.loc['rider_fee', 'inputs'])\\n inc_payout_factor = float(read_income_inputs.loc['income_payout_factor', 'inputs'])\\n contract_bonus = float(read_income_inputs.loc['contract_bonus', 'inputs'])\\n social = float(read_income_inputs.loc['social', 'inputs'])\\n inflation = float(read_income_inputs.loc['inflation', 'inputs'])\\n wtd_cpn_yield = float(read_income_inputs.loc['wtd_coupon_yld', 'inputs'])\\n life_expectancy = int(read_income_inputs.loc['life_expectancy_age', 'inputs'])\\n clients_age = int(read_income_inputs.loc['clients_age', 'inputs'])\\n \\n # -------------For Constant Growth Rates------------------------\\n const_fia_index_ret = float(read_income_inputs.loc['const_fia_index_ret', 'inputs'])\\n const_risky_port_ret = float(read_income_inputs.loc['const_risky_port_ret', 'inputs'])\\n\\n # ---------------INCOME MODEL--------------------------------------------\\n runs = 0\\n returns_dict = {}\\n asset_dict = {}\\n fia_dict = {}\\n\\n income_df = pd.DataFrame(index=years, columns=income_cols)\\n income_df.loc[:, 'year'] = years\\n income_df.loc[:, 'strategy_term'] = income_df.loc[:, 'year'] % term\\n income_df.loc[:, 'strategy_term'] = income_df['strategy_term'].apply(lambda x: 1 if x == 0 else 0)\\n\\n if method == 'normal':\\n # income_df.loc[:, 'index_returns'] = read_normal.loc[:, '{}_{}'.format('r_FIA', str(runs))]\\n # ----------CONSTANT FIA INDEX GROWTH RATE-------------------\\n income_df.loc[:, 'index_returns'] = const_fia_index_ret\\n\\n elif method == 'smallest':\\n income_df.loc[:, 'index_returns'] = read_small.loc[:, '{}_{}'.format('r_FIA', str(runs))]\\n\\n else:\\n income_df.loc[:, 'index_returns'] = read_large.loc[:, '{}_{}'.format('r_FIA', str(runs))]\\n\\n # income_df.loc[:, 'index_returns'] = np.random.normal(fia_ret, fia_risk, size=(len(years), 1))\\n\\n cumprod = (1. + income_df['index_returns']).rolling(window=term).agg(lambda x: x.prod()) - 1\\n income_df.loc[:, 'term_ret'] = np.where(income_df.loc[:, 'strategy_term'] == 1, cumprod, 0)\\n income_df.loc[:, 'term_ret_with_par'] = income_df.loc[:, 'term_ret'] * par_rate\\n income_df.loc[:, 'term_annualize'] = income_df.loc[:, 'term_ret_with_par'].apply(\\n lambda x: (1 + x) ** (1 / term) - 1)\\n income_df.loc[:, 'ann_net_spread'] = income_df.loc[:, 'term_annualize'] - spread\\n income_df.loc[:, 'ann_net_spread'] = np.where(income_df.loc[:, 'strategy_term'] == 1,\\n income_df.loc[:, 'ann_net_spread'], 0)\\n income_df.loc[:, 'term_ret_netspr'] = income_df.loc[:, 'ann_net_spread'].apply(lambda x: (1 + x) ** term - 1)\\n\\n for counter in years:\\n if counter == 0:\\n income_df.loc[counter, 'high_inc_benefit_base'] = premium * (1 + income_bonus)\\n\\n elif counter <= min(bonus_term, income_starts):\\n income_df.loc[counter, 'high_inc_benefit_base'] = income_df.loc[counter - 1, 'high_inc_benefit_base'] * \\\\\\n (1 + income_growth)\\n else:\\n income_df.loc[counter, 'high_inc_benefit_base'] = income_df.loc[counter - 1, 'high_inc_benefit_base']\\n\\n income_df.loc[:, 'rider_fee'] = income_df.loc[:, 'high_inc_benefit_base'] * rider_fee\\n income_df.loc[:, 'eoy_income'] = np.where(income_df.loc[:, 'year'] > income_starts,\\n income_df.loc[:, 'high_inc_benefit_base'] * inc_payout_factor, 0)\\n\\n for counter in years:\\n if counter == 0:\\n income_df.loc[counter, 'contract_value'] = premium * (1 + contract_bonus)\\n\\n elif income_df.loc[counter, 'strategy_term'] == 1:\\n x1 = income_df.loc[counter - 1, 'contract_value'] - income_df.loc[counter, 'rider_fee']\\n x2 = (x1 * (1 + income_df.loc[counter, 'term_ret_netspr'])) - income_df.loc[counter, 'eoy_income']\\n income_df.loc[counter, 'contract_value'] = x2\\n\\n else:\\n x1 = income_df.loc[counter - 1, 'contract_value'] - income_df.loc[counter, 'rider_fee'] - \\\\\\n income_df.loc[counter, 'eoy_income']\\n\\n income_df.loc[counter, 'contract_value'] = x1\\n\\n # variable stores the income number that is used in the base and fia portfolio calcs.\\n\\n income_from_fia = income_df.loc[income_df.index[-1], 'eoy_income']\\n\\n income_df.loc[:, 'contract_value'] = income_df.loc[:, 'contract_value'].apply(lambda x: 0 if x <= 0 else x)\\n\\n sim_fia_cv.loc[:, str(runs)] = income_df.loc[:, 'contract_value']\\n\\n # -------------------------------------BASE MODEL---------------------------------------------\\n\\n base_wts = read_asset_weights.loc[:, 'base']\\n base_assets = list(base_wts.index)\\n base_weights = list(base_wts.values)\\n base_returns = list(read_returns_est.loc[:, 'Annualized Returns'].values)\\n base_std = list(read_returns_est.loc[:, 'Annualized Risk'].values)\\n\\n base_investment = float(read_income_inputs.loc['risky_assets', 'Base'])\\n adv_fees = float(read_income_inputs.loc['advisor_fees', 'Base'])\\n\\n # -------------------required income----------------------------------\\n req_annual_income = float(read_income_inputs.loc['annual_income', 'inputs'])\\n income_needed = req_annual_income - social\\n income_net_fia_income = max(0, income_needed - income_from_fia)\\n cpn_income_base = base_investment * wtd_cpn_yield\\n\\n # ----------------------RANDOM RETURNS--------------------------\\n r_cols = ['r_{}'.format(name) for name in base_assets]\\n boy_value = ['bv_{}'.format(name) for name in base_assets]\\n eoy_value = ['ev_{}'.format(name) for name in base_assets]\\n\\n random_returns = pd.DataFrame(index=income_df.index, columns=r_cols)\\n\\n # for c in range(len(r_cols)):\\n # ret = np.random.normal(base_returns[c], base_std[c], size=(len(random_returns.index), 1))\\n\\n if method == 'smallest':\\n this_run_cols = ['{}_{}'.format(cname, str(runs)) for cname in r_cols]\\n random_returns = read_small.loc[:, this_run_cols]\\n\\n # random_returns.loc[:, r_cols[c]] = np.sort(ret.flatten())\\n # asset_dict.update({'{}_{}'.format(r_cols[c], str(runs)): np.sort(ret.flatten())})\\n\\n elif method == 'largest':\\n this_run_cols = ['{}_{}'.format(cname, str(runs)) for cname in r_cols]\\n random_returns = read_large.loc[:, this_run_cols]\\n\\n # random_returns.loc[:, r_cols[c]] = np.flip(np.sort(ret.flatten()))\\n # asset_dict.update({'{}_{}'.format(r_cols[c], str(runs)): np.flip(np.sort(ret.flatten()))})\\n\\n else:\\n this_run_cols = ['{}_{}'.format(cname, str(runs)) for cname in r_cols]\\n random_returns = read_normal.loc[:, this_run_cols]\\n\\n # random_returns.loc[:, r_cols[c]] = ret.flatten()\\n # asset_dict.update({'{}_{}'.format(r_cols[c], str(runs)): ret.flatten()})\\n\\n # store the simulated assets returns in one dictionary\\n # returns_dict.update({str(runs): random_returns})\\n\\n # collect the asset based returns from all simulation and calculate the median returns.\\n # def get_median_returns(sym):\\n # cols = [sym + '_' + str(c) for c in np.arange(trials)]\\n # asset_df = pd.DataFrame({c: asset_dict.get(c) for c in cols})\\n # return asset_df.median(axis=1)\\n #\\n # asset_median_returns = pd.DataFrame({symbol: get_median_returns(symbol) for symbol in r_cols})\\n #\\n # asset_median_returns.loc[:, 'simulated_portfolio_median_returns'] = asset_median_returns.dot(base_weights)\\n\\n base_df = random_returns.copy()\\n pre_income_base_df = random_returns.copy()\\n\\n # base_investment = float(read_portfolio_inputs.loc['risky_assets', 'Base'])\\n\\n fia_portfolio_df = random_returns.copy()\\n pre_income_port_df = random_returns.copy()\\n port_investment = float(read_income_inputs.loc['risky_assets', 'FIA'])\\n cpn_income_port = port_investment * wtd_cpn_yield\\n\\n # ---------Initial Investments for pre-income account values---------------------\\n pre_income_base_inv = base_investment\\n pre_income_port_inv = port_investment\\n # ----------------------------------------BASE PORTFOLIO----------------------------\\n for name in boy_value:\\n base_df.loc[:, name] = 0.0\\n pre_income_base_df.loc[:, name] = 0.0\\n\\n for counter in years:\\n period_returns = list(random_returns.loc[counter, :])\\n if counter == 0:\\n # ---------------For year 0, the year of investment------------\\n\\n # ------------Calculate the annual portfolio returns - Gross Returns--------------------\\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment for c in range(len(boy_value))]\\n\\n # -------------Record the Pre Income Base Portfolio-----------------------------\\n\\n pre_income_base_df.loc[counter, boy_value] = [base_weights[c] *\\n pre_income_base_inv for c in range(len(boy_value))]\\n pre_income_base_df.loc[counter, 'total'] = pre_income_base_df.loc[counter, boy_value].sum()\\n pre_income_base_inv = pre_income_base_df.loc[counter, boy_value].sum()\\n\\n # ------------------Pre Income Block Ends------------------------\\n\\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\\n\\n base_df.loc[counter, 'total_net_fees'] = 0.0\\n base_df.loc[counter, 'income'] = 0.0\\n # base_investment = base_df.loc[counter, boy_value].sum()\\n base_df.loc[counter, 'total_pre_income'] = base_investment\\n\\n elif (counter > 0) and (counter < income_starts):\\n\\n # ----For years between the start of the investment and start if the income---------------\\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment * (1 + period_returns[c])\\n for c in range(len(boy_value))]\\n\\n # -------------Record the Pre Income Base Portfolio-----------------------------\\n pre_income_base_df.loc[counter, boy_value] = [\\n base_weights[c] * pre_income_base_inv * (1 + period_returns[c])\\n for c in range(len(boy_value))]\\n\\n # pre_income_base_df.loc[counter, 'total'] = pre_income_base_df.loc[counter, boy_value].sum()\\n pre_income_base_df.loc[counter, 'total'] = base_investment * (1 + const_risky_port_ret)\\n pre_income_base_df.loc[counter, 'adv_fees'] = pre_income_base_df.loc[counter, 'total'] * adv_fees\\n pre_income_base_df.loc[counter, 'total_net_fees'] = pre_income_base_df.loc[counter, 'total'] - \\\\\\n pre_income_base_df.loc[counter, 'adv_fees']\\n pre_income_base_inv = pre_income_base_df.loc[counter, 'total_net_fees'] + cpn_income_base\\n\\n # ------------------Pre Income Block Ends------------------------\\n\\n # base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\\n base_df.loc[counter, 'total'] = base_investment * (1 + 0.06)\\n base_df.loc[counter, 'adv_fees'] = base_df.loc[counter, 'total'] * adv_fees\\n base_df.loc[counter, 'total_net_fees'] = base_df.loc[counter, 'total'] - base_df.loc[\\n counter, 'adv_fees']\\n\\n # --coupon payment is invested back into the risky portfolio until the income is withdrawn----\\n base_investment = base_df.loc[counter, 'total_net_fees'] + cpn_income_base\\n base_df.loc[counter, 'total_pre_income'] = base_df.loc[counter, 'total_net_fees']\\n\\n else:\\n\\n # -------------For Years after the income started----------------------\\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment * (1 + period_returns[c])\\n for c in range(len(boy_value))]\\n\\n # -------------Record the Pre Income Base Portfolio-----------------------------\\n pre_income_base_df.loc[counter, boy_value] = [\\n base_weights[c] * pre_income_base_inv * (1 + period_returns[c])\\n for c in range(len(boy_value))]\\n\\n # pre_income_base_df.loc[counter, 'total'] = pre_income_base_df.loc[counter, boy_value].sum()\\n pre_income_base_df.loc[counter, 'total'] = base_investment * (1 + const_risky_port_ret)\\n pre_income_base_df.loc[counter, 'adv_fees'] = pre_income_base_df.loc[counter, 'total'] * adv_fees\\n pre_income_base_df.loc[counter, 'total_net_fees'] = pre_income_base_df.loc[counter, 'total'] - \\\\\\n pre_income_base_df.loc[counter, 'adv_fees']\\n pre_income_base_inv = pre_income_base_df.loc[counter, 'total_net_fees'] + cpn_income_base\\n\\n # ------------------Pre Income Block Ends------------------------\\n\\n # base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\\n base_df.loc[counter, 'total'] = base_investment * (1 + const_risky_port_ret)\\n base_df.loc[counter, 'adv_fees'] = base_df.loc[counter, 'total'] * adv_fees\\n\\n # ---req. income is adjusted for inflation from the second year of withdrawal. Reinvestment of coupon\\n # stops from the year income starts. Req. income is reduced by the coupon payments\\n\\n if counter == income_starts:\\n\\n income_needed = req_annual_income - social\\n base_df.loc[counter, 'income'] = income_needed - cpn_income_base\\n income_needed = req_annual_income\\n\\n else:\\n income_needed = income_needed * (1 + inflation) - social\\n base_df.loc[counter, 'income'] = income_needed - cpn_income_base\\n income_needed = income_needed + social\\n\\n base_df.loc[counter, 'total_net_fees'] = base_df.loc[counter, 'total'] - \\\\\\n base_df.loc[counter, 'adv_fees'] - \\\\\\n base_df.loc[counter, 'income']\\n\\n base_df.loc[counter, 'total_pre_income'] = base_df.loc[counter, 'total'] - \\\\\\n base_df.loc[counter, 'adv_fees']\\n\\n base_investment = base_df.loc[counter, 'total_net_fees']\\n\\n # -------------------Portfolio with PreIncome Values----------------------------\\n sim_base_total_preincome.loc[:, 's_{}'.format(str(runs))] = pre_income_base_df.loc[:, 'total_net_fees']\\n sim_base_total_preincome.fillna(float(read_income_inputs.loc['risky_assets', 'Base']), inplace=True)\\n # --------------------------------PreIncome Block Ends----------------------------\\n\\n base_df.loc[:, 'adj_total'] = base_df.loc[:, 'total_net_fees'].apply(lambda x: x if x > 0 else 0)\\n sim_base_total.loc[:, 's_{}'.format(str(runs))] = base_df.loc[:, 'total_net_fees']\\n sim_base_income.loc[:, 's_{}'.format(str(runs))] = base_df.loc[:, 'income']\\n sim_base_total_pre_income.loc[:, 's_{}'.format(str(runs))] = base_df.loc[:, 'total_pre_income']\\n\\n # ----------------------------FIA PORTFOLIO----------------------------------------------\\n for name in boy_value:\\n fia_portfolio_df.loc[:, name] = 0.0\\n pre_income_port_df.loc[:, name] = 0.0\\n\\n for counter in years:\\n period_returns = list(random_returns.loc[counter, :])\\n if counter == 0:\\n\\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment\\n for c in range(len(boy_value))]\\n\\n # -------------Record the Pre Income Base Portfolio-----------------------------\\n\\n pre_income_port_df.loc[counter, boy_value] = [base_weights[c] *\\n pre_income_port_inv for c in range(len(boy_value))]\\n pre_income_port_df.loc[counter, 'total'] = pre_income_port_df.loc[counter, boy_value].sum()\\n pre_income_port_inv = pre_income_port_df.loc[counter, boy_value].sum()\\n\\n # ------------------Pre Income Block Ends------------------------\\n\\n fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\\n fia_portfolio_df.loc[counter, 'total_net_fees'] = 0.0\\n fia_portfolio_df.loc[counter, 'income'] = 0.0\\n # port_investment = fia_portfolio_df.loc[counter, boy_value].sum()\\n fia_portfolio_df.loc[counter, 'total_pre_income'] = port_investment\\n\\n elif (counter > 0) and (counter < income_starts):\\n\\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment * (1 + period_returns[c])\\n for c in range(len(boy_value))]\\n\\n # ------------------Record the Pre Income Base Portfolio-----------------------------\\n pre_income_port_df.loc[counter, boy_value] = [\\n base_weights[c] * pre_income_port_inv * (1 + period_returns[c])\\n for c in range(len(boy_value))]\\n\\n # pre_income_port_df.loc[counter, 'total'] = pre_income_port_df.loc[counter, boy_value].sum()\\n \\n # -----------------------CONSTANT GROWTH RATE-----------------\\n pre_income_port_df.loc[counter, 'total'] = port_investment * (1 + const_risky_port_ret)\\n\\n pre_income_port_df.loc[counter, 'adv_fees'] = pre_income_port_df.loc[counter, 'total'] * adv_fees\\n pre_income_port_df.loc[counter, 'total_net_fees'] = pre_income_port_df.loc[counter, 'total'] - \\\\\\n pre_income_port_df.loc[counter, 'adv_fees']\\n pre_income_port_inv = pre_income_port_df.loc[counter, 'total_net_fees'] + cpn_income_base\\n\\n # ------------------Pre Income Block Ends------------------------\\n\\n # fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\\n \\n # -------CONSTANT GROWTH RATE-----------------\\n fia_portfolio_df.loc[counter, 'total'] = port_investment * (1 + const_risky_port_ret)\\n\\n fia_portfolio_df.loc[counter, 'adv_fees'] = fia_portfolio_df.loc[counter, 'total'] * adv_fees\\n fia_portfolio_df.loc[counter, 'total_net_fees'] = fia_portfolio_df.loc[counter, 'total'] - \\\\\\n fia_portfolio_df.loc[counter, 'adv_fees']\\n fia_portfolio_df.loc[counter, 'total_pre_income'] = fia_portfolio_df.loc[counter, 'total_net_fees']\\n\\n port_investment = fia_portfolio_df.loc[counter, 'total_net_fees'] + cpn_income_port\\n\\n else:\\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment * (1 + period_returns[c])\\n for c in range(len(boy_value))]\\n\\n # -------------Record the Pre Income Base Portfolio-----------------------------\\n pre_income_port_df.loc[counter, boy_value] = [\\n base_weights[c] * pre_income_port_inv * (1 + period_returns[c])\\n for c in range(len(boy_value))]\\n\\n # pre_income_port_df.loc[counter, 'total'] = pre_income_port_df.loc[counter, boy_value].sum()\\n pre_income_port_df.loc[counter, 'total'] = port_investment * (1 + const_risky_port_ret)\\n pre_income_port_df.loc[counter, 'adv_fees'] = pre_income_port_df.loc[counter, 'total'] * adv_fees\\n pre_income_port_df.loc[counter, 'total_net_fees'] = pre_income_port_df.loc[counter, 'total'] - \\\\\\n pre_income_port_df.loc[counter, 'adv_fees']\\n pre_income_port_inv = pre_income_port_df.loc[counter, 'total_net_fees'] + cpn_income_base\\n\\n # ------------------Pre Income Block Ends------------------------\\n\\n # fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\\n fia_portfolio_df.loc[counter, 'total'] = port_investment * (1 + const_risky_port_ret)\\n fia_portfolio_df.loc[counter, 'adv_fees'] = fia_portfolio_df.loc[counter, 'total'] * adv_fees\\n\\n # ---req. income is adjusted for inflation from the second year of withdrawal. Reinvestment of coupon\\n # stops from the year income starts. Req. income is reduced by the coupon payments\\n\\n if counter == income_starts:\\n\\n income_needed = req_annual_income - social\\n income_net_fia_income = max(0, income_needed - income_from_fia)\\n fia_portfolio_df.loc[counter, 'income'] = max(0, income_net_fia_income - cpn_income_port)\\n income_needed = req_annual_income\\n\\n else:\\n income_needed = income_needed * (1 + inflation) - social\\n income_net_fia_income = max(0, income_needed - income_from_fia)\\n fia_portfolio_df.loc[counter, 'income'] = max(0, income_net_fia_income - cpn_income_port)\\n income_needed = income_needed + social\\n\\n fia_portfolio_df.loc[counter, 'total_net_fees'] = fia_portfolio_df.loc[counter, 'total'] - \\\\\\n fia_portfolio_df.loc[counter, 'adv_fees'] - \\\\\\n fia_portfolio_df.loc[counter, 'income']\\n\\n fia_portfolio_df.loc[counter, 'total_pre_income'] = fia_portfolio_df.loc[counter, 'total'] - \\\\\\n fia_portfolio_df.loc[counter, 'adv_fees']\\n\\n port_investment = fia_portfolio_df.loc[counter, 'total_net_fees']\\n\\n sim_port_total.loc[:, 's_{}'.format(str(runs))] = fia_portfolio_df.loc[:, 'total_net_fees'] + \\\\\\n income_df.loc[:, 'contract_value']\\n\\n sim_port_income.loc[:, 's_{}'.format(str(runs))] = fia_portfolio_df.loc[:, 'income']\\n\\n fia_portfolio_df.loc[:, 'adj_total'] = fia_portfolio_df.loc[:, 'total_net_fees'].apply(\\n lambda x: x if x > 0 else 0)\\n\\n sim_port_total_pre_income.loc[:, 's_{}'.format(str(runs))] = fia_portfolio_df.loc[:, 'total_pre_income']\\n\\n # -------------------Portfolio with PreIncome Values----------------------------\\n sim_port_total_preincome.loc[:, 's_{}'.format(str(runs))] = pre_income_port_df.loc[:, 'total_net_fees'] + \\\\\\n income_df.loc[:, 'contract_value']\\n\\n sim_port_total_preincome.fillna(float(read_income_inputs.loc['risky_assets', 'FIA']), inplace=True)\\n \\n # --------------------------------PreIncome Block Ends----------------------------\\n\\n # ------------------Calculate % of portfolios ending value greater than required LIFETIME cumm. income---------\\n total_income_by_age = sim_base_income.loc[:, sim_base_income.columns[0]].cumsum()\\n total_income_by_acturial_age = total_income_by_age.loc[life_expectancy - clients_age]\\n total_income_by_age.fillna(0, inplace=True)\\n income_dataframe = pd.DataFrame(total_income_by_age)\\n income_dataframe.loc[:, 'remaining_income_by_acturial_age'] = total_income_by_age.apply(\\n lambda x: total_income_by_acturial_age - x)\\n\\n s = income_dataframe.loc[:, 'remaining_income_by_acturial_age']\\n base_prob_of_success = sim_base_total.gt(s, axis=0).sum(axis=1)\\n port_prob_of_success = sim_port_total.gt(s, axis=0).sum(axis=1)\\n\\n # ----------------------------Portfolio sufficient for NEXT YEARS income needs-------------------\\n next_year_income = sim_base_income.loc[:, sim_base_income.columns[0]].shift(-1).fillna(0) # Yearly Income Reqd.\\n base_success_next_year = sim_base_total.gt(next_year_income, axis=0).sum(axis=1)\\n\\n base_for_next_year_need = sim_base_total[sim_base_total.gt(next_year_income, axis=0)]\\n\\n port_success_next_year = sim_port_total.gt(next_year_income, axis=0).sum(axis=1)\\n\\n port_for_next_year_need = sim_port_total[sim_port_total.gt(next_year_income, axis=0)]\\n\\n # ---------------Portfolio for 45 years of simulation---------------------------------------\\n base_success_portfolio = sim_base_total[sim_base_total.gt(next_year_income, axis=0)]\\n port_success_portfolio = sim_port_total[sim_port_total.gt(next_year_income, axis=0)]\\n\\n # ----------------Portfolio Simulation until the acturial age------------------------------\\n acturial_years = life_expectancy - clients_age\\n base_success_portfolio_act_age = base_success_portfolio.loc[acturial_years, :]\\n port_success_portfolio_act_age = port_success_portfolio.loc[acturial_years, :]\\n\\n # -------------------------Base Portfolio TS with max Terminal Value ----------------------------\\n if base_success_portfolio_act_age.isnull().sum() == trials:\\n base_max_portfolio = 0.0\\n else:\\n base_max_portfolio = base_success_portfolio.loc[:, base_success_portfolio_act_age.idxmax()]\\n\\n # -------------------------FIA Portfolio TS with max Terminal Value ----------------------------\\n if port_success_portfolio_act_age.isnull().sum() == trials:\\n port_max_portfolio = 0.0\\n else:\\n port_max_portfolio = port_success_portfolio.loc[:, port_success_portfolio_act_age.idxmax()]\\n\\n # ------------------------------Average age with full income------------------------------\\n base_mean_age = ((life_expectancy - clients_age) - base_success_portfolio.loc[1:life_expectancy - clients_age, :]\\n .isnull().sum()).mean()\\n\\n port_mean_age = ((life_expectancy - clients_age) - port_success_portfolio.loc[1:life_expectancy - clients_age, :]\\n .isnull().sum()).mean()\\n\\n # ----------------------------Median Age with full Income------------------------------------------\\n base_median_age = ((life_expectancy - clients_age) - base_success_portfolio.loc[1:life_expectancy - clients_age, :]\\n .isnull().sum()).median()\\n\\n port_median_age = ((life_expectancy - clients_age) - port_success_portfolio.loc[1:life_expectancy - clients_age, :]\\n .isnull().sum()).median()\\n\\n # --------------Mean Value for all the portfolios at end of the acturial age--------------------\\n base_act_avg_porfolio = base_success_portfolio.loc[life_expectancy - clients_age, :].fillna(0).mean()\\n port_act_avg_porfolio = port_success_portfolio.loc[life_expectancy - clients_age, :].fillna(0).mean()\\n\\n # --------------Median Value for all the portfolios at end of the acturial age--------------------\\n base_act_median_porfolio = base_success_portfolio.loc[life_expectancy - clients_age, :].fillna(0).median()\\n port_act_median_porfolio = port_success_portfolio.loc[life_expectancy - clients_age, :].fillna(0).median()\\n\\n # # --------------Mean Value for all the portfolios in the simulation--------------------\\n # base_sim_mean = base_success_portfolio.loc[1:life_expectancy - clients_age, :].mean().mean()\\n # port_sim_mean = port_success_portfolio.loc[1:life_expectancy - clients_age, :].mean().mean()\\n #\\n # # --------------Median Value for all the portfolios in the simulation--------------------\\n # base_sim_mean = base_success_portfolio.loc[1:life_expectancy - clients_age, :].mean().median()\\n # port_sim_mean = port_success_portfolio.loc[1:life_expectancy - clients_age, :].mean().median()\\n\\n # -------Max Portfolio value at the end of acturial age----------------------------------------\\n base_act_max = base_success_portfolio.loc[life_expectancy - clients_age, :].max()\\n port_act_max = port_success_portfolio.loc[life_expectancy - clients_age, :].max()\\n\\n # -------Min Portfolio value at the end of acturial age----------------------------------------\\n base_act_min = base_success_portfolio.loc[life_expectancy - clients_age, :].min()\\n port_act_min = port_success_portfolio.loc[life_expectancy - clients_age, :].min()\\n\\n # ---------------------Lifetime Average Income----------------------------------\\n base_total_income = sim_base_income.cumsum().loc[acturial_years, :].mean()\\n port_total_income = income_from_fia + sim_port_income\\n port_total_income = port_total_income.cumsum().loc[acturial_years, :].mean()\\n\\n simulation_stats = pd.DataFrame(index=['Average Years', 'Median Years', 'Average Age', 'Median Age',\\n 'Average Portfolio (act.age)', 'Median Portfolio (act.age)',\\n 'Max Portfolio Value', 'Min Portfolio Value',\\n 'Average Lifetime Income'], columns=['Base', 'FIA'])\\n\\n simulation_stats.loc['Average Years', :] = [base_mean_age, base_mean_age]\\n simulation_stats.loc['Median Years', :] = [base_median_age, base_median_age]\\n simulation_stats.loc['Average Age', :] = [base_mean_age + clients_age, base_mean_age + clients_age]\\n simulation_stats.loc['Median Age', :] = [base_median_age + clients_age, base_median_age + clients_age]\\n simulation_stats.loc['Average Portfolio (act.age)', :] = [base_act_avg_porfolio, port_act_avg_porfolio]\\n simulation_stats.loc['Median Portfolio (act.age)', :] = [base_act_median_porfolio, port_act_median_porfolio]\\n simulation_stats.loc['Max Portfolio Value', :] = [base_act_max, port_act_max]\\n simulation_stats.loc['Min Portfolio Value', :] = [base_act_min, port_act_min]\\n simulation_stats.loc['Average Lifetime Income', :] = [base_total_income, port_total_income]\\n comments = ['Average years of portfolios that meet the next years income needs for the lifetime',\\n 'Median years of portfolios that meet the next years income needs for the lifetime',\\n 'Average Clients Age',\\n 'Median Clients Age',\\n 'Average of terminal values for the portfolios at the end of the acturial life',\\n 'Median of terminal values for the portfolios at the end of the acturial life',\\n 'Maximum of terminal values for the portfolios at the end of the acturial life',\\n 'Minimum of terminal values for the portfolios at the end of the acturial life',\\n 'Average of total income generated by all portfolios at the end of the acturial life']\\n\\n simulation_stats.loc[:, 'Notes'] = comments\\n\\n # --------------------------------------------------------------------------------\\n\\n # # -----------------------------------income breakdown for Base portfolio----------------------------------\\n # base_df.to_csv(src + 'base_port_detail.csv')\\n # sim_base_total.to_csv(src + 'base_ending_values.csv')\\n # income_breakdown_base = pd.DataFrame(sim_base_total.quantile(0.5, axis=1))\\n # income_breakdown_base.loc[:, 'income_from_portfolio'] = sim_base_income.quantile(0.5, axis=1)\\n # income_breakdown_base.loc[:, 'fia_income'] = 0.0\\n # income_breakdown_base.loc[:, 'social_security_income'] = social\\n # income_breakdown_base.loc[:, 'coupon_income'] = cpn_income_base\\n #\\n # income_breakdown_base.rename(columns={income_breakdown_base.columns[0]: 'portfolio_ending_value'}, inplace=True)\\n # income_breakdown_base.loc[:, 'income_from_portfolio'][\\n # income_breakdown_base.loc[:, 'portfolio_ending_value'] <= 0] = 0\\n # income_breakdown_base.loc[:, 'total_income'] = income_breakdown_base.loc[:, income_breakdown_base.columns[1:]].sum(\\n # axis=1)\\n #\\n # # --------------------------------------Block Ends-----------------------------------------------------------\\n #\\n # # ---------------------------------------income breakdown for FIA portfolio----------------------------------\\n # fia_portfolio_df.to_csv(src + 'fia_port_detail.csv')\\n # sim_port_total.to_csv(src + 'fiaport_ending_values.csv')\\n #\\n # income_breakdown_port = pd.DataFrame(sim_port_total.quantile(0.5, axis=1))\\n # income_breakdown_port.loc[:, 'income_from_portfolio'] = sim_port_income.quantile(0.5, axis=1)\\n # income_breakdown_port.loc[:, 'fia_income'] = income_from_fia\\n # income_breakdown_port.loc[:, 'social_security_income'] = social\\n # income_breakdown_port.loc[:, 'coupon_income'] = cpn_income_port\\n #\\n # income_breakdown_port.rename(columns={income_breakdown_port.columns[0]: 'portfolio_ending_value'}, inplace=True)\\n # income_breakdown_port.loc[:, 'income_from_portfolio'][\\n # income_breakdown_port.loc[:, 'portfolio_ending_value'] <= 0] = 0\\n # income_breakdown_port.loc[:, 'total_income'] = income_breakdown_port.loc[:, income_breakdown_port.columns[1:]].sum(\\n # axis=1)\\n #\\n # # ----------------------------------Block Ends-------------------------------------------------------------\\n q_cut = [0.0, 0.1, 0.25, 0.5, 0.75, 0.95, 1.0]\\n sim_base_income[sim_base_total < income_needed] = 0.0\\n\\n sim_port_income[sim_port_total < income_net_fia_income] = 0\\n\\n sim_port_income = sim_port_income + income_from_fia\\n\\n # base_quantile = sim_base_total.loc[sim_base_total.index[-1]].quantile([0.05, 0.25, 0.50, 0.75, 0.90])\\n #\\n # port_quantile = sim_port_total.loc[sim_port_total.index[-1]].quantile([0.05, 0.25, 0.50, 0.75, 0.90])\\n\\n base_quantile = sim_base_total.loc[sim_base_total.index[-1]].quantile(q_cut)\\n\\n port_quantile = sim_port_total.loc[sim_port_total.index[-1]].quantile(q_cut)\\n\\n # q_cut = [0.0, .05, 0.25, 0.5, 0.75, 0.95, 1.0]\\n cols = ['Min', '10th', '25th', '50th', '75th', '90th', 'Max']\\n\\n # ------------------------------------------drop year 0-----------------------------------------\\n sim_base_total = sim_base_total[1:]\\n sim_port_total = sim_port_total[1:]\\n\\n # ---------------------------------plot for histogram for porfolios--------------------------------------\\n # base_term_value = sim_base_total.loc[sim_base_total.index[:life_expectancy - clients_age], :]\\n # fact = 1 / len(base_term_value)\\n # base_ann_ret = (base_term_value.iloc[-1] / base_term_value.iloc[0]) ** fact - 1\\n # counts, bins, bars = plt.hist(base_ann_ret)\\n\\n # ------------------------quantile analysis for base terminal value-----------------------------\\n base_qcut = pd.DataFrame(index=sim_base_total.index, columns=cols)\\n for c in range(len(cols)):\\n base_qcut.loc[:, cols[c]] = sim_base_total.quantile(q_cut[c], axis=1)\\n\\n base_qcut.clip(lower=0, inplace=True)\\n\\n sim_base_total.clip(lower=0, inplace=True)\\n\\n # -------------------------------------quantile analysis for base income----------------------------\\n base_income_qcut = pd.DataFrame(index=sim_base_income.index, columns=cols)\\n for c in range(len(cols)):\\n base_income_qcut.loc[:, cols[c]] = sim_base_income.quantile(q_cut[c], axis=1)\\n\\n # ----Remove NaN's prior to the income start years------------\\n # base_income_qcut = base_income_qcut.loc[income_starts:]\\n\\n # ---------------------------------quantile analysis for portfolio terminal value ---------------\\n\\n port_qcut = pd.DataFrame(index=sim_port_total.index, columns=cols)\\n for c in range(len(cols)):\\n port_qcut.loc[:, cols[c]] = sim_port_total.quantile(q_cut[c], axis=1)\\n\\n port_qcut.clip(lower=0, inplace=True)\\n\\n # ----------------------------------quantile analysis for portfolio income----------------------------\\n port_income_qcut = pd.DataFrame(index=sim_port_income.index, columns=cols)\\n for c in range(len(cols)):\\n port_income_qcut.loc[:, cols[c]] = sim_port_income.quantile(q_cut[c], axis=1)\\n\\n # ----Remove NaN's prior to the income start years------------\\n # port_income_qcut = port_income_qcut.loc[income_starts:]\\n\\n # ----------probability ending value will be less than 0 at the end of the horizon -----------------------\\n # base_legacy_risk = (sim_base_total.loc[sim_base_total.index[-1]] < 0).sum() / (trials)\\n\\n base_legacy_risk = (sim_base_total.loc[sim_base_total.index[life_expectancy - clients_age]] < 0).sum() / trials\\n port_legacy_risk = (sim_port_total.loc[sim_port_total.index[life_expectancy - clients_age]] < 0).sum() / trials\\n\\n # port_legacy_risk = (sim_port_total.loc[sim_port_total.index[-1]] <= 0).sum() / (trials)\\n\\n legacy_risk = pd.DataFrame([base_legacy_risk, port_legacy_risk,\\n 'Prob. of portfolio value less than 0 at the end of the expected life'],\\n index=['base', 'fia_portfolio', 'Notes'],\\n columns=['Ruin Probability'])\\n\\n # -----------Year-wise probability of ending value greater than 0 -----------------\\n base_psuccess = sim_base_total.apply(lambda x: x > 0).sum(axis=1) / trials\\n port_psuccess = sim_port_total.apply(lambda x: x > 0).sum(axis=1) / trials\\n\\n # -----------------------WRITING FILES TO EXCEL ---------------------------\\n\\n writer = pd.ExcelWriter(dest_simulation + method + '_leveled_growth_simulation.xlsx', engine='xlsxwriter')\\n read_income_inputs.to_excel(writer, sheet_name='inputs_for_income')\\n\\n read_returns_est.to_excel(writer, sheet_name='asset_returns_estimates')\\n # read_portfolio_inputs.to_excel(writer, sheet_name='portfolio_inputs')\\n\\n age_index = list(range(clients_age + 1, clients_age + len(base_qcut) + 1))\\n # base_qcut.loc[:, 'clients_age'] = age_index\\n # base_qcut.loc[:, 'comment'] = ''\\n # base_qcut.loc[:, 'comment'] = np.where(base_qcut.clients_age == life_expectancy, 'expected_life', \\\"\\\")\\n base_inv = float(read_income_inputs.loc['risky_assets', 'Base'])\\n base_qcut.loc[:, 'age'] = age_index\\n base_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n\\n # -----------------------To start with year 0---------------------------------\\n insert_col = [base_inv, base_inv, base_inv, base_inv, base_inv, base_inv,\\n base_inv, clients_age, np.nan]\\n base_qcut.loc[len(base_qcut) + 1, :] = 0.0\\n base_qcut = base_qcut.shift(1)\\n base_qcut.iloc[0] = insert_col\\n base_qcut.reset_index(drop=True, inplace=True)\\n base_qcut.to_excel(writer, sheet_name='base_ending_value_quantiles')\\n # base_qcut.loc[income_starts:, :].to_excel(writer, sheet_name='base_ending_value_quantiles')\\n\\n # base_income_qcut = base_income_qcut[1:] base_income_qcut.loc[:, 'clients_age'] = age_index\\n # base_income_qcut.loc[:, 'comment'] = '' base_income_qcut.loc[:, 'comment'] = np.where(\\n # base_income_qcut.clients_age == life_expectancy, 'expected_life', \\\"\\\")\\n\\n base_income_qcut = base_income_qcut.loc[1:, :]\\n base_income_qcut.loc[:, 'age'] = age_index\\n base_income_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n base_income_qcut.loc[income_starts:, :].to_excel(writer, sheet_name='base_income_quantiles')\\n\\n # age_index = list(range(clients_age+1, clients_age + len(port_qcut)+1))\\n # port_qcut.loc[:, 'clients_age'] = age_index\\n # port_qcut.loc[:, 'comment'] = ''\\n # port_qcut.loc[:, 'comment'] = np.where(port_qcut.clients_age == life_expectancy, 'expected_life', \\\"\\\")\\n\\n port_qcut.loc[:, 'age'] = age_index\\n port_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n port_qcut.loc[len(port_qcut) + 1, :] = 0.0\\n port_qcut = port_qcut.shift(1)\\n port_qcut.iloc[0] = insert_col\\n port_qcut.reset_index(drop=True, inplace=True)\\n port_qcut.to_excel(writer, sheet_name='fia_port_ending_value_quantiles')\\n # port_qcut.loc[income_starts:, :].to_excel(writer, sheet_name='fia_port_ending_value_quantiles')\\n\\n # port_income_qcut = port_income_qcut[1:] port_income_qcut.loc[:, 'clients_age'] = age_index\\n # port_income_qcut.loc[:, 'comment'] = '' port_income_qcut.loc[:, 'comment'] = np.where(\\n # port_income_qcut.clients_age == life_expectancy, 'expected_life', \\\"\\\")\\n\\n port_income_qcut = port_income_qcut.loc[1:, :]\\n port_income_qcut.loc[:, 'age'] = age_index\\n port_income_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n port_income_qcut.loc[income_starts:, :].to_excel(writer, sheet_name='fia_port_income_quantiles')\\n\\n prob_success_df = pd.concat([base_psuccess, port_psuccess], axis=1)\\n prob_success_df.rename(columns={prob_success_df.columns[0]: 'prob(ending_value>0)_base',\\n prob_success_df.columns[1]: 'prob(ending_value>0)_port'}, inplace=True)\\n\\n # prob_success_df.loc[:, 'clients_age'] = age_index\\n # prob_success_df.loc[:, 'comment'] = ''\\n # prob_success_df.loc[:, 'comment'] = np.where(prob_success_df.clients_age == life_expectancy, 'expected_life', \\\"\\\")\\n\\n prob_success_df.loc[:, 'age'] = age_index\\n prob_success_df.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n prob_success_df.loc[:, 'prob(ending_value>lifetime_req income)_base'] = base_prob_of_success / trials\\n prob_success_df.loc[:, 'prob(ending_value>lifetime_req income)_port'] = port_prob_of_success / trials\\n prob_success_df.loc[:, 'prob(ending_value>next_year_req_income)_base'] = base_success_next_year / trials\\n prob_success_df.loc[:, 'prob(ending_value>next_year_req_income)_port'] = port_success_next_year / trials\\n prob_success_df.loc[:, 'base_max_portfolio_at_acturial_age'] = base_max_portfolio\\n prob_success_df.loc[:, 'port_max_portfolio_at_acturial_age'] = port_max_portfolio\\n\\n # --------------------Percentile Portfolio's based on Acturial Life------------------------\\n base_success = prob_success_df.loc[life_expectancy - clients_age, 'prob(ending_value>next_year_req_income)_base']\\n port_success = prob_success_df.loc[life_expectancy - clients_age, 'prob(ending_value>next_year_req_income)_port']\\n\\n # acturial_age_base_tv = sim_base_total.loc[:life_expectancy - clients_age, ]\\n # percentile_base_tv = sim_base_total.apply(lambda x: np.nanpercentile(x, base_success), axis=1)\\n\\n # ----------------Year wise percentile portfolio to meet next year income. Based on the success at acturial age.\\n # Yearly portfolio values that can provide the next year income below the success rate at end of life (Percentile)-\\n\\n # acturial_age_base = base_for_next_year_need.loc[:life_expectancy - clients_age, ]\\n # acturial_age_base = base_for_next_year_need.copy().fillna(0)\\n percentile_base = base_for_next_year_need.apply(lambda x: np.nanpercentile(x, base_success), axis=1)\\n\\n # ----Pre Income Portfolio based on the Probab. of Success to meet next year's income at the end on the Act. Age\\n base_pre_income_success = sim_base_total_preincome.apply(lambda x: np.nanpercentile(x, base_success), axis=1)\\n base_ann_ret_pre_income = base_pre_income_success.pct_change().fillna(0)\\n\\n # acturial_age_port_tv = sim_port_total.loc[:life_expectancy - clients_age, ]\\n # percentile_port_tv = sim_port_total.apply(lambda x: np.nanpercentile(x, port_success), axis=1)\\n\\n # ----------------Year wise percentile portfolio to meet next year income. Based on the success at acturial age.\\n # Yearly portfolio values that can provide the next year income below the success rate at end of life (Percentile)-\\n\\n # acturial_age_port = port_for_next_year_need.loc[:life_expectancy - clients_age, ]\\n # acturial_age_base = port_for_next_year_need.copy().fillna(0)\\n percentile_port = port_for_next_year_need.apply(lambda x: np.nanpercentile(x, port_success), axis=1)\\n\\n # ----Pre Income Portfolio based on the Probab. of Success to meet next year's income at the end on the Act. Age\\n port_pre_income_success = sim_port_total_preincome.apply(lambda x: np.nanpercentile(x, port_success), axis=1)\\n port_ann_ret_pre_income = port_pre_income_success.pct_change().fillna(0)\\n\\n prob_success_df.loc[:, 'acturial_success_percentile_base_portfolio'] = percentile_base\\n prob_success_df.loc[:, 'acturial_success_percentile_port_portfolio'] = percentile_port\\n\\n prob_success_df.loc[:, 'base_pre_income_ann_ret'] = base_ann_ret_pre_income\\n prob_success_df.loc[:, 'port_pre_income_ann_ret'] = port_ann_ret_pre_income\\n\\n # prob_success_df.loc[:, 'terminalVal_success_percentile_base_portfolio'] = percentile_base_tv\\n # prob_success_df.loc[:, 'terminalVal_success_percentile_port_portfolio'] = percentile_port_tv\\n\\n sim_base_total_preincome.to_excel(writer, sheet_name='base_preincome_portfolios')\\n # -------Add premium to year 0 value to get total portfolio value---------\\n sim_port_total_preincome.iloc[0] = sim_port_total_preincome.iloc[0] + premium\\n sim_port_total_preincome.to_excel(writer, sheet_name='port_preincome_portfolios')\\n\\n # -------------For Simulation slide - BASE Portfolio - Can Delete --------------------\\n # base_qcut_preinc = pd.DataFrame(index=sim_base_total_preincome.index, columns=cols)\\n # for c in range(len(cols)):\\n # base_qcut_preinc.loc[:, cols[c]] = sim_base_total_preincome.quantile(q_cut[c], axis=1)\\n #\\n # # -------------For Simulation slide - Proposed Portfolio --------------------\\n # port_qcut_preinc = pd.DataFrame(index=sim_port_total_preincome.index, columns=cols)\\n # for c in range(len(cols)):\\n # port_qcut_preinc.loc[:, cols[c]] = sim_port_total_preincome.quantile(q_cut[c], axis=1)\\n #\\n # base_qcut_preinc.to_excel(writer, sheet_name='base_preincome_quantiles')\\n # port_qcut_preinc.to_excel(writer, sheet_name='port_preincome_quantiles')\\n\\n prob_success_df.to_excel(writer, sheet_name='success_probability')\\n\\n # --------------BASE - Accumulation and Income Breakdown based on the success percentile portfolio---------------\\n base_df.to_csv(src + 'base_port_detail.csv')\\n sim_base_total.to_csv(src + 'base_ending_values.csv')\\n income_breakdown_base = pd.DataFrame(sim_base_total.quantile(base_success, axis=1))\\n income_breakdown_base.loc[:, 'income_from_risky_assets'] = sim_base_income.quantile(base_success, axis=1) \\\\\\n - social - cpn_income_port\\n income_breakdown_base.loc[:, 'guaranteed_income'] = 0.0\\n income_breakdown_base.loc[:, 'social_security_income'] = social\\n income_breakdown_base.loc[:, 'coupon_income'] = cpn_income_base\\n\\n income_breakdown_base.rename(columns={income_breakdown_base.columns[0]: 'portfolio_ending_value'}, inplace=True)\\n income_breakdown_base.loc[:, 'income_from_risky_assets'][\\n income_breakdown_base.loc[:, 'portfolio_ending_value'] <= 0] = 0\\n income_breakdown_base.loc[:, 'total_income'] = income_breakdown_base.loc[:, income_breakdown_base.columns[1:]].sum(\\n axis=1)\\n\\n # ----------FIA PORTFOLIO - Accumulation and Income Breakdown based on the success percentile portfolio-----------\\n fia_portfolio_df.to_csv(src + 'fia_port_detail.csv')\\n sim_port_total.to_csv(src + 'fiaport_ending_values.csv')\\n\\n income_breakdown_port = pd.DataFrame(sim_port_total.quantile(port_success, axis=1))\\n income_breakdown_port.loc[:, 'income_from_risky_assets'] = sim_port_income.quantile(port_success, axis=1) \\\\\\n - income_from_fia - social - cpn_income_port\\n income_breakdown_port.loc[:, 'guaranteed_income'] = income_from_fia\\n income_breakdown_port.loc[:, 'social_security_income'] = social\\n income_breakdown_port.loc[:, 'coupon_income'] = cpn_income_port\\n\\n income_breakdown_port.rename(columns={income_breakdown_port.columns[0]: 'portfolio_ending_value'}, inplace=True)\\n income_breakdown_port.loc[:, 'income_from_risky_assets'][\\n income_breakdown_port.loc[:, 'portfolio_ending_value'] <= 0] = 0\\n income_breakdown_port.loc[:, 'total_income'] = income_breakdown_port.loc[:, income_breakdown_port.columns[1:]].sum(\\n axis=1)\\n\\n # -------------------Write simulation Statistics-------------------------------------\\n simulation_stats.to_excel(writer, sheet_name='simulation_statistics')\\n\\n # port_psuccess.to_excel(writer, sheet_name='fia_port_success_probability')\\n\\n income_breakdown_base = income_breakdown_base.loc[1:, :]\\n income_breakdown_base.loc[:, 'age'] = age_index\\n income_breakdown_base.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n income_breakdown_base.loc[income_starts:, :].to_excel(writer, sheet_name='base_income_breakdown_median')\\n\\n income_breakdown_port = income_breakdown_port.loc[1:, :]\\n income_breakdown_port.loc[:, 'age'] = age_index\\n income_breakdown_port.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n income_breakdown_port.loc[income_starts:, :].to_excel(writer, sheet_name='fia_income_breakdown_median')\\n\\n legacy_risk.to_excel(writer, sheet_name='ruin_probability')\\n\\n # if method == 'normal':\\n # median_returns_normal.loc[:, 'fia_median_returns'] = median_normal_fia\\n # median_returns_normal.to_excel(writer, sheet_name='gr_port_median_normal')\\n #\\n # elif method == 'smallest':\\n # median_returns_smallest.loc[:, 'fia_median_returns'] = median_smallest_fia\\n # median_returns_smallest.to_excel(writer, sheet_name='gr_port_median_asc')\\n #\\n # else:\\n # median_returns_largest.loc[:, 'fia_median_returns'] = median_largest_fia\\n # median_returns_largest.to_excel(writer, sheet_name='gr_port_median_desc')\\n\\n # ---------------------Histogram for S&P Forecast---------------------------------------\\n sp_returns = read_returns_est.loc['SPXT Index', 'Annualized Returns']\\n sp_risk = read_returns_est.loc['SPXT Index', 'Annualized Risk']\\n sp_random_ret = np.random.normal(loc=sp_returns, scale=sp_risk, size=10000)\\n bins, data = np.histogram(sp_random_ret, bins=20)\\n df_ret = pd.DataFrame(data, columns=['Return_range'])\\n df_bins = pd.DataFrame(bins, columns=['Count'])\\n df_hist = df_ret.join(df_bins)\\n\\n df_hist.to_excel(writer, sheet_name='sp500_histogram')\\n writer.save()\\n\\n print(\\\"simulation completed....\\\")\",\n \"def _run_backtest(self):\\n i = 0\\n\\n while True:\\n i += 1\\n print(i)\\n\\n # Update the market bars\\n if self.data_handler.continue_backtest == True:\\n self.data_handler.update_bars()\\n else:\\n break\\n\\n # Handle the Events\\n while True:\\n try:\\n event = self.events.get(False)\\n except queue.Empty:\\n break\\n else:\\n # The inner-loop acts on the events by calling the appropriate method of the appropriate object\\n if event is not None:\\n if event.type == 'MARKET':\\n self.strategy.calculate_signals(event)\\n self.portfolio.update_timeindex(event)\\n\\n elif event.type == 'SIGNAL':\\n self.signals += 1\\n self.portfolio.update_signal(event)\\n\\n elif event.type == 'ORDER':\\n self.orders += 1\\n self.execution_handler.execute_order(event)\\n\\n elif event.type == 'FILL':\\n self.fills += 1\\n self.portfolio.update_fill(event)\\n\\n # Pauses for a duration of self.heartbeat seconds\\n time.sleep(self.heartbeat)\",\n \"def income_model_asset_based_portfolio_custom(num_of_years=30, trials=100, method='normal', income=True):\\n\\n sim_fia_cv = pd.DataFrame(index=range(num_of_years + 1))\\n\\n sim_base_total = pd.DataFrame(index=range(num_of_years + 1))\\n sim_base_income = pd.DataFrame(index=range(num_of_years + 1))\\n\\n sim_port_total = pd.DataFrame(index=range(num_of_years + 1))\\n sim_port_income = pd.DataFrame(index=range(num_of_years + 1))\\n\\n sim_base_total_pre_income = pd.DataFrame(index=range(num_of_years + 1))\\n sim_port_total_pre_income = pd.DataFrame(index=range(num_of_years + 1))\\n\\n # read_income_inputs = pd.read_csv(src + \\\"income_model_inputs.csv\\\", index_col='Items')\\n read_income_inputs = pd.read_excel(src + \\\"portfolio_information.xlsx\\\", sheet_name='income_model_inputs',\\n index_col=[0])\\n\\n # read_returns_est = pd.read_csv(src + \\\"income_assets_returns_estimates.csv\\\", index_col='Symbol')\\n read_returns_est = pd.read_excel(src + \\\"portfolio_information.xlsx\\\", sheet_name='income_assets_returns_estimates',\\n index_col=[0])\\n\\n clean_names = list(read_returns_est.index)\\n clean_names = [s.split(' ')[0] for s in clean_names]\\n read_returns_est.loc[:, 'names'] = clean_names\\n read_returns_est.set_index('names', drop=True, inplace=True)\\n read_returns_est = read_returns_est[:-1]\\n read_returns_est.rename(index={'SBMMTB3': 'Cash', read_returns_est.index[-1]: 'FIA'}, inplace=True)\\n\\n # ---------------Returns DataFrame based on the use input------------------------------------\\n ann_ret = np.full((num_of_years + 1, len(read_returns_est)), read_returns_est.loc[:, 'Annualized Returns'])\\n read_normal = pd.DataFrame(ann_ret, index=np.arange(num_of_years + 1), columns=read_returns_est.index)\\n # read_normal.rename(columns={read_normal.columns[-1]: 'FIA'}, inplace=True)\\n user_est_fia_return = float(read_income_inputs.loc['fia_forecast', 'inputs'])\\n read_normal.loc[:, 'FIA'] = user_est_fia_return\\n\\n read_returns_est.loc['FIA', 'Annualized Returns'] = user_est_fia_return\\n\\n # read_returns_est.drop(['BM', read_returns_est.index[-1]], axis=0, inplace=True)\\n # read_portfolio_inputs = pd.read_csv(src + \\\"income_portfolio_inputs.csv\\\", index_col='Items')\\n\\n # read_asset_weights = pd.read_csv(src + \\\"asset_weights.csv\\\", index_col='Asset')\\n read_asset_weights = pd.read_excel(src + \\\"portfolio_information.xlsx\\\", sheet_name='asset_weights',\\n index_col=[0])\\n\\n read_asset_weights.drop(read_asset_weights.index[-1], axis=0, inplace=True)\\n\\n # read random returns for simulation\\n # read_normal = pd.read_csv(src + 'sort_normal.csv', index_col=[0], parse_dates=True)\\n # read_small = pd.read_csv(src + 'sort_small_to_large.csv', index_col=[0], parse_dates=True)\\n # read_large = pd.read_csv(src + 'sort_large_to_small.csv', index_col=[0], parse_dates=True)\\n assets_col_names = list(read_normal.columns)\\n\\n tickers = list(read_asset_weights.index)\\n wts = np.array(read_asset_weights.loc[:, 'base'])\\n\\n def asset_median_returns(data, ticker):\\n return data.filter(regex=ticker).median(axis=1)\\n\\n # dataframe for unsorted returns (normal)\\n median_returns_normal = pd.DataFrame({t: asset_median_returns(read_normal, t) for t in tickers})\\n median_returns_normal.loc[:, 'portfolio_return'] = median_returns_normal.dot(wts)\\n median_normal_fia = pd.DataFrame({'FIA': asset_median_returns(read_normal, 'FIA')})\\n #\\n # # dataframe for smallest to largest returns\\n # median_returns_smallest = pd.DataFrame({t: asset_median_returns(read_small, t) for t in tickers})\\n # median_returns_smallest.loc[:, 'portfolio_return'] = median_returns_smallest.dot(wts)\\n # median_smallest_fia = pd.DataFrame({'FIA': asset_median_returns(read_small, 'r_FIA')})\\n #\\n # # dataframe for unsorted returns (normal)\\n # median_returns_largest = pd.DataFrame({t: asset_median_returns(read_large, t) for t in tickers})\\n # median_returns_largest.loc[:, 'portfolio_return'] = median_returns_largest.dot(wts)\\n # median_largest_fia = pd.DataFrame({'FIA': asset_median_returns(read_large, 'r_FIA')})\\n\\n years = list(range(0, num_of_years + 1))\\n income_cols = ['year', 'strategy_term', 'index_returns', 'term_ret', 'term_ret_with_par', 'term_annualize',\\n 'ann_net_spread', 'term_ret_netspr', 'high_inc_benefit_base', 'rider_fee', 'eoy_income',\\n 'contract_value']\\n\\n term = int(read_income_inputs.loc['term', 'inputs'])\\n fia_ret = read_returns_est.loc[read_returns_est.index[-1], 'Annualized Returns']\\n fia_risk = read_returns_est.loc[read_returns_est.index[-1], 'Annualized Risk']\\n par_rate = float(read_income_inputs.loc['par_rate', 'inputs'])\\n spread = float(read_income_inputs.loc['spread', 'inputs'])\\n bonus_term = int(read_income_inputs.loc['bonus_term', 'inputs'])\\n premium = float(read_income_inputs.loc['premium', 'inputs'])\\n income_bonus = float(read_income_inputs.loc['income_bonus', 'inputs'])\\n\\n income_starts = int(read_income_inputs.loc['start_income_years', 'inputs'])\\n income_growth = float(read_income_inputs.loc['income_growth', 'inputs'])\\n rider_fee = float(read_income_inputs.loc['rider_fee', 'inputs'])\\n inc_payout_factor = float(read_income_inputs.loc['income_payout_factor', 'inputs'])\\n contract_bonus = float(read_income_inputs.loc['contract_bonus', 'inputs'])\\n social = float(read_income_inputs.loc['social', 'inputs'])\\n inflation = float(read_income_inputs.loc['inflation', 'inputs'])\\n wtd_cpn_yield = float(read_income_inputs.loc['wtd_coupon_yld', 'inputs'])\\n life_expectancy = int(read_income_inputs.loc['life_expectancy_age', 'inputs'])\\n clients_age = int(read_income_inputs.loc['clients_age', 'inputs'])\\n\\n # ---------------INCOME MODEL--------------------------------------------\\n runs = 0\\n returns_dict = {}\\n asset_dict = {}\\n fia_dict = {}\\n while runs < trials:\\n print(runs)\\n\\n income_df = pd.DataFrame(index=years, columns=income_cols)\\n income_df.loc[:, 'year'] = years\\n income_df.loc[:, 'strategy_term'] = income_df.loc[:, 'year'] % term\\n income_df.loc[:, 'strategy_term'] = income_df['strategy_term'].apply(lambda x: 1 if x == 0 else 0)\\n\\n income_df.loc[:, 'index_returns'] = read_normal.loc[:, 'FIA']\\n # income_df.loc[:, 'index_returns'] = np.random.normal(fia_ret, fia_risk, size=(len(years), 1))\\n\\n cumprod = (1. + income_df['index_returns']).rolling(window=term).agg(lambda x: x.prod()) - 1\\n income_df.loc[:, 'term_ret'] = np.where(income_df.loc[:, 'strategy_term'] == 1, cumprod, 0)\\n income_df.loc[:, 'term_ret_with_par'] = income_df.loc[:, 'term_ret'] * par_rate\\n income_df.loc[:, 'term_annualize'] = income_df.loc[:, 'term_ret_with_par'].apply(\\n lambda x: (1 + x) ** (1 / term) - 1)\\n income_df.loc[:, 'ann_net_spread'] = income_df.loc[:, 'term_annualize'] - spread\\n income_df.loc[:, 'ann_net_spread'] = np.where(income_df.loc[:, 'strategy_term'] == 1,\\n income_df.loc[:, 'ann_net_spread'], 0)\\n income_df.loc[:, 'term_ret_netspr'] = income_df.loc[:, 'ann_net_spread'].apply(lambda x: (1 + x) ** term - 1)\\n\\n for counter in years:\\n if counter == 0:\\n income_df.loc[counter, 'high_inc_benefit_base'] = premium * (1 + income_bonus)\\n\\n elif counter <= min(bonus_term, income_starts):\\n income_df.loc[counter, 'high_inc_benefit_base'] = income_df.loc[counter - 1, 'high_inc_benefit_base'] * \\\\\\n (1 + income_growth)\\n else:\\n income_df.loc[counter, 'high_inc_benefit_base'] = income_df.loc[counter - 1, 'high_inc_benefit_base']\\n\\n income_df.loc[:, 'rider_fee'] = income_df.loc[:, 'high_inc_benefit_base'] * rider_fee\\n income_df.loc[:, 'eoy_income'] = np.where(income_df.loc[:, 'year'] > income_starts,\\n income_df.loc[:, 'high_inc_benefit_base'] * inc_payout_factor, 0)\\n\\n for counter in years:\\n if counter == 0:\\n income_df.loc[counter, 'contract_value'] = premium * (1 + contract_bonus)\\n\\n elif income_df.loc[counter, 'strategy_term'] == 1:\\n x1 = income_df.loc[counter - 1, 'contract_value'] - income_df.loc[counter, 'rider_fee']\\n x2 = (x1 * (1 + income_df.loc[counter, 'term_ret_netspr'])) - income_df.loc[counter, 'eoy_income']\\n income_df.loc[counter, 'contract_value'] = x2\\n\\n else:\\n x1 = income_df.loc[counter - 1, 'contract_value'] - income_df.loc[counter, 'rider_fee'] - \\\\\\n income_df.loc[counter, 'eoy_income']\\n\\n income_df.loc[counter, 'contract_value'] = x1\\n\\n # variable stores the income number that is used in the base and fia portfolio calcs.\\n\\n income_from_fia = income_df.loc[income_df.index[-1], 'eoy_income']\\n\\n income_df.loc[:, 'contract_value'] = income_df.loc[:, 'contract_value'].apply(lambda x: 0 if x <= 0 else x)\\n\\n sim_fia_cv.loc[:, str(runs)] = income_df.loc[:, 'contract_value']\\n\\n # -------------------------------------BASE MODEL---------------------------------------------\\n\\n base_wts = read_asset_weights.loc[:, 'base']\\n base_assets = list(base_wts.index)\\n base_weights = list(base_wts.values)\\n base_returns = list(read_returns_est.loc[:, 'Annualized Returns'].values)\\n base_std = list(read_returns_est.loc[:, 'Annualized Risk'].values)\\n\\n base_investment = float(read_income_inputs.loc['risky_assets', 'Base'])\\n adv_fees = float(read_income_inputs.loc['advisor_fees', 'Base'])\\n\\n # -------------------required income----------------------------------\\n if income:\\n req_annual_income = float(read_income_inputs.loc['annual_income', 'inputs'])\\n income_needed = req_annual_income - social\\n income_net_fia_income = max(0, income_needed - income_from_fia)\\n cpn_income_base = base_investment * wtd_cpn_yield\\n else:\\n req_annual_income = 0.0\\n income_needed = 0.0\\n income_net_fia_income = 0.0\\n cpn_income_base = base_investment * wtd_cpn_yield\\n\\n # ----------------------RANDOM RETURNS--------------------------\\n r_cols = ['r_{}'.format(name) for name in base_assets]\\n boy_value = ['bv_{}'.format(name) for name in base_assets]\\n eoy_value = ['ev_{}'.format(name) for name in base_assets]\\n\\n random_returns = pd.DataFrame(index=income_df.index, columns=r_cols)\\n\\n # for c in range(len(r_cols)):\\n # ret = np.random.normal(base_returns[c], base_std[c], size=(len(random_returns.index), 1))\\n\\n # this_run_cols = ['{}_{}'.format(cname, str(runs)) for cname in r_cols]\\n random_returns = read_normal.loc[:, base_assets]\\n\\n # random_returns.loc[:, r_cols[c]] = ret.flatten()\\n # asset_dict.update({'{}_{}'.format(r_cols[c], str(runs)): ret.flatten()})\\n\\n # store the simulated assets returns in one dictionary\\n # returns_dict.update({str(runs): random_returns})\\n\\n # collect the asset based returns from all simulation and calculate the median returns.\\n # def get_median_returns(sym):\\n # cols = [sym + '_' + str(c) for c in np.arange(trials)]\\n # asset_df = pd.DataFrame({c: asset_dict.get(c) for c in cols})\\n # return asset_df.median(axis=1)\\n #\\n # asset_median_returns = pd.DataFrame({symbol: get_median_returns(symbol) for symbol in r_cols})\\n #\\n # asset_median_returns.loc[:, 'simulated_portfolio_median_returns'] = asset_median_returns.dot(base_weights)\\n\\n base_df = random_returns.copy()\\n\\n # base_investment = float(read_portfolio_inputs.loc['risky_assets', 'Base'])\\n\\n fia_portfolio_df = random_returns.copy()\\n port_investment = float(read_income_inputs.loc['risky_assets', 'FIA'])\\n cpn_income_port = port_investment * wtd_cpn_yield\\n\\n # ----------------------------------------BASE PORTFOLIO----------------------------\\n for name in boy_value:\\n base_df.loc[:, name] = 0.0\\n\\n for counter in years:\\n period_returns = list(random_returns.loc[counter, :])\\n if counter == 0:\\n\\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment for c in range(len(boy_value))]\\n\\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\\n base_df.loc[counter, 'total_net_fees'] = 0.0\\n base_df.loc[counter, 'income'] = 0.0\\n base_investment = base_df.loc[counter, boy_value].sum()\\n\\n elif (counter > 0) and (counter < income_starts):\\n\\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment * (1 + period_returns[c])\\n for c in range(len(boy_value))]\\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\\n base_df.loc[counter, 'adv_fees'] = base_df.loc[counter, 'total'] * adv_fees\\n base_df.loc[counter, 'total_net_fees'] = base_df.loc[counter, 'total'] - base_df.loc[\\n counter, 'adv_fees']\\n\\n # --coupon payment is invested back into the risky portfolio until the income is withdrawn----\\n base_investment = base_df.loc[counter, 'total_net_fees'] + cpn_income_base\\n\\n else:\\n\\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment * (1 + period_returns[c])\\n for c in range(len(boy_value))]\\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\\n base_df.loc[counter, 'adv_fees'] = base_df.loc[counter, 'total'] * adv_fees\\n\\n # ---req. income is adjusted for inflation from the second year of withdrawal. Reinvestment of coupon\\n # stops from the year income starts. Req. income is reduced by the coupon payments\\n\\n if counter == income_starts:\\n\\n income_needed = req_annual_income - social\\n base_df.loc[counter, 'income'] = income_needed - cpn_income_base\\n income_needed = req_annual_income\\n\\n else:\\n income_needed = income_needed * (1 + inflation) - social\\n base_df.loc[counter, 'income'] = income_needed - cpn_income_base\\n income_needed = income_needed + social\\n\\n base_df.loc[counter, 'total_net_fees'] = base_df.loc[counter, 'total'] - \\\\\\n base_df.loc[counter, 'adv_fees'] - \\\\\\n base_df.loc[counter, 'income']\\n\\n base_df.loc[counter, 'total_pre_income'] = base_df.loc[counter, 'total'] - \\\\\\n base_df.loc[counter, 'adv_fees']\\n\\n base_investment = base_df.loc[counter, 'total_net_fees']\\n\\n base_df.loc[:, 'adj_total'] = base_df.loc[:, 'total_net_fees'].apply(lambda x: x if x > 0 else 0)\\n sim_base_total.loc[:, 's_{}'.format(str(runs))] = base_df.loc[:, 'total_net_fees']\\n sim_base_income.loc[:, 's_{}'.format(str(runs))] = base_df.loc[:, 'income']\\n sim_base_total_pre_income.loc[:, 's_{}'.format(str(runs))] = base_df.loc[:, 'total_pre_income']\\n\\n # ----------------------------FIA PORTFOLIO----------------------------------------------\\n for name in boy_value:\\n fia_portfolio_df.loc[:, name] = 0.0\\n\\n for counter in years:\\n period_returns = list(random_returns.loc[counter, :])\\n if counter == 0:\\n\\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment\\n for c in range(len(boy_value))]\\n fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\\n fia_portfolio_df.loc[counter, 'total_net_fees'] = 0.0\\n fia_portfolio_df.loc[counter, 'income'] = 0.0\\n port_investment = fia_portfolio_df.loc[counter, boy_value].sum()\\n\\n elif (counter > 0) and (counter < income_starts):\\n\\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment * (1 + period_returns[c])\\n for c in range(len(boy_value))]\\n fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\\n fia_portfolio_df.loc[counter, 'adv_fees'] = fia_portfolio_df.loc[counter, 'total'] * adv_fees\\n fia_portfolio_df.loc[counter, 'total_net_fees'] = fia_portfolio_df.loc[counter, 'total'] - \\\\\\n fia_portfolio_df.loc[counter, 'adv_fees']\\n\\n port_investment = fia_portfolio_df.loc[counter, 'total_net_fees'] + cpn_income_port\\n\\n else:\\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment * (1 + period_returns[c])\\n for c in range(len(boy_value))]\\n fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\\n fia_portfolio_df.loc[counter, 'adv_fees'] = fia_portfolio_df.loc[counter, 'total'] * adv_fees\\n\\n # ---req. income is adjusted for inflation from the second year of withdrawal. Reinvestment of coupon\\n # stops from the year income starts. Req. income is reduced by the coupon payments\\n\\n if counter == income_starts:\\n\\n income_needed = req_annual_income - social\\n income_net_fia_income = max(0, income_needed - income_from_fia)\\n fia_portfolio_df.loc[counter, 'income'] = max(0, income_net_fia_income - cpn_income_port)\\n income_needed = req_annual_income\\n\\n else:\\n income_needed = income_needed * (1 + inflation) - social\\n income_net_fia_income = max(0, income_needed - income_from_fia)\\n fia_portfolio_df.loc[counter, 'income'] = max(0, income_net_fia_income - cpn_income_port)\\n income_needed = income_needed + social\\n\\n if income:\\n fia_portfolio_df.loc[counter, 'total_net_fees'] = fia_portfolio_df.loc[counter, 'total'] - \\\\\\n fia_portfolio_df.loc[counter, 'adv_fees'] - \\\\\\n fia_portfolio_df.loc[counter, 'income']\\n else:\\n fia_portfolio_df.loc[counter, 'total_net_fees'] = fia_portfolio_df.loc[counter, 'total'] - \\\\\\n fia_portfolio_df.loc[counter, 'adv_fees'] + \\\\\\n income_from_fia\\n\\n fia_portfolio_df.loc[counter, 'total_pre_income'] = fia_portfolio_df.loc[counter, 'total'] - \\\\\\n fia_portfolio_df.loc[counter, 'adv_fees']\\n\\n port_investment = fia_portfolio_df.loc[counter, 'total_net_fees']\\n\\n sim_port_total.loc[:, 's_{}'.format(str(runs))] = fia_portfolio_df.loc[:, 'total_net_fees'] + \\\\\\n income_df.loc[:, 'contract_value']\\n\\n sim_port_income.loc[:, 's_{}'.format(str(runs))] = fia_portfolio_df.loc[:, 'income']\\n\\n fia_portfolio_df.loc[:, 'adj_total'] = fia_portfolio_df.loc[:, 'total_net_fees'].apply(\\n lambda x: x if x > 0 else 0)\\n\\n sim_port_total_pre_income.loc[:, 's_{}'.format(str(runs))] = fia_portfolio_df.loc[:, 'total_pre_income']\\n\\n runs += 1\\n\\n # ------------------Calculate % of portfolios ending value greater than required LIFETIME cumm. income---------\\n total_income_by_age = sim_base_income.loc[:, sim_base_income.columns[0]].cumsum()\\n total_income_by_acturial_age = total_income_by_age.loc[life_expectancy - clients_age]\\n total_income_by_age.fillna(0, inplace=True)\\n income_dataframe = pd.DataFrame(total_income_by_age)\\n income_dataframe.loc[:, 'remaining_income_by_acturial_age'] = total_income_by_age.apply(\\n lambda x: total_income_by_acturial_age - x)\\n\\n s = income_dataframe.loc[:, 'remaining_income_by_acturial_age']\\n base_prob_of_success = sim_base_total.gt(s, axis=0).sum(axis=1)\\n port_prob_of_success = sim_port_total.gt(s, axis=0).sum(axis=1)\\n\\n # ----------------------------Portfolio sufficient for NEXT YEARS income needs-------------------\\n next_year_income = sim_base_income.loc[:, sim_base_income.columns[0]].shift(-1).fillna(0) # Yearly Income Reqd.\\n base_success_next_year = sim_base_total.gt(next_year_income, axis=0).sum(axis=1)\\n\\n base_for_next_year_need = sim_base_total[sim_base_total.gt(next_year_income, axis=0)]\\n\\n port_success_next_year = sim_port_total.gt(next_year_income, axis=0).sum(axis=1)\\n\\n port_for_next_year_need = sim_port_total[sim_port_total.gt(next_year_income, axis=0)]\\n\\n # ---------------Portfolio for 45 years of simulation---------------------------------------\\n base_success_portfolio = sim_base_total[sim_base_total.gt(next_year_income, axis=0)]\\n port_success_portfolio = sim_port_total[sim_port_total.gt(next_year_income, axis=0)]\\n\\n # ----------------Portfolio Simulation until the acturial age------------------------------\\n acturial_years = life_expectancy - clients_age\\n base_success_portfolio_act_age = base_success_portfolio.loc[acturial_years, :]\\n port_success_portfolio_act_age = port_success_portfolio.loc[acturial_years, :]\\n\\n # -------------------------Base Portfolio TS with max Terminal Value ----------------------------\\n if base_success_portfolio_act_age.isnull().sum() == trials:\\n base_max_portfolio = 0.0\\n else:\\n base_max_portfolio = base_success_portfolio.loc[:, base_success_portfolio_act_age.idxmax()]\\n\\n # -------------------------FIA Portfolio TS with max Terminal Value ----------------------------\\n if port_success_portfolio_act_age.isnull().sum() == trials:\\n port_max_portfolio = 0.0\\n else:\\n port_max_portfolio = port_success_portfolio.loc[:, port_success_portfolio_act_age.idxmax()]\\n # ------------------------------Average age with full income------------------------------\\n base_mean_age = ((life_expectancy - clients_age) - base_success_portfolio.loc[1:life_expectancy - clients_age, :]\\n .isnull().sum()).mean()\\n\\n port_mean_age = ((life_expectancy - clients_age) - port_success_portfolio.loc[1:life_expectancy - clients_age, :]\\n .isnull().sum()).mean()\\n\\n # ----------------------------Median Age with full Income------------------------------------------\\n base_median_age = ((life_expectancy - clients_age) - base_success_portfolio.loc[1:life_expectancy - clients_age, :]\\n .isnull().sum()).median()\\n\\n port_median_age = ((life_expectancy - clients_age) - port_success_portfolio.loc[1:life_expectancy - clients_age, :]\\n .isnull().sum()).median()\\n\\n # --------------Mean Value for all the portfolios at end of the acturial age--------------------\\n base_act_avg_porfolio = base_success_portfolio.loc[life_expectancy - clients_age, :].fillna(0).mean()\\n port_act_avg_porfolio = port_success_portfolio.loc[life_expectancy - clients_age, :].fillna(0).mean()\\n\\n # --------------Median Value for all the portfolios at end of the acturial age--------------------\\n base_act_median_porfolio = base_success_portfolio.loc[life_expectancy - clients_age, :].fillna(0).median()\\n port_act_median_porfolio = port_success_portfolio.loc[life_expectancy - clients_age, :].fillna(0).median()\\n\\n # # --------------Mean Value for all the portfolios in the simulation--------------------\\n # base_sim_mean = base_success_portfolio.loc[1:life_expectancy - clients_age, :].mean().mean()\\n # port_sim_mean = port_success_portfolio.loc[1:life_expectancy - clients_age, :].mean().mean()\\n #\\n # # --------------Median Value for all the portfolios in the simulation--------------------\\n # base_sim_mean = base_success_portfolio.loc[1:life_expectancy - clients_age, :].mean().median()\\n # port_sim_mean = port_success_portfolio.loc[1:life_expectancy - clients_age, :].mean().median()\\n\\n # -------Max Portfolio value at the end of acturial age----------------------------------------\\n base_act_max = base_success_portfolio.loc[life_expectancy - clients_age, :].max()\\n port_act_max = port_success_portfolio.loc[life_expectancy - clients_age, :].max()\\n\\n # -------Min Portfolio value at the end of acturial age----------------------------------------\\n base_act_min = base_success_portfolio.loc[life_expectancy - clients_age, :].min()\\n port_act_min = port_success_portfolio.loc[life_expectancy - clients_age, :].min()\\n\\n # ---------------------Lifetime Average Income----------------------------------\\n base_total_income = sim_base_income.cumsum().loc[acturial_years, :].mean()\\n port_total_income = income_from_fia + sim_port_income\\n port_total_income = port_total_income.cumsum().loc[acturial_years, :].mean()\\n\\n simulation_stats = pd.DataFrame(index=['Average Years', 'Median Years', 'Average Age', 'Median Age',\\n 'Average Portfolio (act.age)', 'Median Portfolio (act.age)',\\n 'Max Portfolio Value', 'Min Portfolio Value',\\n 'Average Lifetime Income'], columns=['Base', 'FIA'])\\n\\n simulation_stats.loc['Average Years', :] = [base_mean_age, base_mean_age]\\n simulation_stats.loc['Median Years', :] = [base_median_age, base_median_age]\\n simulation_stats.loc['Average Age', :] = [base_mean_age + clients_age, base_mean_age + clients_age]\\n simulation_stats.loc['Median Age', :] = [base_median_age + clients_age, base_median_age + clients_age]\\n simulation_stats.loc['Average Portfolio (act.age)', :] = [base_act_avg_porfolio, port_act_avg_porfolio]\\n simulation_stats.loc['Median Portfolio (act.age)', :] = [base_act_median_porfolio, port_act_median_porfolio]\\n simulation_stats.loc['Max Portfolio Value', :] = [base_act_max, port_act_max]\\n simulation_stats.loc['Min Portfolio Value', :] = [base_act_min, port_act_min]\\n simulation_stats.loc['Average Lifetime Income', :] = [base_total_income, port_total_income]\\n comments = ['Average years of portfolios that meet the next years income needs for the lifetime',\\n 'Median years of portfolios that meet the next years income needs for the lifetime',\\n 'Average Clients Age',\\n 'Median Clients Age',\\n 'Average of terminal values for the portfolios at the end of the acturial life',\\n 'Median of terminal values for the portfolios at the end of the acturial life',\\n 'Maximum of terminal values for the portfolios at the end of the acturial life',\\n 'Minimum of terminal values for the portfolios at the end of the acturial life',\\n 'Average of total income generated by all portfolios at the end of the acturial life']\\n\\n simulation_stats.loc[:, 'Notes'] = comments\\n\\n # --------------------------------------------------------------------------------\\n\\n # # -----------------------------------income breakdown for Base portfolio----------------------------------\\n # base_df.to_csv(src + 'base_port_detail.csv')\\n # sim_base_total.to_csv(src + 'base_ending_values.csv')\\n # income_breakdown_base = pd.DataFrame(sim_base_total.quantile(0.5, axis=1))\\n # income_breakdown_base.loc[:, 'income_from_portfolio'] = sim_base_income.quantile(0.5, axis=1)\\n # income_breakdown_base.loc[:, 'fia_income'] = 0.0\\n # income_breakdown_base.loc[:, 'social_security_income'] = social\\n # income_breakdown_base.loc[:, 'coupon_income'] = cpn_income_base\\n #\\n # income_breakdown_base.rename(columns={income_breakdown_base.columns[0]: 'portfolio_ending_value'}, inplace=True)\\n # income_breakdown_base.loc[:, 'income_from_portfolio'][\\n # income_breakdown_base.loc[:, 'portfolio_ending_value'] <= 0] = 0\\n # income_breakdown_base.loc[:, 'total_income'] = income_breakdown_base.loc[:, income_breakdown_base.columns[1:]].sum(\\n # axis=1)\\n #\\n # # --------------------------------------Block Ends-----------------------------------------------------------\\n #\\n # # ---------------------------------------income breakdown for FIA portfolio----------------------------------\\n # fia_portfolio_df.to_csv(src + 'fia_port_detail.csv')\\n # sim_port_total.to_csv(src + 'fiaport_ending_values.csv')\\n #\\n # income_breakdown_port = pd.DataFrame(sim_port_total.quantile(0.5, axis=1))\\n # income_breakdown_port.loc[:, 'income_from_portfolio'] = sim_port_income.quantile(0.5, axis=1)\\n # income_breakdown_port.loc[:, 'fia_income'] = income_from_fia\\n # income_breakdown_port.loc[:, 'social_security_income'] = social\\n # income_breakdown_port.loc[:, 'coupon_income'] = cpn_income_port\\n #\\n # income_breakdown_port.rename(columns={income_breakdown_port.columns[0]: 'portfolio_ending_value'}, inplace=True)\\n # income_breakdown_port.loc[:, 'income_from_portfolio'][\\n # income_breakdown_port.loc[:, 'portfolio_ending_value'] <= 0] = 0\\n # income_breakdown_port.loc[:, 'total_income'] = income_breakdown_port.loc[:, income_breakdown_port.columns[1:]].sum(\\n # axis=1)\\n #\\n # # ----------------------------------Block Ends-------------------------------------------------------------\\n q_cut = [0.0, 0.05, 0.25, 0.5, 0.75, 0.95, 1.0]\\n sim_base_income[sim_base_total < income_needed] = 0.0\\n\\n sim_port_income[sim_port_total < income_net_fia_income] = 0\\n\\n sim_port_income = sim_port_income + income_from_fia\\n\\n # base_quantile = sim_base_total.loc[sim_base_total.index[-1]].quantile([0.05, 0.25, 0.50, 0.75, 0.90])\\n #\\n # port_quantile = sim_port_total.loc[sim_port_total.index[-1]].quantile([0.05, 0.25, 0.50, 0.75, 0.90])\\n\\n base_quantile = sim_base_total.loc[sim_base_total.index[-1]].quantile(q_cut)\\n\\n port_quantile = sim_port_total.loc[sim_port_total.index[-1]].quantile(q_cut)\\n\\n # q_cut = [0.0, .05, 0.25, 0.5, 0.75, 0.95, 1.0]\\n cols = ['Min', '5th', '25th', '50th', '75th', '90th', 'Max']\\n\\n # ------------------------------------------drop year 0-----------------------------------------\\n sim_base_total = sim_base_total[1:]\\n sim_port_total = sim_port_total[1:]\\n\\n # ------------------------quantile analysis for base terminal value-----------------------------\\n base_qcut = pd.DataFrame(index=sim_base_total.index, columns=cols)\\n for c in range(len(cols)):\\n base_qcut.loc[:, cols[c]] = sim_base_total.quantile(q_cut[c], axis=1)\\n\\n base_qcut.clip(lower=0, inplace=True)\\n\\n sim_base_total.clip(lower=0, inplace=True)\\n\\n # -------------------------------------quantile analysis for base income----------------------------\\n base_income_qcut = pd.DataFrame(index=sim_base_income.index, columns=cols)\\n for c in range(len(cols)):\\n base_income_qcut.loc[:, cols[c]] = sim_base_income.quantile(q_cut[c], axis=1)\\n\\n # ----Remove NaN's prior to the income start years------------\\n # base_income_qcut = base_income_qcut.loc[income_starts:]\\n\\n # ---------------------------------quantile analysis for portfolio terminal value ---------------\\n\\n port_qcut = pd.DataFrame(index=sim_port_total.index, columns=cols)\\n for c in range(len(cols)):\\n port_qcut.loc[:, cols[c]] = sim_port_total.quantile(q_cut[c], axis=1)\\n\\n port_qcut.clip(lower=0, inplace=True)\\n\\n # ----------------------------------quantile analysis for portfolio income----------------------------\\n port_income_qcut = pd.DataFrame(index=sim_port_income.index, columns=cols)\\n for c in range(len(cols)):\\n port_income_qcut.loc[:, cols[c]] = sim_port_income.quantile(q_cut[c], axis=1)\\n\\n # ----Remove NaN's prior to the income start years------------\\n # port_income_qcut = port_income_qcut.loc[income_starts:]\\n\\n # ----------probability ending value will be less than 0 at the end of the horizon -----------------------\\n # base_legacy_risk = (sim_base_total.loc[sim_base_total.index[-1]] < 0).sum() / (trials)\\n\\n base_legacy_risk = (sim_base_total.loc[sim_base_total.index[life_expectancy - clients_age]] < 0).sum() / trials\\n port_legacy_risk = (sim_port_total.loc[sim_port_total.index[life_expectancy - clients_age]] < 0).sum() / trials\\n\\n # port_legacy_risk = (sim_port_total.loc[sim_port_total.index[-1]] <= 0).sum() / (trials)\\n\\n legacy_risk = pd.DataFrame([base_legacy_risk, port_legacy_risk,\\n 'Prob. of portfolio value less than 0 at the end of the expected life'],\\n index=['base', 'fia_portfolio', 'Notes'],\\n columns=['Ruin Probability'])\\n\\n # -----------Year-wise probability of ending value greater than 0 -----------------\\n base_psuccess = sim_base_total.apply(lambda x: x > 0).sum(axis=1) / trials\\n port_psuccess = sim_port_total.apply(lambda x: x > 0).sum(axis=1) / trials\\n\\n # -----------------------WRITING FILES TO EXCEL ---------------------------\\n\\n writer = pd.ExcelWriter(src + method + '_simulated_income_summary_custom.xlsx', engine='xlsxwriter')\\n read_income_inputs.to_excel(writer, sheet_name='inputs_for_income')\\n\\n read_returns_est.to_excel(writer, sheet_name='asset_returns_estimates')\\n # read_portfolio_inputs.to_excel(writer, sheet_name='portfolio_inputs')\\n\\n age_index = list(range(clients_age + 1, clients_age + len(base_qcut) + 1))\\n # base_qcut.loc[:, 'clients_age'] = age_index\\n # base_qcut.loc[:, 'comment'] = ''\\n # base_qcut.loc[:, 'comment'] = np.where(base_qcut.clients_age == life_expectancy, 'expected_life', \\\"\\\")\\n\\n base_inv = float(read_income_inputs.loc['risky_assets', 'Base'])\\n base_qcut.loc[:, 'age'] = age_index\\n base_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n\\n # --------To start with year 0---------------------------------\\n insert_col = [base_inv, base_inv, base_inv, base_inv, base_inv, base_inv,\\n base_inv, clients_age, np.nan]\\n base_qcut.loc[len(base_qcut) + 1, :] = 0.0\\n base_qcut = base_qcut.shift(1)\\n base_qcut.iloc[0] = insert_col\\n base_qcut.reset_index(drop=True, inplace=True)\\n base_qcut.loc[:, 'Annual Return'] = base_qcut.loc[:, '50th'].pct_change().fillna(0)\\n base_qcut.to_excel(writer, sheet_name='base_ending_value_quantiles')\\n # base_qcut.loc[income_starts:, :].to_excel(writer, sheet_name='base_ending_value_quantiles')\\n\\n # base_income_qcut = base_income_qcut[1:] base_income_qcut.loc[:, 'clients_age'] = age_index\\n # base_income_qcut.loc[:, 'comment'] = '' base_income_qcut.loc[:, 'comment'] = np.where(\\n # base_income_qcut.clients_age == life_expectancy, 'expected_life', \\\"\\\")\\n\\n base_income_qcut = base_income_qcut.loc[1:, :]\\n base_income_qcut.loc[:, 'age'] = age_index\\n base_income_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n\\n base_income_qcut.loc[income_starts:, :].to_excel(writer, sheet_name='base_income_quantiles')\\n\\n # age_index = list(range(clients_age+1, clients_age + len(port_qcut)+1))\\n # port_qcut.loc[:, 'clients_age'] = age_index\\n # port_qcut.loc[:, 'comment'] = ''\\n # port_qcut.loc[:, 'comment'] = np.where(port_qcut.clients_age == life_expectancy, 'expected_life', \\\"\\\")\\n\\n port_qcut.loc[:, 'age'] = age_index\\n port_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n port_qcut.loc[len(port_qcut) + 1, :] = 0.0\\n port_qcut = port_qcut.shift(1)\\n port_qcut.iloc[0] = insert_col\\n port_qcut.reset_index(drop=True, inplace=True)\\n port_qcut.loc[:, 'Annual Return'] = port_qcut.loc[:, '50th'].pct_change().fillna(0)\\n port_qcut.to_excel(writer, sheet_name='fia_port_ending_value_quantiles')\\n # port_qcut.loc[income_starts:, :].to_excel(writer, sheet_name='fia_port_ending_value_quantiles')\\n\\n # port_income_qcut = port_income_qcut[1:] port_income_qcut.loc[:, 'clients_age'] = age_index\\n # port_income_qcut.loc[:, 'comment'] = '' port_income_qcut.loc[:, 'comment'] = np.where(\\n # port_income_qcut.clients_age == life_expectancy, 'expected_life', \\\"\\\")\\n\\n port_income_qcut = port_income_qcut.loc[1:, :]\\n port_income_qcut.loc[:, 'age'] = age_index\\n port_income_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n port_income_qcut.loc[income_starts:, :].to_excel(writer, sheet_name='fia_port_income_quantiles')\\n\\n prob_success_df = pd.concat([base_psuccess, port_psuccess], axis=1)\\n prob_success_df.rename(columns={prob_success_df.columns[0]: 'prob(ending_value>0)_base',\\n prob_success_df.columns[1]: 'prob(ending_value>0)_port'}, inplace=True)\\n\\n # prob_success_df.loc[:, 'clients_age'] = age_index\\n # prob_success_df.loc[:, 'comment'] = ''\\n # prob_success_df.loc[:, 'comment'] = np.where(prob_success_df.clients_age == life_expectancy, 'expected_life', \\\"\\\")\\n\\n prob_success_df.loc[:, 'age'] = age_index\\n prob_success_df.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n prob_success_df.loc[:, 'prob(ending_value>lifetime_req income)_base'] = base_prob_of_success / trials\\n prob_success_df.loc[:, 'prob(ending_value>lifetime_req income)_port'] = port_prob_of_success / trials\\n prob_success_df.loc[:, 'prob(ending_value>next_year_req_income)_base'] = base_success_next_year / trials\\n prob_success_df.loc[:, 'prob(ending_value>next_year_req_income)_port'] = port_success_next_year / trials\\n prob_success_df.loc[:, 'base_max_portfolio_at_acturial_age'] = base_max_portfolio\\n prob_success_df.loc[:, 'port_max_portfolio_at_acturial_age'] = port_max_portfolio\\n\\n # --------------------Percentile Portfolio's based on Acturial Life------------------------\\n base_success = prob_success_df.loc[life_expectancy - clients_age, 'prob(ending_value>next_year_req_income)_base']\\n port_success = prob_success_df.loc[life_expectancy - clients_age, 'prob(ending_value>next_year_req_income)_port']\\n\\n # acturial_age_base_tv = sim_base_total.loc[:life_expectancy - clients_age, ]\\n # percentile_base_tv = sim_base_total.apply(lambda x: np.nanpercentile(x, base_success), axis=1)\\n\\n # ----------------Year wise percentile portfolio to meet next year income. Based on the success at acturial age.\\n # Yearly portfolio values that can provide the next year income below the success rate at end of life (Percentile)-\\n\\n # acturial_age_base = base_for_next_year_need.loc[:life_expectancy - clients_age, ]\\n # acturial_age_base = base_for_next_year_need.copy().fillna(0)\\n percentile_base = base_for_next_year_need.apply(lambda x: np.nanpercentile(x, base_success), axis=1)\\n\\n # acturial_age_port_tv = sim_port_total.loc[:life_expectancy - clients_age, ]\\n # percentile_port_tv = sim_port_total.apply(lambda x: np.nanpercentile(x, port_success), axis=1)\\n\\n # ----------------Year wise percentile portfolio to meet next year income. Based on the success at acturial age.\\n # Yearly portfolio values that can provide the next year income below the success rate at end of life (Percentile)-\\n\\n # acturial_age_port = port_for_next_year_need.loc[:life_expectancy - clients_age, ]\\n # acturial_age_base = port_for_next_year_need.copy().fillna(0)\\n percentile_port = port_for_next_year_need.apply(lambda x: np.nanpercentile(x, port_success), axis=1)\\n\\n prob_success_df.loc[:, 'acturial_success_percentile_base_portfolio'] = percentile_base\\n prob_success_df.loc[:, 'acturial_success_percentile_port_portfolio'] = percentile_port\\n\\n # prob_success_df.loc[:, 'terminalVal_success_percentile_base_portfolio'] = percentile_base_tv\\n # prob_success_df.loc[:, 'terminalVal_success_percentile_port_portfolio'] = percentile_port_tv\\n\\n prob_success_df.to_excel(writer, sheet_name='success_probability')\\n\\n # --------------BASE - Accumulation and Income Breakdown based on the success percentile portfolio---------------\\n base_df.to_csv(src + 'base_port_detail.csv')\\n sim_base_total.to_csv(src + 'base_ending_values.csv')\\n income_breakdown_base = pd.DataFrame(sim_base_total.quantile(base_success, axis=1))\\n income_breakdown_base.loc[:, 'income_from_risky_assets'] = sim_base_income.quantile(base_success, axis=1) \\\\\\n - social - cpn_income_port\\n income_breakdown_base.loc[:, 'guaranteed_income'] = 0.0\\n income_breakdown_base.loc[:, 'social_security_income'] = social\\n income_breakdown_base.loc[:, 'coupon_income'] = cpn_income_base\\n\\n income_breakdown_base.rename(columns={income_breakdown_base.columns[0]: 'portfolio_ending_value'}, inplace=True)\\n income_breakdown_base.loc[:, 'income_from_risky_assets'][\\n income_breakdown_base.loc[:, 'portfolio_ending_value'] <= 0] = 0\\n income_breakdown_base.loc[:, 'total_income'] = income_breakdown_base.loc[:, income_breakdown_base.columns[1:]].sum(\\n axis=1)\\n\\n # ----------FIA PORTFOLIO - Accumulation and Income Breakdown based on the success percentile portfolio-----------\\n fia_portfolio_df.to_csv(src + 'fia_port_detail.csv')\\n sim_port_total.to_csv(src + 'fiaport_ending_values.csv')\\n\\n income_breakdown_port = pd.DataFrame(sim_port_total.quantile(port_success, axis=1))\\n income_breakdown_port.loc[:, 'income_from_risky_assets'] = sim_port_income.quantile(port_success, axis=1) \\\\\\n - income_from_fia - social - cpn_income_port\\n income_breakdown_port.loc[:, 'guaranteed_income'] = income_from_fia\\n income_breakdown_port.loc[:, 'social_security_income'] = social\\n income_breakdown_port.loc[:, 'coupon_income'] = cpn_income_port\\n\\n income_breakdown_port.rename(columns={income_breakdown_port.columns[0]: 'portfolio_ending_value'}, inplace=True)\\n income_breakdown_port.loc[:, 'income_from_risky_assets'][\\n income_breakdown_port.loc[:, 'portfolio_ending_value'] <= 0] = 0\\n income_breakdown_port.loc[:, 'total_income'] = income_breakdown_port.loc[:, income_breakdown_port.columns[1:]].sum(\\n axis=1)\\n\\n # -------------------Write simulation Statistics-------------------------------------\\n simulation_stats.to_excel(writer, sheet_name='simulation_statistics')\\n\\n # port_psuccess.to_excel(writer, sheet_name='fia_port_success_probability')\\n\\n income_breakdown_base = income_breakdown_base.loc[1:, :]\\n income_breakdown_base.loc[:, 'age'] = age_index\\n income_breakdown_base.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n income_breakdown_base.loc[income_starts:, :].to_excel(writer, sheet_name='base_income_breakdown_median')\\n\\n income_breakdown_port = income_breakdown_port.loc[1:, :]\\n income_breakdown_port.loc[:, 'age'] = age_index\\n income_breakdown_port.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n income_breakdown_port.loc[income_starts:, :].to_excel(writer, sheet_name='fia_income_breakdown_median')\\n\\n legacy_risk.to_excel(writer, sheet_name='ruin_probability')\\n\\n median_returns_normal.loc[:, 'fia_median_returns'] = median_normal_fia\\n median_returns_normal.to_excel(writer, sheet_name='gr_port_median_normal')\\n\\n writer.save()\\n\\n # -----------------Plotting charts--------------------------------------------\\n base_qcut.loc[income_starts:].plot(grid=True, title='Quantile Terminal Value - Base Portfolio')\\n plt.savefig(src + \\\"quantile_terminal_base.png\\\")\\n plt.close('all')\\n\\n base_income_qcut.plot(grid=True, title='Quantile Income - Base Portfolio')\\n plt.savefig(src + \\\"quantile_income_base.png\\\")\\n plt.close('all')\\n\\n base_psuccess.plot(grid=True, title='Probability of Success (Portfolio Ending Value > 0) - Base Portfolio')\\n plt.savefig(src + \\\"success_probabilty_base.png\\\")\\n plt.close('all')\\n\\n (1 - base_psuccess).plot(grid=True, title='Probability of Ruin (Portfolio Ending Value < 0) - Base Portfolio')\\n plt.savefig(src + \\\"ruin_probability_base.png\\\")\\n plt.close('all')\\n\\n port_qcut.loc[income_starts:].plot(grid=True, title='Quantile Terminal Value - FIA Portfolio')\\n plt.savefig(src + \\\"quantile_terminal_fia.png\\\")\\n plt.close('all')\\n\\n port_income_qcut.plot(grid=True, title='Quantile Income - FIA Portfolio')\\n plt.savefig(src + \\\"quantile_income_fia.png\\\")\\n plt.close('all')\\n\\n port_psuccess.plot(grid=True, title='Probability of Success (Portfolio Ending Value > 0) - FIA Portfolio')\\n plt.savefig(src + \\\"success_probabilty_fia.png\\\")\\n plt.close('all')\\n\\n (1 - port_psuccess).plot(grid=True, title='Probability of Ruin (Portfolio Ending Value < 0) - FIA Portfolio')\\n plt.savefig(src + \\\"ruin_probability_fia.png\\\")\\n plt.close('all')\\n\\n print(\\\"simulation completed....\\\")\",\n \"def simulate_trading(self):\\n # Create the file output stream\\n posix_now = datetime.datetime.timestamp(datetime.datetime.now())\\n out_path = os.getcwd() + \\\"/OutputResults/backtest_{}\\\".format(posix_now)[:-7:] + \\\".csv\\\"\\n\\n out = open(out_path, \\\"w+\\\")\\n\\n spl = len(self.strat_params_list)\\n for i, sp in enumerate(self.strat_params_list): # http://book.pythontips.com/en/latest/enumerate.html\\n print(\\\"Strategy %s out of %s...\\\" % (i + 1, spl))\\n self._generate_trading_instances(sp)\\n self._run_backtest()\\n stats = self._output_performance()\\n pprint.pprint(stats)\\n\\n tot_ret = float(stats[0][1].replace(\\\"%\\\", \\\"\\\"))\\n cagr = float(stats[1][1].replace(\\\"%\\\", \\\"\\\"))\\n sharpe = float(stats[2][1])\\n max_dd = float(stats[3][1].replace(\\\"%\\\", \\\"\\\"))\\n dd_dur = int(stats[4][1])\\n\\n # This should be more general in future implementations...\\n out.write(\\n \\\"%s,%s,%s,%s,%s,%s,%s,%s\\\\n\\\" % (sp[\\\"ols_window\\\"], sp[\\\"zscore_high\\\"], sp[\\\"zscore_low\\\"],\\n tot_ret, cagr, sharpe, max_dd, dd_dur)\\n )\\n\\n out.close()\",\n \"def _run_backtest(self):\\n i = 0\\n while True:\\n i += 1\\n if self.data_handler.continue_backtest == True:\\n self.data_handler.update_bars()\\n #print(self.data_handler.get_latest_bar_datetime(self.symbol_list[0]))\\n else:\\n break\\n while self.backtest:\\n try:\\n event = self.events.get(False)\\n except Empty:\\n break\\n else:\\n if event is not None:\\n if event.type == EventType.MARKET:\\n try:\\n self.strategy.On_Bars(event)\\n self.portfolio.update_balance(event)\\n self.portfolio.order_check(event)\\n except EquityError:\\n print('Not Engough Equity,Backtest Will be Stop...')\\n self.backtest=False\\n break\\n elif event.type == EventType.ORDER_SEND:\\n self.portfolio.update_order(event)\\n elif event.type == EventType.ORDER_CLOSE:\\n try:\\n self.portfolio.update_order(event)\\n self.portfolio.update_euity(event)\\n except EquityError:\\n print ('Not Engough Equity,Backtest Will be Stop...')\\n self.backtest=False\\n break\\n elif event.type == EventType.ORDER_MODIFY:\\n self.portfolio.update_order(event)\\n time.sleep(self.heartbeat)\",\n \"def before_trading_start(context, data):\\n factors = pipeline_output('ff_example')\\n\\n # get the data we're going to use\\n returns = factors['returns']\\n mkt_cap = factors.sort_values(['market_cap'], ascending=True)\\n be_me = factors.sort_values(['be_me'], ascending=True)\\n\\n # to compose the six portfolios, split our universe into portions\\n half = int(len(mkt_cap)*0.5)\\n small_caps = mkt_cap[:half]\\n big_caps = mkt_cap[half:]\\n \\n thirty = int(len(be_me)*0.3)\\n seventy = int(len(be_me)*0.7)\\n growth = be_me[:thirty]\\n neutral = be_me[thirty:seventy]\\n value = be_me[seventy:]\\n\\n # now use the portions to construct the portfolios.\\n # note: these portfolios are just lists (indices) of equities\\n small_value = small_caps.index.intersection(value.index)\\n small_neutral = small_caps.index.intersection(neutral.index)\\n small_growth = small_caps.index.intersection(growth.index)\\n \\n big_value = big_caps.index.intersection(value.index)\\n big_neutral = big_caps.index.intersection(neutral.index)\\n big_growth = big_caps.index.intersection(growth.index)\\n\\n # take the mean to get the portfolio return, assuming uniform\\n # allocation to its constituent equities.\\n sv = returns[small_value].mean()\\n sn = returns[small_neutral].mean()\\n sg = returns[small_growth].mean()\\n \\n bv = returns[big_value].mean()\\n bn = returns[big_neutral].mean()\\n bg = returns[big_growth].mean()\\n\\n # computing SMB\\n context.smb = (sv + sn + sg)/3 - (bv + bn + bg)/3\\n\\n # computing HML\\n context.hml = (sv + bv)/2 - (sg + bg)/2\",\n \"def generate_portfolio_data(self):\\n self.__load_portfolio_historical_prices()\\n self.__populate_historical_trade_data()\\n self.__calculate_portfolio_returns()\\n self.__calculate_portfolio_performance()\",\n \"def back_testing(self, func=None):\\n # preallocate real statistics dict\\n ism_label = []\\n \\n if not self.moving_average:\\n # iterate on all ism packages\\n for _i, ism_pack in enumerate(self.ism_data):\\n ism_code = ism_pack[0][0]\\n interval = float(ism_pack[0][-5])\\n df = self.back_testing_data[ism_code]\\n # using close price only now, check data is/not empty\\n if df.empty:\\n res = np.nan\\n continue\\n else:\\n run_price = df['ClosePrice_run'].sum()\\n stop_price = df['ClosePrice_stop'].sum()\\n res = ((stop_price-run_price)/run_price)*243/interval # 243 market days/year\\n ism_label.append([ism_code, res])\\n \\n else: # using moving_average\\n # iterate on all ism packages\\n # for _i, ism_pack in enumerate(self.ism_data):\\n # ism_code = ism_pack[0][0]\\n # interval = int(ism_pack[0][-5])\\n # df = self.back_testing_data[ism_code]\\n # \\n # # using close price only now\\n # dim = int(len(df)/(L_shift+R_shift-1)) \\n # close_price = np.array_split(df['ClosePrice'], dim)\\n # f1 = lambda x: float(x[-1]-x[0]) # real return for single stock\\n # f2 = lambda x: float(x[0]) # initial value of single stock\\n # if L_shift+R_shift >= interval:\\n # ls1 = np.zeros((L_shift+R_shift-interval,), dtype=np.float32)\\n # ls2 = np.zeros_like(ls1)\\n # for _j, par in enumerate(close_price):\\n # # sum over different stocks in one ism_pack\\n # par_rolling = par.rolling(interval)\\n # ls1 += np.asarray(par_rolling.apply(f1).dropna())\\n # ls2 += np.asarray(par_rolling.apply(f2).dropna())\\n # ism_label[ism_code] = {}\\n # ism_label[ism_code]['real_return'] = (ls1/ls2/interval)\\n # else:\\n # print(\\\"L_shift+R_shift < interval\\\")\\n pass \\n \\n return ism_label\",\n \"def test_low_stockprice_high_interest(self):\\n stock_prices = np.array([[5, 4, 4, 2],\\n [5, 3, 3, 3],\\n [5, 4, 2, 2],\\n [5, 3, 3, 1]], dtype=float)\\n interest_rate = 2.0 # 200%\\n test_case = StockMarket(5, stock_prices, interest_rate)\\n test_case.dynamic_programming_bottom_up()\\n for portfolio in set(test_case.backtracing_portfolio()):\\n self.assertEqual(0, portfolio)\",\n \"def backtest(self, strategy): \\n \\n # load data\\n data = self.import_data(currency_ids = np.unique(self.signals.currency_id),\\n start_date = self.start_date,\\n end_date = self.end_date)\\n \\n # add cash and columns for currencies\\n data[\\\"capital\\\"] = 0\\n data[\\\"actions\\\"] = \\\"\\\" # should become a dictW \\n data[\\\"description\\\"] = \\\"\\\"\\n data[\\\"capital\\\"].iloc[0] = self.initial_capital\\n data[\\\"worth_usd\\\"] = 0\\n \\n for currency_id in np.unique(self.signals.currency_id):\\n data[currency_id + '_position'] = 0\\n \\n # a sell or buy can influence subsequent positions, so calculate iteratively\\n for observation in range(1, len(data.index)):\\n \\n date = data.index[observation]\\n \\n # investment this period is zero\\n investment_capital_period = 0\\n \\n # amount of currency_ids initially same as last period\\n for currency_id in np.unique(self.signals.currency_id):\\n data[currency_id + '_position'].iloc[observation] = data[currency_id + '_position'].iloc[observation-1] \\n \\n # at each point, compute size of each position (cash and currencies), and record actions\\n if(data.index[observation] in self.signals.index):\\n \\n action_df = pd.DataFrame(columns=list([\\\"Currency\\\",\\\"NominalAmount\\\", \\\"CapitalAmount\\\"]))\\n \\n # could be multiple actions\\n for index, action in self.signals.loc[date].iterrows(): \\n currency_id = action['currency_id']\\n signal = action['signal']\\n \\n # Buy\\n if signal == 1:\\n \\n # buy for 10% currency_id\\n investment_capital = data[\\\"capital\\\"].iloc[observation-1] * 0.10 \\n\\n # estimate how many coins\\n investment_nominal = round(investment_capital / data[currency_id].iloc[observation])\\n \\n # calculate exact capital needed\\n investment_capital_exact = investment_nominal * data[currency_id].iloc[observation]\\n investment_capital_period = investment_capital_period + investment_capital_exact \\n \\n # change the amount of currency hold\\n data[currency_id + '_position'].iloc[observation] = data[currency_id + '_position'].iloc[observation-1] + investment_nominal\\n \\n # report action by appending a Series to the (empty) dataframe\\n action_df = action_df.append(pd.Series({\\\"Currency\\\": currency_id, \\n \\\"NominalAmount\\\": investment_nominal, \\n \\\"CapitalAmount\\\": investment_capital_exact}),ignore_index=True)\\n \\n # report description\\n data[\\\"description\\\"].iloc[observation] = (data[\\\"actions\\\"].iloc[observation] + \\\"\\\\n Buy \\\" + \\n str(investment_nominal) + \\\" \\\" + str(currency_id) + \\n \\\" for \\\" + str(investment_capital_exact))\\n \\n # Sell\\n if signal == -1:\\n \\n # sell currency_id for 10% of total capital\\n investment_capital = data[\\\"capital\\\"].iloc[observation-1] * 0.10 \\n \\n # estimate how many coins\\n investment_nominal = round(investment_capital / data[currency_id].iloc[observation])\\n \\n # calculate exact capital needed\\n investment_capital_exact = investment_nominal * data[currency_id].iloc[observation]\\n investment_capital_period = investment_capital_period - investment_capital_exact\\n \\n # change the amount of currency hold\\n data[currency_id + '_position'].iloc[observation] = data[currency_id + '_position'].iloc[observation-1] - investment_nominal\\n \\n # report action\\n action_df = action_df.append(pd.Series({\\\"Currency\\\": currency_id, \\n \\\"NominalAmount\\\": investment_nominal, \\n \\\"CapitalAmount\\\": investment_capital_exact}),ignore_index=True)\\n \\n # report description\\n data[\\\"description\\\"].iloc[observation] = data[\\\"actions\\\"].iloc[observation] + \\\"Sell \\\" + str(investment_nominal) + \\\" \\\" + str(currency_id) + \\\" for \\\" + str(investment_capital_exact)\\n \\n # report actions\\n data[\\\"actions\\\"].iloc[observation] = action_df.to_json()\\n \\n # calculate resulting cash capital\\n data[\\\"capital\\\"].iloc[observation] = data[\\\"capital\\\"].iloc[observation-1] - investment_capital_period\\n \\n # calculate worth by capital (usd) and each currency * price\\n data[\\\"worth_usd\\\"].iloc[observation] = data[\\\"capital\\\"].iloc[observation] \\n return data\",\n \"def run_back_test(self, algorithm):\\n for date in self.dates:\\n info = dict()\\n info['cash'] = self.cash['cash'][date]\\n info['date'] = date\\n original_order = Order()\\n for stock in self.stocks:\\n original_order.order(stock, self.stock_data[stock].position['Position'][date])\\n make_order = algorithm(self.get_back_dataframe(date), original_order, info)\\n\\n for stock in make_order.orders:\\n self.order_stock(stock, make_order.orders[stock], date)\",\n \"def income_model_asset_based_portfolio_quantile(num_of_years=30, trials=100, method='normal'):\\n\\n sim_fia_cv = pd.DataFrame(index=range(num_of_years + 1))\\n\\n sim_base_total = pd.DataFrame(index=range(num_of_years + 1))\\n sim_base_income = pd.DataFrame(index=range(num_of_years + 1))\\n\\n sim_port_total = pd.DataFrame(index=range(num_of_years + 1))\\n sim_port_income = pd.DataFrame(index=range(num_of_years + 1))\\n\\n sim_base_total_pre_income = pd.DataFrame(index=range(num_of_years + 1))\\n sim_port_total_pre_income = pd.DataFrame(index=range(num_of_years + 1))\\n\\n sim_base_total_preincome = pd.DataFrame(index=range(num_of_years + 1))\\n sim_port_total_preincome = pd.DataFrame(index=range(num_of_years + 1))\\n\\n # read_income_inputs = pd.read_csv(src + \\\"income_model_inputs.csv\\\", index_col='Items')\\n read_income_inputs = pd.read_excel(src + \\\"portfolio_information.xlsx\\\", sheet_name='income_model_inputs',\\n index_col=[0])\\n\\n # read_returns_est = pd.read_csv(src + \\\"income_assets_returns_estimates.csv\\\", index_col='Symbol')\\n read_returns_est = pd.read_excel(src + \\\"portfolio_information.xlsx\\\", sheet_name='income_assets_returns_estimates',\\n index_col=[0])\\n\\n # read_returns_est.drop(['BM', read_returns_est.index[-1]], axis=0, inplace=True)\\n # read_portfolio_inputs = pd.read_csv(src + \\\"income_portfolio_inputs.csv\\\", index_col='Items')\\n\\n # read_asset_weights = pd.read_csv(src + \\\"asset_weights.csv\\\", index_col='Asset')\\n read_asset_weights = pd.read_excel(src + \\\"portfolio_information.xlsx\\\", sheet_name='asset_weights',\\n index_col=[0])\\n\\n read_asset_weights.drop(read_asset_weights.index[-1], axis=0, inplace=True)\\n\\n # read random returns for simulation\\n read_normal = pd.read_csv(src + 'sort_normal.csv', index_col=[0], parse_dates=True)\\n read_small = pd.read_csv(src + 'sort_small_to_large.csv', index_col=[0], parse_dates=True)\\n read_large = pd.read_csv(src + 'sort_large_to_small.csv', index_col=[0], parse_dates=True)\\n assets_col_names = list(read_normal.columns)\\n\\n tickers = list(read_asset_weights.index)\\n wts = np.array(read_asset_weights.loc[:, 'base'])\\n\\n def asset_median_returns(data, ticker):\\n return data.filter(regex=ticker).median(axis=1)\\n\\n # dataframe for unsorted returns (normal)\\n median_returns_normal = pd.DataFrame({t: asset_median_returns(read_normal, t) for t in tickers})\\n median_returns_normal.loc[:, 'portfolio_return'] = median_returns_normal.dot(wts)\\n median_normal_fia = pd.DataFrame({'FIA': asset_median_returns(read_normal, 'r_FIA')})\\n\\n # dataframe for smallest to largest returns\\n median_returns_smallest = pd.DataFrame({t: asset_median_returns(read_small, t) for t in tickers})\\n median_returns_smallest.loc[:, 'portfolio_return'] = median_returns_smallest.dot(wts)\\n median_smallest_fia = pd.DataFrame({'FIA': asset_median_returns(read_small, 'r_FIA')})\\n\\n # dataframe for unsorted returns (normal)\\n median_returns_largest = pd.DataFrame({t: asset_median_returns(read_large, t) for t in tickers})\\n median_returns_largest.loc[:, 'portfolio_return'] = median_returns_largest.dot(wts)\\n median_largest_fia = pd.DataFrame({'FIA': asset_median_returns(read_large, 'r_FIA')})\\n\\n years = list(range(0, num_of_years + 1))\\n income_cols = ['year', 'strategy_term', 'index_returns', 'term_ret', 'term_ret_with_par', 'term_annualize',\\n 'ann_net_spread', 'term_ret_netspr', 'high_inc_benefit_base', 'rider_fee', 'eoy_income',\\n 'contract_value']\\n\\n term = int(read_income_inputs.loc['term', 'inputs'])\\n fia_ret = read_returns_est.loc[read_returns_est.index[-1], 'Annualized Returns']\\n fia_risk = read_returns_est.loc[read_returns_est.index[-1], 'Annualized Risk']\\n par_rate = float(read_income_inputs.loc['par_rate', 'inputs'])\\n spread = float(read_income_inputs.loc['spread', 'inputs'])\\n bonus_term = int(read_income_inputs.loc['bonus_term', 'inputs'])\\n premium = float(read_income_inputs.loc['premium', 'inputs'])\\n income_bonus = float(read_income_inputs.loc['income_bonus', 'inputs'])\\n\\n income_starts = int(read_income_inputs.loc['start_income_years', 'inputs'])\\n income_growth = float(read_income_inputs.loc['income_growth', 'inputs'])\\n rider_fee = float(read_income_inputs.loc['rider_fee', 'inputs'])\\n inc_payout_factor = float(read_income_inputs.loc['income_payout_factor', 'inputs'])\\n contract_bonus = float(read_income_inputs.loc['contract_bonus', 'inputs'])\\n social = float(read_income_inputs.loc['social', 'inputs'])\\n inflation = float(read_income_inputs.loc['inflation', 'inputs'])\\n wtd_cpn_yield = float(read_income_inputs.loc['wtd_coupon_yld', 'inputs'])\\n life_expectancy = int(read_income_inputs.loc['life_expectancy_age', 'inputs'])\\n clients_age = int(read_income_inputs.loc['clients_age', 'inputs'])\\n\\n # ---------------INCOME MODEL--------------------------------------------\\n runs = 0\\n returns_dict = {}\\n asset_dict = {}\\n fia_dict = {}\\n while runs < trials:\\n print(runs)\\n\\n income_df = pd.DataFrame(index=years, columns=income_cols)\\n income_df.loc[:, 'year'] = years\\n income_df.loc[:, 'strategy_term'] = income_df.loc[:, 'year'] % term\\n income_df.loc[:, 'strategy_term'] = income_df['strategy_term'].apply(lambda x: 1 if x == 0 else 0)\\n\\n if method == 'normal':\\n income_df.loc[:, 'index_returns'] = read_normal.loc[:, '{}_{}'.format('r_FIA', str(runs))]\\n\\n elif method == 'smallest':\\n income_df.loc[:, 'index_returns'] = read_small.loc[:, '{}_{}'.format('r_FIA', str(runs))]\\n\\n else:\\n income_df.loc[:, 'index_returns'] = read_large.loc[:, '{}_{}'.format('r_FIA', str(runs))]\\n\\n # income_df.loc[:, 'index_returns'] = np.random.normal(fia_ret, fia_risk, size=(len(years), 1))\\n\\n cumprod = (1. + income_df['index_returns']).rolling(window=term).agg(lambda x: x.prod()) - 1\\n income_df.loc[:, 'term_ret'] = np.where(income_df.loc[:, 'strategy_term'] == 1, cumprod, 0)\\n income_df.loc[:, 'term_ret_with_par'] = income_df.loc[:, 'term_ret'] * par_rate\\n income_df.loc[:, 'term_annualize'] = income_df.loc[:, 'term_ret_with_par'].apply(\\n lambda x: (1 + x) ** (1 / term) - 1)\\n income_df.loc[:, 'ann_net_spread'] = income_df.loc[:, 'term_annualize'] - spread\\n income_df.loc[:, 'ann_net_spread'] = np.where(income_df.loc[:, 'strategy_term'] == 1,\\n income_df.loc[:, 'ann_net_spread'], 0)\\n income_df.loc[:, 'term_ret_netspr'] = income_df.loc[:, 'ann_net_spread'].apply(lambda x: (1 + x) ** term - 1)\\n\\n for counter in years:\\n if counter == 0:\\n income_df.loc[counter, 'high_inc_benefit_base'] = premium * (1 + income_bonus)\\n\\n elif counter <= min(bonus_term, income_starts):\\n income_df.loc[counter, 'high_inc_benefit_base'] = income_df.loc[counter - 1, 'high_inc_benefit_base'] * \\\\\\n (1 + income_growth)\\n else:\\n income_df.loc[counter, 'high_inc_benefit_base'] = income_df.loc[counter - 1, 'high_inc_benefit_base']\\n\\n income_df.loc[:, 'rider_fee'] = income_df.loc[:, 'high_inc_benefit_base'] * rider_fee\\n income_df.loc[:, 'eoy_income'] = np.where(income_df.loc[:, 'year'] > income_starts,\\n income_df.loc[:, 'high_inc_benefit_base'] * inc_payout_factor, 0)\\n\\n for counter in years:\\n if counter == 0:\\n income_df.loc[counter, 'contract_value'] = premium * (1 + contract_bonus)\\n\\n elif income_df.loc[counter, 'strategy_term'] == 1:\\n x1 = income_df.loc[counter - 1, 'contract_value'] - income_df.loc[counter, 'rider_fee']\\n x2 = (x1 * (1 + income_df.loc[counter, 'term_ret_netspr'])) - income_df.loc[counter, 'eoy_income']\\n income_df.loc[counter, 'contract_value'] = x2\\n\\n else:\\n x1 = income_df.loc[counter - 1, 'contract_value'] - income_df.loc[counter, 'rider_fee'] - \\\\\\n income_df.loc[counter, 'eoy_income']\\n\\n income_df.loc[counter, 'contract_value'] = x1\\n\\n # variable stores the income number that is used in the base and fia portfolio calcs.\\n\\n income_from_fia = income_df.loc[income_df.index[-1], 'eoy_income']\\n\\n income_df.loc[:, 'contract_value'] = income_df.loc[:, 'contract_value'].apply(lambda x: 0 if x <= 0 else x)\\n\\n sim_fia_cv.loc[:, str(runs)] = income_df.loc[:, 'contract_value']\\n\\n # -------------------------------------BASE MODEL---------------------------------------------\\n\\n base_wts = read_asset_weights.loc[:, 'base']\\n base_assets = list(base_wts.index)\\n base_weights = list(base_wts.values)\\n base_returns = list(read_returns_est.loc[:, 'Annualized Returns'].values)\\n base_std = list(read_returns_est.loc[:, 'Annualized Risk'].values)\\n\\n base_investment = float(read_income_inputs.loc['risky_assets', 'Base'])\\n adv_fees = float(read_income_inputs.loc['advisor_fees', 'Base'])\\n\\n # -------------------required income----------------------------------\\n req_annual_income = float(read_income_inputs.loc['annual_income', 'inputs'])\\n income_needed = req_annual_income - social\\n income_net_fia_income = max(0, income_needed - income_from_fia)\\n cpn_income_base = base_investment * wtd_cpn_yield\\n\\n # ----------------------RANDOM RETURNS--------------------------\\n r_cols = ['r_{}'.format(name) for name in base_assets]\\n boy_value = ['bv_{}'.format(name) for name in base_assets]\\n eoy_value = ['ev_{}'.format(name) for name in base_assets]\\n\\n random_returns = pd.DataFrame(index=income_df.index, columns=r_cols)\\n\\n for c in range(len(r_cols)):\\n ret = np.random.normal(base_returns[c], base_std[c], size=(len(random_returns.index), 1))\\n\\n if method == 'smallest':\\n this_run_cols = ['{}_{}'.format(cname, str(runs)) for cname in r_cols]\\n random_returns = read_small.loc[:, this_run_cols]\\n\\n # random_returns.loc[:, r_cols[c]] = np.sort(ret.flatten())\\n # asset_dict.update({'{}_{}'.format(r_cols[c], str(runs)): np.sort(ret.flatten())})\\n\\n elif method == 'largest':\\n this_run_cols = ['{}_{}'.format(cname, str(runs)) for cname in r_cols]\\n random_returns = read_large.loc[:, this_run_cols]\\n\\n # random_returns.loc[:, r_cols[c]] = np.flip(np.sort(ret.flatten()))\\n # asset_dict.update({'{}_{}'.format(r_cols[c], str(runs)): np.flip(np.sort(ret.flatten()))})\\n\\n else:\\n this_run_cols = ['{}_{}'.format(cname, str(runs)) for cname in r_cols]\\n random_returns = read_normal.loc[:, this_run_cols]\\n\\n # random_returns.loc[:, r_cols[c]] = ret.flatten()\\n # asset_dict.update({'{}_{}'.format(r_cols[c], str(runs)): ret.flatten()})\\n\\n # store the simulated assets returns in one dictionary\\n # returns_dict.update({str(runs): random_returns})\\n\\n # collect the asset based returns from all simulation and calculate the median returns.\\n # def get_median_returns(sym):\\n # cols = [sym + '_' + str(c) for c in np.arange(trials)]\\n # asset_df = pd.DataFrame({c: asset_dict.get(c) for c in cols})\\n # return asset_df.median(axis=1)\\n #\\n # asset_median_returns = pd.DataFrame({symbol: get_median_returns(symbol) for symbol in r_cols})\\n #\\n # asset_median_returns.loc[:, 'simulated_portfolio_median_returns'] = asset_median_returns.dot(base_weights)\\n\\n base_df = random_returns.copy()\\n pre_income_base_df = random_returns.copy()\\n\\n # base_investment = float(read_portfolio_inputs.loc['risky_assets', 'Base'])\\n\\n fia_portfolio_df = random_returns.copy()\\n pre_income_port_df = random_returns.copy()\\n port_investment = float(read_income_inputs.loc['risky_assets', 'FIA'])\\n cpn_income_port = port_investment * wtd_cpn_yield\\n\\n # ---------Initial Investments for pre-income account values---------------------\\n pre_income_base_inv = base_investment\\n pre_income_port_inv = port_investment\\n # ----------------------------------------BASE PORTFOLIO----------------------------\\n for name in boy_value:\\n base_df.loc[:, name] = 0.0\\n pre_income_base_df.loc[:, name] = 0.0\\n\\n for counter in years:\\n period_returns = list(random_returns.loc[counter, :])\\n if counter == 0:\\n # ---------------For year 0, the year of investment------------\\n\\n # ------------Calculate the annual portfolio returns - Gross Returns--------------------\\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment for c in range(len(boy_value))]\\n\\n # -------------Record the Pre Income Base Portfolio-----------------------------\\n\\n pre_income_base_df.loc[counter, boy_value] = [base_weights[c] *\\n pre_income_base_inv for c in range(len(boy_value))]\\n pre_income_base_df.loc[counter, 'total'] = pre_income_base_df.loc[counter, boy_value].sum()\\n pre_income_base_inv = pre_income_base_df.loc[counter, boy_value].sum()\\n\\n # ------------------Pre Income Block Ends------------------------\\n\\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\\n\\n base_df.loc[counter, 'total_net_fees'] = 0.0\\n base_df.loc[counter, 'income'] = 0.0\\n base_investment = base_df.loc[counter, boy_value].sum()\\n base_df.loc[counter, 'total_pre_income'] = base_investment\\n\\n elif (counter > 0) and (counter < income_starts):\\n\\n # ----For years between the start of the investment and start if the income---------------\\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment * (1 + period_returns[c])\\n for c in range(len(boy_value))]\\n\\n # -------------Record the Pre Income Base Portfolio-----------------------------\\n pre_income_base_df.loc[counter, boy_value] = [\\n base_weights[c] * pre_income_base_inv * (1 + period_returns[c])\\n for c in range(len(boy_value))]\\n\\n pre_income_base_df.loc[counter, 'total'] = pre_income_base_df.loc[counter, boy_value].sum()\\n pre_income_base_df.loc[counter, 'adv_fees'] = pre_income_base_df.loc[counter, 'total'] * adv_fees\\n pre_income_base_df.loc[counter, 'total_net_fees'] = pre_income_base_df.loc[counter, 'total'] - \\\\\\n pre_income_base_df.loc[counter, 'adv_fees']\\n pre_income_base_inv = pre_income_base_df.loc[counter, 'total_net_fees'] + cpn_income_base\\n\\n # ------------------Pre Income Block Ends------------------------\\n\\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\\n base_df.loc[counter, 'adv_fees'] = base_df.loc[counter, 'total'] * adv_fees\\n base_df.loc[counter, 'total_net_fees'] = base_df.loc[counter, 'total'] - base_df.loc[\\n counter, 'adv_fees']\\n\\n # --coupon payment is invested back into the risky portfolio until the income is withdrawn----\\n base_investment = base_df.loc[counter, 'total_net_fees'] + cpn_income_base\\n base_df.loc[counter, 'total_pre_income'] = base_df.loc[counter, 'total_net_fees']\\n\\n else:\\n\\n # -------------For Years after the income started----------------------\\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment * (1 + period_returns[c])\\n for c in range(len(boy_value))]\\n\\n # -------------Record the Pre Income Base Portfolio-----------------------------\\n pre_income_base_df.loc[counter, boy_value] = [\\n base_weights[c] * pre_income_base_inv * (1 + period_returns[c])\\n for c in range(len(boy_value))]\\n\\n pre_income_base_df.loc[counter, 'total'] = pre_income_base_df.loc[counter, boy_value].sum()\\n pre_income_base_df.loc[counter, 'adv_fees'] = pre_income_base_df.loc[counter, 'total'] * adv_fees\\n pre_income_base_df.loc[counter, 'total_net_fees'] = pre_income_base_df.loc[counter, 'total'] - \\\\\\n pre_income_base_df.loc[counter, 'adv_fees']\\n pre_income_base_inv = pre_income_base_df.loc[counter, 'total_net_fees'] + cpn_income_base\\n\\n # ------------------Pre Income Block Ends------------------------\\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\\n base_df.loc[counter, 'adv_fees'] = base_df.loc[counter, 'total'] * adv_fees\\n\\n # ---req. income is adjusted for inflation from the second year of withdrawal. Reinvestment of coupon\\n # stops from the year income starts. Req. income is reduced by the coupon payments\\n\\n if counter == income_starts:\\n\\n income_needed = req_annual_income - social\\n base_df.loc[counter, 'income'] = income_needed - cpn_income_base\\n income_needed = req_annual_income\\n\\n else:\\n income_needed = income_needed * (1 + inflation) - social\\n base_df.loc[counter, 'income'] = income_needed - cpn_income_base\\n income_needed = income_needed + social\\n\\n base_df.loc[counter, 'total_net_fees'] = base_df.loc[counter, 'total'] - \\\\\\n base_df.loc[counter, 'adv_fees'] - \\\\\\n base_df.loc[counter, 'income']\\n\\n base_df.loc[counter, 'total_pre_income'] = base_df.loc[counter, 'total'] - \\\\\\n base_df.loc[counter, 'adv_fees']\\n\\n base_investment = base_df.loc[counter, 'total_net_fees']\\n\\n # ------------------------Portfolio with PreIncome Values----------------------------\\n sim_base_total_preincome.loc[:, 's_{}'.format(str(runs))] = pre_income_base_df.loc[:, 'total_net_fees']\\n sim_base_total_preincome.fillna(float(read_income_inputs.loc['risky_assets', 'Base']), inplace=True)\\n # --------------------------------PreIncome Block Ends----------------------------\\n\\n base_df.loc[:, 'adj_total'] = base_df.loc[:, 'total_net_fees'].apply(lambda x: x if x > 0 else 0)\\n sim_base_total.loc[:, 's_{}'.format(str(runs))] = base_df.loc[:, 'total_net_fees']\\n sim_base_income.loc[:, 's_{}'.format(str(runs))] = base_df.loc[:, 'income']\\n sim_base_total_pre_income.loc[:, 's_{}'.format(str(runs))] = base_df.loc[:, 'total_pre_income']\\n\\n # ----------------------------FIA PORTFOLIO----------------------------------------------\\n for name in boy_value:\\n fia_portfolio_df.loc[:, name] = 0.0\\n pre_income_port_df.loc[:, name] = 0.0\\n\\n for counter in years:\\n period_returns = list(random_returns.loc[counter, :])\\n if counter == 0:\\n\\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment\\n for c in range(len(boy_value))]\\n\\n # -------------Record the Pre Income Base Portfolio-----------------------------\\n\\n pre_income_port_df.loc[counter, boy_value] = [base_weights[c] *\\n pre_income_port_inv for c in range(len(boy_value))]\\n pre_income_port_df.loc[counter, 'total'] = pre_income_port_df.loc[counter, boy_value].sum()\\n pre_income_port_inv = pre_income_port_df.loc[counter, boy_value].sum()\\n\\n # ------------------Pre Income Block Ends------------------------\\n\\n fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\\n fia_portfolio_df.loc[counter, 'total_net_fees'] = 0.0\\n fia_portfolio_df.loc[counter, 'income'] = 0.0\\n port_investment = fia_portfolio_df.loc[counter, boy_value].sum()\\n fia_portfolio_df.loc[counter, 'total_pre_income'] = port_investment\\n\\n elif (counter > 0) and (counter < income_starts):\\n\\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment * (1 + period_returns[c])\\n for c in range(len(boy_value))]\\n\\n # ------------------Record the Pre Income Base Portfolio-----------------------------\\n pre_income_port_df.loc[counter, boy_value] = [\\n base_weights[c] * pre_income_port_inv * (1 + period_returns[c])\\n for c in range(len(boy_value))]\\n\\n pre_income_port_df.loc[counter, 'total'] = pre_income_port_df.loc[counter, boy_value].sum()\\n pre_income_port_df.loc[counter, 'adv_fees'] = pre_income_port_df.loc[counter, 'total'] * adv_fees\\n pre_income_port_df.loc[counter, 'total_net_fees'] = pre_income_port_df.loc[counter, 'total'] - \\\\\\n pre_income_port_df.loc[counter, 'adv_fees']\\n pre_income_port_inv = pre_income_port_df.loc[counter, 'total_net_fees'] + cpn_income_base\\n\\n # ------------------Pre Income Block Ends------------------------\\n\\n fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\\n fia_portfolio_df.loc[counter, 'adv_fees'] = fia_portfolio_df.loc[counter, 'total'] * adv_fees\\n fia_portfolio_df.loc[counter, 'total_net_fees'] = fia_portfolio_df.loc[counter, 'total'] - \\\\\\n fia_portfolio_df.loc[counter, 'adv_fees']\\n fia_portfolio_df.loc[counter, 'total_pre_income'] = fia_portfolio_df.loc[counter, 'total_net_fees']\\n\\n port_investment = fia_portfolio_df.loc[counter, 'total_net_fees'] + cpn_income_port\\n\\n else:\\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment * (1 + period_returns[c])\\n for c in range(len(boy_value))]\\n\\n # -------------Record the Pre Income Base Portfolio-----------------------------\\n pre_income_port_df.loc[counter, boy_value] = [\\n base_weights[c] * pre_income_port_inv * (1 + period_returns[c])\\n for c in range(len(boy_value))]\\n\\n pre_income_port_df.loc[counter, 'total'] = pre_income_port_df.loc[counter, boy_value].sum()\\n pre_income_port_df.loc[counter, 'adv_fees'] = pre_income_port_df.loc[counter, 'total'] * adv_fees\\n pre_income_port_df.loc[counter, 'total_net_fees'] = pre_income_port_df.loc[counter, 'total'] - \\\\\\n pre_income_port_df.loc[counter, 'adv_fees']\\n pre_income_port_inv = pre_income_port_df.loc[counter, 'total_net_fees'] + cpn_income_base\\n\\n # ------------------Pre Income Block Ends------------------------\\n\\n fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\\n fia_portfolio_df.loc[counter, 'adv_fees'] = fia_portfolio_df.loc[counter, 'total'] * adv_fees\\n\\n # ---req. income is adjusted for inflation from the second year of withdrawal. Reinvestment of coupon\\n # stops from the year income starts. Req. income is reduced by the coupon payments\\n\\n if counter == income_starts:\\n\\n income_needed = req_annual_income - social\\n income_net_fia_income = max(0, income_needed - income_from_fia)\\n fia_portfolio_df.loc[counter, 'income'] = max(0, income_net_fia_income - cpn_income_port)\\n income_needed = req_annual_income\\n\\n else:\\n income_needed = income_needed * (1 + inflation) - social\\n income_net_fia_income = max(0, income_needed - income_from_fia)\\n fia_portfolio_df.loc[counter, 'income'] = max(0, income_net_fia_income - cpn_income_port)\\n income_needed = income_needed + social\\n\\n fia_portfolio_df.loc[counter, 'total_net_fees'] = fia_portfolio_df.loc[counter, 'total'] - \\\\\\n fia_portfolio_df.loc[counter, 'adv_fees'] - \\\\\\n fia_portfolio_df.loc[counter, 'income']\\n\\n fia_portfolio_df.loc[counter, 'total_pre_income'] = fia_portfolio_df.loc[counter, 'total'] - \\\\\\n fia_portfolio_df.loc[counter, 'adv_fees']\\n\\n port_investment = fia_portfolio_df.loc[counter, 'total_net_fees']\\n\\n sim_port_total.loc[:, 's_{}'.format(str(runs))] = fia_portfolio_df.loc[:, 'total_net_fees'] + \\\\\\n income_df.loc[:, 'contract_value']\\n\\n sim_port_income.loc[:, 's_{}'.format(str(runs))] = fia_portfolio_df.loc[:, 'income']\\n\\n fia_portfolio_df.loc[:, 'adj_total'] = fia_portfolio_df.loc[:, 'total_net_fees'].apply(\\n lambda x: x if x > 0 else 0)\\n\\n sim_port_total_pre_income.loc[:, 's_{}'.format(str(runs))] = fia_portfolio_df.loc[:, 'total_pre_income']\\n\\n # -------------------Portfolio with PreIncome Values----------------------------\\n sim_port_total_preincome.loc[:, 's_{}'.format(str(runs))] = pre_income_port_df.loc[:, 'total_net_fees'] + \\\\\\n income_df.loc[:, 'contract_value']\\n\\n sim_port_total_preincome.fillna(float(read_income_inputs.loc['risky_assets', 'FIA']), inplace=True)\\n # --------------------------------PreIncome Block Ends----------------------------\\n\\n runs += 1\\n\\n # ------------------Calculate % of portfolios ending value greater than required LIFETIME cumm. income---------\\n total_income_by_age = sim_base_income.loc[:, sim_base_income.columns[0]].cumsum()\\n total_income_by_acturial_age = total_income_by_age.loc[life_expectancy - clients_age]\\n total_income_by_age.fillna(0, inplace=True)\\n income_dataframe = pd.DataFrame(total_income_by_age)\\n income_dataframe.loc[:, 'remaining_income_by_acturial_age'] = total_income_by_age.apply(\\n lambda x: total_income_by_acturial_age - x)\\n\\n s = income_dataframe.loc[:, 'remaining_income_by_acturial_age']\\n base_prob_of_success = sim_base_total.gt(s, axis=0).sum(axis=1)\\n port_prob_of_success = sim_port_total.gt(s, axis=0).sum(axis=1)\\n\\n # ----------------------------Portfolio sufficient for NEXT YEARS income needs-------------------\\n next_year_income = sim_base_income.loc[:, sim_base_income.columns[0]].shift(-1).fillna(0) # Yearly Income Reqd.\\n base_success_next_year = sim_base_total.gt(next_year_income, axis=0).sum(axis=1)\\n\\n base_for_next_year_need = sim_base_total[sim_base_total.gt(next_year_income, axis=0)]\\n\\n port_success_next_year = sim_port_total.gt(next_year_income, axis=0).sum(axis=1)\\n\\n port_for_next_year_need = sim_port_total[sim_port_total.gt(next_year_income, axis=0)]\\n\\n # ---------------Portfolio for 45 years of simulation---------------------------------------\\n base_success_portfolio = sim_base_total[sim_base_total.gt(next_year_income, axis=0)]\\n port_success_portfolio = sim_port_total[sim_port_total.gt(next_year_income, axis=0)]\\n\\n # ----------------Portfolio Simulation until the acturial age------------------------------\\n acturial_years = life_expectancy - clients_age\\n base_success_portfolio_act_age = base_success_portfolio.loc[acturial_years, :]\\n port_success_portfolio_act_age = port_success_portfolio.loc[acturial_years, :]\\n\\n # -------------------------Base Portfolio TS with max Terminal Value ----------------------------\\n if base_success_portfolio_act_age.isnull().sum() == trials:\\n base_max_portfolio = 0.0\\n else:\\n base_max_portfolio = base_success_portfolio.loc[:, base_success_portfolio_act_age.idxmax()]\\n\\n # -------------------------FIA Portfolio TS with max Terminal Value ----------------------------\\n if port_success_portfolio_act_age.isnull().sum() == trials:\\n port_max_portfolio = 0.0\\n else:\\n port_max_portfolio = port_success_portfolio.loc[:, port_success_portfolio_act_age.idxmax()]\\n\\n # ------------------------------Average age with full income------------------------------\\n base_mean_age = ((life_expectancy - clients_age) - base_success_portfolio.loc[1:life_expectancy - clients_age, :]\\n .isnull().sum()).mean()\\n\\n port_mean_age = ((life_expectancy - clients_age) - port_success_portfolio.loc[1:life_expectancy - clients_age, :]\\n .isnull().sum()).mean()\\n\\n # ----------------------------Median Age with full Income------------------------------------------\\n base_median_age = ((life_expectancy - clients_age) - base_success_portfolio.loc[1:life_expectancy - clients_age, :]\\n .isnull().sum()).median()\\n\\n port_median_age = ((life_expectancy - clients_age) - port_success_portfolio.loc[1:life_expectancy - clients_age, :]\\n .isnull().sum()).median()\\n\\n # --------------Mean Value for all the portfolios at end of the acturial age--------------------\\n base_act_avg_porfolio = base_success_portfolio.loc[life_expectancy - clients_age, :].fillna(0).mean()\\n port_act_avg_porfolio = port_success_portfolio.loc[life_expectancy - clients_age, :].fillna(0).mean()\\n\\n # ---------------Median Value for all the portfolios at end of the acturial age--------------------\\n base_act_median_porfolio = base_success_portfolio.loc[life_expectancy - clients_age, :].fillna(0).median()\\n port_act_median_porfolio = port_success_portfolio.loc[life_expectancy - clients_age, :].fillna(0).median()\\n\\n # # --------------Mean Value for all the portfolios in the simulation--------------------\\n # base_sim_mean = base_success_portfolio.loc[1:life_expectancy - clients_age, :].mean().mean()\\n # port_sim_mean = port_success_portfolio.loc[1:life_expectancy - clients_age, :].mean().mean()\\n #\\n # # --------------Median Value for all the portfolios in the simulation--------------------\\n # base_sim_mean = base_success_portfolio.loc[1:life_expectancy - clients_age, :].mean().median()\\n # port_sim_mean = port_success_portfolio.loc[1:life_expectancy - clients_age, :].mean().median()\\n\\n # -------Max Portfolio value at the end of acturial age----------------------------------------\\n base_act_max = base_success_portfolio.loc[life_expectancy - clients_age, :].max()\\n port_act_max = port_success_portfolio.loc[life_expectancy - clients_age, :].max()\\n\\n # ----------------------Min Portfolio value at the end of acturial age----------------------------------------\\n base_act_min = base_success_portfolio.loc[life_expectancy - clients_age, :].min()\\n port_act_min = port_success_portfolio.loc[life_expectancy - clients_age, :].min()\\n\\n # -----------------------------------------Lifetime Average Income-----------------------------------------\\n base_total_income = sim_base_income.cumsum().loc[acturial_years, :].mean()\\n port_total_income = income_from_fia + sim_port_income\\n port_total_income = port_total_income.cumsum().loc[acturial_years, :].mean()\\n\\n simulation_stats = pd.DataFrame(index=['Average Years', 'Median Years', 'Average Age', 'Median Age',\\n 'Average Portfolio (act.age)', 'Median Portfolio (act.age)',\\n 'Max Portfolio Value', 'Min Portfolio Value',\\n 'Average Lifetime Income'], columns=['Base', 'FIA'])\\n\\n simulation_stats.loc['Average Years', :] = [base_mean_age, base_mean_age]\\n simulation_stats.loc['Median Years', :] = [base_median_age, base_median_age]\\n simulation_stats.loc['Average Age', :] = [base_mean_age + clients_age, base_mean_age + clients_age]\\n simulation_stats.loc['Median Age', :] = [base_median_age + clients_age, base_median_age + clients_age]\\n simulation_stats.loc['Average Portfolio (act.age)', :] = [base_act_avg_porfolio, port_act_avg_porfolio]\\n simulation_stats.loc['Median Portfolio (act.age)', :] = [base_act_median_porfolio, port_act_median_porfolio]\\n simulation_stats.loc['Max Portfolio Value', :] = [base_act_max, port_act_max]\\n simulation_stats.loc['Min Portfolio Value', :] = [base_act_min, port_act_min]\\n simulation_stats.loc['Average Lifetime Income', :] = [base_total_income, port_total_income]\\n comments = ['Average years of portfolios that meet the next years income needs for the lifetime',\\n 'Median years of portfolios that meet the next years income needs for the lifetime',\\n 'Average Clients Age',\\n 'Median Clients Age',\\n 'Average of terminal values for the portfolios at the end of the acturial life',\\n 'Median of terminal values for the portfolios at the end of the acturial life',\\n 'Maximum of terminal values for the portfolios at the end of the acturial life',\\n 'Minimum of terminal values for the portfolios at the end of the acturial life',\\n 'Average of total income generated by all portfolios at the end of the acturial life']\\n\\n simulation_stats.loc[:, 'Notes'] = comments\\n\\n # --------------------------------------------------------------------------------\\n\\n # # -----------------------------------income breakdown for Base portfolio----------------------------------\\n # base_df.to_csv(src + 'base_port_detail.csv')\\n # sim_base_total.to_csv(src + 'base_ending_values.csv')\\n # income_breakdown_base = pd.DataFrame(sim_base_total.quantile(0.5, axis=1))\\n # income_breakdown_base.loc[:, 'income_from_portfolio'] = sim_base_income.quantile(0.5, axis=1)\\n # income_breakdown_base.loc[:, 'fia_income'] = 0.0\\n # income_breakdown_base.loc[:, 'social_security_income'] = social\\n # income_breakdown_base.loc[:, 'coupon_income'] = cpn_income_base\\n #\\n # income_breakdown_base.rename(columns={income_breakdown_base.columns[0]: 'portfolio_ending_value'}, inplace=True)\\n # income_breakdown_base.loc[:, 'income_from_portfolio'][\\n # income_breakdown_base.loc[:, 'portfolio_ending_value'] <= 0] = 0\\n # income_breakdown_base.loc[:, 'total_income'] = income_breakdown_base.loc[:, income_breakdown_base.columns[1:]].sum(\\n # axis=1)\\n #\\n # # --------------------------------------Block Ends-----------------------------------------------------------\\n #\\n # # ---------------------------------------income breakdown for FIA portfolio----------------------------------\\n # fia_portfolio_df.to_csv(src + 'fia_port_detail.csv')\\n # sim_port_total.to_csv(src + 'fiaport_ending_values.csv')\\n #\\n # income_breakdown_port = pd.DataFrame(sim_port_total.quantile(0.5, axis=1))\\n # income_breakdown_port.loc[:, 'income_from_portfolio'] = sim_port_income.quantile(0.5, axis=1)\\n # income_breakdown_port.loc[:, 'fia_income'] = income_from_fia\\n # income_breakdown_port.loc[:, 'social_security_income'] = social\\n # income_breakdown_port.loc[:, 'coupon_income'] = cpn_income_port\\n #\\n # income_breakdown_port.rename(columns={income_breakdown_port.columns[0]: 'portfolio_ending_value'}, inplace=True)\\n # income_breakdown_port.loc[:, 'income_from_portfolio'][\\n # income_breakdown_port.loc[:, 'portfolio_ending_value'] <= 0] = 0\\n # income_breakdown_port.loc[:, 'total_income'] = income_breakdown_port.loc[:, income_breakdown_port.columns[1:]].sum(\\n # axis=1)\\n #\\n # # ----------------------------------Block Ends-------------------------------------------------------------\\n q_cut = [0.0, 0.1, 0.25, 0.5, 0.75, 0.95, 1.0]\\n sim_base_income[sim_base_total < income_needed] = 0.0\\n\\n sim_port_income[sim_port_total < income_net_fia_income] = 0\\n\\n sim_port_income = sim_port_income + income_from_fia\\n\\n # base_quantile = sim_base_total.loc[sim_base_total.index[-1]].quantile([0.05, 0.25, 0.50, 0.75, 0.90])\\n #\\n # port_quantile = sim_port_total.loc[sim_port_total.index[-1]].quantile([0.05, 0.25, 0.50, 0.75, 0.90])\\n\\n base_quantile = sim_base_total.loc[sim_base_total.index[-1]].quantile(q_cut)\\n\\n port_quantile = sim_port_total.loc[sim_port_total.index[-1]].quantile(q_cut)\\n\\n # q_cut = [0.0, .05, 0.25, 0.5, 0.75, 0.95, 1.0]\\n cols = ['Min', '10th', '25th', '50th', '75th', '90th', 'Max']\\n\\n # ------------------------------------------drop year 0-----------------------------------------\\n sim_base_total = sim_base_total[1:]\\n sim_port_total = sim_port_total[1:]\\n\\n # ---------------------------------plot for histogram for porfolios--------------------------------------\\n # base_term_value = sim_base_total.loc[sim_base_total.index[:life_expectancy - clients_age], :]\\n # fact = 1 / len(base_term_value)\\n # base_ann_ret = (base_term_value.iloc[-1] / base_term_value.iloc[0]) ** fact - 1\\n # counts, bins, bars = plt.hist(base_ann_ret)\\n\\n # ------------------------quantile analysis for base terminal value-----------------------------\\n base_qcut = pd.DataFrame(index=sim_base_total.index, columns=cols)\\n for c in range(len(cols)):\\n base_qcut.loc[:, cols[c]] = sim_base_total.quantile(q_cut[c], axis=1)\\n\\n base_qcut.clip(lower=0, inplace=True)\\n\\n sim_base_total.clip(lower=0, inplace=True)\\n\\n # -------------------------------------quantile analysis for base income----------------------------\\n base_income_qcut = pd.DataFrame(index=sim_base_income.index, columns=cols)\\n for c in range(len(cols)):\\n base_income_qcut.loc[:, cols[c]] = sim_base_income.quantile(q_cut[c], axis=1)\\n\\n # ----Remove NaN's prior to the income start years------------\\n # base_income_qcut = base_income_qcut.loc[income_starts:]\\n\\n # ---------------------------------quantile analysis for portfolio terminal value ---------------\\n\\n port_qcut = pd.DataFrame(index=sim_port_total.index, columns=cols)\\n for c in range(len(cols)):\\n port_qcut.loc[:, cols[c]] = sim_port_total.quantile(q_cut[c], axis=1)\\n\\n port_qcut.clip(lower=0, inplace=True)\\n\\n # ----------------------------------quantile analysis for portfolio income----------------------------\\n port_income_qcut = pd.DataFrame(index=sim_port_income.index, columns=cols)\\n for c in range(len(cols)):\\n port_income_qcut.loc[:, cols[c]] = sim_port_income.quantile(q_cut[c], axis=1)\\n\\n # ----Remove NaN's prior to the income start years------------\\n # port_income_qcut = port_income_qcut.loc[income_starts:]\\n\\n # ----------probability ending value will be less than 0 at the end of the horizon -----------------------\\n # base_legacy_risk = (sim_base_total.loc[sim_base_total.index[-1]] < 0).sum() / (trials)\\n\\n base_legacy_risk = (sim_base_total.loc[sim_base_total.index[life_expectancy - clients_age]] < 0).sum() / trials\\n port_legacy_risk = (sim_port_total.loc[sim_port_total.index[life_expectancy - clients_age]] < 0).sum() / trials\\n\\n # port_legacy_risk = (sim_port_total.loc[sim_port_total.index[-1]] <= 0).sum() / (trials)\\n\\n legacy_risk = pd.DataFrame([base_legacy_risk, port_legacy_risk,\\n 'Prob. of portfolio value less than 0 at the end of the expected life'],\\n index=['base', 'fia_portfolio', 'Notes'],\\n columns=['Ruin Probability'])\\n\\n # -----------Year-wise probability of ending value greater than 0 -----------------\\n base_psuccess = sim_base_total.apply(lambda x: x > 0).sum(axis=1) / trials\\n port_psuccess = sim_port_total.apply(lambda x: x > 0).sum(axis=1) / trials\\n\\n # ------------------------------------WRITING FILES TO EXCEL ---------------------------\\n\\n writer = pd.ExcelWriter(dest_simulation + method + '_montecarlo_income_summary.xlsx', engine='xlsxwriter')\\n read_income_inputs.to_excel(writer, sheet_name='inputs_for_income')\\n\\n read_returns_est.to_excel(writer, sheet_name='asset_returns_estimates')\\n # read_portfolio_inputs.to_excel(writer, sheet_name='portfolio_inputs')\\n\\n age_index = list(range(clients_age + 1, clients_age + len(base_qcut) + 1))\\n # base_qcut.loc[:, 'clients_age'] = age_index\\n # base_qcut.loc[:, 'comment'] = ''\\n # base_qcut.loc[:, 'comment'] = np.where(base_qcut.clients_age == life_expectancy, 'expected_life', \\\"\\\")\\n base_inv = float(read_income_inputs.loc['risky_assets', 'Base'])\\n base_qcut.loc[:, 'age'] = age_index\\n base_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n\\n # -----------------------To start with year 0---------------------------------\\n insert_col = [base_inv, base_inv, base_inv, base_inv, base_inv, base_inv,\\n base_inv, clients_age, np.nan]\\n base_qcut.loc[len(base_qcut) + 1, :] = 0.0\\n base_qcut = base_qcut.shift(1)\\n base_qcut.iloc[0] = insert_col\\n base_qcut.reset_index(drop=True, inplace=True)\\n base_qcut.to_excel(writer, sheet_name='base_ending_value_quantiles')\\n # base_qcut.loc[income_starts:, :].to_excel(writer, sheet_name='base_ending_value_quantiles')\\n\\n # base_income_qcut = base_income_qcut[1:] base_income_qcut.loc[:, 'clients_age'] = age_index\\n # base_income_qcut.loc[:, 'comment'] = '' base_income_qcut.loc[:, 'comment'] = np.where(\\n # base_income_qcut.clients_age == life_expectancy, 'expected_life', \\\"\\\")\\n\\n base_income_qcut = base_income_qcut.loc[1:, :]\\n base_income_qcut.loc[:, 'age'] = age_index\\n base_income_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n base_income_qcut.loc[income_starts:, :].to_excel(writer, sheet_name='base_income_quantiles')\\n\\n # age_index = list(range(clients_age+1, clients_age + len(port_qcut)+1))\\n # port_qcut.loc[:, 'clients_age'] = age_index\\n # port_qcut.loc[:, 'comment'] = ''\\n # port_qcut.loc[:, 'comment'] = np.where(port_qcut.clients_age == life_expectancy, 'expected_life', \\\"\\\")\\n\\n port_qcut.loc[:, 'age'] = age_index\\n port_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n port_qcut.loc[len(port_qcut) + 1, :] = 0.0\\n port_qcut = port_qcut.shift(1)\\n port_qcut.iloc[0] = insert_col\\n port_qcut.reset_index(drop=True, inplace=True)\\n port_qcut.to_excel(writer, sheet_name='fia_port_ending_value_quantiles')\\n # port_qcut.loc[income_starts:, :].to_excel(writer, sheet_name='fia_port_ending_value_quantiles')\\n\\n # port_income_qcut = port_income_qcut[1:] port_income_qcut.loc[:, 'clients_age'] = age_index\\n # port_income_qcut.loc[:, 'comment'] = '' port_income_qcut.loc[:, 'comment'] = np.where(\\n # port_income_qcut.clients_age == life_expectancy, 'expected_life', \\\"\\\")\\n\\n port_income_qcut = port_income_qcut.loc[1:, :]\\n port_income_qcut.loc[:, 'age'] = age_index\\n port_income_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n port_income_qcut.loc[income_starts:, :].to_excel(writer, sheet_name='fia_port_income_quantiles')\\n\\n prob_success_df = pd.concat([base_psuccess, port_psuccess], axis=1)\\n prob_success_df.rename(columns={prob_success_df.columns[0]: 'prob(ending_value>0)_base',\\n prob_success_df.columns[1]: 'prob(ending_value>0)_port'}, inplace=True)\\n\\n # prob_success_df.loc[:, 'clients_age'] = age_index\\n # prob_success_df.loc[:, 'comment'] = ''\\n # prob_success_df.loc[:, 'comment'] = np.where(prob_success_df.clients_age == life_expectancy, 'expected_life', \\\"\\\")\\n\\n prob_success_df.loc[:, 'age'] = age_index\\n prob_success_df.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n prob_success_df.loc[:, 'prob(ending_value>lifetime_req income)_base'] = base_prob_of_success / trials\\n prob_success_df.loc[:, 'prob(ending_value>lifetime_req income)_port'] = port_prob_of_success / trials\\n prob_success_df.loc[:, 'prob(ending_value>next_year_req_income)_base'] = base_success_next_year / trials\\n prob_success_df.loc[:, 'prob(ending_value>next_year_req_income)_port'] = port_success_next_year / trials\\n prob_success_df.loc[:, 'base_max_portfolio_at_acturial_age'] = base_max_portfolio\\n prob_success_df.loc[:, 'port_max_portfolio_at_acturial_age'] = port_max_portfolio\\n\\n # --------------------Percentile Portfolio's based on Acturial Life------------------------\\n base_success = prob_success_df.loc[life_expectancy - clients_age, 'prob(ending_value>next_year_req_income)_base']\\n port_success = prob_success_df.loc[life_expectancy - clients_age, 'prob(ending_value>next_year_req_income)_port']\\n\\n # acturial_age_base_tv = sim_base_total.loc[:life_expectancy - clients_age, ]\\n # percentile_base_tv = sim_base_total.apply(lambda x: np.nanpercentile(x, base_success), axis=1)\\n\\n # ----------------Year wise percentile portfolio to meet next year income. Based on the success at acturial age.\\n # Yearly portfolio values that can provide the next year income below the success rate at end of life (Percentile)-\\n\\n # acturial_age_base = base_for_next_year_need.loc[:life_expectancy - clients_age, ]\\n # acturial_age_base = base_for_next_year_need.copy().fillna(0)\\n percentile_base = base_for_next_year_need.apply(lambda x: np.nanpercentile(x, base_success), axis=1)\\n\\n # ----Pre Income Portfolio based on the Probab. of Success to meet next year's income at the end on the Act. Age\\n base_pre_income_success = sim_base_total_preincome.apply(lambda x: np.nanpercentile(x, base_success), axis=1)\\n base_ann_ret_pre_income = base_pre_income_success.pct_change().fillna(0)\\n\\n # acturial_age_port_tv = sim_port_total.loc[:life_expectancy - clients_age, ]\\n # percentile_port_tv = sim_port_total.apply(lambda x: np.nanpercentile(x, port_success), axis=1)\\n\\n # ----------------Year wise percentile portfolio to meet next year income. Based on the success at acturial age.\\n # Yearly portfolio values that can provide the next year income below the success rate at end of life (Percentile)-\\n\\n # acturial_age_port = port_for_next_year_need.loc[:life_expectancy - clients_age, ]\\n # acturial_age_base = port_for_next_year_need.copy().fillna(0)\\n percentile_port = port_for_next_year_need.apply(lambda x: np.nanpercentile(x, port_success), axis=1)\\n\\n # ----Pre Income Portfolio based on the Probab. of Success to meet next year's income at the end on the Act. Age\\n port_pre_income_success = sim_port_total_preincome.apply(lambda x: np.nanpercentile(x, port_success), axis=1)\\n port_ann_ret_pre_income = port_pre_income_success.pct_change().fillna(0)\\n\\n prob_success_df.loc[:, 'acturial_success_percentile_base_portfolio'] = percentile_base\\n prob_success_df.loc[:, 'acturial_success_percentile_port_portfolio'] = percentile_port\\n\\n prob_success_df.loc[:, 'base_pre_income_ann_ret'] = base_ann_ret_pre_income\\n prob_success_df.loc[:, 'port_pre_income_ann_ret'] = port_ann_ret_pre_income\\n\\n # prob_success_df.loc[:, 'terminalVal_success_percentile_base_portfolio'] = percentile_base_tv\\n # prob_success_df.loc[:, 'terminalVal_success_percentile_port_portfolio'] = percentile_port_tv\\n\\n sim_base_total_preincome.to_excel(writer, sheet_name='base_preincome_portfolios')\\n # -------------Adding Premium to calculate the total initial investment--------------\\n sim_port_total_preincome.iloc[0] = sim_port_total_preincome.iloc[0] + premium\\n sim_port_total_preincome.to_excel(writer, sheet_name='port_preincome_portfolios')\\n\\n # -------------For Simulation slide - BASE Portfolio - Can Delete --------------------\\n # base_qcut_preinc = pd.DataFrame(index=sim_base_total_preincome.index, columns=cols)\\n # for c in range(len(cols)):\\n # base_qcut_preinc.loc[:, cols[c]] = sim_base_total_preincome.quantile(q_cut[c], axis=1)\\n #\\n # # -------------For Simulation slide - Proposed Portfolio --------------------\\n # port_qcut_preinc = pd.DataFrame(index=sim_port_total_preincome.index, columns=cols)\\n # for c in range(len(cols)):\\n # port_qcut_preinc.loc[:, cols[c]] = sim_port_total_preincome.quantile(q_cut[c], axis=1)\\n #\\n # base_qcut_preinc.to_excel(writer, sheet_name='base_preincome_quantiles')\\n # port_qcut_preinc.to_excel(writer, sheet_name='port_preincome_quantiles')\\n\\n prob_success_df.to_excel(writer, sheet_name='success_probability')\\n\\n # --------------BASE - Accumulation and Income Breakdown based on the success percentile portfolio---------------\\n base_df.to_csv(src + 'base_port_detail.csv')\\n sim_base_total.to_csv(src + 'base_ending_values.csv')\\n income_breakdown_base = pd.DataFrame(sim_base_total.quantile(base_success, axis=1))\\n income_breakdown_base.loc[:, 'income_from_risky_assets'] = sim_base_income.quantile(base_success, axis=1) \\\\\\n - social - cpn_income_port\\n income_breakdown_base.loc[:, 'guaranteed_income'] = 0.0\\n income_breakdown_base.loc[:, 'social_security_income'] = social\\n income_breakdown_base.loc[:, 'coupon_income'] = cpn_income_base\\n\\n income_breakdown_base.rename(columns={income_breakdown_base.columns[0]: 'portfolio_ending_value'}, inplace=True)\\n income_breakdown_base.loc[:, 'income_from_risky_assets'][\\n income_breakdown_base.loc[:, 'portfolio_ending_value'] <= 0] = 0\\n income_breakdown_base.loc[:, 'total_income'] = income_breakdown_base.loc[:, income_breakdown_base.columns[1:]].sum(\\n axis=1)\\n\\n # ----------FIA PORTFOLIO - Accumulation and Income Breakdown based on the success percentile portfolio-----------\\n fia_portfolio_df.to_csv(src + 'fia_port_detail.csv')\\n sim_port_total.to_csv(src + 'fiaport_ending_values.csv')\\n\\n income_breakdown_port = pd.DataFrame(sim_port_total.quantile(port_success, axis=1))\\n income_breakdown_port.loc[:, 'income_from_risky_assets'] = sim_port_income.quantile(port_success, axis=1) \\\\\\n - income_from_fia - social - cpn_income_port\\n income_breakdown_port.loc[:, 'guaranteed_income'] = income_from_fia\\n income_breakdown_port.loc[:, 'social_security_income'] = social\\n income_breakdown_port.loc[:, 'coupon_income'] = cpn_income_port\\n\\n income_breakdown_port.rename(columns={income_breakdown_port.columns[0]: 'portfolio_ending_value'}, inplace=True)\\n income_breakdown_port.loc[:, 'income_from_risky_assets'][\\n income_breakdown_port.loc[:, 'portfolio_ending_value'] <= 0] = 0\\n income_breakdown_port.loc[:, 'total_income'] = income_breakdown_port.loc[:, income_breakdown_port.columns[1:]].sum(\\n axis=1)\\n\\n # -------------------Write simulation Statistics-------------------------------------\\n simulation_stats.to_excel(writer, sheet_name='simulation_statistics')\\n\\n # port_psuccess.to_excel(writer, sheet_name='fia_port_success_probability')\\n\\n income_breakdown_base = income_breakdown_base.loc[1:, :]\\n income_breakdown_base.loc[:, 'age'] = age_index\\n income_breakdown_base.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n income_breakdown_base.loc[income_starts:, :].to_excel(writer, sheet_name='base_income_breakdown_median')\\n\\n income_breakdown_port = income_breakdown_port.loc[1:, :]\\n income_breakdown_port.loc[:, 'age'] = age_index\\n income_breakdown_port.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n income_breakdown_port.loc[income_starts:, :].to_excel(writer, sheet_name='fia_income_breakdown_median')\\n\\n legacy_risk.to_excel(writer, sheet_name='ruin_probability')\\n\\n if method == 'normal':\\n median_returns_normal.loc[:, 'fia_median_returns'] = median_normal_fia\\n median_returns_normal.to_excel(writer, sheet_name='gr_port_median_normal')\\n\\n elif method == 'smallest':\\n median_returns_smallest.loc[:, 'fia_median_returns'] = median_smallest_fia\\n median_returns_smallest.to_excel(writer, sheet_name='gr_port_median_asc')\\n\\n else:\\n median_returns_largest.loc[:, 'fia_median_returns'] = median_largest_fia\\n median_returns_largest.to_excel(writer, sheet_name='gr_port_median_desc')\\n\\n # ---------------------Histogram for S&P Forecast---------------------------------------\\n sp_returns = read_returns_est.loc['SPXT Index', 'Annualized Returns']\\n sp_risk = read_returns_est.loc['SPXT Index', 'Annualized Risk']\\n sp_random_ret = np.random.normal(loc=sp_returns, scale=sp_risk, size=10000)\\n bins, data = np.histogram(sp_random_ret, bins=20)\\n df_ret = pd.DataFrame(data, columns=['Return_range'])\\n df_bins = pd.DataFrame(bins, columns=['Count'])\\n df_hist = df_ret.join(df_bins)\\n df_hist.to_excel(writer, sheet_name='sp500_histogram')\\n\\n # ---------------------Histogram for FIA Portfolios TV>0 at the acturial age---------------------------------------\\n tval_at_horizon = sim_port_total.loc[acturial_years, :]\\n fact = 1 / acturial_years\\n arr_returns = np.array((tval_at_horizon / 1000000) ** fact - 1)\\n clean_ann_ret = arr_returns[~np.isnan(arr_returns)]\\n p_bins, p_data = np.histogram(clean_ann_ret, bins=20)\\n df_ret = pd.DataFrame(p_data, columns=['Return_range'])\\n df_bins = pd.DataFrame(p_bins, columns=['Count'])\\n df_hist = df_ret.join(df_bins)\\n df_hist.to_excel(writer, sheet_name='fia_portfolio_histogram')\\n\\n tval_df = pd.DataFrame(sim_port_total.loc[acturial_years, :])\\n tval_df.rename(columns={tval_df.columns[0]:'Terminal Values'}, inplace=True)\\n tval_df.to_excel(writer, sheet_name='fia_ending_values_hist')\\n writer.save()\\n\\n # -----------------Plotting charts--------------------------------------------\\n base_qcut.loc[income_starts:].plot(grid=True, title='Quantile Terminal Value - Base Portfolio')\\n plt.savefig(src + \\\"quantile_terminal_base.png\\\")\\n plt.close('all')\\n\\n base_income_qcut.plot(grid=True, title='Quantile Income - Base Portfolio')\\n plt.savefig(src + \\\"quantile_income_base.png\\\")\\n plt.close('all')\\n\\n base_psuccess.plot(grid=True, title='Probability of Success (Portfolio Ending Value > 0) - Base Portfolio')\\n plt.savefig(src + \\\"success_probabilty_base.png\\\")\\n plt.close('all')\\n\\n (1 - base_psuccess).plot(grid=True, title='Probability of Ruin (Portfolio Ending Value < 0) - Base Portfolio')\\n plt.savefig(src + \\\"ruin_probability_base.png\\\")\\n plt.close('all')\\n\\n port_qcut.loc[income_starts:].plot(grid=True, title='Quantile Terminal Value - FIA Portfolio')\\n plt.savefig(src + \\\"quantile_terminal_fia.png\\\")\\n plt.close('all')\\n\\n port_income_qcut.plot(grid=True, title='Quantile Income - FIA Portfolio')\\n plt.savefig(src + \\\"quantile_income_fia.png\\\")\\n plt.close('all')\\n\\n port_psuccess.plot(grid=True, title='Probability of Success (Portfolio Ending Value > 0) - FIA Portfolio')\\n plt.savefig(src + \\\"success_probabilty_fia.png\\\")\\n plt.close('all')\\n\\n (1 - port_psuccess).plot(grid=True, title='Probability of Ruin (Portfolio Ending Value < 0) - FIA Portfolio')\\n plt.savefig(src + \\\"ruin_probability_fia.png\\\")\\n plt.close('all')\\n\\n print(\\\"simulation completed....\\\")\",\n \"def perform_backtests(self):\\r\\n \\r\\n for test_name in self.testing_dates:\\r\\n print('\\\\t|--Test #{}'.format(test_name))\\r\\n test_dates = self.testing_dates[test_name]\\r\\n print('\\\\t\\\\t|--Performing Nested Cross-Validation')\\r\\n cross_validation = CrossValidate()\\r\\n cross_validation.output_names = self.output_names\\r\\n cross_validation.feature_names = self.feature_names\\r\\n cross_validation.feature_dict = self.feature_dict\\r\\n cross_validation.full_df = self.final_df_output\\r\\n cross_validation.cv_params = self.testing_dates\\r\\n cross_validation.test_name = test_name\\r\\n cross_validation.walk_forward_cv()\\r\\n self.optimal_params['Test #{}'.format(test_name)] = cross_validation.optimal_params_by_output\\r\\n self.cv_model_metadata['Test #{}'.format(test_name)] = cross_validation.cv_metadata_by_output\\r\\n \\r\\n print('\\\\t\\\\t|--Performing Out-Of-Sample Testing')\\r\\n prediction = Predict()\\r\\n prediction.output_names = self.output_names\\r\\n prediction.feature_names = self.feature_names\\r\\n prediction.feature_dict = self.feature_dict\\r\\n prediction.optimal_params_by_output = cross_validation.optimal_params_by_output\\r\\n prediction.cv_predictions_by_output = cross_validation.cv_predictions_by_output\\r\\n prediction.full_df = self.final_df_output\\r\\n prediction.pred_start = test_dates['pred_start']\\r\\n prediction.pred_end = test_dates['pred_end']\\r\\n prediction.run_prediction()\\r\\n self.full_predictions['Test #{}'.format(test_name)] = prediction.predictions_by_output\\r\\n self.pred_model_metadata['Test #{}'.format(test_name)] = prediction.pred_metadata_by_output\\r\\n \\r\\n print('\\\\nSaving model metadata...')\\r\\n with open(path.deployment_cv_results, 'w') as file:\\r\\n json.dump(self.optimal_params, file)\\r\\n with open(path.deployment_cv_metadata, 'w') as file:\\r\\n json.dump(self.cv_model_metadata, file)\\r\\n with open(path.deployment_pred_model_metadata, 'w') as file:\\r\\n json.dump(self.pred_model_metadata, file)\\r\\n with open(path.deployment_full_predictions, 'w') as file:\\r\\n json.dump(self.full_predictions, file)\",\n \"def run(self, max_risk=0, min_return=0, num=0, init_holdings=None):\\n if not self.dates:\\n self.dates = ['2010-01-01', '2012-12-31']\\n self.load_data()\\n\\n num_months = len(self.df_all)\\n first_purchase = True\\n result = {}\\n baseline_result = {}\\n self.baseline_values = [0]\\n self.update_values = [0]\\n months = []\\n\\n # Define dataframe to save output data \\n headers = ['Date', 'Value'] + self.stocks + ['Variance', 'Returns']\\n self.opt_results_df = pd.DataFrame(columns=headers)\\n row = []\\n\\n self.price_df = pd.DataFrame(columns=self.stocks)\\n\\n # Initialize the plot\\n plt.ylim(ymax = 1.5*self.budget, ymin = -1.5*self.budget)\\n plt.xticks(list(range(0, num_months, 2)), \\n self.df_baseline.index.strftime('%b')[::2], rotation='vertical')\\n plt.locator_params(axis='x', nbins=num_months/2)\\n plt.plot(list(range(0, num_months)), [0]*(num_months), \\n color='red', label=\\\"Break-even\\\", linewidth=0.5)\\n\\n for i in range(3, num_months):\\n\\n # Look at just the data up to the current month\\n df = self.df_all.iloc[0:i+1,:].copy()\\n baseline_df_current = self.df_baseline.iloc[0:i+1,:]\\n print(\\\"\\\\nDate:\\\", df.last_valid_index())\\n months.append(df.last_valid_index().date()) \\n\\n if first_purchase:\\n budget = self.budget\\n initial_budget = self.budget\\n baseline_shares = (budget / baseline_df_current.iloc[-1])\\n baseline_result = {self.baseline[0]: baseline_shares} \\n else:\\n # Compute profit of current portfolio\\n budget = sum([df.iloc[-1][s]*result['stocks'][s] for s in self.stocks]) \\n self.update_values.append(budget - initial_budget)\\n\\n # Compute profit of fund portfolio\\n fund_value = sum([baseline_df_current.iloc[-1][s]*baseline_result[s] \\n for s in self.baseline]) \\n self.baseline_values.append(fund_value - initial_budget)\\n\\n self.budget = budget \\n\\n self.load_data(df=df)\\n\\n self.price_df.loc[i-2] = list(self.price.values)\\n\\n # Output for user on command-line and plot\\n update_values = np.array(self.update_values, dtype=object)\\n baseline_values = np.array(self.baseline_values, dtype=object)\\n plt.plot(range(3, i+1), update_values, \\n color='blue', label=\\\"Optimized portfolio\\\")\\n plt.plot(range(3, i+1), baseline_values, \\n color='gray', label=\\\"Fund portfolio\\\", linewidth=0.5)\\n \\n if first_purchase:\\n plt.legend(loc=\\\"lower left\\\")\\n plt.title(\\\"Start: {start}, End: {end}\\\".format\\\\\\n (start=self.df_all.first_valid_index().date(), \\n end=self.df_all.last_valid_index().date()))\\n\\n plt.savefig(\\\"portfolio.png\\\")\\n plt.pause(0.05)\\n \\n # Making solve run\\n if self.model_type == 'DQM':\\n print(f\\\"\\\\nMulti-Period DQM Run...\\\")\\n \\n self.build_dqm()\\n self.solution['DQM'] = self.solve_dqm()\\n result = self.solution['DQM']\\n else:\\n print(f\\\"\\\\nMulti-Period CQM Run...\\\")\\n\\n # Set budget to 0 to enforce that portfolio is self-financing \\n if self.t_cost and not first_purchase:\\n self.budget = 0 \\n\\n self.solution['CQM'] = self.solve_cqm(max_risk=max_risk, \\n min_return=min_return,\\n init_holdings=init_holdings)\\n result = self.solution['CQM']\\n init_holdings = result['stocks']\\n\\n # Print results to command-line\\n value = sum([self.price[s]*result['stocks'][s] for s in self.stocks])\\n returns = result['return']\\n variance = result['risk'] \\n\\n row = [months[-1].strftime('%Y-%m-%d'), value] + \\\\\\n [result['stocks'][s] for s in self.stocks] + \\\\\\n [variance, returns] \\n self.opt_results_df.loc[i-2] = row \\n \\n first_purchase = False\\n\\n print(self.opt_results_df)\\n print(f'\\\\nRun completed.\\\\n')\\n\\n plt.savefig(\\\"portfolio.png\\\")\\n plt.show(block=False)\",\n \"def _run_backtest(self):\\n i = 0\\n while True:\\n i += 1\\n if self.data_handler.continue_backtest == True:\\n self.data_handler.update_bars()\\n else:\\n break\\n while True:\\n try:\\n event = self.events.get(False)\\n except Empty:\\n break\\n else:\\n if event is not None:\\n if event.type == EventType.MARKET:\\n self.strategy.On_Bars(event)\\n self.portfolio.update_balance(event)\\n self.portfolio.order_check(event)\\n elif event.type == EventType.ORDER_SEND:\\n self.portfolio.update_order(event)\\n elif event.type == EventType.ORDER_CLOSE:\\n self.portfolio.update_order(event)\\n self.portfolio.update_euity(event)\\n elif event.type == EventType.ORDER_MODIFY:\\n self.portfolio.update_order(event)\\n time.sleep(self.heartbeat)\",\n \"def rebalance(context, data):\\n logger.debug('rebalancing on: %s', algo.get_datetime())\\n\\n context.trend_filter = False\\n\\n # new_portfolio = algo.pipeline_output('pipeline').dropna(subset=['overall_rank']).sort_values('momentum', ascending=False)\\n\\n new_portfolio = algo.pipeline_output('pipeline').dropna(subset=['overall_rank']).sort_values('momentum', ascending=False)\\n\\n for equity, row in new_portfolio.iterrows():\\n logger.debug('new portfolio (before filtering) - equity: %s', equity)\\n\\n # print(new_portfolio)\\n\\n # new_portfolio = new_portfolio[new_portfolio['overall_rank'].notna() & new_portfolio['momentum'] > 40][:20]\\n \\n # new_portfolio = new_portfolio[(new_portfolio['momentum_decile'] > 8)][:20]\\n\\n new_portfolio = new_portfolio.nlargest(20, ['overall_rank', 'momentum']) #<- $600K PL in 10 years\\n\\n # new_portfolio = new_portfolio.nlargest(20, ['momentum', 'overall_rank']) #<- 1M PL in 10 years\\n\\n if logger.level is logging.DEBUG:\\n for equity, row in new_portfolio.iterrows():\\n logger.debug('new portfolio - (after filtering) equity: %s', equity)\\n \\n\\n # print(len(new_portfolio.index))\\n\\n # volatility driven weights\\n # new_portfolio['inverse_volatility'] = new_portfolio['volatility'].apply(lambda x: 1 / x)\\n # inv_vola_sum = new_portfolio['inverse_volatility'].sum()\\n # new_portfolio['target_weight'] = new_portfolio['inverse_volatility'].apply(lambda x: x / inv_vola_sum)\\n\\n # portfolio size driven weights\\n # num_equities = len(new_portfolio.index)\\n # new_portfolio['target_weight'] = 1 / num_equities\\\\\\n\\n # logger.info('len existing portfolio: %s', len(context.portfolio.positions))\\n\\n if logger.level is logging.DEBUG:\\n for equity, values in context.portfolio.positions.items():\\n logger.debug('context.portfolio.positions - equity: %s, amount: %s, cost_basis: %s, sold_on: %s, sold_at_price: %s', equity, values.amount, values.cost_basis, values.last_sale_date, values.last_sale_price)\\n\\n \\n order_target(algo.sid('FIBBG000NTFYM5'), 0)\\n logger.debug('selling all bonds')\\n\\n for equity in context.portfolio.positions:\\n if equity is algo.sid('FIBBG000NTFYM5'): \\n continue\\n if equity not in set(new_portfolio.index.tolist()):\\n # logger.info('selling %s', equity)\\n order_target_percent(equity, 0)\\n\\n stock_weights = 1.0 / max(len(context.portfolio.positions), len(new_portfolio.index))\\n\\n logger.debug('len existing portfolio (afer ejection): %s', len(context.portfolio.positions))\\n logger.debug('len new portfolio: %s', len(new_portfolio.index))\\n logger.debug('stock_weights: %s', stock_weights)\\n\\n # print(context.portfolio.positions.get(algo.sid('FIBBG000NTFYM5')))\\n\\n # spy = context.portfolio.positions.get(algo.sid('FIBBG000NTFYM5'))\\n\\n # if (spy is not None) and (spy.amount > 0):\\n # order_target_percent(algo.sid('FIBBG000NTFYM5'), 0)\\n\\n for equity, row in new_portfolio.iterrows():\\n if row.trend_filter is True:\\n # logger.info('buying %s', equity)\\n context.trend_filter = True\\n order_target_percent(equity, stock_weights)\\n else:\\n context.trend_filter = False\\n \\n logger.debug('cash: %s', context.portfolio.cash)\\n logger.debug('portfolio_value: %s', context.portfolio.portfolio_value)\\n logger.debug('num_positions: %s', len(context.portfolio.positions))\\n logger.debug('positions: %s', context.portfolio.positions)\",\n \"def evaluate(self):\\n # define start index test set\\n start_test_set = int(len(self.X) * 2 / 3)\\n\\n # Different methods for cummulativa vs day-ahead forecasting\\n if self.forecast_horizon == 1:\\n # In sample\\n lin_residuals_in_sample = self.y - (self.betas[0] + np.dot(self.X, self.betas[1]))\\n self.rmse_in_sample = np.mean(lin_residuals_in_sample ** 2) ** 0.5\\n self.var_in_sample = np.var(self.y)\\n\\n # Out of sample\\n # Calculate MSE of wls-ev prediction\\n self.mse_wlsev = np.mean((self.y[start_test_set:] - self.ols_predict()) ** 2)\\n # Calculate MSE of benchmark prediction\\n self.mse_benchmark = np.mean((self.y[start_test_set:] - self.benchmark_predict()) ** 2)\\n else:\\n # In Sample with betas estimated on full time series\\n lin_residuals_in_sample = rolling_sum(self.y, self.forecast_horizon) - (\\n self.betas[0] + np.dot(self.X[:-(self.forecast_horizon-1)], self.betas[1]))\\n self.rmse_in_sample = np.mean(lin_residuals_in_sample ** 2) ** 0.5\\n self.var_in_sample = np.var(rolling_sum(self.y, self.forecast_horizon))\\n\\n # Out of sample\\n # calculate realized cummulative returns over forecast horizon sequences\\n cum_rets_realized = rolling_sum(self.y[start_test_set:], self.forecast_horizon)\\n # Calculate MSE of wls-ev prediction, only where realized values are available\\n self.mse_wlsev = np.mean((cum_rets_realized - self.ols_predict()[:-(self.forecast_horizon-1)]) ** 2)\\n # Calculate MSE of benchmark prediction, only where realized values are available\\n self.mse_benchmark = np.mean(\\n (cum_rets_realized - self.benchmark_predict()[:-(self.forecast_horizon-1)]) ** 2)\\n\\n # Calculate out of sample r-squared\\n self.oos_r_squared = 1 - (self.mse_wlsev / self.mse_benchmark)\\n # Calculate in sample r-squared\\n self.in_sample_r_squared = 1.0 - (self.rmse_in_sample ** 2) / self.var_in_sample\",\n \"def run_simulator(simulation_parameters, spectrum_portfolio,\\n ant_heights, site_radii, modulation_and_coding_lut, costs):\\n\\n unprojected_point = {\\n 'type': 'Feature',\\n 'geometry': {\\n 'type': 'Point',\\n 'coordinates': (-0.07496, 51.42411),\\n },\\n 'properties': {\\n 'site_id': 'Crystal Palace Radio Tower'\\n }\\n }\\n\\n unprojected_crs = 'epsg:4326'\\n projected_crs = 'epsg:3857'\\n\\n environments =[\\n 'urban',\\n 'suburban',\\n 'rural'\\n ]\\n\\n for environment in environments:\\n for site_radius in site_radii[environment]:\\n\\n print('--working on {}: {}'.format(environment, site_radius))\\n\\n transmitter, interfering_transmitters, site_area, interfering_site_areas = \\\\\\n produce_sites_and_site_areas(\\n unprojected_point['geometry']['coordinates'],\\n site_radius,\\n unprojected_crs,\\n projected_crs\\n )\\n\\n receivers = generate_receivers(site_area, SIMULATION_PARAMETERS, 1)\\n\\n for frequency, bandwidth, generation in spectrum_portfolio:\\n for ant_height in ant_heights:\\n\\n MANAGER = SimulationManager(\\n transmitter, interfering_transmitters, receivers,\\n site_area, SIMULATION_PARAMETERS\\n )\\n\\n results = MANAGER.estimate_link_budget(\\n frequency, bandwidth, generation, ant_height,\\n environment,\\n MODULATION_AND_CODING_LUT,\\n SIMULATION_PARAMETERS\\n )\\n\\n folder = os.path.join(BASE_PATH, '..', 'results', 'full_tables')\\n filename = 'full_capacity_lut_{}_{}_{}_{}.csv'.format(\\n environment, site_radius, frequency, ant_height)\\n\\n write_full_results(results, environment, site_radius,\\n frequency, bandwidth, generation, ant_height,\\n folder, filename, simulation_parameters)\\n\\n average_site_results = obtain_average_values(\\n results, simulation_parameters\\n )\\n\\n results_directory = os.path.join(BASE_PATH, '..', 'results')\\n\\n write_frequency_lookup_table(average_site_results, environment,\\n site_radius, frequency, bandwidth, generation,\\n ant_height, results_directory,\\n 'average_capacity_lut.csv',\\n simulation_parameters\\n )\\n\\n if frequency == spectrum_portfolio[0][0]:\\n\\n percentile_site_results = obtain_percentile_values(\\n results, simulation_parameters\\n )\\n\\n percentile_site_results = calculate_costs(\\n percentile_site_results, costs, simulation_parameters,\\n site_radius, environment\\n )\\n\\n write_cost_lookup_table(percentile_site_results, environment,\\n site_radius, frequency, bandwidth, generation,\\n ant_height, results_directory,\\n 'percentile_{}_capacity_lut.csv'.format(\\n simulation_parameters['percentile']),\\n simulation_parameters\\n )\\n\\n ## write out as shapes, if desired, for debugging purposes\\n geojson_receivers = convert_results_geojson(results)\\n\\n write_shapefile(\\n geojson_receivers, os.path.join(results_directory, 'shapes'),\\n 'receivers_{}.shp'.format(site_radius),\\n projected_crs\\n )\\n\\n write_shapefile(\\n transmitter, os.path.join(results_directory, 'shapes'),\\n 'transmitter_{}.shp'.format(site_radius),\\n projected_crs\\n )\\n\\n write_shapefile(\\n site_area, os.path.join(results_directory, 'shapes'),\\n 'site_area_{}.shp'.format(site_radius),\\n projected_crs\\n )\\n\\n write_shapefile(\\n interfering_transmitters, os.path.join(results_directory, 'shapes'),\\n 'interfering_transmitters_{}.shp'.format(site_radius),\\n projected_crs\\n )\\n\\n write_shapefile(\\n interfering_site_areas, os.path.join(results_directory, 'shapes'),\\n 'interfering_site_areas_{}.shp'.format(site_radius),\\n projected_crs\\n )\",\n \"def run_test_procedures(self):\\r\\n print('\\\\nDeploying prediction model...\\\\n')\\r\\n self.final_df_output = pd.read_json(path.data_final)\\r\\n self.final_df_output.sort_index(inplace=True)\\r\\n self.fill_testing_dates()\\r\\n self.perform_backtests()\\r\\n self.create_full_predictions_dataframe()\\r\\n print('\\\\nDeployment complete!')\",\n \"def test_TPt(self):\\n\\n test_value = self.portfolio.calculate_total_performance(\\n *self.boarder)[self.test_row_number]\\n calculated_value = self.manual_cumprod(\\n self.portfolio._get_total_portfolio)\\n self.assertAlmostEqual(test_value, calculated_value)\",\n \"def copy_generate_median_portfolio_from_simulation(num_of_years=30, trials=100, method='normal'):\\n sim_fia_cv = pd.DataFrame(index=range(num_of_years + 1))\\n\\n sim_base_total = pd.DataFrame(index=range(num_of_years + 1))\\n sim_base_income = pd.DataFrame(index=range(num_of_years + 1))\\n\\n sim_port_total = pd.DataFrame(index=range(num_of_years + 1))\\n sim_port_income = pd.DataFrame(index=range(num_of_years + 1))\\n\\n # read_income_inputs = pd.read_csv(src + \\\"income_model_inputs.csv\\\", index_col='Items')\\n read_income_inputs = pd.read_excel(src + \\\"portfolio_information.xlsx\\\", sheet_name='income_model_inputs',\\n index_col=[0])\\n\\n # read_returns_est = pd.read_csv(src + \\\"income_assets_returns_estimates.csv\\\", index_col='Symbol')\\n read_returns_est = pd.read_excel(src + \\\"portfolio_information.xlsx\\\", sheet_name='income_assets_returns_estimates',\\n index_col=[0])\\n\\n # read_returns_est.drop(['BM', read_returns_est.index[-1]], axis=0, inplace=True)\\n # read_portfolio_inputs = pd.read_csv(src + \\\"income_portfolio_inputs.csv\\\", index_col='Items')\\n\\n # read_asset_weights = pd.read_csv(src + \\\"asset_weights.csv\\\", index_col='Asset')\\n read_asset_weights = pd.read_excel(src + \\\"portfolio_information.xlsx\\\", sheet_name='asset_weights',\\n index_col=[0])\\n\\n all_assets = list(read_asset_weights.index)\\n read_asset_weights.drop(read_asset_weights.index[-1], axis=0, inplace=True)\\n\\n # read random returns for simulation\\n read_normal = pd.read_csv(src + 'sort_normal.csv', index_col=[0], parse_dates=True)\\n\\n assets_col_names = list(read_normal.columns)\\n\\n tickers = list(read_asset_weights.index)\\n wts = np.array(read_asset_weights.loc[:, 'base'])\\n\\n def asset_median_returns(data, ticker):\\n return data.filter(regex=ticker).median(axis=1)\\n\\n # dataframe for unsorted returns (normal)\\n median_returns_normal = pd.DataFrame({t: asset_median_returns(read_normal, t) for t in tickers})\\n median_returns_normal.loc[:, 'portfolio_return'] = median_returns_normal.dot(wts)\\n median_normal_fia = pd.DataFrame({'FIA': asset_median_returns(read_normal, 'r_FIA')})\\n\\n years = list(range(0, num_of_years + 1))\\n income_cols = ['year', 'strategy_term', 'index_returns', 'term_ret', 'term_ret_with_par', 'term_annualize',\\n 'ann_net_spread', 'term_ret_netspr', 'high_inc_benefit_base', 'rider_fee', 'eoy_income',\\n 'contract_value']\\n\\n term = int(read_income_inputs.loc['term', 'inputs'])\\n fia_ret = read_returns_est.loc[read_returns_est.index[-1], 'Annualized Returns']\\n fia_risk = read_returns_est.loc[read_returns_est.index[-1], 'Annualized Risk']\\n par_rate = float(read_income_inputs.loc['par_rate', 'inputs'])\\n spread = float(read_income_inputs.loc['spread', 'inputs'])\\n bonus_term = int(read_income_inputs.loc['bonus_term', 'inputs'])\\n premium = float(read_income_inputs.loc['premium', 'inputs'])\\n income_bonus = float(read_income_inputs.loc['income_bonus', 'inputs'])\\n\\n income_starts = int(read_income_inputs.loc['start_income_years', 'inputs'])\\n income_growth = float(read_income_inputs.loc['income_growth', 'inputs'])\\n rider_fee = float(read_income_inputs.loc['rider_fee', 'inputs'])\\n inc_payout_factor = float(read_income_inputs.loc['income_payout_factor', 'inputs'])\\n contract_bonus = float(read_income_inputs.loc['contract_bonus', 'inputs'])\\n social = float(read_income_inputs.loc['social', 'inputs'])\\n inflation = float(read_income_inputs.loc['inflation', 'inputs'])\\n wtd_cpn_yield = float(read_income_inputs.loc['wtd_coupon_yld', 'inputs'])\\n life_expectancy = int(read_income_inputs.loc['life_expectancy_age', 'inputs'])\\n clients_age = int(read_income_inputs.loc['clients_age', 'inputs'])\\n\\n # # ---------------INCOME MODEL--------------------------------------------\\n runs = 0\\n returns_dict = {}\\n asset_dict = {}\\n fia_dict = {}\\n while runs <= trials:\\n print(runs)\\n\\n # --------------------BASE MODEL---------------------------------------------\\n\\n base_wts = read_asset_weights.loc[:, 'base']\\n base_assets = list(base_wts.index)\\n base_weights = list(base_wts.values)\\n base_returns = list(read_returns_est.loc[:, 'Annualized Returns'].values)\\n base_std = list(read_returns_est.loc[:, 'Annualized Risk'].values)\\n\\n base_investment = float(read_income_inputs.loc['risky_assets', 'Base'])\\n adv_fees = float(read_income_inputs.loc['advisor_fees', 'Base'])\\n\\n # ----------------------RANDOM RETURNS--------------------------\\n r_cols = ['r_{}'.format(name) for name in base_assets]\\n boy_value = ['bv_{}'.format(name) for name in base_assets]\\n eoy_value = ['ev_{}'.format(name) for name in base_assets]\\n\\n random_returns = pd.DataFrame(index=years, columns=r_cols)\\n #\\n # for c in range(len(r_cols)):\\n # ret = np.random.normal(base_returns[c], base_std[c], size=(len(random_returns.index), 1))\\n\\n this_run_cols = ['{}_{}'.format(cname, str(runs)) for cname in r_cols]\\n random_returns = read_normal.loc[:, this_run_cols]\\n base_df = random_returns.copy()\\n\\n # -------------BASE PORTFOLIO----------------------------\\n for name in boy_value:\\n base_df.loc[:, name] = 0.0\\n\\n for counter in years:\\n period_returns = list(random_returns.loc[counter, :])\\n if counter == 0:\\n\\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment for c in range(len(boy_value))]\\n\\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\\n\\n base_investment = base_df.loc[counter, boy_value].sum()\\n\\n elif (counter > 0) and (counter < income_starts):\\n\\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment * (1 + period_returns[c])\\n for c in range(len(boy_value))]\\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\\n\\n # base_df.loc[counter, 'total_net_fees'] = base_df.loc[counter, 'total']\\n\\n # --coupon payment is invested back into the risky portfolio until the income is withdrawn----\\n base_investment = base_df.loc[counter, 'total']\\n\\n else:\\n\\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment * (1 + period_returns[c])\\n for c in range(len(boy_value))]\\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\\n\\n base_investment = base_df.loc[counter, 'total']\\n\\n base_df.loc[:, 'adj_total'] = base_df.loc[:, 'total'].apply(lambda x: x if x > 0 else 0)\\n\\n asset_dict.update({'{}_{}'.format('s', str(runs)): base_df.loc[:, 'total']})\\n # asset_dict.update({'{}_{}'.format('s', str(runs)): ret.flatten()})\\n runs += 1\\n\\n asset_values = pd.DataFrame(asset_dict)\\n assets_ror = asset_values.pct_change().fillna(0)\\n terminal_values = asset_values.iloc[-1]\\n terminal_df = pd.DataFrame(columns=['Terminal Value'])\\n terminal_df['Terminal Value'] = terminal_values\\n\\n median_index = np.where(asset_values == np.median(np.array(terminal_values)))\\n median_index = median_index[1][0]\\n m_cols = ['{}_{}_{}'.format('r', r, str(median_index)) for r in all_assets]\\n median_portfolio_asset_returns = read_normal.loc[:, m_cols]\\n median_portfolio_asset_returns.loc[:, 'port_ending_values'] = asset_values.loc[:, '{}_{}'.format('s', median_index)]\\n median_portfolio_asset_returns.loc[:, 'ror'] = median_portfolio_asset_returns[\\n 'port_ending_values'].pct_change().fillna(0)\\n desc_sort = median_portfolio_asset_returns.sort_values(by=median_portfolio_asset_returns.columns[-1],\\n ascending=False)\\n asc_sort = median_portfolio_asset_returns.sort_values(by=median_portfolio_asset_returns.columns[-1],\\n ascending=True)\\n unsorted_df = median_portfolio_asset_returns\\n unsorted_df.drop(unsorted_df.columns[-2:], axis=1, inplace=True)\\n desc_sort.drop(desc_sort.columns[-2:], axis=1, inplace=True)\\n asc_sort.drop(asc_sort.columns[-2:], axis=1, inplace=True)\\n\\n # reset index and drop existing index\\n desc_sort.reset_index(drop=True, inplace=True)\\n asc_sort.reset_index(drop=True, inplace=True)\\n\\n unsorted_df.to_csv(src + 'median_returns_unsorted.csv')\\n desc_sort.to_csv(src + 'median_returns_largest.csv')\\n asc_sort.to_csv(src + 'median_returns_smallest.csv')\\n asset_values.to_csv(src + 'ending_values.csv')\\n assets_ror.to_csv(src + 'ending_values_ror.csv')\\n terminal_df.to_csv(src + 'terminal_values.csv')\\n print(\\\"simulation completed....\\\")\",\n \"def solve_model(self): \\n \\n t0 = time.time() #start the clock\\n \\n # a. benchmark case\\n \\n #i. joint pdf of productivity state and tau \\n self.make_joint_pdf(1)\\n \\n #ii. set policy. in RR08 the benchmark economy has no taxes nor subsidies\\n self.tau_benchmark = np.array([0, 0, 0]) #subsidy rate, excempt rate, tax rate \\n self.set_tax_system(self.tau_benchmark) #set tax system\\n \\n #iii. benchmark equilibrium\\n self.Yss_b, self.Kss_b, self.TFPss_b, self.average_firm_size_b, self.E_star_b, _, \\\\\\n _, self.N_ss_b, self.w_ss_b, self.cdf_stationary_b, self.cdf_emp_b = self.solve_stationary_equilibrium()\\n \\n print(\\\"\\\\n-----------------------------------------\\\")\\n print(\\\"Benchmark Stationary Equilibrium\\\")\\n print(\\\"-----------------------------------------\\\")\\n print(f\\\"ss output = {self.Yss_b:.2f}\\\")\\n print(f\\\"ss capital = {self.Kss_b:.2f}\\\")\\n print(f\\\"ss tfp = {self.TFPss_b:.2f}\\\")\\n print(f\\\"ss wage = {self.w_ss_b:.2f}\\\")\\n print(f\\\"entry mass = {self.E_star_b:.3f}\\\")\\n print(f\\\"avg. firm size = {self.average_firm_size_b:.2f}\\\")\\n \\n #b. plot (note that the distributions plotted here are unaffected by the distortionary policies)\\n \\n if self.plott:\\n #i. initialize\\n employed = [4.99, 49.99]\\n firm_size_by_employee = np.zeros(len(employed)+1)\\n share_employment = np.zeros(len(employed)+1)\\n \\n \\n #i. percentage of firms that employ employed\\n \\n for i_e in range(len(employed)):\\n summ = np.sum(firm_size_by_employee)\\n interpolate = self.interpol(self.labor_demand_rel, self.cdf_stationary_b, employed[i_e])[0] #labor_demand_rel is labor demand with the lowest value normalized to 1\\n firm_size_by_employee[i_e] = interpolate - summ\\n firm_size_by_employee[-1] = 1 - np.sum(firm_size_by_employee)\\n \\n plt.pie(firm_size_by_employee, labels=['<5','5<50','50 =<'], autopct=\\\"%.1f%%\\\")\\n plt.title('Size of Firms by Firm Size (Number of Employees)')\\n plt.savefig('firm_size_rr08.pdf')\\n plt.show()\\n \\n \\n #ii. employment percentage by firm size\\n for i_e in range(len(employed)):\\n summ = np.sum(share_employment)\\n interpolate = self.interpol(self.labor_demand_rel, self.cdf_emp_b , employed[i_e])[0]\\n share_employment[i_e] = interpolate - summ\\n share_employment[-1] = 1 - np.sum(share_employment)\\n \\n plt.pie(share_employment, labels=['<5','5<50','50 =<'], autopct=\\\"%.1f%%\\\")\\n plt.title('Employment Share by Firm Size (Number of Employees)')\\n plt.savefig('employment_by_firm_size_rr08.pdf')\\n plt.show()\\n \\n #iii. productivity cdf and employment cdf\\n plt.plot(self.grid_s, self.cdf_stationary_b)\\n plt.plot(self.grid_s, self.cdf_emp_b)\\n plt.title('Stationary CDF' )\\n plt.xlabel('Productivity level')\\n plt.ylabel('Cumulative Sum')\\n plt.legend(['Firms by Productivity Level','Share of Employment'])\\n plt.savefig('cdf_rr08.pdf')\\n plt.show()\\n \\n \\n \\n #c. distortion case\\n \\n #i. joint pdf of productivity state and tau \\n self.make_joint_pdf(0)\\n \\n #ii. compute stationary economy for each tau\\n \\n for idx, tau in enumerate(self.tau_vector):\\n \\n #iii. find the subsidy rate that generates the same capital stock as in benchmark economy\\n self.tau_s[idx] = self.find_subsidy_rate(tau)\\n \\n # set tax system with newly found tau_s and given tau\\n tauv = np.array([-self.tau_s[idx], self.excempt_frac, tau]) #subsidy rate, excempt rate, tax rate \\n self.set_tax_system(tauv) #set tax system\\n \\n #v. distorted stationary equilibrium\\n self.Yss_d[idx], self.Kss_d[idx], self.TFPss_d[idx], self.average_firm_size_d[idx], self.E_star_d[idx], \\\\\\n self.Y_set_d[idx,:], self.subsidy_size_d[idx], self.N_ss_d[idx], self.w_ss_d[idx],\\\\\\n _, _ = self.solve_stationary_equilibrium()\\n \\n print(\\\"\\\\n-----------------------------------------\\\")\\n print(\\\"Distorted Stationary Equilibrium\\\")\\n print(\\\"-----------------------------------------\\\\n\\\")\\n if self.distortion_case == 1:\\n print(\\\"Tax/Subidy Uncorrelated with Firm Level Producitivity\\\\n\\\")\\n elif self.distortion_case == 2:\\n print(\\\"Tax/Subidy Negatively Correlated with Firm Level Producitivity\\\")\\n print(\\\"(low productivity firms recieve subsidy, high productivity taxed)\\\\n\\\")\\n elif self.distortion_case == 2:\\n print(\\\"Tax/Subidy Positively Correlated with Firm Level Producitivity\\\")\\n print(\\\"(high productivity firms recieve subsidy, low productivity taxed)\\\\n\\\")\\n if self.policy_type == 1 :\\n print(\\\"Tax Type: Tax on output\\\\n\\\")\\n elif self.policy_type == 2 :\\n print(\\\"Tax Type: Tax on capital\\\\n\\\")\\n elif self.policy_type == 3 :\\n print(\\\"Tax Type: Tax on labor\\\\n\\\")\\n print(f\\\"fraction of firms recieving subsidy = {self.subsidy_frac:.2f}\\\")\\n print(f\\\"fraction of firms taxed = {1-self.subsidy_frac-self.excempt_frac:.2f}\\\")\\n print(f\\\"fraction of firms excempt = {self.excempt_frac:.2f}\\\")\\n print(\\\"-----------------------------------------\\\\n\\\")\\n \\n print(tabulate([['relative Yss', round(self.Yss_d[0]/self.Yss_b, 2), round(self.Yss_d[1]/self.Yss_b, 2), round(self.Yss_d[2]/self.Yss_b, 2), round(self.Yss_d[3]/self.Yss_b, 2)],\\n ['relative TFPss', round(self.TFPss_d[0]/self.TFPss_b, 2), round(self.TFPss_d[1]/self.TFPss_b, 2), round(self.TFPss_d[2]/self.TFPss_b, 2), round(self.TFPss_d[3]/self.TFPss_b, 2)], \\n ['relative entry mass', round(self.E_star_d[0]/self.E_star_b, 2), round(self.E_star_d[1]/self.E_star_b, 2), round(self.E_star_d[2]/self.E_star_b, 2), round(self.E_star_d[3]/self.E_star_b, 2)],\\n ['share of subsidized output', round(self.Y_set_d[0,0], 2), round(self.Y_set_d[1,0], 2), round(self.Y_set_d[2,0], 2), round(self.Y_set_d[3,0], 2)],\\n ['total subsidy paid of output', round(self.subsidy_size_d[0], 2), round(self.subsidy_size_d[1], 2), round(self.subsidy_size_d[2], 2), round(self.subsidy_size_d[3], 2)],\\n ['subsidy rate (tau_s)', round(self.tau_s[0], 2), round(self.tau_s[1], 2), round(self.tau_s[2], 2), round(self.tau_s[3], 2)],\\n [], \\n ['relative Kss', round(self.Kss_d[0]/self.Kss_b, 2), round(self.Kss_d[1]/self.Kss_b, 2), round(self.Kss_d[2]/self.Kss_b, 2), round(self.Kss_d[3]/self.Kss_b, 2)], \\n ['relative wss', round(self.w_ss_d[0]/self.w_ss_b, 2), round(self.w_ss_d[1]/self.w_ss_b, 2), round(self.w_ss_d[2]/self.w_ss_b, 2), round(self.w_ss_d[3]/self.w_ss_b, 2)], \\n ['relative Nss', round(self.N_ss_d[0]/self.N_ss_b, 2), round(self.N_ss_d[1]/self.N_ss_b, 2), round(self.N_ss_d[2]/self.N_ss_b, 2), round(self.N_ss_d[3]/self.N_ss_b, 2)], \\n ['relative avg. firm size', round(self.average_firm_size_d[0]/self.average_firm_size_b, 2), round(self.average_firm_size_d[1]/self.average_firm_size_b, 2), round(self.average_firm_size_d[2]/self.average_firm_size_b, 2), round(self.average_firm_size_d[3]/self.average_firm_size_b, 2)]],\\n headers=['Variable', 'Tax = '+str(self.tau_vector[0]), \\\"Tax = \\\"+str(self.tau_vector[1]), 'Tax = '+str(self.tau_vector[2]), 'Tax = '+str(self.tau_vector[3])]))\\n \\n\\n t1 = time.time()\\n print(f'\\\\nTotal Run Time: {t1-t0:.2f} seconds')\",\n \"def __calculate_portfolio_returns(self):\\n\\n p_bar = tqdm(range(1), desc=\\\" Calculating returns\\\", leave=False)\\n\\n trade_data = self.historical_trade_data\\n\\n # Helper functions to calculate cash inflow and outflow\\n def f_min(x):\\n return x.apply(lambda x: min(x, 0))\\n\\n def f_max(x):\\n return x.apply(lambda x: max(x, 0))\\n\\n # Calculate cash inflow and outflow\\n trade_data[\\n pd.MultiIndex.from_product(\\n [[\\\"Period cash inflow\\\"], self.tickers_list + [\\\"Total\\\"]]\\n )\\n ] = -1 * trade_data[\\\"Investment delta\\\"][:].apply(lambda x: f_min(x), axis=0)\\n\\n trade_data[\\n pd.MultiIndex.from_product(\\n [[\\\"Period cash outflow\\\"], self.tickers_list + [\\\"Total\\\"]]\\n )\\n ] = trade_data[\\\"Investment delta\\\"][:].apply(lambda x: f_max(x), axis=1)\\n\\n # Calculate period return\\n\\n trade_data[\\n pd.MultiIndex.from_product(\\n [[\\\"Period absolute return\\\"], self.tickers_list + [\\\"Total\\\"]]\\n )\\n ] = (trade_data[\\\"End Value\\\"] + trade_data[\\\"Period cash inflow\\\"]) - (\\n trade_data[\\\"End Value\\\"].shift(1).fillna(0)\\n + trade_data[\\\"Period cash outflow\\\"]\\n )\\n\\n trade_data[\\n pd.MultiIndex.from_product(\\n [[\\\"Period percentage return\\\"], self.tickers_list + [\\\"Total\\\"]]\\n )\\n ] = (trade_data[\\\"End Value\\\"] + trade_data[\\\"Period cash inflow\\\"]) / (\\n trade_data[\\\"End Value\\\"].shift(1).fillna(0)\\n + trade_data[\\\"Period cash outflow\\\"]\\n ) - 1\\n\\n trade_data[\\\"Period percentage return\\\"].fillna(0, inplace=True)\\n\\n self.historical_trade_data = trade_data\\n\\n self.portfolio_returns = self.historical_trade_data[\\\"Period percentage return\\\"][\\n \\\"Total\\\"\\n ]\\n\\n p_bar.n += 1\\n p_bar.refresh()\",\n \"def calculatedResults(meanReturns, covMatrix, riskFreeRate=0, constraintSet=(0,1)):\\n # Max Sharpe Ratio Portfolio\\n maxSR_Portfolio = maxSR(meanReturns, covMatrix)\\n maxSR_returns, maxSR_std = portfolioPerformance(maxSR_Portfolio['x'], meanReturns, covMatrix)\\n \\n maxSR_allocation = pd.DataFrame(maxSR_Portfolio['x'], index=meanReturns.index, columns=['allocation'])\\n maxSR_allocation.allocation = [round(i*100,0) for i in maxSR_allocation.allocation]\\n \\n # Min Volatility Portfolio\\n minVol_Portfolio = minimizeVariance(meanReturns, covMatrix)\\n minVol_returns, minVol_std = portfolioPerformance(minVol_Portfolio['x'], meanReturns, covMatrix)\\n \\n minVol_allocation = pd.DataFrame(minVol_Portfolio['x'], index=meanReturns.index, columns=['allocation'])\\n minVol_allocation.allocation = [round(i*100,0) for i in minVol_allocation.allocation]\\n\\n # Efficient Frontier\\n efficientList = []\\n targetReturns = np.linspace(minVol_returns, maxSR_returns, 20)\\n for target in targetReturns:\\n efficientList.append(efficientOpt(meanReturns, covMatrix, target)['fun'])\\n \\n maxSR_returns, maxSR_std = round(maxSR_returns*100,2), round(maxSR_std*100,2)\\n minVol_returns, minVol_std = round(minVol_returns*100,2), round(minVol_std*100,2)\\n \\n return maxSR_returns, maxSR_std, maxSR_allocation, minVol_returns, minVol_std, minVol_allocation, efficientList, targetReturns\",\n \"def main():\\n\\n # Hypothesis:\\n # The `impact` encapsulates the volatility, stability and overall\\n # fluctuation of the market; in particular, movements that would\\n # affect one's portfolio, e.g. unexpected (i.e. not predicted)\\n # increases or drops in prices.\\n # For the StrategyLearner should directly affect the learned\\n # policy, particularly, in terms of willingness to take risks by\\n # betting on the behavior of the market.\\n # This can be translated into three metrics:\\n # - Number of entries:\\n # These should be reduced as market impact increases which\\n # shows the learning agent being more cautious about its bets\\n # - Cumulative return:\\n # Directly related to the point mentioned above, as market\\n # impact increases and the agent's willingness to take risks\\n # decreaes, so is the overall performance of the strategy\\n # - Training episodes:\\n # This applies specifically to the Q-Learning agent, but it\\n # is interesting to see how as the market impact increases,\\n # the number of complete training episodes (i.e. a complete\\n # pass on the trading data) is not affected. One would think\\n # that the agent would converge faster when the impact is\\n # large as it would quickly realize that the most optimal\\n # strategy is to not do anything. However, impact does not\\n # affect the rate of convergence, but rather the strategy\\n # that the agent converges to\\n\\n # Set the seed for reproducibility\\n random.seed(1481090000)\\n\\n # Experiment parameters\\n symbol = 'JPM'\\n # In-sample: January 1, 2008 to December 31 2009\\n start_date = dt.datetime(2008, 1, 1)\\n end_date = dt.datetime(2009, 12, 31)\\n starting_value = 100000\\n commission = 0.0\\n # Values to use to evaluate the effect of the impact\\n impact_values = [0.0, 0.005, 0.01, 0.05, 0.1, 0.25, 0.5, 1.0]\\n\\n all_entries = []\\n all_returns = []\\n all_episodes = []\\n\\n for impact in impact_values:\\n log.info(\\\"Evaluating the effect of impact=%s\\\", impact)\\n strategy_learner = StrategyLearner(verbose=False, impact=impact)\\n\\n log.info(\\\"Training StrategyLearner\\\")\\n strategy_learner.addEvidence(\\n symbol=symbol,\\n sd=start_date,\\n ed=end_date,\\n sv=starting_value\\n )\\n\\n log.info(\\\"Querying StrategyLearner to generate trades\\\")\\n trades = strategy_learner.testPolicy(\\n symbol=symbol,\\n sd=start_date,\\n ed=end_date,\\n sv=starting_value\\n )\\n\\n log.info(\\\"Transforming StrategyLearner trades into marketsim orders\\\")\\n orders = _convert_trades_to_marketisim_orders(symbol, trades)\\n\\n log.info(\\\"Computing portfolio values for %d orders\\\", orders.shape[0])\\n port_vals = compute_portvals(\\n orders,\\n start_val=starting_value,\\n commission=commission,\\n impact=impact\\n )\\n\\n cumulative_return = _compute_cumulative_return(port_vals)\\n\\n all_entries.append(strategy_learner.metadata['entries'])\\n all_returns.append(cumulative_return)\\n all_episodes.append(strategy_learner.metadata['training_episodes'])\\n\\n _plot_and_save_number_of_entries_per_impact_value(impact_values, all_entries)\\n _plot_and_save_number_of_episodes_per_impact_value(impact_values, all_episodes)\\n _plot_and_save_cumulative_return_per_impact_value(impact_values, all_returns)\",\n \"def test(self, num_decisions=1000):\\n all_grid_states = []\\n all_grid_rewards = []\\n\\n all_grid_states_cluster = []\\n all_surrounding_states_cluster = []\\n\\n all_system_rewards = []\\n all_system_states = []\\n all_system_states_cluster = []\\n\\n all_actions = []\\n all_dissipation = []\\n\\n os.system(\\\"mkdir \\\" + self.folder_name + \\\"Test/\\\")\\n filename = self.folder_name + \\\"Test/\\\" + \\\"TEST.h5\\\"\\n self.system.reset_context(filename)\\n tag = \\\"_test_init\\\"\\n self.system.run_decorrelation(20)\\n\\n grid_dist, surrounding_dist, _, _, _, _ = self.system.get_state_reward()\\n state = self._get_state(grid_dist, surrounding_dist)\\n\\n all_dissipation.append(self.system.get_dissipation())\\n for i in range(num_decisions):\\n action_index = self._get_action(state, episode=10000)\\n tag = \\\"_test_\\\" + str(i)\\n actions = [self.all_actions[i] for i in action_index]\\n all_actions.append(actions)\\n self.system.update_action(actions)\\n system_states, system_rewards, system_states_cluster = self.system.run_step(\\n is_detailed=True, tag=tag)\\n\\n grid_dist, surrounding_dist, grid_reward, surrounding_reward, grid_states_cluster, surrounding_states_cluster = self.system.get_state_reward()\\n\\n # The \\\"grid states\\\" and dissipation are recorded at the end of a decision\\n # Dissipation here is total entropy production (not epr)\\n # Actions are recorded at the beginning of the decision\\n\\n state = self._get_state(grid_dist, surrounding_dist)\\n reward = self._get_reward(grid_reward, surrounding_reward)\\n\\n all_grid_states.append(state)\\n all_grid_rewards.append(reward)\\n\\n all_grid_states_cluster.append(grid_states_cluster)\\n all_surrounding_states_cluster.append(surrounding_states_cluster)\\n\\n all_dissipation.append(self.system.get_dissipation())\\n\\n # The \\\"System States\\\" are recorded every 0.25 seconds. Excludes 0th second\\n all_system_states.append(system_states)\\n # Just to have a 1D array use extend\\n all_system_rewards.append(system_rewards)\\n all_system_states_cluster.append(system_states_cluster)\\n\\n if (i % 100 == 99):\\n np.save(self.folder_name + \\\"grid_rewards\\\",\\n np.array(all_grid_rewards))\\n np.save(self.folder_name + \\\"grid_states\\\",\\n np.array(all_grid_states, dtype=object))\\n np.save(self.folder_name + \\\"grid_states_cluster\\\",\\n np.array(all_grid_states_cluster, dtype=object))\\n\\n np.save(self.folder_name + \\\"surrounding_states_cluster\\\",\\n np.array(all_surrounding_states_cluster))\\n\\n np.save(self.folder_name + \\\"actions\\\", np.array(all_actions))\\n\\n np.save(self.folder_name + \\\"dissipation\\\",\\n np.array(all_dissipation))\\n np.save(self.folder_name + \\\"system_states\\\",\\n np.array(all_system_states))\\n np.save(self.folder_name + \\\"system_states_cluster\\\",\\n np.array(all_system_states_cluster))\\n np.save(self.folder_name + \\\"system_rewards\\\",\\n np.array(all_system_rewards))\",\n \"def __init__(\\n self,\\n portfolio,\\n market=None,\\n commission_min=5.00,\\n commission_pct=0.0,\\n buy_percent=1.0,\\n sell_percent=1.0,\\n pm_threshold=0.0,\\n pm_order=1.0,\\n risk_free_return=1.0,\\n name=None\\n ):\\n\\n # Assumptions\\n self.name = name if name else portfolio.name\\n self.commission_min = commission_min\\n self.commission_pct = commission_pct\\n self.buy_percent = buy_percent\\n self.sell_percent = sell_percent\\n self.pm_threshold = pm_threshold\\n self.pm_order = pm_order\\n self.risk_free_return = risk_free_return\\n self.performance = {}\\n\\n # Inputs\\n self.portfolio = portfolio\\n self.market = copy.deepcopy(market) if market else Asset(np.ones(len(self.portfolio.dates)))\\n\\n # Trading states\\n self.long_open = {symbol:False for symbol in portfolio.assets.keys()}\\n self.short_open = {symbol:False for symbol in portfolio.assets.keys()}\\n\\n # Keep track of intermidiate results for performance\\n self.trade_data = []\\n recordings = [\\n 'buy price', 'buy shares', 'buy fees', 'buy date',\\n 'sell price', 'sell shares', 'sell fees', 'sell date',\\n 'gain', 'profit', 'loss', 'return', 'win/loose',\\n 'min balance', 'min date', 'max balance', 'max date',\\n 'drawdown', 'drawdown days',\\n 'volatility', 'expected_return', 'beta', 'lpm', 'hpm',\\n 'max', 'mean', 'min'\\n ]\\n self.record = {symbol:pd.DataFrame(columns=recordings) for symbol in portfolio.assets.keys()}\\n self.max = {symbol:[portfolio.assets[symbol].c.iloc[0], None] for symbol in portfolio.assets.keys()}\\n self.min = {symbol:[999999999999999, None] for symbol in portfolio.assets.keys()}\\n self.drawdown = {symbol:[999999999999999, None] for symbol in portfolio.assets.keys()}\",\n \"def first_stage_test(solver, sub_u_rate):\\n bins_num = 100\\n\\n classifier = joblib.load(join(join(_result_path, solver), str(sub_u_rate)) + '/logistic.pkl')\\n\\n # evaluate positive set, which contains spies\\n positive = np.load(\\\"./processed_data/train/raw/train_p.npy\\\")\\n positive_x = positive[:, : -1]\\n result_p = np.array(classifier.predict_proba(positive_x)[:, 1])\\n plt.hist(result_p, bins=bins_num)\\n plt.savefig(join(join(_result_path, solver), str(sub_u_rate)) + '/positive.png')\\n plt.show()\\n print(\\\"\\\\npositive set results: average: \\\" + str(np.mean(result_p)) + \\\" variance:\\\" + str(np.var(result_p)))\\n print(\\\"max: \\\" + str(result_p.max()) + \\\" min: \\\" + str(result_p.min()))\\n\\n # evaluate spy set\\n spy = np.load(_spy_path)\\n spy_x = spy[:, : -1]\\n result_spy = np.array(classifier.predict_proba(spy_x)[:, 1])\\n plt.hist(result_spy, bins=bins_num)\\n plt.savefig(join(join(_result_path, solver), str(sub_u_rate)) + '/spy.png')\\n plt.show()\\n print(\\\"\\\\nspy set results: average: \\\" + str(np.mean(result_spy)) + \\\" variance:\\\" + str(np.var(result_spy)))\\n print(\\\"max: \\\" + str(result_spy.max()) + \\\" min: \\\" + str(result_spy.min()))\\n\\n # evaluate sub-unlabeled set\\n sub_u = np.load(\\\"./processed_data/train/sub_u_\\\" + str(sub_u_rate) + \\\".npy\\\")\\n sub_u_x = sub_u[:, :-1]\\n result_sub_u = np.array(classifier.predict_proba(sub_u_x)[:, 1])\\n plt.hist(result_sub_u, bins=bins_num)\\n plt.savefig(join(join(_result_path, solver), str(sub_u_rate)) + '/sub-u.png')\\n plt.show()\\n print(\\\"\\\\nsub-unlabeled set results: average: \\\" + str(np.mean(result_sub_u)) + \\\" variance:\\\" + str(np.var(result_sub_u)))\\n print(\\\"max: \\\" + str(result_sub_u.max()) + \\\" min: \\\" + str(result_sub_u.min()))\\n\\n # evaluate the whole unlabeled set\\n unlabeled = np.load(\\\"./processed_data/train/raw/train_u.npy\\\")\\n unlabeled_x = unlabeled[:, :-1]\\n result_unlabeled = np.array(classifier.predict_proba(unlabeled_x)[:, 1])\\n plt.hist(result_unlabeled, bins=bins_num)\\n plt.savefig(join(join(_result_path, solver), str(sub_u_rate)) + '/unlabeled.png')\\n plt.show()\\n print(\\\"\\\\nunlabeled set results: average: \\\" + str(np.mean(result_unlabeled)) + \\\" variance:\\\" + str(\\n np.var(result_unlabeled)))\\n print(\\\"max: \\\" + str(result_unlabeled.max()) + \\\" min: \\\" + str(result_unlabeled.min()))\",\n \"def __init__(\\n self,\\n meta_data: pd.DataFrame,\\n factor_data: pd.DataFrame,\\n signal_data: pd.DataFrame,\\n settings: dict,\\n ):\\n logger.info(\\\"Initiating Backtest.\\\")\\n df = pd.concat([meta_data, factor_data, signal_data], axis=1)\\n self.settings = settings\\n\\n self.timeframe = settings.get('timeframe')\\n self.mq_name = settings.get('mq', 'default')\\n self.friction_loss = settings.get('friction_loss', 0)\\n self.return_base = settings.get('return_base', 'pre_close')\\n self.pos_ = settings.get('position_pctg', 1)\\n self.stop_loss = settings.get('stop_loss', 100)\\n self.action_on_0 = settings.get('sig_0_action', 'close')\\n day_close = settings.get('day_close', None)\\n if day_close is not None:\\n self.trade_flag = [time.fromisoformat(t) for t in day_close.split(' ')]\\n else:\\n self.trade_flag = 0\\n\\n self.period = settings.get(\\\"period\\\", None)\\n\\n df.sort_index(ascending=True, inplace=True)\\n df['date'] = df.index.date if self.timeframe[0] == 'T' else df.index\\n\\n if self.period is not None:\\n (self.start_dt, self.end_dt) = (datetime.strptime(i, \\\"%Y-%m-%d\\\") for i in self.period.split(' '))\\n df = df[(df['date'] >= self.start_dt.date()) & (df['date'] <= self.end_dt.date())].copy()\\n\\n if df.empty:\\n raise ValueError('Empty DataFrame.')\\n\\n if self.return_base == 'pre_close':\\n df['return'] = df['close'].diff()\\n elif self.return_base == 'open':\\n df['return'] = df['close'] - df['open']\\n else:\\n raise AttributeError(\\n f'Wrong parameter passed: return_base. current: {self.return_base}, '\\n f'must be pre_close or open.'\\n )\\n\\n self.df = df\\n\\n # initiate some params\\n self.collect_perfs = {}\\n\\n # initiate redis_mq\\n self.rds_handler = rh.RedisHandle(self.mq_name)\",\n \"def calc_performance(self):\\n for symbol in self.portfolio.assets.keys():\\n\\n # Total the Performance of all the trades\\n start = self.portfolio.trades[symbol].index[0]\\n end = self.portfolio.trades[symbol].index[-1]\\n trades = len(self.record[symbol])\\n profit = self.record[symbol]['profit'].sum()\\n loss = self.record[symbol]['loss'].sum()\\n # Total or average the trade info for all the trades\\n try:\\n wins = len(self.record[symbol].groupby('win/loose').groups['w'])\\n except (ValueError, KeyError):\\n wins = 0\\n try:\\n losses = len(self.record[symbol].groupby('win/loose').groups['l'])\\n except (ValueError, KeyError):\\n losses = 0\\n try:\\n washes = len(self.record[symbol].groupby('win/loose').groups['-'])\\n except (ValueError, KeyError):\\n washes = 0\\n max_drawdown = self.record[symbol]['drawdown'].max()\\n average_drawdown = self.record[symbol]['drawdown'].mean()\\n max_drawdown_time = self.record[symbol]['drawdown days'].max()\\n average_drawdown_time = self.record[symbol]['drawdown days'].mean()\\n # Average the risk and market comparisons for all trades\\n vol_risk = self.record[symbol]['volatility'].mean()\\n beta = self.record[symbol]['beta'].mean()\\n lpm_risk = self.record[symbol]['lpm'].mean()\\n e_r = self.record[symbol]['expected_return'].mean()\\n # Calculate Risk measures\\n treynor_ratio = (e_r - self.risk_free_return) / beta\\n sharpe_ratio = (e_r - self.risk_free_return) / vol_risk\\n # Package up the data for each symbol\\n self.performance[symbol] = {\\n 'start': start,\\n 'end': end,\\n 'trades': trades,\\n 'wins': wins,\\n 'losses': losses,\\n 'washes': washes,\\n 'profit': profit,\\n 'loss': loss,\\n 'net_profit': profit - loss,\\n 'profit_factor': profit / loss if loss != 0 else 1.0,\\n 'percent_profitable': wins / trades if trades != 0 else 0.0,\\n 'average_trade_net_profit' : (profit - loss) / trades if trades != 0 else 0.0,\\n 'max_drawdown' : max_drawdown,\\n 'average_drawdown' : average_drawdown,\\n 'max_drawdown_days' : max_drawdown_time,\\n 'average_drawdown_days' : average_drawdown_time,\\n 'volatility_risk' : vol_risk,\\n 'beta' : beta,\\n 'lower_partial_moment_risk' : lpm_risk,\\n 't_r' : treynor_ratio,\\n 's_r' : sharpe_ratio\\n }\\n\\n return self\",\n \"def test_Rt(self):\\n\\n test_value = self.portfolio._get_asset_portfolio[self.test_row_number]\\n\\n test_prices = self.portfolio._df_asset.iloc(axis=0)[\\n self.test_row_number].values\\n test_weights = self.weights.iloc(axis=0)[\\n self.test_row_number].values\\n calculated_value = sum(self.list_multiplication(test_prices,\\n test_weights))\\n self.assertAlmostEqual(test_value, calculated_value)\",\n \"def generate_portfolio(S_0, params):\\n\\tpublic_client = gdax.PublicClient()\\n\\tallvar = []\\n\\tsumvar = 0\\n\\tfor coin in params:\\n\\t\\ttheta = coin[0]\\n\\t\\tv = theta[0]\\n\\t\\tT = coin[1]\\n\\t\\tprod_id = coin[2]\\n\\t\\t# Get the current value of the coin, i.e. how much you bought\\n\\t\\tname = prod_id + '-USD'\\n\\t\\tstats = public_client.get_product_24hr_stats(name)\\n\\t\\tvalue = (float(stats['high']) + float(stats['low']))/2\\n\\t\\tallvar.append([prod_id, value, v])\\n\\t\\tsumvar += v\\n\\tpriority = sorted(allvar, key=lambda i: i[2])\\n\\tportfolio = []\\n\\tfor i in priority:\\n\\t\\tinvestment = S_0*i[2]/sumvar\\n\\t\\tcurrency = investment/i[1]\\n\\t\\tportfolio.append((i[0], currency, investment)) # id, investment, currency\\n\\tprint(\\\"\\\\nYour suggested investments are: \\\\n\\\")\\n\\tfor coin in portfolio:\\n\\t\\tprint(\\\"%s: %s for %s USD\\\" % (coin[0], coin[1], coin[2]))\\n\\t# Prompt to save the portfolio\\n\\tdone = False\\n\\twhile done != True:\\n\\t\\tinp = input(\\\"\\\\nWould you like to save this portfolio? (y/n)\\t\\\")\\n\\t\\ttry:\\n\\t\\t\\tif inp.lower() == 'y':\\n\\t\\t\\t\\tpublic_client = gdax.PublicClient()\\n\\t\\t\\t\\tcurrent_date = np.datetime64(public_client.get_time().get(\\\"iso\\\").split('T')[0])\\n\\t\\t\\t\\t# Save the file\\n\\t\\t\\t\\twith open(\\\"portfolios/%s.txt\\\" % (current_date), \\\"w\\\") as f:\\n\\t\\t\\t\\t\\tfor coin in portfolio:\\n\\t\\t\\t\\t\\t\\tf.write(str(coin[0]) + ', ' + str(coin[1]) + ', ' + str(coin[2]) + '\\\\n')\\n\\t\\t\\t\\tprint(\\\"Portfolio saved. Exiting.\\\\n\\\")\\n\\t\\t\\t\\tdone = True\\n\\t\\t\\tif inp.lower() == 'n':\\n\\t\\t\\t\\tprint(\\\"Program complete. Exiting.\\\\n\\\")\\n\\t\\t\\t\\tdone = True\\n\\t\\texcept ValueError:\\n\\t\\t\\tprint(\\\"Your input could not be interpreted.\\\")\",\n \"def run():\\n\\n # Set up environment and agent\\n e = Environment() # create environment (also adds some dummy traffic)\\n a = e.create_agent(LearningAgent) # create agent\\n e.set_primary_agent(a, enforce_deadline=False) # set agent to track\\n\\n # Now simulate it\\n sim = Simulator(e, update_delay=0.0) # reduce update_delay to speed up simulation\\n sim.run(n_trials=num_of_experiments) # press Esc or close pygame window to quit\\n \\n pd.Series(a.success).to_pickle('success_' + exp_id + '.pickle')\\n a.Q_table.to_pickle('qtable_' + exp_id + '.pickle')\\n pd.Series(a.q_delta_avg).to_pickle('convergence_' + exp_id + '.pickle')\\n pd.Series(a.t_total).to_pickle('steps_' + exp_id + '.pickle')\",\n \"def income_model_user_defined_returns(num_of_years=30, trials=100, method='normal'):\\n\\n print(\\\"Running method income_model_user_defined_returns()\\\")\\n\\n sim_fia_cv = pd.DataFrame(index=range(num_of_years + 1))\\n\\n sim_base_total = pd.DataFrame(index=range(num_of_years + 1))\\n sim_base_income = pd.DataFrame(index=range(num_of_years + 1))\\n\\n sim_port_total = pd.DataFrame(index=range(num_of_years + 1))\\n sim_port_income = pd.DataFrame(index=range(num_of_years + 1))\\n\\n read_income_inputs = pd.read_excel(src + \\\"portfolio_information.xlsx\\\", sheet_name='income_model_inputs',\\n index_col=[0])\\n\\n read_returns_est = pd.read_excel(src + \\\"portfolio_information.xlsx\\\", sheet_name='income_assets_returns_estimates',\\n index_col=[0])\\n clean_names = list(read_returns_est.index)\\n clean_names = [s.split(' ')[0] for s in clean_names]\\n read_returns_est.loc[:, 'names'] = clean_names\\n read_returns_est.set_index('names', drop=True, inplace=True)\\n read_returns_est = read_returns_est[:-1]\\n read_returns_est.rename(index={'SBMMTB3': 'Cash', read_returns_est.index[-1]: 'FIA'}, inplace=True)\\n\\n # ---------------Returns DataFrame based on the user input------------------------------------\\n ann_ret = np.full((num_of_years + 1, len(read_returns_est)), read_returns_est.loc[:, 'Annualized Returns'])\\n read_normal = pd.DataFrame(ann_ret, index=np.arange(num_of_years + 1), columns=read_returns_est.index)\\n # read_normal.rename(columns={read_normal.columns[-1]: 'FIA'}, inplace=True)\\n user_est_fia_return = float(read_income_inputs.loc['fia_forecast', 'inputs'])\\n read_normal.loc[:, 'FIA'] = user_est_fia_return\\n\\n read_asset_weights = pd.read_excel(src + \\\"portfolio_information.xlsx\\\", sheet_name='asset_weights',\\n index_col=[0])\\n\\n # read_asset_weights.drop(read_asset_weights.index[-1], axis=0, inplace=True)\\n\\n # read random returns for simulation\\n read_normal = pd.read_csv(src + 'median_returns_unsorted.csv', index_col=[0], parse_dates=True)\\n cols = [read_normal.columns[c].split('_')[1] for c in np.arange(len(read_normal.columns))]\\n read_normal.rename(columns=dict(zip(list(read_normal.columns), cols)), inplace=True)\\n\\n # ann_ret = np.full((num_of_years+1, len(read_returns_est)), read_returns_est.loc[:, 'Annualized Returns'])\\n # read_normal = pd.DataFrame(ann_ret, index=np.arange(num_of_years+1), columns=read_returns_est.index)\\n # # read_normal.rename(columns={read_normal.columns[-1]: 'FIA'}, inplace=True)\\n # user_est_fia_return = float(read_income_inputs.loc['fia_forecast', 'inputs'])\\n # read_normal.loc[:, 'FIA'] = user_est_fia_return\\n\\n assets_col_names = list(read_normal.columns)\\n\\n tickers = list(read_asset_weights.index)\\n wts = np.array(read_asset_weights.loc[:, 'base'])\\n\\n def asset_median_returns(data, ticker):\\n return data.filter(regex=ticker).median(axis=1)\\n\\n # dataframe for unsorted returns (normal)\\n median_returns_normal = read_normal.copy()\\n median_returns_normal.loc[:, 'portfolio_return'] = median_returns_normal.dot(wts)\\n median_normal_fia = pd.DataFrame({'FIA': asset_median_returns(read_normal, 'FIA')})\\n\\n years = list(range(0, num_of_years + 1))\\n income_cols = ['year', 'strategy_term', 'index_returns', 'term_ret', 'term_ret_with_par', 'term_annualize',\\n 'ann_net_spread', 'term_ret_netspr', 'high_inc_benefit_base', 'rider_fee', 'eoy_income',\\n 'contract_value']\\n\\n term = int(read_income_inputs.loc['term', 'inputs'])\\n fia_ret = read_returns_est.loc[read_returns_est.index[-1], 'Annualized Returns']\\n fia_risk = read_returns_est.loc[read_returns_est.index[-1], 'Annualized Risk']\\n par_rate = float(read_income_inputs.loc['par_rate', 'inputs'])\\n spread = float(read_income_inputs.loc['spread', 'inputs'])\\n bonus_term = int(read_income_inputs.loc['bonus_term', 'inputs'])\\n premium = float(read_income_inputs.loc['premium', 'inputs'])\\n income_bonus = float(read_income_inputs.loc['income_bonus', 'inputs'])\\n\\n income_starts = int(read_income_inputs.loc['start_income_years', 'inputs'])\\n income_growth = float(read_income_inputs.loc['income_growth', 'inputs'])\\n rider_fee = float(read_income_inputs.loc['rider_fee', 'inputs'])\\n inc_payout_factor = float(read_income_inputs.loc['income_payout_factor', 'inputs'])\\n contract_bonus = float(read_income_inputs.loc['contract_bonus', 'inputs'])\\n social = float(read_income_inputs.loc['social', 'inputs'])\\n inflation = float(read_income_inputs.loc['inflation', 'inputs'])\\n wtd_cpn_yield = float(read_income_inputs.loc['wtd_coupon_yld', 'inputs'])\\n life_expectancy = int(read_income_inputs.loc['life_expectancy_age', 'inputs'])\\n clients_age = int(read_income_inputs.loc['clients_age', 'inputs'])\\n\\n # ---------------INCOME MODEL--------------------------------------------\\n runs = 0\\n returns_dict = {}\\n asset_dict = {}\\n fia_dict = {}\\n\\n income_df = pd.DataFrame(index=years, columns=income_cols)\\n income_df.loc[:, 'year'] = years\\n income_df.loc[:, 'strategy_term'] = income_df.loc[:, 'year'] % term\\n income_df.loc[:, 'strategy_term'] = income_df['strategy_term'].apply(lambda x: 1 if x == 0 else 0)\\n income_df.loc[:, 'index_returns'] = read_normal.loc[:, 'FIA']\\n\\n # income_df.loc[:, 'index_returns'] = np.random.normal(fia_ret, fia_risk, size=(len(years), 1))\\n cumprod = (1. + income_df['index_returns']).rolling(window=term).agg(lambda x: x.prod()) - 1\\n income_df.loc[:, 'term_ret'] = np.where(income_df.loc[:, 'strategy_term'] == 1, cumprod, 0)\\n income_df.loc[:, 'term_ret_with_par'] = income_df.loc[:, 'term_ret'] * par_rate\\n income_df.loc[:, 'term_annualize'] = income_df.loc[:, 'term_ret_with_par'].apply(\\n lambda x: (1 + x) ** (1 / term) - 1)\\n income_df.loc[:, 'ann_net_spread'] = income_df.loc[:, 'term_annualize'] - spread\\n income_df.loc[:, 'ann_net_spread'] = np.where(income_df.loc[:, 'strategy_term'] == 1,\\n income_df.loc[:, 'ann_net_spread'], 0)\\n income_df.loc[:, 'term_ret_netspr'] = income_df.loc[:, 'ann_net_spread'].apply(lambda x: (1 + x) ** term - 1)\\n\\n for counter in years:\\n if counter == 0:\\n income_df.loc[counter, 'high_inc_benefit_base'] = premium * (1 + income_bonus)\\n\\n elif counter <= min(bonus_term, income_starts):\\n income_df.loc[counter, 'high_inc_benefit_base'] = income_df.loc[counter - 1, 'high_inc_benefit_base'] * \\\\\\n (1 + income_growth)\\n else:\\n income_df.loc[counter, 'high_inc_benefit_base'] = income_df.loc[counter - 1, 'high_inc_benefit_base']\\n\\n income_df.loc[:, 'rider_fee'] = income_df.loc[:, 'high_inc_benefit_base'] * rider_fee\\n income_df.loc[:, 'eoy_income'] = np.where(income_df.loc[:, 'year'] > income_starts,\\n income_df.loc[:, 'high_inc_benefit_base'] * inc_payout_factor, 0)\\n\\n for counter in years:\\n if counter == 0:\\n income_df.loc[counter, 'contract_value'] = premium * (1 + contract_bonus)\\n\\n elif income_df.loc[counter, 'strategy_term'] == 1:\\n x1 = income_df.loc[counter - 1, 'contract_value'] - income_df.loc[counter, 'rider_fee']\\n x2 = (x1 * (1 + income_df.loc[counter, 'term_ret_netspr'])) - income_df.loc[counter, 'eoy_income']\\n income_df.loc[counter, 'contract_value'] = x2\\n\\n else:\\n x1 = income_df.loc[counter - 1, 'contract_value'] - income_df.loc[counter, 'rider_fee'] - \\\\\\n income_df.loc[counter, 'eoy_income']\\n\\n income_df.loc[counter, 'contract_value'] = x1\\n\\n # variable stores the income number that is used in the base and fia portfolio calcs.\\n\\n income_from_fia = income_df.loc[income_df.index[-1], 'eoy_income']\\n\\n income_df.loc[:, 'contract_value'] = income_df.loc[:, 'contract_value'].apply(lambda x: 0 if x <= 0 else x)\\n\\n sim_fia_cv.loc[:, str(runs)] = income_df.loc[:, 'contract_value']\\n\\n # --------------------BASE MODEL---------------------------------------------\\n base_wts = read_asset_weights.loc[:, 'base']\\n base_assets = list(base_wts.index)\\n base_weights = list(base_wts.values)\\n base_returns = list(read_returns_est.loc[:, 'Annualized Returns'].values)\\n base_std = list(read_returns_est.loc[:, 'Annualized Risk'].values)\\n\\n base_investment = float(read_income_inputs.loc['risky_assets', 'Base'])\\n adv_fees = float(read_income_inputs.loc['advisor_fees', 'Base'])\\n\\n # -------------------required income----------------------------------\\n req_annual_income = float(read_income_inputs.loc['annual_income', 'inputs'])\\n income_needed = req_annual_income - social\\n income_net_fia_income = max(0, income_needed - income_from_fia)\\n cpn_income_base = base_investment * wtd_cpn_yield\\n\\n # ----------------------RANDOM RETURNS--------------------------\\n r_cols = base_assets\\n boy_value = ['bv_{}'.format(name) for name in base_assets]\\n eoy_value = ['ev_{}'.format(name) for name in base_assets]\\n\\n random_returns = pd.DataFrame(index=income_df.index, columns=r_cols)\\n\\n for c in range(len(r_cols)):\\n ret = np.random.normal(base_returns[c], base_std[c], size=(len(random_returns.index), 1))\\n\\n random_returns = read_normal.copy()\\n\\n base_df = random_returns.copy()\\n fia_portfolio_df = random_returns.copy()\\n port_investment = float(read_income_inputs.loc['risky_assets', 'FIA'])\\n cpn_income_port = port_investment * wtd_cpn_yield\\n\\n # -------------BASE PORTFOLIO----------------------------\\n for name in boy_value:\\n base_df.loc[:, name] = 0.0\\n\\n for counter in years:\\n period_returns = list(random_returns.loc[counter, :])\\n if counter == 0:\\n\\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment for c in range(len(boy_value))]\\n\\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\\n base_df.loc[counter, 'total_net_fees'] = 0.0\\n base_df.loc[counter, 'income'] = 0.0\\n base_investment = base_df.loc[counter, boy_value].sum()\\n\\n elif (counter > 0) and (counter < income_starts):\\n\\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment * (1 + period_returns[c])\\n for c in range(len(boy_value))]\\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\\n base_df.loc[counter, 'adv_fees'] = base_df.loc[counter, 'total'] * adv_fees\\n base_df.loc[counter, 'total_net_fees'] = base_df.loc[counter, 'total'] - base_df.loc[\\n counter, 'adv_fees']\\n\\n # --coupon payment is invested back into the risky portfolio until the income is withdrawn----\\n base_investment = base_df.loc[counter, 'total_net_fees'] + cpn_income_base\\n\\n else:\\n\\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment * (1 + period_returns[c])\\n for c in range(len(boy_value))]\\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\\n base_df.loc[counter, 'adv_fees'] = base_df.loc[counter, 'total'] * adv_fees\\n\\n # ---req. income is adjusted for inflation from the second year of withdrawal. Reinvestment of coupon\\n # stops from the year income starts. Req. income is reduced by the coupon payments\\n\\n if counter == income_starts:\\n\\n income_needed = req_annual_income - social\\n base_df.loc[counter, 'income'] = income_needed - cpn_income_base\\n income_needed = req_annual_income\\n\\n else:\\n income_needed = income_needed * (1 + inflation) - social\\n base_df.loc[counter, 'income'] = income_needed - cpn_income_base\\n income_needed = income_needed + social\\n\\n base_df.loc[counter, 'total_net_fees'] = base_df.loc[counter, 'total'] - \\\\\\n base_df.loc[counter, 'adv_fees'] - \\\\\\n base_df.loc[counter, 'income']\\n\\n base_investment = base_df.loc[counter, 'total_net_fees']\\n\\n base_df.loc[:, 'adj_total'] = base_df.loc[:, 'total_net_fees'].apply(lambda x: x if x > 0 else 0)\\n sim_base_total.loc[:, 's_{}'.format(str(runs))] = base_df.loc[:, 'total_net_fees']\\n sim_base_income.loc[:, 's_{}'.format(str(runs))] = base_df.loc[:, 'income']\\n\\n # ----------------------------FIA PORTFOLIO----------------------------------------------\\n for name in boy_value:\\n fia_portfolio_df.loc[:, name] = 0.0\\n\\n for counter in years:\\n period_returns = list(random_returns.loc[counter, :])\\n if counter == 0:\\n\\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment\\n for c in range(len(boy_value))]\\n fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\\n fia_portfolio_df.loc[counter, 'total_net_fees'] = 0.0\\n fia_portfolio_df.loc[counter, 'income'] = 0.0\\n port_investment = fia_portfolio_df.loc[counter, boy_value].sum()\\n\\n elif (counter > 0) and (counter < income_starts):\\n\\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment * (1 + period_returns[c])\\n for c in range(len(boy_value))]\\n fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\\n fia_portfolio_df.loc[counter, 'adv_fees'] = fia_portfolio_df.loc[counter, 'total'] * adv_fees\\n fia_portfolio_df.loc[counter, 'total_net_fees'] = fia_portfolio_df.loc[counter, 'total'] - \\\\\\n fia_portfolio_df.loc[counter, 'adv_fees']\\n\\n port_investment = fia_portfolio_df.loc[counter, 'total_net_fees'] + cpn_income_port\\n\\n else:\\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment * (1 + period_returns[c])\\n for c in range(len(boy_value))]\\n fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\\n fia_portfolio_df.loc[counter, 'adv_fees'] = fia_portfolio_df.loc[counter, 'total'] * adv_fees\\n\\n # ---req. income is adjusted for inflation from the second year of withdrawal. Reinvestment of coupon\\n # stops from the year income starts. Req. income is reduced by the coupon payments\\n\\n if counter == income_starts:\\n\\n income_needed = req_annual_income - social\\n income_net_fia_income = max(0, income_needed - income_from_fia)\\n fia_portfolio_df.loc[counter, 'income'] = max(0, income_net_fia_income - cpn_income_port)\\n income_needed = req_annual_income\\n\\n else:\\n income_needed = income_needed * (1 + inflation) - social\\n income_net_fia_income = max(0, income_needed - income_from_fia)\\n fia_portfolio_df.loc[counter, 'income'] = max(0, income_net_fia_income - cpn_income_port)\\n income_needed = income_needed + social\\n\\n fia_portfolio_df.loc[counter, 'total_net_fees'] = fia_portfolio_df.loc[counter, 'total'] - \\\\\\n fia_portfolio_df.loc[counter, 'adv_fees'] - \\\\\\n fia_portfolio_df.loc[counter, 'income']\\n\\n port_investment = fia_portfolio_df.loc[counter, 'total_net_fees']\\n\\n sim_port_total.loc[:, 's_{}'.format(str(runs))] = fia_portfolio_df.loc[:, 'total_net_fees'] + \\\\\\n income_df.loc[:, 'contract_value']\\n\\n sim_port_income.loc[:, 's_{}'.format(str(runs))] = fia_portfolio_df.loc[:, 'income']\\n\\n fia_portfolio_df.loc[:, 'adj_total'] = fia_portfolio_df.loc[:, 'total_net_fees'].apply(\\n lambda x: x if x > 0 else 0)\\n\\n # ---------income breakdown for Base portfolio----------------------------------\\n base_df.to_csv(src + 'base_port_detail.csv')\\n sim_base_total.to_csv(src + 'base_ending_values.csv')\\n income_breakdown_base = pd.DataFrame(sim_base_total.quantile(0.5, axis=1))\\n income_breakdown_base.loc[:, 'income_from_portfolio'] = sim_base_income.quantile(0.5, axis=1)\\n income_breakdown_base.loc[:, 'fia_income'] = 0.0\\n income_breakdown_base.loc[:, 'social_security_income'] = social\\n income_breakdown_base.loc[:, 'coupon_income'] = cpn_income_base\\n\\n income_breakdown_base.rename(columns={income_breakdown_base.columns[0]: 'portfolio_ending_value'}, inplace=True)\\n income_breakdown_base.loc[:, 'income_from_portfolio'][\\n income_breakdown_base.loc[:, 'portfolio_ending_value'] <= 0] = 0\\n income_breakdown_base.loc[:, 'total_income'] = income_breakdown_base.loc[:, income_breakdown_base.columns[1:]].sum(\\n axis=1)\\n\\n # ------------Block Ends-------------------------------------------------------------\\n\\n # ---------income breakdown for FIA portfolio----------------------------------\\n fia_portfolio_df.to_csv(src + 'fia_port_detail.csv')\\n sim_port_total.to_csv(src + 'fiaport_ending_values.csv')\\n\\n income_breakdown_port = pd.DataFrame(sim_port_total.quantile(0.5, axis=1))\\n income_breakdown_port.loc[:, 'income_from_portfolio'] = sim_port_income.quantile(0.5, axis=1)\\n income_breakdown_port.loc[:, 'fia_income'] = income_from_fia\\n income_breakdown_port.loc[:, 'social_security_income'] = social\\n income_breakdown_port.loc[:, 'coupon_income'] = cpn_income_port\\n\\n income_breakdown_port.rename(columns={income_breakdown_port.columns[0]: 'portfolio_ending_value'}, inplace=True)\\n income_breakdown_port.loc[:, 'income_from_portfolio'][\\n income_breakdown_port.loc[:, 'portfolio_ending_value'] <= 0] = 0\\n income_breakdown_port.loc[:, 'total_income'] = income_breakdown_port.loc[:, income_breakdown_port.columns[1:]].sum(\\n axis=1)\\n\\n # ------------Block Ends-------------------------------------------------------------\\n q_cut = [0.0, 0.05, 0.25, 0.5, 0.75, 0.95, 1.0]\\n sim_base_income[sim_base_total < income_needed] = 0.0\\n\\n sim_port_income[sim_port_total < income_net_fia_income] = 0\\n\\n sim_port_income = sim_port_income + income_from_fia\\n\\n # base_quantile = sim_base_total.loc[sim_base_total.index[-1]].quantile([0.05, 0.25, 0.50, 0.75, 0.90])\\n\\n # port_quantile = sim_port_total.loc[sim_port_total.index[-1]].quantile([0.05, 0.25, 0.50, 0.75, 0.90])\\n\\n base_quantile = sim_base_total.loc[sim_base_total.index[-1]].quantile(q_cut)\\n\\n port_quantile = sim_port_total.loc[sim_port_total.index[-1]].quantile(q_cut)\\n\\n # q_cut = [0.0, .05, 0.25, 0.5, 0.75, 0.95, 1.0]\\n cols = ['Min', '5th', '25th', '50th', '75th', '90th', 'Max']\\n\\n # ----drop year 0--------\\n sim_base_total = sim_base_total[1:]\\n sim_port_total = sim_port_total[1:]\\n\\n # ----------------quantile analysis for base terminal value--------------------------\\n base_qcut = pd.DataFrame(index=sim_base_total.index, columns=cols)\\n for c in range(len(cols)):\\n base_qcut.loc[:, cols[c]] = sim_base_total.quantile(q_cut[c], axis=1)\\n\\n base_qcut.clip(lower=0, inplace=True)\\n\\n # ----------------------quantile analysis for base income----------------------------\\n base_income_qcut = pd.DataFrame(index=sim_base_income.index, columns=cols)\\n for c in range(len(cols)):\\n base_income_qcut.loc[:, cols[c]] = sim_base_income.quantile(q_cut[c], axis=1)\\n\\n # ----Remove NaN's prior to the income start years------------\\n # base_income_qcut = base_income_qcut.loc[income_starts:]\\n\\n # -------------quantile analysis for portfolio terminal value ----------------\\n port_qcut = pd.DataFrame(index=sim_port_total.index, columns=cols)\\n for c in range(len(cols)):\\n port_qcut.loc[:, cols[c]] = sim_port_total.quantile(q_cut[c], axis=1)\\n\\n port_qcut.clip(lower=0, inplace=True)\\n\\n # ---------------quantile analysis for portfolio income----------------------------\\n port_income_qcut = pd.DataFrame(index=sim_port_income.index, columns=cols)\\n for c in range(len(cols)):\\n port_income_qcut.loc[:, cols[c]] = sim_port_income.quantile(q_cut[c], axis=1)\\n\\n # ----Remove NaN's prior to the income start years------------\\n # port_income_qcut = port_income_qcut.loc[income_starts:]\\n\\n # ----------probability ending value will be less than 0 at the end of the horizon -----------------------\\n base_legacy_risk = (sim_base_total.loc[sim_base_total.index[life_expectancy - clients_age]] < 0).sum() / (\\n trials + 1)\\n port_legacy_risk = (sim_port_total.loc[sim_port_total.index[life_expectancy - clients_age]] < 0).sum() / (\\n trials + 1)\\n\\n legacy_risk = pd.DataFrame([base_legacy_risk, port_legacy_risk,\\n 'Prob. of portfolio value less than 0 at the end of the expected life'],\\n index=['base', 'fia_portfolio', 'Notes'],\\n columns=['Ruin Probability'])\\n\\n # -----------Year-wise probability of ending value greater than 0 -----------------\\n base_psuccess = sim_base_total.apply(lambda x: x > 0).sum(axis=1) / (trials + 1)\\n port_psuccess = sim_port_total.apply(lambda x: x > 0).sum(axis=1) / (trials + 1)\\n\\n # -----------------------WRITING FILES TO EXCEL ---------------------------\\n col_names = ['50th', 'age', 'comment']\\n writer = pd.ExcelWriter(src + method + '_simulated_income_summary_custom.xlsx', engine='xlsxwriter')\\n read_income_inputs.to_excel(writer, sheet_name='inputs_for_income')\\n\\n read_returns_est.to_excel(writer, sheet_name='asset_returns_estimates')\\n\\n age_index = list(range(clients_age + 1, clients_age + len(base_qcut) + 1))\\n base_qcut.loc[:, 'age'] = age_index\\n base_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n base_qcut.loc[income_starts:, col_names].to_excel(writer, sheet_name='base_ending_value_quantiles')\\n\\n base_income_qcut = base_income_qcut.loc[1:, :]\\n base_income_qcut.loc[:, 'age'] = age_index\\n base_income_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n base_income_qcut.loc[income_starts:, col_names].to_excel(writer, sheet_name='base_income_quantiles')\\n\\n port_qcut.loc[:, 'age'] = age_index\\n port_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n port_qcut.loc[income_starts:, col_names].to_excel(writer, sheet_name='fia_port_ending_value_quantiles')\\n\\n port_income_qcut = port_income_qcut.loc[1:, :]\\n port_income_qcut.loc[:, 'age'] = age_index\\n port_income_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n port_income_qcut.loc[income_starts:, col_names].to_excel(writer, sheet_name='fia_port_income_quantiles')\\n\\n income_breakdown_base = income_breakdown_base.loc[1:, :]\\n income_breakdown_base.loc[:, 'age'] = age_index\\n income_breakdown_base.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n income_breakdown_base.loc[income_starts:, :].to_excel(writer, sheet_name='base_income_breakdown_median')\\n\\n income_breakdown_port = income_breakdown_port.loc[1:, :]\\n income_breakdown_port.loc[:, 'age'] = age_index\\n income_breakdown_port.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n income_breakdown_port.loc[:, 'ending_contract_value'] = income_df.loc[:, 'contract_value']\\n income_breakdown_port.loc[income_starts:, :].to_excel(writer, sheet_name='fia_income_breakdown_median')\\n\\n median_returns_normal.to_excel(writer, sheet_name='gr_port_median_normal')\\n\\n terminal_val = pd.read_csv(src + 'terminal_values.csv', index_col=[0])\\n ending_val = pd.read_csv(src + 'ending_values.csv', index_col=[0])\\n ending_val_ror = pd.read_csv(src + 'ending_values_ror.csv', index_col=[0])\\n\\n terminal_val.to_excel(writer, sheet_name='terminal_values')\\n ending_val.to_excel(writer, sheet_name='port_ending_values')\\n ending_val_ror.to_excel(writer, sheet_name='port_annual_growth')\\n\\n writer.save()\\n\\n # -----------------Plotting charts--------------------------------------------\\n base_qcut.loc[income_starts:].plot(grid=True, title='Quantile Terminal Value - Base Portfolio')\\n plt.savefig(src + \\\"quantile_terminal_base.png\\\")\\n plt.close('all')\\n\\n base_income_qcut.plot(grid=True, title='Quantile Income - Base Portfolio')\\n plt.savefig(src + \\\"quantile_income_base.png\\\")\\n plt.close('all')\\n\\n base_psuccess.plot(grid=True, title='Probability of Success (Portfolio Ending Value > 0) - Base Portfolio')\\n plt.savefig(src + \\\"success_probabilty_base.png\\\")\\n plt.close('all')\\n\\n (1 - base_psuccess).plot(grid=True, title='Probability of Ruin (Portfolio Ending Value < 0) - Base Portfolio')\\n plt.savefig(src + \\\"ruin_probability_base.png\\\")\\n plt.close('all')\\n\\n port_qcut.loc[income_starts:].plot(grid=True, title='Quantile Terminal Value - FIA Portfolio')\\n plt.savefig(src + \\\"quantile_terminal_fia.png\\\")\\n plt.close('all')\\n\\n port_income_qcut.plot(grid=True, title='Quantile Income - FIA Portfolio')\\n plt.savefig(src + \\\"quantile_income_fia.png\\\")\\n plt.close('all')\\n\\n port_psuccess.plot(grid=True, title='Probability of Success (Portfolio Ending Value > 0) - FIA Portfolio')\\n plt.savefig(src + \\\"success_probabilty_fia.png\\\")\\n plt.close('all')\\n\\n (1 - port_psuccess).plot(grid=True, title='Probability of Ruin (Portfolio Ending Value < 0) - FIA Portfolio')\\n plt.savefig(src + \\\"ruin_probability_fia.png\\\")\\n plt.close('all')\\n\\n print(\\\"simulation completed for {}\\\".format(method))\",\n \"def get_market_metrics(market_portfolio: pd.DataFrame, t_costs: float, index_id: str, index_name: str,\\n test_data_start_date: datetime.date, test_data_end_date: datetime.date, market_logs=False) -> \\\\\\n Tuple[pd.Series, pd.Series, pd.Series]:\\n\\n market_portfolio_metrics = pd.Series([]).rename('Market')\\n market_portfolio_metrics.index.name = 'Metrics'\\n\\n excess_return_series = calc_excess_returns(\\n market_portfolio.loc[:, 'daily_return'].groupby(level=['datadate']).mean()).rename('daily_excess_return')\\n excess_return_series = excess_return_series.reset_index()\\n excess_return_series.loc[:, 'datadate'] = excess_return_series['datadate'].dt.strftime(\\n '%Y-%m-%d')\\n excess_return_series.set_index('datadate', inplace=True)\\n cumulative_excess_return = (excess_return_series.get('daily_excess_return') + 1).cumprod().rename(\\n 'Cumulative Market Return')\\n cumulative_excess_return.index.name = 'Time'\\n # cumulative_return.plot(title='Cumulative Market Performance')\\n # plt.legend(loc='best')\\n # plt.show()\\n\\n # JOB: Calculate metrics\\n # noinspection DuplicatedCode\\n annualized_sharpe = calc_sharpe(market_portfolio.loc[:, ['daily_return']].groupby(level=['datadate']).mean(),\\n annualize=True)\\n annualized_sharpe_atc = calc_sharpe(\\n market_portfolio.loc[:, ['daily_return']].groupby(level=['datadate']).mean() - 4 * t_costs,\\n annualize=True)\\n\\n annualized_sortino = calc_sortino(market_portfolio.loc[:, ['daily_return']].groupby(level=['datadate']).mean(),\\n annualize=True)\\n annualized_sortino_atc = calc_sortino(\\n market_portfolio.loc[:, ['daily_return']].groupby(level=['datadate']).mean() - 4 * t_costs,\\n annualize=True)\\n\\n mean_daily_return = market_portfolio.groupby(level=['datadate'])['daily_return'].mean().mean()\\n mean_daily_excess_return = calc_excess_returns(\\n market_portfolio.groupby(level=['datadate'])['daily_return'].mean().rename('daily_return')).mean()\\n\\n market_portfolio_metrics.loc['Mean Daily Return'] = mean_daily_return\\n market_portfolio_metrics.loc['Annualized Return'] = annualize_metric(mean_daily_return)\\n market_portfolio_metrics.loc['Mean Daily Excess Return'] = mean_daily_excess_return\\n market_portfolio_metrics.loc['Annualized Excess Return'] = annualize_metric(mean_daily_excess_return)\\n market_portfolio_metrics.loc['Annualized Sharpe'] = annualized_sharpe\\n market_portfolio_metrics.loc['Annualized Sortino'] = annualized_sortino\\n\\n # JOB: Add metrics incl. transaction costs of 5 bps per half-turn\\n market_portfolio_metrics.loc['Mean Daily Return_atc'] = mean_daily_return - 4 * t_costs\\n market_portfolio_metrics.loc['Annualized Return_atc'] = annualize_metric(mean_daily_return - 4 * t_costs)\\n market_portfolio_metrics.loc['Mean Daily Excess Return_atc'] = mean_daily_excess_return - 4 * t_costs\\n market_portfolio_metrics.loc['Annualized Excess Return_atc'] = annualize_metric(\\n mean_daily_excess_return - 4 * t_costs)\\n market_portfolio_metrics.loc['Annualized Sharpe_atc'] = annualized_sharpe_atc\\n market_portfolio_metrics.loc['Annualized Sortino_atc'] = annualized_sortino_atc\\n\\n data_record = {\\n 'ID': config.run_id,\\n 'Experiment Run End': datetime.datetime.now().isoformat(),\\n 'Parent Model Type': 'Market',\\n 'Model Type': 'Market',\\n 'Index ID': index_id,\\n 'Index Name': index_name,\\n 'Study Period ID': config.study_period_id,\\n 'Study Period Length': None,\\n 'Period Range': None,\\n 'Study Period Start Date': None,\\n 'Study Period End Date': None,\\n 'Test Set Size': None,\\n 'Days Test Set': None,\\n 'Constituent Number': None,\\n 'Average Cross Section Size': None,\\n 'Test Set Start Date': test_data_start_date.isoformat(),\\n 'Test Set End Date': test_data_end_date.isoformat(),\\n 'Total Accuracy': None,\\n\\n 'Top-k Accuracy Scores': None,\\n 'Top-k Mean Daily Return': market_portfolio_metrics['Mean Daily Return'],\\n 'Top-k Mean Daily Excess Return': market_portfolio_metrics['Mean Daily Excess Return'],\\n 'Top-k Annualized Excess Return': market_portfolio_metrics['Annualized Excess Return'],\\n 'Top-k Annualized Return': market_portfolio_metrics['Annualized Return'],\\n 'Top-k Annualized Sharpe': market_portfolio_metrics['Annualized Sharpe'],\\n 'Top-k Annualized Sortino': market_portfolio_metrics['Annualized Sortino'],\\n 'Mean Daily Return (Short)': None,\\n 'Mean Daily Return (Long)': None,\\n\\n 'Top-k Mean Daily Return_atc': market_portfolio_metrics['Mean Daily Return_atc'],\\n 'Top-k Annualized Return_atc': market_portfolio_metrics['Annualized Return_atc'],\\n 'Top-k Mean Daily Excess Return_atc': market_portfolio_metrics['Mean Daily Excess Return_atc'],\\n 'Top-k Annualized Excess Return_atc': market_portfolio_metrics['Annualized Excess Return_atc'],\\n 'Top-k Annualized Sharpe_atc': market_portfolio_metrics['Annualized Sharpe_atc'],\\n 'Top-k Annualized Sortino_atc': market_portfolio_metrics['Annualized Sortino_atc'],\\n 'Top-k Mean Daily Return (Short)_atc': None,\\n 'Top-k Mean Daily Return (Long)_atc': None,\\n\\n 'Model Configs': None,\\n 'Total Epochs': None,\\n\\n 'Return Series': excess_return_series['daily_excess_return'].to_dict(),\\n 'Prediction Error': None\\n }\\n\\n if market_logs:\\n write_to_logs(data_record)\\n\\n return market_portfolio_metrics, excess_return_series, cumulative_excess_return\",\n \"def test_portfolio_balance(\\n session, account_checking, account_savings, account_sp500, asset_krw, asset_sp500\\n):\\n portfolio = Portfolio()\\n portfolio.base_asset = asset_krw\\n portfolio.add_accounts(account_checking, account_savings, account_sp500)\\n\\n assert portfolio.balance(parse_date(\\\"2016-05-20\\\")) == {}\\n\\n deposit(account_checking, asset_krw, 1500, parse_date(\\\"2016-05-01\\\"))\\n deposit(account_savings, asset_krw, 3000, parse_date(\\\"2016-05-01\\\"))\\n deposit(account_sp500, asset_sp500, 120, parse_date(\\\"2016-05-01\\\"))\\n\\n assert portfolio.balance(parse_date(\\\"2016-05-20\\\")) == {\\n asset_krw: 4500,\\n asset_sp500: 120,\\n }\\n\\n deposit(account_savings, asset_krw, 4000, parse_date(\\\"2016-05-02\\\"))\\n deposit(account_savings, asset_krw, 5000, parse_date(\\\"2016-05-03\\\"))\\n\\n assert portfolio.balance(parse_date(\\\"2016-05-20\\\")) == {\\n asset_krw: 13500,\\n asset_sp500: 120,\\n }\\n\\n balance_adjustment(account_savings, asset_krw, 10000, parse_date(\\\"2016-05-04\\\"))\\n\\n assert portfolio.balance(parse_date(\\\"2016-05-20\\\")) == {\\n asset_krw: 11500,\\n asset_sp500: 120,\\n }\\n\\n session.delete(portfolio)\\n session.commit()\",\n \"def test_Pt(self):\\n\\n test_value = self.portfolio.calculate_asset_performance(\\n *self.boarder)[self.test_row_number]\\n calculated_value = self.manual_cumprod(\\n self.portfolio._get_asset_portfolio)\\n self.assertAlmostEqual(test_value, calculated_value)\",\n \"def test_Rit(self):\\n\\n manual_calculated = list(self._manual_calculate_formal(self.prices))\\n\\n self.portfolio._generate_asset()\\n test_column = self._convert_df_to_list(self.portfolio._df_asset)\\n\\n self.assertAlmostEqual(self._clear_column(manual_calculated),\\n self._clear_column(test_column))\",\n \"def run():\\n\\n for simulation in range(0, N_SIMULATIONS):\\n\\n # Set up environment and agent\\n e = Environment() # create environment (also adds some dummy traffic)\\n a = e.create_agent(LearningAgent) # create agent\\n e.set_primary_agent(a, enforce_deadline=True) # specify agent to track\\n # NOTE: You can set enforce_deadline=False while debugging to allow longer trials\\n # TODO: Change later enforce_deadline=True\\n\\n # Now simulate it\\n sim = Simulator(e, update_delay=0.001, display=False) # create simulator (uses pygame when display=True, if available)\\n # NOTE: To speed up simulation, reduce update_delay and/or set display=False\\n\\n sim.run(n_trials=N_TRIALS) # run for a specified number of trials\\n # NOTE: To quit midway, press Esc or close pygame window, or hit Ctrl+C on the command-line\\n\\n if simulation == N_SIMULATIONS - 1:\\n\\n with open('results.csv', 'a') as csvfile:\\n fieldnames = ['alpha', 'gamma', 'epsilon', 'success_rate', 'last_failure']\\n writer = csv.DictWriter(csvfile, fieldnames=fieldnames)\\n\\n for index in range(0,len(simulation_rates)):\\n writer.writerow({\\n 'alpha': get_simulation_params(0)[0],\\n 'gamma': get_simulation_params(0)[1],\\n 'epsilon': get_simulation_params(0)[2],\\n 'success_rate': simulation_rates[index],\\n 'last_failure': last_errors[index]})\\n\\n\\n if N_SIMULATIONS > 1: #multiple simulation AND last simulation\\n\\n plt.figure(1)\\n\\n plt.subplot(211)\\n plt.plot(simulation_rates)\\n plt.title('Success Rate/Simulation')\\n plt.xlabel('# Simulation')\\n plt.ylabel('Success Rate')\\n\\n plt.subplot(212)\\n plt.plot(last_errors)\\n plt.title('Last failed trial per simulation')\\n plt.xlabel('# Simulation')\\n plt.ylabel('Last failed trial')\\n\\n plt.show()\",\n \"def backtest(cache):\\n if not cache:\\n history = getBars(MARKET, TF)\\n else:\\n cachefile = \\\"cache/{}-{}.csv\\\".format(MARKET, TF)\\n try:\\n history = pd.read_csv(\\n cachefile, index_col=\\\"datetime\\\", parse_dates=True)\\n except:\\n history = getBars(MARKET, TF)\\n history.to_csv(cachefile)\\n history['ma'] = history['close'].rolling(MEAN).mean()\\n weAreLong = False\\n PRICE_DIPPED = False\\n\\n entry = 0.0\\n exit = None\\n pl = 0.0\\n history['pandl'] = 0.0\\n trades = 1\\n\\n ### BEGIN STRATEGY DEFINITION ###\\n count = 1\\n\\n for i in history.index:\\n candle_close_rate = history['ma'][i]\\n ma = history['close'][i]\\n if count > MEAN:\\n # playing revert to mean (RTM)\\n if not weAreLong and buySignaled(candle_close_rate, ma, PRICE_DIPPED):\\n entry = candle_close_rate\\n exit = candle_close_rate * (1.0 + (EXIT_PERCENT / 100.0))\\n stop = candle_close_rate * (1.0 - (STOP_PERC / 100.0))\\n weAreLong = True\\n history['pandl'][i] = pl\\n elif weAreLong and history['high'][i] >= exit:\\n weAreLong = False\\n pl += (((exit - entry) / entry) * 100.0 ) - (2.0 * FEE)\\n history['pandl'][i] = pl\\n trades += 1\\n elif weAreLong and candle_close_rate <= stop:\\n weAreLong = False\\n pl += (((candle_close_rate - entry) / entry) * 100.0 ) - (2.0 * FEE)\\n history['pandl'][i] = pl\\n trades += 1\\n else:\\n if weAreLong:\\n # fpl = ((history['close'][i] * (1.0 - FEE)) - (entry * (1.0 + FEE)))\\n # fpl = ( (history['close'][i] * (100.0 - FEE)) - (entry * (100.0 + FEE)) ) / 100.0\\n fpl = ((candle_close_rate - entry) / entry) * 100.0\\n history['pandl'][i] = pl + fpl\\n else:\\n fpl = 0.0\\n history['pandl'][i] = pl + fpl\\n count += 1\\n if candle_close_rate <= ma:\\n PRICE_DIPPED = True\\n else:\\n PRICE_DIPPED = False\\n ### END STRATEGY DEFINITION ###\\n\\n days = (len(history) * TF) / (60 * 24)\\n sharpe_ratio = getSharpe(list(history['pandl']), days)\\n fig, ax = plt.subplots(1)\\n plt.plot(history['pandl'])\\n fig.autofmt_xdate()\\n plt.ylabel('cumulative %')\\n props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)\\n # place a text box in upper left in axes coords\\n textstr = \\\"\\\"\\\"{}\\n Days: {}\\n Trades: {}\\n Settings:\\n TF = {}\\n MEAN = {}\\n BREAKOUT = {}\\n RTM = {}\\n RTM_PERCENT = {}\\n BO_PERCENT = {}\\n EXIT_PERCENT = {},\\n STOP = {}\\n Sharpe = {}\\n \\\"\\\"\\\".format(MARKET,\\n days,\\n trades,\\n TF,\\n MEAN,\\n BREAKOUT,\\n RTM,\\n RTM_PERCENT,\\n BO_PERCENT,\\n EXIT_PERCENT,\\n STOP_PERC,\\n sharpe_ratio)\\n ax.text(0.05, 0.95, textstr, transform=ax.transAxes, fontsize=14,\\n verticalalignment='top', bbox=props)\\n plt.title(\\\"BACKTEST {}\\\".format(MARKET))\\n plt.savefig(BACKTESTFILE)\\n print(\\\"{},{},{},{},{},{},{},{},{},{},{},{}\\\".format(days,\\n trades,\\n MARKET,\\n TF,\\n MEAN,\\n BREAKOUT,\\n RTM,\\n RTM_PERCENT,\\n BO_PERCENT,\\n EXIT_PERCENT,\\n STOP_PERC,\\n sharpe_ratio))\\n # plt.show()\",\n \"def start_trading(self):\\n backtest_result_dir = os.path.join(self.result_dir, str(self.backtest_date))\\n os.mkdir(backtest_result_dir)\\n backtest_scores_path = os.path.join(backtest_result_dir, 'scores.csv')\\n # all_backtest_scores_exists = os.path.isfile(self.all_backtest_scores_path)\\n\\n last_backtest_scores = open(self.last_backtest_scores_path, \\\"w\\\")\\n backtest_scores = open(backtest_scores_path, \\\"w\\\")\\n\\n\\n fieldnames = [ 'Backtest Name', 'Backtest Date', 'Strategy', 'Start Date', 'End Date', 'Instrument(s)', 'Params'] + \\\\\\n ['Number of signals', 'Number of orders', 'Number of trades', 'Total USD Return', 'Total BTC Return',\\n 'Sharpe Ratio', 'BTC Sharpe Ratio', 'Max Drawdown', 'BTC Max Drawdown', 'Drawdown Duration', 'BTC Drawdown Duration',\\n 'Monthly BTC Return', 'Yearly BTC Return', 'Avg. winning trade', 'Median duration', 'Avg. losing trade', 'Median returns winning', 'Largest losing trade',\\n 'Gross loss', 'Largest winning trade', 'Avg duration', 'Avg returns losing', 'Median returns losing', 'Profit factor',\\n 'Winning round trips', 'Percent profitable', 'Total profit', 'Shortest duration', 'Median returns all round trips',\\n 'Losing round trips', 'Longest duration', 'Avg returns all round trips', 'Gross profit', 'Avg returns winning',\\n 'Total number of round trips', 'Ratio Avg. Win:Avg. Loss', 'Avg. trade net profit', 'Even round trips',\\n 'Configuration Filename', 'Leverage']\\n\\n try:\\n with last_backtest_scores as a, backtest_scores as b:\\n writer_a = csv.DictWriter(a, fieldnames=fieldnames)\\n writer_b = csv.DictWriter(b, fieldnames=fieldnames)\\n writer_a.writeheader()\\n writer_b.writeheader()\\n\\n # if not all_backtest_scores_exists:\\n # writer_c.writeheader()\\n\\n for i, (start, end) in enumerate(zip(self.start_dates, self.end_dates)):\\n for j, instruments in enumerate(self.instrument_list):\\n for k, params in enumerate(self.strategy_params):\\n num_backtest = i * (self.num_instruments * self.num_params + 1) + j * (self.num_params + 1) + k + 1\\n print(\\\"Strategy %s out of %s\\\" % (num_backtest, self.num_backtests))\\n self._generate_trading_instances(start, end, instruments, params)\\n self._run()\\n stats = self._process_results()\\n\\n general_stats = stats['general']\\n pnl_stats = stats['pnl']['All trades'].to_dict()\\n summary_stats = stats['summary']['All trades'].to_dict()\\n duration_stats = stats['duration']['All trades'].to_dict()\\n return_stats = stats['returns']['All trades'].to_dict()\\n params_value = '/'.join([ '{}:{}'.format(item[0], item[1]) for item in params.items() ])\\n\\n row = {\\n 'Backtest Name': self.backtest_name,\\n 'Backtest Date': self.backtest_date,\\n 'Strategy': self.strategy.strategy_name,\\n 'Start Date': start,\\n 'End Date': end,\\n 'Instrument(s)': format_instrument_list(instruments),\\n 'Params': params_value,\\n 'Number of signals': self.signals,\\n 'Number of orders': self.orders,\\n 'Number of trades': self.fills,\\n **general_stats,\\n **pnl_stats,\\n **summary_stats,\\n **duration_stats,\\n **return_stats,\\n 'Configuration Filename': self.configuration_filename,\\n 'Leverage': self.default_leverage\\n }\\n\\n writer_a.writerow(row)\\n writer_b.writerow(row)\\n\\n except IOError:\\n print('I/O Error')\\n\\n all_scores_csv = pd.concat([ pd.read_csv(self.all_backtest_scores_path), pd.read_csv(backtest_scores_path)])\\n all_scores_csv.to_csv(self.all_backtest_scores_path, columns=fieldnames, index=False, encoding='utf-8-sig')\\n\\n self._open_results_in_excel()\",\n \"def summary(self):\\n print '%s Portfolio\\\\'s %s Strategy' % (self.portfolio.name, self.name)\\n print '-' * COL_DASH_WIDTH\\n\\n self.display_trades()\\n\\n for symbol in self.portfolio.assets.keys():\\n perf = self.performance[symbol]\\n\\n print '\\\\nSummary for %s from %s (first trade) to %s (last trade)' % (symbol, perf['start'], perf['end'])\\n print '.' * COL_DASH_WIDTH\\n print 'Summary:'\\n data = [[fmtn(perf['trades']), fmtn(perf['wins']), fmtn(perf['losses']), fmtn(perf['washes'])]]\\n print tabulate.tabulate(data, headers=['Total Trades', '# Wins', '# Losses', '# Washes'])\\n\\n print '\\\\nPerformance:'\\n data = [[\\n fmtn(perf['profit']), fmtn(perf['loss']), fmtn(perf['net_profit']),\\n fmtp(perf['profit_factor']), fmtp(perf['percent_profitable']), fmtn(perf['average_trade_net_profit'])\\n ]]\\n print tabulate.tabulate(data, headers=['Profit', 'Loss', 'Net Profit', 'Profit Factor', 'Percent Profitable', 'Average Net Profit per Trade'])\\n\\n print '\\\\nDrawdown:'\\n data = [[fmtn(perf['max_drawdown']), fmtn(perf['average_drawdown']), fmtn(perf['max_drawdown_days']), fmtn(perf['average_drawdown_days'])]]\\n print tabulate.tabulate(data, headers=['Max', 'Average', 'Max Days', 'Average Days'])\\n\\n print '\\\\nRisk:'\\n data = [[fmtn(perf['volatility_risk']), fmtn(perf['beta']), fmtn(perf['lower_partial_moment_risk']), fmtn(perf['t_r']), fmtn(perf['s_r'])]]\\n print tabulate.tabulate(data, headers=['Volatility', 'Beta', 'Lower Partial Moment', 'Treynor Ratio', 'Sharpe Ratio'])\",\n \"def run_grid_search():\\n\\n best_score = 0\\n best_learning_rate = 0\\n best_discount_rate = 0\\n best_initial_q_hat = 0\\n trial_results = []\\n number_of_trials = 30\\n # TODO These ought to be done with numpy.arange but I don't have that package installed at the moment\\n for learning_rate_raw in range(5, 50, 5):\\n for discount_rate_raw in range(5, 20, 5):\\n for initial_q_hat in range(0, 10, 1):\\n learning_rate = learning_rate_raw * 0.01\\n discount_rate = discount_rate_raw * 0.05\\n # Set up environment and agent\\n e = Environment() # create environment (also adds some dummy traffic)\\n a = e.create_agent(LearningAgent, learning_rate, discount_rate, initial_q_hat) # create agent\\n e.set_primary_agent(a, enforce_deadline=True) # specify agent to track\\n # NOTE: You can set enforce_deadline=False while debugging to allow longer trials\\n\\n # Now simulate it\\n sim = Simulator(e, update_delay=0, display=False) # create simulator (uses pygame when display=True, if available)\\n # NOTE: To speed up simulation, reduce update_delay and/or set display=False\\n\\n sim.run(n_trials=number_of_trials) # run for a specified number of trials\\n # NOTE: To quit midway, press Esc or close pygame window, or hit Ctrl+C on the command-line\\n score = a.get_score()\\n if score > best_score:\\n best_score = score\\n best_learning_rate = learning_rate\\n best_discount_rate = discount_rate\\n best_initial_q_hat = initial_q_hat\\n trial_results.append((learning_rate, discount_rate, initial_q_hat, score, a.get_proportion_of_states_visited(), len(a.get_failed_trials())/ float(number_of_trials)))\\n print \\\"Gridsearch finished, best learning rate: %.2f, best discount rate: %.2f, best initial q hat %i\\\" % (best_learning_rate, best_discount_rate, best_initial_q_hat)\\n\\n with open('gridsearch_results.csv', 'wb') as csvfile:\\n spamwriter = csv.writer(csvfile, delimiter=';', quotechar='|', quoting=csv.QUOTE_MINIMAL)\\n spamwriter.writerow(('learning rate', 'discount factor', 'initial q-hat value', 'score', 'states visited', 'failed trials'))\\n for result in trial_results:\\n spamwriter.writerow(result)\",\n \"def test(args):\\n brain_name = env.brain_names[0]\\n brain = env.brains[brain_name]\\n env_info = env.reset(train_mode=True)[brain_name]\\n\\n num_agents = len(env_info.agents)\\n print('Number of agents:', num_agents)\\n\\n # dim of each action\\n action_size = brain.vector_action_space_size\\n print('Size of each action:', action_size)\\n\\n # dim of the state space\\n states = env_info.vector_observations\\n state_size = states.shape[1]\\n\\n agent = MADDPG(state_size, action_size, actor_layer_dim_1=args.actor_layer_dim_1,\\n actor_layer_dim_2=args.actor_layer_dim_2,\\n actor_layer_dim_3=args.actor_layer_dim_3,\\n critic_layer_dim_1=args.critic_layer_dim_1,\\n critic_layer_dim_2=args.critic_layer_dim_2,\\n critic_layer_dim_3=args.critic_layer_dim_3)\\n\\n agent.load(\\n \\\"chkpts/{}/{:02d}_best_model.checkpoint\\\".format(args.model_path, args.loop_counter))\\n\\n test_scores = []\\n for i_episode in tqdm(range(1, 1+args.test_n_run)):\\n # initialize the scores\\n scores = np.zeros(num_agents)\\n env_info = env.reset(train_mode=True)[\\n brain_name] # reset the environment\\n states = env_info.vector_observations # get the current states\\n dones = [False]*num_agents\\n while not np.any(dones):\\n actions = agent.act(states) # select actions\\n # send the actions to the environment\\n env_info = env.step(actions)[brain_name]\\n next_states = env_info.vector_observations # get the next states\\n rewards = env_info.rewards # get the rewards\\n dones = env_info.local_done # see if episode has finished\\n scores += rewards # update the scores\\n # roll over the states to next time step\\n states = next_states\\n\\n test_scores.append(np.max(scores))\\n\\n avg_score = sum(test_scores)/len(test_scores)\\n print(\\\"Test Score: {}\\\".format(avg_score))\\n\\n return avg_score\",\n \"def main():\\n # Retrieve csv_file name an max_investment from argument passed in console:\\n arg_csv_file, arg_max_investment = set_arg()\\n if arg_csv_file:\\n csv_file = arg_csv_file\\n else:\\n csv_file = 'dataset1_Python+P7.csv'\\n if arg_max_investment:\\n max_investment = float(arg_max_investment)\\n else:\\n max_investment = 500.00\\n\\n # Retrieve dataset:\\n base_dataset = open_convert_and_clean_csv(csv_file)\\n\\n # Retrieve solution:\\n start = time.perf_counter()\\n print()\\n print(f\\\"Processing with file '{csv_file}' containing {len(base_dataset)} shares...\\\")\\n print(f\\\"Maximum investment: {max_investment}€\\\")\\n print(\\\"Please wait...\\\")\\n computable_dataset = add_roi_to_dataset(convert_dataset_to_cents(base_dataset))\\n best_roi, combination = best_combination_dynamic(computable_dataset, max_investment)\\n\\n # Formatting results:\\n combination.sort(key=lambda x: x[2], reverse=True)\\n combination_in_euros = convert_dataset_to_euros(combination)\\n best_roi /= 100\\n # Following calculus is done on cent prices (combination) to avoid approximations with floats\\n shares_cost = calculate_shares_cost_sum(combination) / 100\\n\\n # Printing results:\\n print()\\n print(f\\\"Length of dataset: {len(computable_dataset)}\\\")\\n print(f\\\"Duration of Analysis: {elapsed_time_formatted(start)}\\\")\\n print()\\n print(f\\\"Best Return on investment after 2 years: {round(best_roi, 2)}€\\\")\\n print(f\\\"Number of shares to buy : {len(combination_in_euros)}\\\")\\n print(f\\\"Total cost: {round(shares_cost, 2)}€\\\")\\n print()\\n print(f\\\"Best combination of shares ordered by performance: \\\")\\n for share in combination_in_euros:\\n print(f\\\"{share[0]} | Price: {share[1]}€ | profit: {share[2]}%\\\")\\n print()\",\n \"def test_marketsim(description, group, inputs, outputs, grader):\\n\\n points_earned = 0.0 # initialize points for this test case\\n try:\\n # Try to import student code (only once)\\n if not main_code in globals():\\n import importlib\\n # * Import module\\n mod = importlib.import_module(main_code)\\n globals()[main_code] = mod\\n # * Import methods to test\\n for m in ['compute_portvals']:\\n globals()[m] = getattr(mod, m)\\n\\n incorrect = False\\n msgs = []\\n\\n if group == 'author':\\n try:\\n # globals()['author'] = getattr(marketsim, 'author')\\n auth_string = run_with_timeout(marketsim.author,seconds_per_test_case,(),{})\\n if auth_string == 'tb34':\\n incorrect = True\\n msgs.append(\\\" Incorrect author name (tb34)\\\")\\n points_earned = -20\\n elif auth_string == '':\\n incorrect = True\\n msgs.append(\\\" Empty author name\\\")\\n points_earned = -20\\n except Exception as e:\\n incorrect = True\\n msgs.append(\\\" Exception occured when calling author() method: {}\\\".format(e))\\n points_earned = -20\\n else:\\n # Unpack test case\\n orders_file = inputs['orders_file']\\n start_val = inputs['start_val']\\n\\n portvals = None\\n fullpath_orders_file = orders_file#get_orders_data_file(orders_file)\\n portvals = run_with_timeout(compute_portvals,seconds_per_test_case,(fullpath_orders_file,start_val),{})\\n # Verify against expected outputs and assign points\\n\\n # * Check return type is correct, coax into Series\\n assert (type(portvals) == pd.Series) or (type(portvals) == pd.DataFrame and len(portvals.columns) == 1), \\\"You must return a Series or single-column DataFrame!\\\"\\n if type(portvals) == pd.DataFrame:\\n portvals = portvals[portvals.columns[0]] # convert single-column DataFrame to Series\\n if group == 'basic':\\n if len(portvals) != outputs['num_days']:\\n incorrect=True\\n msgs.append(\\\" Incorrect number of days: {}, expected {}\\\".format(len(portvals), outputs['num_days']))\\n else:\\n points_earned += 2.0\\n if abs(portvals[-1]-outputs['last_day_portval']) > (0.001*outputs['last_day_portval']):\\n incorrect=True\\n msgs.append(\\\" Incorrect final value: {}, expected {}\\\".format(portvals[-1],outputs['last_day_portval']))\\n else:\\n points_earned += 5.0\\n adr,sr = get_stats(portvals)\\n if abs(sr-outputs['sharpe_ratio']) > abs(0.001*outputs['sharpe_ratio']):\\n incorrect=True\\n msgs.append(\\\" Incorrect sharpe ratio: {}, expected {}\\\".format(sr,outputs['sharpe_ratio']))\\n else:\\n points_earned += 1.5\\n if abs(adr-outputs['avg_daily_ret']) > abs(0.001*outputs['avg_daily_ret']):\\n incorrect=True\\n msgs.append(\\\" Incorrect avg daily return: {}, expected {}\\\".format(adr,outputs['avg_daily_ret']))\\n else:\\n points_earned += 1.0\\n elif group=='leverage':\\n if abs(portvals[-1]-outputs['last_day_portval']) > (0.001*outputs['last_day_portval']):\\n incorrect = True\\n msgs.append(\\\" Incorrect final value: {}, expected {}\\\".format(portvals[-1],outputs['last_day_portval']))\\n else:\\n points_earned += 1.0\\n if incorrect:\\n # inputs_str = \\\" orders_file: {}\\\\n\\\" \\\\\\n # \\\" start_val: {}\\\\n\\\".format(orders_file, start_val)\\n raise IncorrectOutput, \\\"Test failed on one or more output criteria.\\\\n Inputs:\\\\n{}\\\\n Failures:\\\\n{}\\\".format(inputs, \\\"\\\\n\\\".join(msgs))\\n except Exception as e:\\n # Test result: failed\\n msg = \\\"Test case description: {}\\\\n\\\".format(description)\\n \\n # Generate a filtered stacktrace, only showing erroneous lines in student file(s)\\n \\n tb_list = tb.extract_tb(sys.exc_info()[2])\\n if 'grading_traceback' in dir(e):\\n tb_list = e.grading_traceback\\n for i in xrange(len(tb_list)):\\n row = tb_list[i]\\n tb_list[i] = (os.path.basename(row[0]), row[1], row[2], row[3]) # show only filename instead of long absolute path\\n tb_list = [row for row in tb_list if row[0] == 'marketsim.py']\\n if tb_list:\\n msg += \\\"Traceback:\\\\n\\\"\\n msg += ''.join(tb.format_list(tb_list)) # contains newlines\\n msg += \\\"{}: {}\\\".format(e.__class__.__name__, e.message)\\n\\n # Report failure result to grader, with stacktrace\\n grader.add_result(GradeResult(outcome='failed', points=points_earned, msg=msg))\\n raise\\n else:\\n # Test result: passed (no exceptions)\\n grader.add_result(GradeResult(outcome='passed', points=points_earned, msg=None))\",\n \"def rtest_predictoutcome():\\n\\n #define cohort size\\n npatients = 2\\n\\n #init healthy patients\\n simulator = AbbcEnvironment(patients=npatients)\\n\\n #simulate healthy patients for long term in short term increments\\n nstep = int(long_term/short_term)\\n\\n #define action taken : -1 means patients will be simulated as healthy\\n action = np.repeat(-1, npatients)\\n\\n #init episode list\\n episode = [simulator.state]\\n\\n #main simulation loop to generate episodes\\n for step in range(nstep):\\n episode += simulator.take_action(action=action, simtime=short_term)\\n\\n #episode length is 1+2*nstep consisting of intit state (5xnpat) followed by\\n # next state and reward (1xnpat) repeating each time step.\\n #print(episode)\\n #print(len(episode))\\n\\n #---semi gradient temporal difference (0) algorithm ---\\n #init hyperparameters\\n alpha = .1 #learning rate\\n #init Value function model\\n agent = AbbcAgent(discount=1.0)\\n #loop over episodes\\n for patient in range(npatients):\\n #state = [nstep]\\n #state += episode[0][:,patient] #get inital state\\n state = np.append(episode[0][:,patient],nstep).reshape((6,1)) #get inital state\\n\\n print(state)\\n #loop over time steps in episode\\n for k in range(1,nstep+1):\\n #get next state and reward\\n #nextstate = [nstep-k]\\n #nextstate = episode[k*2-1][:,patient]\\n nextstate = np.append(episode[k*2-1][:,patient],nstep-k).reshape((6,1))\\n\\n reward = episode[k*2][patient]\\n\\n #get magnitude for forces\\n magnitude = alpha * (reward + agent.discount * agent.get_value(nextstate)\\n - agent.get_value(state))\\n #compute forces\\n forces = computeforces(agent.prednet, state, 0, \\\"iden\\\")\\n\\n #update model\\n for layer in forces:\\n index = layer[\\\"layer\\\"]\\n agent.prednet[index][\\\"weight\\\"] += magnitude * layer[\\\"fweight\\\"]\\n agent.prednet[index][\\\"bias\\\"] += magnitude * layer[\\\"fbias\\\"]\\n\\n state = np.copy(nextstate)\\n\\n\\n #make predictions\\n state = np.append(episode[0][:,patient],nstep).reshape((6,1)) #get inital state\\n print(agent.get_value(state))\\n\\n #Value function approximates outcome return at time horizon.\\n assert(False)\\n\\n ##define action taken\\n #action = np.repeat(2, npatients)\\n ##main simulation loop\\n #for step in range(nstep):\\n # _, drugy_reward[step,:] = simulator.take_action(action=action, simtime=short_term)\",\n \"def MonteCarlo(data, iterations, strike, maturity):\\n\\n log_returns = np.log(1 + data.pct_change())\\n\\n rf = 0.025 # risk-free rate\\n\\n stdv = log_returns.std() * np.sqrt(250) # Annualized standard deviation\\n\\n T = maturity # set the maturity\\n t_intervals = 250 \\n delta_t = T / t_intervals\\n\\n iterations = iterations \\n\\n Z = np.random.standard_normal((t_intervals+1, iterations))\\n S = np.zeros_like(Z) # price matrix\\n\\n # set the first row of the price matrix to be the last price in the dataframe\\n S[0] = data.iloc[-1]\\n\\n # Perform simulations\\n for t in range(1, t_intervals + 1):\\n S[t] = S[t - 1] * np.exp((rf - 0.5 * stdv**2)\\n * delta_t + stdv*np.sqrt(delta_t)*Z[t])\\n\\n # Calculate Payoff\\n strike = strike\\n pyff = np.maximum(S[-1] - strike, 0)\\n\\n C = np.exp(-rf * T) * np.sum(pyff) / iterations # calculate call oprice\\n print(\\\"The price of the call option on %s is:\\\" % data.name, C)\\n\\n # Visulize Monte Carlo\\n fig = plt.figure(figsize=(9, 6))\\n plt.xlim(0, t_intervals)\\n plt.grid(True)\\n plt.xlabel('Days')\\n plt.ylabel('Price')\\n plt.title('Monte Carlo Simulation')\\n plt.plot(S[:,:])\\n plt.show()\\n\\n return C\",\n \"def evaluate_benchmarks(self):\\n\\n # iterate over replicates\\n results = {}\\n for replicate_id, replicate in self.replicates:\\n\\n # evaluate benchmark for current replicate\\n bmark = SimulationBenchmark(replicate.copy(),\\n graph=self.graphs[replicate_id],\\n **self.params)\\n\\n # store results\\n results[replicate_id] = dict(\\n\\n labels_MAE=bmark.scores['labels'].MAE,\\n level_only_MAE=bmark.scores['level_only'].MAE,\\n spatial_only_MAE=bmark.scores['spatial_only'].MAE,\\n community_MAE=bmark.scores['labels_comm'].MAE,\\n\\n labels_PCT=bmark.scores['labels'].percent_correct,\\n level_only_PCT=bmark.scores['level_only'].percent_correct,\\n spatial_only_PCT=bmark.scores['spatial_only'].percent_correct,\\n community_PCT=bmark.scores['labels_comm'].percent_correct)\\n\\n # compile dataframe\\n results = pd.DataFrame.from_dict(results, orient='index')\\n results.index.set_names(self.multiindex, inplace=True)\\n\\n return results\",\n \"def performance_vs_index(self, index='SPY', dateIni='Ini', dateFin='Fin'):\\n if dateFin == 'Fin':\\n dateFin = self.data.index[-1]\\n if dateIni == 'Ini':\\n dateIni = self.data.index[0]\\n portfolioGains = round(self.data.loc[self.data.index[-1], 'Profit/Loss%'], 2)\\n else:\\n pData = self.data.loc[dateIni:dateFin]\\n pData.loc[:,'Profit/Loss'] = pData['Gains'].cumsum()\\n pData.loc[:,'Profit/Loss%'] = pData['Profit/Loss'] / pData['Invested'] * 100\\n portfolioGains = round(pData.loc[pData.index[-1], 'Profit/Loss%'], 2)\\n indexData = yf.Ticker(index).history(start=dateIni, end=dateFin)\\n indexData['Var%'] = (indexData.Close - indexData.Close[0]) / indexData.Close[0] * 100\\n indexGains = round(indexData.loc[indexData.index[-1], 'Var%'], 2)\\n return portfolioGains, indexGains, portfolioGains - indexGains\",\n \"def portfolio_returns(self,weights,returns):\\n \\n return weights.T @ returns\",\n \"def optimize(\\n target_name: str,\\n setting: dict,\\n start: datetime,\\n end: datetime,\\n rate: float,\\n slippage: float,\\n size: float,\\n pricetick: float,\\n capital: int,\\n):\\n backtest_me = BacktestMainEngine()\\n backtest_me.set_parameters(\\n start=start,\\n end=end,\\n rate=rate,\\n slippage=slippage,\\n size=size,\\n pricetick=pricetick,\\n capital=capital)\\n backtest_me.setting = setting\\n # 启动价差引擎\\n backtest_me.engines['St'].start()\\n # 显示回测结果\\n df = backtest_me.calculate_result()\\n statistics = backtest_me.calculate_statistics()\\n backtest_me.save_chart(setting)\\n for k, v in setting.items():\\n statistics[k] = v\\n # statistics['start'] = start.date()\\n # statistics['end'] = end.date()\\n # statistics['rate'] = rate\\n # statistics['slippage'] = slippage\\n # statistics['size'] = size\\n # statistics['pricetick'] = pricetick\\n statistics['capital'] = capital\\n statistics['capital'] = capital\\n if statistics['max_ddpercent']:\\n statistics['收益回撤比'] = statistics['total_return'] / statistics['max_ddpercent']\\n if statistics['profit_days'] + statistics['loss_days']:\\n statistics['胜率'] = statistics['profit_days'] / (statistics['profit_days'] + statistics['loss_days'])\\n return statistics\",\n \"def calculate_performance(controls_f, df, pop_type, neg, output):\\n\\n print('\\\\n Calculating performance...')\\n log_write(output['log'], '\\\\n\\\\nPerformance based on results per %s\\\\n\\\\n' % pop_type)\\n\\n # Remove strains with missing penetrance and few cell count\\n df = df.iloc[df['Penetrance'].isna().values == 0, :]\\n df = df[df['Num_cells'] >= 15].reset_index(drop=True)\\n\\n # Plot penetrance distribution\\n plt.figure(figsize=(6, 6))\\n sns.set(font_scale=1.25)\\n sns.set_style('white')\\n sns.kdeplot(df['Penetrance'].values, color='mediumblue', shade=True)\\n plt.xlabel('Penetrance (%)')\\n mean_penetrance = df['Penetrance'].mean()\\n plt.title('Mean penetrance: %.4f' % mean_penetrance)\\n fig = plt.gcf()\\n fig.savefig(output['Penetrance'].replace('.', '_%s.' % pop_type), dpi=150, bbox_inches='tight')\\n fig.clf()\\n plt.close(fig)\\n log_write(output['log'], 'Mean penetrance: %.2f\\\\n' % mean_penetrance)\\n log_write(output['log'], 'Mean WT penetrance: %.2f\\\\n' % df[df['Strain ID'].isin(neg)]['Penetrance'].mean())\\n\\n # Plot WT percentile distribution\\n plt.figure(figsize=(6, 6))\\n sns.set(font_scale=1.25)\\n sns.set_style('white')\\n sns.kdeplot(df['WT_percentile_at_threshold'].values, color='mediumblue', shade=True)\\n plt.xlabel('WT Percentile at the score of maximum difference')\\n mean_percentile = df['WT_percentile_at_threshold'].mean()\\n plt.title('Mean percentile: %.4f' % mean_percentile)\\n fig = plt.gcf()\\n fig.savefig(output['WT_Percentile'].replace('.', '_%s.' % pop_type), dpi=150, bbox_inches='tight')\\n fig.clf()\\n plt.close(fig)\\n log_write(output['log'], 'Mean WT percentile at threshold: %.2f\\\\n' % mean_percentile)\\n\\n # Plot correlation\\n plt.figure(figsize=(6, 6))\\n sns.set(font_scale=1.25)\\n sns.set_style('white')\\n limits = [-0.1, 100.1]\\n ticks = [0, 20, 40, 60, 80, 100]\\n sns.lineplot(x=limits, y=limits, color='k', dashes=True, linewidth=1)\\n sns.scatterplot(x='Penetrance', y='KS_Penetrance', color='mediumblue', data=df, s=60, alpha=0.5, linewidth=0)\\n plt.xlim(limits)\\n plt.xticks(ticks)\\n plt.ylim(limits)\\n plt.yticks(ticks)\\n plt.xlabel('Penetrance')\\n plt.ylabel('KS Penetrance')\\n correlation = stats.pearsonr(df['Penetrance'], df['KS_Penetrance'])[0]\\n plt.title('Correlation: %.4f' % correlation)\\n fig = plt.gcf()\\n fig.savefig(output['KS_Correlation'].replace('.', '_%s.' % pop_type), dpi=150, bbox_inches='tight')\\n fig.clf()\\n plt.close(fig)\\n log_write(output['log'], 'KS correlation: %.4f\\\\n' % correlation)\\n\\n # Get positive controls\\n if controls_f:\\n df_cont = pd.read_csv(controls_f, low_memory=False)\\n pos = df_cont['Strain ID'].values\\n\\n # Calculate performance with maximum difference\\n aupr, aupr_b, auroc = calculate_auc(df, neg, pos)\\n correlation = plot_penetrance_agreement(df, df_cont, neg,\\n output['PeneAgreement'].replace('.', '_%s.' % pop_type))\\n log_write(output['log'], 'AUPR: %.4f\\\\n' % aupr)\\n log_write(output['log'], 'AUPR balanced: %.4f\\\\n' % aupr_b)\\n log_write(output['log'], 'AUROC: %.4f\\\\n' % auroc)\\n log_write(output['log'], 'Correlation: %.4f\\\\n' % correlation)\\n\\n # Plot penetrance of controls\\n plt.figure(figsize=(12, 4))\\n sns.set(font_scale=1.25)\\n sns.set_style('white')\\n plt.scatter(x=df[df['Strain ID'].isin(neg)].index.values,\\n y=df[df['Strain ID'].isin(neg)]['Penetrance'].values,\\n color='dodgerblue', alpha=0.3, label='Negative control', s=20)\\n plt.scatter(x=df[df['Strain ID'].isin(pos)].index.values,\\n y=df[df['Strain ID'].isin(pos)]['Penetrance'].values,\\n color='red', alpha=0.7, label='Positive control', s=20)\\n plt.xticks([])\\n plt.yticks([0, 25, 50, 75, 100])\\n plt.xlabel('Genes')\\n plt.ylabel('Penetrance (%)')\\n plt.legend(loc='upper right')\\n plt.savefig(output['PenetranceControls'].replace('.', '_%s.' % pop_type),\\n dpi=150, bbox_inches='tight')\",\n \"def compare(numberOfSimulations, initialValue, sigma, T, strike, r = 0):\\n \\n numberOfTests = 100\\n \\n \\n #the two lists that will contain our average errors for the different tests\\n errorsStandardMonteCarlo = []\\n errorsMonteCarloWithAV = []\\n \\n #our benchmark: the analytic price of the call option\\n analyticPriceBS = blackScholesPriceCall(initialValue, r, sigma, T, initialValue)\\n\\n #look at how lambda functions are defined in Python \\n payoff = lambda x : max(x - initialValue, 0)\\n \\n #alternatively:\\n #def payoff(x):\\n # return max(x - initialValue, 0)\\n \\n #note how to construct an object of a class\\n \\n blackScholesGenerator = GenerateBlackScholes(numberOfSimulations, T, initialValue, sigma, r)\\n #k=0,..,numberOfTests - 1\\n for k in range(numberOfTests):\\n \\n \\n\\n #first, the valuation with the standard Monte-Carlo:\\n realizationsOfTheProcessWithStandardMC = blackScholesGenerator.generateRealizations()\\n \\n priceCalculatorWithStandardMC = SimpleEuropeanOption(realizationsOfTheProcessWithStandardMC, r)\\n priceStandardMC = priceCalculatorWithStandardMC.getPrice(payoff, T) \\n errorWithStandardMC= abs(priceStandardMC - analyticPriceBS)/analyticPriceBS\\n #note how to append new values to a list\\n errorsStandardMonteCarlo.append(errorWithStandardMC)\\n \\n #then, the one with Antithetic Variables:\\n realizationsOfTheProcessWithAV = blackScholesGenerator.generateRealizationsAntitheticVariables()\\n \\n priceCalculatorWithAV = SimpleEuropeanOption(realizationsOfTheProcessWithAV, r)\\n priceWithAV = priceCalculatorWithAV.getPrice(payoff, T) \\n errorWithAV= abs(priceWithAV - analyticPriceBS)/analyticPriceBS\\n #note how to append new values to a list\\n errorsMonteCarloWithAV.append(errorWithAV)\\n\\n #we get and return the respective average errors: mean is imported from\\n #statistics\\n averageErrorStandardMC = mean(errorsStandardMonteCarlo)\\n #you can also use numpy\\n averageErrorAV = np.mean(errorsMonteCarloWithAV)\\n \\n return averageErrorStandardMC, averageErrorAV\",\n \"def backtest(start=None, end=None,\\n capital=100000, leverage=1, commission=0,\\n slippage=0, price='open', stop_loss=0, order='B',\\n universe='all', limit=5, columns=None, conditions=None,\\n sort_by='price', sort_mode=True,\\n connection=None, tablename=None,\\n where_clause=None, data=None,\\n strategy=None, output=None): \\n if (data is None) and ((tablename is None) or (connection is None)):\\n raise ValueError('No proper data source.\\\\nEither specify a database connection and tablename or provide a dataframe')\\n\\n if data is None:\\n data = fetch_data(universe=universe, start=start, end=end,\\n connection=connection, tablename=tablename,\\n where_clause=where_clause)\\n\\n # Check whether any data is available\\n isNotEmpty = lambda x: True if len(data) > 0 else False\\n\\n if isNotEmpty(data):\\n data = prepare_data(data, columns) \\n final = apply_prices(data, conditions, price, stop_loss, order) \\n else:\\n raise ValueError('No data fetched from database')\\n\\n if isNotEmpty(final): \\n result = run_strategy(final, sort_by, sort_mode, limit, strategy)\\n else:\\n raise ValueError('No data after filtering all conditions')\\n\\n if output:\\n return output(result)\\n else:\\n return get_output(result, capital, leverage, commission, slippage)\",\n \"def update_portfolio(self, portfolio: PortfolioController):\\n now = portfolio.get_history(seconds_back=0)\\n future = portfolio.get_history(seconds_back=-self.update_interval)\\n\\n for fund in portfolio.funds:\\n best_currency = max(portfolio.currencies, key=lambda currency: future_value(fund, currency, now, future))\\n if best_currency != fund.currency:\\n portfolio.request_transfer(fund, best_currency)\",\n \"def tracking_error(benchmark_returns: pd.Series,\\n portfolio_returns: pd.Series) -> float:\\n return (benchmark_returns-portfolio_returns).std(ddof=1)\",\n \"def hist_data(self):\\n # database address\\n jydb_address = \\\"117.122.223.35\\\"\\n jydb_user_id = \\\"zpy\\\"\\n jydb_user_pwd = \\\"Z1pe1y1@zpy\\\"\\n jydb_db_name = \\\"jydb02\\\"\\n # preallocate data set\\n back_testing_data = {}\\n \\n # on-line mode\\n conn = pymysql.connect(jydb_address, jydb_user_id, jydb_user_pwd, jydb_db_name)\\n # not using moving average\\n if not self.moving_average: \\n # iterate on all ism packages\\n for _i, ism_pack in enumerate(tqdm(self.ism_data)):\\n ismcode = ism_pack[0][0]\\n ProRunDate = ism_pack[0][5]\\n ProStopDate = ism_pack[0][6]\\n MaxStoreSum = ism_pack[0][7]\\n MinInvestShare = ism_pack[0][8]\\n InnerCode_ls = flatten(list(map(lambda x: [y[0] for y in x[1:]], ism_pack[1:])))\\n SecuCode_ls = flatten(list(map(lambda x: [y[1] for y in x[1:]], ism_pack[1:])))\\n PriorType_ls = flatten(list(map(lambda x: [y[2] for y in x[1:]], ism_pack[1:])))\\n # collect data from source conn\\n flag_run = \\\"SELECT InnerCode, OpenPrice, ClosePrice FROM QT_DailyQuote WHERE \\\" + \\\\\\n \\\"InnerCode IN (\\\" + \\\",\\\".join(InnerCode_ls) + \\\") AND \\\" + \\\\\\n \\\"TradingDay=\\\\'\\\" + ProRunDate + \\\"\\\\'\\\"\\n flag_stop = \\\"SELECT InnerCode, OpenPrice, ClosePrice FROM QT_DailyQuote WHERE \\\" + \\\\\\n \\\"InnerCode IN (\\\" + \\\",\\\".join(InnerCode_ls) + \\\") AND \\\" + \\\\\\n \\\"TradingDay=\\\\'\\\" + ProStopDate + \\\"\\\\'\\\"\\n run_price = pd.read_sql(flag_run, conn)\\n stop_price = pd.read_sql(flag_stop, conn)\\n back_testing_data[ismcode] = pd.merge(run_price, stop_price, on='InnerCode', \\n suffixes=('_run', '_stop'))\\n else: # using moving average \\n # iterate on all ism packages\\n for _i, ism_pack in enumerate(self.ism_data):\\n ismcode = ism_pack[0][0]\\n ProRunDate = ism_pack[0][5]\\n TradingDay_begin = former_market_date(ProRunDate, self.L_shift, conn)\\n TradingDay_end = future_market_date(ProRunDate, self.R_shift, conn)\\n InnerCode_ls = flatten(list(map(lambda x: [y[0] for y in x[1:]], ism_pack[1:])))\\n SecuCode_ls = flatten(list(map(lambda x: [y[1] for y in x[1:]], ism_pack[1:])))\\n PriorType_ls = flatten(list(map(lambda x: [y[2] for y in x[1:]], ism_pack[1:])))\\n flag = \\\"SELECT InnerCode, TradingDay, OpenPrice, ClosePrice FROM QT_DailyQuote WHERE \\\" + \\\\\\n \\\"InnerCode IN (\\\" + \\\",\\\".join(InnerCode_ls) + \\\") AND \\\" + \\\\\\n \\\"TradingDay BETWEEN \\\\'\\\" + TradingDay_begin + \\\"\\\\' AND \\\\'\\\" + TradingDay_end + \\\"\\\\'\\\"\\n back_testing_data[ismcode] = pd.read_sql(flag, conn)\\n \\n # close sql connection\\n conn.close()\\n \\n return back_testing_data\",\n \"def update_portfolio(self):\\n if not self._dirty_portfolio:\\n return\\n\\n portfolio = self._portfolio\\n pt = self.position_tracker\\n\\n portfolio.positions = pt.get_positions()\\n position_stats = pt.stats\\n\\n portfolio.positions_value = position_value = (\\n position_stats.net_value\\n )\\n portfolio.positions_exposure = position_stats.net_exposure\\n self._cash_flow(self._get_payout_total(pt.positions))\\n\\n start_value = portfolio.portfolio_value\\n\\n # update the new starting value\\n portfolio.portfolio_value = end_value = portfolio.cash + position_value\\n\\n pnl = end_value - start_value\\n if start_value != 0:\\n returns = pnl / start_value\\n else:\\n returns = 0.0\\n\\n portfolio.pnl += pnl\\n portfolio.returns = (\\n (1 + portfolio.returns) *\\n (1 + returns) -\\n 1\\n )\\n\\n # the portfolio has been fully synced\\n self._dirty_portfolio = False\",\n \"def test_stock_quantity_own_all(self):\\n self.portfolio = './app/test/payloads/port_own_all.json'\\n self.stock = ['ETH', 'BTC']\\n self.stock_url = 'https://min-api.cryptocompare.com/data/pricemultifull?fsyms=ETH&tsyms=USD'\\n\\n expected_data = {'ETH': 1.01, 'BTC': 0.098}\\n\\n #Run stock_quantity on test data\\n self.add_perf = PortfolioAddPerf(self.portfolio, self.stock, self.stock_url)\\n return_data = self.add_perf.stock_quantity()\\n\\n self.assertEqual(expected_data, return_data)\",\n \"def runDTBacktest(df, \\n testname=None,\\n input_calculators=input_calculators,\\n output_calculators=output_calculators,\\n trainLearner=trainDecisionTree,\\n make_trades=stdConfidenceTrades,\\n calc_gains=getBuySellGains):\\n df_inds = runCalculators(df.close, input_calculators)\\n df_outs = runCalculators(df.close, output_calculators)\\n df = pd.concat([df, df_inds, df_outs], axis=1, join_axes=[df.index])\\n df = df.dropna()\\n df_inds = df[df_inds.columns]\\n df_outs = df[df_outs.columns]\\n \\n train_split = 0.9\\n train_in, test_in = splitData(df_inds, train_split)\\n train_out, test_out = splitData(df_outs, train_split)\\n \\n decTree = trainLearner(train_in, train_out)\\n preds = decTree.predict(test_in)\\n test_out['preds'] = preds\\n trades = make_trades(preds)\\n \\n test_df = pd.concat([df, test_in, test_out], axis=1, join_axes=[test_in.index])\\n totalGain = calc_gains(test_df['close'], trades)\\n \\n return {\\n 'testname':testname,\\n 'gain' : totalGain,\\n 'dt' : decTree,\\n 'df':df,\\n 'preds':preds,\\n 'trades':trades,\\n 'test_df':test_df\\n }\",\n \"def BayesPaperStats(maxIters, numRuns):\\n\\n comm = MPI.COMM_WORLD\\n rank = comm.Get_rank()\\n\\n assert comm.Get_size() == numRuns, \\\"Please ensure there is one process running per run i.e \\\" + str(numRuns) + \\\" processes.\\\"\\n \\n problemBounds = {\\\"Bfield\\\": choco.uniform(10, 1300), \\\"T\\\": choco.uniform(50, 230), \\\"Btheta\\\": choco.uniform(0, 90), \\\"Etheta\\\": choco.uniform(0, 90)}\\n\\n # The target for each algorithm. This was determined by using the values in the literature, so there is clearly some deviation either due to the detuning or computation.\\n globalFoM = 1.033\\n\\n if rank == 0:\\n timeList = []\\n iterationList = []\\n\\n # Set up the database for the chocolate optimiser.\\n connection = choco.SQLiteConnection(\\\"sqlite:///bayes_paper_\\\" + str(rank) + \\\"_db.db\\\")\\n\\n # Define which solver will be used.\\n solver = choco.Bayes(connection, problemBounds, utility_function = \\\"ei\\\", n_bootstrap = int(np.ceil(maxIters/10)), clear_db = True)\\n\\n # Clear the database. TODO: To do this?\\n connection.clear()\\n\\n # Start timing.\\n startTime = time.time()\\n timeElapsed = None\\n iterationSuccess = None\\n\\n # Start optimisation.\\n for iteration in range(maxIters):\\n\\n # Make one suggestion.\\n try:\\n token, nextParams = solver.next()\\n except:\\n print(\\\"Error suggesting a new point. Here are the last set of parameters sampled:\\\")\\n print(str(nextParams))\\n print(\\\"Iteration number: \\\" + str(iteration))\\n continue\\n\\n # Check what FoM this gives. Go negative as this is a minimisation routine.\\n fEval = abs(FitnessPaper(**nextParams))\\n\\n # Update best FoM.\\n if fEval >= globalFoM:\\n # The algorithm has managed to surpass or equal the paper value.\\n iterationSuccess = iteration\\n timeElapsed = time.time() - startTime\\n \\n if rank == 0:\\n iterationList.append(iterationSuccess)\\n timeList.append(timeElapsed)\\n\\n break\\n \\n # Tell the optimiser about the result.\\n solver.update(token, fEval)\\n\\n # Run complete. Send results to main process. Tags are unique identifiers.\\n if rank != 0:\\n comm.send(timeElapsed, dest = 0, tag = 1)\\n comm.send(iterationSuccess, dest = 0, tag = 2)\\n\\n # Wait for all the processes to end.\\n comm.Barrier()\\n\\n if rank == 0:\\n # Aggregate the data.\\n for process in range(comm.Get_size() - 1):\\n # Get the data.\\n individualTime = None\\n individualTime = comm.recv(individualTime, source = process + 1, tag = 1)\\n\\n individualIter = None\\n individualIter = comm.recv(individualIter, source = process + 1, tag = 2)\\n\\n if individualIter is not None:\\n # Both values must therefore be non-null.\\n iterationList.append(individualIter)\\n timeList.append(individualTime)\\n\\n avgRuntime = np.average(timeList)\\n avgIters = np.average(iterationList)\\n try:\\n\\n fastestTime = np.min(timeList)\\n\\n except ValueError:\\n \\n # List is empty.\\n fastestTime = float('NaN')\\n\\n numSuccess = len(iterationList)\\n successRate = numSuccess/numRuns\\n\\n print(\\\"Bayesian optimisation paper testing complete! Here are the stats:\\\")\\n print(\\\"Number of successful runs: \\\" + str(numSuccess) + \\\" (Success rate of \\\" + str(successRate) + \\\")\\\")\\n print(\\\"Average iterations required for success: \\\" + str(avgIters))\\n print(\\\"Average time required for success: \\\" + str(avgRuntime))\\n print(\\\"Fastest convergence time: \\\" + str(fastestTime))\\n print(\\\"------------------------------------------------------------------------------------------------------------------\\\")\\n \\n return\",\n \"def run():\\n return estimate(0,1,0)\",\n \"def main():\\n\\n # choose number of data-points and sample a pair of vectors: the input\\n # values and the corresponding target values\\n N = 500\\n inputs, targets = sample_data(N, arbitrary_function_2, seed=1)\\n\\n # specify the centres and scale of some rbf basis functions\\n default_centres = np.linspace(0,1,21)\\n default_scale = 0.03\\n default_reg_param = 0.08\\n\\n # get the cross-validation folds\\n num_folds = 4\\n folds = create_cv_folds(N, num_folds)\\n\\n # evaluate then plot the performance of different reg params\\n evaluate_reg_param(inputs, targets, folds, default_centres, default_scale)\\n # evaluate then plot the performance of different scales\\n evaluate_scale(inputs, targets, folds, default_centres, default_reg_param)\\n # evaluate then plot the performance of different numbers of basis\\n # function centres.\\n evaluate_num_centres(\\n inputs, targets, folds, default_scale, default_reg_param)\\n\\n plt.show()\",\n \"def main() -> None:\\n\\n download_data_tools.initial_message()\\n\\n # S&P 500 companies, initial year and time step\\n stocks: List[str] = download_data_tools.get_stocks(['all'])\\n dates: List[str] = ['1992-01', '2012-12']\\n time_step: str = '1d'\\n\\n # Basic folders\\n download_data_tools.start_folders()\\n\\n # Run analysis\\n # Download data\\n portfolio_download_data(stocks, dates, time_step)\\n\\n print('Ay vamos!!!')\",\n \"def main(args):\\n\\n # Compose the model list\\n modellist = []\\n if args['model']:\\n modellist.append(bmark.ModelInfo(args['model'], os.getcwd(), args['classname']))\\n\\n # Load the benchmark settings\\n benchmark = None\\n benchmark = bmark.load_benchmark(args['benchmark'])\\n corresponding_data = False\\n if 'corresponding_data' in benchmark:\\n corresponding_data = benchmark['corresponding_data']\\n\\n # Only extend if not cached\\n cache_df = None\\n if not args['cache']:\\n modellist.extend(benchmark['models'])\\n else:\\n cache_df = pd.read_csv(args['cache'])\\n\\n # Extract comparator settings from benchmark description\\n eval_comparator = comparator.EqualityComparator()\\n if 'comparator' in benchmark:\\n if benchmark['comparator'] == 'nvc':\\n eval_comparator = comparator.NVCComparator()\\n\\n # Run the model evaluation\\n is_silent = (args['output'] in ['html', 'server'])\\n eva = None\\n if benchmark['type'] == 'adaption':\\n eva = evaluator.AdaptionEvaluator(\\n modellist,\\n eval_comparator,\\n benchmark['data.test'],\\n train_datafile=benchmark['data.train'],\\n train_data_person=benchmark['data.train_person'],\\n silent=is_silent,\\n corresponding_data=corresponding_data,\\n domain_encoders=benchmark['domain_encoders'],\\n cache_df=cache_df\\n )\\n elif benchmark['type'] == 'coverage':\\n # Check for benchmark validity\\n if benchmark['data.train'] or benchmark['data.train_person']:\\n print('WARNING: Ignoring specified training and train_person data ' \\\\\\n + 'for coverage evaluation...')\\n\\n eva = evaluator.CoverageEvaluator(\\n modellist,\\n eval_comparator,\\n benchmark['data.test'],\\n train_datafile=benchmark['data.train'],\\n train_data_person=benchmark['data.train_person'],\\n silent=is_silent,\\n corresponding_data=corresponding_data,\\n domain_encoders=benchmark['domain_encoders'],\\n cache_df=cache_df\\n )\\n else:\\n raise ValueError('Unknown benchmark type: {}'.format(benchmark['type']))\\n\\n with silence_stdout(is_silent):\\n res_df = eva.evaluate()\\n\\n if 'save' in args:\\n res_df.to_csv(args['save'], index=False)\\n\\n # Run the metric visualizer\\n htmlcrtr = html_creator.HTMLCreator([\\n viz_plot.AccuracyVisualizer(),\\n viz_plot.BoxplotVisualizer(),\\n viz_plot.TableVisualizer()\\n ])\\n\\n # Prepare the benchmark output information and visualize the evaluation results\\n benchmark_info = {\\n 'name': os.path.basename(args['benchmark']),\\n 'data.train': os.path.basename(\\n benchmark['data.train']) if benchmark['data.train'] else '',\\n 'data.train_person': os.path.basename(\\n benchmark['data.train_person']) if benchmark['data.train_person'] else '',\\n 'data.test': os.path.basename(benchmark['data.test']),\\n 'type': benchmark['type'],\\n 'corresponding_data': benchmark['corresponding_data'],\\n 'domains': list(res_df['domain'].unique()),\\n 'response_types': list(res_df['response_type'].unique()),\\n }\\n\\n if args['output'] == 'browser':\\n html = htmlcrtr.to_html(res_df, benchmark_info, embedded=False)\\n server.load_in_default_browser(html.encode('utf8'))\\n elif args['output'] == 'server':\\n html = htmlcrtr.to_html(res_df, benchmark_info, embedded=True)\\n sys.stdout.buffer.write(html.encode('utf-8'))\\n elif args['output'] == 'html':\\n html = htmlcrtr.to_html(res_df, benchmark_info, embedded=False)\\n print(html)\",\n \"def optimizeForReturn(required_return, stock_db, use_genetic):\\n print('Optimizing portfolio for %f' % required_return)\\n pf = PortfolioFactory(stock_db, required_return, use_genetic=use_genetic)\\n desired_portfolio = pf.desired_portfolio\\n print('Required Return: %f' % required_return)\\n print('Expected Return: %f' % math.pow(\\n desired_portfolio.average_return, Config.DAYS_IN_YEAR))\\n print('Downside Risk: %f' % desired_portfolio.downside_risk)\\n print('Downside Correl: %f' % desired_portfolio.downside_correl)\\n print('Score: %f' % desired_portfolio.score)\\n\\n # Write desired portfolio.\\n DataIO.writeDesiredPortfolio(\\n desired_portfolio, stock_db,\\n 'output/DesiredPortfolio_%.0f_%.4f_%s.csv' % (\\n Config.MINIMUM_AMOUNT_DATA, required_return, Config.TODAY.date()))\\n\\n print('Finished for %f' % required_return)\\n\\n return desired_portfolio\",\n \"def ttest():\\n # open test results and perform regression analysis\\n alphas = []\\n betas = []\\n iterations = {}\\n with open(f\\\"Results/conclusion2.csv\\\") as f:\\n csv_reader = csv.reader(f, delimiter=',')\\n\\n for run in csv_reader:\\n max, max_i = get_max_run(run)\\n if int(run[0]) not in iterations:\\n iterations[int(run[0])] = {100 - int(run[1])-1: int(max)}\\n else:\\n iterations[int(run[0])][100 - int(run[1])-1] = int(max)\\n\\n for iteration in iterations:\\n mono_levels = list(iterations[iteration].keys())\\n pop_sizes = [iterations[iteration][i] for i in mono_levels]\\n\\n regress_result = regress(pop_sizes, mono_levels)\\n alphas += [regress_result[1]]\\n betas += [regress_result[0]]\\n\\n # plot scatter and regression line\\n avg_alpha = sum(alphas)/len(alphas)\\n avg_beta = sum(betas)/len(betas)\\n stddev_beta = np.std(betas)\\n vis.scatter_mono(iterations, avg_alpha, avg_beta)\\n\\n # perform t-test\\n ttest_result = stats.ttest_ind(betas, [0 for i in betas], equal_var=True)\\n t_stat = ttest_result[0]\\n p_value = ttest_result[1]\\n print(f'Results from t-test:')\\n print(f'Avg beta: {avg_beta}, stddev beta: {stddev_beta}.')\\n print(f't-stat: {t_stat}, p-value: {p_value}.')\",\n \"def print_portfolio_info(returns, avg_rets, weights):\\n ret = (weights * avg_rets).sum()\\n std = (weights * returns).sum(1).std()\\n sharpe = ret / std\\n print(\\\"Optimal weights:\\\\n{}\\\\n\\\".format(weights))\\n print(\\\"Expected return: {}\\\".format(ret))\\n print(\\\"Expected variance: {}\\\".format(std ** 2))\\n print(\\\"Expected Sharpe: {}\\\".format(sharpe))\",\n \"def main(args):\\n\\n print(now(), \\\"benchmark_test.py running.\\\")\\n out_dir = args.output\\n if not args.from_scratch:\\n print(\\n f\\\"Testing poisons from {args.poisons_path}, in the transfer learning setting...\\\\n\\\".format()\\n )\\n\\n ####################################################\\n # Frozen Feature Extractor (ffe)\\n print(\\\"Frozen Feature Extractor test:\\\")\\n args.num_poisons = 25\\n args.trainset_size = 2500\\n args.val_period = 20\\n args.optimizer = \\\"SGD\\\"\\n args.lr = 0.01\\n args.lr_schedule = [30]\\n args.epochs = 40\\n\\n args.end2end = False\\n\\n # white-box attack\\n args.output = os.path.join(out_dir, \\\"ffe-wb\\\")\\n args.model = \\\"resnet18\\\"\\n args.model_path = whitebox_modelpath\\n poison_test.main(args)\\n\\n # grey box attack\\n args.model = \\\"resnet18\\\"\\n args.model_path = greybox_modelpath\\n args.output = os.path.join(out_dir, \\\"ffe-gb\\\")\\n poison_test.main(args)\\n\\n # black box attacks\\n args.output = os.path.join(out_dir, \\\"ffe-bb\\\")\\n\\n args.model = \\\"MobileNetV2\\\"\\n args.model_path = blackbox_modelpath[0]\\n poison_test.main(args)\\n\\n args.model_path = blackbox_modelpath[1]\\n args.model = \\\"VGG11\\\"\\n poison_test.main(args)\\n ####################################################\\n\\n ####################################################\\n # End-To-End Fine Tuning (e2e)\\n print(\\\"End-To-End Fine Tuning test:\\\")\\n args.num_poisons = 25\\n args.trainset_size = 2500\\n args.val_period = 20\\n args.optimizer = \\\"SGD\\\"\\n args.lr = 0.01\\n args.lr_schedule = [30]\\n args.epochs = 40\\n\\n args.end2end = True\\n\\n # white-box attack\\n args.output = os.path.join(out_dir, \\\"e2e-wb\\\")\\n args.model = \\\"resnet18\\\"\\n args.model_path = whitebox_modelpath\\n poison_test.main(args)\\n\\n # grey box attack\\n args.model = \\\"resnet18\\\"\\n args.model_path = greybox_modelpath\\n args.output = os.path.join(out_dir, \\\"e2e-gb\\\")\\n poison_test.main(args)\\n\\n # black box attacks\\n args.output = os.path.join(out_dir, \\\"e2e-bb\\\")\\n\\n args.model = \\\"MobileNetV2\\\"\\n args.model_path = blackbox_modelpath[0]\\n poison_test.main(args)\\n\\n args.model = \\\"VGG11\\\"\\n args.model_path = blackbox_modelpath[1]\\n poison_test.main(args)\\n ####################################################\\n\\n else:\\n print(\\n f\\\"Testing poisons from {args.poisons_path}, in the from scratch training setting...\\\\n\\\".format()\\n )\\n\\n ####################################################\\n # From Scratch Training (fst)\\n args.num_poisons = 500\\n args.trainset_size = 50000\\n args.val_period = 20\\n args.optimizer = \\\"SGD\\\"\\n args.lr = 0.1\\n args.lr_schedule = [100, 150]\\n args.epochs = 200\\n args.model_path = \\\"\\\"\\n args.output = os.path.join(out_dir, \\\"fst\\\")\\n\\n args.model = \\\"resnet18\\\"\\n poison_test.main(args)\\n\\n args.model = \\\"MobileNetV2\\\"\\n poison_test.main(args)\\n\\n args.model = \\\"VGG11\\\"\\n poison_test.main(args)\\n ####################################################\",\n \"def test_zero_lookback_window(self, mock_get_prices):\\n class BuyBelow10(Moonshot):\\n \\\"\\\"\\\"\\n A basic test strategy that buys below 10.\\n \\\"\\\"\\\"\\n DB = 'test-db'\\n LOOKBACK_WINDOW = 0\\n\\n def prices_to_signals(self, prices):\\n signals = prices.loc[\\\"Close\\\"] < 10\\n return signals.astype(int)\\n\\n def _mock_get_prices():\\n\\n dt_idx = pd.DatetimeIndex([\\\"2018-05-01\\\",\\\"2018-05-02\\\",\\\"2018-05-03\\\", \\\"2018-05-04\\\"])\\n fields = [\\\"Close\\\",\\\"Volume\\\"]\\n idx = pd.MultiIndex.from_product([fields, dt_idx], names=[\\\"Field\\\", \\\"Date\\\"])\\n\\n prices = pd.DataFrame(\\n {\\n \\\"FI12345\\\": [\\n #Close\\n 9,\\n 11,\\n 10.50,\\n 9.99,\\n # Volume\\n 5000,\\n 16000,\\n 8800,\\n 9900\\n ],\\n \\\"FI23456\\\": [\\n # Close\\n 9.89,\\n 11,\\n 8.50,\\n 10.50,\\n # Volume\\n 15000,\\n 14000,\\n 28800,\\n 17000\\n\\n ],\\n },\\n index=idx\\n )\\n\\n return prices\\n\\n def mock_download_master_file(f, *args, **kwargs):\\n\\n master_fields = [\\\"Timezone\\\", \\\"Symbol\\\", \\\"SecType\\\", \\\"Currency\\\", \\\"PriceMagnifier\\\", \\\"Multiplier\\\"]\\n securities = pd.DataFrame(\\n {\\n \\\"FI12345\\\": [\\n \\\"America/New_York\\\",\\n \\\"ABC\\\",\\n \\\"STK\\\",\\n \\\"USD\\\",\\n None,\\n None\\n ],\\n \\\"FI23456\\\": [\\n \\\"America/New_York\\\",\\n \\\"DEF\\\",\\n \\\"STK\\\",\\n \\\"USD\\\",\\n None,\\n None,\\n ]\\n },\\n index=master_fields\\n )\\n securities.columns.name = \\\"Sid\\\"\\n securities.T.to_csv(f, index=True, header=True)\\n f.seek(0)\\n\\n mock_get_prices.return_value = _mock_get_prices()\\n\\n with patch(\\\"moonshot.strategies.base.download_master_file\\\", new=mock_download_master_file):\\n results = BuyBelow10().backtest(start_date=\\\"2018-05-01\\\", end_date=\\\"2018-05-04\\\")\\n\\n get_prices_call = mock_get_prices.mock_calls[0]\\n _, args, kwargs = get_prices_call\\n self.assertListEqual(kwargs[\\\"codes\\\"], [\\\"test-db\\\"])\\n self.assertEqual(kwargs[\\\"start_date\\\"], \\\"2018-05-01\\\")\\n self.assertEqual(kwargs[\\\"end_date\\\"], \\\"2018-05-04\\\")\\n self.assertEqual(kwargs[\\\"fields\\\"], ['Open', 'Close', 'Volume'])\\n self.assertIsNone(kwargs[\\\"timezone\\\"])\\n self.assertTrue(kwargs[\\\"infer_timezone\\\"])\",\n \"def cAPM_Constant(df_train, df_test, MaxRPT_r1, MaxRPT, no_rounds):\\n\\n # number of periods in samples\\n T = df_train.shape[0]\\n T_test = df_test.shape[0]\\n\\n # number of agents\\n K = df_test.shape[1]\\n\\n # individual forecasts\\n F = df_train.iloc[:, 1:].values\\n F_test = df_test.iloc[:, :].values\\n\\n # outcomes\\n outcomes = df_train.iloc[:, 0].values\\n\\n # initialize matrices containing all predictions and bets\\n pred_mat = np.full((no_rounds, K), np.nan, dtype=float)\\n bet_mat = np.full((no_rounds, K), np.nan, dtype=float)\\n\\n # budget initialization\\n budgets = np.full(K, 1, dtype=float)\\n\\n # training the market\\n for i in range(T):\\n\\n # first round betting\\n pred_mat[0, :] = F[i, :]\\n bet_mat[0, :] = budgets * MaxRPT_r1\\n budgets = budgets * (1-MaxRPT_r1)\\n\\n # rest of rounds betting\\n for j in range(1, no_rounds):\\n\\n pred_mat[j, :] = F[i, :]\\n bet_mat[j, :] = budgets * MaxRPT\\n budgets = budgets * (1-MaxRPT)\\n\\n # use IQR to determine outlier error threshold\\n abs_errors = np.abs(outcomes[i] - pred_mat)\\n Q3, Q1 = np.percentile(abs_errors, [75, 25])\\n IQR = Q3 - Q1\\n oet = Q3 + (IQR*1.5)\\n\\n # reward the agents based on accuracy of their predictions\\n accuracy = np.maximum(100 * (1 - abs_errors/oet), 1)\\n score = np.log(accuracy)\\n revenue = np.multiply(score, bet_mat)\\n budgets = budgets + np.sum(revenue, axis=0)\\n\\n # rescaling the budget (only for computational reasons)\\n budgets = budgets/np.sum(budgets)\\n\\n # using the market for the out-of-sample predictions\\n pred = np.full(T_test, np.nan, dtype=float)\\n for i in range(T_test):\\n\\n # reset test budget\\n budgets_test = np.copy(budgets)\\n\\n # first round betting\\n pred_mat[0, :] = F_test[i, :]\\n bet_mat[0, :] = budgets_test * MaxRPT_r1\\n budgets_test = budgets_test * (1-MaxRPT_r1)\\n\\n # rest of rounds betting\\n for j in range(1, no_rounds):\\n\\n pred_mat[j, :] = F_test[i, :]\\n bet_mat[j, :] = budgets_test * MaxRPT\\n budgets_test = budgets_test * (1-MaxRPT)\\n\\n # final round market prediction is the c-APM prediction\\n pred[i] = np.dot(\\n pred_mat[no_rounds-1, :],\\n bet_mat[no_rounds-1, :])/np.sum(bet_mat[no_rounds-1, :])\\n\\n # output\\n df_pred = pd.DataFrame(\\n {\\\"c-APM (Constant)\\\": pred},\\n index=df_test.index\\n )\\n\\n return df_pred\",\n \"def test_class():\\n riskfree = .03\\n maturity = 30/365\\n moneyness = np.linspace(-.04, .04, 10)\\n premium = np.ones_like(moneyness) * .05\\n call = True\\n data = {'riskfree': riskfree, 'maturity': maturity,\\n 'moneyness': moneyness, 'call': call, 'premium': premium}\\n\\n sigma = .13\\n bsm = BSmodel(sigma, data)\\n\\n weights = [.63]\\n means = [-.01, .09]\\n stds = [.16, .05]\\n param = weights + means + stds\\n mbs = MBSmodel(param, data)\\n\\n param_a, param_p, param_c = 4, 1.5, -.05\\n gb2 = GB2model([param_a, param_p, param_c], data)\\n print(gb2.get_pnames())\\n\\n plt.figure()\\n for model in [bsm, mbs, gb2]:\\n plt.plot(moneyness, model.density(moneyness), label=model.get_name())\\n plt.legend()\\n plt.show()\\n\\n plt.figure()\\n for model in [bsm, mbs, gb2]:\\n plt.plot(moneyness, model.premium(), label=model.get_name())\\n plt.legend()\\n plt.show()\\n\\n plt.figure()\\n for model in [bsm, mbs, gb2]:\\n plt.plot(moneyness, model.impvol(), label=model.get_name())\\n plt.legend()\\n plt.show()\\n\\n print('BS objective function = %.4f' % bsm.objective(sigma))\\n print('GB2 objective function = %.4f'\\n % gb2.objective([param_a, param_p, param_c]))\",\n \"def full_screen(self,ploton=False):\\n self._screener_init() # initialise the model with a single expensive test\\n\\n for i in range(self.nthreads): # at the start, give the workers a job to do each\\n self._select_and_run_experiment(i)\\n\\n while self.model.b >= self.cy: # spend budget till cant afford any more expensive tests\\n i = self._record_experiment(final=False)\\n self._select_and_run_experiment(i)\\n if ploton:\\n self.model.plot(self.model.x,self.y,self.z)\\n\\n for i in range(self.nthreads): # finish up any remaining jobs and record their results\\n self._record_experiment(final=True)\\n\\n return self.history\",\n \"def run_simulation(n, experiments, iterations, budget, recovery_count, performance_factor, current_top,\\r\\n precomputed=True, dataset=None):\\r\\n T = 0\\r\\n k = 5\\r\\n delta = 0.1\\r\\n range_m = 1*np.arange(1, budget+1)\\r\\n scores, true_top = init(n, precomputed=precomputed, dataset=dataset)\\r\\n true_ranks = get_ranks(scores)\\r\\n\\r\\n for exp in tqdm.tqdm(range(experiments), desc=\\\"experiments\\\"):\\r\\n for itr in tqdm.tqdm(range(iterations), desc=\\\"iterations\\\"):\\r\\n t = 1\\r\\n count = 0\\r\\n A = np.arange(n)\\r\\n P = np.zeros((n, n))\\r\\n S = np.zeros(n)\\r\\n r_count = 0\\r\\n data = []\\r\\n for b in range(budget):\\r\\n m = range_m[b]\\r\\n\\r\\n est, t, A, P, S, count, r_count, data = sparse_borda(n, t, T, k, A, P, S, delta, scores, m*n, count, r_count, data)\\r\\n\\r\\n ranking, ranks, top = get_ranking(n, est)\\r\\n if(top == true_top):\\r\\n recovery_count[b][exp] += 1\\r\\n performance_factor[b][exp] += ranks[true_top]\\r\\n current_top[b][exp] += true_ranks[top]\\r\\n\\r\\n current_top /= iterations\\r\\n performance_factor /= iterations\\r\\n\\r\\n return ranking, ranks, data, scores, true_top, est, recovery_count, performance_factor, current_top\",\n \"def run(self):\\n results = self._optimization.run()\\n self._optimization.sim.fdtd.close()\\n \\n # plot optimization recap figure\\n plt.show()\\n \\n return [results[0], np.array(results[1])]\",\n \"def main():\\n trades = get_trades()\\n _print_trades(trades)\\n\\n print(\\\"\\\\n# Cost basis per asset\\\")\\n _cost_basis_per_asset(trades)\\n\\n for year in range(2015, 2019):\\n trades_for_year = _filter_trades_by_time(trades, year)\\n _print_balances(trades_for_year, year)\\n _print_agg_trades(trades_for_year, year)\",\n \"def test_run_beta_diversity_through_plots_parallel(self):\\r\\n run_beta_diversity_through_plots(\\r\\n self.test_data['biom'][0],\\r\\n self.test_data['map'][0],\\r\\n self.test_out,\\r\\n call_commands_serially,\\r\\n self.params,\\r\\n self.qiime_config,\\r\\n tree_fp=self.test_data['tree'][0],\\r\\n parallel=True,\\r\\n status_update_callback=no_status_updates)\\r\\n\\r\\n unweighted_unifrac_dm_fp = join(\\r\\n self.test_out,\\r\\n 'unweighted_unifrac_dm.txt')\\r\\n weighted_unifrac_dm_fp = join(self.test_out, 'weighted_unifrac_dm.txt')\\r\\n unweighted_unifrac_pc_fp = join(\\r\\n self.test_out,\\r\\n 'unweighted_unifrac_pc.txt')\\r\\n weighted_unifrac_pc_fp = join(self.test_out, 'weighted_unifrac_pc.txt')\\r\\n weighted_unifrac_html_fp = join(self.test_out,\\r\\n 'weighted_unifrac_emperor_pcoa_plot', 'index.html')\\r\\n\\r\\n # check for expected relations between values in the unweighted unifrac\\r\\n # distance matrix\\r\\n dm = parse_distmat_to_dict(open(unweighted_unifrac_dm_fp))\\r\\n self.assertTrue(dm['f1']['f2'] < dm['f1']['p1'],\\r\\n \\\"Distance between pair of fecal samples is larger than distance\\\"\\r\\n \\\" between fecal and palm sample (unweighted unifrac).\\\")\\r\\n self.assertEqual(dm['f1']['f1'], 0)\\r\\n # check for expected relations between values in the weighted unifrac\\r\\n # distance matrix\\r\\n dm = parse_distmat_to_dict(open(weighted_unifrac_dm_fp))\\r\\n self.assertTrue(dm['f1']['f2'] < dm['f1']['p1'],\\r\\n \\\"Distance between pair of fecal samples is larger than distance\\\"\\r\\n \\\" between fecal and palm sample (unweighted unifrac).\\\")\\r\\n self.assertEqual(dm['f1']['f1'], 0)\\r\\n\\r\\n # check that final output files have non-zero size\\r\\n self.assertTrue(getsize(unweighted_unifrac_pc_fp) > 0)\\r\\n self.assertTrue(getsize(weighted_unifrac_pc_fp) > 0)\\r\\n self.assertTrue(getsize(weighted_unifrac_html_fp) > 0)\\r\\n\\r\\n # Check that the log file is created and has size > 0\\r\\n log_fp = glob(join(self.test_out, 'log*.txt'))[0]\\r\\n self.assertTrue(getsize(log_fp) > 0)\",\n \"def test_run_beta_diversity_through_plots(self):\\r\\n run_beta_diversity_through_plots(\\r\\n self.test_data['biom'][0],\\r\\n self.test_data['map'][0],\\r\\n self.test_out,\\r\\n call_commands_serially,\\r\\n self.params,\\r\\n self.qiime_config,\\r\\n tree_fp=self.test_data['tree'][0],\\r\\n parallel=False,\\r\\n status_update_callback=no_status_updates)\\r\\n\\r\\n unweighted_unifrac_dm_fp = join(\\r\\n self.test_out,\\r\\n 'unweighted_unifrac_dm.txt')\\r\\n weighted_unifrac_dm_fp = join(self.test_out, 'weighted_unifrac_dm.txt')\\r\\n unweighted_unifrac_pc_fp = join(\\r\\n self.test_out,\\r\\n 'unweighted_unifrac_pc.txt')\\r\\n weighted_unifrac_pc_fp = join(self.test_out, 'weighted_unifrac_pc.txt')\\r\\n weighted_unifrac_html_fp = join(self.test_out,\\r\\n 'weighted_unifrac_emperor_pcoa_plot', 'index.html')\\r\\n\\r\\n # check for expected relations between values in the unweighted unifrac\\r\\n # distance matrix\\r\\n dm = parse_distmat_to_dict(open(unweighted_unifrac_dm_fp))\\r\\n self.assertTrue(dm['f1']['f2'] < dm['f1']['p1'],\\r\\n \\\"Distance between pair of fecal samples is larger than distance\\\"\\r\\n \\\" between fecal and palm sample (unweighted unifrac).\\\")\\r\\n self.assertEqual(dm['f1']['f1'], 0)\\r\\n # check for expected relations between values in the weighted unifrac\\r\\n # distance matrix\\r\\n dm = parse_distmat_to_dict(open(weighted_unifrac_dm_fp))\\r\\n self.assertTrue(dm['f1']['f2'] < dm['f1']['p1'],\\r\\n \\\"Distance between pair of fecal samples is larger than distance\\\"\\r\\n \\\" between fecal and palm sample (unweighted unifrac).\\\")\\r\\n self.assertEqual(dm['f1']['f1'], 0)\\r\\n\\r\\n # check that final output files have non-zero size\\r\\n self.assertTrue(getsize(unweighted_unifrac_pc_fp) > 0)\\r\\n self.assertTrue(getsize(weighted_unifrac_pc_fp) > 0)\\r\\n self.assertTrue(getsize(weighted_unifrac_html_fp) > 0)\\r\\n\\r\\n # Check that the log file is created and has size > 0\\r\\n log_fp = glob(join(self.test_out, 'log*.txt'))[0]\\r\\n self.assertTrue(getsize(log_fp) > 0)\",\n \"def parameter_optimization(self):\\n out = open(self.csv_dir + self.strategy_id + '_gridsearch.csv', \\\"w\\\")\\n spl = len(self.para_list)\\n for i, sp in enumerate(self.para_list):\\n print(\\\"Strategy %s out of %s...\\\" % (i + 1, spl))\\n self._generate_trading_instances(sp)\\n self._run_backtest()\\n stats = self.portfolio.get_statistics()\\n tot_profit = float(stats[0][1])\\n sharpe = float(stats[1][1])\\n max_dd = float(stats[2][1])\\n win_rate = float(stats[7][1].replace(\\\"%\\\", \\\"\\\"))\\n profit_factor = float(stats[8][1])\\n\\n out.write(\\n \\\"%s,%s,%s,%s,%s,%s,%s\\\\n\\\" %\\n (sp[\\\"takeprofit\\\"], sp[\\\"period\\\"], tot_profit, sharpe, max_dd, win_rate, profit_factor)\\n )\\n out.close()\",\n \"def main():\\n\\n convergence_rates_e1 = pd.DataFrame(index=['Mean', 'St.D'])\\n convergence_rates_e4 = pd.DataFrame(index=['Mean', 'St.D'])\\n success_rates = pd.DataFrame(index=['1e-1', '1e-2', '1e-3', '1e-4', '1e-5'])\\n peak_ratios = pd.DataFrame(index=['1e-1', '1e-2', '1e-3', '1e-4', '1e-5'])\\n\\n for function in range(1, 21):\\n results = pickle.load(open('results/benchmarking_result_{}.pkl'.format(function), 'rb'))\\n\\n col_name = 'F{}'.format(function)\\n index = function-1\\n\\n convergence_rates_e1.insert(index, col_name, results.ConvergenceRates[0])\\n convergence_rates_e4.insert(index, col_name, results.ConvergenceRates[3])\\n\\n success_rates.insert(index, col_name, results.SuccessRate)\\n peak_ratios.insert(index, col_name, results.PeakRatio)\\n\\n for i in results.SimulationSwarms:\\n\\n x_axis = [k for k, _ in i.items()]\\n y_axis = [v for _, v in i.items()]\\n\\n plt.subplot(4, 5, function) # subplot indexes from 1\\n plt.plot(x_axis, y_axis, 'k-')\\n\\n plt.savefig('results/nmmso_benchmark.png')\\n plt.show()\\n\\n pd.set_option('display.max_columns', None) # make sure all columns are printed below\\n print(convergence_rates_e1)\\n print(convergence_rates_e4)\\n print(success_rates)\\n print(peak_ratios)\\n\\n # Table V from Fieldsend et al.\\n table4 = pd.Series([\\n peak_ratios.stack().median(),\\n peak_ratios.stack().mean(),\\n peak_ratios.stack().std()\\n ])\\n print(table4)\\n\\n # do we want to reproduce Fig. 3\",\n \"def run_performance():\\n # Create a Struct data instance from config\\n inputs = Struct(config)\\n inputs.throttle = throttle\\n # Get oxidizer properties at the given temperature\\n n2o = n2o_properties(inputs.ox.T_tank)\\n # Our integration variables are oxidizer mass and liquid oxidizer volume\\n Mox = n2o.rho_l*(inputs.ox.liquid_V) + n2o.rho_g*(inputs.ox.tank_V-inputs.ox.liquid_V)\\n if inputs.options.output_on:\\n print(\\\"Initial oxidizer mass: {} kg.\\\".format(Mox))\\n\\n start = time.perf_counter() # Start timer for integration\\n\\n time, record = integration(inputs) # Time = time for integration, record = output data\\n F_thrust = record.F_thrust\\n p_cc = record.p_cc\\n p_oxtank = record.p_oxtank\\n p_oxpresstank = record.p_oxpresstank\\n p_fueltank = record.p_fueltank\\n p_fuelpresstank = record.p_fuelpresstank\\n p_oxmanifold = record.p_oxmanifold\\n T_oxtank = record.T_oxtank\\n T_cc = record.T_cc\\n area_core = record.area_core\\n OF = record.OF_i\\n gamma_ex = record.gamma_ex\\n m_dot_ox = record.m_dot_ox\\n m_dot_fuel = record.m_dot_fuel\\n p_crit = record.p_crit\\n m_dot_ox_crit = record.m_dot_ox_crit\\n M_e = record.M_e\\n p_exit = record.p_exit\\n p_shock = record.p_shock\\n\\n time_elapsed = start-time.perf_counter() # Stop the timer and print elapsed time\\n if inputs.options.output_on:\\n print(\\\"Time elapsed for this timestep: {} sec.\\\".format(time_elapsed))\",\n \"def test_TRit(self):\\n\\n prices = self._convert_df_to_list(self.prices)\\n currency = self._convert_df_to_list(self.currency)\\n\\n manual_total = self.list_multiplication(prices, currency)\\n manual_calculated = list(self._manual_calculate_formal(manual_total))\\n\\n self.portfolio._generate_total()\\n test_column = self._convert_df_to_list(self.portfolio._df_total)\\n\\n self.assertAlmostEqual(self._clear_column(manual_calculated),\\n self._clear_column(test_column))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7306378","0.7142306","0.7061594","0.7044508","0.6963376","0.6885208","0.6866841","0.68580073","0.65411276","0.6384601","0.63703954","0.63657266","0.6328172","0.63091695","0.6296223","0.629528","0.629528","0.6279004","0.62647766","0.61990505","0.6168383","0.6153074","0.61273015","0.6035174","0.60299695","0.6022568","0.6004109","0.5995819","0.59722424","0.59544635","0.5952904","0.59512544","0.5942454","0.59403914","0.5907672","0.5896248","0.58644855","0.58519703","0.5823265","0.5788199","0.57657665","0.5761373","0.57591796","0.57375914","0.5710116","0.56949574","0.56715924","0.5668516","0.56582326","0.5633553","0.56132275","0.56096715","0.5608253","0.55837774","0.5576816","0.5570632","0.55603075","0.55484927","0.5548295","0.5547154","0.5546463","0.5511979","0.55003715","0.5475879","0.5467516","0.5455084","0.54527515","0.5451653","0.5445965","0.5444299","0.5439173","0.54300386","0.5429524","0.5429123","0.54289496","0.5423203","0.5409735","0.53986126","0.5396078","0.53943336","0.53719044","0.53689003","0.53674334","0.53568447","0.5355906","0.5350791","0.5349625","0.53471756","0.5329208","0.5328127","0.5323971","0.5322313","0.5318063","0.5317459","0.5317356","0.5310627","0.53071195","0.53051764","0.53015363","0.5285473"],"string":"[\n \"0.7306378\",\n \"0.7142306\",\n \"0.7061594\",\n \"0.7044508\",\n \"0.6963376\",\n \"0.6885208\",\n \"0.6866841\",\n \"0.68580073\",\n \"0.65411276\",\n \"0.6384601\",\n \"0.63703954\",\n \"0.63657266\",\n \"0.6328172\",\n \"0.63091695\",\n \"0.6296223\",\n \"0.629528\",\n \"0.629528\",\n \"0.6279004\",\n \"0.62647766\",\n \"0.61990505\",\n \"0.6168383\",\n \"0.6153074\",\n \"0.61273015\",\n \"0.6035174\",\n \"0.60299695\",\n \"0.6022568\",\n \"0.6004109\",\n \"0.5995819\",\n \"0.59722424\",\n \"0.59544635\",\n \"0.5952904\",\n \"0.59512544\",\n \"0.5942454\",\n \"0.59403914\",\n \"0.5907672\",\n \"0.5896248\",\n \"0.58644855\",\n \"0.58519703\",\n \"0.5823265\",\n \"0.5788199\",\n \"0.57657665\",\n \"0.5761373\",\n \"0.57591796\",\n \"0.57375914\",\n \"0.5710116\",\n \"0.56949574\",\n \"0.56715924\",\n \"0.5668516\",\n \"0.56582326\",\n \"0.5633553\",\n \"0.56132275\",\n \"0.56096715\",\n \"0.5608253\",\n \"0.55837774\",\n \"0.5576816\",\n \"0.5570632\",\n \"0.55603075\",\n \"0.55484927\",\n \"0.5548295\",\n \"0.5547154\",\n \"0.5546463\",\n \"0.5511979\",\n \"0.55003715\",\n \"0.5475879\",\n \"0.5467516\",\n \"0.5455084\",\n \"0.54527515\",\n \"0.5451653\",\n \"0.5445965\",\n \"0.5444299\",\n \"0.5439173\",\n \"0.54300386\",\n \"0.5429524\",\n \"0.5429123\",\n \"0.54289496\",\n \"0.5423203\",\n \"0.5409735\",\n \"0.53986126\",\n \"0.5396078\",\n \"0.53943336\",\n \"0.53719044\",\n \"0.53689003\",\n \"0.53674334\",\n \"0.53568447\",\n \"0.5355906\",\n \"0.5350791\",\n \"0.5349625\",\n \"0.53471756\",\n \"0.5329208\",\n \"0.5328127\",\n \"0.5323971\",\n \"0.5322313\",\n \"0.5318063\",\n \"0.5317459\",\n \"0.5317356\",\n \"0.5310627\",\n \"0.53071195\",\n \"0.53051764\",\n \"0.53015363\",\n \"0.5285473\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.68904"},"document_rank":{"kind":"string","value":"5"}}},{"rowIdx":14366,"cells":{"query":{"kind":"string","value":"Initializes the Backtest. A Queue is used to hold the Events. The Signals, Orders, and Fills are counted."},"document":{"kind":"string","value":"def __init__(self, csv_dir, symbol_list, initial_capital, heartbeat, start_date,\n data_handler, execution_handler, portfolio, strategy, strat_params_list=None):\n self.csv_dir = csv_dir\n self.symbol_list = symbol_list\n self.initial_capital = initial_capital\n self.heartbeat = heartbeat\n self.start_date = start_date\n\n self.data_handler_class = data_handler\n self.execution_handler_class = execution_handler\n self.portfolio_class = portfolio\n self.strategy_class = strategy\n\n self.strat_params_list = strat_params_list\n\n self.events = queue.Queue()\n\n self.signals = 0\n self.orders = 0\n self.fills = 0\n self.num_strats = 1\n\n # Call this in the initialization to populate the other classes with our data\n # self._generate_trading_instances()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def __init__(self):\n self._events = collections.deque()\n self._alarms = 0\n self._alarm_active = True\n self._kq = select.kqueue()\n self._kq_events = {}\n self.traceback = ()\n self.event_count = 0\n self._no_tb = False","def on_init(self, queue=None, **kwargs):\n self.queue = queue if queue else Queue()","def __init__(self) -> None:\n self._queue = []","def __post_init__(self) -> None:\n self.gtex += [None]\n self.bm += [None]\n self._q: queue.Queue = queue.Queue(maxsize=self.maxsize)","def __init__(self, queue: Queue):\n super().__init__()\n self._cursor = 0\n self._queue = queue\n self._all_transferred = threading.Event()","def __init__(self):\n self.queue = Queue()","def __init__(self):\n\n super(VirtualTimeScheduler, self).__init__()\n self.event_queue = Queue.PriorityQueue()","def setUpClass(cls):\n cls.queue = RabbitQueue(QUEUE_CONN_PARAMS)","def __init__(self):\n self.queue = []\n self.queue.append(Queue())\n self.queue.append(Queue())\n self.tag = 0 # using to record which queue contain the data","def init_queues(self):\n for step in self.get_children():\n if step.kind == StepKinds.queue:\n step.init_object(self.context, None)","def __init__(self):\n self.queue = []","def __init__(self):\n self.queue = []","def __init__(self):\n self.queue = []","def __init__(self):\n self.queue = []","def __init__(self):\n self.queue = []","def __init__(self):\n self._queue_items = []","def __init__(self):\n self._event = multiprocessing.Event()\n self._queue = multiprocessing.JoinableQueue()\n self._results = multiprocessing.Queue()\n self._spawn_workers()\n self.population = self._seed_population()","def __init__(self):\r\n self.queue = []","def __init__(self):\r\n self.queue = []","def __init__(self):\n self.data = Queue()","def setup_queue(self):\n self.logger.info('declaring queue %s', self.queue)\n if self.otq:\n self._channel.queue_declare(self.on_queue_declareok, self.queue, auto_delete=True)\n else:\n self._channel.queue_declare(self.on_queue_declareok, self.queue)","def __init__(self):\n self.queues=[]","def __init__(self, numthreads):\n self.queue = Queue.Queue()\n for _ in range(numthreads):\n Worker(self.queue)\n logger.debug(\"Event worker pool started with %s threads.\" % numthreads)","def __init__(self):\n self.push_queue = []\n self.pop_queue = []","def __init__(self):\n self.queue = deque()","def __init__(self):\n self.queue = deque()","def __init__(self):\n self.queue = deque()","def __init__(self):\n self.queue = deque()","def __init__(self): \n self.queue = []","def init(self):\n self.t_f = 0\n self.h_b = [] # Heap of running tasks.\n self.h_c = [] # Heap of waiting tasks.\n self.h_d = [] # Heap of deadlines.\n self.pmin = min([task.period for task in self.task_list]) \\\n * self.sim.cycles_per_ms\n self.evt_bc = False\n self.activations = []\n self.waiting_schedule = False","def Initialize(self):\n self.queue_workers = [\n gevent.spawn(self._EventQueueWorker) for _ in xrange(self.num_workers)]\n self.gc_worker = gevent.spawn(self._GarbageCollectorWorker)","def __init__(self, user, queueTwoMin, queueTenMin, queueHour, queueAlerts, queueTermination, testDic = None):\n # Start Time, keep track of the elapsed time\n self.originalTime = time.time()\n # User that uses the monitoring app, must exist !\n self.user = user\n\n # Queue to transmit all data\n self.queueTwoMin = queueTwoMin\n self.queueTenMin = queueTenMin\n self.queueHour = queueHour\n self.queueAlerts = queueAlerts\n\n # Queue for termination\n self.queueTermination = queueTermination\n\n # Alert Storage, to check whether raised alert are to be sent\n self.alertsDic = {}\n if testDic:\n self.alertsDic = testDic\n self.mailer = mailSender.MailSender(mailSender.mailrecipient)\n\n # Start monitoring\n self.monitorAll()","def __init__(self):\r\n self.queue = []\r\n self.current = False","def __init__(self):\n self.queue = deque()\n self.aux_queue = deque()","def __init__(self):\n Queue.__init__(self)","def __init__(self):\n # stack is first in last out so in python we can use append to add and\n # pop front\n\n # we want to implement a queue which is first in first out\n self.stacks = [[], []]\n self.activeStackIndex = 0","def __init__(self):\r\n\r\n self.__is_healthy = True\r\n self.__log = PriorityQueue(maxsize=100)","def __init__(self):\n # NOTE: DO not modify this method\n self.items = SimpleQueue()","def __init__(self, queue: Queue):\n logging.Handler.__init__(self)\n self.queue = queue","def __init__(self):\n self.dequeue = DeQueue()","def __init__(self, queue, usercallback):\n self.queue = queue\n self.usercallback = usercallback","def test_init():\n from parenthetics import Queue\n my_queue = Queue()\n assert isinstance(my_queue, Queue)","def __init__(self):\n\t\tself.interrupt = False\n\t\tself.direction = OUTPUT.MOTOR_DOWN\n\t\tself.moving = False\n\t\tself.currentFloor = -1\n\n\t\tself.orderQueue = OrderQueue.load_from_file()\n\t\tself.callbackQueue = Queue()\n\t\tself.newOrderQueue = Queue()\n\t\tself.startedOrderQueue = Queue()\n\t\tself.signalPoller = SignalPoller(self.callbackQueue)\n\t\tself.doorTimer = DoorTimer(self.close_door, self.callbackQueue)\n\t\tself.initialize_lights()\n\t\tself.initialize_networkHandler()\n\t\tself.update_and_send_elevator_info()\n\t\tself.set_callbacks()\n\t\tself.networkHandler.start()\n\t\tself.signalPoller.start()\n\t\tself.drive()\n\t\tself.run()","def __init__(self, queue):\n logging.Handler.__init__(self)\n self.queue = queue","def __init__(self):\n self.queue = deque()\n self.val = 0","def __init__(self):\n self.l_queue, self.r_queue = [], []","def __init__(self):\n self._data = [None]*ArrayQueue.DEFAULT_CAPACITY\n self._size = 0\n self._front = 0","def __init__(self):\n self._queue = defaultdict(\n OrderedDict\n ) # type: Dict[OrderedDict[TelnetCommand, ExpectedResponse]]","def init_email_queue():\n g.setdefault('email_queue', [])","def __init__(self):\n self.event_list = []\n self._subscribers = set()","def __init__(self):\n self._head = None\n self._tail = None\n self._size = 0 # number of queue elements","def __init__(self):\n self.frontier_index = 0 # the number of items ever added to the frontier\n self.frontierpq = [] # the frontier priority queue","def __init__(self, queue):\r\n logging.Handler.__init__(self)\r\n self.queue = queue","def __init__(self):\r\n self.queue = []\r\n self.num = 0\r\n self.topele =-1","def __init__(self, queue):\n super(Worker, self).__init__()\n self.queue = queue\n logger.debug(\"Worker thread started.\")\n self.start()","def __init__(self):\n self._data_queue = []\n self._access_queue_lock = Lock()","def __init__(self):\n self.shared_counter = 0\n self.queue = list()\n self.queue_size = 0","def __init__(self, probes=None, queue=None, cfg=None, run_till_idle=True):\n self.probes = probes if probes is not None else []\n self.queue = queue\n self.run_till_idle = run_till_idle\n self.loop = asyncio.get_event_loop()\n self.cfg = cfg or get_config()","def testEventHandlerInit(self):\n stub = NetworkObjectStub()\n\n e1 = Event(5, stub, 'message')\n e2 = Event(0, stub, 'message')\n e3 = Event(7, stub, 'message')\n e4 = PacketEvent(1, 'sender2', stub, 4, 'message5')\n eventList = [e1, e2, e3, e4]\n\n eventHandler = EventHandler('network', eventList)\n self.assertEqual('network', eventHandler._network)\n self.assertEqual(4, eventHandler._queue.qsize())","def __init__(self) -> None:\n self._queue: Queue[Dict[str, int]] = Queue() # pylint:disable=E1136\n self._pilothouse = Pilothouse(self._queue)","def __init__(self, context):\n self.context = context\n \n self._m_ticket = self._m_ticket_initial\n\n self._d_orders = {}\n self._d_orders['trades'] = OrderedDict()\n self._d_orders['history'] = OrderedDict()\n self._d_orders['cancelled'] = OrderedDict()","def __init__(self):\n queue = collections.deque()","def __init__(self, timer: Timer):\n self.all_event_notifier = AllEventNotifier()\n self.empty_queue_event_notifier = EmptyQueueEventNotifier(self.all_event_notifier)\n self.end_trading_event_notifier = EndTradingEventNotifier(self.all_event_notifier)\n self.scheduler = Scheduler(timer)","def __init__(self, queue, level, duration, module=None, research=None):\n if len(queue.queue_entries) == 0:\n self.started_at = datetime.now()\n self.queue = queue\n self.level = level\n self.duration = duration\n self.module = module\n self.research = research\n self.type = module.type if module else research.type","def __init__(self, battle_queue: 'BattleQueue') -> None:\n self.battle_queue = battle_queue\n self.is_manual = True","def setUp(self):\n self.pcp = ControllerQueue(None, None)\n DummyWorkItem.results = {}","def __init__ (self, size: int):\n self.size = size\n self.queue = []\n self.sum = 0","def __init__(self):\n self.q = deque()","def init(self):\n\t\t# Event list: time\n\t\tself.event_lst = [0, float('inf')]\n\t\t# Rate list of each edge edge->(time->rate)\n\t\t# \tRate here is the residual capacity\n\t\t#\tEnsure that there is one initial timestamp with full capacity and one infinite timestamp with no capacity\n\t\tself.rate_lst = {e:{0:self.topo.topo[e[0]][e[1]]['Capacity'], float('inf'):0} for e in self.topo.edges}","def __init__(self, number):\n self.number = number\n self.totalCustomerWaitTime = 0\n self.customersServed = 0\n self.currentCustomer = None\n self.queue = LinkedQueue()","def __init__(self):\n self.messageSet = set()\n self.messageQueue = deque()","def setUp(self):\n # This Queue and Pipe let heartbeat send and receive messages to the\n # fake child client processes, and have those messages processed here.\n self.queue = Queue()\n self.consumer_master, self.consumer_slave = multiprocessing.Pipe()\n self.monitor_master, self.monitor_slave = multiprocessing.Pipe()\n\n # Only one consumer\n self.consumer = ProcessData(process=self.MockProcess(id=0,\n name='TestConsumer'),\n pipe=self.consumer_slave)\n\n self.monitor = ProcessData(process=self.MockProcess(id=0,\n name='TestMonitor'),\n pipe=self.monitor_slave)\n\n # Messages that StorageHeartbeat puts on the socket for the server\n # Are quickly decoded again and put on this queue for verification\n self.socket_queue = Queue()\n\n # We use MockSocket to impersonate a real socket\n self.socket = self.MockSocket(self.socket_queue, self)\n\n self.dut = StorageHeartbeat(consumers=[self.consumer],\n monitor=self.monitor,\n report_in=self.queue,\n runtime=10,\n poll_period=5,\n client_socket=self.socket)","def __init__(self, queue: CityOverheadTimeQueue, name: int):\n super().__init__()\n self._name = name\n self._queue = queue\n self.data_incoming = True","def __init__(self, queue_id=None):\n super().__init__()\n self.queue_id = queue_id","def __init__(self, size):\n self.size = size\n self.queue = []","def __init__(self):\n self.started = Event()","def __init__(\n self,\n event_dispatcher: Optional[type[EventDispatcher] | CustomEventDispatcher] = None,\n logger: Optional[_logging.Logger] = None,\n start_on_init: bool = False,\n event_queue: Optional[queue.Queue[UserEvent | Signal]] = None,\n batch_size: Optional[int] = None,\n flush_interval: Optional[float] = None,\n timeout_interval: Optional[float] = None,\n notification_center: Optional[_notification_center.NotificationCenter] = None,\n ):\n self.event_dispatcher = event_dispatcher or EventDispatcher\n self.logger = _logging.adapt_logger(logger or _logging.NoOpLogger())\n self.event_queue = event_queue or queue.Queue(maxsize=self._DEFAULT_QUEUE_CAPACITY)\n self.batch_size: int = (\n batch_size # type: ignore[assignment]\n if self._validate_instantiation_props(batch_size, 'batch_size', self._DEFAULT_BATCH_SIZE)\n else self._DEFAULT_BATCH_SIZE\n )\n self.flush_interval: timedelta = (\n timedelta(seconds=flush_interval) # type: ignore[arg-type]\n if self._validate_instantiation_props(flush_interval, 'flush_interval', self._DEFAULT_FLUSH_INTERVAL)\n else timedelta(seconds=self._DEFAULT_FLUSH_INTERVAL)\n )\n self.timeout_interval: timedelta = (\n timedelta(seconds=timeout_interval) # type: ignore[arg-type]\n if self._validate_instantiation_props(timeout_interval, 'timeout_interval', self._DEFAULT_TIMEOUT_INTERVAL)\n else timedelta(seconds=self._DEFAULT_TIMEOUT_INTERVAL)\n )\n\n self.notification_center = notification_center or _notification_center.NotificationCenter(self.logger)\n self._current_batch: list[UserEvent] = []\n\n if not validator.is_notification_center_valid(self.notification_center):\n self.logger.error(enums.Errors.INVALID_INPUT.format('notification_center'))\n self.logger.debug('Creating notification center for use.')\n self.notification_center = _notification_center.NotificationCenter(self.logger)\n\n self.executor: Optional[threading.Thread] = None\n if start_on_init is True:\n self.start()","def __init__(self):\n\n\t\tself.count = 0\n\t\tself.messages = []","def __init__(self, event_queue, connection_options):\n super(GerritEventsStream, self).__init__()\n self._event_queue = event_queue\n self._connection_options = connection_options\n self._running = True","def __init__(self, threads_count):\n\n self.queue = Queue(threads_count)\n\n self.threads = []\n self.device = None\n\n self.create_workers(threads_count)\n self.start_workers()","def __init__(self, queueLength):\r\n self.queueLength = queueLength\r\n self.data = []\r\n return","def __init__(self):\n self.q1 = queue()\n self.q2 = queue()","def __init__(self, size):\n self.size = size\n self.queue = deque([])\n self.cur_sum = 0","def __init__(self, thoonk, feed):\n Queue.__init__(self, thoonk, feed)\n\n self.feed_publishes = 'feed.publishes:%s' % feed\n self.feed_published = 'feed.published:%s' % feed\n self.feed_cancelled = 'feed.cancelled:%s' % feed\n self.feed_retried = 'feed.retried:%s' % feed\n self.feed_finishes = 'feed.finishes:%s' % feed\n self.feed_claimed = 'feed.claimed:%s' % feed\n self.feed_stalled = 'feed.stalled:%s' % feed\n self.feed_running = 'feed.running:%s' % feed\n \n self.job_finish = 'job.finish:%s' % feed","def __init__(self, act, sig):\n self.__active = act\n self.__counter = 0\n self.__interval = 0\n \n self.__qf = framework.QF.getInstance()\n\n # Add check for unique signal here.\n event.Event.__init__(self,sig)","def __init__(self):\n # MessageHandlers - index is MessageType and data is a list of\n # tuples (priority, MessageHandler object) that is sorted by\n # priorities.\n #XXX: rockstar - WTF?! Why is there a list comprehension being used\n # and still only returning an empty list?\n # pylint: disable-msg=W0612\n self.message_handlers = [\n [] for i in range(self.NUMBER_OF_MESSAGE_TYPES)\n ]\n self.lock = threading.Lock()\n self.logger = Logger().getLogger('backend.core.MessageBus')","def __init__(self):\n self.events = {}\n self.eventmatchers = {}\n self.alive = True","def __init__(self, worker):\n self._worker = worker\n self._jobs = Queue()\n self._results, self._errors = [], []\n self._jobfinished = Condition()","def __init__(self):\n print(\"\\nStarting a new order.\\nThank you for visiting our on-line store!\")\n self.__items_list = []\n self.__purchase_date = datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n self.__order_number = Order.__last_order_number_used + 1\n Order.__last_order_number_used = self.__order_number","def __init__(self, size):\n self.size = size\n self.queue = []\n self.sum = 0","def __init__(self):\n self._order_handlers = []\n self._target_handlers = {}\n\n self._robot = None\n self._lock = threading.Lock()","def test_initialize(self):\n SignalHook(self.test_extension, self.signal, self._on_signal_fired)\n\n self.assertEqual(len(self._on_signal_fired.calls), 0)\n self.signal.send(self)\n self.assertEqual(len(self._on_signal_fired.calls), 1)","def __init__(self):\n self.stack1 = []\n self.stack2 = []\n\n # stack1[0] is the queue front\n # stack2[0] is the queue back","def __init__(self, policy , nstates , initial_stock , incomingOrdersQueue, outgoingOrdersQueue, incomingDeliveriesQueue, outgoingDeliveriesQueue):\r\n self.currentStock = initial_stock\r\n self.currentOrders = 0\r\n self.costsIncurred = 0\r\n \r\n self.incomingOrdersQueue = incomingOrdersQueue\r\n self.outgoingOrdersQueue = outgoingOrdersQueue\r\n self.incomingDeliveriesQueue = incomingDeliveriesQueue\r\n self.outgoingDeliveriesQueue = outgoingDeliveriesQueue\r\n \r\n self.lastOrderQuantity = 0\r\n\r\n\r\n self.policy = policy\r\n self.nstates = nstates\r\n self.states = []\r\n\r\n\r\n return","def setup_queue(self, method_frame):\n logger.info('Declaring queue %s', self.queue_name)\n # self._channel.queue_declare(self.on_queue_declareok, queue_name)\n\n self._channel.queue_declare(self.on_queue_declareok, exclusive=False, durable=True, queue=self.queue_name)","def __init__(self, *args, **kwargs):\n self.events = {}","def __init__(self):\n self._shutdown_lock = threading.Lock()\n self._work_queue = queue.Queue()\n self.ftp_server = ftpclient.setup()","def __init__(self, size):\n self.queue = collections.deque(maxlen=size)","def __init__(self):\n self._events = {}\n self.modules = {}","def __init__(self, *args):\n\n super(Recorder, self).__init__('RECORDER', *args)\n\n self.client = InfluxDBClient(\n host=self.configprops.influx_host,\n port=self.configprops.influx_port\n )\n\n databases_raw = self.client.get_list_database()\n\n databases = list((i['name'] for i in databases_raw))\n\n if self.configprops.influx_database not in databases:\n self.client.create_database(self.configprops.influx_database)\n\n self.client.switch_database(self.configprops.influx_database)\n\n self.current_position = {'lat': 0, 'lon': 0}","def start(self):\n self._connect()\n self._init_exchange()\n self._init_queue()\n self._bind_queue()"],"string":"[\n \"def __init__(self):\\n self._events = collections.deque()\\n self._alarms = 0\\n self._alarm_active = True\\n self._kq = select.kqueue()\\n self._kq_events = {}\\n self.traceback = ()\\n self.event_count = 0\\n self._no_tb = False\",\n \"def on_init(self, queue=None, **kwargs):\\n self.queue = queue if queue else Queue()\",\n \"def __init__(self) -> None:\\n self._queue = []\",\n \"def __post_init__(self) -> None:\\n self.gtex += [None]\\n self.bm += [None]\\n self._q: queue.Queue = queue.Queue(maxsize=self.maxsize)\",\n \"def __init__(self, queue: Queue):\\n super().__init__()\\n self._cursor = 0\\n self._queue = queue\\n self._all_transferred = threading.Event()\",\n \"def __init__(self):\\n self.queue = Queue()\",\n \"def __init__(self):\\n\\n super(VirtualTimeScheduler, self).__init__()\\n self.event_queue = Queue.PriorityQueue()\",\n \"def setUpClass(cls):\\n cls.queue = RabbitQueue(QUEUE_CONN_PARAMS)\",\n \"def __init__(self):\\n self.queue = []\\n self.queue.append(Queue())\\n self.queue.append(Queue())\\n self.tag = 0 # using to record which queue contain the data\",\n \"def init_queues(self):\\n for step in self.get_children():\\n if step.kind == StepKinds.queue:\\n step.init_object(self.context, None)\",\n \"def __init__(self):\\n self.queue = []\",\n \"def __init__(self):\\n self.queue = []\",\n \"def __init__(self):\\n self.queue = []\",\n \"def __init__(self):\\n self.queue = []\",\n \"def __init__(self):\\n self.queue = []\",\n \"def __init__(self):\\n self._queue_items = []\",\n \"def __init__(self):\\n self._event = multiprocessing.Event()\\n self._queue = multiprocessing.JoinableQueue()\\n self._results = multiprocessing.Queue()\\n self._spawn_workers()\\n self.population = self._seed_population()\",\n \"def __init__(self):\\r\\n self.queue = []\",\n \"def __init__(self):\\r\\n self.queue = []\",\n \"def __init__(self):\\n self.data = Queue()\",\n \"def setup_queue(self):\\n self.logger.info('declaring queue %s', self.queue)\\n if self.otq:\\n self._channel.queue_declare(self.on_queue_declareok, self.queue, auto_delete=True)\\n else:\\n self._channel.queue_declare(self.on_queue_declareok, self.queue)\",\n \"def __init__(self):\\n self.queues=[]\",\n \"def __init__(self, numthreads):\\n self.queue = Queue.Queue()\\n for _ in range(numthreads):\\n Worker(self.queue)\\n logger.debug(\\\"Event worker pool started with %s threads.\\\" % numthreads)\",\n \"def __init__(self):\\n self.push_queue = []\\n self.pop_queue = []\",\n \"def __init__(self):\\n self.queue = deque()\",\n \"def __init__(self):\\n self.queue = deque()\",\n \"def __init__(self):\\n self.queue = deque()\",\n \"def __init__(self):\\n self.queue = deque()\",\n \"def __init__(self): \\n self.queue = []\",\n \"def init(self):\\n self.t_f = 0\\n self.h_b = [] # Heap of running tasks.\\n self.h_c = [] # Heap of waiting tasks.\\n self.h_d = [] # Heap of deadlines.\\n self.pmin = min([task.period for task in self.task_list]) \\\\\\n * self.sim.cycles_per_ms\\n self.evt_bc = False\\n self.activations = []\\n self.waiting_schedule = False\",\n \"def Initialize(self):\\n self.queue_workers = [\\n gevent.spawn(self._EventQueueWorker) for _ in xrange(self.num_workers)]\\n self.gc_worker = gevent.spawn(self._GarbageCollectorWorker)\",\n \"def __init__(self, user, queueTwoMin, queueTenMin, queueHour, queueAlerts, queueTermination, testDic = None):\\n # Start Time, keep track of the elapsed time\\n self.originalTime = time.time()\\n # User that uses the monitoring app, must exist !\\n self.user = user\\n\\n # Queue to transmit all data\\n self.queueTwoMin = queueTwoMin\\n self.queueTenMin = queueTenMin\\n self.queueHour = queueHour\\n self.queueAlerts = queueAlerts\\n\\n # Queue for termination\\n self.queueTermination = queueTermination\\n\\n # Alert Storage, to check whether raised alert are to be sent\\n self.alertsDic = {}\\n if testDic:\\n self.alertsDic = testDic\\n self.mailer = mailSender.MailSender(mailSender.mailrecipient)\\n\\n # Start monitoring\\n self.monitorAll()\",\n \"def __init__(self):\\r\\n self.queue = []\\r\\n self.current = False\",\n \"def __init__(self):\\n self.queue = deque()\\n self.aux_queue = deque()\",\n \"def __init__(self):\\n Queue.__init__(self)\",\n \"def __init__(self):\\n # stack is first in last out so in python we can use append to add and\\n # pop front\\n\\n # we want to implement a queue which is first in first out\\n self.stacks = [[], []]\\n self.activeStackIndex = 0\",\n \"def __init__(self):\\r\\n\\r\\n self.__is_healthy = True\\r\\n self.__log = PriorityQueue(maxsize=100)\",\n \"def __init__(self):\\n # NOTE: DO not modify this method\\n self.items = SimpleQueue()\",\n \"def __init__(self, queue: Queue):\\n logging.Handler.__init__(self)\\n self.queue = queue\",\n \"def __init__(self):\\n self.dequeue = DeQueue()\",\n \"def __init__(self, queue, usercallback):\\n self.queue = queue\\n self.usercallback = usercallback\",\n \"def test_init():\\n from parenthetics import Queue\\n my_queue = Queue()\\n assert isinstance(my_queue, Queue)\",\n \"def __init__(self):\\n\\t\\tself.interrupt = False\\n\\t\\tself.direction = OUTPUT.MOTOR_DOWN\\n\\t\\tself.moving = False\\n\\t\\tself.currentFloor = -1\\n\\n\\t\\tself.orderQueue = OrderQueue.load_from_file()\\n\\t\\tself.callbackQueue = Queue()\\n\\t\\tself.newOrderQueue = Queue()\\n\\t\\tself.startedOrderQueue = Queue()\\n\\t\\tself.signalPoller = SignalPoller(self.callbackQueue)\\n\\t\\tself.doorTimer = DoorTimer(self.close_door, self.callbackQueue)\\n\\t\\tself.initialize_lights()\\n\\t\\tself.initialize_networkHandler()\\n\\t\\tself.update_and_send_elevator_info()\\n\\t\\tself.set_callbacks()\\n\\t\\tself.networkHandler.start()\\n\\t\\tself.signalPoller.start()\\n\\t\\tself.drive()\\n\\t\\tself.run()\",\n \"def __init__(self, queue):\\n logging.Handler.__init__(self)\\n self.queue = queue\",\n \"def __init__(self):\\n self.queue = deque()\\n self.val = 0\",\n \"def __init__(self):\\n self.l_queue, self.r_queue = [], []\",\n \"def __init__(self):\\n self._data = [None]*ArrayQueue.DEFAULT_CAPACITY\\n self._size = 0\\n self._front = 0\",\n \"def __init__(self):\\n self._queue = defaultdict(\\n OrderedDict\\n ) # type: Dict[OrderedDict[TelnetCommand, ExpectedResponse]]\",\n \"def init_email_queue():\\n g.setdefault('email_queue', [])\",\n \"def __init__(self):\\n self.event_list = []\\n self._subscribers = set()\",\n \"def __init__(self):\\n self._head = None\\n self._tail = None\\n self._size = 0 # number of queue elements\",\n \"def __init__(self):\\n self.frontier_index = 0 # the number of items ever added to the frontier\\n self.frontierpq = [] # the frontier priority queue\",\n \"def __init__(self, queue):\\r\\n logging.Handler.__init__(self)\\r\\n self.queue = queue\",\n \"def __init__(self):\\r\\n self.queue = []\\r\\n self.num = 0\\r\\n self.topele =-1\",\n \"def __init__(self, queue):\\n super(Worker, self).__init__()\\n self.queue = queue\\n logger.debug(\\\"Worker thread started.\\\")\\n self.start()\",\n \"def __init__(self):\\n self._data_queue = []\\n self._access_queue_lock = Lock()\",\n \"def __init__(self):\\n self.shared_counter = 0\\n self.queue = list()\\n self.queue_size = 0\",\n \"def __init__(self, probes=None, queue=None, cfg=None, run_till_idle=True):\\n self.probes = probes if probes is not None else []\\n self.queue = queue\\n self.run_till_idle = run_till_idle\\n self.loop = asyncio.get_event_loop()\\n self.cfg = cfg or get_config()\",\n \"def testEventHandlerInit(self):\\n stub = NetworkObjectStub()\\n\\n e1 = Event(5, stub, 'message')\\n e2 = Event(0, stub, 'message')\\n e3 = Event(7, stub, 'message')\\n e4 = PacketEvent(1, 'sender2', stub, 4, 'message5')\\n eventList = [e1, e2, e3, e4]\\n\\n eventHandler = EventHandler('network', eventList)\\n self.assertEqual('network', eventHandler._network)\\n self.assertEqual(4, eventHandler._queue.qsize())\",\n \"def __init__(self) -> None:\\n self._queue: Queue[Dict[str, int]] = Queue() # pylint:disable=E1136\\n self._pilothouse = Pilothouse(self._queue)\",\n \"def __init__(self, context):\\n self.context = context\\n \\n self._m_ticket = self._m_ticket_initial\\n\\n self._d_orders = {}\\n self._d_orders['trades'] = OrderedDict()\\n self._d_orders['history'] = OrderedDict()\\n self._d_orders['cancelled'] = OrderedDict()\",\n \"def __init__(self):\\n queue = collections.deque()\",\n \"def __init__(self, timer: Timer):\\n self.all_event_notifier = AllEventNotifier()\\n self.empty_queue_event_notifier = EmptyQueueEventNotifier(self.all_event_notifier)\\n self.end_trading_event_notifier = EndTradingEventNotifier(self.all_event_notifier)\\n self.scheduler = Scheduler(timer)\",\n \"def __init__(self, queue, level, duration, module=None, research=None):\\n if len(queue.queue_entries) == 0:\\n self.started_at = datetime.now()\\n self.queue = queue\\n self.level = level\\n self.duration = duration\\n self.module = module\\n self.research = research\\n self.type = module.type if module else research.type\",\n \"def __init__(self, battle_queue: 'BattleQueue') -> None:\\n self.battle_queue = battle_queue\\n self.is_manual = True\",\n \"def setUp(self):\\n self.pcp = ControllerQueue(None, None)\\n DummyWorkItem.results = {}\",\n \"def __init__ (self, size: int):\\n self.size = size\\n self.queue = []\\n self.sum = 0\",\n \"def __init__(self):\\n self.q = deque()\",\n \"def init(self):\\n\\t\\t# Event list: time\\n\\t\\tself.event_lst = [0, float('inf')]\\n\\t\\t# Rate list of each edge edge->(time->rate)\\n\\t\\t# \\tRate here is the residual capacity\\n\\t\\t#\\tEnsure that there is one initial timestamp with full capacity and one infinite timestamp with no capacity\\n\\t\\tself.rate_lst = {e:{0:self.topo.topo[e[0]][e[1]]['Capacity'], float('inf'):0} for e in self.topo.edges}\",\n \"def __init__(self, number):\\n self.number = number\\n self.totalCustomerWaitTime = 0\\n self.customersServed = 0\\n self.currentCustomer = None\\n self.queue = LinkedQueue()\",\n \"def __init__(self):\\n self.messageSet = set()\\n self.messageQueue = deque()\",\n \"def setUp(self):\\n # This Queue and Pipe let heartbeat send and receive messages to the\\n # fake child client processes, and have those messages processed here.\\n self.queue = Queue()\\n self.consumer_master, self.consumer_slave = multiprocessing.Pipe()\\n self.monitor_master, self.monitor_slave = multiprocessing.Pipe()\\n\\n # Only one consumer\\n self.consumer = ProcessData(process=self.MockProcess(id=0,\\n name='TestConsumer'),\\n pipe=self.consumer_slave)\\n\\n self.monitor = ProcessData(process=self.MockProcess(id=0,\\n name='TestMonitor'),\\n pipe=self.monitor_slave)\\n\\n # Messages that StorageHeartbeat puts on the socket for the server\\n # Are quickly decoded again and put on this queue for verification\\n self.socket_queue = Queue()\\n\\n # We use MockSocket to impersonate a real socket\\n self.socket = self.MockSocket(self.socket_queue, self)\\n\\n self.dut = StorageHeartbeat(consumers=[self.consumer],\\n monitor=self.monitor,\\n report_in=self.queue,\\n runtime=10,\\n poll_period=5,\\n client_socket=self.socket)\",\n \"def __init__(self, queue: CityOverheadTimeQueue, name: int):\\n super().__init__()\\n self._name = name\\n self._queue = queue\\n self.data_incoming = True\",\n \"def __init__(self, queue_id=None):\\n super().__init__()\\n self.queue_id = queue_id\",\n \"def __init__(self, size):\\n self.size = size\\n self.queue = []\",\n \"def __init__(self):\\n self.started = Event()\",\n \"def __init__(\\n self,\\n event_dispatcher: Optional[type[EventDispatcher] | CustomEventDispatcher] = None,\\n logger: Optional[_logging.Logger] = None,\\n start_on_init: bool = False,\\n event_queue: Optional[queue.Queue[UserEvent | Signal]] = None,\\n batch_size: Optional[int] = None,\\n flush_interval: Optional[float] = None,\\n timeout_interval: Optional[float] = None,\\n notification_center: Optional[_notification_center.NotificationCenter] = None,\\n ):\\n self.event_dispatcher = event_dispatcher or EventDispatcher\\n self.logger = _logging.adapt_logger(logger or _logging.NoOpLogger())\\n self.event_queue = event_queue or queue.Queue(maxsize=self._DEFAULT_QUEUE_CAPACITY)\\n self.batch_size: int = (\\n batch_size # type: ignore[assignment]\\n if self._validate_instantiation_props(batch_size, 'batch_size', self._DEFAULT_BATCH_SIZE)\\n else self._DEFAULT_BATCH_SIZE\\n )\\n self.flush_interval: timedelta = (\\n timedelta(seconds=flush_interval) # type: ignore[arg-type]\\n if self._validate_instantiation_props(flush_interval, 'flush_interval', self._DEFAULT_FLUSH_INTERVAL)\\n else timedelta(seconds=self._DEFAULT_FLUSH_INTERVAL)\\n )\\n self.timeout_interval: timedelta = (\\n timedelta(seconds=timeout_interval) # type: ignore[arg-type]\\n if self._validate_instantiation_props(timeout_interval, 'timeout_interval', self._DEFAULT_TIMEOUT_INTERVAL)\\n else timedelta(seconds=self._DEFAULT_TIMEOUT_INTERVAL)\\n )\\n\\n self.notification_center = notification_center or _notification_center.NotificationCenter(self.logger)\\n self._current_batch: list[UserEvent] = []\\n\\n if not validator.is_notification_center_valid(self.notification_center):\\n self.logger.error(enums.Errors.INVALID_INPUT.format('notification_center'))\\n self.logger.debug('Creating notification center for use.')\\n self.notification_center = _notification_center.NotificationCenter(self.logger)\\n\\n self.executor: Optional[threading.Thread] = None\\n if start_on_init is True:\\n self.start()\",\n \"def __init__(self):\\n\\n\\t\\tself.count = 0\\n\\t\\tself.messages = []\",\n \"def __init__(self, event_queue, connection_options):\\n super(GerritEventsStream, self).__init__()\\n self._event_queue = event_queue\\n self._connection_options = connection_options\\n self._running = True\",\n \"def __init__(self, threads_count):\\n\\n self.queue = Queue(threads_count)\\n\\n self.threads = []\\n self.device = None\\n\\n self.create_workers(threads_count)\\n self.start_workers()\",\n \"def __init__(self, queueLength):\\r\\n self.queueLength = queueLength\\r\\n self.data = []\\r\\n return\",\n \"def __init__(self):\\n self.q1 = queue()\\n self.q2 = queue()\",\n \"def __init__(self, size):\\n self.size = size\\n self.queue = deque([])\\n self.cur_sum = 0\",\n \"def __init__(self, thoonk, feed):\\n Queue.__init__(self, thoonk, feed)\\n\\n self.feed_publishes = 'feed.publishes:%s' % feed\\n self.feed_published = 'feed.published:%s' % feed\\n self.feed_cancelled = 'feed.cancelled:%s' % feed\\n self.feed_retried = 'feed.retried:%s' % feed\\n self.feed_finishes = 'feed.finishes:%s' % feed\\n self.feed_claimed = 'feed.claimed:%s' % feed\\n self.feed_stalled = 'feed.stalled:%s' % feed\\n self.feed_running = 'feed.running:%s' % feed\\n \\n self.job_finish = 'job.finish:%s' % feed\",\n \"def __init__(self, act, sig):\\n self.__active = act\\n self.__counter = 0\\n self.__interval = 0\\n \\n self.__qf = framework.QF.getInstance()\\n\\n # Add check for unique signal here.\\n event.Event.__init__(self,sig)\",\n \"def __init__(self):\\n # MessageHandlers - index is MessageType and data is a list of\\n # tuples (priority, MessageHandler object) that is sorted by\\n # priorities.\\n #XXX: rockstar - WTF?! Why is there a list comprehension being used\\n # and still only returning an empty list?\\n # pylint: disable-msg=W0612\\n self.message_handlers = [\\n [] for i in range(self.NUMBER_OF_MESSAGE_TYPES)\\n ]\\n self.lock = threading.Lock()\\n self.logger = Logger().getLogger('backend.core.MessageBus')\",\n \"def __init__(self):\\n self.events = {}\\n self.eventmatchers = {}\\n self.alive = True\",\n \"def __init__(self, worker):\\n self._worker = worker\\n self._jobs = Queue()\\n self._results, self._errors = [], []\\n self._jobfinished = Condition()\",\n \"def __init__(self):\\n print(\\\"\\\\nStarting a new order.\\\\nThank you for visiting our on-line store!\\\")\\n self.__items_list = []\\n self.__purchase_date = datetime.datetime.now().strftime(\\\"%Y-%m-%d %H:%M:%S\\\")\\n self.__order_number = Order.__last_order_number_used + 1\\n Order.__last_order_number_used = self.__order_number\",\n \"def __init__(self, size):\\n self.size = size\\n self.queue = []\\n self.sum = 0\",\n \"def __init__(self):\\n self._order_handlers = []\\n self._target_handlers = {}\\n\\n self._robot = None\\n self._lock = threading.Lock()\",\n \"def test_initialize(self):\\n SignalHook(self.test_extension, self.signal, self._on_signal_fired)\\n\\n self.assertEqual(len(self._on_signal_fired.calls), 0)\\n self.signal.send(self)\\n self.assertEqual(len(self._on_signal_fired.calls), 1)\",\n \"def __init__(self):\\n self.stack1 = []\\n self.stack2 = []\\n\\n # stack1[0] is the queue front\\n # stack2[0] is the queue back\",\n \"def __init__(self, policy , nstates , initial_stock , incomingOrdersQueue, outgoingOrdersQueue, incomingDeliveriesQueue, outgoingDeliveriesQueue):\\r\\n self.currentStock = initial_stock\\r\\n self.currentOrders = 0\\r\\n self.costsIncurred = 0\\r\\n \\r\\n self.incomingOrdersQueue = incomingOrdersQueue\\r\\n self.outgoingOrdersQueue = outgoingOrdersQueue\\r\\n self.incomingDeliveriesQueue = incomingDeliveriesQueue\\r\\n self.outgoingDeliveriesQueue = outgoingDeliveriesQueue\\r\\n \\r\\n self.lastOrderQuantity = 0\\r\\n\\r\\n\\r\\n self.policy = policy\\r\\n self.nstates = nstates\\r\\n self.states = []\\r\\n\\r\\n\\r\\n return\",\n \"def setup_queue(self, method_frame):\\n logger.info('Declaring queue %s', self.queue_name)\\n # self._channel.queue_declare(self.on_queue_declareok, queue_name)\\n\\n self._channel.queue_declare(self.on_queue_declareok, exclusive=False, durable=True, queue=self.queue_name)\",\n \"def __init__(self, *args, **kwargs):\\n self.events = {}\",\n \"def __init__(self):\\n self._shutdown_lock = threading.Lock()\\n self._work_queue = queue.Queue()\\n self.ftp_server = ftpclient.setup()\",\n \"def __init__(self, size):\\n self.queue = collections.deque(maxlen=size)\",\n \"def __init__(self):\\n self._events = {}\\n self.modules = {}\",\n \"def __init__(self, *args):\\n\\n super(Recorder, self).__init__('RECORDER', *args)\\n\\n self.client = InfluxDBClient(\\n host=self.configprops.influx_host,\\n port=self.configprops.influx_port\\n )\\n\\n databases_raw = self.client.get_list_database()\\n\\n databases = list((i['name'] for i in databases_raw))\\n\\n if self.configprops.influx_database not in databases:\\n self.client.create_database(self.configprops.influx_database)\\n\\n self.client.switch_database(self.configprops.influx_database)\\n\\n self.current_position = {'lat': 0, 'lon': 0}\",\n \"def start(self):\\n self._connect()\\n self._init_exchange()\\n self._init_queue()\\n self._bind_queue()\"\n]","isConversational":false},"negative_scores":{"kind":"list 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\"0.5962927\",\n \"0.5950622\",\n \"0.5947035\",\n \"0.59462637\",\n \"0.5940163\",\n \"0.5925378\",\n \"0.5911286\",\n \"0.5877753\",\n \"0.58582985\",\n \"0.58581513\",\n \"0.5851362\",\n \"0.5843048\",\n \"0.5836419\",\n \"0.5835441\",\n \"0.5820532\",\n \"0.58130723\",\n \"0.581276\",\n \"0.58116996\",\n \"0.5799714\",\n \"0.5796245\",\n \"0.5761747\",\n \"0.5752927\",\n \"0.5743419\",\n \"0.5741704\",\n \"0.5734497\",\n \"0.57333827\",\n \"0.570881\",\n \"0.57035094\",\n \"0.57009095\",\n \"0.56891733\",\n \"0.5681483\",\n \"0.5675453\",\n \"0.567426\",\n \"0.5671851\",\n \"0.56638384\",\n \"0.56586593\",\n \"0.5655799\",\n \"0.5652284\",\n \"0.5647706\",\n \"0.56461513\",\n \"0.56366235\",\n \"0.56345636\",\n \"0.563348\",\n \"0.5630453\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":14367,"cells":{"query":{"kind":"string","value":"Generates the trading instance objects from their class types. This method attaches all of the trading objects (DataHandler, Strategy, Portfolio, and ExecutionHandler) to various internal members. This ties together all the other classes to the Backtester object."},"document":{"kind":"string","value":"def _generate_trading_instances(self, strategy_params_dict):\n print(\"Creating DataHandler, Strategy, Portfolio, and ExecutionHandler for\")\n print(\"strategy parameter list: %s...\" % strategy_params_dict)\n\n # Set internal data members equal to the classes we passed in earlier, along with necessary parameters.\n # https://softwareengineering.stackexchange.com/questions/131403/what-is-the-name-of-in-python/131415\n self.data_handler = self.data_handler_class(self.events, self.csv_dir, self.symbol_list)\n self.strategy = self.strategy_class(self.data_handler, self.events, **strategy_params_dict)\n self.portfolio = self.portfolio_class(self.data_handler, self.events, self.start_date, self.initial_capital)\n self.execution_handler = self.execution_handler_class(self.events) # The Event Queue sent to ExecutionHandler"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _generate_trading_instances(self):\n print(\"Creating DataHandler, Strategy, Portfolio, and ExecutionHandler for\")\n\n # Set internal data members equal to the classes we passed in earlier, along with necessary parameters.\n # https://softwareengineering.stackexchange.com/questions/131403/what-is-the-name-of-in-python/131415\n self.data_handler = self.data_handler_class(self.events, self.csv_dir, self.symbol_list)\n self.strategy = self.strategy_class(self.data_handler, self.events)\n self.portfolio = self.portfolio_class(self.data_handler, self.events, self.start_date, self.initial_capital)\n self.execution_handler = self.execution_handler_class(self.events) # The Event Queue sent to ExecutionHandler","def _generate_trading_instances(self):\n print(\n \"Initizalization...\"\n )\n\n self.data_handler = self.data_handler_cls(self.events, self.csv_dir, self.symbol_list, self.start_date,\n self.end_date)\n self.portfolio = self.portfolio_cls(self.data_handler, self.events, self.initial_capital, self.start_date,\n self.strategy_id, self.spread, self.commission,self.csv_dir)\n self.strategy = self.strategy_cls(self.data_handler, self.events, self.portfolio, self.spread, self.commission)\n self.plot = self.plot_cls(self.csv_dir, self.portfolio, self.strategy_id)","def _generate_trading_instances(self, start_date, end_date, instruments, params):\n configuration = self.configuration\n configuration.start_date = start_date\n configuration.end_date = end_date\n configuration.instruments = instruments\n\n logger.info(\"Creating DataHandler, Strategy, Portfolio and ExecutionHandler\")\n logger.info(\"Start date: %s\" % start_date)\n logger.info(\"End date: %s\" % end_date)\n logger.info(\"Instrument(s): %s...\" % instruments)\n logger.info(\"Params: %s...\" % params)\n\n self.data_handler = self.data_handler_cls(self.events, configuration)\n self.strategy = self.strategy_cls(self.data_handler, self.events, configuration, **params)\n self.portfolio = self.portfolio_cls(self.data_handler, self.events, configuration)\n self.execution_handler = self.execution_handler_cls(self.data_handler, self.events, configuration)","def _generate_trading_instances(self, sp):\n print(\n \"Initialization...\"\n )\n self.data_handler = self.data_handler_cls(self.events, self.csv_dir, self.symbol_list, self.start_date,\n self.end_date)\n self.portfolio = self.portfolio_cls(self.data_handler, self.events, self.initial_capital, self.start_date,\n self.strategy_id, self.spread, self.commission,self.csv_dir)\n self.strategy = self.strategy_cls(self.data_handler, self.events, self.portfolio, self.spread, self.commission,\n sp)\n self.plot = self.plot_cls(self.csv_dir, self.portfolio, self.strategy_id)","def create_all(self, registry):\n for cls in registry.values():\n self.create_class(cls)","def _fill_class_dicts():\n global _taxonomy_classes\n global _data_classes\n if not _taxonomy_classes:\n _taxonomy_classes = get_taxonomies()\n if not _data_classes:\n stack = []\n next_module = data\n while next_module is not None:\n stack += _inspect_module(next_module)\n if stack:\n next_module = stack.pop()\n else:\n next_module = None","def make_objects(self):\n pass","def __init__(self):\n if DynamicImporter._instance is not None:\n raise Exception(\"DynamicImporter instance already exists!\")\n DynamicImporter._instance = self\n\n current_path = Path(__file__).parent\n test_path = current_path / \"testdata\"\n files = test_path.rglob(\"*.py\")\n\n for file in files:\n\n if file.name in [\"__init__.py\", \"test_module.py\", \"test_registry.py\", \"connections.py\"]:\n continue\n\n name = file.stem\n module = import_module(f\"testdata.{name}\")\n class_title = f\"{name.title()}Test\"\n\n try:\n _class = getattr(module, class_title) # get the class\n self.class_list[class_title] = _class # add the class to the class list\n except AttributeError: # don't throw exceptions for files that don't have a test\n continue","def __init__(self):\n for base in AutomationSetup.__bases__:\n base.__init__(self)","def setUpClass(cls):\n\n Base._Base__nb_objects = 0\n cls.b1 = Base()\n cls.b2 = Base()\n cls.b3 = Base(22)\n cls.b4 = Base(2.2)\n cls.b5 = Base(\"two\")\n cls.r1 = Rectangle(10, 7, 2, 8)\n cls.r2 = Rectangle(2, 4)","def setup_class(self):\n\n class SubFLRW(FLRW):\n def w(self, z):\n return super().w(z)\n\n self.cls = SubFLRW\n # H0, Om0, Ode0\n self.cls_args = (70 * u.km / u.s / u.Mpc, 0.27 * u.one, 0.689 * u.one)\n self.cls_kwargs = dict(Tcmb0=3.0 * u.K, name=self.__class__.__name__, meta={\"a\": \"b\"})","def init_elect_types(self):\n self.wta = WinnerTakeAll()\n self.proportional = Proportional()\n self.schulze = Schulze()\n\n session.add_all([self.wta, self.proportional, self.schulze])","def _create_Work(classname, dataclass):\n globals()[classname] = type(classname, (Work, dataclass), {})","def XtremObjFactory(object_type, object_data, parent_connection):\r\n for cls in XtremObject.__subclasses__():\r\n if cls.is_class_for(object_type):\r\n return cls(object_data, parent_connection)","def _setup(self):\n\n # Get user data\n self.symbols = self._get_symbols()\n self.data_dict = self._get_data()\n self.portfolio = self.initialize_portfolio()\n\n if 'slippage' in self.portfolio:\n self.slippage = self.portfolio['slippage']\n else:\n self.slippage = None\n\n # Keep track of all trades\n self.trade_manager = TradeManager(\n self.symbols, self.portfolio, self.sql_config\n )\n\n # Initialize state variables that are updated each iteration\n self.date = None\n self.data = None\n self.symbol = None\n self.currency = None\n self.last_buy = None\n self.num_unresolved = 0\n self.unresolved_trade = False","def setup_class(klass):","def setup_class(klass):","def make_class(attributes, base_classes=()):\r\n \"*** YOUR CODE HERE ***\"","def _classes_(cls):\n for base_cls in cls.__bases__:\n # Avoid infinite loop\n if base_cls == Sandbox:\n continue\n\n yield base_cls","def _prepare_wsdl_objects(self):\r\n\r\n\t# Default behavior is to not request transit information\r\n\tself.ReturnTransitAndCommit = False\r\n\r\n # This is the primary data structure for processShipment requests.\r\n self.RequestedShipment = self.client.factory.create('RequestedShipment')\r\n self.RequestedShipment.ShipTimestamp = datetime.now()\r\n \r\n TotalWeight = self.client.factory.create('Weight')\r\n # Start at nothing.\r\n TotalWeight.Value = 0.0\r\n # Default to pounds.\r\n TotalWeight.Units = 'LB'\r\n # This is the total weight of the entire shipment. Shipments may\r\n # contain more than one package.\r\n self.RequestedShipment.TotalWeight = TotalWeight\r\n \r\n # This is the top level data structure for Shipper information.\r\n ShipperParty = self.client.factory.create('Party')\r\n ShipperParty.Address = self.client.factory.create('Address')\r\n ShipperParty.Contact = self.client.factory.create('Contact')\r\n \r\n # Link the ShipperParty to our master data structure.\r\n self.RequestedShipment.Shipper = ShipperParty\r\n\r\n # This is the top level data structure for Recipient information.\r\n RecipientParty = self.client.factory.create('Party')\r\n RecipientParty.Contact = self.client.factory.create('Contact')\r\n RecipientParty.Address = self.client.factory.create('Address')\r\n \r\n # Link the RecipientParty object to our master data structure.\r\n self.RequestedShipment.Recipient = RecipientParty\r\n \r\n Payor = self.client.factory.create('Payor')\r\n # Grab the account number from the FedexConfig object by default.\r\n Payor.AccountNumber = self._config_obj.account_number\r\n # Assume US.\r\n Payor.CountryCode = 'US'\r\n \r\n ShippingChargesPayment = self.client.factory.create('Payment')\r\n ShippingChargesPayment.Payor = Payor\r\n\r\n self.RequestedShipment.ShippingChargesPayment = ShippingChargesPayment\r\n \r\n # ACCOUNT or LIST\r\n self.RequestedShipment.RateRequestTypes = ['ACCOUNT'] \r\n \r\n # Start with no packages, user must add them.\r\n self.RequestedShipment.PackageCount = 0\r\n self.RequestedShipment.RequestedPackageLineItems = []\r\n \r\n # This is good to review if you'd like to see what the data structure\r\n # looks like.\r\n self.logger.debug(self.RequestedShipment)","def setUpClass(cls):\r\n cls.jon = Person.create(name='Jon', age=143)\r\n cls.eric = Person.create(name='Eric', age=25)\r\n cls.blake = Person.create(name='Blake', age=14)\r\n\r\n cls.physics = Course.create(name='Physics 264', credits=1.0)\r\n cls.beekeeping = Course.create(name='Beekeeping', credits=15.0)\r\n cls.theoretics = Course.create(name='Theoretical Theoretics', credits=-3.5)\r\n\r\n cls.eric_in_physics = EnrolledIn.create(cls.eric, cls.physics, date_enrolled=datetime.now(),\r\n enthusiasm=10) # eric loves physics\r\n cls.jon_in_beekeeping = EnrolledIn.create(cls.jon, cls.beekeeping, date_enrolled=datetime.now(),\r\n enthusiasm=1) # jon hates beekeeping\r\n\r\n cls.blake_in_theoretics = EnrolledIn.create(cls.blake, cls.theoretics, date_enrolled=datetime.now(),\r\n enthusiasm=8)\r\n\r\n cls.blake_beekeeping = TaughtBy.create(cls.beekeeping, cls.blake, overall_mood='Pedantic')\r\n cls.jon_physics = TaughtBy.create(cls.physics, cls.jon, overall_mood='Creepy')\r\n cls.eric_theoretics = TaughtBy.create(cls.theoretics, cls.eric, overall_mood='Obtuse')","def __init__(self):\n self.classes = {}","def setup_class(cls):","def setup_class(cls):","def _prepare_wsdl_objects(self):\r\n self.DeletionControlType = self.client.factory.create('DeletionControlType')\r\n self.TrackingId = self.client.factory.create('TrackingId')\r\n self.TrackingId.TrackingIdType = self.client.factory.create('TrackingIdType')","def setup_class(cls):\n cls._patch_logger()\n cls.multiplexer = Multiplexer(\n [DummyConnection(connection_id=DUMMY_CONNECTION_PUBLIC_ID)]\n )\n cls.outbox = OutBox(cls.multiplexer)\n private_key_pem_path = os.path.join(CUR_PATH, \"data\", \"fet_private_key.txt\")\n eth_private_key_pem_path = os.path.join(CUR_PATH, \"data\", \"fet_private_key.txt\")\n cls.wallet = Wallet(\n {FETCHAI: private_key_pem_path, ETHEREUM: eth_private_key_pem_path}\n )\n cls.ledger_apis = LedgerApis({FETCHAI: DEFAULT_FETCHAI_CONFIG}, FETCHAI)\n cls.agent_name = \"test\"\n cls.ownership_state = OwnershipState()\n cls.preferences = Preferences()\n cls.decision_maker = DecisionMaker(\n agent_name=cls.agent_name,\n max_reactions=MAX_REACTIONS,\n outbox=cls.outbox,\n wallet=cls.wallet,\n ledger_apis=cls.ledger_apis,\n )\n cls.multiplexer.connect()\n\n cls.tx_id = \"transaction0\"\n cls.tx_sender_addr = \"agent_1\"\n cls.tx_counterparty_addr = \"pk\"\n cls.info = {\"some_info_key\": \"some_info_value\"}\n cls.ledger_id = \"fetchai\"\n\n cls.decision_maker.start()","def setUpClass(cls):\n super().setUpClass()\n\n cls.accessor = OCPReportDBAccessor(cls.schema)\n cls.report_schema = cls.accessor.report_schema\n cls.all_tables = list(OCP_REPORT_TABLE_MAP.values())\n cls.creator = ReportObjectCreator(cls.schema)\n cls.date_accessor = DateHelper()\n cls.manifest_accessor = ReportManifestDBAccessor()\n cls.dh = DateHelper()","def load_asset_classes(self) -> None:\n provider = AlphaVantageProvider()\n trends_provider = GoogleTrendsProvider()\n self.asset_classes.append(AssetClass.CurrencyClass(provider, trends_provider))\n self.asset_classes.append(AssetClass.StockClass(provider, trends_provider))","def gen_extractors():\n return [klass() for klass in gen_extractor_classes()]","def build_etl_classes(self):\n\n self.clear_etl_classes()\n\n for config in list(self.configs.values()):\n\n etl_class = self.build(config)\n\n self.add_etl_class(etl_class)","def __init__(self, obj, datamodel=None):\n with RecursiveConverter.in_progress:\n self.obj = obj\n self.class_name = obj.__class__.__name__\n self.datamodel = datamodel\n self.is_root = datamodel is None\n if self.is_root:\n RecursiveConverter.converted_modules = {}\n RecursiveConverter.typedefs = []\n self.datamodel = VHDLModule('-', obj)\n\n # recursively convert all child modules\n self.childs = []\n\n def conv(self, node):\n if isinstance(node, VHDLList):\n if node.elements_compatible_typed:\n if isinstance(node.elems[0], VHDLModule):\n if self.is_compatible_with_converted_module(node.elems[0]):\n return\n self.childs.append(RecursiveConverter(node.elems[0].current, node.elems[0]))\n\n else:\n # dynamic list..need to convert all modules\n for x in node.elems:\n if isinstance(x, VHDLModule):\n if self.is_compatible_with_converted_module(x):\n return\n self.childs.append(RecursiveConverter(x.current, x))\n elif isinstance(node, VHDLModule):\n if self.is_compatible_with_converted_module(node):\n return\n self.childs.append(RecursiveConverter(node.current, node))\n\n if self.is_root:\n logger.info(f'Creating top.vhd ...')\n self.top_vhdl = TopGenerator(obj)\n\n # maybe some input/output is a convertible module?\n for node in self.inputs:\n conv(self, node)\n\n for node in self.outputs:\n conv(self, node)\n\n # iterate all functions and discover local variables that may need to be converted\n for x in self.obj.__dict__.values():\n if isinstance(x, PyhaFunc):\n for key, val in x.get_local_types().items():\n if isinstance(val, Hardware):\n node = init_vhdl_type(key, val)\n conv(self, node)\n\n # convert instance elements before the instance itself, recursive\n for node in self.datamodel.elems:\n conv(self, node)\n\n self.red_node = get_objects_rednode(obj)\n convert_name = self.get_module_converted_name(self.datamodel)\n logger.info(f'{convert_name} to VHDL ...')\n\n self.conv = convert(self.red_node, obj) # actual conversion happens here\n\n self.vhdl_conversion = str(self.conv)\n RecursiveConverter.converted_modules[convert_name] = (self.datamodel, self.vhdl_conversion)\n RecursiveConverter.typedefs.extend(self.conv.build_typedefs())","def __init__(self,classes=['normalizeText','tagger','stem','stopWord','spellChecker']):\n self._support = prebotSupport()\n self._classes = classes\n if(\"tagger\" in self._classes):\n self._tagger = tagger()\n if(\"normalizeText\" in self._classes):\n self._normalize = normalizeText()\n if(\"spellChecker\" in self._classes):\n self._spellChecker = spellChecker()\n if(\"stopWord\" in self._classes):\n self._stopWord = stopWord()\n if(\"stem\" in self._classes):\n self._stem = stemming()","def setUpClass(cls):\n #First get the source directory setup correctly. For these tests\n #we are going to use the config libraries instead of duplicating\n #the code. Only the one module being wrapped will get copied.\n cls.fdir = path.join(path.abspath(\".\"), \"ftypes\")\n cls.source = path.join(cls.fdir, \"symmetry_module.f90\")\n cls._check_dir()\n \n settings.use_filesystem_cache = False\n cls.parser = CodeParser()\n cls.parser.parse(cls.source)\n\n cls.dependencies = [\"{}_c\".format(m) for m in cls.parser.modules]\n cls.originals = list(cls.parser.modules.keys())\n cls.writers = {}\n\n #Setup the default test data the we use to run the python wrapper module\n #methods after ftypes is done.\n cls.lattice = array([[.5,.5,0],[0,.5,.5],[.5,0,.5]])\n cls.atomtypes = array([1])\n cls.basis = array([[0],[0],[0]])","def dump_classes():\n credentials = Credentials()\n mountpoint = MountPoint()\n workload = Workload()\n migrationtarget = MigrationTarget()\n migration = Migration()\n\n with open('../tier_one/python_test_migration', 'wb') as f:\n dill.dump(credentials, f)\n dill.dump(mountpoint, f)\n dill.dump(workload, f)\n dill.dump(migrationtarget, f)\n dill.dump(migration, f)","def makemodules(self):\n names = get_unique_name_list_from_class_list(self.cls_modules)\n for cls, name in zip(self.cls_modules, names):\n self.makemodule(name, cls)","def __init_fixture_methods(self):\n # init our self.(class_setup|setup|teardown|class_teardown)_fixtures lists\n for fixture_type in fixture_types:\n setattr(self, \"%s_fixtures\" % fixture_type, [])\n\n # for setup methods, we want oldest class first. for teardowns, we want newest class first\n hierarchy = list(reversed(type(self).mro()))\n for cls in hierarchy[1:]:\n # mixins on TestCase instances that derive from, say, object, won't be set up properly\n if hasattr(cls, '_fixture_methods'):\n # the metaclass stored the class's fixtures in a _fixture_methods instance variable\n for fixture_type, fixture_methods in cls._fixture_methods.iteritems():\n bound_fixture_methods = [instancemethod(func, self, self.__class__) for func in fixture_methods]\n if fixture_type.endswith('setup'):\n # for setup methods, we want methods defined further back in the\n # class hierarchy to execute first\n getattr(self, \"%s_fixtures\" % fixture_type).extend(bound_fixture_methods)\n else:\n # for teardown methods though, we want the opposite\n setattr(self, \"%s_fixtures\" % fixture_type, bound_fixture_methods + getattr(self, \"%s_fixtures\" % fixture_type))","def generate():\n strategies = []\n\n # Add Centrifugal Bumblepuppy 16+H (RFind based)\n strategies.extend(\n generate_meta_strategy_pair(CentrifugalBumblepuppy16h))\n\n # Add Centrifugal Bumblepuppy 16+H (RFind based)\n strategies.extend(\n generate_meta_strategy_pair(CentrifugalBumblepuppy4))\n\n # Add decision tree strategies\n strategies.extend(\n generate_meta_strategy_pair(DecisionTreeStrategy))\n\n # Add decision tree strategies\n strategies.extend(\n generate_meta_strategy_pair(DecisionTreeV10Strategy))\n\n # Add HPS Dojo strategies\n strategies.extend(\n generate_meta_strategy_pair(HPSDojoStrategy))\n\n # Add testing please ignore strategies\n strategies.extend(\n generate_meta_strategy_pair(TestingPlsIgnoreStrategy))\n\n # Add testimono strategy\n strategies.extend(\n generate_meta_strategy_pair(TestimonoStrategy))\n\n # Add RPS Geometry\n strategies.extend(\n generate_meta_strategy_pair(GeometryV4Strategy))\n\n # Add Iocaine Powder strategy\n strategies.extend(\n generate_meta_strategy_pair(IocanePowderStrategy))\n\n # Add are you a lucker strategies\n strategies.extend(\n generate_meta_strategy_pair(AreYouALuckerStrategy))\n\n # Add Greenberg strategies\n strategies.extend(\n generate_meta_strategy_pair(GreenbergStrategy))\n\n # Add RPS Meta Fix strategies\n strategies.extend(\n generate_meta_strategy_pair(RPSMetaFixStrategy))\n\n # By default, rotate everything\n do_rotations = [True for _ in strategies]\n\n # Anti Trivial\n strategies.extend(\n generate_meta_strategy_pair(\n AntiTrivialStrategy, mirroring=False))\n do_rotations.extend([False])\n\n return strategies, do_rotations","def make_orders(self):\n\n # orders to cancel from all of the strategies\n ocancel = self.get_cancel_orders()\n\n # orders to update from all of the strategies\n oupdate = self.get_update_orders()\n\n # new orders from all of the strategies\n onew = self.get_new_orders()\n \n # do we need to cancel, update, or make new orders?\n tocancel = bool(ocancel[const.BDAQID] or ocancel[const.BFID])\n toupdate = bool(oupdate[const.BDAQID] or oupdate[const.BFID])\n tonew = bool(onew[const.BDAQID] or onew[const.BFID])\n\n if tocancel:\n betlog.betlog.debug('cancelling orders: {0}'.format(ocancel))\n\n if toupdate:\n betlog.betlog.debug('updating orders: {0}'.format(oupdate))\n\n if tonew:\n betlog.betlog.debug('making new orders: {0}'.format(onew))\n\n if (tocancel or toupdate or tonew):\n \n # we could instead do 'monkey patching' here so we don't\n # need to check this every tick...\n if self.gconf.PracticeMode:\n # we don't make any real money bets in practice mode\n print 'bets not made since in practice mode'\n return\n\n # call multithreaded make orders so that we make all order\n # requests (cancelling, updating, making new) for BDAQ and\n # BF simultaneously.\n corders, uorders, neworders = multi.\\\n make_orders(ocancel, oupdate, onew)\n\n # save the full order information to the order store (this will\n # handle writing to the DB, etc.)\n self.ostore.add_orders(corders, uorders, neworders)\n\n else:\n \n # we need to set latest cancel, update, new orders to be\n # empty.\n self.ostore.latest = [{const.BDAQID: {}, const.BFID: {}}, \n {const.BDAQID: {}, const.BFID: {}}, \n {const.BDAQID: {}, const.BFID: {}}]","def generate():\n strategies = []\n\n # Add Centrifugal Bumblepuppy 16+H (RFind based)\n strategies.extend(\n generate_meta_strategy_pair(CentrifugalBumblepuppy16h))\n\n # Add Centrifugal Bumblepuppy 16+H (RFind based)\n strategies.extend(\n generate_meta_strategy_pair(CentrifugalBumblepuppy4))\n\n # Add decision tree strategies\n strategies.extend(\n generate_meta_strategy_pair(DecisionTreeStrategy))\n\n # Add decision tree strategies\n strategies.extend(\n generate_meta_strategy_pair(DecisionTreeV10Strategy))\n\n # Add HPS Dojo strategies\n strategies.extend(\n generate_meta_strategy_pair(HPSDojoStrategy))\n\n # Add testing please ignore strategies\n strategies.extend(\n generate_meta_strategy_pair(TestingPlsIgnoreStrategy))\n\n # Add testimono strategy\n strategies.extend(\n generate_meta_strategy_pair(TestimonoStrategy))\n\n # Add RPS Geometry\n strategies.extend(\n generate_meta_strategy_pair(GeometryV4Strategy))\n\n # Add Iocaine Powder strategy\n strategies.extend(\n generate_meta_strategy_pair(IocanePowderStrategy))\n\n # Add are you a lucker strategies\n strategies.extend(\n generate_meta_strategy_pair(AreYouALuckerStrategy))\n\n # By default, rotate everything\n do_rotations = [True for _ in strategies]\n\n # Anti Trivial\n strategies.extend(\n generate_meta_strategy_pair(\n AntiTrivialStrategy, mirroring=False))\n do_rotations.extend([False])\n\n return strategies, do_rotations","def init_classes():\r\n\r\n\tglobal data\r\n\r\n\tif data is None:\r\n\t\twith app.app_context():\r\n\t\t\tprint \"initializing db\"\r\n\t\r\n\t\t\tdata = status.DataManager( db_path=dbPath, ext=pycklerext )\r\n\t\r\n\t\t\tprint \"db loaded\"\r\n\r\n\telse:\r\n\t\twith app.app_context():\r\n\t\t\tprint \"updating db\"\r\n\t\t\tdata.loadlast()\r\n\t\t\tprint \"db updated\"\r\n\r\n\twith app.app_context():\r\n\t\tg.modules = {\r\n\t\t\t'memall': get_mem_all,\r\n\t\t\t'memone': get_mem_one\r\n\t\t}\r\n\t\tg.glanularity = 60","def __init__(self):\n self.stock = Stock()\n self.foundations = {suit: Foundation(suit) for suit in SUITS}\n self.waste = Waste()\n # Deal cards face-down into each of the seven tableaus from the stock.\n # Each tableau has as many cards as its number.\n self.tableaus = [Tableau(self.stock.deal(num, face_up=False)) for num in range(1, 8)]","def generate():\n strategies = []\n\n # Add Centrifugal Bumblepuppy 16+H (RFind based)\n strategies.extend(\n generate_meta_strategy_pair(CentrifugalBumblepuppy16h))\n\n # Add Centrifugal Bumblepuppy 16+H (RFind based)\n strategies.extend(\n generate_meta_strategy_pair(CentrifugalBumblepuppy4))\n\n # Add decision tree strategies\n strategies.extend(\n generate_meta_strategy_pair(DecisionTreeStrategy))\n\n # Add decision tree strategies\n strategies.extend(\n generate_meta_strategy_pair(DecisionTreeV10Strategy))\n\n # Add HPS Dojo strategies\n strategies.extend(\n generate_meta_strategy_pair(HPSDojoStrategy))\n\n # Add testing please ignore strategies\n strategies.extend(\n generate_meta_strategy_pair(TestingPlsIgnoreStrategy))\n\n # Add testimono strategy\n strategies.extend(\n generate_meta_strategy_pair(TestimonoStrategy))\n\n # Add RPS Geometry\n strategies.extend(\n generate_meta_strategy_pair(GeometryV4Strategy))\n\n # Add Iocaine Powder strategy\n strategies.extend(\n generate_meta_strategy_pair(IocanePowderStrategy))\n\n # Add are you a lucker strategies\n strategies.extend(\n generate_meta_strategy_pair(AreYouALuckerStrategy))\n\n # Add RPS Meta Fix strategies\n strategies.extend(\n generate_meta_strategy_pair(RPSMetaFixStrategy))\n\n # By default, rotate everything\n do_rotations = [True for _ in strategies]\n\n # Anti Trivial\n strategies.extend(\n generate_meta_strategy_pair(\n AntiTrivialStrategy, mirroring=False))\n do_rotations.extend([False])\n\n return strategies, do_rotations","def test_instances(self):\n a = CommonMixin()\n a.__dict__.update(**self.data)\n\n b = CommonMixin()\n b.__dict__.update(**self.data)\n\n c = ExampleClass()\n c.__dict__.update(**self.data)\n\n return a, b, c","def create_ast(client: TypeAny) -> Globals:\n ast = Globals(client=client)\n\n modules: TypeList[TypeTuple[str, TypeAny]] = [\n (\"xgboost\", xgb),\n (\"xgboost.core\", xgb.core),\n (\"xgboost.sklearn\", xgb.sklearn),\n ]\n\n classes: TypeList[TypeTuple[str, str, TypeAny]] = [\n (\"xgboost.DMatrix\", \"xgboost.DMatrix\", xgb.core.DMatrix),\n (\"xgboost.core.DMatrix\", \"xgboost.core.DMatrix\", xgb.core.DMatrix),\n (\"xgboost.core.Booster\", \"xgboost.core.Booster\", xgb.core.Booster),\n (\n \"xgboost.core.XGBoostError\",\n \"xgboost.core.XGBoostError\",\n xgb.core.XGBoostError,\n ),\n # classifiers\n (\"xgboost.XGBClassifier\", \"xgboost.XGBClassifier\", xgb.XGBClassifier),\n (\"xgboost.XGBRFClassifier\", \"xgboost.XGBRFClassifier\", xgb.XGBRFClassifier),\n # (\"xgboost.dask.DaskXGBRFClassifier\"), Currently dask is not supported in syft\n # regreessors\n (\"xgboost.XGBRegressor\", \"xgboost.XGBRegressor\", xgb.XGBRegressor),\n (\"xgboost.XGBRFRegressor\", \"xgboost.XGBRFRegressor\", xgb.XGBRFRegressor),\n # (\"xgboost.dask.DaskXGBRFRegressor\"), Currently dask is not supported in syft\n ]\n\n methods = [\n (\"xgboost.train\", \"xgboost.core.Booster\"),\n (\"xgboost.core.Booster.predict\", \"numpy.ndarray\"),\n # classifiers\n (\"xgboost.XGBClassifier.fit\", \"xgboost.XGBClassifier\"),\n (\"xgboost.XGBClassifier.predict\", \"numpy.ndarray\"),\n (\"xgboost.XGBRFClassifier.fit\", \"xgboost.XGBRFClassifier\"),\n (\"xgboost.XGBRFClassifier.predict\", \"numpy.ndarray\"),\n # regressors\n (\"xgboost.XGBRegressor.fit\", \"xgboost.XGBRegressor\"),\n (\"xgboost.XGBRegressor.predict\", \"numpy.ndarray\"),\n (\"xgboost.XGBRFRegressor.fit\", \"xgboost.XGBRFClassifier\"),\n (\"xgboost.XGBRFRegressor.predict\", \"numpy.ndarray\"),\n ]\n\n add_modules(ast, modules)\n add_classes(ast, classes)\n add_methods(ast, methods)\n\n for klass in ast.classes:\n klass.create_pointer_class()\n klass.create_send_method()\n klass.create_storable_object_attr_convenience_methods()\n\n return ast","def setUpClass(cls):\n\t\t\t\t\t\n\t\tww.weightwatcher.torch = torch\n\t\tcls.weights_dir = Test_SafeTensorsDir._make_tmp_weights_dir(format=MODEL_FILE_FORMATS.SAFETENSORS)\n\t\t\n\t\treturn","def setUpClass(cls):\n\t\t\t\t\t\n\t\tww.weightwatcher.torch = torch\n\t\tcls.weights_dir = Test_WWFlatFiles._make_tmp_weights_dir()\n\t\t\n\t\treturn","def generate():\n strategies = []\n\n # Add Centrifugal Bumblepuppy 16+H (RFind based)\n strategies.extend(\n generate_meta_strategy_pair(CentrifugalBumblepuppy16h))\n\n # Add Centrifugal Bumblepuppy 16+H (RFind based)\n strategies.extend(\n generate_meta_strategy_pair(CentrifugalBumblepuppy4))\n\n # Add decision tree strategies\n strategies.extend(\n generate_meta_strategy_pair(DecisionTreeStrategy))\n\n # Add decision tree strategies\n strategies.extend(\n generate_meta_strategy_pair(DecisionTreeV10Strategy))\n\n # Add HPS Dojo strategies\n strategies.extend(\n generate_meta_strategy_pair(HPSDojoStrategy))\n\n # Add testing please ignore strategies\n strategies.extend(\n generate_meta_strategy_pair(TestingPlsIgnoreStrategy))\n\n # Add testimono strategy\n strategies.extend(\n generate_meta_strategy_pair(TestimonoStrategy))\n\n # Add RPS Geometry\n strategies.extend(\n generate_meta_strategy_pair(GeometryV4Strategy))\n\n # Add Iocaine Powder strategy\n strategies.extend(\n generate_meta_strategy_pair(IocanePowderStrategy))\n\n # By default, rotate everything\n do_rotations = [True for _ in strategies]\n\n # Anti Trivial\n strategies.extend(\n generate_meta_strategy_pair(\n AntiTrivialStrategy, mirroring=False))\n do_rotations.extend([False])\n\n return strategies, do_rotations","def setUpClass(cls):\n\t\t\t\t\t\n\t\tww.weightwatcher.torch = torch\n\t\tcls.weights_dir = Test_SafeTensorsDir._make_tmp_weights_dir(format=MODEL_FILE_FORMATS.SAFETENSORS, layer_map=False)\n\t\t\n\t\treturn","def _prepare_wsdl_objects(self):\r\n # This is the primary data structure for processShipment requests.\r\n self.RequestedShipment = self.client.factory.create('RequestedShipment')\r\n self.RequestedShipment.ShipTimestamp = datetime.now()\r\n \r\n TotalWeight = self.client.factory.create('Weight')\r\n # Start at nothing.\r\n TotalWeight.Value = 0.0\r\n # Default to pounds.\r\n TotalWeight.Units = 'LB'\r\n # This is the total weight of the entire shipment. Shipments may\r\n # contain more than one package.\r\n self.RequestedShipment.TotalWeight = TotalWeight\r\n \r\n # This is the top level data structure for Shipper information.\r\n ShipperParty = self.client.factory.create('Party')\r\n ShipperParty.Address = self.client.factory.create('Address')\r\n ShipperParty.Contact = self.client.factory.create('Contact')\r\n \r\n # Link the ShipperParty to our master data structure.\r\n self.RequestedShipment.Shipper = ShipperParty\r\n\r\n # This is the top level data structure for Recipient information.\r\n RecipientParty = self.client.factory.create('Party')\r\n RecipientParty.Contact = self.client.factory.create('Contact')\r\n RecipientParty.Address = self.client.factory.create('Address')\r\n \r\n # Link the RecipientParty object to our master data structure.\r\n self.RequestedShipment.Recipient = RecipientParty\r\n \r\n Payor = self.client.factory.create('Payor')\r\n # Grab the account number from the FedexConfig object by default.\r\n Payor.AccountNumber = self._config_obj.account_number\r\n # Assume US.\r\n Payor.CountryCode = 'US'\r\n \r\n ShippingChargesPayment = self.client.factory.create('Payment')\r\n ShippingChargesPayment.Payor = Payor\r\n\r\n self.RequestedShipment.ShippingChargesPayment = ShippingChargesPayment\r\n self.RequestedShipment.LabelSpecification = self.client.factory.create('LabelSpecification')\r\n # ACCOUNT or LIST\r\n self.RequestedShipment.RateRequestTypes = ['ACCOUNT'] \r\n \r\n # Start with no packages, user must add them.\r\n self.RequestedShipment.PackageCount = 0\r\n self.RequestedShipment.RequestedPackageLineItems = []\r\n \r\n # This is good to review if you'd like to see what the data structure\r\n # looks like.\r\n self.logger.debug(self.RequestedShipment)","def register_classes():\n DiffuseCompChain.register_class()\n CatalogCompChain.register_class()\n DiffuseAnalysisChain.register_class()","def local_classes(self, classnames, typesets=frozenset(['cy', 'py'])):\n saved = {}\n for name in classnames:\n if 'c' in typesets and name in self.cython_ctypes:\n saved[name, 'c'] = _undot_class_name(name, self.cython_ctypes)\n if 'cy' in typesets and name in self.cython_cytypes:\n saved[name, 'cy'] = _undot_class_name(name, self.cython_cytypes)\n if 'py' in typesets and name in self.cython_pytypes:\n saved[name, 'py'] = _undot_class_name(name, self.cython_pytypes)\n self.clearmemo()\n yield\n for name in classnames:\n if 'c' in typesets and name in self.cython_ctypes:\n _redot_class_name(name, self.cython_ctypes, saved[name, 'c'])\n if 'cy' in typesets and name in self.cython_cytypes:\n _redot_class_name(name, self.cython_cytypes, saved[name, 'cy'])\n if 'py' in typesets and name in self.cython_pytypes:\n _redot_class_name(name, self.cython_pytypes, saved[name, 'py'])\n self.clearmemo()","def __create_custom_objects():\n # make some preparation to properly load objects from keras_contribute\n instance_holder = {\"instance\": None}\n\n class ClassWrapper(CRF):\n def __init__(self, *args, **kwargs):\n instance_holder[\"instance\"] = self\n super(ClassWrapper, self).__init__(*args, **kwargs)\n\n def loss(*args):\n method = getattr(instance_holder[\"instance\"], \"loss_function\")\n return method(*args)\n\n def accuracy(*args):\n method = getattr(instance_holder[\"instance\"], \"accuracy\")\n return method(*args)\n\n return {\"ClassWrapper\": ClassWrapper, \"CRF\": ClassWrapper, \"crf_loss\": loss,\n \"crf_viterbi_accuracy\": accuracy}","def create_effect_classes(self):\r\n effects.polulate(self.effect_packages)","def __init__(self, data):\n self.bees = [Bee(b) for b in data[\"bees\"]]\n self.flowers = [Flower(f) for f in data[\"flowers\"]]\n self.hives = [Hive(h) for h in data[\"hives\"]]","def setup_class(cls):\n super(TestCliTransferFetchAINetwork, cls).setup_class()\n cls.agent_name2 = \"agent_\" + \"\".join(\n random.choices(string.ascii_lowercase, k=5) # nosec\n )\n cls.create_agents(cls.agent_name2)\n\n cls.gen_key(cls.agent_name)\n cls.gen_key(cls.agent_name2)","def start_trading(self):\n backtest_result_dir = os.path.join(self.result_dir, str(self.backtest_date))\n os.mkdir(backtest_result_dir)\n backtest_scores_path = os.path.join(backtest_result_dir, 'scores.csv')\n # all_backtest_scores_exists = os.path.isfile(self.all_backtest_scores_path)\n\n last_backtest_scores = open(self.last_backtest_scores_path, \"w\")\n backtest_scores = open(backtest_scores_path, \"w\")\n\n\n fieldnames = [ 'Backtest Name', 'Backtest Date', 'Strategy', 'Start Date', 'End Date', 'Instrument(s)', 'Params'] + \\\n ['Number of signals', 'Number of orders', 'Number of trades', 'Total USD Return', 'Total BTC Return',\n 'Sharpe Ratio', 'BTC Sharpe Ratio', 'Max Drawdown', 'BTC Max Drawdown', 'Drawdown Duration', 'BTC Drawdown Duration',\n 'Monthly BTC Return', 'Yearly BTC Return', 'Avg. winning trade', 'Median duration', 'Avg. losing trade', 'Median returns winning', 'Largest losing trade',\n 'Gross loss', 'Largest winning trade', 'Avg duration', 'Avg returns losing', 'Median returns losing', 'Profit factor',\n 'Winning round trips', 'Percent profitable', 'Total profit', 'Shortest duration', 'Median returns all round trips',\n 'Losing round trips', 'Longest duration', 'Avg returns all round trips', 'Gross profit', 'Avg returns winning',\n 'Total number of round trips', 'Ratio Avg. Win:Avg. Loss', 'Avg. trade net profit', 'Even round trips',\n 'Configuration Filename', 'Leverage']\n\n try:\n with last_backtest_scores as a, backtest_scores as b:\n writer_a = csv.DictWriter(a, fieldnames=fieldnames)\n writer_b = csv.DictWriter(b, fieldnames=fieldnames)\n writer_a.writeheader()\n writer_b.writeheader()\n\n # if not all_backtest_scores_exists:\n # writer_c.writeheader()\n\n for i, (start, end) in enumerate(zip(self.start_dates, self.end_dates)):\n for j, instruments in enumerate(self.instrument_list):\n for k, params in enumerate(self.strategy_params):\n num_backtest = i * (self.num_instruments * self.num_params + 1) + j * (self.num_params + 1) + k + 1\n print(\"Strategy %s out of %s\" % (num_backtest, self.num_backtests))\n self._generate_trading_instances(start, end, instruments, params)\n self._run()\n stats = self._process_results()\n\n general_stats = stats['general']\n pnl_stats = stats['pnl']['All trades'].to_dict()\n summary_stats = stats['summary']['All trades'].to_dict()\n duration_stats = stats['duration']['All trades'].to_dict()\n return_stats = stats['returns']['All trades'].to_dict()\n params_value = '/'.join([ '{}:{}'.format(item[0], item[1]) for item in params.items() ])\n\n row = {\n 'Backtest Name': self.backtest_name,\n 'Backtest Date': self.backtest_date,\n 'Strategy': self.strategy.strategy_name,\n 'Start Date': start,\n 'End Date': end,\n 'Instrument(s)': format_instrument_list(instruments),\n 'Params': params_value,\n 'Number of signals': self.signals,\n 'Number of orders': self.orders,\n 'Number of trades': self.fills,\n **general_stats,\n **pnl_stats,\n **summary_stats,\n **duration_stats,\n **return_stats,\n 'Configuration Filename': self.configuration_filename,\n 'Leverage': self.default_leverage\n }\n\n writer_a.writerow(row)\n writer_b.writerow(row)\n\n except IOError:\n print('I/O Error')\n\n all_scores_csv = pd.concat([ pd.read_csv(self.all_backtest_scores_path), pd.read_csv(backtest_scores_path)])\n all_scores_csv.to_csv(self.all_backtest_scores_path, columns=fieldnames, index=False, encoding='utf-8-sig')\n\n self._open_results_in_excel()","def __init__(self):\n super().__init__()\n self.name = '' # name of this istance (alias)\n self.type = type(self).__name__ # specific type within this class\n self.verbosity = None # verbosity level (see message handler)\n self.globalAttributes = {} # this is a dictionary that contains parameters that are set at the level of the base classes defining the types\n self._knownAttribute = [] # this is a list of strings representing the allowed attribute in the xml input for the class\n self._knownAttribute += ['name','verbosity'] # attributes that are known\n self.printTag = 'BaseType' # the tag that refers to this class in all the specific printing\n self.variableGroups = {} # the variables this class needs to be aware of\n self.metadataKeys = set() # list of registered metadata keys to expect from this entity\n self.metadataParams = {} # dictionary of registered metadata keys with repect to their indexes","def _decorate(cls):\n global_validators = [session_required, catch_typeerror]\n # Cheat methods _hosts_name_label\n # -------------\n # Methods that have a trivial implementation for all classes.\n # 1. get_by_uuid == getting by ref, so just return uuid for\n # all get_by_uuid() methods.\n \n for api_cls in classes.keys():\n # We'll let the autoplug classes implement these functions\n # themselves - its much cleaner to do it in the base class\n \n get_by_uuid = '%s_get_by_uuid' % api_cls\n get_uuid = '%s_get_uuid' % api_cls\n get_all_records = '%s_get_all_records' % api_cls \n\n def _get_by_uuid(_1, _2, ref):\n return xen_api_success(ref)\n\n def _get_uuid(_1, _2, ref):\n return xen_api_success(ref)\n\n def unpack(v):\n return v.get('Value')\n\n def _get_all_records(_api_cls):\n return lambda s, session: \\\n xen_api_success(dict([(ref, unpack(getattr(cls, '%s_get_record' % _api_cls)(s, session, ref)))\\\n for ref in unpack(getattr(cls, '%s_get_all' % _api_cls)(s, session))]))\n\n setattr(cls, get_by_uuid, _get_by_uuid)\n setattr(cls, get_uuid, _get_uuid)\n setattr(cls, get_all_records, _get_all_records(api_cls))\n\n # Autoplugging classes\n # --------------------\n # These have all of their methods grabbed out from the implementation\n # class, and wrapped up to be compatible with the Xen-API.\n\n# def getter(ref, type):\n# return XendAPIStore.get(ref, type)\n\n def wrap_method(name, new_f):\n try:\n f = getattr(cls, name)\n wrapped_f = (lambda * args: new_f(f, *args))\n wrapped_f.api = f.api\n wrapped_f.async = f.async\n setattr(cls, name, wrapped_f)\n except AttributeError:\n # Logged below (API call: %s not found)\n pass\n\n\n def setter_event_wrapper(api_cls, attr_name):\n setter_name = '%s_set_%s' % (api_cls, attr_name)\n wrap_method(\n setter_name,\n lambda setter, s, session, ref, *args:\n _setter_event_dispatch(s, setter, api_cls, attr_name,\n session, ref, args))\n\n\n def ctor_event_wrapper(api_cls):\n ctor_name = '%s_create' % api_cls\n wrap_method(\n ctor_name,\n lambda ctor, s, session, *args:\n _ctor_event_dispatch(s, ctor, api_cls, session, args))\n\n\n def dtor_event_wrapper(api_cls):\n dtor_name = '%s_destroy' % api_cls\n wrap_method(\n dtor_name,\n lambda dtor, s, session, ref, *args:\n _dtor_event_dispatch(s, dtor, api_cls, session, ref, args))\n\n\n # Wrapping validators around XMLRPC calls\n # ---------------------------------------\n for api_cls, validator in classes.items():\n def doit(n, takes_instance, async_support=False,\n return_type=None):\n n_ = n.replace('.', '_')\n try:\n f = getattr(cls, n_)\n if n not in argcounts:\n argcounts[n] = f.func_code.co_argcount - 1\n \n validators = takes_instance and validator and \\\n [validator] or []\n \n validators += global_validators\n for v in validators:\n f = v(f)\n f.api = n\n f.async = async_support\n if return_type:\n f.return_type = return_type\n \n setattr(cls, n_, f)\n except AttributeError:\n log.warn(\"API call: %s not found\" % n)\n\n \n ro_attrs = getattr(cls, '%s_attr_ro' % api_cls, []) \\\n + cls.Base_attr_ro\n rw_attrs = getattr(cls, '%s_attr_rw' % api_cls, []) \\\n + cls.Base_attr_rw\n methods = getattr(cls, '%s_methods' % api_cls, []) \\\n + cls.Base_methods\n funcs = getattr(cls, '%s_funcs' % api_cls, []) \\\n + cls.Base_funcs\n\n # wrap validators around readable class attributes\n for attr_name in ro_attrs + rw_attrs:\n doit('%s.get_%s' % (api_cls, attr_name), True,\n async_support=False)\n\n # wrap validators around writable class attrributes\n for attr_name in rw_attrs:\n doit('%s.set_%s' % (api_cls, attr_name), True,\n async_support=False)\n setter_event_wrapper(api_cls, attr_name)\n\n # wrap validators around methods\n for method_name, return_type in methods:\n doit('%s.%s' % (api_cls, method_name), True,\n async_support=True)\n\n # wrap validators around class functions\n for func_name, return_type in funcs:\n \n doit('%s.%s' % (api_cls, func_name), False,\n async_support=True,\n return_type=return_type)\n \n ctor_event_wrapper(api_cls)\n dtor_event_wrapper(api_cls)","def iter_cls(*classes, blacklist=tuple()):\n for bases in permutations(classes):\n if bases not in blacklist:\n yield type('_'.join(c.__name__ for c in bases), bases, {})","def __prepare_wsdl_objects(self):\r\n pass","def process_class(self, parent, cls):\n if cls.typemap.flat_name in self.class_map:\n raise RuntimeError(\"process_class: class {} already exists in class_map\"\n .format(cls.typemap.flat_name))\n self.class_map[cls.typemap.flat_name] = cls\n for var in cls.variables:\n self.add_var_getter_setter(parent, cls, var)\n cls.functions = self.define_function_suffix(cls.functions)","def setUpClass(cls):\n\n check_spec_test_env()\n\n ws_object_version = [\n _ws_obj(1, 1, 1), # root/origin object\n _ws_obj(1, 2, 1), # copy object\n _ws_obj(1, 3, 1), # provenance object\n _ws_obj(1, 4, 1), # reference object\n _ws_obj(1, 5, 1, is_public=False), # private copy obj\n _ws_obj(1, 6, 1, is_public=False), # private prov obj\n _ws_obj(1, 7, 1, is_public=False), # private ref obj\n ]\n create_test_docs(\"ws_object_version\", ws_object_version)\n ws_type_version = [{\"_key\": \"Module.Type1-1.0\"}]\n create_test_docs(\"ws_type_version\", ws_type_version)\n ws_obj_instance_of_type = [\n {\n \"_from\": \"ws_object_version/1:1:1\",\n \"_to\": \"ws_type_version/Module.Type1-1.0\",\n },\n {\n \"_from\": \"ws_object_version/1:2:1\",\n \"_to\": \"ws_type_version/Module.Type1-1.0\",\n },\n {\n \"_from\": \"ws_object_version/1:3:1\",\n \"_to\": \"ws_type_version/Module.Type1-1.0\",\n },\n {\n \"_from\": \"ws_object_version/1:4:1\",\n \"_to\": \"ws_type_version/Module.Type1-1.0\",\n },\n ]\n create_test_docs(\"ws_obj_instance_of_type\", ws_obj_instance_of_type)\n ws_prov_descendant_of = [\n {\"_from\": \"ws_object_version/1:1:1\", \"_to\": \"ws_object_version/1:3:1\"},\n {\"_from\": \"ws_object_version/1:1:1\", \"_to\": \"ws_object_version/1:6:1\"},\n ]\n create_test_docs(\"ws_prov_descendant_of\", ws_prov_descendant_of)\n ws_refers_to = [\n {\"_from\": \"ws_object_version/1:1:1\", \"_to\": \"ws_object_version/1:4:1\"},\n {\"_from\": \"ws_object_version/1:1:1\", \"_to\": \"ws_object_version/1:7:1\"},\n ]\n create_test_docs(\"ws_refers_to\", ws_refers_to)\n ws_copied_from = [\n {\"_from\": \"ws_object_version/1:1:1\", \"_to\": \"ws_object_version/1:2:1\"},\n {\"_from\": \"ws_object_version/1:1:1\", \"_to\": \"ws_object_version/1:5:1\"},\n ]\n create_test_docs(\"ws_copied_from\", ws_copied_from)","def generate_test_instances(self):\n for testproblem in self.testproblems:\n for solverconfig in self.solverconfigs:\n self.instances.append(TestInstance(testproblem, solverconfig))","def classes(self):\n raise NotImplementedError(\"Please implement this yourself.\")","def _create_chain(class_type_list, kwargs_list):\n chain = None # module with preprocessing chain\n modules = [] # list of modules (not connected via preprocessing)\n for i, pre_id in enumerate(class_type_list):\n chain = CModule.create(\n pre_id, preprocess=chain, **kwargs_list[i])\n modules.append(CModule.create(pre_id, **kwargs_list[i]))\n return chain, modules","def setUpClass(cls):\n\t\t\n\t\tww.weightwatcher.torch = torch\t\t\n\t\treturn","def generate():\n strategies = []\n\n # Add Centrifugal Bumblepuppy 16+H (RFind based)\n strategies.extend(\n generate_meta_strategy_pair(CentrifugalBumblepuppy16h))\n\n # Add decision tree strategies\n strategies.extend(\n generate_meta_strategy_pair(DecisionTreeStrategy))\n\n # Add decision tree strategies\n strategies.extend(\n generate_meta_strategy_pair(DecisionTreeV10Strategy))\n\n # Add HPS Dojo strategies\n strategies.extend(\n generate_meta_strategy_pair(HPSDojoStrategy))\n\n # Add testing please ignore strategies\n strategies.extend(\n generate_meta_strategy_pair(TestingPlsIgnoreStrategy))\n\n # Add testimono strategy\n strategies.extend(\n generate_meta_strategy_pair(TestimonoStrategy))\n\n # Add RPS Geometry\n strategies.extend(\n generate_meta_strategy_pair(GeometryV4Strategy))\n\n # Add Iocaine Powder strategy\n strategies.extend(\n generate_meta_strategy_pair(IocanePowderStrategy))\n\n # By default, rotate everything\n do_rotations = [True for _ in strategies]\n\n # Anti Trivial\n strategies.extend(\n generate_meta_strategy_pair(\n AntiTrivialStrategy, mirroring=False))\n do_rotations.extend([False])\n\n return strategies, do_rotations","def setUpClass(cls):\n\t\t\t\t\t\n\t\tww.weightwatcher.torch = torch\n\t\tcls.weights_dir = Test_PyStateDictDir._make_tmp_weights_dir(format=MODEL_FILE_FORMATS.PYTORCH)\n\t\t\n\t\treturn","def __init__(self):\n super(Modules, self).__init__()\n \n global superclasses\n superclasses['universe'] = []\n superclasses['actions'] = ['universe']\n superclasses['booleans'] = ['universe']\n\n global instances\n instances['universe'] = set()\n instances['actions'] = set()\n instances['booleans'] = set()","def setup_class(self):\n pass","def setup(cls):\n super().setup()\n cls.search_behaviour = cast(\n GenericSearchBehaviour, cls._skill.skill_context.behaviours.search\n )\n cls.tx_behaviour = cast(\n GenericTransactionBehaviour, cls._skill.skill_context.behaviours.transaction\n )\n cls.strategy = cast(GenericStrategy, cls._skill.skill_context.strategy)\n\n cls.logger = cls._skill.skill_context.logger","def __init__(self):\n self.redis = RedisClient()\n self.crawlers = [crawler_cls() for crawler_cls in crawlers_cls]","def get_models(self):\n classes = self._get_classes()\n models = {\n name: cls\n for name,\n cls in classes\n if cls not in self.exclude_classes and\n issubclass(cls, (Base, experimentalBase))\n }\n models.update(self.custom_constructors)\n return models","def __init__(self, processManager, clientManager):\n self.processManager = processManager\n self.clientManager = clientManager\n self.engine_types = {}\n self.engine_allocations = {}\n self.engine_instances = {}","def setUpClass(cls):\n\n\t\timport ai\n\t\tcls.list_of_AIs = []\n\t\tAIs_available = [module[1] for module in pkgutil.walk_packages(path=['ai'], prefix='ai.')]\n\n\t\tfor AI_module in AIs_available:\n\t\t\tmodule = importlib.import_module(name=AI_module)\n\t\t\tcls.list_of_AIs.append([clss[1] for clss in inspect.getmembers(module, inspect.isclass) if\n\t\t\t\t\t\t\t clss[0].startswith(\"AI\")][-1]())\n\n\t\tassert len(cls.list_of_AIs) > 0, \"Make sure we imported some AIs.\"","def setUp(self):\n self.simulation = FooSimulation(count=5, g=6, h=9, i=12)\n self.foo = Foo(simulation=self.simulation, name='foo', a=4, b=42, c=\"Hello\")\n self.handler = Handler(ProxyLock(self.simulation),\n ProxyLock(self.foo),\n [Attribute(\"count\"), Attribute(\"g\")],\n [Attribute('b')],\n [Attribute('a')])","def setUpClass(cls):\n\t\t\n\t\tww.weightwatcher.torch = torch\n\t\tstate_dict = models.vgg11(weights='VGG11_Weights.IMAGENET1K_V1').state_dict()\n\t\tmodel_name = 'vgg11'\n\n\n\t\twith TemporaryDirectory(dir=TEST_TMP_DIR, prefix=\"ww_\") as model_dir:\n\t\t\tprint(f\"setting up class using {model_dir} as model_dir\")\n\t\t\n\t\t\tstate_dict_filename = os.path.join(model_dir, \"pytorch_model.bin\")\n\t\t\ttorch.save(state_dict, state_dict_filename)\n\t\t\t\n\t\t\tcls.config = ww.WeightWatcher.extract_pytorch_bins(model_dir=model_dir, model_name=model_name)\n\t\t\tcls.weights_dir = cls.config['weights_dir']\n\t\t\t\n\t\treturn","def generate():\n strategies = []\n\n # Add Centrifugal Bumblepuppy 16+H (RFind based)\n strategies.extend(\n generate_meta_strategy_pair(CentrifugalBumblepuppy16h))\n\n # Add decision tree strategies\n strategies.extend(\n generate_meta_strategy_pair(DecisionTreeStrategy))\n\n # Add decision tree strategies\n strategies.extend(\n generate_meta_strategy_pair(DecisionTreeV10Strategy))\n\n # Add HPS Dojo strategies\n strategies.extend(\n generate_meta_strategy_pair(HPSDojoStrategy))\n\n # Add testing please ignore strategies\n strategies.extend(\n generate_meta_strategy_pair(TestingPlsIgnoreStrategy))\n\n # Add testimono strategy\n strategies.extend(\n generate_meta_strategy_pair(TestimonoStrategy))\n\n # Add RPS Geometry\n strategies.extend(\n generate_meta_strategy_pair(GeometryV4Strategy))\n\n # By default, rotate everything\n do_rotations = [True for _ in strategies]\n\n # Anti Trivial\n strategies.extend(\n generate_meta_strategy_pair(\n AntiTrivialStrategy, mirroring=False))\n do_rotations.extend([False])\n\n return strategies, do_rotations","def init_objects():\n gravity = Gravity()\n planet_stencil = PlanetStencil()\n return gravity, planet_stencil","def wrap_simple_classes(self):\n # Base ref-counted classes are abstract because it is necessary to set up reference counting.\n # Wrapper classes do that in their public constructor.\n klasses = []\n for struct in self.repo.structs.values():\n if struct.is_class and struct.c_name not in self.base_classes:\n wrapped_name = struct.vala_name + \"Ref\"\n wrapped_c_name = 'Cef' + wrapped_name\n members = [\n StructMember(\"GData*\", \"private_data\", \"private_data\"),\n StructMember(\"volatile int\", \"ref_count\", \"ref_count\")\n ]\n\n # Vala definition\n klass = Struct(\n c_name=wrapped_c_name,\n vala_name=wrapped_name,\n c_header=\"valacef_api.h\",\n members=members)\n klass.set_parent(struct)\n klass.set_is_class(True)\n construct = Function(\n c_name=wrapped_c_name + \"New\",\n vala_name=wrapped_name,\n c_header=\"valacef_api.h\")\n construct.construct = True\n klass.add_method(construct)\n\n priv_set = Function(\n c_name=wrapped_c_name + \"PrivSet\",\n vala_name=\"priv_set\",\n c_header=\"valacef_api.h\",\n params=[\n (\"const char*\", \"key\"),\n (\"T\", \"data\"),\n ],\n vala_generics=[\"T\"],\n vala_simple_generics=True\n )\n klass.add_method(priv_set)\n priv_get = Function(\n c_name=wrapped_c_name + \"PrivGet\",\n vala_name=\"priv_get\",\n c_header=\"valacef_api.h\",\n params=[\n (\"const char*\", \"key\"),\n ],\n ret_type=\"T\",\n vala_generics=[\"T\"],\n vala_simple_generics=True\n )\n klass.add_method(priv_get)\n klass.add_method(Function(\n c_name=wrapped_c_name + \"PrivDel\",\n vala_name=\"priv_del\",\n c_header=\"valacef_api.h\",\n params=[\n (\"const char*\", \"key\"),\n ],\n ))\n\n klasses.append(klass)\n\n # C definition\n c_klass = Struct(\n c_name=wrapped_c_name,\n vala_name=wrapped_name,\n c_header=\"stdlib.h;capi/cef_base_capi.h\",\n members=members)\n c_klass.set_parent(struct)\n c_klass.set_is_class(True)\n construct = Function(wrapped_c_name + \"New\", wrapped_name, \"\", wrapped_c_name + '*', body=[\n '%s* self = (%s*) calloc(1, sizeof(%s));' % (wrapped_c_name, wrapped_c_name, wrapped_c_name),\n '%s((void*) self, sizeof(%s), sizeof(%s));' % (\n 'cef_base_ref_counted_init_ref_counting', struct.c_name, wrapped_c_name),\n 'g_datalist_init(&(self->private_data));',\n 'return self;'\n ])\n construct.construct = True\n c_klass.add_method(construct)\n\n priv_set = Function(wrapped_c_name + \"PrivSet\", \"priv_set\", \"\", params=[\n (wrapped_c_name + \"*\", \"self\"),\n (\"const char*\", \"key\"),\n (\"void*\", \"data\"),\n ('GDestroyNotify', 'destroy'),\n ],\n body=[\n 'g_assert (self != NULL);',\n 'g_assert (key != NULL);',\n 'g_datalist_id_set_data_full(',\n '&self->private_data, g_quark_from_string(key), data, data ? destroy : (GDestroyNotify) NULL);',\n ])\n c_klass.add_method(priv_set)\n priv_get = Function(wrapped_c_name + \"PrivGet\", \"priv_get\", \"\", params=[\n (wrapped_c_name + \"*\", \"self\"),\n (\"const char*\", \"key\"),\n ],\n ret_type=\"void*\",\n body=[\n 'g_assert (self != NULL);',\n 'g_assert (key != NULL);',\n 'return g_datalist_get_data(&self->private_data, key);',\n ])\n c_klass.add_method(priv_get)\n c_klass.add_method(Function(wrapped_c_name + \"PrivDel\", \"priv_del\", \"\", params=[\n (wrapped_c_name + \"*\", \"self\"),\n (\"const char*\", \"key\"),\n ],\n body=[\n 'g_return_if_fail (self != NULL);',\n 'g_return_if_fail (key != NULL);',\n 'g_datalist_remove_data(&self->private_data, key);',\n ]))\n self.add_c_glue(c_klass)\n\n self.repo.add_struct(*klasses)","def instantiate_all_classes(self, node):\n self.instantiate_classes(node)\n\n for cls in node.classes:\n self.instantiate_classes(cls)\n\n for ns in node.namespaces:\n self.instantiate_all_classes(ns)","def setUp(self):\n self.cashFlowDate=Date(1,October,2018)\n self.fixingDate=Date(1,November,2018)\n self.foreignAmount=1000.0\n self.familyName=\"ECB\"\n self.fixingDays=2\n self.sourceCurrency=USDCurrency()\n self.targetCurrency=EURCurrency()\n self.fixingCalendar=UnitedStates()\n self.todayDate=Date(11, November, 2018)\n self.tsDayCounter=Actual360()\n self.flatForwardUSD=FlatForward(self.todayDate, 0.005, self.tsDayCounter)\n self.sourceYts=RelinkableYieldTermStructureHandle(self.flatForwardUSD)\n self.flatForwardEUR=FlatForward(self.todayDate, 0.03, self.tsDayCounter);\n self.targetYts=RelinkableYieldTermStructureHandle(self.flatForwardEUR)\n self.fxindex=FxIndex(self.familyName,self.fixingDays,self.sourceCurrency,self.targetCurrency,self.fixingCalendar,self.sourceYts,self.targetYts)\n self.fxlinkedcashflow=FXLinkedCashFlow(self.cashFlowDate,self.fixingDate,self.foreignAmount,self.fxindex)","def __init__(self):\n self.weapon = Weapon()\n self.armor = Armor()","def setup():\n env.total_time = 0\n boeing_737 = Airplane(env, 'Boeing 737', 20, 6, 1, False)\n boeing_737.make_rows()\n seats = boeing_737.get_seats()\n passengers = []\n\n # create a passenger for every seat on the plane\n for j in range(0, boeing_737.get_number_of_seats()):\n passenger = Passenger(env, seats[j], boeing_737)\n passengers.append(passenger)\n\n _algorithms = BoardingAlgorithm(env, boeing_737, passengers)\n\n return boeing_737, passengers, _algorithms","def _init_objects(self) -> None:\n self.position = selectors.get_position(self.exchange, self.symbol)\n self.broker = Broker(self.position, self.exchange, self.symbol, self.timeframe)\n\n if self.hp is None and len(self.hyperparameters()) > 0:\n self.hp = {}\n for dna in self.hyperparameters():\n self.hp[dna['name']] = dna['default']","def setUpClass(cls):\n clean_db() # remove all objects created by another tests\n # save the link to the content type\n cls.ctype = ContentType.objects.get_for_model(TestModel)\n # create an object\n cls.object = TestModel.objects.create(name=\"Test object\")\n # create two images related to the object\n cls.image1 = models.Image.objects.create(\n image=get_image_in_memory_data(),\n position=0,\n content_type=cls.ctype,\n object_id=cls.object.id\n )\n cls.image2 = models.Image.objects.create(\n image=get_image_in_memory_data(),\n position=1,\n content_type=cls.ctype,\n object_id=cls.object.id\n )\n # create another object without related images\n cls.alone_object = TestModel.objects.create(\n name=\"Alone test object\"\n )","def import_all_handlers(self):\n import os\n exclude_list=[\"base\"]\n\n #\n # the list of handlers (excluding base. Add more you dont want\n # to be loaded or inspected to exclude_list above.)\n #\n mods=[]\n module_path = os.path.abspath(os.path.join(os.path.dirname( __file__ ), 'handlers'))\n #print(\"importing handlers from: \" + module_path)\n for mod in os.listdir( module_path ):\n mod = mod.split(\".\")[0]\n if not mod.startswith(\"_\") and not mod in exclude_list:\n #print(\" now processing: \" + str(mod))\n mods.append(mod)\n \n #print(\"mods: \" + str(mods))\n class_list = []\n # load all the models from their modules (mods)\n #print(str(mods))\n import importlib\n for m in mods:\n #print(\"importing: \" + 'pow_comments.handlers.' + m) \n try:\n mod = importlib.import_module('pow_comments.handlers.' + m)\n except:\n pass\n #print(dir(mod))","def test_regen_methods(self, tmpdir, treantclass):\n with tmpdir.as_cwd():\n t1 = treantclass('newone')\n t2 = treantclass('newone')\n assert t1.uuid == t2.uuid\n\n t3 = treantclass('newone', new=True)\n assert t3.uuid != t2.uuid\n\n t4 = treantclass(t3.filepath)\n assert t4.uuid == t3.uuid","def create_effect_instances(self):\r\n raise NotImplementedError()","def _initObjects(self):\n\n print \"DEBUG: Initializing Entities\"\n ObjectType.initializeObjectTypes()","def process_class_list(self, module, classes):","def _prepare_wsdl_objects(self):\r\n self.TrackPackageIdentifier = self.client.factory.create('TrackPackageIdentifier')\r\n # Default to tracking number.\r\n self.TrackPackageIdentifier.Type = 'TRACKING_NUMBER_OR_DOORTAG'","def import_all_known_classes(debug=False):\r\n\r\n output = {}\r\n for cls in KnownClass.objects:\r\n if debug:\r\n print \"Importing %s.%s\"%(cls.module_name, cls.class_name)\r\n x = get_class(cls.module_name, cls.class_name)\r\n output[(cls.module_name, cls.class_name)] = x()\r\n return output","def test_05_transaction_create_buy_stock(self):\n portfolio = Portfolio.get_portfolio_by_slug(\"test\")\n user = \"automated unit tester\"\n\n buy_stock_aapl = Transaction.buy_stock(\n portfolio=portfolio,\n asset=\"AAPL\",\n t_currency=TRANSACTION_CURRENCY_USD,\n amount=256,\n unit_price=162.94,\n user=user\n )\n\n buy_stock_ibm = Transaction.buy_stock(\n portfolio=portfolio,\n asset=\"IBM\",\n t_currency=TRANSACTION_CURRENCY_USD,\n amount=128,\n unit_price=145.56,\n user=user\n )\n\n buy_stock_msft = Transaction.buy_stock(\n portfolio=portfolio,\n asset=\"MSFT\",\n t_currency=TRANSACTION_CURRENCY_EUR,\n amount=64,\n unit_price=76.22,\n user=user\n )\n\n self.assertTrue(isinstance(buy_stock_aapl, Transaction),\n msg=\"Transaction is NOT returning a valid object while buying an APPLE stock\")\n print(\"Transaction buy_stock method is returning a valid APPLE transaction: {}\".format(\n buy_stock_aapl))\n\n self.assertTrue(isinstance(buy_stock_ibm, Transaction),\n msg=\"Transaction is NOT returning a valid object while buying an IBM stock\")\n print(\"Transaction buy_stock method is returning a valid IBM transaction: {}\".format(\n buy_stock_ibm))\n\n self.assertTrue(isinstance(buy_stock_msft, Transaction),\n msg=\"Transaction is NOT returning a valid object while buying an MICROSOFT stock\")\n print(\"Transaction buy_stock method is returning a valid MICROSOFT transaction: {}\".format(\n buy_stock_msft))","def cls():\n patcher = patch_debounce()\n patcher.start()\n _import = __import__('homeassistant.components.homekit.type_thermostats',\n fromlist=['Thermostat'])\n patcher_tuple = namedtuple('Cls', ['thermostat'])\n yield patcher_tuple(thermostat=_import.Thermostat)\n patcher.stop()","def __init__(self, klass, *args, **kwargs):\n self._klass = klass(*args, **kwargs)","def setup_class(cls):\n cls.ledger_apis = LedgerApis({FETCHAI: DEFAULT_FETCHAI_CONFIG}, FETCHAI)\n cls.ledger_state_proxy = LedgerStateProxy(ledger_apis=cls.ledger_apis)","def get_classes(self):\n return","def _reconstruct_object(deserialized_data):\n for key, value in deserialized_data.items():\n key = key.strip('__')\n if key in registry:\n # Gather the keyword arguments for class *key*\n kwargs = dict(value.items())\n return registry[key](**kwargs)","def setUpClass(cls):\n super().setUpClass() # creates the first object\n # create the second onject of the same model\n cls.second_object = TestModel.objects.create(\n name=\"SecondTestObject\"\n )\n # create an object of anther model\n cls.another_object = AnotherTestModel.objects.create(\n name=\"AnotherTestObject\"\n )"],"string":"[\n \"def _generate_trading_instances(self):\\n print(\\\"Creating DataHandler, Strategy, Portfolio, and ExecutionHandler for\\\")\\n\\n # Set internal data members equal to the classes we passed in earlier, along with necessary parameters.\\n # https://softwareengineering.stackexchange.com/questions/131403/what-is-the-name-of-in-python/131415\\n self.data_handler = self.data_handler_class(self.events, self.csv_dir, self.symbol_list)\\n self.strategy = self.strategy_class(self.data_handler, self.events)\\n self.portfolio = self.portfolio_class(self.data_handler, self.events, self.start_date, self.initial_capital)\\n self.execution_handler = self.execution_handler_class(self.events) # The Event Queue sent to ExecutionHandler\",\n \"def _generate_trading_instances(self):\\n print(\\n \\\"Initizalization...\\\"\\n )\\n\\n self.data_handler = self.data_handler_cls(self.events, self.csv_dir, self.symbol_list, self.start_date,\\n self.end_date)\\n self.portfolio = self.portfolio_cls(self.data_handler, self.events, self.initial_capital, self.start_date,\\n self.strategy_id, self.spread, self.commission,self.csv_dir)\\n self.strategy = self.strategy_cls(self.data_handler, self.events, self.portfolio, self.spread, self.commission)\\n self.plot = self.plot_cls(self.csv_dir, self.portfolio, self.strategy_id)\",\n \"def _generate_trading_instances(self, start_date, end_date, instruments, params):\\n configuration = self.configuration\\n configuration.start_date = start_date\\n configuration.end_date = end_date\\n configuration.instruments = instruments\\n\\n logger.info(\\\"Creating DataHandler, Strategy, Portfolio and ExecutionHandler\\\")\\n logger.info(\\\"Start date: %s\\\" % start_date)\\n logger.info(\\\"End date: %s\\\" % end_date)\\n logger.info(\\\"Instrument(s): %s...\\\" % instruments)\\n logger.info(\\\"Params: %s...\\\" % params)\\n\\n self.data_handler = self.data_handler_cls(self.events, configuration)\\n self.strategy = self.strategy_cls(self.data_handler, self.events, configuration, **params)\\n self.portfolio = self.portfolio_cls(self.data_handler, self.events, configuration)\\n self.execution_handler = self.execution_handler_cls(self.data_handler, self.events, configuration)\",\n \"def _generate_trading_instances(self, sp):\\n print(\\n \\\"Initialization...\\\"\\n )\\n self.data_handler = self.data_handler_cls(self.events, self.csv_dir, self.symbol_list, self.start_date,\\n self.end_date)\\n self.portfolio = self.portfolio_cls(self.data_handler, self.events, self.initial_capital, self.start_date,\\n self.strategy_id, self.spread, self.commission,self.csv_dir)\\n self.strategy = self.strategy_cls(self.data_handler, self.events, self.portfolio, self.spread, self.commission,\\n sp)\\n self.plot = self.plot_cls(self.csv_dir, self.portfolio, self.strategy_id)\",\n \"def create_all(self, registry):\\n for cls in registry.values():\\n self.create_class(cls)\",\n \"def _fill_class_dicts():\\n global _taxonomy_classes\\n global _data_classes\\n if not _taxonomy_classes:\\n _taxonomy_classes = get_taxonomies()\\n if not _data_classes:\\n stack = []\\n next_module = data\\n while next_module is not None:\\n stack += _inspect_module(next_module)\\n if stack:\\n next_module = stack.pop()\\n else:\\n next_module = None\",\n \"def make_objects(self):\\n pass\",\n \"def __init__(self):\\n if DynamicImporter._instance is not None:\\n raise Exception(\\\"DynamicImporter instance already exists!\\\")\\n DynamicImporter._instance = self\\n\\n current_path = Path(__file__).parent\\n test_path = current_path / \\\"testdata\\\"\\n files = test_path.rglob(\\\"*.py\\\")\\n\\n for file in files:\\n\\n if file.name in [\\\"__init__.py\\\", \\\"test_module.py\\\", \\\"test_registry.py\\\", \\\"connections.py\\\"]:\\n continue\\n\\n name = file.stem\\n module = import_module(f\\\"testdata.{name}\\\")\\n class_title = f\\\"{name.title()}Test\\\"\\n\\n try:\\n _class = getattr(module, class_title) # get the class\\n self.class_list[class_title] = _class # add the class to the class list\\n except AttributeError: # don't throw exceptions for files that don't have a test\\n continue\",\n \"def __init__(self):\\n for base in AutomationSetup.__bases__:\\n base.__init__(self)\",\n \"def setUpClass(cls):\\n\\n Base._Base__nb_objects = 0\\n cls.b1 = Base()\\n cls.b2 = Base()\\n cls.b3 = Base(22)\\n cls.b4 = Base(2.2)\\n cls.b5 = Base(\\\"two\\\")\\n cls.r1 = Rectangle(10, 7, 2, 8)\\n cls.r2 = Rectangle(2, 4)\",\n \"def setup_class(self):\\n\\n class SubFLRW(FLRW):\\n def w(self, z):\\n return super().w(z)\\n\\n self.cls = SubFLRW\\n # H0, Om0, Ode0\\n self.cls_args = (70 * u.km / u.s / u.Mpc, 0.27 * u.one, 0.689 * u.one)\\n self.cls_kwargs = dict(Tcmb0=3.0 * u.K, name=self.__class__.__name__, meta={\\\"a\\\": \\\"b\\\"})\",\n \"def init_elect_types(self):\\n self.wta = WinnerTakeAll()\\n self.proportional = Proportional()\\n self.schulze = Schulze()\\n\\n session.add_all([self.wta, self.proportional, self.schulze])\",\n \"def _create_Work(classname, dataclass):\\n globals()[classname] = type(classname, (Work, dataclass), {})\",\n \"def XtremObjFactory(object_type, object_data, parent_connection):\\r\\n for cls in XtremObject.__subclasses__():\\r\\n if cls.is_class_for(object_type):\\r\\n return cls(object_data, parent_connection)\",\n \"def _setup(self):\\n\\n # Get user data\\n self.symbols = self._get_symbols()\\n self.data_dict = self._get_data()\\n self.portfolio = self.initialize_portfolio()\\n\\n if 'slippage' in self.portfolio:\\n self.slippage = self.portfolio['slippage']\\n else:\\n self.slippage = None\\n\\n # Keep track of all trades\\n self.trade_manager = TradeManager(\\n self.symbols, self.portfolio, self.sql_config\\n )\\n\\n # Initialize state variables that are updated each iteration\\n self.date = None\\n self.data = None\\n self.symbol = None\\n self.currency = None\\n self.last_buy = None\\n self.num_unresolved = 0\\n self.unresolved_trade = False\",\n \"def setup_class(klass):\",\n \"def setup_class(klass):\",\n \"def make_class(attributes, base_classes=()):\\r\\n \\\"*** YOUR CODE HERE ***\\\"\",\n \"def _classes_(cls):\\n for base_cls in cls.__bases__:\\n # Avoid infinite loop\\n if base_cls == Sandbox:\\n continue\\n\\n yield base_cls\",\n \"def _prepare_wsdl_objects(self):\\r\\n\\r\\n\\t# Default behavior is to not request transit information\\r\\n\\tself.ReturnTransitAndCommit = False\\r\\n\\r\\n # This is the primary data structure for processShipment requests.\\r\\n self.RequestedShipment = self.client.factory.create('RequestedShipment')\\r\\n self.RequestedShipment.ShipTimestamp = datetime.now()\\r\\n \\r\\n TotalWeight = self.client.factory.create('Weight')\\r\\n # Start at nothing.\\r\\n TotalWeight.Value = 0.0\\r\\n # Default to pounds.\\r\\n TotalWeight.Units = 'LB'\\r\\n # This is the total weight of the entire shipment. Shipments may\\r\\n # contain more than one package.\\r\\n self.RequestedShipment.TotalWeight = TotalWeight\\r\\n \\r\\n # This is the top level data structure for Shipper information.\\r\\n ShipperParty = self.client.factory.create('Party')\\r\\n ShipperParty.Address = self.client.factory.create('Address')\\r\\n ShipperParty.Contact = self.client.factory.create('Contact')\\r\\n \\r\\n # Link the ShipperParty to our master data structure.\\r\\n self.RequestedShipment.Shipper = ShipperParty\\r\\n\\r\\n # This is the top level data structure for Recipient information.\\r\\n RecipientParty = self.client.factory.create('Party')\\r\\n RecipientParty.Contact = self.client.factory.create('Contact')\\r\\n RecipientParty.Address = self.client.factory.create('Address')\\r\\n \\r\\n # Link the RecipientParty object to our master data structure.\\r\\n self.RequestedShipment.Recipient = RecipientParty\\r\\n \\r\\n Payor = self.client.factory.create('Payor')\\r\\n # Grab the account number from the FedexConfig object by default.\\r\\n Payor.AccountNumber = self._config_obj.account_number\\r\\n # Assume US.\\r\\n Payor.CountryCode = 'US'\\r\\n \\r\\n ShippingChargesPayment = self.client.factory.create('Payment')\\r\\n ShippingChargesPayment.Payor = Payor\\r\\n\\r\\n self.RequestedShipment.ShippingChargesPayment = ShippingChargesPayment\\r\\n \\r\\n # ACCOUNT or LIST\\r\\n self.RequestedShipment.RateRequestTypes = ['ACCOUNT'] \\r\\n \\r\\n # Start with no packages, user must add them.\\r\\n self.RequestedShipment.PackageCount = 0\\r\\n self.RequestedShipment.RequestedPackageLineItems = []\\r\\n \\r\\n # This is good to review if you'd like to see what the data structure\\r\\n # looks like.\\r\\n self.logger.debug(self.RequestedShipment)\",\n \"def setUpClass(cls):\\r\\n cls.jon = Person.create(name='Jon', age=143)\\r\\n cls.eric = Person.create(name='Eric', age=25)\\r\\n cls.blake = Person.create(name='Blake', age=14)\\r\\n\\r\\n cls.physics = Course.create(name='Physics 264', credits=1.0)\\r\\n cls.beekeeping = Course.create(name='Beekeeping', credits=15.0)\\r\\n cls.theoretics = Course.create(name='Theoretical Theoretics', credits=-3.5)\\r\\n\\r\\n cls.eric_in_physics = EnrolledIn.create(cls.eric, cls.physics, date_enrolled=datetime.now(),\\r\\n enthusiasm=10) # eric loves physics\\r\\n cls.jon_in_beekeeping = EnrolledIn.create(cls.jon, cls.beekeeping, date_enrolled=datetime.now(),\\r\\n enthusiasm=1) # jon hates beekeeping\\r\\n\\r\\n cls.blake_in_theoretics = EnrolledIn.create(cls.blake, cls.theoretics, date_enrolled=datetime.now(),\\r\\n enthusiasm=8)\\r\\n\\r\\n cls.blake_beekeeping = TaughtBy.create(cls.beekeeping, cls.blake, overall_mood='Pedantic')\\r\\n cls.jon_physics = TaughtBy.create(cls.physics, cls.jon, overall_mood='Creepy')\\r\\n cls.eric_theoretics = TaughtBy.create(cls.theoretics, cls.eric, overall_mood='Obtuse')\",\n \"def __init__(self):\\n self.classes = {}\",\n \"def setup_class(cls):\",\n \"def setup_class(cls):\",\n \"def _prepare_wsdl_objects(self):\\r\\n self.DeletionControlType = self.client.factory.create('DeletionControlType')\\r\\n self.TrackingId = self.client.factory.create('TrackingId')\\r\\n self.TrackingId.TrackingIdType = self.client.factory.create('TrackingIdType')\",\n \"def setup_class(cls):\\n cls._patch_logger()\\n cls.multiplexer = Multiplexer(\\n [DummyConnection(connection_id=DUMMY_CONNECTION_PUBLIC_ID)]\\n )\\n cls.outbox = OutBox(cls.multiplexer)\\n private_key_pem_path = os.path.join(CUR_PATH, \\\"data\\\", \\\"fet_private_key.txt\\\")\\n eth_private_key_pem_path = os.path.join(CUR_PATH, \\\"data\\\", \\\"fet_private_key.txt\\\")\\n cls.wallet = Wallet(\\n {FETCHAI: private_key_pem_path, ETHEREUM: eth_private_key_pem_path}\\n )\\n cls.ledger_apis = LedgerApis({FETCHAI: DEFAULT_FETCHAI_CONFIG}, FETCHAI)\\n cls.agent_name = \\\"test\\\"\\n cls.ownership_state = OwnershipState()\\n cls.preferences = Preferences()\\n cls.decision_maker = DecisionMaker(\\n agent_name=cls.agent_name,\\n max_reactions=MAX_REACTIONS,\\n outbox=cls.outbox,\\n wallet=cls.wallet,\\n ledger_apis=cls.ledger_apis,\\n )\\n cls.multiplexer.connect()\\n\\n cls.tx_id = \\\"transaction0\\\"\\n cls.tx_sender_addr = \\\"agent_1\\\"\\n cls.tx_counterparty_addr = \\\"pk\\\"\\n cls.info = {\\\"some_info_key\\\": \\\"some_info_value\\\"}\\n cls.ledger_id = \\\"fetchai\\\"\\n\\n cls.decision_maker.start()\",\n \"def setUpClass(cls):\\n super().setUpClass()\\n\\n cls.accessor = OCPReportDBAccessor(cls.schema)\\n cls.report_schema = cls.accessor.report_schema\\n cls.all_tables = list(OCP_REPORT_TABLE_MAP.values())\\n cls.creator = ReportObjectCreator(cls.schema)\\n cls.date_accessor = DateHelper()\\n cls.manifest_accessor = ReportManifestDBAccessor()\\n cls.dh = DateHelper()\",\n \"def load_asset_classes(self) -> None:\\n provider = AlphaVantageProvider()\\n trends_provider = GoogleTrendsProvider()\\n self.asset_classes.append(AssetClass.CurrencyClass(provider, trends_provider))\\n self.asset_classes.append(AssetClass.StockClass(provider, trends_provider))\",\n \"def gen_extractors():\\n return [klass() for klass in gen_extractor_classes()]\",\n \"def build_etl_classes(self):\\n\\n self.clear_etl_classes()\\n\\n for config in list(self.configs.values()):\\n\\n etl_class = self.build(config)\\n\\n self.add_etl_class(etl_class)\",\n \"def __init__(self, obj, datamodel=None):\\n with RecursiveConverter.in_progress:\\n self.obj = obj\\n self.class_name = obj.__class__.__name__\\n self.datamodel = datamodel\\n self.is_root = datamodel is None\\n if self.is_root:\\n RecursiveConverter.converted_modules = {}\\n RecursiveConverter.typedefs = []\\n self.datamodel = VHDLModule('-', obj)\\n\\n # recursively convert all child modules\\n self.childs = []\\n\\n def conv(self, node):\\n if isinstance(node, VHDLList):\\n if node.elements_compatible_typed:\\n if isinstance(node.elems[0], VHDLModule):\\n if self.is_compatible_with_converted_module(node.elems[0]):\\n return\\n self.childs.append(RecursiveConverter(node.elems[0].current, node.elems[0]))\\n\\n else:\\n # dynamic list..need to convert all modules\\n for x in node.elems:\\n if isinstance(x, VHDLModule):\\n if self.is_compatible_with_converted_module(x):\\n return\\n self.childs.append(RecursiveConverter(x.current, x))\\n elif isinstance(node, VHDLModule):\\n if self.is_compatible_with_converted_module(node):\\n return\\n self.childs.append(RecursiveConverter(node.current, node))\\n\\n if self.is_root:\\n logger.info(f'Creating top.vhd ...')\\n self.top_vhdl = TopGenerator(obj)\\n\\n # maybe some input/output is a convertible module?\\n for node in self.inputs:\\n conv(self, node)\\n\\n for node in self.outputs:\\n conv(self, node)\\n\\n # iterate all functions and discover local variables that may need to be converted\\n for x in self.obj.__dict__.values():\\n if isinstance(x, PyhaFunc):\\n for key, val in x.get_local_types().items():\\n if isinstance(val, Hardware):\\n node = init_vhdl_type(key, val)\\n conv(self, node)\\n\\n # convert instance elements before the instance itself, recursive\\n for node in self.datamodel.elems:\\n conv(self, node)\\n\\n self.red_node = get_objects_rednode(obj)\\n convert_name = self.get_module_converted_name(self.datamodel)\\n logger.info(f'{convert_name} to VHDL ...')\\n\\n self.conv = convert(self.red_node, obj) # actual conversion happens here\\n\\n self.vhdl_conversion = str(self.conv)\\n RecursiveConverter.converted_modules[convert_name] = (self.datamodel, self.vhdl_conversion)\\n RecursiveConverter.typedefs.extend(self.conv.build_typedefs())\",\n \"def __init__(self,classes=['normalizeText','tagger','stem','stopWord','spellChecker']):\\n self._support = prebotSupport()\\n self._classes = classes\\n if(\\\"tagger\\\" in self._classes):\\n self._tagger = tagger()\\n if(\\\"normalizeText\\\" in self._classes):\\n self._normalize = normalizeText()\\n if(\\\"spellChecker\\\" in self._classes):\\n self._spellChecker = spellChecker()\\n if(\\\"stopWord\\\" in self._classes):\\n self._stopWord = stopWord()\\n if(\\\"stem\\\" in self._classes):\\n self._stem = stemming()\",\n \"def setUpClass(cls):\\n #First get the source directory setup correctly. For these tests\\n #we are going to use the config libraries instead of duplicating\\n #the code. Only the one module being wrapped will get copied.\\n cls.fdir = path.join(path.abspath(\\\".\\\"), \\\"ftypes\\\")\\n cls.source = path.join(cls.fdir, \\\"symmetry_module.f90\\\")\\n cls._check_dir()\\n \\n settings.use_filesystem_cache = False\\n cls.parser = CodeParser()\\n cls.parser.parse(cls.source)\\n\\n cls.dependencies = [\\\"{}_c\\\".format(m) for m in cls.parser.modules]\\n cls.originals = list(cls.parser.modules.keys())\\n cls.writers = {}\\n\\n #Setup the default test data the we use to run the python wrapper module\\n #methods after ftypes is done.\\n cls.lattice = array([[.5,.5,0],[0,.5,.5],[.5,0,.5]])\\n cls.atomtypes = array([1])\\n cls.basis = array([[0],[0],[0]])\",\n \"def dump_classes():\\n credentials = Credentials()\\n mountpoint = MountPoint()\\n workload = Workload()\\n migrationtarget = MigrationTarget()\\n migration = Migration()\\n\\n with open('../tier_one/python_test_migration', 'wb') as f:\\n dill.dump(credentials, f)\\n dill.dump(mountpoint, f)\\n dill.dump(workload, f)\\n dill.dump(migrationtarget, f)\\n dill.dump(migration, f)\",\n \"def makemodules(self):\\n names = get_unique_name_list_from_class_list(self.cls_modules)\\n for cls, name in zip(self.cls_modules, names):\\n self.makemodule(name, cls)\",\n \"def __init_fixture_methods(self):\\n # init our self.(class_setup|setup|teardown|class_teardown)_fixtures lists\\n for fixture_type in fixture_types:\\n setattr(self, \\\"%s_fixtures\\\" % fixture_type, [])\\n\\n # for setup methods, we want oldest class first. for teardowns, we want newest class first\\n hierarchy = list(reversed(type(self).mro()))\\n for cls in hierarchy[1:]:\\n # mixins on TestCase instances that derive from, say, object, won't be set up properly\\n if hasattr(cls, '_fixture_methods'):\\n # the metaclass stored the class's fixtures in a _fixture_methods instance variable\\n for fixture_type, fixture_methods in cls._fixture_methods.iteritems():\\n bound_fixture_methods = [instancemethod(func, self, self.__class__) for func in fixture_methods]\\n if fixture_type.endswith('setup'):\\n # for setup methods, we want methods defined further back in the\\n # class hierarchy to execute first\\n getattr(self, \\\"%s_fixtures\\\" % fixture_type).extend(bound_fixture_methods)\\n else:\\n # for teardown methods though, we want the opposite\\n setattr(self, \\\"%s_fixtures\\\" % fixture_type, bound_fixture_methods + getattr(self, \\\"%s_fixtures\\\" % fixture_type))\",\n \"def generate():\\n strategies = []\\n\\n # Add Centrifugal Bumblepuppy 16+H (RFind based)\\n strategies.extend(\\n generate_meta_strategy_pair(CentrifugalBumblepuppy16h))\\n\\n # Add Centrifugal Bumblepuppy 16+H (RFind based)\\n strategies.extend(\\n generate_meta_strategy_pair(CentrifugalBumblepuppy4))\\n\\n # Add decision tree strategies\\n strategies.extend(\\n generate_meta_strategy_pair(DecisionTreeStrategy))\\n\\n # Add decision tree strategies\\n strategies.extend(\\n generate_meta_strategy_pair(DecisionTreeV10Strategy))\\n\\n # Add HPS Dojo strategies\\n strategies.extend(\\n generate_meta_strategy_pair(HPSDojoStrategy))\\n\\n # Add testing please ignore strategies\\n strategies.extend(\\n generate_meta_strategy_pair(TestingPlsIgnoreStrategy))\\n\\n # Add testimono strategy\\n strategies.extend(\\n generate_meta_strategy_pair(TestimonoStrategy))\\n\\n # Add RPS Geometry\\n strategies.extend(\\n generate_meta_strategy_pair(GeometryV4Strategy))\\n\\n # Add Iocaine Powder strategy\\n strategies.extend(\\n generate_meta_strategy_pair(IocanePowderStrategy))\\n\\n # Add are you a lucker strategies\\n strategies.extend(\\n generate_meta_strategy_pair(AreYouALuckerStrategy))\\n\\n # Add Greenberg strategies\\n strategies.extend(\\n generate_meta_strategy_pair(GreenbergStrategy))\\n\\n # Add RPS Meta Fix strategies\\n strategies.extend(\\n generate_meta_strategy_pair(RPSMetaFixStrategy))\\n\\n # By default, rotate everything\\n do_rotations = [True for _ in strategies]\\n\\n # Anti Trivial\\n strategies.extend(\\n generate_meta_strategy_pair(\\n AntiTrivialStrategy, mirroring=False))\\n do_rotations.extend([False])\\n\\n return strategies, do_rotations\",\n \"def make_orders(self):\\n\\n # orders to cancel from all of the strategies\\n ocancel = self.get_cancel_orders()\\n\\n # orders to update from all of the strategies\\n oupdate = self.get_update_orders()\\n\\n # new orders from all of the strategies\\n onew = self.get_new_orders()\\n \\n # do we need to cancel, update, or make new orders?\\n tocancel = bool(ocancel[const.BDAQID] or ocancel[const.BFID])\\n toupdate = bool(oupdate[const.BDAQID] or oupdate[const.BFID])\\n tonew = bool(onew[const.BDAQID] or onew[const.BFID])\\n\\n if tocancel:\\n betlog.betlog.debug('cancelling orders: {0}'.format(ocancel))\\n\\n if toupdate:\\n betlog.betlog.debug('updating orders: {0}'.format(oupdate))\\n\\n if tonew:\\n betlog.betlog.debug('making new orders: {0}'.format(onew))\\n\\n if (tocancel or toupdate or tonew):\\n \\n # we could instead do 'monkey patching' here so we don't\\n # need to check this every tick...\\n if self.gconf.PracticeMode:\\n # we don't make any real money bets in practice mode\\n print 'bets not made since in practice mode'\\n return\\n\\n # call multithreaded make orders so that we make all order\\n # requests (cancelling, updating, making new) for BDAQ and\\n # BF simultaneously.\\n corders, uorders, neworders = multi.\\\\\\n make_orders(ocancel, oupdate, onew)\\n\\n # save the full order information to the order store (this will\\n # handle writing to the DB, etc.)\\n self.ostore.add_orders(corders, uorders, neworders)\\n\\n else:\\n \\n # we need to set latest cancel, update, new orders to be\\n # empty.\\n self.ostore.latest = [{const.BDAQID: {}, const.BFID: {}}, \\n {const.BDAQID: {}, const.BFID: {}}, \\n {const.BDAQID: {}, const.BFID: {}}]\",\n \"def generate():\\n strategies = []\\n\\n # Add Centrifugal Bumblepuppy 16+H (RFind based)\\n strategies.extend(\\n generate_meta_strategy_pair(CentrifugalBumblepuppy16h))\\n\\n # Add Centrifugal Bumblepuppy 16+H (RFind based)\\n strategies.extend(\\n generate_meta_strategy_pair(CentrifugalBumblepuppy4))\\n\\n # Add decision tree strategies\\n strategies.extend(\\n generate_meta_strategy_pair(DecisionTreeStrategy))\\n\\n # Add decision tree strategies\\n strategies.extend(\\n generate_meta_strategy_pair(DecisionTreeV10Strategy))\\n\\n # Add HPS Dojo strategies\\n strategies.extend(\\n generate_meta_strategy_pair(HPSDojoStrategy))\\n\\n # Add testing please ignore strategies\\n strategies.extend(\\n generate_meta_strategy_pair(TestingPlsIgnoreStrategy))\\n\\n # Add testimono strategy\\n strategies.extend(\\n generate_meta_strategy_pair(TestimonoStrategy))\\n\\n # Add RPS Geometry\\n strategies.extend(\\n generate_meta_strategy_pair(GeometryV4Strategy))\\n\\n # Add Iocaine Powder strategy\\n strategies.extend(\\n generate_meta_strategy_pair(IocanePowderStrategy))\\n\\n # Add are you a lucker strategies\\n strategies.extend(\\n generate_meta_strategy_pair(AreYouALuckerStrategy))\\n\\n # By default, rotate everything\\n do_rotations = [True for _ in strategies]\\n\\n # Anti Trivial\\n strategies.extend(\\n generate_meta_strategy_pair(\\n AntiTrivialStrategy, mirroring=False))\\n do_rotations.extend([False])\\n\\n return strategies, do_rotations\",\n \"def init_classes():\\r\\n\\r\\n\\tglobal data\\r\\n\\r\\n\\tif data is None:\\r\\n\\t\\twith app.app_context():\\r\\n\\t\\t\\tprint \\\"initializing db\\\"\\r\\n\\t\\r\\n\\t\\t\\tdata = status.DataManager( db_path=dbPath, ext=pycklerext )\\r\\n\\t\\r\\n\\t\\t\\tprint \\\"db loaded\\\"\\r\\n\\r\\n\\telse:\\r\\n\\t\\twith app.app_context():\\r\\n\\t\\t\\tprint \\\"updating db\\\"\\r\\n\\t\\t\\tdata.loadlast()\\r\\n\\t\\t\\tprint \\\"db updated\\\"\\r\\n\\r\\n\\twith app.app_context():\\r\\n\\t\\tg.modules = {\\r\\n\\t\\t\\t'memall': get_mem_all,\\r\\n\\t\\t\\t'memone': get_mem_one\\r\\n\\t\\t}\\r\\n\\t\\tg.glanularity = 60\",\n \"def __init__(self):\\n self.stock = Stock()\\n self.foundations = {suit: Foundation(suit) for suit in SUITS}\\n self.waste = Waste()\\n # Deal cards face-down into each of the seven tableaus from the stock.\\n # Each tableau has as many cards as its number.\\n self.tableaus = [Tableau(self.stock.deal(num, face_up=False)) for num in range(1, 8)]\",\n \"def generate():\\n strategies = []\\n\\n # Add Centrifugal Bumblepuppy 16+H (RFind based)\\n strategies.extend(\\n generate_meta_strategy_pair(CentrifugalBumblepuppy16h))\\n\\n # Add Centrifugal Bumblepuppy 16+H (RFind based)\\n strategies.extend(\\n generate_meta_strategy_pair(CentrifugalBumblepuppy4))\\n\\n # Add decision tree strategies\\n strategies.extend(\\n generate_meta_strategy_pair(DecisionTreeStrategy))\\n\\n # Add decision tree strategies\\n strategies.extend(\\n generate_meta_strategy_pair(DecisionTreeV10Strategy))\\n\\n # Add HPS Dojo strategies\\n strategies.extend(\\n generate_meta_strategy_pair(HPSDojoStrategy))\\n\\n # Add testing please ignore strategies\\n strategies.extend(\\n generate_meta_strategy_pair(TestingPlsIgnoreStrategy))\\n\\n # Add testimono strategy\\n strategies.extend(\\n generate_meta_strategy_pair(TestimonoStrategy))\\n\\n # Add RPS Geometry\\n strategies.extend(\\n generate_meta_strategy_pair(GeometryV4Strategy))\\n\\n # Add Iocaine Powder strategy\\n strategies.extend(\\n generate_meta_strategy_pair(IocanePowderStrategy))\\n\\n # Add are you a lucker strategies\\n strategies.extend(\\n generate_meta_strategy_pair(AreYouALuckerStrategy))\\n\\n # Add RPS Meta Fix strategies\\n strategies.extend(\\n generate_meta_strategy_pair(RPSMetaFixStrategy))\\n\\n # By default, rotate everything\\n do_rotations = [True for _ in strategies]\\n\\n # Anti Trivial\\n strategies.extend(\\n generate_meta_strategy_pair(\\n AntiTrivialStrategy, mirroring=False))\\n do_rotations.extend([False])\\n\\n return strategies, do_rotations\",\n \"def test_instances(self):\\n a = CommonMixin()\\n a.__dict__.update(**self.data)\\n\\n b = CommonMixin()\\n b.__dict__.update(**self.data)\\n\\n c = ExampleClass()\\n c.__dict__.update(**self.data)\\n\\n return a, b, c\",\n \"def create_ast(client: TypeAny) -> Globals:\\n ast = Globals(client=client)\\n\\n modules: TypeList[TypeTuple[str, TypeAny]] = [\\n (\\\"xgboost\\\", xgb),\\n (\\\"xgboost.core\\\", xgb.core),\\n (\\\"xgboost.sklearn\\\", xgb.sklearn),\\n ]\\n\\n classes: TypeList[TypeTuple[str, str, TypeAny]] = [\\n (\\\"xgboost.DMatrix\\\", \\\"xgboost.DMatrix\\\", xgb.core.DMatrix),\\n (\\\"xgboost.core.DMatrix\\\", \\\"xgboost.core.DMatrix\\\", xgb.core.DMatrix),\\n (\\\"xgboost.core.Booster\\\", \\\"xgboost.core.Booster\\\", xgb.core.Booster),\\n (\\n \\\"xgboost.core.XGBoostError\\\",\\n \\\"xgboost.core.XGBoostError\\\",\\n xgb.core.XGBoostError,\\n ),\\n # classifiers\\n (\\\"xgboost.XGBClassifier\\\", \\\"xgboost.XGBClassifier\\\", xgb.XGBClassifier),\\n (\\\"xgboost.XGBRFClassifier\\\", \\\"xgboost.XGBRFClassifier\\\", xgb.XGBRFClassifier),\\n # (\\\"xgboost.dask.DaskXGBRFClassifier\\\"), Currently dask is not supported in syft\\n # regreessors\\n (\\\"xgboost.XGBRegressor\\\", \\\"xgboost.XGBRegressor\\\", xgb.XGBRegressor),\\n (\\\"xgboost.XGBRFRegressor\\\", \\\"xgboost.XGBRFRegressor\\\", xgb.XGBRFRegressor),\\n # (\\\"xgboost.dask.DaskXGBRFRegressor\\\"), Currently dask is not supported in syft\\n ]\\n\\n methods = [\\n (\\\"xgboost.train\\\", \\\"xgboost.core.Booster\\\"),\\n (\\\"xgboost.core.Booster.predict\\\", \\\"numpy.ndarray\\\"),\\n # classifiers\\n (\\\"xgboost.XGBClassifier.fit\\\", \\\"xgboost.XGBClassifier\\\"),\\n (\\\"xgboost.XGBClassifier.predict\\\", \\\"numpy.ndarray\\\"),\\n (\\\"xgboost.XGBRFClassifier.fit\\\", \\\"xgboost.XGBRFClassifier\\\"),\\n (\\\"xgboost.XGBRFClassifier.predict\\\", \\\"numpy.ndarray\\\"),\\n # regressors\\n (\\\"xgboost.XGBRegressor.fit\\\", \\\"xgboost.XGBRegressor\\\"),\\n (\\\"xgboost.XGBRegressor.predict\\\", \\\"numpy.ndarray\\\"),\\n (\\\"xgboost.XGBRFRegressor.fit\\\", \\\"xgboost.XGBRFClassifier\\\"),\\n (\\\"xgboost.XGBRFRegressor.predict\\\", \\\"numpy.ndarray\\\"),\\n ]\\n\\n add_modules(ast, modules)\\n add_classes(ast, classes)\\n add_methods(ast, methods)\\n\\n for klass in ast.classes:\\n klass.create_pointer_class()\\n klass.create_send_method()\\n klass.create_storable_object_attr_convenience_methods()\\n\\n return ast\",\n \"def setUpClass(cls):\\n\\t\\t\\t\\t\\t\\n\\t\\tww.weightwatcher.torch = torch\\n\\t\\tcls.weights_dir = Test_SafeTensorsDir._make_tmp_weights_dir(format=MODEL_FILE_FORMATS.SAFETENSORS)\\n\\t\\t\\n\\t\\treturn\",\n \"def setUpClass(cls):\\n\\t\\t\\t\\t\\t\\n\\t\\tww.weightwatcher.torch = torch\\n\\t\\tcls.weights_dir = Test_WWFlatFiles._make_tmp_weights_dir()\\n\\t\\t\\n\\t\\treturn\",\n \"def generate():\\n strategies = []\\n\\n # Add Centrifugal Bumblepuppy 16+H (RFind based)\\n strategies.extend(\\n generate_meta_strategy_pair(CentrifugalBumblepuppy16h))\\n\\n # Add Centrifugal Bumblepuppy 16+H (RFind based)\\n strategies.extend(\\n generate_meta_strategy_pair(CentrifugalBumblepuppy4))\\n\\n # Add decision tree strategies\\n strategies.extend(\\n generate_meta_strategy_pair(DecisionTreeStrategy))\\n\\n # Add decision tree strategies\\n strategies.extend(\\n generate_meta_strategy_pair(DecisionTreeV10Strategy))\\n\\n # Add HPS Dojo strategies\\n strategies.extend(\\n generate_meta_strategy_pair(HPSDojoStrategy))\\n\\n # Add testing please ignore strategies\\n strategies.extend(\\n generate_meta_strategy_pair(TestingPlsIgnoreStrategy))\\n\\n # Add testimono strategy\\n strategies.extend(\\n generate_meta_strategy_pair(TestimonoStrategy))\\n\\n # Add RPS Geometry\\n strategies.extend(\\n generate_meta_strategy_pair(GeometryV4Strategy))\\n\\n # Add Iocaine Powder strategy\\n strategies.extend(\\n generate_meta_strategy_pair(IocanePowderStrategy))\\n\\n # By default, rotate everything\\n do_rotations = [True for _ in strategies]\\n\\n # Anti Trivial\\n strategies.extend(\\n generate_meta_strategy_pair(\\n AntiTrivialStrategy, mirroring=False))\\n do_rotations.extend([False])\\n\\n return strategies, do_rotations\",\n \"def setUpClass(cls):\\n\\t\\t\\t\\t\\t\\n\\t\\tww.weightwatcher.torch = torch\\n\\t\\tcls.weights_dir = Test_SafeTensorsDir._make_tmp_weights_dir(format=MODEL_FILE_FORMATS.SAFETENSORS, layer_map=False)\\n\\t\\t\\n\\t\\treturn\",\n \"def _prepare_wsdl_objects(self):\\r\\n # This is the primary data structure for processShipment requests.\\r\\n self.RequestedShipment = self.client.factory.create('RequestedShipment')\\r\\n self.RequestedShipment.ShipTimestamp = datetime.now()\\r\\n \\r\\n TotalWeight = self.client.factory.create('Weight')\\r\\n # Start at nothing.\\r\\n TotalWeight.Value = 0.0\\r\\n # Default to pounds.\\r\\n TotalWeight.Units = 'LB'\\r\\n # This is the total weight of the entire shipment. Shipments may\\r\\n # contain more than one package.\\r\\n self.RequestedShipment.TotalWeight = TotalWeight\\r\\n \\r\\n # This is the top level data structure for Shipper information.\\r\\n ShipperParty = self.client.factory.create('Party')\\r\\n ShipperParty.Address = self.client.factory.create('Address')\\r\\n ShipperParty.Contact = self.client.factory.create('Contact')\\r\\n \\r\\n # Link the ShipperParty to our master data structure.\\r\\n self.RequestedShipment.Shipper = ShipperParty\\r\\n\\r\\n # This is the top level data structure for Recipient information.\\r\\n RecipientParty = self.client.factory.create('Party')\\r\\n RecipientParty.Contact = self.client.factory.create('Contact')\\r\\n RecipientParty.Address = self.client.factory.create('Address')\\r\\n \\r\\n # Link the RecipientParty object to our master data structure.\\r\\n self.RequestedShipment.Recipient = RecipientParty\\r\\n \\r\\n Payor = self.client.factory.create('Payor')\\r\\n # Grab the account number from the FedexConfig object by default.\\r\\n Payor.AccountNumber = self._config_obj.account_number\\r\\n # Assume US.\\r\\n Payor.CountryCode = 'US'\\r\\n \\r\\n ShippingChargesPayment = self.client.factory.create('Payment')\\r\\n ShippingChargesPayment.Payor = Payor\\r\\n\\r\\n self.RequestedShipment.ShippingChargesPayment = ShippingChargesPayment\\r\\n self.RequestedShipment.LabelSpecification = self.client.factory.create('LabelSpecification')\\r\\n # ACCOUNT or LIST\\r\\n self.RequestedShipment.RateRequestTypes = ['ACCOUNT'] \\r\\n \\r\\n # Start with no packages, user must add them.\\r\\n self.RequestedShipment.PackageCount = 0\\r\\n self.RequestedShipment.RequestedPackageLineItems = []\\r\\n \\r\\n # This is good to review if you'd like to see what the data structure\\r\\n # looks like.\\r\\n self.logger.debug(self.RequestedShipment)\",\n \"def register_classes():\\n DiffuseCompChain.register_class()\\n CatalogCompChain.register_class()\\n DiffuseAnalysisChain.register_class()\",\n \"def local_classes(self, classnames, typesets=frozenset(['cy', 'py'])):\\n saved = {}\\n for name in classnames:\\n if 'c' in typesets and name in self.cython_ctypes:\\n saved[name, 'c'] = _undot_class_name(name, self.cython_ctypes)\\n if 'cy' in typesets and name in self.cython_cytypes:\\n saved[name, 'cy'] = _undot_class_name(name, self.cython_cytypes)\\n if 'py' in typesets and name in self.cython_pytypes:\\n saved[name, 'py'] = _undot_class_name(name, self.cython_pytypes)\\n self.clearmemo()\\n yield\\n for name in classnames:\\n if 'c' in typesets and name in self.cython_ctypes:\\n _redot_class_name(name, self.cython_ctypes, saved[name, 'c'])\\n if 'cy' in typesets and name in self.cython_cytypes:\\n _redot_class_name(name, self.cython_cytypes, saved[name, 'cy'])\\n if 'py' in typesets and name in self.cython_pytypes:\\n _redot_class_name(name, self.cython_pytypes, saved[name, 'py'])\\n self.clearmemo()\",\n \"def __create_custom_objects():\\n # make some preparation to properly load objects from keras_contribute\\n instance_holder = {\\\"instance\\\": None}\\n\\n class ClassWrapper(CRF):\\n def __init__(self, *args, **kwargs):\\n instance_holder[\\\"instance\\\"] = self\\n super(ClassWrapper, self).__init__(*args, **kwargs)\\n\\n def loss(*args):\\n method = getattr(instance_holder[\\\"instance\\\"], \\\"loss_function\\\")\\n return method(*args)\\n\\n def accuracy(*args):\\n method = getattr(instance_holder[\\\"instance\\\"], \\\"accuracy\\\")\\n return method(*args)\\n\\n return {\\\"ClassWrapper\\\": ClassWrapper, \\\"CRF\\\": ClassWrapper, \\\"crf_loss\\\": loss,\\n \\\"crf_viterbi_accuracy\\\": accuracy}\",\n \"def create_effect_classes(self):\\r\\n effects.polulate(self.effect_packages)\",\n \"def __init__(self, data):\\n self.bees = [Bee(b) for b in data[\\\"bees\\\"]]\\n self.flowers = [Flower(f) for f in data[\\\"flowers\\\"]]\\n self.hives = [Hive(h) for h in data[\\\"hives\\\"]]\",\n \"def setup_class(cls):\\n super(TestCliTransferFetchAINetwork, cls).setup_class()\\n cls.agent_name2 = \\\"agent_\\\" + \\\"\\\".join(\\n random.choices(string.ascii_lowercase, k=5) # nosec\\n )\\n cls.create_agents(cls.agent_name2)\\n\\n cls.gen_key(cls.agent_name)\\n cls.gen_key(cls.agent_name2)\",\n \"def start_trading(self):\\n backtest_result_dir = os.path.join(self.result_dir, str(self.backtest_date))\\n os.mkdir(backtest_result_dir)\\n backtest_scores_path = os.path.join(backtest_result_dir, 'scores.csv')\\n # all_backtest_scores_exists = os.path.isfile(self.all_backtest_scores_path)\\n\\n last_backtest_scores = open(self.last_backtest_scores_path, \\\"w\\\")\\n backtest_scores = open(backtest_scores_path, \\\"w\\\")\\n\\n\\n fieldnames = [ 'Backtest Name', 'Backtest Date', 'Strategy', 'Start Date', 'End Date', 'Instrument(s)', 'Params'] + \\\\\\n ['Number of signals', 'Number of orders', 'Number of trades', 'Total USD Return', 'Total BTC Return',\\n 'Sharpe Ratio', 'BTC Sharpe Ratio', 'Max Drawdown', 'BTC Max Drawdown', 'Drawdown Duration', 'BTC Drawdown Duration',\\n 'Monthly BTC Return', 'Yearly BTC Return', 'Avg. winning trade', 'Median duration', 'Avg. losing trade', 'Median returns winning', 'Largest losing trade',\\n 'Gross loss', 'Largest winning trade', 'Avg duration', 'Avg returns losing', 'Median returns losing', 'Profit factor',\\n 'Winning round trips', 'Percent profitable', 'Total profit', 'Shortest duration', 'Median returns all round trips',\\n 'Losing round trips', 'Longest duration', 'Avg returns all round trips', 'Gross profit', 'Avg returns winning',\\n 'Total number of round trips', 'Ratio Avg. Win:Avg. Loss', 'Avg. trade net profit', 'Even round trips',\\n 'Configuration Filename', 'Leverage']\\n\\n try:\\n with last_backtest_scores as a, backtest_scores as b:\\n writer_a = csv.DictWriter(a, fieldnames=fieldnames)\\n writer_b = csv.DictWriter(b, fieldnames=fieldnames)\\n writer_a.writeheader()\\n writer_b.writeheader()\\n\\n # if not all_backtest_scores_exists:\\n # writer_c.writeheader()\\n\\n for i, (start, end) in enumerate(zip(self.start_dates, self.end_dates)):\\n for j, instruments in enumerate(self.instrument_list):\\n for k, params in enumerate(self.strategy_params):\\n num_backtest = i * (self.num_instruments * self.num_params + 1) + j * (self.num_params + 1) + k + 1\\n print(\\\"Strategy %s out of %s\\\" % (num_backtest, self.num_backtests))\\n self._generate_trading_instances(start, end, instruments, params)\\n self._run()\\n stats = self._process_results()\\n\\n general_stats = stats['general']\\n pnl_stats = stats['pnl']['All trades'].to_dict()\\n summary_stats = stats['summary']['All trades'].to_dict()\\n duration_stats = stats['duration']['All trades'].to_dict()\\n return_stats = stats['returns']['All trades'].to_dict()\\n params_value = '/'.join([ '{}:{}'.format(item[0], item[1]) for item in params.items() ])\\n\\n row = {\\n 'Backtest Name': self.backtest_name,\\n 'Backtest Date': self.backtest_date,\\n 'Strategy': self.strategy.strategy_name,\\n 'Start Date': start,\\n 'End Date': end,\\n 'Instrument(s)': format_instrument_list(instruments),\\n 'Params': params_value,\\n 'Number of signals': self.signals,\\n 'Number of orders': self.orders,\\n 'Number of trades': self.fills,\\n **general_stats,\\n **pnl_stats,\\n **summary_stats,\\n **duration_stats,\\n **return_stats,\\n 'Configuration Filename': self.configuration_filename,\\n 'Leverage': self.default_leverage\\n }\\n\\n writer_a.writerow(row)\\n writer_b.writerow(row)\\n\\n except IOError:\\n print('I/O Error')\\n\\n all_scores_csv = pd.concat([ pd.read_csv(self.all_backtest_scores_path), pd.read_csv(backtest_scores_path)])\\n all_scores_csv.to_csv(self.all_backtest_scores_path, columns=fieldnames, index=False, encoding='utf-8-sig')\\n\\n self._open_results_in_excel()\",\n \"def __init__(self):\\n super().__init__()\\n self.name = '' # name of this istance (alias)\\n self.type = type(self).__name__ # specific type within this class\\n self.verbosity = None # verbosity level (see message handler)\\n self.globalAttributes = {} # this is a dictionary that contains parameters that are set at the level of the base classes defining the types\\n self._knownAttribute = [] # this is a list of strings representing the allowed attribute in the xml input for the class\\n self._knownAttribute += ['name','verbosity'] # attributes that are known\\n self.printTag = 'BaseType' # the tag that refers to this class in all the specific printing\\n self.variableGroups = {} # the variables this class needs to be aware of\\n self.metadataKeys = set() # list of registered metadata keys to expect from this entity\\n self.metadataParams = {} # dictionary of registered metadata keys with repect to their indexes\",\n \"def _decorate(cls):\\n global_validators = [session_required, catch_typeerror]\\n # Cheat methods _hosts_name_label\\n # -------------\\n # Methods that have a trivial implementation for all classes.\\n # 1. get_by_uuid == getting by ref, so just return uuid for\\n # all get_by_uuid() methods.\\n \\n for api_cls in classes.keys():\\n # We'll let the autoplug classes implement these functions\\n # themselves - its much cleaner to do it in the base class\\n \\n get_by_uuid = '%s_get_by_uuid' % api_cls\\n get_uuid = '%s_get_uuid' % api_cls\\n get_all_records = '%s_get_all_records' % api_cls \\n\\n def _get_by_uuid(_1, _2, ref):\\n return xen_api_success(ref)\\n\\n def _get_uuid(_1, _2, ref):\\n return xen_api_success(ref)\\n\\n def unpack(v):\\n return v.get('Value')\\n\\n def _get_all_records(_api_cls):\\n return lambda s, session: \\\\\\n xen_api_success(dict([(ref, unpack(getattr(cls, '%s_get_record' % _api_cls)(s, session, ref)))\\\\\\n for ref in unpack(getattr(cls, '%s_get_all' % _api_cls)(s, session))]))\\n\\n setattr(cls, get_by_uuid, _get_by_uuid)\\n setattr(cls, get_uuid, _get_uuid)\\n setattr(cls, get_all_records, _get_all_records(api_cls))\\n\\n # Autoplugging classes\\n # --------------------\\n # These have all of their methods grabbed out from the implementation\\n # class, and wrapped up to be compatible with the Xen-API.\\n\\n# def getter(ref, type):\\n# return XendAPIStore.get(ref, type)\\n\\n def wrap_method(name, new_f):\\n try:\\n f = getattr(cls, name)\\n wrapped_f = (lambda * args: new_f(f, *args))\\n wrapped_f.api = f.api\\n wrapped_f.async = f.async\\n setattr(cls, name, wrapped_f)\\n except AttributeError:\\n # Logged below (API call: %s not found)\\n pass\\n\\n\\n def setter_event_wrapper(api_cls, attr_name):\\n setter_name = '%s_set_%s' % (api_cls, attr_name)\\n wrap_method(\\n setter_name,\\n lambda setter, s, session, ref, *args:\\n _setter_event_dispatch(s, setter, api_cls, attr_name,\\n session, ref, args))\\n\\n\\n def ctor_event_wrapper(api_cls):\\n ctor_name = '%s_create' % api_cls\\n wrap_method(\\n ctor_name,\\n lambda ctor, s, session, *args:\\n _ctor_event_dispatch(s, ctor, api_cls, session, args))\\n\\n\\n def dtor_event_wrapper(api_cls):\\n dtor_name = '%s_destroy' % api_cls\\n wrap_method(\\n dtor_name,\\n lambda dtor, s, session, ref, *args:\\n _dtor_event_dispatch(s, dtor, api_cls, session, ref, args))\\n\\n\\n # Wrapping validators around XMLRPC calls\\n # ---------------------------------------\\n for api_cls, validator in classes.items():\\n def doit(n, takes_instance, async_support=False,\\n return_type=None):\\n n_ = n.replace('.', '_')\\n try:\\n f = getattr(cls, n_)\\n if n not in argcounts:\\n argcounts[n] = f.func_code.co_argcount - 1\\n \\n validators = takes_instance and validator and \\\\\\n [validator] or []\\n \\n validators += global_validators\\n for v in validators:\\n f = v(f)\\n f.api = n\\n f.async = async_support\\n if return_type:\\n f.return_type = return_type\\n \\n setattr(cls, n_, f)\\n except AttributeError:\\n log.warn(\\\"API call: %s not found\\\" % n)\\n\\n \\n ro_attrs = getattr(cls, '%s_attr_ro' % api_cls, []) \\\\\\n + cls.Base_attr_ro\\n rw_attrs = getattr(cls, '%s_attr_rw' % api_cls, []) \\\\\\n + cls.Base_attr_rw\\n methods = getattr(cls, '%s_methods' % api_cls, []) \\\\\\n + cls.Base_methods\\n funcs = getattr(cls, '%s_funcs' % api_cls, []) \\\\\\n + cls.Base_funcs\\n\\n # wrap validators around readable class attributes\\n for attr_name in ro_attrs + rw_attrs:\\n doit('%s.get_%s' % (api_cls, attr_name), True,\\n async_support=False)\\n\\n # wrap validators around writable class attrributes\\n for attr_name in rw_attrs:\\n doit('%s.set_%s' % (api_cls, attr_name), True,\\n async_support=False)\\n setter_event_wrapper(api_cls, attr_name)\\n\\n # wrap validators around methods\\n for method_name, return_type in methods:\\n doit('%s.%s' % (api_cls, method_name), True,\\n async_support=True)\\n\\n # wrap validators around class functions\\n for func_name, return_type in funcs:\\n \\n doit('%s.%s' % (api_cls, func_name), False,\\n async_support=True,\\n return_type=return_type)\\n \\n ctor_event_wrapper(api_cls)\\n dtor_event_wrapper(api_cls)\",\n \"def iter_cls(*classes, blacklist=tuple()):\\n for bases in permutations(classes):\\n if bases not in blacklist:\\n yield type('_'.join(c.__name__ for c in bases), bases, {})\",\n \"def __prepare_wsdl_objects(self):\\r\\n pass\",\n \"def process_class(self, parent, cls):\\n if cls.typemap.flat_name in self.class_map:\\n raise RuntimeError(\\\"process_class: class {} already exists in class_map\\\"\\n .format(cls.typemap.flat_name))\\n self.class_map[cls.typemap.flat_name] = cls\\n for var in cls.variables:\\n self.add_var_getter_setter(parent, cls, var)\\n cls.functions = self.define_function_suffix(cls.functions)\",\n \"def setUpClass(cls):\\n\\n check_spec_test_env()\\n\\n ws_object_version = [\\n _ws_obj(1, 1, 1), # root/origin object\\n _ws_obj(1, 2, 1), # copy object\\n _ws_obj(1, 3, 1), # provenance object\\n _ws_obj(1, 4, 1), # reference object\\n _ws_obj(1, 5, 1, is_public=False), # private copy obj\\n _ws_obj(1, 6, 1, is_public=False), # private prov obj\\n _ws_obj(1, 7, 1, is_public=False), # private ref obj\\n ]\\n create_test_docs(\\\"ws_object_version\\\", ws_object_version)\\n ws_type_version = [{\\\"_key\\\": \\\"Module.Type1-1.0\\\"}]\\n create_test_docs(\\\"ws_type_version\\\", ws_type_version)\\n ws_obj_instance_of_type = [\\n {\\n \\\"_from\\\": \\\"ws_object_version/1:1:1\\\",\\n \\\"_to\\\": \\\"ws_type_version/Module.Type1-1.0\\\",\\n },\\n {\\n \\\"_from\\\": \\\"ws_object_version/1:2:1\\\",\\n \\\"_to\\\": \\\"ws_type_version/Module.Type1-1.0\\\",\\n },\\n {\\n \\\"_from\\\": \\\"ws_object_version/1:3:1\\\",\\n \\\"_to\\\": \\\"ws_type_version/Module.Type1-1.0\\\",\\n },\\n {\\n \\\"_from\\\": \\\"ws_object_version/1:4:1\\\",\\n \\\"_to\\\": \\\"ws_type_version/Module.Type1-1.0\\\",\\n },\\n ]\\n create_test_docs(\\\"ws_obj_instance_of_type\\\", ws_obj_instance_of_type)\\n ws_prov_descendant_of = [\\n {\\\"_from\\\": \\\"ws_object_version/1:1:1\\\", \\\"_to\\\": \\\"ws_object_version/1:3:1\\\"},\\n {\\\"_from\\\": \\\"ws_object_version/1:1:1\\\", \\\"_to\\\": \\\"ws_object_version/1:6:1\\\"},\\n ]\\n create_test_docs(\\\"ws_prov_descendant_of\\\", ws_prov_descendant_of)\\n ws_refers_to = [\\n {\\\"_from\\\": \\\"ws_object_version/1:1:1\\\", \\\"_to\\\": \\\"ws_object_version/1:4:1\\\"},\\n {\\\"_from\\\": \\\"ws_object_version/1:1:1\\\", \\\"_to\\\": \\\"ws_object_version/1:7:1\\\"},\\n ]\\n create_test_docs(\\\"ws_refers_to\\\", ws_refers_to)\\n ws_copied_from = [\\n {\\\"_from\\\": \\\"ws_object_version/1:1:1\\\", \\\"_to\\\": \\\"ws_object_version/1:2:1\\\"},\\n {\\\"_from\\\": \\\"ws_object_version/1:1:1\\\", \\\"_to\\\": \\\"ws_object_version/1:5:1\\\"},\\n ]\\n create_test_docs(\\\"ws_copied_from\\\", ws_copied_from)\",\n \"def generate_test_instances(self):\\n for testproblem in self.testproblems:\\n for solverconfig in self.solverconfigs:\\n self.instances.append(TestInstance(testproblem, solverconfig))\",\n \"def classes(self):\\n raise NotImplementedError(\\\"Please implement this yourself.\\\")\",\n \"def _create_chain(class_type_list, kwargs_list):\\n chain = None # module with preprocessing chain\\n modules = [] # list of modules (not connected via preprocessing)\\n for i, pre_id in enumerate(class_type_list):\\n chain = CModule.create(\\n pre_id, preprocess=chain, **kwargs_list[i])\\n modules.append(CModule.create(pre_id, **kwargs_list[i]))\\n return chain, modules\",\n \"def setUpClass(cls):\\n\\t\\t\\n\\t\\tww.weightwatcher.torch = torch\\t\\t\\n\\t\\treturn\",\n \"def generate():\\n strategies = []\\n\\n # Add Centrifugal Bumblepuppy 16+H (RFind based)\\n strategies.extend(\\n generate_meta_strategy_pair(CentrifugalBumblepuppy16h))\\n\\n # Add decision tree strategies\\n strategies.extend(\\n generate_meta_strategy_pair(DecisionTreeStrategy))\\n\\n # Add decision tree strategies\\n strategies.extend(\\n generate_meta_strategy_pair(DecisionTreeV10Strategy))\\n\\n # Add HPS Dojo strategies\\n strategies.extend(\\n generate_meta_strategy_pair(HPSDojoStrategy))\\n\\n # Add testing please ignore strategies\\n strategies.extend(\\n generate_meta_strategy_pair(TestingPlsIgnoreStrategy))\\n\\n # Add testimono strategy\\n strategies.extend(\\n generate_meta_strategy_pair(TestimonoStrategy))\\n\\n # Add RPS Geometry\\n strategies.extend(\\n generate_meta_strategy_pair(GeometryV4Strategy))\\n\\n # Add Iocaine Powder strategy\\n strategies.extend(\\n generate_meta_strategy_pair(IocanePowderStrategy))\\n\\n # By default, rotate everything\\n do_rotations = [True for _ in strategies]\\n\\n # Anti Trivial\\n strategies.extend(\\n generate_meta_strategy_pair(\\n AntiTrivialStrategy, mirroring=False))\\n do_rotations.extend([False])\\n\\n return strategies, do_rotations\",\n \"def setUpClass(cls):\\n\\t\\t\\t\\t\\t\\n\\t\\tww.weightwatcher.torch = torch\\n\\t\\tcls.weights_dir = Test_PyStateDictDir._make_tmp_weights_dir(format=MODEL_FILE_FORMATS.PYTORCH)\\n\\t\\t\\n\\t\\treturn\",\n \"def __init__(self):\\n super(Modules, self).__init__()\\n \\n global superclasses\\n superclasses['universe'] = []\\n superclasses['actions'] = ['universe']\\n superclasses['booleans'] = ['universe']\\n\\n global instances\\n instances['universe'] = set()\\n instances['actions'] = set()\\n instances['booleans'] = set()\",\n \"def setup_class(self):\\n pass\",\n \"def setup(cls):\\n super().setup()\\n cls.search_behaviour = cast(\\n GenericSearchBehaviour, cls._skill.skill_context.behaviours.search\\n )\\n cls.tx_behaviour = cast(\\n GenericTransactionBehaviour, cls._skill.skill_context.behaviours.transaction\\n )\\n cls.strategy = cast(GenericStrategy, cls._skill.skill_context.strategy)\\n\\n cls.logger = cls._skill.skill_context.logger\",\n \"def __init__(self):\\n self.redis = RedisClient()\\n self.crawlers = [crawler_cls() for crawler_cls in crawlers_cls]\",\n \"def get_models(self):\\n classes = self._get_classes()\\n models = {\\n name: cls\\n for name,\\n cls in classes\\n if cls not in self.exclude_classes and\\n issubclass(cls, (Base, experimentalBase))\\n }\\n models.update(self.custom_constructors)\\n return models\",\n \"def __init__(self, processManager, clientManager):\\n self.processManager = processManager\\n self.clientManager = clientManager\\n self.engine_types = {}\\n self.engine_allocations = {}\\n self.engine_instances = {}\",\n \"def setUpClass(cls):\\n\\n\\t\\timport ai\\n\\t\\tcls.list_of_AIs = []\\n\\t\\tAIs_available = [module[1] for module in pkgutil.walk_packages(path=['ai'], prefix='ai.')]\\n\\n\\t\\tfor AI_module in AIs_available:\\n\\t\\t\\tmodule = importlib.import_module(name=AI_module)\\n\\t\\t\\tcls.list_of_AIs.append([clss[1] for clss in inspect.getmembers(module, inspect.isclass) if\\n\\t\\t\\t\\t\\t\\t\\t clss[0].startswith(\\\"AI\\\")][-1]())\\n\\n\\t\\tassert len(cls.list_of_AIs) > 0, \\\"Make sure we imported some AIs.\\\"\",\n \"def setUp(self):\\n self.simulation = FooSimulation(count=5, g=6, h=9, i=12)\\n self.foo = Foo(simulation=self.simulation, name='foo', a=4, b=42, c=\\\"Hello\\\")\\n self.handler = Handler(ProxyLock(self.simulation),\\n ProxyLock(self.foo),\\n [Attribute(\\\"count\\\"), Attribute(\\\"g\\\")],\\n [Attribute('b')],\\n [Attribute('a')])\",\n \"def setUpClass(cls):\\n\\t\\t\\n\\t\\tww.weightwatcher.torch = torch\\n\\t\\tstate_dict = models.vgg11(weights='VGG11_Weights.IMAGENET1K_V1').state_dict()\\n\\t\\tmodel_name = 'vgg11'\\n\\n\\n\\t\\twith TemporaryDirectory(dir=TEST_TMP_DIR, prefix=\\\"ww_\\\") as model_dir:\\n\\t\\t\\tprint(f\\\"setting up class using {model_dir} as model_dir\\\")\\n\\t\\t\\n\\t\\t\\tstate_dict_filename = os.path.join(model_dir, \\\"pytorch_model.bin\\\")\\n\\t\\t\\ttorch.save(state_dict, state_dict_filename)\\n\\t\\t\\t\\n\\t\\t\\tcls.config = ww.WeightWatcher.extract_pytorch_bins(model_dir=model_dir, model_name=model_name)\\n\\t\\t\\tcls.weights_dir = cls.config['weights_dir']\\n\\t\\t\\t\\n\\t\\treturn\",\n \"def generate():\\n strategies = []\\n\\n # Add Centrifugal Bumblepuppy 16+H (RFind based)\\n strategies.extend(\\n generate_meta_strategy_pair(CentrifugalBumblepuppy16h))\\n\\n # Add decision tree strategies\\n strategies.extend(\\n generate_meta_strategy_pair(DecisionTreeStrategy))\\n\\n # Add decision tree strategies\\n strategies.extend(\\n generate_meta_strategy_pair(DecisionTreeV10Strategy))\\n\\n # Add HPS Dojo strategies\\n strategies.extend(\\n generate_meta_strategy_pair(HPSDojoStrategy))\\n\\n # Add testing please ignore strategies\\n strategies.extend(\\n generate_meta_strategy_pair(TestingPlsIgnoreStrategy))\\n\\n # Add testimono strategy\\n strategies.extend(\\n generate_meta_strategy_pair(TestimonoStrategy))\\n\\n # Add RPS Geometry\\n strategies.extend(\\n generate_meta_strategy_pair(GeometryV4Strategy))\\n\\n # By default, rotate everything\\n do_rotations = [True for _ in strategies]\\n\\n # Anti Trivial\\n strategies.extend(\\n generate_meta_strategy_pair(\\n AntiTrivialStrategy, mirroring=False))\\n do_rotations.extend([False])\\n\\n return strategies, do_rotations\",\n \"def init_objects():\\n gravity = Gravity()\\n planet_stencil = PlanetStencil()\\n return gravity, planet_stencil\",\n \"def wrap_simple_classes(self):\\n # Base ref-counted classes are abstract because it is necessary to set up reference counting.\\n # Wrapper classes do that in their public constructor.\\n klasses = []\\n for struct in self.repo.structs.values():\\n if struct.is_class and struct.c_name not in self.base_classes:\\n wrapped_name = struct.vala_name + \\\"Ref\\\"\\n wrapped_c_name = 'Cef' + wrapped_name\\n members = [\\n StructMember(\\\"GData*\\\", \\\"private_data\\\", \\\"private_data\\\"),\\n StructMember(\\\"volatile int\\\", \\\"ref_count\\\", \\\"ref_count\\\")\\n ]\\n\\n # Vala definition\\n klass = Struct(\\n c_name=wrapped_c_name,\\n vala_name=wrapped_name,\\n c_header=\\\"valacef_api.h\\\",\\n members=members)\\n klass.set_parent(struct)\\n klass.set_is_class(True)\\n construct = Function(\\n c_name=wrapped_c_name + \\\"New\\\",\\n vala_name=wrapped_name,\\n c_header=\\\"valacef_api.h\\\")\\n construct.construct = True\\n klass.add_method(construct)\\n\\n priv_set = Function(\\n c_name=wrapped_c_name + \\\"PrivSet\\\",\\n vala_name=\\\"priv_set\\\",\\n c_header=\\\"valacef_api.h\\\",\\n params=[\\n (\\\"const char*\\\", \\\"key\\\"),\\n (\\\"T\\\", \\\"data\\\"),\\n ],\\n vala_generics=[\\\"T\\\"],\\n vala_simple_generics=True\\n )\\n klass.add_method(priv_set)\\n priv_get = Function(\\n c_name=wrapped_c_name + \\\"PrivGet\\\",\\n vala_name=\\\"priv_get\\\",\\n c_header=\\\"valacef_api.h\\\",\\n params=[\\n (\\\"const char*\\\", \\\"key\\\"),\\n ],\\n ret_type=\\\"T\\\",\\n vala_generics=[\\\"T\\\"],\\n vala_simple_generics=True\\n )\\n klass.add_method(priv_get)\\n klass.add_method(Function(\\n c_name=wrapped_c_name + \\\"PrivDel\\\",\\n vala_name=\\\"priv_del\\\",\\n c_header=\\\"valacef_api.h\\\",\\n params=[\\n (\\\"const char*\\\", \\\"key\\\"),\\n ],\\n ))\\n\\n klasses.append(klass)\\n\\n # C definition\\n c_klass = Struct(\\n c_name=wrapped_c_name,\\n vala_name=wrapped_name,\\n c_header=\\\"stdlib.h;capi/cef_base_capi.h\\\",\\n members=members)\\n c_klass.set_parent(struct)\\n c_klass.set_is_class(True)\\n construct = Function(wrapped_c_name + \\\"New\\\", wrapped_name, \\\"\\\", wrapped_c_name + '*', body=[\\n '%s* self = (%s*) calloc(1, sizeof(%s));' % (wrapped_c_name, wrapped_c_name, wrapped_c_name),\\n '%s((void*) self, sizeof(%s), sizeof(%s));' % (\\n 'cef_base_ref_counted_init_ref_counting', struct.c_name, wrapped_c_name),\\n 'g_datalist_init(&(self->private_data));',\\n 'return self;'\\n ])\\n construct.construct = True\\n c_klass.add_method(construct)\\n\\n priv_set = Function(wrapped_c_name + \\\"PrivSet\\\", \\\"priv_set\\\", \\\"\\\", params=[\\n (wrapped_c_name + \\\"*\\\", \\\"self\\\"),\\n (\\\"const char*\\\", \\\"key\\\"),\\n (\\\"void*\\\", \\\"data\\\"),\\n ('GDestroyNotify', 'destroy'),\\n ],\\n body=[\\n 'g_assert (self != NULL);',\\n 'g_assert (key != NULL);',\\n 'g_datalist_id_set_data_full(',\\n '&self->private_data, g_quark_from_string(key), data, data ? destroy : (GDestroyNotify) NULL);',\\n ])\\n c_klass.add_method(priv_set)\\n priv_get = Function(wrapped_c_name + \\\"PrivGet\\\", \\\"priv_get\\\", \\\"\\\", params=[\\n (wrapped_c_name + \\\"*\\\", \\\"self\\\"),\\n (\\\"const char*\\\", \\\"key\\\"),\\n ],\\n ret_type=\\\"void*\\\",\\n body=[\\n 'g_assert (self != NULL);',\\n 'g_assert (key != NULL);',\\n 'return g_datalist_get_data(&self->private_data, key);',\\n ])\\n c_klass.add_method(priv_get)\\n c_klass.add_method(Function(wrapped_c_name + \\\"PrivDel\\\", \\\"priv_del\\\", \\\"\\\", params=[\\n (wrapped_c_name + \\\"*\\\", \\\"self\\\"),\\n (\\\"const char*\\\", \\\"key\\\"),\\n ],\\n body=[\\n 'g_return_if_fail (self != NULL);',\\n 'g_return_if_fail (key != NULL);',\\n 'g_datalist_remove_data(&self->private_data, key);',\\n ]))\\n self.add_c_glue(c_klass)\\n\\n self.repo.add_struct(*klasses)\",\n \"def instantiate_all_classes(self, node):\\n self.instantiate_classes(node)\\n\\n for cls in node.classes:\\n self.instantiate_classes(cls)\\n\\n for ns in node.namespaces:\\n self.instantiate_all_classes(ns)\",\n \"def setUp(self):\\n self.cashFlowDate=Date(1,October,2018)\\n self.fixingDate=Date(1,November,2018)\\n self.foreignAmount=1000.0\\n self.familyName=\\\"ECB\\\"\\n self.fixingDays=2\\n self.sourceCurrency=USDCurrency()\\n self.targetCurrency=EURCurrency()\\n self.fixingCalendar=UnitedStates()\\n self.todayDate=Date(11, November, 2018)\\n self.tsDayCounter=Actual360()\\n self.flatForwardUSD=FlatForward(self.todayDate, 0.005, self.tsDayCounter)\\n self.sourceYts=RelinkableYieldTermStructureHandle(self.flatForwardUSD)\\n self.flatForwardEUR=FlatForward(self.todayDate, 0.03, self.tsDayCounter);\\n self.targetYts=RelinkableYieldTermStructureHandle(self.flatForwardEUR)\\n self.fxindex=FxIndex(self.familyName,self.fixingDays,self.sourceCurrency,self.targetCurrency,self.fixingCalendar,self.sourceYts,self.targetYts)\\n self.fxlinkedcashflow=FXLinkedCashFlow(self.cashFlowDate,self.fixingDate,self.foreignAmount,self.fxindex)\",\n \"def __init__(self):\\n self.weapon = Weapon()\\n self.armor = Armor()\",\n \"def setup():\\n env.total_time = 0\\n boeing_737 = Airplane(env, 'Boeing 737', 20, 6, 1, False)\\n boeing_737.make_rows()\\n seats = boeing_737.get_seats()\\n passengers = []\\n\\n # create a passenger for every seat on the plane\\n for j in range(0, boeing_737.get_number_of_seats()):\\n passenger = Passenger(env, seats[j], boeing_737)\\n passengers.append(passenger)\\n\\n _algorithms = BoardingAlgorithm(env, boeing_737, passengers)\\n\\n return boeing_737, passengers, _algorithms\",\n \"def _init_objects(self) -> None:\\n self.position = selectors.get_position(self.exchange, self.symbol)\\n self.broker = Broker(self.position, self.exchange, self.symbol, self.timeframe)\\n\\n if self.hp is None and len(self.hyperparameters()) > 0:\\n self.hp = {}\\n for dna in self.hyperparameters():\\n self.hp[dna['name']] = dna['default']\",\n \"def setUpClass(cls):\\n clean_db() # remove all objects created by another tests\\n # save the link to the content type\\n cls.ctype = ContentType.objects.get_for_model(TestModel)\\n # create an object\\n cls.object = TestModel.objects.create(name=\\\"Test object\\\")\\n # create two images related to the object\\n cls.image1 = models.Image.objects.create(\\n image=get_image_in_memory_data(),\\n position=0,\\n content_type=cls.ctype,\\n object_id=cls.object.id\\n )\\n cls.image2 = models.Image.objects.create(\\n image=get_image_in_memory_data(),\\n position=1,\\n content_type=cls.ctype,\\n object_id=cls.object.id\\n )\\n # create another object without related images\\n cls.alone_object = TestModel.objects.create(\\n name=\\\"Alone test object\\\"\\n )\",\n \"def import_all_handlers(self):\\n import os\\n exclude_list=[\\\"base\\\"]\\n\\n #\\n # the list of handlers (excluding base. Add more you dont want\\n # to be loaded or inspected to exclude_list above.)\\n #\\n mods=[]\\n module_path = os.path.abspath(os.path.join(os.path.dirname( __file__ ), 'handlers'))\\n #print(\\\"importing handlers from: \\\" + module_path)\\n for mod in os.listdir( module_path ):\\n mod = mod.split(\\\".\\\")[0]\\n if not mod.startswith(\\\"_\\\") and not mod in exclude_list:\\n #print(\\\" now processing: \\\" + str(mod))\\n mods.append(mod)\\n \\n #print(\\\"mods: \\\" + str(mods))\\n class_list = []\\n # load all the models from their modules (mods)\\n #print(str(mods))\\n import importlib\\n for m in mods:\\n #print(\\\"importing: \\\" + 'pow_comments.handlers.' + m) \\n try:\\n mod = importlib.import_module('pow_comments.handlers.' + m)\\n except:\\n pass\\n #print(dir(mod))\",\n \"def test_regen_methods(self, tmpdir, treantclass):\\n with tmpdir.as_cwd():\\n t1 = treantclass('newone')\\n t2 = treantclass('newone')\\n assert t1.uuid == t2.uuid\\n\\n t3 = treantclass('newone', new=True)\\n assert t3.uuid != t2.uuid\\n\\n t4 = treantclass(t3.filepath)\\n assert t4.uuid == t3.uuid\",\n \"def create_effect_instances(self):\\r\\n raise NotImplementedError()\",\n \"def _initObjects(self):\\n\\n print \\\"DEBUG: Initializing Entities\\\"\\n ObjectType.initializeObjectTypes()\",\n \"def process_class_list(self, module, classes):\",\n \"def _prepare_wsdl_objects(self):\\r\\n self.TrackPackageIdentifier = self.client.factory.create('TrackPackageIdentifier')\\r\\n # Default to tracking number.\\r\\n self.TrackPackageIdentifier.Type = 'TRACKING_NUMBER_OR_DOORTAG'\",\n \"def import_all_known_classes(debug=False):\\r\\n\\r\\n output = {}\\r\\n for cls in KnownClass.objects:\\r\\n if debug:\\r\\n print \\\"Importing %s.%s\\\"%(cls.module_name, cls.class_name)\\r\\n x = get_class(cls.module_name, cls.class_name)\\r\\n output[(cls.module_name, cls.class_name)] = x()\\r\\n return output\",\n \"def test_05_transaction_create_buy_stock(self):\\n portfolio = Portfolio.get_portfolio_by_slug(\\\"test\\\")\\n user = \\\"automated unit tester\\\"\\n\\n buy_stock_aapl = Transaction.buy_stock(\\n portfolio=portfolio,\\n asset=\\\"AAPL\\\",\\n t_currency=TRANSACTION_CURRENCY_USD,\\n amount=256,\\n unit_price=162.94,\\n user=user\\n )\\n\\n buy_stock_ibm = Transaction.buy_stock(\\n portfolio=portfolio,\\n asset=\\\"IBM\\\",\\n t_currency=TRANSACTION_CURRENCY_USD,\\n amount=128,\\n unit_price=145.56,\\n user=user\\n )\\n\\n buy_stock_msft = Transaction.buy_stock(\\n portfolio=portfolio,\\n asset=\\\"MSFT\\\",\\n t_currency=TRANSACTION_CURRENCY_EUR,\\n amount=64,\\n unit_price=76.22,\\n user=user\\n )\\n\\n self.assertTrue(isinstance(buy_stock_aapl, Transaction),\\n msg=\\\"Transaction is NOT returning a valid object while buying an APPLE stock\\\")\\n print(\\\"Transaction buy_stock method is returning a valid APPLE transaction: {}\\\".format(\\n buy_stock_aapl))\\n\\n self.assertTrue(isinstance(buy_stock_ibm, Transaction),\\n msg=\\\"Transaction is NOT returning a valid object while buying an IBM stock\\\")\\n print(\\\"Transaction buy_stock method is returning a valid IBM transaction: {}\\\".format(\\n buy_stock_ibm))\\n\\n self.assertTrue(isinstance(buy_stock_msft, Transaction),\\n msg=\\\"Transaction is NOT returning a valid object while buying an MICROSOFT stock\\\")\\n print(\\\"Transaction buy_stock method is returning a valid MICROSOFT transaction: {}\\\".format(\\n buy_stock_msft))\",\n \"def cls():\\n patcher = patch_debounce()\\n patcher.start()\\n _import = __import__('homeassistant.components.homekit.type_thermostats',\\n fromlist=['Thermostat'])\\n patcher_tuple = namedtuple('Cls', ['thermostat'])\\n yield patcher_tuple(thermostat=_import.Thermostat)\\n patcher.stop()\",\n \"def __init__(self, klass, *args, **kwargs):\\n self._klass = klass(*args, **kwargs)\",\n \"def setup_class(cls):\\n cls.ledger_apis = LedgerApis({FETCHAI: DEFAULT_FETCHAI_CONFIG}, FETCHAI)\\n cls.ledger_state_proxy = LedgerStateProxy(ledger_apis=cls.ledger_apis)\",\n \"def get_classes(self):\\n return\",\n \"def _reconstruct_object(deserialized_data):\\n for key, value in deserialized_data.items():\\n key = key.strip('__')\\n if key in registry:\\n # Gather the keyword arguments for class *key*\\n kwargs = dict(value.items())\\n return registry[key](**kwargs)\",\n \"def setUpClass(cls):\\n super().setUpClass() # creates the first object\\n # create the second onject of the same model\\n cls.second_object = TestModel.objects.create(\\n name=\\\"SecondTestObject\\\"\\n )\\n # create an object of anther model\\n cls.another_object = AnotherTestModel.objects.create(\\n name=\\\"AnotherTestObject\\\"\\n )\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7944597","0.71021026","0.6479502","0.6362634","0.56881684","0.56321007","0.5572392","0.55376","0.5502691","0.5442817","0.5403161","0.5379905","0.5372698","0.5296769","0.52877635","0.5230109","0.5230109","0.5185296","0.5165134","0.5138819","0.5119774","0.50943846","0.5055732","0.5055732","0.50314504","0.5024732","0.5023778","0.50164944","0.5010884","0.500447","0.49895838","0.49856988","0.4972179","0.49713397","0.49664292","0.49492413","0.49484128","0.49363628","0.49000448","0.48910233","0.48894718","0.48888084","0.48818204","0.48768982","0.48719195","0.48573953","0.48536244","0.48525813","0.48515794","0.48494124","0.48482117","0.48469117","0.484675","0.4836746","0.48249826","0.4823301","0.4820473","0.48191383","0.48150852","0.4813872","0.48119408","0.48110926","0.4809112","0.4805668","0.47996843","0.47984377","0.47875538","0.4786764","0.4784536","0.47825208","0.4782198","0.47755435","0.47680905","0.47666264","0.4753126","0.4751795","0.4748286","0.47471118","0.47453728","0.47424802","0.47330827","0.47297928","0.47237763","0.4720082","0.47196808","0.47167927","0.47157574","0.47143516","0.4706401","0.47042122","0.4697461","0.46959928","0.46947068","0.46913576","0.4687222","0.4682186","0.46792802","0.46790442","0.46761534","0.4662473"],"string":"[\n \"0.7944597\",\n \"0.71021026\",\n \"0.6479502\",\n \"0.6362634\",\n \"0.56881684\",\n \"0.56321007\",\n \"0.5572392\",\n \"0.55376\",\n \"0.5502691\",\n \"0.5442817\",\n \"0.5403161\",\n \"0.5379905\",\n \"0.5372698\",\n \"0.5296769\",\n \"0.52877635\",\n \"0.5230109\",\n \"0.5230109\",\n \"0.5185296\",\n \"0.5165134\",\n \"0.5138819\",\n \"0.5119774\",\n \"0.50943846\",\n \"0.5055732\",\n \"0.5055732\",\n \"0.50314504\",\n \"0.5024732\",\n \"0.5023778\",\n \"0.50164944\",\n \"0.5010884\",\n \"0.500447\",\n \"0.49895838\",\n \"0.49856988\",\n \"0.4972179\",\n \"0.49713397\",\n \"0.49664292\",\n \"0.49492413\",\n \"0.49484128\",\n \"0.49363628\",\n \"0.49000448\",\n \"0.48910233\",\n \"0.48894718\",\n \"0.48888084\",\n \"0.48818204\",\n \"0.48768982\",\n \"0.48719195\",\n \"0.48573953\",\n \"0.48536244\",\n \"0.48525813\",\n \"0.48515794\",\n \"0.48494124\",\n \"0.48482117\",\n \"0.48469117\",\n \"0.484675\",\n \"0.4836746\",\n \"0.48249826\",\n \"0.4823301\",\n \"0.4820473\",\n \"0.48191383\",\n \"0.48150852\",\n \"0.4813872\",\n \"0.48119408\",\n \"0.48110926\",\n \"0.4809112\",\n \"0.4805668\",\n \"0.47996843\",\n \"0.47984377\",\n \"0.47875538\",\n \"0.4786764\",\n \"0.4784536\",\n \"0.47825208\",\n \"0.4782198\",\n \"0.47755435\",\n \"0.47680905\",\n \"0.47666264\",\n \"0.4753126\",\n \"0.4751795\",\n \"0.4748286\",\n \"0.47471118\",\n \"0.47453728\",\n \"0.47424802\",\n \"0.47330827\",\n \"0.47297928\",\n \"0.47237763\",\n \"0.4720082\",\n \"0.47196808\",\n \"0.47167927\",\n \"0.47157574\",\n \"0.47143516\",\n \"0.4706401\",\n \"0.47042122\",\n \"0.4697461\",\n \"0.46959928\",\n \"0.46947068\",\n \"0.46913576\",\n \"0.4687222\",\n \"0.4682186\",\n \"0.46792802\",\n \"0.46790442\",\n \"0.46761534\",\n \"0.4662473\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.71861804"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":14368,"cells":{"query":{"kind":"string","value":"Executes the backtest. This is where the signal handling of the Backtesting engine is carried out. There are two while loops, the outerloop (heartbeat) and the nested innerloop, which checks if there is an event in the Event Queue object. The inner loop acts on the Event by calling the appropriate method"},"document":{"kind":"string","value":"def _run_backtest(self):\n i = 0\n\n while True:\n i += 1\n print(i)\n\n # Update the market bars\n if self.data_handler.continue_backtest == True:\n self.data_handler.update_bars()\n else:\n break\n\n # Handle the Events\n while True:\n try:\n event = self.events.get(False)\n except queue.Empty:\n break\n else:\n # The inner-loop acts on the events by calling the appropriate method of the appropriate object\n if event is not None:\n if event.type == 'MARKET':\n self.strategy.calculate_signals(event)\n self.portfolio.update_timeindex(event)\n\n elif event.type == 'SIGNAL':\n self.signals += 1\n self.portfolio.update_signal(event)\n\n elif event.type == 'ORDER':\n self.orders += 1\n self.execution_handler.execute_order(event)\n\n elif event.type == 'FILL':\n self.fills += 1\n self.portfolio.update_fill(event)\n\n # Pauses for a duration of self.heartbeat seconds\n time.sleep(self.heartbeat)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _run_backtest(self):\n i = 0\n \n while True:\n i += 1\n print(i)\n \n # Update the market bars\n if self.data_handler.continue_backtest == True:\n self.data_handler.update_bars()\n else:\n break\n \n # Handle the Events\n while True:\n try:\n event = self.events.get(False)\n except queue.Empty:\n break\n else:\n # The inner-loop acts on the events by calling the appropriate method of the appropriate object\n if event is not None:\n if event.type == 'MARKET':\n self.strategy.calculate_signals(event)\n self.portfolio.update_timeindex(event)\n \n elif event.type == 'SIGNAL':\n self.signals += 1\n self.portfolio.update_signal(event)\n \n elif event.type == 'ORDER':\n self.orders += 1\n self.execution_handler.execute_order(event)\n \n elif event.type == 'FILL':\n self.fills += 1\n self.portfolio.update_fill(event)\n \n # Pauses for a duration of self.heartbeat seconds\n time.sleep(self.heartbeat)","def _run_backtest(self):\n i = 0\n while True:\n i += 1\n if self.data_handler.continue_backtest == True:\n self.data_handler.update_bars()\n else:\n break\n while True:\n try:\n event = self.events.get(False)\n except Empty:\n break\n else:\n if event is not None:\n if event.type == EventType.MARKET:\n self.strategy.On_Bars(event)\n self.portfolio.update_balance(event)\n self.portfolio.order_check(event)\n elif event.type == EventType.ORDER_SEND:\n self.portfolio.update_order(event)\n elif event.type == EventType.ORDER_CLOSE:\n self.portfolio.update_order(event)\n self.portfolio.update_euity(event)\n elif event.type == EventType.ORDER_MODIFY:\n self.portfolio.update_order(event)\n time.sleep(self.heartbeat)","def _run_backtest(self):\n i = 0\n while True:\n i += 1\n if self.data_handler.continue_backtest == True:\n self.data_handler.update_bars()\n #print(self.data_handler.get_latest_bar_datetime(self.symbol_list[0]))\n else:\n break\n while self.backtest:\n try:\n event = self.events.get(False)\n except Empty:\n break\n else:\n if event is not None:\n if event.type == EventType.MARKET:\n try:\n self.strategy.On_Bars(event)\n self.portfolio.update_balance(event)\n self.portfolio.order_check(event)\n except EquityError:\n print('Not Engough Equity,Backtest Will be Stop...')\n self.backtest=False\n break\n elif event.type == EventType.ORDER_SEND:\n self.portfolio.update_order(event)\n elif event.type == EventType.ORDER_CLOSE:\n try:\n self.portfolio.update_order(event)\n self.portfolio.update_euity(event)\n except EquityError:\n print ('Not Engough Equity,Backtest Will be Stop...')\n self.backtest=False\n break\n elif event.type == EventType.ORDER_MODIFY:\n self.portfolio.update_order(event)\n time.sleep(self.heartbeat)","def run(self):\n\n self.halt = False\n\n print(\"Starting heartbeat.\")\n while not self.halt:\n event = events.TickEvent()\n AppState.get_state().get_event_manager().post_event(event)\n AppState.get_state().get_clock().tick(settings.MAX_FPS)","def run(self) -> None:\n\n while not self.stop_event.is_set():\n if self.my_queue:\n # if heartbeat received at '/heartbeat' route from the monitored peer,\n # sleep until next\n self.my_queue.clear()\n time.sleep(7)\n\n else:\n # else drop peer data from database and inform central server appending '0'\n # to my queue\n self.db_access.drop_peer(self.peer_id)\n self.my_queue.append(0)\n break","def event_loop(self):\n while self.ack is False:\n gevent.sleep(self.loop_interval)\n output_service = self.get_directory_service_proxy().get_service(\"mock-output-service\")\n output_service.put(\"test-worker-work-result\")\n self.ack = True","def __call__(self, event_queue, in_queue, out_queue):\n\n running = True\n self.in_q = in_queue # command received from the main thread\n self.out_q = out_queue # responses, commands to the main thread\n self.ev_q = event_queue # return pending events to the main thread\n shutdown = False\n self.light_state = False # current state of beat light\n\n # send first beat light message\n if self.btic.BeatLight() is True:\n self.out_q.put(\"beaton\")\n else:\n self.out_q.put(\"beatoff\")\n\n # run thread loop\n while running is True:\n if self.die_pending is False:\n self.sendPendingEvents()\n self.processCommands()\n\n \"\"\"\n if self.btic.BeatLightToggle() == True:\n self.out_q.put(\"beat\")\n wx.WakeUpIdle()\n \"\"\"\n\n # display beat light on UI\n light = self.btic.BeatLight()\n if light != self.light_state:\n self.light_state = light\n if light is True:\n self.out_q.put(\"beatoff\")\n else:\n self.out_q.put(\"beaton\")\n wx.WakeUpIdle() \n \n if self.allClear() is True:\n time.sleep(.01)\n #pass\n else:\n # stop the loop/thread when all is cleaned up\n self.sendPendingEvents()\n if self.allClear() is True:\n self.clearBank() \n self.die_pending = False\n running = False\n else:\n time.sleep(.01)\n # pass","def awaitVerification(self):\r\n method = moduleName + '.' + self.className + '.' + 'awaitVerification'\r\n while True:\r\n try:\r\n self._stopevent.wait(self._sleepperiod)\r\n verification = self.localCommQueue.get_nowait()\r\n if verification == terminationVerificationMsg.COMMIT:\r\n #The parent AE agrees that we can shutdown. Terminate\r\n break\r\n elif verification == terminationVerificationMsg.ROLLBACK:\r\n #Roll back the termination\r\n raise Exceptions.WorkerThreadTerminationRollback()\r\n elif verification == terminationVerificationMsg.ERROR:\r\n errorMsg = \"Worker thread for landmark %s is improperly indexed\" %self.queueID\r\n Graph.logQ.put( [logType , logLevel.ERROR , method , errorMsg])\r\n raise Exceptions.WorkerThreadIndexError(errorMsg)\r\n else:\r\n #Should not happen\r\n errorMsg = \"Unexpected shutdown verification response for worker thread on landmark %s\" %self.queueID\r\n Graph.logQ.put( [logType , logLevel.ERROR , method , errorMsg])\r\n raise Exceptions.WorkerThreadIndexError(errorMsg)\r\n break\r\n except queue.Empty:\r\n pass\r\n except Exceptions.WorkerThreadTerminationRollback:\r\n raise Exceptions.WorkerThreadTerminationRollback()\r\n except Exception as e:\r\n errorMsg = \"Unexpected error during shutdown verification process for worker thread on landmark %s. Traceback= %s\" %(self.queueID, e)\r\n Graph.logQ.put( [logType , logLevel.ERROR , method , errorMsg])\r\n raise e","def _eventloop(self):\n logging.debug(\"%s - eventloop started\" % self.name)\n while not self.stopped:\n event = self.inqueue.get()\n if not event: break\n self.doevent(event)\n logging.debug(\"%s - eventloop stopped\" % self.name)","def _do_test(self):\n\n process_all_events()\n\n if self.list:\n (callback, args, kwargs) = self.list.pop(0)\n callback(*args, **kwargs)\n else:\n safe_exit(force=1)","def main_loop(self):\n LOGGER.info('Entering main event loop...')\n try:\n while self._handle_faucet_events():\n while not self._faucet_events.event_socket_connected:\n LOGGER.info('Attempting faucet event sock connection...')\n time.sleep(1)\n try:\n self._faucet_events.connect()\n self._restore_states()\n self._faucet_collector.set_state_restored(True)\n except Exception as e:\n LOGGER.error(\"Cannot restore states or connect to faucet: %s\", e)\n self._faucet_collector.set_state_restored(False, e)\n except KeyboardInterrupt:\n LOGGER.info('Keyboard interrupt. Exiting.')\n self._faucet_events.disconnect()\n except Exception as e:\n LOGGER.error(\"Exception: %s\", e)\n raise","def run(self):\n \n # Wrap the outer loop in a try block so we can do an orderly shutdown\n # should an exception occur:\n try:\n # Send out a STARTUP event:\n self.dispatchEvent(weewx.Event(weewx.STARTUP))\n \n syslog.syslog(syslog.LOG_INFO, \"engine: Starting main packet loop.\")\n\n last_gc = int(time.time())\n\n # This is the outer loop. \n while True:\n\n # See if garbage collection is scheduled:\n if int(time.time()) - last_gc > self.gc_interval:\n ngc = gc.collect()\n syslog.syslog(syslog.LOG_INFO, \"engine: garbage collected %d objects\" % ngc)\n last_gc = int(time.time())\n\n # First, let any interested services know the packet LOOP is\n # about to start\n self.dispatchEvent(weewx.Event(weewx.PRE_LOOP))\n \n # Get ready to enter the main packet loop. An exception of type\n # BreakLoop will get thrown when a service wants to break the\n # loop and interact with the console.\n try:\n \n # And this is the main packet LOOP. It will continuously\n # generate LOOP packets until some service breaks it by\n # throwing an exception (usually when an archive period\n # has passed).\n for packet in self.console.genLoopPackets():\n \n # Package the packet as an event, then dispatch it.\n self.dispatchEvent(weewx.Event(weewx.NEW_LOOP_PACKET, packet=packet))\n\n # Allow services to break the loop by throwing\n # an exception:\n self.dispatchEvent(weewx.Event(weewx.CHECK_LOOP, packet=packet))\n\n syslog.syslog(syslog.LOG_CRIT, \"engine: Internal error. Packet loop has exited.\")\n \n except BreakLoop:\n \n # Send out an event saying the packet LOOP is done:\n self.dispatchEvent(weewx.Event(weewx.POST_LOOP))\n\n finally:\n # The main loop has exited. Shut the engine down.\n syslog.syslog(syslog.LOG_DEBUG, \"engine: Main loop exiting. Shutting engine down.\")\n self.shutDown()","def heartbeat_event(self, event):\r\n while not self.imm_jobs.empty():\r\n imm_job = self.imm_jobs.get_nowait()\r\n imm_job(self)\r\n \r\n if self.do_reconfigure:\r\n self.selmgr.reconfigure(self.current_consensus())\r\n self.do_reconfigure = False\r\n \r\n if self.run_all_jobs:\r\n while not self.low_prio_jobs.empty() and self.run_all_jobs:\r\n imm_job = self.low_prio_jobs.get_nowait()\r\n imm_job(self)\r\n self.run_all_jobs = False\r\n return\r\n\r\n # If event is stream:NEW*/DETACHED or circ BUILT/FAILED, \r\n # don't run low prio jobs.. No need to delay streams for them.\r\n if PathBuilder.is_urgent_event(event): return\r\n \r\n # Do the low prio jobs one at a time in case a \r\n # higher priority event is queued \r\n if not self.low_prio_jobs.empty():\r\n delay_job = self.low_prio_jobs.get_nowait()\r\n delay_job(self)","def run(self):\n self.workhorse_.run()\n try:\n while(True):\n self.workhorse_.heartbeat()\n self.periodic_snapshot()\n except workflow.NoMoreWork:\n print \"Fini.\"\n exit(0)\n exit(-1)","def test_heartbeat(self):\n pass","def processEvent(self):\n # Note: break out of event dispatch loop when closedown event is received\n # and closing flag is set. This is to prevent DoS attack by faked closedown\n # event type, and to ensure that prior events received are all processed.\n delay_on_error_min = 0.125 # Back off retry interval on error..\n delay_on_error_max = 20.0 # ..\n delay_on_error = delay_on_error_min # ..\n while True:\n if delay_on_error < delay_on_error_max:\n delay_on_error *= 2\n try:\n # PLEASE NOTE: In the event that the HTTPC is run as duplex, not simplex\n # then the post methods will be delayed if nothing is sent down to the client\n # from the server. This timeout is controlled by QUEUE_WAIT_TIMEOUT in EventRouterHTTPS.py\n if self._simplex == True:\n self._queueEvent.wait()\n self._queueEvent.clear()\n \n if not self._queue.empty():\n Trace(\"%s queue.get ...\"%(self.getUri()), \"EventLib.EventRelayHTTPC\")\n ###msgbody = self._queue.get()\n ###Trace(\"%s get msgbody: %s\"%(self.getUri(),msgbody), \"EventLib.EventRelayHTTPC\")\n ###self._event.set()\n msgbody = self.getQueuedItem()\n [typ,env] = msgbody\n if typ == \"closedown\":\n if self._closing: break\n else:\n # process request as an HTTP POST request\n data = makeEnvelopeData(env)\n headers = { \"Content-type\": \"text/plain\",\n \"Accept\": \"text/plain\",\n \"Content-length\": str(len(data)) }\n self._httpcon.request(\"POST\", \"/request_path_ignored\", data, headers)\n response = self._httpcon.getresponse()\n delay_on_error = delay_on_error_min\n elif self._simplex == False:\n # Nothing in queue:\n # issue a GET for incoming events\n _log.info(\"%s HTTP get ...\"%(self.getUri()))\n headers = { \"Accept\": \"text/plain\" }\n self._httpcon.request(\"GET\", \"/request_path_ignored\", None, headers)\n response = self._httpcon.getresponse()\n if response.status == 200:\n delay_on_error = delay_on_error_min\n msgbody = response.read()\n Trace(\"%s get msgbody: %s\"%(self.getUri(),msgbody), \"EventLib.EventRelayHTTPC\")\n # Parse message and act accordingly\n msgdata = parseMessageData(msgbody)\n Trace(\"%s get msgdata: %s\"%(self.getUri(),str(msgdata)), \"EventLib.EventRelayHTTPC\")\n if msgdata == None:\n #TODO: Log \"Request body malformed\"\n pass\n elif msgdata[0] == \"forward\":\n # msgdata = [\"forward\", [['R1', 'R2', 'R3'], 'ev:typ', 'ev:src', 'payload']]\n event = makeEvent(evtype=msgdata[1][1],source=msgdata[1][2],payload=msgdata[1][3])\n env = constructEnvelope(msgdata[1][0], event)\n self.forward(event, env)\n elif msgdata[0] == \"idle\":\n # Idle response gives client a chance to send if anything is queued\n pass\n else:\n #TODO: handle closedown message?\n Warn( \"%s Request body unrecognized option: %s\"%(self.getUri(),msgdata[0]), \"EventRelayHTTPC\")\n pass\n elif response.status == 503:\n Trace( \"%s processEvent error response: %u, %s\"%(self.getUri(),response.status,response.reason), \"EventLib.EventRelayHTTPC\")\n # Remote end closed down\n break\n else:\n # TODO: (log error response)\n Warn( \"%s processEvent error response: %u, %s\"%(self.getUri(),response.status,response.reason), \"EventLib.EventRelayHTTPC\")\n time.sleep(delay_on_error)\n \n except httplib.BadStatusLine, e:\n # This can happen at closedown\n Info( \"%s processEvent bad response: %s\"%(self.getUri(), str(e)), \"EventLib.EventRelayHTTPC\")\n time.sleep(delay_on_error)\n except httplib.CannotSendRequest, e:\n # This can happen at closedown\n Info( \"%s Cannot send request: %s\"%(self.getUri(), str(e)), \"EventLib.EventRelayHTTPC\")\n time.sleep(delay_on_error)\n except httplib.ResponseNotReady, e:\n # This can happen at startup and sometimes other times:\n # maybe multiple requests on a single HTTP connection object?\n Info( \"%s Response not ready: (%s)\"%(self.getUri(), str(e)), \"EventLib.EventRelayHTTPC\")\n time.sleep(delay_on_error)\n except socket.error, e:\n Warn( \"%s Socket error: %s\"%(self.getUri(), str(e)), \"EventLib.EventRelayHTTPC\")\n time.sleep(delay_on_error)\n return","def simulateCore(self):\n while len(self.event_q) > 0:\n evts = self.nextEvents()\n self.handleEvents(evts)\n self.gatherSystemStatistics(self.scheduler.system)\n self.dumpEventQueue()","def _event_loop(self):\n while True:\n self.scheduler.run(blocking=True)\n time.sleep(1)","def run():\n\n while True:\n\n # get event, blah\n event_name, event_data = revent.get_event(block=True, timeout=5)\n\n if event_name is not None:\n print 'received: %s' % event_name\n\n if event_name.endswith('_oembed_details'):\n handle_new_oembed_details(event_data)\n\n elif event_name == 'new_tweet':\n handle_new_tweet(event_data)\n\n # and we're done\n assert revent.verify_msg(event_name, event_data), \\\n \"Could not verify %s\" % event_name","def run():\r\n event = threading.Event()\r\n while (event.is_set() == False):\r\n # perform database backup\r\n backup()\r\n\r\n # sleep for the predefined amount interval\r\n event.wait(BACKUP_INTERVAL)","def _heartbeat_loop(self):\n # set last time so that \"if t_now - t_last >= HEARTBEAT_LOG_INTERVAL\"\n # below evalutes to True on the first run\n t_last = time.time() - HEARTBEAT_LOG_INTERVAL - 1\n while True:\n alive = 0\n # count alive processes \n for p in PROCESSES:\n if p.is_alive():\n alive += 1\n\n # no processes are alive - exit heartbeat loop\n if alive == 0:\n return\n\n t_now = time.time()\n if t_now - t_last >= HEARTBEAT_LOG_INTERVAL:\n # log heartbeat\n obj = { \n 'timestamp': time.time(),\n 'child_procs_total': self._procs_total,\n 'child_procs_alive': alive,\n 'probe_req_queue_len': self._probe_request_queue.qsize(),\n 'probe_resp_queue_len': \\\n self._probe_response_queue.qsize(), \n }\n \n # push to shared mem\n self._sm.set(config.BASE['SHARED_MEM_HEARTBEAT_KEY'],\n json.dumps(obj), HEARTBEAT_TTL)\n LOG.debug('pushed a heartbeat to the shared memory')\n\n t_last = t_now\n\n time.sleep(HEARTBEAT_LOOP_INTERVAL)","def test_cbbackupmgr_with_eventing(self):\n if \"5.5\" > self.cb_version[:3]:\n self.fail(\"This eventing test is only for cb version 5.5 and later. \")\n from pytests.eventing.eventing_constants import HANDLER_CODE\n from lib.testconstants import STANDARD_BUCKET_PORT\n\n self.src_bucket_name = self.input.param('src_bucket_name', 'src_bucket')\n self.eventing_log_level = self.input.param('eventing_log_level', 'INFO')\n self.dst_bucket_name = self.input.param('dst_bucket_name', 'dst_bucket')\n self.dst_bucket_name1 = self.input.param('dst_bucket_name1', 'dst_bucket1')\n self.metadata_bucket_name = self.input.param('metadata_bucket_name', 'metadata')\n self.create_functions_buckets = self.input.param('create_functions_buckets', True)\n self.docs_per_day = self.input.param(\"doc-per-day\", 1)\n self.use_memory_manager = self.input.param('use_memory_manager', True)\n self.backup_before_eventing = self.input.param('backup_before_eventing', False)\n bucket_params = self._create_bucket_params(server=self.master, size=256,\n replicas=self.num_replicas)\n self.cluster.create_standard_bucket(name=self.src_bucket_name, port=STANDARD_BUCKET_PORT + 1,\n bucket_params=bucket_params)\n self.buckets = RestConnection(self.master).get_buckets()\n self.src_bucket = RestConnection(self.master).get_buckets()\n self.cluster.create_standard_bucket(name=self.dst_bucket_name, port=STANDARD_BUCKET_PORT + 1,\n bucket_params=bucket_params)\n self.backup_create()\n if (self.backup_before_eventing):\n self.backup_cluster()\n self.cluster.create_standard_bucket(name=self.metadata_bucket_name, port=STANDARD_BUCKET_PORT + 1,\n bucket_params=bucket_params)\n self.buckets = RestConnection(self.master).get_buckets()\n self.gens_load = self.generate_docs(self.docs_per_day)\n self.expiry = 3\n\n self.restServer = self.get_nodes_from_services_map(service_type=\"eventing\")\n self.rest = RestConnection(self.restServer)\n\n\n self.load(self.gens_load, buckets=self.buckets, flag=self.item_flag, verify_data=False,\n batch_size=self.batch_size)\n function_name = \"Function_{0}_{1}\".format(randint(1, 1000000000), self._testMethodName)\n self.function_name = function_name[0:90]\n body = self.create_save_function_body(self.function_name, HANDLER_CODE.BUCKET_OPS_ON_UPDATE, worker_count=3)\n bk_events_created = False\n rs_events_created = False\n try:\n self.deploy_function(body)\n bk_events_created = True\n self.backup_cluster()\n rest_bk = RestConnection(self.backupset.cluster_host)\n bk_fxn = rest_bk.get_all_functions()\n\n backup_index = 0\n\n if self.backup_before_eventing:\n backup_index = 1\n self.backupset.start = 1\n self.backupset.end = 2\n\n if bk_fxn != \"\":\n self._verify_backup_events_definition(json.loads(bk_fxn), body, backup_index = backup_index)\n\n self.backup_restore()\n\n rest_rs = RestConnection(self.backupset.restore_cluster_host)\n\n if self.backup_before_eventing:\n self.assertTrue('metadata' in [bucket.name for bucket in rest_rs.get_buckets()])\n\n self.bkrs_resume_function(body, rest_rs)\n rs_events_created = True\n self._verify_restore_events_definition(bk_fxn)\n except Exception as e:\n self.fail(e)\n finally:\n master_nodes = [self.backupset.cluster_host,\n self.backupset.restore_cluster_host]\n for node in master_nodes:\n rest = RestConnection(node)\n self.bkrs_undeploy_and_delete_function(body, rest, node)\n self.rest = RestConnection(self.master)","def stopeventmonitor(self):\n self.doeventloop = False\n if self.service is not None:\n self.service.breakloop()\n # reset the service, otherwise nextEvent won\"t work\n self.initeventservice(shutdown=True)\n if self.eventmonthread is not None:\n if emane.VERSION >= emane.EMANE091:\n self.eventmonthread._Thread__stop()\n self.eventmonthread.join()\n self.eventmonthread = None","def run(self):\n\n (robotProc, iRMsg, robotStat, robotInfo, robotCmd, bcMsg, cbpaeRun, wsInfo) = self.prepVars()\n\n broadcasterProc = self.startBroadcaster(cbpaeRun, bcMsg, iRMsg)\n\n# =============================================================================\n# # pass additional queues to the robot processes by overloading this method\n# =============================================================================\n robotProc = self.startRobots(robotProc, iRMsg, bcMsg, robotInfo, robotCmd, robotStat)\n\n guiProc = self.startGui(wsInfo, robotInfo, robotCmd)\n\n# =============================================================================\n# # This is the main loop checking robotProcs\n# =============================================================================\n rJoinable = self.checkRJoinable(robotProc, robotStat)\n\n self.stopBroadcaster(cbpaeRun)\n\n self.clearQueues(iRMsg, robotCmd, robotInfo)\n\n self.joinRobotProc(robotProc)\n\n self.logBasicInfo()\n\n print (\"CBPAE Trial Finished!!!\")","def run(self):\n \n # Loop through all checkers to do an initial state check\n for checker in self.checkers:\n checker.update_last_state()\n\n # Send initial heartbeat\n self._send_heartbeat()\n \n # Main loop\n while True: \n html = \"\"\n for checker in self.checkers:\n if checker.just_changed_state():\n log.warn(\"Checker {} has changed state.\"\n .format(checker.name))\n html += \"<li>\" + checker.html() + \"</li>\\n\"\n \n if isinstance(checker, Process) and checker.state() == FAIL:\n log.warn(\"Process {} is not running.\"\n .format(checker.name))\n html += (\"<li>Attempting to restart \" + \n escape(checker.name) + \"...</li>\\n\")\n try:\n checker.restart()\n except MaxRetriesError, e:\n self.shutdown_reason = str(e)\n return\n time.sleep(5)\n html += (\"<li>State after restart: \" + \n checker.html() + \"</li>\\n\")\n\n if html:\n html = \"<h2>STATE CHANGED:</h2>\\n<ul>\\n\" + html + \"</ul>\\n\" \n html += self.html()\n html += run_commands(self.state_change_cmds)\n self.send_email_with_time(html=html,\n subject=\"Babysitter detected\"\n \" state change.\")\n\n if self._need_to_send_heartbeat():\n self._send_heartbeat()\n\n # Check if a new data subdir has been created\n if self.base_data_dir and self.sub_data_dir:\n if self._find_last_numeric_subdir() != self.sub_data_dir:\n self._send_heartbeat(\"<p>New subdir found so about to restart \"\n \"babysitter. Below are the last stats \"\n \"for the old data subdirectory.</p>\\n\")\n raise NewDataDirError()\n \n time.sleep(UPDATE_PERIOD)","def loop_run(self):\n super(EventLoop, self).loop_run()\n self.inq = self.cothread.EventQueue()","def handle_wake_up(_):\n loop.awaken()","async def test_events_handled_on_event_loop(self):\n session = _create_test_session(asyncio.get_running_loop())\n\n handle_event_spy = MagicMock(\n side_effect=session._handle_scriptrunner_event_on_event_loop\n )\n session._handle_scriptrunner_event_on_event_loop = handle_event_spy\n\n # Send a ScriptRunner event from another thread\n thread = threading.Thread(\n target=lambda: session._on_scriptrunner_event(\n sender=MagicMock(), event=ScriptRunnerEvent.SCRIPT_STARTED\n )\n )\n thread.start()\n thread.join()\n\n # _handle_scriptrunner_event_on_event_loop won't have been called\n # yet, because we haven't yielded the eventloop.\n handle_event_spy.assert_not_called()\n\n # Yield to let the AppSession's callbacks run.\n # _handle_scriptrunner_event_on_event_loop will be called here.\n await asyncio.sleep(0)\n\n handle_event_spy.assert_called_once()","def test_dispatch_event(self):\n msg_helper = MessageHelper()\n worker_helper = WorkerHelper()\n broker = self.setup_broker(worker_helper)\n self.assertEqual(broker.get_messages('vumi', 'fooconn.event'), [])\n msg = msg_helper.make_ack()\n yield worker_helper.dispatch_event(msg, 'fooconn')\n self.assertEqual(broker.get_messages('vumi', 'fooconn.event'), [msg])","def test_sighup(self):\n self.render_config_template(\n )\n\n proc = self.start_beat()\n self.wait_until(lambda: self.log_contains(\"mockbeat start running.\"))\n proc.proc.send_signal(signal.SIGHUP)\n proc.check_wait()\n assert self.log_contains(\"mockbeat stopped.\")","def main_loop(self):\n # run for only the allotted time (lifetime)\n for _ in range(self.lifetime * self.ticks_per_second):\n start_time = time()\n new_message, queue_len = self.communicator.get_message()\n if new_message is None: # no incoming messages\n self.do_random_task()\n else:\n # Convert string message back into tuple of ints\n new_message = list(map(int, new_message.split('@@@')))\n self.handle_incoming_message(new_message, queue_len)\n\n # this accounts for the time already taken in test_communication\n # and other activities from the total time allotted for the loop iteration\n already_taken = time() - start_time\n sleep_time = max(1/self.ticks_per_second - already_taken, 0)\n sleep(sleep_time)","def cb_test( self, ):\r\n # this shows how to run stuff in the helper -- call thru queue, post to queue\r\n self.post_to_queue( \"call\", self.helper_thread.test_test_ports , ( ) )","def heartbeat_thread(self):\n while self.heart_should_beat:\n # set the bot's heartbeat event to end the wait immediately\n self.send_heartbeat()\n self.heartbeat_event.wait(timeout=self.heartbeat_period)","def _dispatch(f = None):\n #first install signal handler\n #this way we can quit the program easily from the command line\n #also, this makes libevent block on the first loop\n #otherwise when there are no events in the beginning, loop will not\n #block and our main dispatch loop would claim 100% CPU time\n def interrupt():\n quit(EXIT_CODE_SIGINT)\n event_interrupt = SignalEvent(SIGINT, interrupt)\n\n #the heartbeat makes sure the main loop below at least\n #makes a cycle every second. otherwise, if there are no pending signals\n #libevent._loop would block indefinitly, causing our loop never to check\n #if it still must be _running...\n event_heartbeat = TimeoutEvent(1.0, None, True)\n\n #as a convenience, user can provide a callable *f* to start a new task\n #lets start it here\n if callable(f):\n Tasklet.new(f)()\n\n global _running\n _running = True\n e = None\n try:\n #this is it, the main dispatch loop...\n #tasklets are scheduled to run by stackless,\n #and if no more are runnable, we wait for IO events to happen\n #that will trigger tasks to become runnable\n #ad infinitum...\n while _running:\n #first let any tasklets run until they have all become blocked on IO\n try:\n while stackless.getruncount() > 1:\n stackless.schedule()\n except TaskletExit:\n pass\n except:\n logging.exception(\"unhandled exception in dispatch schedule\")\n\n #now block on IO till any IO is ready.\n #care has been taken to not callback directly into python\n #from libevent. that would add c-data on the stack which would\n #make stackless need to use hard-switching, which is slow.\n #so we call 'loop' which blocks until something available.\n try:\n event.loop()\n except TaskletExit:\n raise\n except:\n logging.exception(\"unhandled exception in event loop\")\n\n #we iterate over the available triggered events and\n #call the callback which is available as the 'data' object of the event\n #some callbacks may trigger direct action (for instance timeouts, signals)\n #others might resume a waiting task (socket io).\n while event.has_next():\n try:\n e, event_type, fd = event.next()\n e.data(event_type)\n except TaskletExit:\n raise\n except:\n logging.exception(\"unhandled exception in event callback\")\n\n finally:\n del e\n event_interrupt.close()\n del event_interrupt\n event_heartbeat.close()\n del event_heartbeat\n\n if DEBUG_LEAK:\n logging.warn(\"alive objects:\")\n gc.collect()\n _print_objects(gc.get_objects())\n logging.warn('garbage:')\n _print_objects(gc.garbage)\n\n sys._exit(_exitcode)","def rfactor_event_loop():\n if RfactorLiveEvent.event.is_set():\n is_live = RfactorLiveEvent.get_nowait()\n # -- Update rFactor live state to front end\n if is_live is not None:\n eel.rfactor_live(is_live)\n\n if RfactorStatusEvent.event.is_set():\n status = RfactorStatusEvent.get_nowait()\n # -- Update rFactor status message in front end\n if status is not None:\n logging.debug('Updating rf2 status message: %s', status)\n eel.rfactor_status(status)\n\n RfactorStatusEvent.reset()","def control_loop():\n global EVENTS, AGENDA, TRACE_LEVEL\n \n epoch = 0\n go = True\n while go:\n if TRACE_LEVEL > 0:\n print \"[epoch {0}]\".format(epoch)\n\n event_count = 0\n \n # process events\n for event in EVENTS:\n event_fn, event_data = event[0], event[1]\n if TRACE_LEVEL > 3:\n print \"\\t\\tEvaluating event: ({0} {1})\".format(event_fn, event_data)\n event_fn(event_data) # eval the function\n if TRACE_LEVEL > 3:\n print \"\\t\\tEvent eval success.\"\n\n if event_count > EVENT_LIMIT:\n break\n event_count += 1\n \n EVENTS = []\n \n # check for stopping condition\n if AGENDA:\n # run the top KSA; LIFO stack agenda\n ksa = AGENDA.pop()\n if TRACE_LEVEL > 0:\n print \"\\tAGENDA Running: {0}\".format(ksa)\n ksa_fn, ksa_data = ksa[0], ksa[1]\n if TRACE_LEVEL > 3:\n print \"\\t\\tEvaluating KSA: ({0} {1})\".format(ksa_fn, ksa_data)\n ksa_fn(ksa_data)\n if TRACE_LEVEL > 3:\n print \"\\t\\tKSA eval success.\"\n else:\n go = False\n\n epoch += 1\n \n print(\"\\n\\nAgenda is empty. Stopping.\")","def test_heartbeat_only(cls):\n # type: () -> None\n\n uniclient_thread = ErrorAssertUniClientThread(cls.TEST_ZMQ_ENDPOINT)\n uniclient_thread.start()\n\n uniworker_thread = ConsoleUniWorkerThread(cls.TEST_ZMQ_ENDPOINT)\n uniworker_thread.start()\n\n uniclient_thread.wait_for_worker(INITIAL_CONNECTION_TIME_SECS)\n # Wait for 60 seconds, make sure the client and worker remain connected.\n sleep(60)\n #sleep(30000)\n assert uniclient_thread.is_connected()\n assert uniworker_thread.is_connected()\n\n # Shut down the worker and client\n uniworker_thread.join()\n uniclient_thread.join()","def on_shutdown(self):\n while self._queue:\n self._get()","def watch_for_heartbeat_messages(self):\n while True:\n message = self.socket_manager.get_heartbeat_message()\n self.ensure_sender_is_known(message)\n if message.direction == \"0\":\n self.respond_to_heartbeat_message(message)\n elif message.direction == \"1\":\n self.handle_heartbeat_response(message)","def loop(self):\n keys.mode = 'main'\n for line in client.readlines('/event'):\n if not self.alive:\n break\n self.dispatch(*line.split(' ', 1))\n self.alive = False","def run():\n logger.verbose(\"bwmon: Thread started\")\n while True:\n lock.wait()\n logger.verbose(\"bwmon: Event received. Running.\")\n database.db_lock.acquire()\n nmdbcopy = copy.deepcopy(database.db)\n database.db_lock.release()\n try:\n if getDefaults(nmdbcopy) and len(bwlimit.tc(\"class show dev %s\" % dev_default)) > 0:\n # class show to check if net:InitNodeLimit:bwlimit.init has run.\n sync(nmdbcopy)\n else: logger.log(\"bwmon: BW limits DISABLED.\")\n except: logger.log_exc(\"bwmon failed\")\n lock.clear()","def loop(self):\n while self.dispatch(True) is not QUIT:\n pass","def loop_once(self):\n while 1:\n if not self._active_nodes:\n # If everything has died stop looping\n self.triggershutdown()\n raise RuntimeError(\"Unexpectedly no active workers available\")\n try:\n eventcall = self.queue.get(timeout=2.0)\n break\n except Empty:\n continue\n callname, kwargs = eventcall\n assert callname, kwargs\n method = \"worker_\" + callname\n call = getattr(self, method)\n self.log(\"calling method\", method, kwargs)\n call(**kwargs)\n if self.sched.tests_finished:\n self.triggershutdown()","def test_job(self):\r\n while not self._coordinator.should_stop():\r\n with self._condition:\r\n self._condition.wait_for(self._true_if_testing)\r\n if self._coordinator.should_stop():\r\n break\r\n should_stop = False\r\n try:\r\n should_stop = self.do_full_test()\r\n except:\r\n traceback.print_exc()\r\n self._is_testing = False\r\n if should_stop is True:\r\n break\r\n logger.debug('Exiting thread %s' % threading.current_thread().name)","def handle_event(self, test_event):\n with self.lock:\n # Keep track of whether terminate was received. We do this so\n # that a process can call the 'terminate' event on its own, to\n # close down a formatter at the appropriate time. Then the\n # atexit() cleanup can call the \"terminate if it hasn't been\n # called yet\".\n if test_event is not None:\n event_type = test_event.get(\"event\", \"\")\n # We intentionally allow event_type to be checked anew\n # after this check below since this check may rewrite\n # the event type\n if event_type == EventBuilder.TYPE_JOB_RESULT:\n # Possibly convert the job status (timeout,\n # exceptional exit) # to an appropriate test_result event.\n self._maybe_remap_job_result_event(test_event)\n event_type = test_event.get(\"event\", \"\")\n\n # Remap timeouts to expected timeouts.\n if event_type in EventBuilder.RESULT_TYPES:\n self._maybe_remap_expected_timeout(test_event)\n self._maybe_remap_expected_failure(test_event)\n event_type = test_event.get(\"event\", \"\")\n\n if event_type == \"terminate\":\n self.terminate_called = True\n elif event_type in EventBuilder.RESULT_TYPES:\n # Clear the most recently started test for the related\n # worker.\n worker_index = test_event.get(\"worker_index\", None)\n if worker_index is not None:\n self.started_tests_by_worker.pop(worker_index, None)\n status = test_event[\"status\"]\n if status in EventBuilder.TESTRUN_ERROR_STATUS_VALUES:\n # A test/job status value in any of those status values\n # causes a testrun failure. If such a test fails, check\n # whether it can be rerun. If it can be rerun, add it\n # to the rerun job.\n self._maybe_add_test_to_rerun_list(test_event)\n\n # Build the test key.\n test_key = self._make_key(test_event)\n if test_key is None:\n raise Exception(\n \"failed to find test filename for \"\n \"test event {}\".format(test_event))\n\n # Save the most recent test event for the test key. This\n # allows a second test phase to overwrite the most recent\n # result for the test key (unique per method). We do final\n # reporting at the end, so we'll report based on final\n # results. We do this so that a re-run caused by, perhaps,\n # the need to run a low-load, single-worker test run can\n # have the final run's results to always be used.\n if test_key in self.result_events:\n self.test_method_rerun_count += 1\n self.result_events[test_key] = test_event\n elif event_type == EventBuilder.TYPE_TEST_START:\n # Track the start time for the test method.\n self.track_start_time(\n test_event[\"test_class\"],\n test_event[\"test_name\"],\n test_event[\"event_time\"])\n # Track of the most recent test method start event\n # for the related worker. This allows us to figure\n # out whether a process timeout or exceptional exit\n # can be charged (i.e. assigned) to a test method.\n worker_index = test_event.get(\"worker_index\", None)\n if worker_index is not None:\n self.started_tests_by_worker[worker_index] = test_event\n\n elif event_type == EventBuilder.TYPE_MARK_TEST_RERUN_ELIGIBLE:\n self._mark_test_for_rerun_eligibility(test_event)\n elif (event_type ==\n EventBuilder.TYPE_MARK_TEST_EXPECTED_FAILURE):\n self._mark_test_as_expected_failure(test_event)","async def test_handle_backmsg_exception(self):\n session = _create_test_session(asyncio.get_running_loop())\n\n # Create a mocked ForwardMsgQueue that tracks \"enqueue\" and \"clear\"\n # function calls together in a list. We'll assert the content\n # and order of these calls.\n forward_msg_queue_events: List[Any] = []\n CLEAR_QUEUE = object()\n\n mock_queue = MagicMock(spec=ForwardMsgQueue)\n mock_queue.enqueue = MagicMock(\n side_effect=lambda msg: forward_msg_queue_events.append(msg)\n )\n mock_queue.clear = MagicMock(\n side_effect=lambda: forward_msg_queue_events.append(CLEAR_QUEUE)\n )\n\n session._browser_queue = mock_queue\n\n # Create an exception and have the session handle it.\n FAKE_EXCEPTION = RuntimeError(\"I am error\")\n session.handle_backmsg_exception(FAKE_EXCEPTION)\n\n # Messages get sent in an eventloop callback, which hasn't had a chance\n # to run yet. Our message queue should be empty.\n self.assertEqual([], forward_msg_queue_events)\n\n # Run callbacks\n await asyncio.sleep(0)\n\n # Build our \"expected events\" list. We need to mock different\n # AppSessionState values for our AppSession to build the list.\n expected_events = []\n\n with patch.object(session, \"_state\", new=AppSessionState.APP_IS_RUNNING):\n expected_events.extend(\n [\n session._create_script_finished_message(\n ForwardMsg.FINISHED_SUCCESSFULLY\n ),\n CLEAR_QUEUE,\n session._create_new_session_message(page_script_hash=\"\"),\n session._create_session_status_changed_message(),\n ]\n )\n\n with patch.object(session, \"_state\", new=AppSessionState.APP_NOT_RUNNING):\n expected_events.extend(\n [\n session._create_script_finished_message(\n ForwardMsg.FINISHED_SUCCESSFULLY\n ),\n session._create_session_status_changed_message(),\n session._create_exception_message(FAKE_EXCEPTION),\n ]\n )\n\n # Assert the results!\n self.assertEqual(expected_events, forward_msg_queue_events)","def event_loop(self, index):\n logger.debug(\"Starting event loop \"+str(index))\n self.stop_flag = Event()\n stop_flag = self.stop_flag # Saving a reference.\n # stop_flag is an object that will signal the current input thread to exit or not exit once it's done processing a callback.\n # It'll be called just before self.stop_flag will be overwritten. However, we've got a reference to it and now can check the exact flag this thread itself constructed.\n # Praise the holy garbage collector.\n stop_flag.clear()\n while not stop_flag.isSet():\n if self.get_current_proxy() is not None:\n try:\n key = self.queue.get(False, 0.1)\n except Queue.Empty:\n # here an active event_loop spends most of the time\n sleep(0.1)\n except AttributeError:\n # typically happens upon program termination\n pass\n else:\n # here event_loop is usually busy\n self.process_key(key)\n else:\n # No current proxy set yet, not processing anything\n sleep(0.1)\n logger.debug(\"Stopping event loop \"+str(index))","def rmp_thread():\r\n rsp_queue = Queue.Queue()\r\n cmd_queue = Queue.Queue()\r\n in_flags = Queue.Queue()\r\n out_flags = Queue.Queue()\r\n\r\n \"\"\"\r\n Create the thread to run RMP\r\n \"\"\"\r\n my_thread = threading.Thread(target=RMP, args=(rmp_addr,rsp_queue,cmd_queue,in_flags,out_flags,UPDATE_DELAY_SEC,LOG_DATA))\r\n my_thread.daemon = True\r\n my_thread.start()\r\n\r\n \"\"\"\r\n Initialize my event handler class\r\n \"\"\"\r\n EventHandler = RMPEventHandlers(cmd_queue,rsp_queue,in_flags)\r\n\r\n \"\"\"\r\n -------------------------------------------------------------------------------\r\n User loop starts here modify to make it do what you want.\r\n\r\n You can pipe std_in from another application to the command queue and the response to std out or\r\n let the event handlers define everything. That is up to the user. In this example we transition modes,\r\n send motion commands (zeroed), play audio songs, and print the response dictionary. The application\r\n terminates the thread and exits when all the songs have been played. It is just an example of how to\r\n spawn a RMP thread, handle events, and send/receive data\r\n -------------------------------------------------------------------------------\r\n \"\"\"\r\n\r\n \"\"\"\r\n Generate a goto tractor event\r\n \"\"\"\r\n #EventHandler.GotoTractor()\r\n #EventHandler.GotoStandby()\r\n EventHandler.GotoBalance()\r\n\r\n \"\"\"\r\n Run until signaled to stop\r\n Perform the actions defined based on the flags passed out\r\n \"\"\"\r\n while (True == EventHandler._continue):\r\n EventHandler.handle_event[RMP_TX_RDY]()\r\n EventHandler.handle_event[RMP_RSP_DATA_RDY]()\r\n time.sleep(1.0)\r\n EventHandler.handle_event[RMP_ZERO]()\r\n EventHandler.handle_event[RMP_RSP_DATA_RDY]()\r\n time.sleep(1.0)\r\n EventHandler.handle_event[RMP_FORWARD]()\r\n EventHandler.handle_event[RMP_RSP_DATA_RDY]()\r\n time.sleep(1.0)\r\n pass\r\n #while not out_flags.empty():\r\n # EventHandler.handle_event[out_flags.get()]()\r\n \"\"\"\r\n Generate a goto standby event\r\n \"\"\"\r\n\r\n \"\"\"\r\n Allow enough time for the command to be sent\r\n then send the signal to kill the thread\r\n \"\"\"\r\n time.sleep(1.0)\r\n in_flags.put(RMP_KILL)\r\n\r\n \"\"\"\r\n Wait for the thread to die\r\n \"\"\"\r\n while my_thread.isAlive():\r\n pass\r\n\r\n \"\"\"\r\n Exit main\r\n \"\"\"\r\n sys.exit()","def __main_loop(self):\n\n while not self.stop:\n self.__lock_data = True\n self.__bus_messages_copy = deepcopy(self.__bus_messages)\n self.__line_messages_copy = deepcopy(self.__line_messages)\n self.__global_messages_copy = deepcopy(self.__global_messages)\n self.__bus_messages = {}\n self.__line_messages = {}\n self.__global_messages = {\"kick reason\": \"\", \"free text\": \"\"}\n buses_to_kick_copy = deepcopy(self.__buses_to_kick)\n self.__buses_to_kick = list()\n self.__lock_data = False\n\n for bus in buses_to_kick_copy: # handles the buses that need to be kicked\n message = \"kicked for reason:\" + self.__global_messages_copy[\"kick reason\"]\n if bus.line_num in self.__line_messages_copy.keys():\n message += self.__line_messages_copy[bus.line_num][\"kick reason\"]\n if bus.line_num in self.__line_messages_copy.keys() and bus.id in self.__bus_messages_copy[\n bus.line_num].keys():\n message += self.__bus_messages_copy[bus.line_num][bus.id][\"kick reason\"]\n print(f\"sending message{message.strip()}\")\n bus.send_to_bus(message.strip())\n\n global_message = self.__build_global_update()\n for line, buses in self.__bus_controller.bus_dict.items():\n line_message = self.__build_line_update(line)\n for bus in buses:\n bus_message = self.__build_bus_update(bus)\n message = global_message + line_message + bus_message\n message = message.strip(\"\\n\")\n if message != \"\":\n bus.send_to_bus(message)\n\n sleep(MessagesSender.SLEEP_TIME)\n\n self.__shut_down()\n print(\"polling thread stopped\")","def run(self):\n\n self.introduction_function()\n\n while True:\n try:\n message_received = self.queue_receive.get_nowait()\n except Exception as e:\n self.queue_receive_error_handler(e)\n else:\n self.execute_message_received(message_received)\n\n if self.development_function() is None:\n break\n\n self.conclusion_function()","def test_pollingBackoff(self):\n\n # Speed up the backoff process\n self.patch(ControllerQueue, \"queuePollingBackoff\", ((1.0, 60.0),))\n\n # Wait for backoff\n while self.node1._actualPollInterval == self.node1.queuePollInterval:\n d = Deferred()\n reactor.callLater(1.0, lambda : d.callback(None))\n yield d\n\n self.assertEqual(self.node1._actualPollInterval, 60.0)\n\n # TODO: this exact test should run against LocalQueuer as well.\n def operation(txn):\n # TODO: how does \"enqueue\" get associated with the transaction?\n # This is not the fact with a raw t.w.enterprise transaction.\n # Should probably do something with components.\n return txn.enqueue(DummyWorkItem, a=3, b=4, jobID=100, workID=1,\n notBefore=datetime.datetime.utcnow())\n yield inTransaction(self.store.newTransaction, operation)\n\n # Backoff terminated\n while self.node1._actualPollInterval != self.node1.queuePollInterval:\n d = Deferred()\n reactor.callLater(0.1, lambda : d.callback(None))\n yield d\n self.assertEqual(self.node1._actualPollInterval, self.node1.queuePollInterval)\n\n # Wait for it to be executed. Hopefully this does not time out :-\\.\n yield JobItem.waitEmpty(self.store.newTransaction, reactor, 60)\n\n # Wait for backoff\n while self.node1._actualPollInterval == self.node1.queuePollInterval:\n d = Deferred()\n reactor.callLater(1.0, lambda : d.callback(None))\n yield d\n\n self.assertEqual(self.node1._actualPollInterval, 60.0)","def run(self):\n if self.pollable:\n self.poll()\n if not self.EventsFactory.is_alive():\n self.EventsFactory.start()\n while True:\n if not self.EventsFactory.is_alive():\n self.logger.error(f'{self} events factory has died..')\n raise SubThreadException(self.EventsFactory)\n update_start_time = time.time()\n self.handle_events()\n wait_for(lambda: time.time() - update_start_time > self.handle_events_every and not self._busy_mutext.locked(),\n logger=self.logger, message='Waiting for work timeout to finish.')","def main_loop():\n while len(fake_threads) > 0:\n pulse(0.1)","async def test_no_hb(self):\n await self.async_setup()\n pyinsteon.managers.heartbeat_manager.HB_CHECK_BUFFER = 1\n self._hb_mgr = pyinsteon.managers.heartbeat_manager.HeartbeatManager(\n self._address, self._group, 0\n )\n await asyncio.sleep(1.1)\n assert self._heartbeat","def test_heartbeat(self):\n self.dut._poll_processes = MagicMock()\n self.dut._do_heartbeat()\n self.assertEqual(self.dut._poll_processes.call_count, 3)","def test_lbheartbeat(self):\n pass","def run(self): \n #\n\n \n # forever loop\n while True: \n \n for app in self.app_list:\n self.check(app) \n #print(\"check\")\n \n gevent.sleep(SLEEP_SECONDS)","def test_shutdown(self):\n hook = SignalHook(self.test_extension, self.signal,\n self._on_signal_fired)\n hook.disable_hook()\n\n self.assertEqual(len(self._on_signal_fired.calls), 0)\n self.signal.send(self)\n self.assertEqual(len(self._on_signal_fired.calls), 0)","def heartbeat_event(self, event):\r\n pass","def run(self):\n self.stopped = False\n # receives incoming 'host up' requests\n serverSocket = socket.socket(socket.AF_INET, socket.SOCK_DGRAM, 0)\n serverSocket.bind(('255.255.255.255', Globals.BROADCAST_PORT))\n \n # wait for UDP broadcast, send TCP ACK\n while 1:\n \n # open a socket and listen for a message\n value,address = serverSocket.recvfrom(256)\n host,port = address\n \n # this actually prevents a seg fault ;( for some reason\n if self.stopped:\n return\n \n if value == 'host up':\n \n sendSocket = socket.socket(socket.AF_INET, \n socket.SOCK_STREAM, 0)\n sendSocket.connect((host, Globals.ACK_PORT))\n sendSocket.send('host up ack')\n sendSocket.close()\n sendSocket = None\n self._addHost(host)\n \n elif value.find('host down') == 0:\n self._removeHost(host)\n \n elif value.find('add group') == 0:\n self._postEvent(value)\n \n elif value.find('remove group') == 0:\n self._postEvent(value)\n \n elif value.find('group beat') == 0:\n self._postEvent(value)\n \n serverSocket.close()","def run(self):\n self._app.processEvents()\n try:\n while not self._stop:\n # GRobot._app.processEvents()\n while self._app.hasPendingEvents():\n self._app.processEvents()\n gevent.sleep(0.01)\n except Exception, e:\n logger.error(e)\n logger.debug('Goodbye GRobot')","def loop(self):\n while not self.should_exit:\n self._run_once()\n\n self.on_exit()","def event_queue_proc(self,event):\r\n event()","async def heartbeat_loop():\n self.debug(\"Started heartbeat loop\")\n while True:\n try:\n await self.heartbeat()\n await asyncio.sleep(HEARTBEAT_INTERVAL)\n except asyncio.CancelledError:\n self.debug(\"Stopped heartbeat loop\")\n raise\n except asyncio.TimeoutError:\n self.debug(\"Heartbeat failed due to timeout, disconnecting\")\n break\n except Exception as ex: # pylint: disable=broad-except\n self.exception(\"Heartbeat failed (%s), disconnecting\", ex)\n break\n\n transport = self.transport\n self.transport = None\n transport.close()","def fsm_loop(self):\n while not rospy.is_shutdown():\n rospy.sleep(0.1)\n self.reset_fsm_preempted()\n self.reset_continued()\n\n state = self.get_state()\n if state is None:\n rospy.logerr(\"State is None. Error\")\n continue\n #\n if state not in self._fxns.keys():\n rospy.logdebug(\"{}: no function for given state. May be a bug\".format(state))\n continue\n ###\n # call the correct callback\n try:\n self._fxns[state](self._goal)\n except ActionServerError as error:\n # current state aborted, so return to the previous state\n rospy.logerr(traceback.format_exc())\n rospy.logdebug(\"{}: Aborted, reverting to previous state\".format(self._state_enums(state)))\n # self.print_prior_states()\n # pdb.set_trace()\n self.revert_state()\n\n # set result\n self._fill_result()","async def listen_and_arbitrate(isTest, backend):\n if not check_address(address):\n # Always exit. Unusable with a bad address\n fatal_error(True, \"Invalid address %s\" % address, 7)\n\n scheduler = SchedulerQueue()\n scanner = backend.Scanner()\n headers = {'Authorization': api_key} if api_key else {}\n async with aiohttp.ClientSession(headers=headers) as session:\n # Get base_nonce and bounty registry parameters\n await get_base_nonce(session)\n voting_window = await get_vote_window(session)\n reveal_window = await get_reveal_window(session)\n\n if not voting_window or not get_reveal_window:\n # Cannot vote/settle without this info\n fatal_error(True, \"Failed to get bounty windows.\", 14)\n\n if not await post_stake(session):\n # Always exit, because it is unusable without staking\n fatal_error(True, \"Failed to Stake Arbiter.\", 9)\n\n async with websockets.connect(ws_url, extra_headers=headers) as ws:\n while True:\n message = json.loads(await ws.recv())\n if message[\"event\"] == \"block\":\n number = message[\"data\"][\"number\"]\n if number % 100 == 0:\n logging.info('Block %s', number)\n\n asyncio.get_event_loop().create_task(scheduler.execute_scheduled(number))\n elif message[\"event\"] == \"bounty\":\n bounty = message[\"data\"]\n asyncio.get_event_loop().create_task(handle_bounty(isTest, session, scheduler, reveal_window, voting_window, scanner, bounty))","def event_loop():\n\n class TimeTrackingTestLoop(asyncio.BaseEventLoop):\n\n stuck_threshold = 100\n\n def __init__(self):\n super().__init__()\n self._time = 0\n self._timers = []\n self._selector = Mock()\n self.clear()\n\n # Loop internals\n\n def _run_once(self):\n super()._run_once()\n # Update internals\n self.busy_count += 1\n self._timers = sorted(when for when in self._timers if when > loop.time())\n # Time advance\n if self.time_to_go:\n when = self._timers.pop(0)\n step = when - loop.time()\n self.steps.append(step)\n self.advance_time(step)\n self.busy_count = 0\n\n def _process_events(self, event_list):\n return\n\n def _write_to_self(self):\n return\n\n # Time management\n\n def time(self):\n return self._time\n\n def advance_time(self, advance):\n if advance:\n self._time += advance\n\n def call_at(self, when, callback, *args, **kwargs):\n self._timers.append(when)\n return super().call_at(when, callback, *args, **kwargs)\n\n @property\n def stuck(self):\n return self.busy_count > self.stuck_threshold\n\n @property\n def time_to_go(self):\n return self._timers and (self.stuck or not self._ready)\n\n # Resource management\n\n def clear(self):\n self.steps = []\n self.open_resources = 0\n self.resources = 0\n self.busy_count = 0\n\n @contextmanager\n def assert_cleanup(self):\n self.clear()\n yield self\n assert self.open_resources == 0\n self.clear()\n\n loop = TimeTrackingTestLoop()\n asyncio.set_event_loop(loop)\n with loop.assert_cleanup():\n yield loop\n loop.close()","def prepareflow(self):\r\n self.time = 0\r\n #self.timeout = timeout\r\n Dummy = things(0,0,0)\r\n Dummy.threadqueue.append(-1)\r\n Dummy.s=self\r\n Dummy.name = 'End of simulation.'\r\n heappush(self.queue, (self.timeout, (Dummy, 1000)))\r\n #while true:\r\n #(now, (item, i)) = heappop(self.queue)\r\n #if now >= timeout: break\r","def run(self):\n if self.worker_is_alive:\n self.master_heartbeat_thread.join()","def run(self):\n self._no_tb = False\n self.traceback = None\n queues = (queue.attr, queue.clean) + queue.queues\n try:\n self.start.emit()\n while True:\n events = 0\n while len(self._events):\n events += 1\n if events == 50:\n self._queue(0)\n events = 0\n self.event_count += 1\n func, args, kwargs, tb_slot, tbs_time, tb_call, tbc_time = self._events.popleft()\n self._construct_tb((tb_slot, \"signal connection <%.4f>\" % (tbs_time - log.start_time)),\n (tb_call, \"signal emitter <%.4f>\" % (tbc_time - log.start_time)))\n func(*args, **kwargs)\n self._clear_tb()\n\n for q in queues:\n if len(q.active):\n break\n else:\n # Die if no events or outstanding processes\n break\n\n self._queue()\n\n finally:\n self.stop.emit()","def mainloop(self, run, *args, **kwds):\r\n #self.m.make()\r\n self.time = 0\r\n Dummy = things(0,0,0)\r\n Dummy.threadqueue.append(-1)\r\n Dummy.s=self\r\n Dummy.name = 'End of simulation.'\r\n heappush(self.queue, (self.timeout, (Dummy, 1000)))\r\n #count = 0\r\n while self.time < self.timeout:\r\n run(*args, **kwds)\r\n (now, (item, i)) = heappop(self.queue)\r\n self.CalculateResourse(now)\r\n self.time = now\r\n item.creat()\r\n #count += 1\r\n #print 'Number of loops' , count\r","def test_gameHandleEvents(self):\n # this kinda gonna be reiterating the other tests??\n # the tests of all the individual methods below make this test work\n pass","def run(self):\n while True:\n try:\n logging.debug(\">>> Waiting for action\")\n talk_action = self.action_queue.get(True, 150)\n except Queue.Empty:\n break\n else:\n self.handle(talk_action)\n self.close()","async def handle_hello(self, data, shard):\n if shard.id != self.id:\n return\n self.received_heartbeat_ack = True\n self.heartbeat_interval = data[\"d\"][\"heartbeat_interval\"] / 1000\n self.loop.create_task(self.heartbeat_loop())\n self.logger.debug(\"Started heartbeat loop\")","def run_forever(self):\n while True:\n if not self._mailbox:\n self._event.wait()\n self._event = _event.Event()\n else:\n # leave the message in the mailbox until after it's\n # been processed so the event doesn't get triggered\n # while in the received method\n self._pool.spawn_n(\n self.received, self._mailbox[0])\n self._mailbox.popleft()","def ProcessEvents(self):\n self.work_queue.put(self.__ProcessEventsAsync)","def _run(self) -> None:\n try:\n while True:\n loop_time = self._get_time()\n loop_time_flush_interval = self._get_time(self.flush_interval.total_seconds())\n\n if loop_time >= self.flushing_interval_deadline:\n self._flush_batch()\n self.flushing_interval_deadline = loop_time + loop_time_flush_interval\n self.logger.debug('Flush interval deadline. Flushed batch.')\n\n try:\n interval = self.flushing_interval_deadline - loop_time\n item = self.event_queue.get(True, interval)\n\n if item is None:\n continue\n\n except queue.Empty:\n continue\n\n if item == self._SHUTDOWN_SIGNAL:\n self.logger.debug('Received shutdown signal.')\n break\n\n if item == self._FLUSH_SIGNAL:\n self.logger.debug('Received flush signal.')\n self._flush_batch()\n continue\n\n if isinstance(item, UserEvent):\n self._add_to_batch(item)\n\n except Exception as exception:\n self.logger.error(f'Uncaught exception processing buffer. Error: {exception}')\n\n finally:\n self.logger.info('Exiting processing loop. Attempting to flush pending events.')\n self._flush_batch()","def test_run_ended(self):","def mainloop(self):\n global agent_types\n while 1:\n self.evSimulate.wait()\n self.logger.debug('Entered Mainloop')\n if self.bQuit == True: \n return\n \n self.evSimulate.clear()\n \n if self.szType != None and self.simtime != None and self.iIdLow!= -1 and self.iIdHigh != -1:\n \n if dictSettings[S_SYNCHRONOUSWRITES]:\n for i in xrange(self.iIdLow, self.iIdHigh):\n \n if agent_types[self.szType]['__lstDeletedIds__']: \n p = bisect.bisect_right(agent_types[self.szType]['__lstDeletedIds__'], i)\n if p == 0 and agent_types[self.szType]['__lstDeletedIds__'][0] == i:\n continue \n if agent_types[self.szType]['__lstDeletedIds__'][p - 1] == i:\n continue \n \n ag = agent_types[self.szType]['__handler__'](self.db, self.szType, i, self.simtime, self.globalData)\n ag.readState()\n ag.update(self.simtime)\n else:\n for i in xrange(self.iIdLow, self.iIdHigh, dictSettings[S_NUMAGENTSTOFETCHATONCE]):\n \n\n iQueryLowIndex = i\n iQueryHighIndex = i + dictSettings[S_NUMAGENTSTOFETCHATONCE]\n if (self.iIdHigh - i) < dictSettings[S_NUMAGENTSTOFETCHATONCE]:\n iQueryHighIndex = self.iIdHigh\n \n lstDictAgentData = self.db.readObjects(self.szType, iQueryLowIndex, iQueryHighIndex)\n \n for j in lstDictAgentData: \n ag = agent_types[self.szType]['__handler__'](self.db, self.szType, j['id'], self.simtime, self.globalData)\n ag.setState(j)\n ag.update(self.simtime)\n \n self.logger.debug('Done simulating')\n \n self.evAPI.set()","def loop(self):\n _logger.info(\"Bus.loop listen imbus on db postgres\")\n # PATCH !!\n with odoo.sql_db.db_connect(_get_imbus_db()).cursor() as cr:\n conn = cr._cnx\n cr.execute(\"listen imbus\")\n cr.commit();\n while True:\n if select.select([conn], [], [], TIMEOUT) == ([], [], []):\n pass\n else:\n conn.poll()\n channels = []\n while conn.notifies:\n channels.extend(json.loads(conn.notifies.pop().payload))\n # dispatch to local threads/greenlets\n events = set()\n for channel in channels:\n events.update(self.channels.pop(hashable(channel), []))\n for event in events:\n event.set()","def _EventQueueWorker(self):\n while not self.stop:\n gevent.sleep(0)\n\n try:\n with Timer('task_process_time', False) as t:\n # Pull the next task off the queue.\n task, enqueue_times = model_provider.GetQueues().GetTask()\n\n # Retrieve the Tab Types for the NIDs so we know what handler to use.\n nid_to_type = model_provider.GetNames().GetTypes(task.keys())\n\n # Add the queued state parts to the main states.\n greenlets = [\n gevent.spawn(\n model_provider.GetStates().ReduceInto,\n nid, cid_to_sstate, nid_to_type[nid])\n for nid, cid_to_sstate in task.iteritems()]\n\n gevent.joinall(greenlets)\n\n LOG.info('Processed tasks %3dN %5dC %7.3fs' % (\n len(task), len(enqueue_times), t.Elapsed))\n\n model_provider.GetLatency().RecordForProcessed(enqueue_times)\n\n except Exception:\n LOG.error('Exception in Queue Worker loop')\n LOG.error(traceback.format_exc())\n gevent.sleep(1)","def thread_for_rescue_detection(self):\n try:\n self.loop_over_frames()\n except Exception as e:\n pass\n finally:\n self.clean_up()","def __heart(self):\n\n while not self.__stop_threads:\n start_time = time.time()\n self.__pulse_all()\n # print(f\"total pulse time = {time()-start_time} seconds\")\n sleep(BusController.PULSE_DELAY)\n print(\"stopped the heartbeats\")","def eventmonitorloop(self):\n if self.service is None:\n return\n logger.info(\"Subscribing to EMANE location events (not generating them). \" \\\n \"(%s) \" % threading.currentThread().getName())\n while self.doeventloop is True:\n if emane.VERSION >= emane.EMANE091:\n uuid, seq, events = self.service.nextEvent()\n if not self.doeventloop:\n break # this occurs with 0.9.1 event service\n for event in events:\n (nem, eid, data) = event\n if eid == LocationEvent.IDENTIFIER:\n self.handlelocationevent2(nem, eid, data)\n else:\n (event, platform, nem, cmp, data) = self.service.nextEvent()\n if event == emaneeventlocation.EVENT_ID:\n self.handlelocationevent(event, platform, nem, cmp, data)\n logger.info(\"Unsubscribing from EMANE location events. (%s) \" % threading.currentThread().getName())","def _bg_thread_main(self) -> None:\n while not self._done:\n self._run_server_cycle()","async def _main_coro(self):\n\t\twhile True:\n\t\t\ttry:\n\t\t\t\t# Gets a frame from the log queue in a separate thread\n\t\t\t\tfor task in as_completed([self._event_loop.run_in_executor(self._thread_executor, self.log_queue.get, True, 0.1)]):\n\t\t\t\t\tframe = await task\n\t\t\t\t\t# If the current frame is a STOP frame, increase the counter.\n\t\t\t\t\t# Handles the frame payload otherwise\n\t\t\t\t\tif frame.header == Frame.STOP:\n\t\t\t\t\t\tself._stop_counter += 1\n\t\t\t\t\telse:\n\t\t\t\t\t\tawait self._record_logs(frame.payload)\n\t\t\texcept Empty:\n\t\t\t\t# Event loop works while TestParser and Processor generate tasks\n\t\t\t\tif self._stop_counter == 2:\n\t\t\t\t\tbreak","def postloop(self):\n print 'Bye!'","def run(self):\n while True:\n self._event.clear()\n self._queue.get().run(self._event)","def main_loop(self):\n import time\n while not self.ask_for_stop:\n self.run_pending()\n time.sleep(self.delay)\n # FIXME this will look at self.ask_for_stop only every self.delay seconds\n # see https://stackoverflow.com/questions/5114292/break-interrupt-a-time-sleep-in-python","def timerEvent(self, event):\n self.ext_fb_trigger()\n if self._check_pulse():\n if self._generator_poll is None:\n if self._generator_feedback is not None:\n print('In timerEvent 2')\n self._stop_loop_feedback()\n return\n try:\n next(self._generator_poll) # Run the next iteration\n if self._generator_feedback is None:\n return\n try:\n next(self._generator_feedback)\n except Exception as e:\n print(e)\n self._stop_loop_feedback()\n except StopIteration:\n # print('In timerEvent 5')\n self._stop_loop_feedback() # Iteration has finished, kill the timer\n self._stop_loop_poll() # Iteration has finished, kill the timer","def polling( self, ):\r\n \"\"\"\r\n queue protocol, data = ( action, function, function_args )\r\n action = a string\r\n function = a function\r\n function_args = arguments to function which will be called function( function_args ) This should be a tuple\r\n \"\"\"\r\n\r\n # !! may be phasing out\r\n# if self.request_to_pause:\r\n# self.paused = True\r\n#\r\n# while self.request_to_pause: # think function part not used ??\r\n# time.sleep( .1 )\r\n#\r\n# self.paused = False\r\n\r\n# if self.parameters.start_helper_delay > 0:\r\n# helper_start = True\r\n# else:\r\n# helper_start = False\r\n # two bits here just used once, have a polling0 then swtch over to this\r\n\r\n if self.is_first_gui_loop:\r\n # perhaps a polling method polling0 that then calls polling\r\n # should be moved to gui !! turn back on unless messing up whole app\r\n # print(\"lifting...\")\r\n# self.gui.root.attributes(\"-topmost\", True) # seems to work\r\n# self.gui.root.lift() # did not work\r\n self.is_first_gui_loop = False\r\n# self.gui.root.attributes(\"-topmost\", False) # seems to work\r\n try:\r\n if self.start_helper and ( self.start_helper_after < time.time() ):\r\n # if self.start_helper_after < time.time() :\r\n self.start_helper = False\r\n\r\n msg = f\"We have an start_helper_function setting in the parameter file = {self.parameters.start_helper_function}\"\r\n print( msg )\r\n self.gui.print_info_string( msg )\r\n to_eval = f\"self.ext_processing. {self.parameters.start_helper_function}\"\r\n a_function = eval( to_eval ) # or hava a way to pass eval to other side ??\r\n # a_function() # run in gt of ext_processing module -- not a good idea\r\n # next was to run in the background thread\r\n self.post_to_queue( \"call\", a_function , self.parameters.start_helper_args )\r\n# else:\r\n# print( self.start_helper_after < time.time() )\r\n# print( self.start_helper )\r\n# print( \"\")\r\n if self.gui_recieve_lock.locked():\r\n self.receive()\r\n # self.start_helper_after time to start helper if used\r\n # loop till queue empty\r\n ( action, function, function_args ) = self.rec_from_queue()\r\n while action != \"\":\r\n if action == \"call\":\r\n #print( \"controller making call\" )\r\n sys.stdout.flush()\r\n function( *function_args )\r\n elif action == \"rec\":\r\n self.gui.print_rec_string( function_args[ 0 ] )\r\n elif action == \"send\":\r\n # but where is it actually sent ??\r\n self.gui.print_send_string( function_args[ 0 ] )\r\n elif action == \"info\":\r\n self.gui.print_info_string( function_args[ 0 ] )\r\n\r\n ( action, function, function_args ) = self.rec_from_queue()\r\n\r\n self.task_tick += 1 # for delay in list send\r\n\r\n if self.list_send: # used for ir_processing and motor processing\r\n #self.loggerit( str( self.task_tick % self.parameters.send_array_mod ) )\r\n if ( ( self.task_tick % self.parameters.send_array_mod ) == 0 ): # 5 might be in parms\r\n #---\r\n #print \"send ix_array\", self.send_list_ix\r\n #self.send( \"xxx\\n\" )\r\n self.send( str( self.send_list[ self.send_list_ix ] ) + \"\\n\" )\r\n\r\n self.send_list_ix += 1\r\n if ( self.send_list_ix >= len( self.send_list ) ):\r\n self.list_send = False\r\n\r\n except Exception as ex_arg:\r\n # !! think there is a type of logging that gives traceback info\r\n self.logger.error( \"polling Exception in smart_terminal: \" + str( ex_arg ) )\r\n # ?? need to look at which we catch maybe just rsh\r\n (atype, avalue, atraceback) = sys.exc_info()\r\n a_join = \"\".join( traceback.format_list ( traceback.extract_tb( atraceback ) ) )\r\n self.logger.error( a_join )\r\n\r\n a_ex_record = ExceptionRecord( time.time) # add at end limit self.ex_max\r\n self.exception_records.append( a_ex_record )\r\n if ( len( self.exception_records ) > self.parameters.ex_max ):\r\n msg = \"too many exceptions polling in smart terminal may die\"\r\n print( msg )\r\n self.logger.error( msg )\r\n self.logger.error( \"too many exceptions what to do???\" )\r\n self.polling_fail = True\r\n #raise Exception( \"too many\" )\r\n #self.restart() # may leave return on stack ??\r\n # here we need to set the next task?? lets try, as function may not have returned\r\n # self.task_list.what_next = self.task_list.advance_next\r\n\r\n finally:\r\n if self.polling_fail:\r\n pass\r\n else:\r\n self.gui.root.after( self.parameters.gt_delta_t, self.polling ) # reschedule event\r\n\r\n return","async def do_run(self, event_bus: EndpointAPI) -> None:\n ...","async def heartbeat_loop(self):\n while self.connected.is_set():\n if not self.received_heartbeat_ack:\n self.failed_heartbeats += 1\n self.logger.info(\n \"WebSocket did not respond to a heartbeat! Failed \"\n \"attempts: \" + str(\n self.failed_heartbeats))\n if self.failed_heartbeats > 2:\n self.logger.warning(\n \"Gateway stopped responding, reconnecting!\")\n await self.close()\n await self.connect(self.gateway_url)\n return\n self.received_heartbeat_ack = False\n await self.send({\n \"op\": 1,\n \"d\" : self.heartbeat_count\n })\n if self.heartbeat_count is not None:\n self.heartbeat_count += 1\n else:\n self.heartbeat_count = 0\n await sleep(self.heartbeat_interval)","def run(self):\n # The first heartbeat happens immediately\n self.log.info('starting heartbeater')\n interval = 0\n self.agent.set_agent_advertise_addr()\n\n self.reader, self.writer = os.pipe()\n p = select.poll()\n p.register(self.reader, select.POLLIN)\n try:\n while True:\n if p.poll(interval * 1000):\n if os.read(self.reader, 1) == 'a':\n break\n\n self.do_heartbeat()\n interval_multiplier = random.uniform(\n self.min_jitter_multiplier,\n self.max_jitter_multiplier)\n interval = self.agent.heartbeat_timeout * interval_multiplier\n log_msg = 'sleeping before next heartbeat, interval: {0}'\n self.log.info(log_msg.format(interval))\n finally:\n os.close(self.reader)\n os.close(self.writer)\n self.reader = None\n self.writer = None","def test_endGameHandlers1(self):\n # check that it starts as False\n assert_equal(self.testGame.running, False)\n # set explicitly to True, make sure it is set\n self.testGame.running = True\n assert_equal(self.testGame.running, True)\n # create a Quit type Pygame event, and run with method, then check\n eventEscape = pg.event.Event(pg.KEYDOWN)\n pass","def _poll_event_queue(self):\n if not self.event_queue.empty():\n obj = self.event_queue.get(block=False)\n if isinstance(obj, tuple):\n if len(obj) == 1:\n obj[0]()\n elif len(obj) == 2:\n if isinstance(obj[1], list):\n obj[0](*obj[1])\n elif isinstance(obj[1], dict):\n obj[0](**obj[1])\n elif len(obj) == 3:\n obj[0](*obj[1], **obj[2])\n self.view.after(100, self._poll_event_queue)","async def run(self):\n while True:\n await asyncio.sleep(0)\n # See if any sockets have anything\n try:\n socks, events = self.poller.poll(1000)\n for sock, event in zip(socks,events):\n if sock in self.subscriptions:\n states = sock.recv_json()\n await self.main_server.sync_states(states)\n\n # Nothing to report - Poller did not find any sockets with updates\n except ValueError:\n pass\n # Exiting\n except KeyboardInterrupt:\n break","def run(self):\n time_to_quit = False\n while True:\n time_to_quit = self.run_to_yield_or_quit()\n if time_to_quit:\n print(self, 'quitting')\n break\n else:\n time.sleep(self.polling_interval)","def test_process_packet_heartbeat(self):\n\n pkt = {'type': 'heartbeat',\n 'endpoint': ''\n }\n self.ns.process_packet(pkt)\n assert not self.environ['socketio'].error.called","async def test_estop_callback(\n decoy: Decoy,\n) -> None:\n engine_store = decoy.mock(cls=EngineStore)\n\n subject = get_estop_listener(engine_store=engine_store)\n\n decoy.when(engine_store.current_run_id).then_return(None, \"fake_run_id\")\n\n disengage_event = EstopStateNotification(\n old_state=EstopState.PHYSICALLY_ENGAGED, new_state=EstopState.LOGICALLY_ENGAGED\n )\n\n subject(disengage_event)\n\n engage_event = EstopStateNotification(\n old_state=EstopState.LOGICALLY_ENGAGED, new_state=EstopState.PHYSICALLY_ENGAGED\n )\n\n subject(engage_event)\n\n subject(engage_event)\n\n decoy.verify(engine_store.engine.estop(maintenance_run=False), times=1)"],"string":"[\n \"def _run_backtest(self):\\n i = 0\\n \\n while True:\\n i += 1\\n print(i)\\n \\n # Update the market bars\\n if self.data_handler.continue_backtest == True:\\n self.data_handler.update_bars()\\n else:\\n break\\n \\n # Handle the Events\\n while True:\\n try:\\n event = self.events.get(False)\\n except queue.Empty:\\n break\\n else:\\n # The inner-loop acts on the events by calling the appropriate method of the appropriate object\\n if event is not None:\\n if event.type == 'MARKET':\\n self.strategy.calculate_signals(event)\\n self.portfolio.update_timeindex(event)\\n \\n elif event.type == 'SIGNAL':\\n self.signals += 1\\n self.portfolio.update_signal(event)\\n \\n elif event.type == 'ORDER':\\n self.orders += 1\\n self.execution_handler.execute_order(event)\\n \\n elif event.type == 'FILL':\\n self.fills += 1\\n self.portfolio.update_fill(event)\\n \\n # Pauses for a duration of self.heartbeat seconds\\n time.sleep(self.heartbeat)\",\n \"def _run_backtest(self):\\n i = 0\\n while True:\\n i += 1\\n if self.data_handler.continue_backtest == True:\\n self.data_handler.update_bars()\\n else:\\n break\\n while True:\\n try:\\n event = self.events.get(False)\\n except Empty:\\n break\\n else:\\n if event is not None:\\n if event.type == EventType.MARKET:\\n self.strategy.On_Bars(event)\\n self.portfolio.update_balance(event)\\n self.portfolio.order_check(event)\\n elif event.type == EventType.ORDER_SEND:\\n self.portfolio.update_order(event)\\n elif event.type == EventType.ORDER_CLOSE:\\n self.portfolio.update_order(event)\\n self.portfolio.update_euity(event)\\n elif event.type == EventType.ORDER_MODIFY:\\n self.portfolio.update_order(event)\\n time.sleep(self.heartbeat)\",\n \"def _run_backtest(self):\\n i = 0\\n while True:\\n i += 1\\n if self.data_handler.continue_backtest == True:\\n self.data_handler.update_bars()\\n #print(self.data_handler.get_latest_bar_datetime(self.symbol_list[0]))\\n else:\\n break\\n while self.backtest:\\n try:\\n event = self.events.get(False)\\n except Empty:\\n break\\n else:\\n if event is not None:\\n if event.type == EventType.MARKET:\\n try:\\n self.strategy.On_Bars(event)\\n self.portfolio.update_balance(event)\\n self.portfolio.order_check(event)\\n except EquityError:\\n print('Not Engough Equity,Backtest Will be Stop...')\\n self.backtest=False\\n break\\n elif event.type == EventType.ORDER_SEND:\\n self.portfolio.update_order(event)\\n elif event.type == EventType.ORDER_CLOSE:\\n try:\\n self.portfolio.update_order(event)\\n self.portfolio.update_euity(event)\\n except EquityError:\\n print ('Not Engough Equity,Backtest Will be Stop...')\\n self.backtest=False\\n break\\n elif event.type == EventType.ORDER_MODIFY:\\n self.portfolio.update_order(event)\\n time.sleep(self.heartbeat)\",\n \"def run(self):\\n\\n self.halt = False\\n\\n print(\\\"Starting heartbeat.\\\")\\n while not self.halt:\\n event = events.TickEvent()\\n AppState.get_state().get_event_manager().post_event(event)\\n AppState.get_state().get_clock().tick(settings.MAX_FPS)\",\n \"def run(self) -> None:\\n\\n while not self.stop_event.is_set():\\n if self.my_queue:\\n # if heartbeat received at '/heartbeat' route from the monitored peer,\\n # sleep until next\\n self.my_queue.clear()\\n time.sleep(7)\\n\\n else:\\n # else drop peer data from database and inform central server appending '0'\\n # to my queue\\n self.db_access.drop_peer(self.peer_id)\\n self.my_queue.append(0)\\n break\",\n \"def event_loop(self):\\n while self.ack is False:\\n gevent.sleep(self.loop_interval)\\n output_service = self.get_directory_service_proxy().get_service(\\\"mock-output-service\\\")\\n output_service.put(\\\"test-worker-work-result\\\")\\n self.ack = True\",\n \"def __call__(self, event_queue, in_queue, out_queue):\\n\\n running = True\\n self.in_q = in_queue # command received from the main thread\\n self.out_q = out_queue # responses, commands to the main thread\\n self.ev_q = event_queue # return pending events to the main thread\\n shutdown = False\\n self.light_state = False # current state of beat light\\n\\n # send first beat light message\\n if self.btic.BeatLight() is True:\\n self.out_q.put(\\\"beaton\\\")\\n else:\\n self.out_q.put(\\\"beatoff\\\")\\n\\n # run thread loop\\n while running is True:\\n if self.die_pending is False:\\n self.sendPendingEvents()\\n self.processCommands()\\n\\n \\\"\\\"\\\"\\n if self.btic.BeatLightToggle() == True:\\n self.out_q.put(\\\"beat\\\")\\n wx.WakeUpIdle()\\n \\\"\\\"\\\"\\n\\n # display beat light on UI\\n light = self.btic.BeatLight()\\n if light != self.light_state:\\n self.light_state = light\\n if light is True:\\n self.out_q.put(\\\"beatoff\\\")\\n else:\\n self.out_q.put(\\\"beaton\\\")\\n wx.WakeUpIdle() \\n \\n if self.allClear() is True:\\n time.sleep(.01)\\n #pass\\n else:\\n # stop the loop/thread when all is cleaned up\\n self.sendPendingEvents()\\n if self.allClear() is True:\\n self.clearBank() \\n self.die_pending = False\\n running = False\\n else:\\n time.sleep(.01)\\n # pass\",\n \"def awaitVerification(self):\\r\\n method = moduleName + '.' + self.className + '.' + 'awaitVerification'\\r\\n while True:\\r\\n try:\\r\\n self._stopevent.wait(self._sleepperiod)\\r\\n verification = self.localCommQueue.get_nowait()\\r\\n if verification == terminationVerificationMsg.COMMIT:\\r\\n #The parent AE agrees that we can shutdown. Terminate\\r\\n break\\r\\n elif verification == terminationVerificationMsg.ROLLBACK:\\r\\n #Roll back the termination\\r\\n raise Exceptions.WorkerThreadTerminationRollback()\\r\\n elif verification == terminationVerificationMsg.ERROR:\\r\\n errorMsg = \\\"Worker thread for landmark %s is improperly indexed\\\" %self.queueID\\r\\n Graph.logQ.put( [logType , logLevel.ERROR , method , errorMsg])\\r\\n raise Exceptions.WorkerThreadIndexError(errorMsg)\\r\\n else:\\r\\n #Should not happen\\r\\n errorMsg = \\\"Unexpected shutdown verification response for worker thread on landmark %s\\\" %self.queueID\\r\\n Graph.logQ.put( [logType , logLevel.ERROR , method , errorMsg])\\r\\n raise Exceptions.WorkerThreadIndexError(errorMsg)\\r\\n break\\r\\n except queue.Empty:\\r\\n pass\\r\\n except Exceptions.WorkerThreadTerminationRollback:\\r\\n raise Exceptions.WorkerThreadTerminationRollback()\\r\\n except Exception as e:\\r\\n errorMsg = \\\"Unexpected error during shutdown verification process for worker thread on landmark %s. Traceback= %s\\\" %(self.queueID, e)\\r\\n Graph.logQ.put( [logType , logLevel.ERROR , method , errorMsg])\\r\\n raise e\",\n \"def _eventloop(self):\\n logging.debug(\\\"%s - eventloop started\\\" % self.name)\\n while not self.stopped:\\n event = self.inqueue.get()\\n if not event: break\\n self.doevent(event)\\n logging.debug(\\\"%s - eventloop stopped\\\" % self.name)\",\n \"def _do_test(self):\\n\\n process_all_events()\\n\\n if self.list:\\n (callback, args, kwargs) = self.list.pop(0)\\n callback(*args, **kwargs)\\n else:\\n safe_exit(force=1)\",\n \"def main_loop(self):\\n LOGGER.info('Entering main event loop...')\\n try:\\n while self._handle_faucet_events():\\n while not self._faucet_events.event_socket_connected:\\n LOGGER.info('Attempting faucet event sock connection...')\\n time.sleep(1)\\n try:\\n self._faucet_events.connect()\\n self._restore_states()\\n self._faucet_collector.set_state_restored(True)\\n except Exception as e:\\n LOGGER.error(\\\"Cannot restore states or connect to faucet: %s\\\", e)\\n self._faucet_collector.set_state_restored(False, e)\\n except KeyboardInterrupt:\\n LOGGER.info('Keyboard interrupt. Exiting.')\\n self._faucet_events.disconnect()\\n except Exception as e:\\n LOGGER.error(\\\"Exception: %s\\\", e)\\n raise\",\n \"def run(self):\\n \\n # Wrap the outer loop in a try block so we can do an orderly shutdown\\n # should an exception occur:\\n try:\\n # Send out a STARTUP event:\\n self.dispatchEvent(weewx.Event(weewx.STARTUP))\\n \\n syslog.syslog(syslog.LOG_INFO, \\\"engine: Starting main packet loop.\\\")\\n\\n last_gc = int(time.time())\\n\\n # This is the outer loop. \\n while True:\\n\\n # See if garbage collection is scheduled:\\n if int(time.time()) - last_gc > self.gc_interval:\\n ngc = gc.collect()\\n syslog.syslog(syslog.LOG_INFO, \\\"engine: garbage collected %d objects\\\" % ngc)\\n last_gc = int(time.time())\\n\\n # First, let any interested services know the packet LOOP is\\n # about to start\\n self.dispatchEvent(weewx.Event(weewx.PRE_LOOP))\\n \\n # Get ready to enter the main packet loop. An exception of type\\n # BreakLoop will get thrown when a service wants to break the\\n # loop and interact with the console.\\n try:\\n \\n # And this is the main packet LOOP. It will continuously\\n # generate LOOP packets until some service breaks it by\\n # throwing an exception (usually when an archive period\\n # has passed).\\n for packet in self.console.genLoopPackets():\\n \\n # Package the packet as an event, then dispatch it.\\n self.dispatchEvent(weewx.Event(weewx.NEW_LOOP_PACKET, packet=packet))\\n\\n # Allow services to break the loop by throwing\\n # an exception:\\n self.dispatchEvent(weewx.Event(weewx.CHECK_LOOP, packet=packet))\\n\\n syslog.syslog(syslog.LOG_CRIT, \\\"engine: Internal error. Packet loop has exited.\\\")\\n \\n except BreakLoop:\\n \\n # Send out an event saying the packet LOOP is done:\\n self.dispatchEvent(weewx.Event(weewx.POST_LOOP))\\n\\n finally:\\n # The main loop has exited. Shut the engine down.\\n syslog.syslog(syslog.LOG_DEBUG, \\\"engine: Main loop exiting. Shutting engine down.\\\")\\n self.shutDown()\",\n \"def heartbeat_event(self, event):\\r\\n while not self.imm_jobs.empty():\\r\\n imm_job = self.imm_jobs.get_nowait()\\r\\n imm_job(self)\\r\\n \\r\\n if self.do_reconfigure:\\r\\n self.selmgr.reconfigure(self.current_consensus())\\r\\n self.do_reconfigure = False\\r\\n \\r\\n if self.run_all_jobs:\\r\\n while not self.low_prio_jobs.empty() and self.run_all_jobs:\\r\\n imm_job = self.low_prio_jobs.get_nowait()\\r\\n imm_job(self)\\r\\n self.run_all_jobs = False\\r\\n return\\r\\n\\r\\n # If event is stream:NEW*/DETACHED or circ BUILT/FAILED, \\r\\n # don't run low prio jobs.. No need to delay streams for them.\\r\\n if PathBuilder.is_urgent_event(event): return\\r\\n \\r\\n # Do the low prio jobs one at a time in case a \\r\\n # higher priority event is queued \\r\\n if not self.low_prio_jobs.empty():\\r\\n delay_job = self.low_prio_jobs.get_nowait()\\r\\n delay_job(self)\",\n \"def run(self):\\n self.workhorse_.run()\\n try:\\n while(True):\\n self.workhorse_.heartbeat()\\n self.periodic_snapshot()\\n except workflow.NoMoreWork:\\n print \\\"Fini.\\\"\\n exit(0)\\n exit(-1)\",\n \"def test_heartbeat(self):\\n pass\",\n \"def processEvent(self):\\n # Note: break out of event dispatch loop when closedown event is received\\n # and closing flag is set. This is to prevent DoS attack by faked closedown\\n # event type, and to ensure that prior events received are all processed.\\n delay_on_error_min = 0.125 # Back off retry interval on error..\\n delay_on_error_max = 20.0 # ..\\n delay_on_error = delay_on_error_min # ..\\n while True:\\n if delay_on_error < delay_on_error_max:\\n delay_on_error *= 2\\n try:\\n # PLEASE NOTE: In the event that the HTTPC is run as duplex, not simplex\\n # then the post methods will be delayed if nothing is sent down to the client\\n # from the server. This timeout is controlled by QUEUE_WAIT_TIMEOUT in EventRouterHTTPS.py\\n if self._simplex == True:\\n self._queueEvent.wait()\\n self._queueEvent.clear()\\n \\n if not self._queue.empty():\\n Trace(\\\"%s queue.get ...\\\"%(self.getUri()), \\\"EventLib.EventRelayHTTPC\\\")\\n ###msgbody = self._queue.get()\\n ###Trace(\\\"%s get msgbody: %s\\\"%(self.getUri(),msgbody), \\\"EventLib.EventRelayHTTPC\\\")\\n ###self._event.set()\\n msgbody = self.getQueuedItem()\\n [typ,env] = msgbody\\n if typ == \\\"closedown\\\":\\n if self._closing: break\\n else:\\n # process request as an HTTP POST request\\n data = makeEnvelopeData(env)\\n headers = { \\\"Content-type\\\": \\\"text/plain\\\",\\n \\\"Accept\\\": \\\"text/plain\\\",\\n \\\"Content-length\\\": str(len(data)) }\\n self._httpcon.request(\\\"POST\\\", \\\"/request_path_ignored\\\", data, headers)\\n response = self._httpcon.getresponse()\\n delay_on_error = delay_on_error_min\\n elif self._simplex == False:\\n # Nothing in queue:\\n # issue a GET for incoming events\\n _log.info(\\\"%s HTTP get ...\\\"%(self.getUri()))\\n headers = { \\\"Accept\\\": \\\"text/plain\\\" }\\n self._httpcon.request(\\\"GET\\\", \\\"/request_path_ignored\\\", None, headers)\\n response = self._httpcon.getresponse()\\n if response.status == 200:\\n delay_on_error = delay_on_error_min\\n msgbody = response.read()\\n Trace(\\\"%s get msgbody: %s\\\"%(self.getUri(),msgbody), \\\"EventLib.EventRelayHTTPC\\\")\\n # Parse message and act accordingly\\n msgdata = parseMessageData(msgbody)\\n Trace(\\\"%s get msgdata: %s\\\"%(self.getUri(),str(msgdata)), \\\"EventLib.EventRelayHTTPC\\\")\\n if msgdata == None:\\n #TODO: Log \\\"Request body malformed\\\"\\n pass\\n elif msgdata[0] == \\\"forward\\\":\\n # msgdata = [\\\"forward\\\", [['R1', 'R2', 'R3'], 'ev:typ', 'ev:src', 'payload']]\\n event = makeEvent(evtype=msgdata[1][1],source=msgdata[1][2],payload=msgdata[1][3])\\n env = constructEnvelope(msgdata[1][0], event)\\n self.forward(event, env)\\n elif msgdata[0] == \\\"idle\\\":\\n # Idle response gives client a chance to send if anything is queued\\n pass\\n else:\\n #TODO: handle closedown message?\\n Warn( \\\"%s Request body unrecognized option: %s\\\"%(self.getUri(),msgdata[0]), \\\"EventRelayHTTPC\\\")\\n pass\\n elif response.status == 503:\\n Trace( \\\"%s processEvent error response: %u, %s\\\"%(self.getUri(),response.status,response.reason), \\\"EventLib.EventRelayHTTPC\\\")\\n # Remote end closed down\\n break\\n else:\\n # TODO: (log error response)\\n Warn( \\\"%s processEvent error response: %u, %s\\\"%(self.getUri(),response.status,response.reason), \\\"EventLib.EventRelayHTTPC\\\")\\n time.sleep(delay_on_error)\\n \\n except httplib.BadStatusLine, e:\\n # This can happen at closedown\\n Info( \\\"%s processEvent bad response: %s\\\"%(self.getUri(), str(e)), \\\"EventLib.EventRelayHTTPC\\\")\\n time.sleep(delay_on_error)\\n except httplib.CannotSendRequest, e:\\n # This can happen at closedown\\n Info( \\\"%s Cannot send request: %s\\\"%(self.getUri(), str(e)), \\\"EventLib.EventRelayHTTPC\\\")\\n time.sleep(delay_on_error)\\n except httplib.ResponseNotReady, e:\\n # This can happen at startup and sometimes other times:\\n # maybe multiple requests on a single HTTP connection object?\\n Info( \\\"%s Response not ready: (%s)\\\"%(self.getUri(), str(e)), \\\"EventLib.EventRelayHTTPC\\\")\\n time.sleep(delay_on_error)\\n except socket.error, e:\\n Warn( \\\"%s Socket error: %s\\\"%(self.getUri(), str(e)), \\\"EventLib.EventRelayHTTPC\\\")\\n time.sleep(delay_on_error)\\n return\",\n \"def simulateCore(self):\\n while len(self.event_q) > 0:\\n evts = self.nextEvents()\\n self.handleEvents(evts)\\n self.gatherSystemStatistics(self.scheduler.system)\\n self.dumpEventQueue()\",\n \"def _event_loop(self):\\n while True:\\n self.scheduler.run(blocking=True)\\n time.sleep(1)\",\n \"def run():\\n\\n while True:\\n\\n # get event, blah\\n event_name, event_data = revent.get_event(block=True, timeout=5)\\n\\n if event_name is not None:\\n print 'received: %s' % event_name\\n\\n if event_name.endswith('_oembed_details'):\\n handle_new_oembed_details(event_data)\\n\\n elif event_name == 'new_tweet':\\n handle_new_tweet(event_data)\\n\\n # and we're done\\n assert revent.verify_msg(event_name, event_data), \\\\\\n \\\"Could not verify %s\\\" % event_name\",\n \"def run():\\r\\n event = threading.Event()\\r\\n while (event.is_set() == False):\\r\\n # perform database backup\\r\\n backup()\\r\\n\\r\\n # sleep for the predefined amount interval\\r\\n event.wait(BACKUP_INTERVAL)\",\n \"def _heartbeat_loop(self):\\n # set last time so that \\\"if t_now - t_last >= HEARTBEAT_LOG_INTERVAL\\\"\\n # below evalutes to True on the first run\\n t_last = time.time() - HEARTBEAT_LOG_INTERVAL - 1\\n while True:\\n alive = 0\\n # count alive processes \\n for p in PROCESSES:\\n if p.is_alive():\\n alive += 1\\n\\n # no processes are alive - exit heartbeat loop\\n if alive == 0:\\n return\\n\\n t_now = time.time()\\n if t_now - t_last >= HEARTBEAT_LOG_INTERVAL:\\n # log heartbeat\\n obj = { \\n 'timestamp': time.time(),\\n 'child_procs_total': self._procs_total,\\n 'child_procs_alive': alive,\\n 'probe_req_queue_len': self._probe_request_queue.qsize(),\\n 'probe_resp_queue_len': \\\\\\n self._probe_response_queue.qsize(), \\n }\\n \\n # push to shared mem\\n self._sm.set(config.BASE['SHARED_MEM_HEARTBEAT_KEY'],\\n json.dumps(obj), HEARTBEAT_TTL)\\n LOG.debug('pushed a heartbeat to the shared memory')\\n\\n t_last = t_now\\n\\n time.sleep(HEARTBEAT_LOOP_INTERVAL)\",\n \"def test_cbbackupmgr_with_eventing(self):\\n if \\\"5.5\\\" > self.cb_version[:3]:\\n self.fail(\\\"This eventing test is only for cb version 5.5 and later. \\\")\\n from pytests.eventing.eventing_constants import HANDLER_CODE\\n from lib.testconstants import STANDARD_BUCKET_PORT\\n\\n self.src_bucket_name = self.input.param('src_bucket_name', 'src_bucket')\\n self.eventing_log_level = self.input.param('eventing_log_level', 'INFO')\\n self.dst_bucket_name = self.input.param('dst_bucket_name', 'dst_bucket')\\n self.dst_bucket_name1 = self.input.param('dst_bucket_name1', 'dst_bucket1')\\n self.metadata_bucket_name = self.input.param('metadata_bucket_name', 'metadata')\\n self.create_functions_buckets = self.input.param('create_functions_buckets', True)\\n self.docs_per_day = self.input.param(\\\"doc-per-day\\\", 1)\\n self.use_memory_manager = self.input.param('use_memory_manager', True)\\n self.backup_before_eventing = self.input.param('backup_before_eventing', False)\\n bucket_params = self._create_bucket_params(server=self.master, size=256,\\n replicas=self.num_replicas)\\n self.cluster.create_standard_bucket(name=self.src_bucket_name, port=STANDARD_BUCKET_PORT + 1,\\n bucket_params=bucket_params)\\n self.buckets = RestConnection(self.master).get_buckets()\\n self.src_bucket = RestConnection(self.master).get_buckets()\\n self.cluster.create_standard_bucket(name=self.dst_bucket_name, port=STANDARD_BUCKET_PORT + 1,\\n bucket_params=bucket_params)\\n self.backup_create()\\n if (self.backup_before_eventing):\\n self.backup_cluster()\\n self.cluster.create_standard_bucket(name=self.metadata_bucket_name, port=STANDARD_BUCKET_PORT + 1,\\n bucket_params=bucket_params)\\n self.buckets = RestConnection(self.master).get_buckets()\\n self.gens_load = self.generate_docs(self.docs_per_day)\\n self.expiry = 3\\n\\n self.restServer = self.get_nodes_from_services_map(service_type=\\\"eventing\\\")\\n self.rest = RestConnection(self.restServer)\\n\\n\\n self.load(self.gens_load, buckets=self.buckets, flag=self.item_flag, verify_data=False,\\n batch_size=self.batch_size)\\n function_name = \\\"Function_{0}_{1}\\\".format(randint(1, 1000000000), self._testMethodName)\\n self.function_name = function_name[0:90]\\n body = self.create_save_function_body(self.function_name, HANDLER_CODE.BUCKET_OPS_ON_UPDATE, worker_count=3)\\n bk_events_created = False\\n rs_events_created = False\\n try:\\n self.deploy_function(body)\\n bk_events_created = True\\n self.backup_cluster()\\n rest_bk = RestConnection(self.backupset.cluster_host)\\n bk_fxn = rest_bk.get_all_functions()\\n\\n backup_index = 0\\n\\n if self.backup_before_eventing:\\n backup_index = 1\\n self.backupset.start = 1\\n self.backupset.end = 2\\n\\n if bk_fxn != \\\"\\\":\\n self._verify_backup_events_definition(json.loads(bk_fxn), body, backup_index = backup_index)\\n\\n self.backup_restore()\\n\\n rest_rs = RestConnection(self.backupset.restore_cluster_host)\\n\\n if self.backup_before_eventing:\\n self.assertTrue('metadata' in [bucket.name for bucket in rest_rs.get_buckets()])\\n\\n self.bkrs_resume_function(body, rest_rs)\\n rs_events_created = True\\n self._verify_restore_events_definition(bk_fxn)\\n except Exception as e:\\n self.fail(e)\\n finally:\\n master_nodes = [self.backupset.cluster_host,\\n self.backupset.restore_cluster_host]\\n for node in master_nodes:\\n rest = RestConnection(node)\\n self.bkrs_undeploy_and_delete_function(body, rest, node)\\n self.rest = RestConnection(self.master)\",\n \"def stopeventmonitor(self):\\n self.doeventloop = False\\n if self.service is not None:\\n self.service.breakloop()\\n # reset the service, otherwise nextEvent won\\\"t work\\n self.initeventservice(shutdown=True)\\n if self.eventmonthread is not None:\\n if emane.VERSION >= emane.EMANE091:\\n self.eventmonthread._Thread__stop()\\n self.eventmonthread.join()\\n self.eventmonthread = None\",\n \"def run(self):\\n\\n (robotProc, iRMsg, robotStat, robotInfo, robotCmd, bcMsg, cbpaeRun, wsInfo) = self.prepVars()\\n\\n broadcasterProc = self.startBroadcaster(cbpaeRun, bcMsg, iRMsg)\\n\\n# =============================================================================\\n# # pass additional queues to the robot processes by overloading this method\\n# =============================================================================\\n robotProc = self.startRobots(robotProc, iRMsg, bcMsg, robotInfo, robotCmd, robotStat)\\n\\n guiProc = self.startGui(wsInfo, robotInfo, robotCmd)\\n\\n# =============================================================================\\n# # This is the main loop checking robotProcs\\n# =============================================================================\\n rJoinable = self.checkRJoinable(robotProc, robotStat)\\n\\n self.stopBroadcaster(cbpaeRun)\\n\\n self.clearQueues(iRMsg, robotCmd, robotInfo)\\n\\n self.joinRobotProc(robotProc)\\n\\n self.logBasicInfo()\\n\\n print (\\\"CBPAE Trial Finished!!!\\\")\",\n \"def run(self):\\n \\n # Loop through all checkers to do an initial state check\\n for checker in self.checkers:\\n checker.update_last_state()\\n\\n # Send initial heartbeat\\n self._send_heartbeat()\\n \\n # Main loop\\n while True: \\n html = \\\"\\\"\\n for checker in self.checkers:\\n if checker.just_changed_state():\\n log.warn(\\\"Checker {} has changed state.\\\"\\n .format(checker.name))\\n html += \\\"<li>\\\" + checker.html() + \\\"</li>\\\\n\\\"\\n \\n if isinstance(checker, Process) and checker.state() == FAIL:\\n log.warn(\\\"Process {} is not running.\\\"\\n .format(checker.name))\\n html += (\\\"<li>Attempting to restart \\\" + \\n escape(checker.name) + \\\"...</li>\\\\n\\\")\\n try:\\n checker.restart()\\n except MaxRetriesError, e:\\n self.shutdown_reason = str(e)\\n return\\n time.sleep(5)\\n html += (\\\"<li>State after restart: \\\" + \\n checker.html() + \\\"</li>\\\\n\\\")\\n\\n if html:\\n html = \\\"<h2>STATE CHANGED:</h2>\\\\n<ul>\\\\n\\\" + html + \\\"</ul>\\\\n\\\" \\n html += self.html()\\n html += run_commands(self.state_change_cmds)\\n self.send_email_with_time(html=html,\\n subject=\\\"Babysitter detected\\\"\\n \\\" state change.\\\")\\n\\n if self._need_to_send_heartbeat():\\n self._send_heartbeat()\\n\\n # Check if a new data subdir has been created\\n if self.base_data_dir and self.sub_data_dir:\\n if self._find_last_numeric_subdir() != self.sub_data_dir:\\n self._send_heartbeat(\\\"<p>New subdir found so about to restart \\\"\\n \\\"babysitter. Below are the last stats \\\"\\n \\\"for the old data subdirectory.</p>\\\\n\\\")\\n raise NewDataDirError()\\n \\n time.sleep(UPDATE_PERIOD)\",\n \"def loop_run(self):\\n super(EventLoop, self).loop_run()\\n self.inq = self.cothread.EventQueue()\",\n \"def handle_wake_up(_):\\n loop.awaken()\",\n \"async def test_events_handled_on_event_loop(self):\\n session = _create_test_session(asyncio.get_running_loop())\\n\\n handle_event_spy = MagicMock(\\n side_effect=session._handle_scriptrunner_event_on_event_loop\\n )\\n session._handle_scriptrunner_event_on_event_loop = handle_event_spy\\n\\n # Send a ScriptRunner event from another thread\\n thread = threading.Thread(\\n target=lambda: session._on_scriptrunner_event(\\n sender=MagicMock(), event=ScriptRunnerEvent.SCRIPT_STARTED\\n )\\n )\\n thread.start()\\n thread.join()\\n\\n # _handle_scriptrunner_event_on_event_loop won't have been called\\n # yet, because we haven't yielded the eventloop.\\n handle_event_spy.assert_not_called()\\n\\n # Yield to let the AppSession's callbacks run.\\n # _handle_scriptrunner_event_on_event_loop will be called here.\\n await asyncio.sleep(0)\\n\\n handle_event_spy.assert_called_once()\",\n \"def test_dispatch_event(self):\\n msg_helper = MessageHelper()\\n worker_helper = WorkerHelper()\\n broker = self.setup_broker(worker_helper)\\n self.assertEqual(broker.get_messages('vumi', 'fooconn.event'), [])\\n msg = msg_helper.make_ack()\\n yield worker_helper.dispatch_event(msg, 'fooconn')\\n self.assertEqual(broker.get_messages('vumi', 'fooconn.event'), [msg])\",\n \"def test_sighup(self):\\n self.render_config_template(\\n )\\n\\n proc = self.start_beat()\\n self.wait_until(lambda: self.log_contains(\\\"mockbeat start running.\\\"))\\n proc.proc.send_signal(signal.SIGHUP)\\n proc.check_wait()\\n assert self.log_contains(\\\"mockbeat stopped.\\\")\",\n \"def main_loop(self):\\n # run for only the allotted time (lifetime)\\n for _ in range(self.lifetime * self.ticks_per_second):\\n start_time = time()\\n new_message, queue_len = self.communicator.get_message()\\n if new_message is None: # no incoming messages\\n self.do_random_task()\\n else:\\n # Convert string message back into tuple of ints\\n new_message = list(map(int, new_message.split('@@@')))\\n self.handle_incoming_message(new_message, queue_len)\\n\\n # this accounts for the time already taken in test_communication\\n # and other activities from the total time allotted for the loop iteration\\n already_taken = time() - start_time\\n sleep_time = max(1/self.ticks_per_second - already_taken, 0)\\n sleep(sleep_time)\",\n \"def cb_test( self, ):\\r\\n # this shows how to run stuff in the helper -- call thru queue, post to queue\\r\\n self.post_to_queue( \\\"call\\\", self.helper_thread.test_test_ports , ( ) )\",\n \"def heartbeat_thread(self):\\n while self.heart_should_beat:\\n # set the bot's heartbeat event to end the wait immediately\\n self.send_heartbeat()\\n self.heartbeat_event.wait(timeout=self.heartbeat_period)\",\n \"def _dispatch(f = None):\\n #first install signal handler\\n #this way we can quit the program easily from the command line\\n #also, this makes libevent block on the first loop\\n #otherwise when there are no events in the beginning, loop will not\\n #block and our main dispatch loop would claim 100% CPU time\\n def interrupt():\\n quit(EXIT_CODE_SIGINT)\\n event_interrupt = SignalEvent(SIGINT, interrupt)\\n\\n #the heartbeat makes sure the main loop below at least\\n #makes a cycle every second. otherwise, if there are no pending signals\\n #libevent._loop would block indefinitly, causing our loop never to check\\n #if it still must be _running...\\n event_heartbeat = TimeoutEvent(1.0, None, True)\\n\\n #as a convenience, user can provide a callable *f* to start a new task\\n #lets start it here\\n if callable(f):\\n Tasklet.new(f)()\\n\\n global _running\\n _running = True\\n e = None\\n try:\\n #this is it, the main dispatch loop...\\n #tasklets are scheduled to run by stackless,\\n #and if no more are runnable, we wait for IO events to happen\\n #that will trigger tasks to become runnable\\n #ad infinitum...\\n while _running:\\n #first let any tasklets run until they have all become blocked on IO\\n try:\\n while stackless.getruncount() > 1:\\n stackless.schedule()\\n except TaskletExit:\\n pass\\n except:\\n logging.exception(\\\"unhandled exception in dispatch schedule\\\")\\n\\n #now block on IO till any IO is ready.\\n #care has been taken to not callback directly into python\\n #from libevent. that would add c-data on the stack which would\\n #make stackless need to use hard-switching, which is slow.\\n #so we call 'loop' which blocks until something available.\\n try:\\n event.loop()\\n except TaskletExit:\\n raise\\n except:\\n logging.exception(\\\"unhandled exception in event loop\\\")\\n\\n #we iterate over the available triggered events and\\n #call the callback which is available as the 'data' object of the event\\n #some callbacks may trigger direct action (for instance timeouts, signals)\\n #others might resume a waiting task (socket io).\\n while event.has_next():\\n try:\\n e, event_type, fd = event.next()\\n e.data(event_type)\\n except TaskletExit:\\n raise\\n except:\\n logging.exception(\\\"unhandled exception in event callback\\\")\\n\\n finally:\\n del e\\n event_interrupt.close()\\n del event_interrupt\\n event_heartbeat.close()\\n del event_heartbeat\\n\\n if DEBUG_LEAK:\\n logging.warn(\\\"alive objects:\\\")\\n gc.collect()\\n _print_objects(gc.get_objects())\\n logging.warn('garbage:')\\n _print_objects(gc.garbage)\\n\\n sys._exit(_exitcode)\",\n \"def rfactor_event_loop():\\n if RfactorLiveEvent.event.is_set():\\n is_live = RfactorLiveEvent.get_nowait()\\n # -- Update rFactor live state to front end\\n if is_live is not None:\\n eel.rfactor_live(is_live)\\n\\n if RfactorStatusEvent.event.is_set():\\n status = RfactorStatusEvent.get_nowait()\\n # -- Update rFactor status message in front end\\n if status is not None:\\n logging.debug('Updating rf2 status message: %s', status)\\n eel.rfactor_status(status)\\n\\n RfactorStatusEvent.reset()\",\n \"def control_loop():\\n global EVENTS, AGENDA, TRACE_LEVEL\\n \\n epoch = 0\\n go = True\\n while go:\\n if TRACE_LEVEL > 0:\\n print \\\"[epoch {0}]\\\".format(epoch)\\n\\n event_count = 0\\n \\n # process events\\n for event in EVENTS:\\n event_fn, event_data = event[0], event[1]\\n if TRACE_LEVEL > 3:\\n print \\\"\\\\t\\\\tEvaluating event: ({0} {1})\\\".format(event_fn, event_data)\\n event_fn(event_data) # eval the function\\n if TRACE_LEVEL > 3:\\n print \\\"\\\\t\\\\tEvent eval success.\\\"\\n\\n if event_count > EVENT_LIMIT:\\n break\\n event_count += 1\\n \\n EVENTS = []\\n \\n # check for stopping condition\\n if AGENDA:\\n # run the top KSA; LIFO stack agenda\\n ksa = AGENDA.pop()\\n if TRACE_LEVEL > 0:\\n print \\\"\\\\tAGENDA Running: {0}\\\".format(ksa)\\n ksa_fn, ksa_data = ksa[0], ksa[1]\\n if TRACE_LEVEL > 3:\\n print \\\"\\\\t\\\\tEvaluating KSA: ({0} {1})\\\".format(ksa_fn, ksa_data)\\n ksa_fn(ksa_data)\\n if TRACE_LEVEL > 3:\\n print \\\"\\\\t\\\\tKSA eval success.\\\"\\n else:\\n go = False\\n\\n epoch += 1\\n \\n print(\\\"\\\\n\\\\nAgenda is empty. Stopping.\\\")\",\n \"def test_heartbeat_only(cls):\\n # type: () -> None\\n\\n uniclient_thread = ErrorAssertUniClientThread(cls.TEST_ZMQ_ENDPOINT)\\n uniclient_thread.start()\\n\\n uniworker_thread = ConsoleUniWorkerThread(cls.TEST_ZMQ_ENDPOINT)\\n uniworker_thread.start()\\n\\n uniclient_thread.wait_for_worker(INITIAL_CONNECTION_TIME_SECS)\\n # Wait for 60 seconds, make sure the client and worker remain connected.\\n sleep(60)\\n #sleep(30000)\\n assert uniclient_thread.is_connected()\\n assert uniworker_thread.is_connected()\\n\\n # Shut down the worker and client\\n uniworker_thread.join()\\n uniclient_thread.join()\",\n \"def on_shutdown(self):\\n while self._queue:\\n self._get()\",\n \"def watch_for_heartbeat_messages(self):\\n while True:\\n message = self.socket_manager.get_heartbeat_message()\\n self.ensure_sender_is_known(message)\\n if message.direction == \\\"0\\\":\\n self.respond_to_heartbeat_message(message)\\n elif message.direction == \\\"1\\\":\\n self.handle_heartbeat_response(message)\",\n \"def loop(self):\\n keys.mode = 'main'\\n for line in client.readlines('/event'):\\n if not self.alive:\\n break\\n self.dispatch(*line.split(' ', 1))\\n self.alive = False\",\n \"def run():\\n logger.verbose(\\\"bwmon: Thread started\\\")\\n while True:\\n lock.wait()\\n logger.verbose(\\\"bwmon: Event received. Running.\\\")\\n database.db_lock.acquire()\\n nmdbcopy = copy.deepcopy(database.db)\\n database.db_lock.release()\\n try:\\n if getDefaults(nmdbcopy) and len(bwlimit.tc(\\\"class show dev %s\\\" % dev_default)) > 0:\\n # class show to check if net:InitNodeLimit:bwlimit.init has run.\\n sync(nmdbcopy)\\n else: logger.log(\\\"bwmon: BW limits DISABLED.\\\")\\n except: logger.log_exc(\\\"bwmon failed\\\")\\n lock.clear()\",\n \"def loop(self):\\n while self.dispatch(True) is not QUIT:\\n pass\",\n \"def loop_once(self):\\n while 1:\\n if not self._active_nodes:\\n # If everything has died stop looping\\n self.triggershutdown()\\n raise RuntimeError(\\\"Unexpectedly no active workers available\\\")\\n try:\\n eventcall = self.queue.get(timeout=2.0)\\n break\\n except Empty:\\n continue\\n callname, kwargs = eventcall\\n assert callname, kwargs\\n method = \\\"worker_\\\" + callname\\n call = getattr(self, method)\\n self.log(\\\"calling method\\\", method, kwargs)\\n call(**kwargs)\\n if self.sched.tests_finished:\\n self.triggershutdown()\",\n \"def test_job(self):\\r\\n while not self._coordinator.should_stop():\\r\\n with self._condition:\\r\\n self._condition.wait_for(self._true_if_testing)\\r\\n if self._coordinator.should_stop():\\r\\n break\\r\\n should_stop = False\\r\\n try:\\r\\n should_stop = self.do_full_test()\\r\\n except:\\r\\n traceback.print_exc()\\r\\n self._is_testing = False\\r\\n if should_stop is True:\\r\\n break\\r\\n logger.debug('Exiting thread %s' % threading.current_thread().name)\",\n \"def handle_event(self, test_event):\\n with self.lock:\\n # Keep track of whether terminate was received. We do this so\\n # that a process can call the 'terminate' event on its own, to\\n # close down a formatter at the appropriate time. Then the\\n # atexit() cleanup can call the \\\"terminate if it hasn't been\\n # called yet\\\".\\n if test_event is not None:\\n event_type = test_event.get(\\\"event\\\", \\\"\\\")\\n # We intentionally allow event_type to be checked anew\\n # after this check below since this check may rewrite\\n # the event type\\n if event_type == EventBuilder.TYPE_JOB_RESULT:\\n # Possibly convert the job status (timeout,\\n # exceptional exit) # to an appropriate test_result event.\\n self._maybe_remap_job_result_event(test_event)\\n event_type = test_event.get(\\\"event\\\", \\\"\\\")\\n\\n # Remap timeouts to expected timeouts.\\n if event_type in EventBuilder.RESULT_TYPES:\\n self._maybe_remap_expected_timeout(test_event)\\n self._maybe_remap_expected_failure(test_event)\\n event_type = test_event.get(\\\"event\\\", \\\"\\\")\\n\\n if event_type == \\\"terminate\\\":\\n self.terminate_called = True\\n elif event_type in EventBuilder.RESULT_TYPES:\\n # Clear the most recently started test for the related\\n # worker.\\n worker_index = test_event.get(\\\"worker_index\\\", None)\\n if worker_index is not None:\\n self.started_tests_by_worker.pop(worker_index, None)\\n status = test_event[\\\"status\\\"]\\n if status in EventBuilder.TESTRUN_ERROR_STATUS_VALUES:\\n # A test/job status value in any of those status values\\n # causes a testrun failure. If such a test fails, check\\n # whether it can be rerun. If it can be rerun, add it\\n # to the rerun job.\\n self._maybe_add_test_to_rerun_list(test_event)\\n\\n # Build the test key.\\n test_key = self._make_key(test_event)\\n if test_key is None:\\n raise Exception(\\n \\\"failed to find test filename for \\\"\\n \\\"test event {}\\\".format(test_event))\\n\\n # Save the most recent test event for the test key. This\\n # allows a second test phase to overwrite the most recent\\n # result for the test key (unique per method). We do final\\n # reporting at the end, so we'll report based on final\\n # results. We do this so that a re-run caused by, perhaps,\\n # the need to run a low-load, single-worker test run can\\n # have the final run's results to always be used.\\n if test_key in self.result_events:\\n self.test_method_rerun_count += 1\\n self.result_events[test_key] = test_event\\n elif event_type == EventBuilder.TYPE_TEST_START:\\n # Track the start time for the test method.\\n self.track_start_time(\\n test_event[\\\"test_class\\\"],\\n test_event[\\\"test_name\\\"],\\n test_event[\\\"event_time\\\"])\\n # Track of the most recent test method start event\\n # for the related worker. This allows us to figure\\n # out whether a process timeout or exceptional exit\\n # can be charged (i.e. assigned) to a test method.\\n worker_index = test_event.get(\\\"worker_index\\\", None)\\n if worker_index is not None:\\n self.started_tests_by_worker[worker_index] = test_event\\n\\n elif event_type == EventBuilder.TYPE_MARK_TEST_RERUN_ELIGIBLE:\\n self._mark_test_for_rerun_eligibility(test_event)\\n elif (event_type ==\\n EventBuilder.TYPE_MARK_TEST_EXPECTED_FAILURE):\\n self._mark_test_as_expected_failure(test_event)\",\n \"async def test_handle_backmsg_exception(self):\\n session = _create_test_session(asyncio.get_running_loop())\\n\\n # Create a mocked ForwardMsgQueue that tracks \\\"enqueue\\\" and \\\"clear\\\"\\n # function calls together in a list. We'll assert the content\\n # and order of these calls.\\n forward_msg_queue_events: List[Any] = []\\n CLEAR_QUEUE = object()\\n\\n mock_queue = MagicMock(spec=ForwardMsgQueue)\\n mock_queue.enqueue = MagicMock(\\n side_effect=lambda msg: forward_msg_queue_events.append(msg)\\n )\\n mock_queue.clear = MagicMock(\\n side_effect=lambda: forward_msg_queue_events.append(CLEAR_QUEUE)\\n )\\n\\n session._browser_queue = mock_queue\\n\\n # Create an exception and have the session handle it.\\n FAKE_EXCEPTION = RuntimeError(\\\"I am error\\\")\\n session.handle_backmsg_exception(FAKE_EXCEPTION)\\n\\n # Messages get sent in an eventloop callback, which hasn't had a chance\\n # to run yet. Our message queue should be empty.\\n self.assertEqual([], forward_msg_queue_events)\\n\\n # Run callbacks\\n await asyncio.sleep(0)\\n\\n # Build our \\\"expected events\\\" list. We need to mock different\\n # AppSessionState values for our AppSession to build the list.\\n expected_events = []\\n\\n with patch.object(session, \\\"_state\\\", new=AppSessionState.APP_IS_RUNNING):\\n expected_events.extend(\\n [\\n session._create_script_finished_message(\\n ForwardMsg.FINISHED_SUCCESSFULLY\\n ),\\n CLEAR_QUEUE,\\n session._create_new_session_message(page_script_hash=\\\"\\\"),\\n session._create_session_status_changed_message(),\\n ]\\n )\\n\\n with patch.object(session, \\\"_state\\\", new=AppSessionState.APP_NOT_RUNNING):\\n expected_events.extend(\\n [\\n session._create_script_finished_message(\\n ForwardMsg.FINISHED_SUCCESSFULLY\\n ),\\n session._create_session_status_changed_message(),\\n session._create_exception_message(FAKE_EXCEPTION),\\n ]\\n )\\n\\n # Assert the results!\\n self.assertEqual(expected_events, forward_msg_queue_events)\",\n \"def event_loop(self, index):\\n logger.debug(\\\"Starting event loop \\\"+str(index))\\n self.stop_flag = Event()\\n stop_flag = self.stop_flag # Saving a reference.\\n # stop_flag is an object that will signal the current input thread to exit or not exit once it's done processing a callback.\\n # It'll be called just before self.stop_flag will be overwritten. However, we've got a reference to it and now can check the exact flag this thread itself constructed.\\n # Praise the holy garbage collector.\\n stop_flag.clear()\\n while not stop_flag.isSet():\\n if self.get_current_proxy() is not None:\\n try:\\n key = self.queue.get(False, 0.1)\\n except Queue.Empty:\\n # here an active event_loop spends most of the time\\n sleep(0.1)\\n except AttributeError:\\n # typically happens upon program termination\\n pass\\n else:\\n # here event_loop is usually busy\\n self.process_key(key)\\n else:\\n # No current proxy set yet, not processing anything\\n sleep(0.1)\\n logger.debug(\\\"Stopping event loop \\\"+str(index))\",\n \"def rmp_thread():\\r\\n rsp_queue = Queue.Queue()\\r\\n cmd_queue = Queue.Queue()\\r\\n in_flags = Queue.Queue()\\r\\n out_flags = Queue.Queue()\\r\\n\\r\\n \\\"\\\"\\\"\\r\\n Create the thread to run RMP\\r\\n \\\"\\\"\\\"\\r\\n my_thread = threading.Thread(target=RMP, args=(rmp_addr,rsp_queue,cmd_queue,in_flags,out_flags,UPDATE_DELAY_SEC,LOG_DATA))\\r\\n my_thread.daemon = True\\r\\n my_thread.start()\\r\\n\\r\\n \\\"\\\"\\\"\\r\\n Initialize my event handler class\\r\\n \\\"\\\"\\\"\\r\\n EventHandler = RMPEventHandlers(cmd_queue,rsp_queue,in_flags)\\r\\n\\r\\n \\\"\\\"\\\"\\r\\n -------------------------------------------------------------------------------\\r\\n User loop starts here modify to make it do what you want.\\r\\n\\r\\n You can pipe std_in from another application to the command queue and the response to std out or\\r\\n let the event handlers define everything. That is up to the user. In this example we transition modes,\\r\\n send motion commands (zeroed), play audio songs, and print the response dictionary. The application\\r\\n terminates the thread and exits when all the songs have been played. It is just an example of how to\\r\\n spawn a RMP thread, handle events, and send/receive data\\r\\n -------------------------------------------------------------------------------\\r\\n \\\"\\\"\\\"\\r\\n\\r\\n \\\"\\\"\\\"\\r\\n Generate a goto tractor event\\r\\n \\\"\\\"\\\"\\r\\n #EventHandler.GotoTractor()\\r\\n #EventHandler.GotoStandby()\\r\\n EventHandler.GotoBalance()\\r\\n\\r\\n \\\"\\\"\\\"\\r\\n Run until signaled to stop\\r\\n Perform the actions defined based on the flags passed out\\r\\n \\\"\\\"\\\"\\r\\n while (True == EventHandler._continue):\\r\\n EventHandler.handle_event[RMP_TX_RDY]()\\r\\n EventHandler.handle_event[RMP_RSP_DATA_RDY]()\\r\\n time.sleep(1.0)\\r\\n EventHandler.handle_event[RMP_ZERO]()\\r\\n EventHandler.handle_event[RMP_RSP_DATA_RDY]()\\r\\n time.sleep(1.0)\\r\\n EventHandler.handle_event[RMP_FORWARD]()\\r\\n EventHandler.handle_event[RMP_RSP_DATA_RDY]()\\r\\n time.sleep(1.0)\\r\\n pass\\r\\n #while not out_flags.empty():\\r\\n # EventHandler.handle_event[out_flags.get()]()\\r\\n \\\"\\\"\\\"\\r\\n Generate a goto standby event\\r\\n \\\"\\\"\\\"\\r\\n\\r\\n \\\"\\\"\\\"\\r\\n Allow enough time for the command to be sent\\r\\n then send the signal to kill the thread\\r\\n \\\"\\\"\\\"\\r\\n time.sleep(1.0)\\r\\n in_flags.put(RMP_KILL)\\r\\n\\r\\n \\\"\\\"\\\"\\r\\n Wait for the thread to die\\r\\n \\\"\\\"\\\"\\r\\n while my_thread.isAlive():\\r\\n pass\\r\\n\\r\\n \\\"\\\"\\\"\\r\\n Exit main\\r\\n \\\"\\\"\\\"\\r\\n sys.exit()\",\n \"def __main_loop(self):\\n\\n while not self.stop:\\n self.__lock_data = True\\n self.__bus_messages_copy = deepcopy(self.__bus_messages)\\n self.__line_messages_copy = deepcopy(self.__line_messages)\\n self.__global_messages_copy = deepcopy(self.__global_messages)\\n self.__bus_messages = {}\\n self.__line_messages = {}\\n self.__global_messages = {\\\"kick reason\\\": \\\"\\\", \\\"free text\\\": \\\"\\\"}\\n buses_to_kick_copy = deepcopy(self.__buses_to_kick)\\n self.__buses_to_kick = list()\\n self.__lock_data = False\\n\\n for bus in buses_to_kick_copy: # handles the buses that need to be kicked\\n message = \\\"kicked for reason:\\\" + self.__global_messages_copy[\\\"kick reason\\\"]\\n if bus.line_num in self.__line_messages_copy.keys():\\n message += self.__line_messages_copy[bus.line_num][\\\"kick reason\\\"]\\n if bus.line_num in self.__line_messages_copy.keys() and bus.id in self.__bus_messages_copy[\\n bus.line_num].keys():\\n message += self.__bus_messages_copy[bus.line_num][bus.id][\\\"kick reason\\\"]\\n print(f\\\"sending message{message.strip()}\\\")\\n bus.send_to_bus(message.strip())\\n\\n global_message = self.__build_global_update()\\n for line, buses in self.__bus_controller.bus_dict.items():\\n line_message = self.__build_line_update(line)\\n for bus in buses:\\n bus_message = self.__build_bus_update(bus)\\n message = global_message + line_message + bus_message\\n message = message.strip(\\\"\\\\n\\\")\\n if message != \\\"\\\":\\n bus.send_to_bus(message)\\n\\n sleep(MessagesSender.SLEEP_TIME)\\n\\n self.__shut_down()\\n print(\\\"polling thread stopped\\\")\",\n \"def run(self):\\n\\n self.introduction_function()\\n\\n while True:\\n try:\\n message_received = self.queue_receive.get_nowait()\\n except Exception as e:\\n self.queue_receive_error_handler(e)\\n else:\\n self.execute_message_received(message_received)\\n\\n if self.development_function() is None:\\n break\\n\\n self.conclusion_function()\",\n \"def test_pollingBackoff(self):\\n\\n # Speed up the backoff process\\n self.patch(ControllerQueue, \\\"queuePollingBackoff\\\", ((1.0, 60.0),))\\n\\n # Wait for backoff\\n while self.node1._actualPollInterval == self.node1.queuePollInterval:\\n d = Deferred()\\n reactor.callLater(1.0, lambda : d.callback(None))\\n yield d\\n\\n self.assertEqual(self.node1._actualPollInterval, 60.0)\\n\\n # TODO: this exact test should run against LocalQueuer as well.\\n def operation(txn):\\n # TODO: how does \\\"enqueue\\\" get associated with the transaction?\\n # This is not the fact with a raw t.w.enterprise transaction.\\n # Should probably do something with components.\\n return txn.enqueue(DummyWorkItem, a=3, b=4, jobID=100, workID=1,\\n notBefore=datetime.datetime.utcnow())\\n yield inTransaction(self.store.newTransaction, operation)\\n\\n # Backoff terminated\\n while self.node1._actualPollInterval != self.node1.queuePollInterval:\\n d = Deferred()\\n reactor.callLater(0.1, lambda : d.callback(None))\\n yield d\\n self.assertEqual(self.node1._actualPollInterval, self.node1.queuePollInterval)\\n\\n # Wait for it to be executed. Hopefully this does not time out :-\\\\.\\n yield JobItem.waitEmpty(self.store.newTransaction, reactor, 60)\\n\\n # Wait for backoff\\n while self.node1._actualPollInterval == self.node1.queuePollInterval:\\n d = Deferred()\\n reactor.callLater(1.0, lambda : d.callback(None))\\n yield d\\n\\n self.assertEqual(self.node1._actualPollInterval, 60.0)\",\n \"def run(self):\\n if self.pollable:\\n self.poll()\\n if not self.EventsFactory.is_alive():\\n self.EventsFactory.start()\\n while True:\\n if not self.EventsFactory.is_alive():\\n self.logger.error(f'{self} events factory has died..')\\n raise SubThreadException(self.EventsFactory)\\n update_start_time = time.time()\\n self.handle_events()\\n wait_for(lambda: time.time() - update_start_time > self.handle_events_every and not self._busy_mutext.locked(),\\n logger=self.logger, message='Waiting for work timeout to finish.')\",\n \"def main_loop():\\n while len(fake_threads) > 0:\\n pulse(0.1)\",\n \"async def test_no_hb(self):\\n await self.async_setup()\\n pyinsteon.managers.heartbeat_manager.HB_CHECK_BUFFER = 1\\n self._hb_mgr = pyinsteon.managers.heartbeat_manager.HeartbeatManager(\\n self._address, self._group, 0\\n )\\n await asyncio.sleep(1.1)\\n assert self._heartbeat\",\n \"def test_heartbeat(self):\\n self.dut._poll_processes = MagicMock()\\n self.dut._do_heartbeat()\\n self.assertEqual(self.dut._poll_processes.call_count, 3)\",\n \"def test_lbheartbeat(self):\\n pass\",\n \"def run(self): \\n #\\n\\n \\n # forever loop\\n while True: \\n \\n for app in self.app_list:\\n self.check(app) \\n #print(\\\"check\\\")\\n \\n gevent.sleep(SLEEP_SECONDS)\",\n \"def test_shutdown(self):\\n hook = SignalHook(self.test_extension, self.signal,\\n self._on_signal_fired)\\n hook.disable_hook()\\n\\n self.assertEqual(len(self._on_signal_fired.calls), 0)\\n self.signal.send(self)\\n self.assertEqual(len(self._on_signal_fired.calls), 0)\",\n \"def heartbeat_event(self, event):\\r\\n pass\",\n \"def run(self):\\n self.stopped = False\\n # receives incoming 'host up' requests\\n serverSocket = socket.socket(socket.AF_INET, socket.SOCK_DGRAM, 0)\\n serverSocket.bind(('255.255.255.255', Globals.BROADCAST_PORT))\\n \\n # wait for UDP broadcast, send TCP ACK\\n while 1:\\n \\n # open a socket and listen for a message\\n value,address = serverSocket.recvfrom(256)\\n host,port = address\\n \\n # this actually prevents a seg fault ;( for some reason\\n if self.stopped:\\n return\\n \\n if value == 'host up':\\n \\n sendSocket = socket.socket(socket.AF_INET, \\n socket.SOCK_STREAM, 0)\\n sendSocket.connect((host, Globals.ACK_PORT))\\n sendSocket.send('host up ack')\\n sendSocket.close()\\n sendSocket = None\\n self._addHost(host)\\n \\n elif value.find('host down') == 0:\\n self._removeHost(host)\\n \\n elif value.find('add group') == 0:\\n self._postEvent(value)\\n \\n elif value.find('remove group') == 0:\\n self._postEvent(value)\\n \\n elif value.find('group beat') == 0:\\n self._postEvent(value)\\n \\n serverSocket.close()\",\n \"def run(self):\\n self._app.processEvents()\\n try:\\n while not self._stop:\\n # GRobot._app.processEvents()\\n while self._app.hasPendingEvents():\\n self._app.processEvents()\\n gevent.sleep(0.01)\\n except Exception, e:\\n logger.error(e)\\n logger.debug('Goodbye GRobot')\",\n \"def loop(self):\\n while not self.should_exit:\\n self._run_once()\\n\\n self.on_exit()\",\n \"def event_queue_proc(self,event):\\r\\n event()\",\n \"async def heartbeat_loop():\\n self.debug(\\\"Started heartbeat loop\\\")\\n while True:\\n try:\\n await self.heartbeat()\\n await asyncio.sleep(HEARTBEAT_INTERVAL)\\n except asyncio.CancelledError:\\n self.debug(\\\"Stopped heartbeat loop\\\")\\n raise\\n except asyncio.TimeoutError:\\n self.debug(\\\"Heartbeat failed due to timeout, disconnecting\\\")\\n break\\n except Exception as ex: # pylint: disable=broad-except\\n self.exception(\\\"Heartbeat failed (%s), disconnecting\\\", ex)\\n break\\n\\n transport = self.transport\\n self.transport = None\\n transport.close()\",\n \"def fsm_loop(self):\\n while not rospy.is_shutdown():\\n rospy.sleep(0.1)\\n self.reset_fsm_preempted()\\n self.reset_continued()\\n\\n state = self.get_state()\\n if state is None:\\n rospy.logerr(\\\"State is None. Error\\\")\\n continue\\n #\\n if state not in self._fxns.keys():\\n rospy.logdebug(\\\"{}: no function for given state. May be a bug\\\".format(state))\\n continue\\n ###\\n # call the correct callback\\n try:\\n self._fxns[state](self._goal)\\n except ActionServerError as error:\\n # current state aborted, so return to the previous state\\n rospy.logerr(traceback.format_exc())\\n rospy.logdebug(\\\"{}: Aborted, reverting to previous state\\\".format(self._state_enums(state)))\\n # self.print_prior_states()\\n # pdb.set_trace()\\n self.revert_state()\\n\\n # set result\\n self._fill_result()\",\n \"async def listen_and_arbitrate(isTest, backend):\\n if not check_address(address):\\n # Always exit. Unusable with a bad address\\n fatal_error(True, \\\"Invalid address %s\\\" % address, 7)\\n\\n scheduler = SchedulerQueue()\\n scanner = backend.Scanner()\\n headers = {'Authorization': api_key} if api_key else {}\\n async with aiohttp.ClientSession(headers=headers) as session:\\n # Get base_nonce and bounty registry parameters\\n await get_base_nonce(session)\\n voting_window = await get_vote_window(session)\\n reveal_window = await get_reveal_window(session)\\n\\n if not voting_window or not get_reveal_window:\\n # Cannot vote/settle without this info\\n fatal_error(True, \\\"Failed to get bounty windows.\\\", 14)\\n\\n if not await post_stake(session):\\n # Always exit, because it is unusable without staking\\n fatal_error(True, \\\"Failed to Stake Arbiter.\\\", 9)\\n\\n async with websockets.connect(ws_url, extra_headers=headers) as ws:\\n while True:\\n message = json.loads(await ws.recv())\\n if message[\\\"event\\\"] == \\\"block\\\":\\n number = message[\\\"data\\\"][\\\"number\\\"]\\n if number % 100 == 0:\\n logging.info('Block %s', number)\\n\\n asyncio.get_event_loop().create_task(scheduler.execute_scheduled(number))\\n elif message[\\\"event\\\"] == \\\"bounty\\\":\\n bounty = message[\\\"data\\\"]\\n asyncio.get_event_loop().create_task(handle_bounty(isTest, session, scheduler, reveal_window, voting_window, scanner, bounty))\",\n \"def event_loop():\\n\\n class TimeTrackingTestLoop(asyncio.BaseEventLoop):\\n\\n stuck_threshold = 100\\n\\n def __init__(self):\\n super().__init__()\\n self._time = 0\\n self._timers = []\\n self._selector = Mock()\\n self.clear()\\n\\n # Loop internals\\n\\n def _run_once(self):\\n super()._run_once()\\n # Update internals\\n self.busy_count += 1\\n self._timers = sorted(when for when in self._timers if when > loop.time())\\n # Time advance\\n if self.time_to_go:\\n when = self._timers.pop(0)\\n step = when - loop.time()\\n self.steps.append(step)\\n self.advance_time(step)\\n self.busy_count = 0\\n\\n def _process_events(self, event_list):\\n return\\n\\n def _write_to_self(self):\\n return\\n\\n # Time management\\n\\n def time(self):\\n return self._time\\n\\n def advance_time(self, advance):\\n if advance:\\n self._time += advance\\n\\n def call_at(self, when, callback, *args, **kwargs):\\n self._timers.append(when)\\n return super().call_at(when, callback, *args, **kwargs)\\n\\n @property\\n def stuck(self):\\n return self.busy_count > self.stuck_threshold\\n\\n @property\\n def time_to_go(self):\\n return self._timers and (self.stuck or not self._ready)\\n\\n # Resource management\\n\\n def clear(self):\\n self.steps = []\\n self.open_resources = 0\\n self.resources = 0\\n self.busy_count = 0\\n\\n @contextmanager\\n def assert_cleanup(self):\\n self.clear()\\n yield self\\n assert self.open_resources == 0\\n self.clear()\\n\\n loop = TimeTrackingTestLoop()\\n asyncio.set_event_loop(loop)\\n with loop.assert_cleanup():\\n yield loop\\n loop.close()\",\n \"def prepareflow(self):\\r\\n self.time = 0\\r\\n #self.timeout = timeout\\r\\n Dummy = things(0,0,0)\\r\\n Dummy.threadqueue.append(-1)\\r\\n Dummy.s=self\\r\\n Dummy.name = 'End of simulation.'\\r\\n heappush(self.queue, (self.timeout, (Dummy, 1000)))\\r\\n #while true:\\r\\n #(now, (item, i)) = heappop(self.queue)\\r\\n #if now >= timeout: break\\r\",\n \"def run(self):\\n if self.worker_is_alive:\\n self.master_heartbeat_thread.join()\",\n \"def run(self):\\n self._no_tb = False\\n self.traceback = None\\n queues = (queue.attr, queue.clean) + queue.queues\\n try:\\n self.start.emit()\\n while True:\\n events = 0\\n while len(self._events):\\n events += 1\\n if events == 50:\\n self._queue(0)\\n events = 0\\n self.event_count += 1\\n func, args, kwargs, tb_slot, tbs_time, tb_call, tbc_time = self._events.popleft()\\n self._construct_tb((tb_slot, \\\"signal connection <%.4f>\\\" % (tbs_time - log.start_time)),\\n (tb_call, \\\"signal emitter <%.4f>\\\" % (tbc_time - log.start_time)))\\n func(*args, **kwargs)\\n self._clear_tb()\\n\\n for q in queues:\\n if len(q.active):\\n break\\n else:\\n # Die if no events or outstanding processes\\n break\\n\\n self._queue()\\n\\n finally:\\n self.stop.emit()\",\n \"def mainloop(self, run, *args, **kwds):\\r\\n #self.m.make()\\r\\n self.time = 0\\r\\n Dummy = things(0,0,0)\\r\\n Dummy.threadqueue.append(-1)\\r\\n Dummy.s=self\\r\\n Dummy.name = 'End of simulation.'\\r\\n heappush(self.queue, (self.timeout, (Dummy, 1000)))\\r\\n #count = 0\\r\\n while self.time < self.timeout:\\r\\n run(*args, **kwds)\\r\\n (now, (item, i)) = heappop(self.queue)\\r\\n self.CalculateResourse(now)\\r\\n self.time = now\\r\\n item.creat()\\r\\n #count += 1\\r\\n #print 'Number of loops' , count\\r\",\n \"def test_gameHandleEvents(self):\\n # this kinda gonna be reiterating the other tests??\\n # the tests of all the individual methods below make this test work\\n pass\",\n \"def run(self):\\n while True:\\n try:\\n logging.debug(\\\">>> Waiting for action\\\")\\n talk_action = self.action_queue.get(True, 150)\\n except Queue.Empty:\\n break\\n else:\\n self.handle(talk_action)\\n self.close()\",\n \"async def handle_hello(self, data, shard):\\n if shard.id != self.id:\\n return\\n self.received_heartbeat_ack = True\\n self.heartbeat_interval = data[\\\"d\\\"][\\\"heartbeat_interval\\\"] / 1000\\n self.loop.create_task(self.heartbeat_loop())\\n self.logger.debug(\\\"Started heartbeat loop\\\")\",\n \"def run_forever(self):\\n while True:\\n if not self._mailbox:\\n self._event.wait()\\n self._event = _event.Event()\\n else:\\n # leave the message in the mailbox until after it's\\n # been processed so the event doesn't get triggered\\n # while in the received method\\n self._pool.spawn_n(\\n self.received, self._mailbox[0])\\n self._mailbox.popleft()\",\n \"def ProcessEvents(self):\\n self.work_queue.put(self.__ProcessEventsAsync)\",\n \"def _run(self) -> None:\\n try:\\n while True:\\n loop_time = self._get_time()\\n loop_time_flush_interval = self._get_time(self.flush_interval.total_seconds())\\n\\n if loop_time >= self.flushing_interval_deadline:\\n self._flush_batch()\\n self.flushing_interval_deadline = loop_time + loop_time_flush_interval\\n self.logger.debug('Flush interval deadline. Flushed batch.')\\n\\n try:\\n interval = self.flushing_interval_deadline - loop_time\\n item = self.event_queue.get(True, interval)\\n\\n if item is None:\\n continue\\n\\n except queue.Empty:\\n continue\\n\\n if item == self._SHUTDOWN_SIGNAL:\\n self.logger.debug('Received shutdown signal.')\\n break\\n\\n if item == self._FLUSH_SIGNAL:\\n self.logger.debug('Received flush signal.')\\n self._flush_batch()\\n continue\\n\\n if isinstance(item, UserEvent):\\n self._add_to_batch(item)\\n\\n except Exception as exception:\\n self.logger.error(f'Uncaught exception processing buffer. Error: {exception}')\\n\\n finally:\\n self.logger.info('Exiting processing loop. Attempting to flush pending events.')\\n self._flush_batch()\",\n \"def test_run_ended(self):\",\n \"def mainloop(self):\\n global agent_types\\n while 1:\\n self.evSimulate.wait()\\n self.logger.debug('Entered Mainloop')\\n if self.bQuit == True: \\n return\\n \\n self.evSimulate.clear()\\n \\n if self.szType != None and self.simtime != None and self.iIdLow!= -1 and self.iIdHigh != -1:\\n \\n if dictSettings[S_SYNCHRONOUSWRITES]:\\n for i in xrange(self.iIdLow, self.iIdHigh):\\n \\n if agent_types[self.szType]['__lstDeletedIds__']: \\n p = bisect.bisect_right(agent_types[self.szType]['__lstDeletedIds__'], i)\\n if p == 0 and agent_types[self.szType]['__lstDeletedIds__'][0] == i:\\n continue \\n if agent_types[self.szType]['__lstDeletedIds__'][p - 1] == i:\\n continue \\n \\n ag = agent_types[self.szType]['__handler__'](self.db, self.szType, i, self.simtime, self.globalData)\\n ag.readState()\\n ag.update(self.simtime)\\n else:\\n for i in xrange(self.iIdLow, self.iIdHigh, dictSettings[S_NUMAGENTSTOFETCHATONCE]):\\n \\n\\n iQueryLowIndex = i\\n iQueryHighIndex = i + dictSettings[S_NUMAGENTSTOFETCHATONCE]\\n if (self.iIdHigh - i) < dictSettings[S_NUMAGENTSTOFETCHATONCE]:\\n iQueryHighIndex = self.iIdHigh\\n \\n lstDictAgentData = self.db.readObjects(self.szType, iQueryLowIndex, iQueryHighIndex)\\n \\n for j in lstDictAgentData: \\n ag = agent_types[self.szType]['__handler__'](self.db, self.szType, j['id'], self.simtime, self.globalData)\\n ag.setState(j)\\n ag.update(self.simtime)\\n \\n self.logger.debug('Done simulating')\\n \\n self.evAPI.set()\",\n \"def loop(self):\\n _logger.info(\\\"Bus.loop listen imbus on db postgres\\\")\\n # PATCH !!\\n with odoo.sql_db.db_connect(_get_imbus_db()).cursor() as cr:\\n conn = cr._cnx\\n cr.execute(\\\"listen imbus\\\")\\n cr.commit();\\n while True:\\n if select.select([conn], [], [], TIMEOUT) == ([], [], []):\\n pass\\n else:\\n conn.poll()\\n channels = []\\n while conn.notifies:\\n channels.extend(json.loads(conn.notifies.pop().payload))\\n # dispatch to local threads/greenlets\\n events = set()\\n for channel in channels:\\n events.update(self.channels.pop(hashable(channel), []))\\n for event in events:\\n event.set()\",\n \"def _EventQueueWorker(self):\\n while not self.stop:\\n gevent.sleep(0)\\n\\n try:\\n with Timer('task_process_time', False) as t:\\n # Pull the next task off the queue.\\n task, enqueue_times = model_provider.GetQueues().GetTask()\\n\\n # Retrieve the Tab Types for the NIDs so we know what handler to use.\\n nid_to_type = model_provider.GetNames().GetTypes(task.keys())\\n\\n # Add the queued state parts to the main states.\\n greenlets = [\\n gevent.spawn(\\n model_provider.GetStates().ReduceInto,\\n nid, cid_to_sstate, nid_to_type[nid])\\n for nid, cid_to_sstate in task.iteritems()]\\n\\n gevent.joinall(greenlets)\\n\\n LOG.info('Processed tasks %3dN %5dC %7.3fs' % (\\n len(task), len(enqueue_times), t.Elapsed))\\n\\n model_provider.GetLatency().RecordForProcessed(enqueue_times)\\n\\n except Exception:\\n LOG.error('Exception in Queue Worker loop')\\n LOG.error(traceback.format_exc())\\n gevent.sleep(1)\",\n \"def thread_for_rescue_detection(self):\\n try:\\n self.loop_over_frames()\\n except Exception as e:\\n pass\\n finally:\\n self.clean_up()\",\n \"def __heart(self):\\n\\n while not self.__stop_threads:\\n start_time = time.time()\\n self.__pulse_all()\\n # print(f\\\"total pulse time = {time()-start_time} seconds\\\")\\n sleep(BusController.PULSE_DELAY)\\n print(\\\"stopped the heartbeats\\\")\",\n \"def eventmonitorloop(self):\\n if self.service is None:\\n return\\n logger.info(\\\"Subscribing to EMANE location events (not generating them). \\\" \\\\\\n \\\"(%s) \\\" % threading.currentThread().getName())\\n while self.doeventloop is True:\\n if emane.VERSION >= emane.EMANE091:\\n uuid, seq, events = self.service.nextEvent()\\n if not self.doeventloop:\\n break # this occurs with 0.9.1 event service\\n for event in events:\\n (nem, eid, data) = event\\n if eid == LocationEvent.IDENTIFIER:\\n self.handlelocationevent2(nem, eid, data)\\n else:\\n (event, platform, nem, cmp, data) = self.service.nextEvent()\\n if event == emaneeventlocation.EVENT_ID:\\n self.handlelocationevent(event, platform, nem, cmp, data)\\n logger.info(\\\"Unsubscribing from EMANE location events. (%s) \\\" % threading.currentThread().getName())\",\n \"def _bg_thread_main(self) -> None:\\n while not self._done:\\n self._run_server_cycle()\",\n \"async def _main_coro(self):\\n\\t\\twhile True:\\n\\t\\t\\ttry:\\n\\t\\t\\t\\t# Gets a frame from the log queue in a separate thread\\n\\t\\t\\t\\tfor task in as_completed([self._event_loop.run_in_executor(self._thread_executor, self.log_queue.get, True, 0.1)]):\\n\\t\\t\\t\\t\\tframe = await task\\n\\t\\t\\t\\t\\t# If the current frame is a STOP frame, increase the counter.\\n\\t\\t\\t\\t\\t# Handles the frame payload otherwise\\n\\t\\t\\t\\t\\tif frame.header == Frame.STOP:\\n\\t\\t\\t\\t\\t\\tself._stop_counter += 1\\n\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\tawait self._record_logs(frame.payload)\\n\\t\\t\\texcept Empty:\\n\\t\\t\\t\\t# Event loop works while TestParser and Processor generate tasks\\n\\t\\t\\t\\tif self._stop_counter == 2:\\n\\t\\t\\t\\t\\tbreak\",\n \"def postloop(self):\\n print 'Bye!'\",\n \"def run(self):\\n while True:\\n self._event.clear()\\n self._queue.get().run(self._event)\",\n \"def main_loop(self):\\n import time\\n while not self.ask_for_stop:\\n self.run_pending()\\n time.sleep(self.delay)\\n # FIXME this will look at self.ask_for_stop only every self.delay seconds\\n # see https://stackoverflow.com/questions/5114292/break-interrupt-a-time-sleep-in-python\",\n \"def timerEvent(self, event):\\n self.ext_fb_trigger()\\n if self._check_pulse():\\n if self._generator_poll is None:\\n if self._generator_feedback is not None:\\n print('In timerEvent 2')\\n self._stop_loop_feedback()\\n return\\n try:\\n next(self._generator_poll) # Run the next iteration\\n if self._generator_feedback is None:\\n return\\n try:\\n next(self._generator_feedback)\\n except Exception as e:\\n print(e)\\n self._stop_loop_feedback()\\n except StopIteration:\\n # print('In timerEvent 5')\\n self._stop_loop_feedback() # Iteration has finished, kill the timer\\n self._stop_loop_poll() # Iteration has finished, kill the timer\",\n \"def polling( self, ):\\r\\n \\\"\\\"\\\"\\r\\n queue protocol, data = ( action, function, function_args )\\r\\n action = a string\\r\\n function = a function\\r\\n function_args = arguments to function which will be called function( function_args ) This should be a tuple\\r\\n \\\"\\\"\\\"\\r\\n\\r\\n # !! may be phasing out\\r\\n# if self.request_to_pause:\\r\\n# self.paused = True\\r\\n#\\r\\n# while self.request_to_pause: # think function part not used ??\\r\\n# time.sleep( .1 )\\r\\n#\\r\\n# self.paused = False\\r\\n\\r\\n# if self.parameters.start_helper_delay > 0:\\r\\n# helper_start = True\\r\\n# else:\\r\\n# helper_start = False\\r\\n # two bits here just used once, have a polling0 then swtch over to this\\r\\n\\r\\n if self.is_first_gui_loop:\\r\\n # perhaps a polling method polling0 that then calls polling\\r\\n # should be moved to gui !! turn back on unless messing up whole app\\r\\n # print(\\\"lifting...\\\")\\r\\n# self.gui.root.attributes(\\\"-topmost\\\", True) # seems to work\\r\\n# self.gui.root.lift() # did not work\\r\\n self.is_first_gui_loop = False\\r\\n# self.gui.root.attributes(\\\"-topmost\\\", False) # seems to work\\r\\n try:\\r\\n if self.start_helper and ( self.start_helper_after < time.time() ):\\r\\n # if self.start_helper_after < time.time() :\\r\\n self.start_helper = False\\r\\n\\r\\n msg = f\\\"We have an start_helper_function setting in the parameter file = {self.parameters.start_helper_function}\\\"\\r\\n print( msg )\\r\\n self.gui.print_info_string( msg )\\r\\n to_eval = f\\\"self.ext_processing. {self.parameters.start_helper_function}\\\"\\r\\n a_function = eval( to_eval ) # or hava a way to pass eval to other side ??\\r\\n # a_function() # run in gt of ext_processing module -- not a good idea\\r\\n # next was to run in the background thread\\r\\n self.post_to_queue( \\\"call\\\", a_function , self.parameters.start_helper_args )\\r\\n# else:\\r\\n# print( self.start_helper_after < time.time() )\\r\\n# print( self.start_helper )\\r\\n# print( \\\"\\\")\\r\\n if self.gui_recieve_lock.locked():\\r\\n self.receive()\\r\\n # self.start_helper_after time to start helper if used\\r\\n # loop till queue empty\\r\\n ( action, function, function_args ) = self.rec_from_queue()\\r\\n while action != \\\"\\\":\\r\\n if action == \\\"call\\\":\\r\\n #print( \\\"controller making call\\\" )\\r\\n sys.stdout.flush()\\r\\n function( *function_args )\\r\\n elif action == \\\"rec\\\":\\r\\n self.gui.print_rec_string( function_args[ 0 ] )\\r\\n elif action == \\\"send\\\":\\r\\n # but where is it actually sent ??\\r\\n self.gui.print_send_string( function_args[ 0 ] )\\r\\n elif action == \\\"info\\\":\\r\\n self.gui.print_info_string( function_args[ 0 ] )\\r\\n\\r\\n ( action, function, function_args ) = self.rec_from_queue()\\r\\n\\r\\n self.task_tick += 1 # for delay in list send\\r\\n\\r\\n if self.list_send: # used for ir_processing and motor processing\\r\\n #self.loggerit( str( self.task_tick % self.parameters.send_array_mod ) )\\r\\n if ( ( self.task_tick % self.parameters.send_array_mod ) == 0 ): # 5 might be in parms\\r\\n #---\\r\\n #print \\\"send ix_array\\\", self.send_list_ix\\r\\n #self.send( \\\"xxx\\\\n\\\" )\\r\\n self.send( str( self.send_list[ self.send_list_ix ] ) + \\\"\\\\n\\\" )\\r\\n\\r\\n self.send_list_ix += 1\\r\\n if ( self.send_list_ix >= len( self.send_list ) ):\\r\\n self.list_send = False\\r\\n\\r\\n except Exception as ex_arg:\\r\\n # !! think there is a type of logging that gives traceback info\\r\\n self.logger.error( \\\"polling Exception in smart_terminal: \\\" + str( ex_arg ) )\\r\\n # ?? need to look at which we catch maybe just rsh\\r\\n (atype, avalue, atraceback) = sys.exc_info()\\r\\n a_join = \\\"\\\".join( traceback.format_list ( traceback.extract_tb( atraceback ) ) )\\r\\n self.logger.error( a_join )\\r\\n\\r\\n a_ex_record = ExceptionRecord( time.time) # add at end limit self.ex_max\\r\\n self.exception_records.append( a_ex_record )\\r\\n if ( len( self.exception_records ) > self.parameters.ex_max ):\\r\\n msg = \\\"too many exceptions polling in smart terminal may die\\\"\\r\\n print( msg )\\r\\n self.logger.error( msg )\\r\\n self.logger.error( \\\"too many exceptions what to do???\\\" )\\r\\n self.polling_fail = True\\r\\n #raise Exception( \\\"too many\\\" )\\r\\n #self.restart() # may leave return on stack ??\\r\\n # here we need to set the next task?? lets try, as function may not have returned\\r\\n # self.task_list.what_next = self.task_list.advance_next\\r\\n\\r\\n finally:\\r\\n if self.polling_fail:\\r\\n pass\\r\\n else:\\r\\n self.gui.root.after( self.parameters.gt_delta_t, self.polling ) # reschedule event\\r\\n\\r\\n return\",\n \"async def do_run(self, event_bus: EndpointAPI) -> None:\\n ...\",\n \"async def heartbeat_loop(self):\\n while self.connected.is_set():\\n if not self.received_heartbeat_ack:\\n self.failed_heartbeats += 1\\n self.logger.info(\\n \\\"WebSocket did not respond to a heartbeat! Failed \\\"\\n \\\"attempts: \\\" + str(\\n self.failed_heartbeats))\\n if self.failed_heartbeats > 2:\\n self.logger.warning(\\n \\\"Gateway stopped responding, reconnecting!\\\")\\n await self.close()\\n await self.connect(self.gateway_url)\\n return\\n self.received_heartbeat_ack = False\\n await self.send({\\n \\\"op\\\": 1,\\n \\\"d\\\" : self.heartbeat_count\\n })\\n if self.heartbeat_count is not None:\\n self.heartbeat_count += 1\\n else:\\n self.heartbeat_count = 0\\n await sleep(self.heartbeat_interval)\",\n \"def run(self):\\n # The first heartbeat happens immediately\\n self.log.info('starting heartbeater')\\n interval = 0\\n self.agent.set_agent_advertise_addr()\\n\\n self.reader, self.writer = os.pipe()\\n p = select.poll()\\n p.register(self.reader, select.POLLIN)\\n try:\\n while True:\\n if p.poll(interval * 1000):\\n if os.read(self.reader, 1) == 'a':\\n break\\n\\n self.do_heartbeat()\\n interval_multiplier = random.uniform(\\n self.min_jitter_multiplier,\\n self.max_jitter_multiplier)\\n interval = self.agent.heartbeat_timeout * interval_multiplier\\n log_msg = 'sleeping before next heartbeat, interval: {0}'\\n self.log.info(log_msg.format(interval))\\n finally:\\n os.close(self.reader)\\n os.close(self.writer)\\n self.reader = None\\n self.writer = None\",\n \"def test_endGameHandlers1(self):\\n # check that it starts as False\\n assert_equal(self.testGame.running, False)\\n # set explicitly to True, make sure it is set\\n self.testGame.running = True\\n assert_equal(self.testGame.running, True)\\n # create a Quit type Pygame event, and run with method, then check\\n eventEscape = pg.event.Event(pg.KEYDOWN)\\n pass\",\n \"def _poll_event_queue(self):\\n if not self.event_queue.empty():\\n obj = self.event_queue.get(block=False)\\n if isinstance(obj, tuple):\\n if len(obj) == 1:\\n obj[0]()\\n elif len(obj) == 2:\\n if isinstance(obj[1], list):\\n obj[0](*obj[1])\\n elif isinstance(obj[1], dict):\\n obj[0](**obj[1])\\n elif len(obj) == 3:\\n obj[0](*obj[1], **obj[2])\\n self.view.after(100, self._poll_event_queue)\",\n \"async def run(self):\\n while True:\\n await asyncio.sleep(0)\\n # See if any sockets have anything\\n try:\\n socks, events = self.poller.poll(1000)\\n for sock, event in zip(socks,events):\\n if sock in self.subscriptions:\\n states = sock.recv_json()\\n await self.main_server.sync_states(states)\\n\\n # Nothing to report - Poller did not find any sockets with updates\\n except ValueError:\\n pass\\n # Exiting\\n except KeyboardInterrupt:\\n break\",\n \"def run(self):\\n time_to_quit = False\\n while True:\\n time_to_quit = self.run_to_yield_or_quit()\\n if time_to_quit:\\n print(self, 'quitting')\\n break\\n else:\\n time.sleep(self.polling_interval)\",\n \"def test_process_packet_heartbeat(self):\\n\\n pkt = {'type': 'heartbeat',\\n 'endpoint': ''\\n }\\n self.ns.process_packet(pkt)\\n assert not self.environ['socketio'].error.called\",\n \"async def test_estop_callback(\\n decoy: Decoy,\\n) -> None:\\n engine_store = decoy.mock(cls=EngineStore)\\n\\n subject = get_estop_listener(engine_store=engine_store)\\n\\n decoy.when(engine_store.current_run_id).then_return(None, \\\"fake_run_id\\\")\\n\\n disengage_event = EstopStateNotification(\\n old_state=EstopState.PHYSICALLY_ENGAGED, new_state=EstopState.LOGICALLY_ENGAGED\\n )\\n\\n subject(disengage_event)\\n\\n engage_event = EstopStateNotification(\\n old_state=EstopState.LOGICALLY_ENGAGED, new_state=EstopState.PHYSICALLY_ENGAGED\\n )\\n\\n subject(engage_event)\\n\\n subject(engage_event)\\n\\n decoy.verify(engine_store.engine.estop(maintenance_run=False), times=1)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7250805","0.7247759","0.6956706","0.6701028","0.6637288","0.6541509","0.64544195","0.64051026","0.62292856","0.621116","0.6163216","0.6132048","0.61309826","0.60833305","0.60645205","0.60457534","0.6026709","0.6022797","0.5971004","0.59705234","0.59382254","0.58634293","0.58442754","0.58312064","0.5826717","0.5822629","0.581594","0.58116394","0.5810759","0.58028376","0.5800037","0.5770179","0.5767031","0.5758558","0.57574075","0.5754007","0.57481414","0.573627","0.57357514","0.57274044","0.5712289","0.5687974","0.5686876","0.56590074","0.5646835","0.56224173","0.56176674","0.56095296","0.55994767","0.55892766","0.5552684","0.555087","0.55471575","0.55420685","0.5537129","0.5536818","0.5528508","0.55219877","0.5514585","0.55099094","0.5508056","0.5506925","0.550222","0.54990226","0.5476","0.5466922","0.5466189","0.54597974","0.5459624","0.5456868","0.54541844","0.5453189","0.5449551","0.54436016","0.54355407","0.54316634","0.542184","0.54214734","0.5420866","0.5420818","0.5418795","0.5418457","0.5416717","0.54158133","0.5415643","0.54153955","0.5399259","0.53962445","0.5394099","0.5387775","0.5382931","0.53823984","0.53773355","0.53722584","0.5364296","0.5355155","0.53541386","0.5353437","0.5348273","0.53452986"],"string":"[\n \"0.7250805\",\n \"0.7247759\",\n \"0.6956706\",\n \"0.6701028\",\n \"0.6637288\",\n \"0.6541509\",\n \"0.64544195\",\n \"0.64051026\",\n \"0.62292856\",\n \"0.621116\",\n \"0.6163216\",\n \"0.6132048\",\n \"0.61309826\",\n \"0.60833305\",\n \"0.60645205\",\n \"0.60457534\",\n \"0.6026709\",\n \"0.6022797\",\n \"0.5971004\",\n \"0.59705234\",\n \"0.59382254\",\n \"0.58634293\",\n \"0.58442754\",\n \"0.58312064\",\n \"0.5826717\",\n \"0.5822629\",\n \"0.581594\",\n \"0.58116394\",\n \"0.5810759\",\n \"0.58028376\",\n \"0.5800037\",\n \"0.5770179\",\n \"0.5767031\",\n \"0.5758558\",\n \"0.57574075\",\n \"0.5754007\",\n \"0.57481414\",\n \"0.573627\",\n \"0.57357514\",\n \"0.57274044\",\n \"0.5712289\",\n \"0.5687974\",\n \"0.5686876\",\n \"0.56590074\",\n \"0.5646835\",\n \"0.56224173\",\n \"0.56176674\",\n \"0.56095296\",\n \"0.55994767\",\n \"0.55892766\",\n \"0.5552684\",\n \"0.555087\",\n \"0.55471575\",\n \"0.55420685\",\n \"0.5537129\",\n \"0.5536818\",\n \"0.5528508\",\n \"0.55219877\",\n \"0.5514585\",\n \"0.55099094\",\n \"0.5508056\",\n \"0.5506925\",\n \"0.550222\",\n \"0.54990226\",\n \"0.5476\",\n \"0.5466922\",\n \"0.5466189\",\n \"0.54597974\",\n \"0.5459624\",\n \"0.5456868\",\n \"0.54541844\",\n \"0.5453189\",\n \"0.5449551\",\n \"0.54436016\",\n \"0.54355407\",\n \"0.54316634\",\n \"0.542184\",\n \"0.54214734\",\n \"0.5420866\",\n \"0.5420818\",\n \"0.5418795\",\n \"0.5418457\",\n \"0.5416717\",\n \"0.54158133\",\n \"0.5415643\",\n \"0.54153955\",\n \"0.5399259\",\n \"0.53962445\",\n \"0.5394099\",\n \"0.5387775\",\n \"0.5382931\",\n \"0.53823984\",\n \"0.53773355\",\n \"0.53722584\",\n \"0.5364296\",\n \"0.5355155\",\n \"0.53541386\",\n \"0.5353437\",\n \"0.5348273\",\n \"0.53452986\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.72942674"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":14369,"cells":{"query":{"kind":"string","value":"Outputs the strategy performance and other metrics from the backtest."},"document":{"kind":"string","value":"def _output_performance(self):\n self.portfolio.create_equity_curve_dataframe()\n\n print(\"Creating summary statistics...\")\n stats = self.portfolio.output_summary_stats()\n\n print(\"Creating equity curve...\")\n print(self.portfolio.equity_curve.tail(10))\n pprint.pprint(stats)\n\n print(\"Signals: %s\" % self.signals)\n print(\"Orders: %s\" % self.orders)\n print(\"Fills: %s\" % self.fills)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def report_performance(self):\n performance = self.amygdala.visualize(self.timestep, \n self.name, \n self.log_dir)\n print('Final performance is {0:.3}'.format(performance))\n self.backup()\n return performance","def _output_performance(self):\n self.portfolio.create_equity_curve_dataframe()\n \n print(\"Creating summary statistics...\")\n stats = self.portfolio.output_summary_stats()\n \n print(\"Creating equity curve...\")\n print(self.portfolio.equity_curve.tail(10))\n pprint.pprint(stats)\n \n print(\"Signals: %s\" % self.signals)\n print(\"Orders: %s\" % self.orders)\n print(\"Fills: %s\" % self.fills)","def print_performance_info(self):\n pass","def test(self):\n statistics = self.__progress(self.testing, self.__val_fn)\n print('Loss: {}'.format(statistics[0]))\n print('Precision: {:.3%}'.format(statistics[1]))\n print('Recall: {:.3%}'.format(statistics[2]))\n print('Accuracy: {:.3%}'.format(statistics[3]))\n self.report['test_loss'] = statistics[0]\n self.report['test_precision'] = statistics[1]\n self.report['test_recall'] = statistics[2]\n self.report['test_accuracy'] = statistics[3]","def print_statistics(self) -> None:\n e = self.current_epoch\n if len(self.loss_history[\"test_loss\"]) > 0:\n template = 'Epoch: {} Training loss: {:.4f}, Test loss: {:.4f}'\n print(template.format(e, self.loss_history[\"training_loss\"][-1],\n self.loss_history[\"test_loss\"][-1]))\n else:\n template = 'Epoch: {} Training loss: {:.4f}'\n print(template.format(e, self.loss_history[\"training_loss\"][-1]))","def simulate_trading(self):\n # Create the file output stream\n posix_now = datetime.datetime.timestamp(datetime.datetime.now())\n out_path = os.getcwd() + \"/OutputResults/backtest_{}\".format(posix_now)[:-7:] + \".csv\"\n\n out = open(out_path, \"w+\")\n\n spl = len(self.strat_params_list)\n for i, sp in enumerate(self.strat_params_list): # http://book.pythontips.com/en/latest/enumerate.html\n print(\"Strategy %s out of %s...\" % (i + 1, spl))\n self._generate_trading_instances(sp)\n self._run_backtest()\n stats = self._output_performance()\n pprint.pprint(stats)\n\n tot_ret = float(stats[0][1].replace(\"%\", \"\"))\n cagr = float(stats[1][1].replace(\"%\", \"\"))\n sharpe = float(stats[2][1])\n max_dd = float(stats[3][1].replace(\"%\", \"\"))\n dd_dur = int(stats[4][1])\n\n # This should be more general in future implementations...\n out.write(\n \"%s,%s,%s,%s,%s,%s,%s,%s\\n\" % (sp[\"ols_window\"], sp[\"zscore_high\"], sp[\"zscore_low\"],\n tot_ret, cagr, sharpe, max_dd, dd_dur)\n )\n\n out.close()","def create_test_report(test_generator, test_history):\n \n df_res_labels = create_res_labels_df(test_generator, test_history)\n \n print_metric_to_console = False\n lvls=['']\n \n metrics_dict = {}\n \n n_samples = df_res_labels.shape[0]\n print('.'*50)\n print('showing test metrics for {} samples'.format(n_samples))\n print('`'*50)\n \n lvl_metrics_dict = {}\n for lvl in lvls:\n y_tr = df_res_labels['y_true' + lvl]\n y_pre = df_res_labels['y_pred' + lvl] \n \n lvl_metrics_dict = {}\n \n # Macro / Micro Driven Metrics\n for avg in ['macro', 'micro']:\n \n met_name = 'precision' + ('_'+ avg) \n res = metrics.precision_score(y_tr, y_pre, average=avg)\n lvl_metrics_dict[met_name] = res\n \n met_name = 'f1' + ('_'+ avg) \n res = metrics.f1_score(y_tr, y_pre, average=avg)\n lvl_metrics_dict[met_name] = res\n \n met_name = 'recall' + ('_'+ avg) \n res = metrics.recall_score(y_tr, y_pre, average=avg)\n lvl_metrics_dict[met_name] = res\n \n met_name = 'accuracy' \n res = metrics.accuracy_score(y_tr, y_pre)\n lvl_metrics_dict[met_name] = res\n \n metrics_dict[lvl] = lvl_metrics_dict\n \n df_test_results = pd.DataFrame(metrics_dict).sort_values(by=lvls, ascending=False)\n df_test_results=df_test_results.reindex(columns=lvls)\n \n print(df_test_results)\n print('- '*70)\n \n plot_confusion_matrix(df_res_labels)\n \n return df_res_labels","def print_stats(self):\n if self.df_avg is None:\n self.collect_stats()\n\n print(\"Simulation Results\")\n print(tabulate(self.df_avg, headers=\"keys\", showindex=False, tablefmt=\"fancy_grid\"))\n print(\"FleetManager stats\")\n print(tabulate(self.manager_df, headers=\"keys\", showindex=False, tablefmt=\"fancy_grid\"))\n print(\"Customer stats\")\n print(tabulate(self.customer_df, headers=\"keys\", showindex=False, tablefmt=\"fancy_grid\"))\n print(\"Transport stats\")\n print(tabulate(self.transport_df, headers=\"keys\", showindex=False, tablefmt=\"fancy_grid\"))\n print(\"Station stats\")\n print(tabulate(self.station_df, headers=\"keys\", showindex=False, tablefmt=\"fancy_grid\"))","def print_results(self):\n self.accuracy = round(accuracy_score(self.y_val, self.y_pred, 'weighted'), 4)\n self.f1 = round(f1_score(self.y_val, self.y_pred, average='weighted'), 4)\n self.precision = round(precision_score(self.y_val, self.y_pred, average='weighted'), 4)\n\n print(f'Results for {self.title}:')\n print(f'{self.title} accuracy: {self.accuracy}')\n print(f'{self.title} f-score: {self.f1}')\n print(f'{self.title} precision: {self.precision}')","def showWorstStats(self) :\n Scenario.messageWorstStats()\n self.showWorstGainWon()\n self.showWorstBetUse()\n self.showNbLevelLose()","def log_results(best_model, model_name, max_features, train_score, test_score,\n score_fp):\n\n # ensure the directorys where metrics are stored are created\n if not os.path.exists(os.path.dirname(score_fp)):\n os.makedirs(os.path.dirname(score_fp), exist_ok=True)\n\n st = datetime.fromtimestamp(time.time()).strftime('%Y-%m-%d %H:%M:%S')\n with open(score_fp, 'a+') as f:\n f.write(st + '\\n')\n f.write('-' * 100 + '\\n')\n f.write('Model Run: {}\\n\\n'.format(model_name))\n f.write('Params: {}\\n\\n'.format(best_model.get_params())) \n f.write('Max features: {}\\n\\n'.format(max_features))\n f.write('Train Score: {}\\n\\n'.format(train_score))\n f.write('Test Score: {}\\n\\n'.format(test_score))","def final_strategy_test():\r\n print('-- Testing final_strategy --')\r\n print('Win rate:', compare_strategies(final_strategy))","def run_test(self):\n self.output_analytics = self.run_inference()\n self.output_df = pd.DataFrame(self.output_analytics)","def output_report(x, y, clf, test_flag=False):\n\n # Calculate recall at k time series\n y, y_prob, y_pred, recall_at_k, avg_recall = calculate_recall_at_k_time_series(x, y, clf)\n # Output confusion_matrix\n print(\"Confusion matrix: \\n\", confusion_matrix(y, y_pred))\n # Output recall_at_k\n print(\"Recall at {}, {}, {}, {}, {}: \".format(5, 10, 20, 50, 100), end=' ')\n for top in [5, 10, 20, 50, 100]:\n if top == 100:\n print(str(round(recall_at_k[top - 1], 2)) + \" accordingly\")\n else:\n print(\"{}, \".format(round(recall_at_k[top - 1], 2)), end='')\n print(\"Average recalls over 100: \", round(avg_recall, 2))\n if test_flag:\n _, recall_at_k, _, _, _ = calculate_recall_at_k(y_prob[:, 1], y, k_max=y.shape[0])\n print(\n \"Positions of escalation flags: \", ([1] if recall_at_k[0] != 0 else []) +\n [i + 1 for i in range(1, len(recall_at_k)) if\n recall_at_k[i] != recall_at_k[i - 1]])","def print_score(classifier,X_test,y_test):\n print(\"Test results:\\n\")\n print('Accuracy Score: {0:.4f}\\n'.format(accuracy_score(y_test,classifier.predict(X_test))))\n print('Classification Report:\\n{}\\n'.format(classification_report(y_test,classifier.predict(X_test))))\n print('Confusion Matrix:\\n{}\\n'.format(confusion_matrix(y_test,classifier.predict(X_test))))","def summary(self):\n print '%s Portfolio\\'s %s Strategy' % (self.portfolio.name, self.name)\n print '-' * COL_DASH_WIDTH\n\n self.display_trades()\n\n for symbol in self.portfolio.assets.keys():\n perf = self.performance[symbol]\n\n print '\\nSummary for %s from %s (first trade) to %s (last trade)' % (symbol, perf['start'], perf['end'])\n print '.' * COL_DASH_WIDTH\n print 'Summary:'\n data = [[fmtn(perf['trades']), fmtn(perf['wins']), fmtn(perf['losses']), fmtn(perf['washes'])]]\n print tabulate.tabulate(data, headers=['Total Trades', '# Wins', '# Losses', '# Washes'])\n\n print '\\nPerformance:'\n data = [[\n fmtn(perf['profit']), fmtn(perf['loss']), fmtn(perf['net_profit']),\n fmtp(perf['profit_factor']), fmtp(perf['percent_profitable']), fmtn(perf['average_trade_net_profit'])\n ]]\n print tabulate.tabulate(data, headers=['Profit', 'Loss', 'Net Profit', 'Profit Factor', 'Percent Profitable', 'Average Net Profit per Trade'])\n\n print '\\nDrawdown:'\n data = [[fmtn(perf['max_drawdown']), fmtn(perf['average_drawdown']), fmtn(perf['max_drawdown_days']), fmtn(perf['average_drawdown_days'])]]\n print tabulate.tabulate(data, headers=['Max', 'Average', 'Max Days', 'Average Days'])\n\n print '\\nRisk:'\n data = [[fmtn(perf['volatility_risk']), fmtn(perf['beta']), fmtn(perf['lower_partial_moment_risk']), fmtn(perf['t_r']), fmtn(perf['s_r'])]]\n print tabulate.tabulate(data, headers=['Volatility', 'Beta', 'Lower Partial Moment', 'Treynor Ratio', 'Sharpe Ratio'])","def print_scores(result_collector):\n # print(\"\\n# Metric: Cohen's kappa\")\n # result_collector.set_metric(['k_cohen', 'k'])\n # result_collector.print_all_results()\n print(\"\\n# Metric: Macro avg. F1\")\n result_collector.set_metric([\"macro_avg\", \"fscore\"])\n # result_collector.print_all_results()\n result_collector.print_result_for_level(\"cc\")\n result_collector.print_result_for_level(\"ro\", print_header=False)\n result_collector.print_result_for_level(\"fu\", print_header=False)\n result_collector.print_result_for_level(\"at\", print_header=False)\n\n # print(\"\\nMetric: Positive attachment F1\")\n # result_collector.set_metric(['classwise', '1', 'fscore'])\n # result_collector.print_result_for_level('at')\n print(\"\\n# Metric: Labelled attachment score\")\n result_collector.set_metric([\"accuracy\"])\n result_collector.print_result_for_level(\"lat\")","def print_stats(self):\n if self.n_iter % 5 != 0:\n return\n\n s_iter = \"%7i - \" % self.n_iter\n s_stat = ' || '.join([\n '{}: {:7.4f}'.format(k, np.mean(v)) for k, v in self.stats.items()\n if type(v) is list and len(v) > 0\n ])\n for k in self.stats.keys():\n if type(self.stats[k]) is list:\n del self.stats[k][:]\n\n # transformer learning rate\n # learning rates\n s_lr = \" - \"\n for k, v in self.optimizers.items():\n s_lr = s_lr + (\" - %s LR: \" % k) + \" / \".join(\n \"{:.4e}\".format(group['lr']) for group in v.param_groups)\n\n # processing speed\n new_time = time.time()\n diff = new_time - self.last_time\n s_speed = \"{:7.2f} sent/s - {:8.2f} words/s - \".format(\n self.stats['processed_s'] * 1.0 / diff,\n self.stats['processed_w'] * 1.0 / diff\n )\n self.stats['processed_s'] = 0\n self.stats['processed_w'] = 0\n self.last_time = new_time\n\n # log speed + stats + learning rate\n logger.info(s_iter + s_speed + s_stat + s_lr)","def test_print_results(self):\n calculated = super().predict_and_print()\n self.assertEqual(calculated, EXP_PRINT_OUTPUT_BASE.format(.18, .1, 0.186, self.test_model.model.train_time) +\n \"Max tree max_depth: 1\\n\"\n \"Number of n_estimators: 1\\n\"\n \"Impurity method: entropy\\n\")","def sprint_statistics(\n self,\n dataset_name: str,\n scoring_functions: List[autoPyTorchMetric],\n metric: autoPyTorchMetric\n ) -> str:\n search_results = self.get_search_results(scoring_functions, metric)\n success_status = (StatusType.SUCCESS, StatusType.DONOTADVANCE)\n sio = io.StringIO()\n sio.write(\"autoPyTorch results:\\n\")\n sio.write(f\"\\tDataset name: {dataset_name}\\n\")\n sio.write(f\"\\tOptimisation Metric: {metric}\\n\")\n\n num_runs = len(search_results.status_types)\n num_success = sum([s in success_status for s in search_results.status_types])\n num_crash = sum([s == StatusType.CRASHED for s in search_results.status_types])\n num_timeout = sum([s == StatusType.TIMEOUT for s in search_results.status_types])\n num_memout = sum([s == StatusType.MEMOUT for s in search_results.status_types])\n\n if num_success > 0:\n best_score = metric._sign * np.max(metric._sign * search_results.opt_scores)\n sio.write(f\"\\tBest validation score: {best_score}\\n\")\n\n sio.write(f\"\\tNumber of target algorithm runs: {num_runs}\\n\")\n sio.write(f\"\\tNumber of successful target algorithm runs: {num_success}\\n\")\n sio.write(f\"\\tNumber of crashed target algorithm runs: {num_crash}\\n\")\n sio.write(f\"\\tNumber of target algorithms that exceeded the time \"\n f\"limit: {num_timeout}\\n\")\n sio.write(f\"\\tNumber of target algorithms that exceeded the memory \"\n f\"limit: {num_memout}\\n\")\n\n return sio.getvalue()","def output_test():\n\toutput_comparison_page(TEST_EVENT_LIST, TEST_COMPARISON_PAGE_FILEPATH)","def print_outcomes(sim_output, strategy_name):\n\n # mean and confidence interval text of game reward\n reward_mean_CI_text = Format.format_estimate_interval(\n estimate=sim_output.get_ave_reward(),\n interval=sim_output.get_CI_reward(alpha=P.alpha),\n deci=1)\n\n # print game reward statistics\n print(strategy_name)\n print(\" Estimate of the mean game reward and {:.{prec}%} confidence interval:\".format(1 - P.alpha, prec=0),\n reward_mean_CI_text)","def showBestStats(self) :\n Scenario.messageBestStats()\n self.showBestStatLevelReached()\n self.showNbCoupFindFirstAttempt()\n self.showBestGainWon()\n self.showBestBetUse()\n self.showNbLevelWon()","def show_stats(self, output_type='count'):\n if not self._stats:\n raise TypeError(\"self._stats is not defined. Try running run_parser first!\")\n self._stats.print_spec(output_type)","def _t_test_results(self):\n t, df, p = self.api.m.math_utils.welchs_t_test(\n self.lkgr.values, self.fkbr.values)\n lines = [\n 'LKGR values: %r' % self.lkgr.values,\n 'FKBR values: %r' % self.fkbr.values,\n 't-statistic: %r' % t,\n 'deg. of freedom: %r' % df,\n 'p-value: %r' % p,\n 'Confidence score: %r' % (100 * (1 - p))\n ]\n return '\\n'.join(lines)","def statistics_on_test(self, predicted_results, result):\n # Print confusion matrix and mean average precision score\n predicted_results_binary = self.predicted_results_to_binary(predicted_results)\n print(\"\\nConfusion matrix : \")\n print(confusion_matrix(result, predicted_results_binary))\n print(\"\\nAverage precision score : \", average_precision_score(result, predicted_results_binary))","def dump(self):\n if self.logger is None:\n return\n Ys = self.get_ys()\n vals = []\n for i in range(min(20, len(Ys))):\n vals.append(round(Ys[i],2)) \n dt = dtime(self.t0) \n \n message = '{0} {1} {2} {3} {4:.6f} {5:.2f} {6:.2f} {7!s} {8!s}'.format(\n dt, int(self.count_evals.value / dt), self.count_runs.value, self.count_evals.value, \\\n self.best_y.value, self.get_y_mean(), self.get_y_standard_dev(), vals, self.best_x[:])\n self.logger.info(message)","def main(output_file):\n with open(output_file, 'w+') as fl:\n poor_perf_stats = pstats.Stats('poor_perf.log', stream=fl)\n good_perf_stats = pstats.Stats('good_perf.log', stream=fl)\n\n poor_perf_stats.sort_stats('cumtime')\n\n fl.write('--------------------------------------------\\n')\n fl.write('POOR PERFORMANCE STATS\\n')\n fl.write(f\"Time: {poor_perf_stats.total_tt}\\n\")\n fl.write(f\"Function Calls: {poor_perf_stats.total_calls}\\n\")\n fl.write(f\"Top cumulative times\\n\")\n poor_perf_stats.print_stats(20)\n\n fl.write('--------------------------------------------\\n')\n fl.write('GOOD PERFORMANCE STATS\\n')\n fl.write(f\"Time: {good_perf_stats.total_tt}\\n\")\n fl.write(f\"Function Calls: {good_perf_stats.total_calls}\\n\")\n fl.write(f\"Top 20 cumulative times\\n\")\n good_perf_stats.print_stats(20)","def printReport(self): \n \n print('Distribution: ', self._distribution_type)\n print('Distribution Type: ', str(self._measure_type).replace('MeasureType.','')) \n print('Type Detection Match: ', str(self._measure_type_match))\n print('MLE: ', str(self._mle))\n print('Goodness of Fit: ', str(self._gof)) \n print('Goodness of Fit Pass: ', str(self._pass)) \n print('Overall Score: ', str(self._score)) \n print('-------------')","def print_evaluations(ytrue, ypred, model):\n\n print(f'How does model {model} score:')\n print(f'The accuracy of the model is: {round(accuracy_score(ytrue, ypred), 3)}')\n print(f'The precision of the model is: {round(precision_score(ytrue, ypred, pos_label=\"bastille_\" ), 3)}')\n print(f'The recall of the model is: {round(recall_score(ytrue, ypred, pos_label=\"bastille_\"), 3)}')\n print(f'The f1-score of the model is: {round(f1_score(ytrue, ypred, pos_label=\"bastille_\"), 3)}')","def get_report(X_train, y_train, X_test, y_test, model, output):\n os = SMOTE(random_state=0)\n os_data_X, os_data_y = os.fit_sample(X_train, y_train)\n os_data_X = pd.DataFrame(data=os_data_X, columns=X_train.columns)\n\n # tune hyper parameters and fit model\n hyperparameters = {\n 'C': np.logspace(-4, 4, 20)\n }\n clf = GridSearchCV(model, hyperparameters, cv=5,\n verbose=0, scoring=make_scorer(recall_score))\n best_model = clf.fit(os_data_X, os_data_y)\n\n # measure accuracies\n train_predictions = best_model.predict(X_train)\n train_accuracy = accuracy_score(y_train, train_predictions)\n test_predictions = best_model.predict(X_test)\n test_accuracy = accuracy_score(y_test, test_predictions)\n test_recall = recall_score(y_test, test_predictions)\n test_precision = precision_score(y_test, test_predictions)\n auc_score = roc_auc_score(y_test, best_model.predict_proba(X_test)[:, 1])\n if (output == \"results_baseline\"):\n accuracies_output_df['baseline'] = [\n test_accuracy, train_accuracy, test_recall, test_precision, auc_score]\n else:\n accuracies_output_df['alternate model'] = [\n test_accuracy, train_accuracy, test_recall, test_precision, auc_score]\n\n # plot and report confusion matrix\n plot_confusion_matrix(best_model, X_test, y_test,\n display_labels=['No Default', 'Default'])\n report = classification_report(y_test, test_predictions, output_dict=True)\n report_df = pd.DataFrame(report)\n report_df = report_df.rename(columns={'0': 'Non Default',\n '1': 'Default'})\n report_df.index.name = \"measurment\"\n report_df.to_csv(f'./{output}/classification_report.csv')\n plt.savefig(f'./{output}/confusion_matrix.png')\n plt.clf()\n\n # compute and save roc curve\n fpr, tpr, thresholds = roc_curve(\n y_test, best_model.predict_proba(X_test)[:, 1])\n plt.plot(fpr, tpr)\n plt.title('ROC report')\n plt.plot((0, 1), (0, 1), '--k')\n plt.xlabel('false positive rate')\n plt.ylabel('true positive rate')\n plt.savefig(f'./{output}/roc.png')","def output_metrics(self):\n print('')\n for key in sorted(self.metrics):\n print('{}:'.format(key), end='')\n for k, v in self.metrics[key].items():\n if type(v[-1]) is list:\n print('\\t' + k + ': ' + ''.join('{:5.3f} '.format(vs) for vs in v[-1]), end='')\n else:\n print('\\t{}: {:5.3f}'.format(k, v[-1]), end='')\n print('\\n', end='')","def print_results(self) -> None:\n print(\"=\" * 70, file=sys.stderr)\n total = 0.0\n max_points = 0.0\n for problem in self.problems:\n total += problem.run_tests()\n max_points += problem.max_grade\n print(f\"Total Grade: {total}/{max_points}\", file=sys.stderr)","def print_metric(self):\r\n print(f'\\n\\n{self.sort} metric of size {self.n}')\r\n print(f'algorithm: {self.algo}')\r\n print(f'number of comparisons: {self.comps}')\r\n print(f'number of exchanges: {self.exs}')\r\n print(f'regression equation for comparisons: {self.comp_eq}')\r\n print(f'regression equation for exchanges: {self.ex_eq}')\r\n print(f'presorted data: {self.predata}')\r\n print(f'postsorted data: {self.postdata}')","def print_results(self, regressor=False):\n if regressor:\n for i, model in enumerate(self.list_of_models):\n print \"Model: \", model\n print \"R2 score: \", self.r2_scores[i]\n print \"MSE: \", self.mse_scores[i]\n else:\n for i, model in enumerate(self.list_of_models):\n print \"Model: \", model\n print \"F1 score: \", self.f1_scores[i]\n print \"recall score: \", self.recall_scores[i]\n print \"precision score: \", self.precision_scores[i]\n print \"accuracy score: \", self.accuracy_scores[i]","def print_tests_results(self):\n\n for test in self.test_report:\n for detail in test:\n print detail + ': ', test[detail]","def log_test_statistics(self, data, statistics, predictions):\n\n independent_list = []\n for var in self.model_arguments()[\"INDEPENDENT\"]:\n independent_list.append(var[\"VARIABLE\"])\n dependent_var = self.model_arguments()[\"DEPENDENT\"][\"VARIABLE\"]\n\n self.log.info(\"Dependent (Target) Variable: %s\", dependent_var)\n for var in independent_list:\n self.log.info(\"Independent (Input) Variable(s): %s\", var)\n\n self.log.info(\"Training Epochs: %d\", self.model_epochs())\n\n self.log.info(\"Test Compounds %s R-squared (R^2): %f\", dependent_var, statistics[\"R2\"])\n self.log.info(\"Test Compounds %s Mean Unsigned Error (MUE): %f\", dependent_var, statistics[\"MUE\"])\n self.log.info(\"Test Compounds %s RMS Error: %f\", dependent_var, statistics[\"RMSE\"])\n\n self.log.info(\"Test Compounds Kendall's Rank Coefficient (tau): %f, p-value: %f\",\n statistics[\"kendall\"][\"tau\"], statistics[\"kendall\"][\"p-value\"])\n self.log.info(\"Test Compounds Spearman Coefficient (rho): %f, p-value: %f\",\n statistics[\"spearman\"][\"rho\"], statistics[\"spearman\"][\"p-value\"])\n self.log.info(\"Test Compounds %s Mean Unsigned Error (MUE): %f\", dependent_var, statistics[\"MUE\"])\n self.log.info(\"Test Compounds Results\")\n self.log.info(\"ID, Tested Rank, Pred. Rank, Tested %s, Pred. %s\", dependent_var, dependent_var)\n self.log.info(\"======================================================\")\n\n predict_list = self.model_prediction_records(data, predictions, statistics)\n\n for idx in range(len(predict_list)):\n line = \"{:10s} {:4.1f} {:4.1f} {:10.4f} {:10.4f}\".format(predict_list[idx][0],\n predict_list[idx][1],\n predict_list[idx][2],\n predict_list[idx][3],\n predict_list[idx][4])\n self.log.info(line)","def print_score(y_actual, y_pred, measure):\n print(\"\\t\\tWeighted Average Scores Over Each Output Class\\n\")\n print(\"\\t\\tPrecision\\tRecall\\t\\tF1_Score\")\n for column_name, column in y_actual.iteritems():\n report = classification_report(y_actual[column_name], y_pred[column_name], output_dict=True )\n prec = report[measure]['precision']\n recall = report[measure]['recall']\n f1 = report[measure]['f1-score']\n print(\"%20.2f %15.2f % 15.2f\" % (prec, recall, f1) + \"\\t\\t\" + column_name )","def analyze_end_performance(self) -> None:\n self.logger.info(f\"###### FINAL RESULTS FOR EACH GAME###### \\n\")\n total_results = [0, 0, 0, 0]\n for game in self.instance_predictions:\n self.logger.info(f\"Simulated game {game.home_team.name} vs {game.away_team.name} \"\n f\"{len(self.instance_predictions[game])} times.\")\n confusion_matrix = [0, 0, 0, 0]\n for predicted_winner in self.instance_predictions[game]:\n actual_winner = game.home_team if game.home_team.scores.get(\n GamePeriod.TOTAL) > game.away_team.scores.get(GamePeriod.TOTAL) else game.away_team\n actual_home_team_won = True if actual_winner == game.home_team else False\n predicted_home_team_won = True if predicted_winner == game.home_team else False\n confusion_matrix = recursive_build_confusion_matrix(confusion_matrix, actual_home_team_won,\n predicted_home_team_won)\n total_results[0] += confusion_matrix[0]\n total_results[1] += confusion_matrix[1]\n total_results[2] += confusion_matrix[2]\n total_results[3] += confusion_matrix[3]\n\n for k, v in analyze_confusion_matrix(confusion_matrix, len(self.instance_predictions[game])).items():\n self.logger.info(f\"{k} : {v}\")\n self.logger.info(\"\\n###### FINAL RESULTS FOR ALL GAMES###### \\n\")\n final_stats = analyze_confusion_matrix(total_results, sum(total_results))\n for k, v in final_stats.items():\n self.logger.info(f\"{k} : {v}\")\n self.write_stats_to_json(final_stats, self.outfile)","def print_output(self):\n print(\"Reference score: \" + str(self.PotTax_reference.sum().TFI))\n print(\"Intervention score: \" + str(self.PotTax_intervention.sum().TFI))\n return","def logging_loop(self, num_gpus):\n # Launch the test worker to get performance metrics\n self.test_worker = self_play.SelfPlay.options(\n num_cpus=0, num_gpus=num_gpus,\n ).remote(\n self.checkpoint,\n self.Game,\n self.config,\n self.config.seed + self.config.num_workers,\n )\n self.test_worker.continuous_self_play.remote(\n self.shared_storage_worker, None, True\n )\n\n # Write everything in TensorBoard\n writer = SummaryWriter(self.config.results_path)\n\n print(\n \"\\nTraining...\\nRun tensorboard --logdir ./results and go to http://localhost:6006/ to see in real time the training performance.\\n\"\n )\n\n # Save hyperparameters to TensorBoard\n hp_table = [\n f\"| {key} | {value} |\" for key, value in self.config.__dict__.items()\n ]\n writer.add_text(\n \"Hyperparameters\",\n \"| Parameter | Value |\\n|-------|-------|\\n\" + \"\\n\".join(hp_table),\n )\n # Save model representation\n writer.add_text(\n \"Model summary\", self.summary,\n )\n # Loop for updating the training performance\n counter = 0\n keys = [\n \"total_reward\",\n \"wormzero_reward\",\n \"opponent_reward\",\n \"episode_length\",\n \"mean_value\",\n \"training_step\",\n \"lr\",\n \"total_loss\",\n \"value_loss\",\n \"policy_loss\",\n \"num_played_games\",\n \"num_played_steps\",\n \"num_reanalysed_games\",\n ]\n info = ray.get(self.shared_storage_worker.get_info.remote(keys))\n try:\n while info[\"training_step\"] < self.config.training_steps:\n info = ray.get(self.shared_storage_worker.get_info.remote(keys))\n writer.add_scalar(\n \"1.Total_reward/1.Total_reward\", info[\"total_reward\"], counter,\n )\n writer.add_scalar(\n \"1.Total_reward/2.Mean_value\", info[\"mean_value\"], counter,\n )\n writer.add_scalar(\n \"1.Total_reward/3.Episode_length\", info[\"episode_length\"], counter,\n )\n writer.add_scalar(\n \"1.Total_reward/4.WormZero_reward\", info[\"wormzero_reward\"], counter,\n )\n writer.add_scalar(\n \"1.Total_reward/5.Opponent_reward\",\n info[\"opponent_reward\"],\n counter,\n )\n writer.add_scalar(\n \"2.Workers/1.Self_played_games\", info[\"num_played_games\"], counter,\n )\n writer.add_scalar(\n \"2.Workers/2.Training_steps\", info[\"training_step\"], counter\n )\n writer.add_scalar(\n \"2.Workers/3.Self_played_steps\", info[\"num_played_steps\"], counter\n )\n writer.add_scalar(\n \"2.Workers/4.Reanalysed_games\",\n info[\"num_reanalysed_games\"],\n counter,\n )\n writer.add_scalar(\n \"2.Workers/5.Training_steps_per_self_played_step_ratio\",\n info[\"training_step\"] / max(1, info[\"num_played_steps\"]),\n counter,\n )\n writer.add_scalar(\"2.Workers/6.Learning_rate\", info[\"lr\"], counter)\n writer.add_scalar(\n \"3.Loss/1.Total_weighted_loss\", info[\"total_loss\"], counter\n )\n writer.add_scalar(\"3.Loss/Value_loss\", info[\"value_loss\"], counter)\n writer.add_scalar(\"3.Loss/Policy_loss\", info[\"policy_loss\"], counter)\n print(\n f'Last test reward: {info[\"total_reward\"]:.2f}. Training step: {info[\"training_step\"]}/{self.config.training_steps}. Played games: {info[\"num_played_games\"]}. Loss: {info[\"total_loss\"]:.2f}',\n end=\"\\r\",\n )\n counter += 1\n time.sleep(0.5)\n except KeyboardInterrupt:\n pass\n\n self.terminate_workers()\n\n if self.config.save_model:\n # Persist replay buffer to disk\n print(\"\\n\\nPersisting replay buffer games to disk...\")\n pickle.dump(\n {\n \"buffer\": self.replay_buffer,\n \"num_played_games\": self.checkpoint[\"num_played_games\"],\n \"num_played_steps\": self.checkpoint[\"num_played_steps\"],\n \"num_reanalysed_games\": self.checkpoint[\"num_reanalysed_games\"],\n },\n open(os.path.join(self.config.results_path, \"replay_buffer.pkl\"), \"wb\"),\n )","def show_score(clf, X_test, y_test):\n y_pred = predict(clf, X_test)\n print metrics.classification_report(y_test.astype(np.int), y_pred)","def log_evaluation(tester, name, description):\r\n\tfor dataset, output in tester.preds.items():\r\n\t\tresults = pandas.DataFrame.from_dict(output)\r\n\t\tpath = os.path.join(\r\n\t\t\tEXPERIMENT_PATH, tester.config[\"name\"] + '-' + dataset)\r\n\t\twith open(path + \".csv\", \"w\") as f:\r\n\t\t\tresults.to_csv(f, sep=\"\\t\", encoding='utf-8',\r\n\t\t\t\tfloat_format='%.3f', index=False)","def test_execution_profiling(self):\n self._test_reports_helper({\"--profile-execution\": \"\"}, [\"report.txt\"])","def _process_results(self):\n self.portfolio.create_backtest_result_dataframe()\n stats = self._show_stats()\n return stats","def advancedStats():","def print_results(self, f1_score, precision_score, recall_score):\n print(\"Algorithm: %s\" % self.name)\n self.print_score(f1_score, \"F1 Score\")\n self.print_score(precision_score, \"Precision Score\")\n self.print_score(recall_score, \"Recall Score\")","def compute_log_metrics(pipe: Pipeline,\n x_test: pd.core.frame.DataFrame,\n y_test: np.ndarray,\n y_test_onehot: np.ndarray):\n test_prob = pipe.predict_proba(x_test)\n test_pred = pipe.predict(x_test)\n\n acc = metrics.accuracy_score(y_test, test_pred)\n precision = metrics.precision_score(y_test, test_pred, average='macro')\n recall = metrics.recall_score(y_test, test_pred, average='macro')\n f1_score = metrics.f1_score(y_test, test_pred, average='macro')\n roc_auc = metrics.roc_auc_score(y_test_onehot,\n test_prob,\n average='macro',\n multi_class='ovr')\n avg_prc = metrics.average_precision_score(y_test_onehot,\n test_prob,\n average='macro')\n print(\"\\nEVALUATION\\n\"\n f\"\\tAccuracy = {acc:.4f}\\n\"\n f\"\\tPrecision (macro) = {precision:.4f}\\n\"\n f\"\\tRecall (macro) = {recall:.4f}\\n\"\n f\"\\tF1 score (macro) = {f1_score:.4f}\\n\"\n f\"\\tROC AUC (macro) = {roc_auc:.4f}\\n\"\n f\"\\tAverage precision (macro) = {avg_prc:.4f}\")\n\n # Bedrock Logger: captures model metrics\n bdrk.log_metrics(\n {\n \"Accuracy\": acc,\n \"Precision (macro)\": precision,\n \"Recall (macro)\": recall,\n \"F1 Score (macro)\": f1_score,\n \"ROC AUC (macro)\": roc_auc,\n \"Avg precision (macro)\": avg_prc,\n }\n )\n # `log_chart_data` assumes binary classification\n # For multiclass labels, we can use a \"micro-average\" by\n # quantifying score on all classes jointly\n # See https://scikit-learn.org/stable/auto_examples/model_selection/plot_precision_recall.html # noqa: E501\n # This will allow us to use the same `log_chart_data` method\n bdrk.log_binary_classifier_metrics(\n y_test_onehot.ravel().astype(int).tolist(), # list of int\n test_prob.ravel().astype(float).tolist() # list of float\n )\n\n return test_prob, test_pred","def save_performances(self):\r\n nb_datasets = len(self.results)\r\n resu = [[] for k in range(nb_datasets)]\r\n\r\n # fetch results\r\n for k in range(nb_datasets):\r\n best = np.argmax(self.results[k]['mean_test_score'])\r\n resu[k].append(('score', self.results[k]['mean_test_score'][best]))\r\n resu[k] = resu[k] + list(self.results[k]['params'][best].items())\r\n\r\n # write results in csv\r\n for k, resu in enumerate(resu):\r\n with open('results/final_results_{}.csv'.format(k), 'a') as file:\r\n writer = csv.writer(file)\r\n writer.writerow(resu)","def print_statistics(self):\n print 'Ran %s iterations in %0.3f seconds\\n' % (\n self.iterations, self.elapsed_time)\n\n print 'Overall Equity'\n for index in range(len(self.holdem_ranges)):\n range_short_form = '%r' % self.holdem_ranges[index]\n print 'P%s) %-15s %0.3f' % (\n index,\n range_short_form,\n float(self.win_stats.get(index, 0))/self.iterations)\n print '\\n'\n print 'Hand distribution for each player'\n for stats in self.player_stats:\n stats.print_report()","def print_results():\n now_time = time.time()\n diff_time_in_sec = now_time - start_time\n generated_per_second = total / diff_time_in_sec\n generated_per_hour = 3600 * generated_per_second\n saved_per_second = success / diff_time_in_sec\n saved_per_hour = 3600 * saved_per_second\n\n os.system('cls' if os.name == 'nt' else 'clear')\n print(f\"{'Generated:' : <16}{total : <12}\")\n print(f\"{'New graphs:' : <16}{success : <12}\")\n print(f\"{'Success rate:' : <16}{round((success / total) * 100, 3) : <7} %\")\n print(f\"{'Speed:' : <16}{round(generated_per_hour) : <7} graphs/h\")\n print(f\"{'Save speed:' : <16}{round(saved_per_hour) : <7} graphs/h\")","def show_current_scattering_statistics(self, out=sys.stdout):\n print(\"\", file=out)\n print(\"Model and map statistics:\", file=out)\n print(\" mean mFo map height @ carbon: %s\" % format_value(\"%.2f\",\n flex.max(self.carbon_fo_values)), file=out)\n if (self.calpha_mean_two_fofc > 0):\n print(\" mean 2mFo-DFc map height @ C-alpha: %s\" % format_value(\n \"%.2f\", self.calpha_mean_two_fofc), file=out)\n print(\" mean B-factor: %s\" % format_value(\"%.2f\", self.b_mean_all), file=out)\n if (self.b_mean_calpha > 0):\n print(\" mean C-alpha B-factor: %s\" % format_value(\"%.2f\",\n self.b_mean_calpha), file=out)\n print(\" mean water B-factor: %s\" % format_value(\"%.2f\",\n self.b_mean_hoh), file=out)\n n_water_fofc_peaks = 0\n n_water_anom_peaks = 0\n water_sel = self.water_selection()\n print(\" %d water molecules\" % len(water_sel), file=out)\n for i_seq in water_sel :\n map_stats = self.map_stats(i_seq)\n if (map_stats.fofc >= 3.0):\n n_water_fofc_peaks += 1\n if (map_stats.anom is not None) and (map_stats.anom >= 3.0):\n n_water_anom_peaks += 1\n print(\" %d waters have mFo-DFc map >= 3.0 sigma\" % \\\n n_water_fofc_peaks, file=out)\n if (self.anomalous_flag):\n print(\" %d waters have anomalous map >= 3.0 sigma\" % \\\n n_water_anom_peaks, file=out)\n print(\"\", file=out)","def stats(self):","def stats(self):\n pass","def print_stats(session, feature_batch, label_batch, cost, accuracy, x, y, keep_prob, valid_features, valid_labels):\n result_cost = session.run(cost, feed_dict={x:feature_batch, y:label_batch, keep_prob: 1.0})\n result_accuracy = session.run(accuracy, feed_dict={x:valid_features, y:valid_labels, keep_prob: 1.0})\n print(\"Cost: {0:.2f} Accuracy: {1:.2f}% \" .format(result_cost,result_accuracy*100), end='')\n return result_cost, result_accuracy","def print_outcomes(MultipleGameSets, strategy_name):\n\n # mean and confidence interval text of patient survival time\n reward_mean_PI_text = Format.format_estimate_interval(\n estimate=MultipleGameSets.get_mean_total_reward(),\n interval=MultipleGameSets.get_PI_total_reward(alpha=P.ALPHA),\n deci=1)\n\n # print survival time statistics\n print(strategy_name)\n print(\" Estimate of mean rewards for gamblers and {:.{prec}%} prediction interval:\".format(1 - P.ALPHA, prec=0),\n reward_mean_PI_text)","def export_results(self):\n problemIDs = list(set([result.problemID for result in self.results]))\n configIDs = list(set([result.configID for result in self.results]))\n\n labels = []\n labels.extend(TestResults._fields)\n labels.extend(SizeMetrics._fields) \n # Remove unused columns\n labels.remove(\"size_metrics\")\n labels.remove(\"problemID\")\n labels.remove(\"configID\")\n\n # output = pd.Panel(items=labels, major_axis=problemIDs, minor_axis=configIDs)\n multiindex = pd.MultiIndex.from_product([problemIDs, configIDs], names=[\"problems\", \"configs\"])\n\n output = pd.DataFrame(index=multiindex, columns=labels)\n output.columns.names = [\"stats\"]\n\n for result in self.results:\n problemID = result.problemID\n configID = result.configID\n for label in [label for label in TestResults._fields if label in labels]:\n output.loc[(problemID, configID), label] = getattr(result, label)\n for label in [label for label in SizeMetrics._fields if label in labels]:\n output.loc[(problemID, configID), label] = getattr(result.size_metrics, label)\n\n # Compute Statistics\n output.fillna(value=np.nan, inplace=True)\n output.sort_index(inplace=True)\n try:\n TestFramework.compute_mosek_error(output, \"opt_val\", \"mosek_config\")\n except (KeyError): # pragma: no cover\n print(\"TestFramework.compute_mosek_error: 'mosek_config' or 'opt_val' field not found.\")\n try:\n TestFramework.compute_performance(output, \"solve_time\")\n except (KeyError): # pragma: no cover\n print(\"TestFramework.compute_performance: 'solve_time' field not found.\")\n return output","def log_output_data(self):\r\n with tf.name_scope('model_output'):\r\n for i in range(self.action_handler.get_number_actions()):\r\n variable_name = str(self.action_handler.action_list_names[i])\r\n tf.summary.histogram(variable_name + '_output', self.actor_last_row_layer[i])","def __str__(self):\n outs = str(self.cluster_subspace).split(\"\\n\")[:6]\n\n if self.regression_data is not None:\n # This might need to be redefined to take \"expectation\" using measure\n feature_avg = np.average(self.feature_matrix, axis=0)\n feature_std = np.std(self.feature_matrix, axis=0)\n outs += [\n f\"Regression Data : estimator={self.regression_data.estimator_name}\",\n f\" module={self.regression_data.module}\",\n f\" parameters={self.regression_data.parameters}\",\n f\"Target Property : \"\n f\"mean={np.mean(self.regression_data.property_vector):0.4f} \"\n f\"std={np.std(self.regression_data.property_vector):0.4f}\",\n ]\n fit_var = sum(\n self._subspace.function_total_multiplicities[1:] * self.eci[1:] ** 2\n )\n outs += [\n f\"ECI-based Property : mean={self.eci[0]:0.4f}\"\n f\" std={np.sqrt(fit_var):0.4f}\",\n \"Fit Summary\",\n ]\n\n for i, term in enumerate(self._subspace.external_terms):\n outs.append(f\"{repr(term)}={self.coefs[len(self.eci) + i]:0.3f}\")\n\n if self.regression_data is not None:\n outs += [\n \" ---------------------------------------------------------------------\"\n \"-------------------------------\",\n \" | ID Orbit ID Degree Cluster Diameter ECI Feature AVG\"\n \" Feature STD ECI * STD |\",\n f\" | 0 0 0 NA \"\n f\"{self.eci[0]:^7.3f}{feature_avg[0]:^15.3f}\"\n f\"{feature_std[0]:^15.3f}{feature_std[0] * self.eci[0]:^13.3f}|\",\n ]\n else:\n outs += [\n \" ---------------------------------------------------------\",\n \" | ID Orbit ID Degree Cluster Diameter ECI |\",\n f\" | 0 0 0 NA \"\n f\"{self.eci[0]:^7.3f} |\",\n ]\n\n for degree, orbits in self.cluster_subspace.orbits_by_size.items():\n for orbit in orbits:\n for i, bits in enumerate(orbit.bit_combos):\n line = (\n f\" |{orbit.bit_id + i:^6}{orbit.id:^12}{degree:^10}\"\n f\"{orbit.base_cluster.diameter:^20.4f}\"\n f\"{self.eci[orbit.bit_id + i]:^7.3f}\"\n )\n if self.regression_data is not None:\n line += (\n f\"{feature_avg[orbit.bit_id + i]:^15.3f}\"\n f\"{feature_std[orbit.bit_id + i]:^15.3f}\"\n f\"{feature_std[orbit.bit_id + i] * self.eci[orbit.bit_id + i]:^13.3f}\" # noqa\n )\n line += \"|\"\n outs.append(line)\n outs.append(\" \" + (len(outs[-1]) - 1) * \"-\")\n return \"\\n\".join(outs)","def stats(self):\n raise NotImplementedError(\"Must implement in frontend subclass.\")","def print_outcomes(multi_cohort, strategy_name):\n\n # mean and confidence interval text of rewards\n survival_mean_PI_text = Format.format_estimate_interval(\n estimate=multi_cohort.get_mean_total_reward(),\n interval=multi_cohort.get_PI_total_reward(alpha=0.05),\n deci=1)\n\n # print reward statistics\n print(strategy_name)\n print(\"Estimate of mean rewards ($) and {:.{prec}%} prediction interval:\".format(1 - 0.05, prec=0),\n survival_mean_PI_text)","def print_gesture_score(self):\n performance_time = time.strptime(self.performance_title[:19], '%Y-%m-%dT%H-%M-%S')\n plot_title = time.strftime('%Y-%m-%dT%H-%M-%S', performance_time) + \"-gesture-plot\"\n generate_posthoc_gesture_score.plot_gesture_only_score(plot_title, self.gestures)\n print(\"Saved gesture plot: \" + plot_title)","def performance_analytics(self):\n return pf.create_returns_tear_sheet(self.port_rets, return_fig=True)","def log_best_performer(self) -> None:\n best = self.get_highest_accuracy()\n self.logger.info(f\"\\n\\nThe model with the highest accuracy {best[0]} has the following characteristics: \\n\")\n for k, v in best[1].items():\n if k != 'best_performer':\n self.logger.info(f\"{k} : {v}\")\n else:\n self.logger.info(f\"Best Accuracy: {v['Accuracy']}\")\n self.logger.info(\"Features used: \")\n for f in v['Labels']:\n self.logger.info(f)\n for nw, w in v['Vars'].items():\n self.logger.info(f\"{nw}: {w}\")","def print_scores(self):\n ### FILL IN ###","def log_training_results(engine: Engine):\n train_evaluator.run(self.train_dl)\n metrics: Dict[str, float] = train_evaluator.state.metrics\n avg_accuracy: float = metrics['accuracy']\n avg_bce: float = metrics['bce']\n pbar.log_message(\n f'Training Results - Epoch: {engine.state.epoch} Avg accuracy: {avg_accuracy:.4f} Avg loss: {avg_bce:.4f}')","def measure_test(self):\n return self.execute(Sgp40I2cCmdExecuteSelfTest())","def __holdout_test(self):\n\t\tprint(\"\\t\\tHoldout Validation Results:\")\n\n\t\tprint(\"\\t\\t\\tAccuracy: \", analysis.accuracy(self.y_pred, self.y_test))\n\t\tprint(\"\\t\\t\\tPrecision: \", analysis.precision(self.y_pred, self.y_test))\n\t\tprint(\"\\t\\t\\tSpecificity: \", analysis.specificity(self.y_pred, self.y_test))\n\t\tprint(\"\\t\\t\\tRecall: \", analysis.recall(self.y_pred, self.y_test))","def print_stats(session, feature_batch, label_batch, cost, accuracy):\n # TODO: Implement Function\n cst = session.run(cost, feed_dict={x:feature_batch, y:label_batch, keep_prob:1.0})\n acc = session.run(accuracy, feed_dict={x:valid_features, y:valid_labels, keep_prob:1.0}) \n print(\"cost:{:.3f};accuracy:{:.3f} <br>\".format(cst,acc))","def summary(self, test_type='t-test'):\n summary = f'Results for running {self.cv_method} evaluation for {self.method} '\n summary += f'on {self.n_model} models:\\n\\n'\n name_length = max([max(len(m.name) for m in self.models) + 1, 6])\n means = self.get_means()\n sems = self.get_sem()\n if means is None:\n means = np.nan * np.ones(self.n_model)\n if sems is None:\n sems = np.nan * np.ones(self.n_model)\n try:\n p_zero = self.test_zero(test_type=test_type)\n p_noise = self.test_noise(test_type=test_type)\n except ValueError:\n p_zero = np.nan * np.ones(self.n_model)\n p_noise = np.nan * np.ones(self.n_model)\n # header of the results table\n summary += 'Model' + (' ' * (name_length - 5))\n summary += '| Eval \\u00B1 SEM |'\n summary += ' p (against 0) |'\n summary += ' p (against NC) |\\n'\n summary += '-' * (name_length + 51)\n summary += '\\n'\n for i, m in enumerate(self.models):\n summary += m.name + (' ' * (name_length - len(m.name)))\n summary += f'| {means[i]: 5.3f} \\u00B1 {sems[i]:4.3f} |'\n if p_zero[i] < 0.001:\n summary += ' < 0.001 |'\n else:\n summary += f'{p_zero[i]:>13.3f} |'\n if p_noise[i] < 0.001:\n summary += ' < 0.001 |'\n else:\n summary += f'{p_noise[i]:>14.3f} |'\n summary += '\\n'\n summary += '\\n'\n if self.cv_method == 'crossvalidation':\n summary += 'No p-values available as crossvalidation provides no variance estimate'\n elif test_type == 't-test':\n summary += 'p-values are based on uncorrected t-tests'\n elif test_type == 'bootstrap':\n summary += 'p-values are based on percentiles of the bootstrap samples'\n elif test_type == 'ranksum':\n summary += 'p-values are based on ranksum tests'\n return summary","def log_inference(tester, name, description):\r\n\tfor dataset, output in tester.preds.items():\r\n\t\tresults = pandas.DataFrame.from_dict(output)\r\n\t\tpath = os.path.join(\r\n\t\t\tEXPERIMENT_PATH, tester.config[\"name\"] + '-' + dataset)\r\n\t\twith open(path + \".csv\", \"w\") as f:\r\n\t\t\tresults.to_csv(f, sep=\"\\t\", encoding='utf-8', \r\n\t\t\t\tfloat_format='%.3f', index=False)\r\n\r\n\t\twith open(path + \"-predictions.csv\", \"w\") as f:\r\n\t\t\tresults[[\"tag\", \"y_hat\"]].to_csv(\r\n\t\t\t\tf, index=False, float_format='%.3f', header=False)","def _generate_report(self):\n total_duration = 0.0\n total_nb_tests = 0\n total_nb_success = 0\n nb_modules = 0\n payload = []\n\n res_table = prettytable.PrettyTable(\n padding_width=2,\n field_names=['Module', 'Duration', 'nb. Test Run', 'Success'])\n res_table.align['Module'] = \"l\"\n res_table.align['Duration'] = \"r\"\n res_table.align['Success'] = \"r\"\n\n # for each scenario we draw a row for the table\n for item in self.summary:\n if item['task_status'] is True:\n nb_modules += 1\n total_duration += item['overall_duration']\n total_nb_tests += item['nb_tests']\n total_nb_success += item['nb_success']\n try:\n success_avg = 100 * item['nb_success'] / item['nb_tests']\n except ZeroDivisionError:\n success_avg = 0\n success_str = f\"{success_avg:0.2f}%\"\n duration_str = time.strftime(\"%H:%M:%S\",\n time.gmtime(item['overall_duration']))\n res_table.add_row([item['test_name'], duration_str,\n item['nb_tests'], success_str])\n payload.append({'module': item['test_name'],\n 'details': {'duration': item['overall_duration'],\n 'nb tests': item['nb_tests'],\n 'success rate': success_str,\n 'success': item['success'],\n 'failures': item['failures']}})\n\n total_duration_str = time.strftime(\"%H:%M:%S\",\n time.gmtime(total_duration))\n try:\n self.result = 100 * total_nb_success / total_nb_tests\n except ZeroDivisionError:\n self.result = 100\n success_rate = f\"{self.result:0.2f}\"\n success_rate_str = str(success_rate) + '%'\n res_table.add_row([\"\", \"\", \"\", \"\"])\n res_table.add_row([\"TOTAL:\", total_duration_str, total_nb_tests,\n success_rate_str])\n\n LOGGER.info(\"Rally Summary Report:\\n\\n%s\\n\", res_table.get_string())\n LOGGER.info(\"Rally '%s' success_rate is %s%% in %s/%s modules\",\n self.case_name, success_rate, nb_modules,\n len(self.summary))\n self.details['summary'] = {'duration': total_duration,\n 'nb tests': total_nb_tests,\n 'nb success': success_rate}\n self.details[\"modules\"] = payload","def print_results(self):\n pass","def show_all(self):\n self.explained_variance_score()\n self.max_error()\n self.mean_absolute_error()\n self.mean_squared_error()\n self.median_absolute_error()\n self.r2_score()\n self.mean_poisson_deviance()\n self.mean_gamma_deviance()\n self.feature_importance()\n self.learning_curve()","def write_output(self) -> None:\n self.home.round(2).to_csv(var.indicators_base_cumsum + \"home_\" + str(self.year) + \".csv\")\n self.away.round(2).to_csv(var.indicators_base_cumsum + \"away_\" + str(self.year) + \".csv\")","def dump(self):\n fmt='%20s:%10.4fs%6.1f%%'\n print('\\n----------------TIME MANAGER PROFILE----------------\\n\\n')\n total_t=time.time()-self.tic0\n for rec in self.record:\n print(fmt % (rec[0],rec[1],100.0*rec[1]/total_t))\n print(fmt % ('TOTAL ELAPSED TIME', total_t, 100.0))\n print('\\n----------------TIME MANAGER PROFILE----------------\\n\\n')","def eval_perf_total(model, X_train, y_train, X_test, y_test):\n\n y_hat_train = model.predict(X_train)\n y_hat_test = model.predict(X_test)\n \n train_mae = metrics.mean_absolute_error(y_train, y_hat_train)\n train_mse = metrics.mean_squared_error(y_train, y_hat_train)\n train_rmse = np.sqrt(metrics.mean_squared_error(y_train, y_hat_train))\n train_r = metrics.r2_score(y_train, y_hat_train)\n\n print('Evaluating Performance on Training Data:\\n')\n print(f' Train Mean Absolute Error: {train_mae:,.2f}')\n print(f' Train Mean Squared Error: {train_mse:,.2f}\\n')\n print(f'Train Root Mean Squared Error: {train_rmse:,.2f}')\n print(f'Train R-Square Value: {round(train_r,2)}')\n\n print('\\n'+'---'*25+'\\n')\n\n test_mae = metrics.mean_absolute_error(y_test, y_hat_test)\n test_mse = metrics.mean_squared_error(y_test, y_hat_test)\n test_rmse = np.sqrt(metrics.mean_squared_error(y_test, y_hat_test))\n test_r = metrics.r2_score(y_test, y_hat_test)\n\n print('Evaluating Performance on Testing Data:\\n')\n print(f' Test Mean Absolute Error: {test_mae:,.2f}')\n print(f' Test Mean Squared Error: {test_mse:,.2f}\\n')\n print(f'Test Root Mean Squared Error: {test_rmse:,.2f}')\n print(f'Test R-Square Value: {round(test_r,2)}')","def print_metrics(result):\n logging.log(LOG_LEVEL_OUTPUT_INFO,\n '------------------------------------------------')\n logging.log(LOG_LEVEL_OUTPUT_INFO, ' KEY METRICS: ')\n logging.log(LOG_LEVEL_OUTPUT_INFO,\n '------------------------------------------------')\n logging.log(LOG_LEVEL_OUTPUT_INFO, '* pages_count: %d',\n get_counter_metric(result, 'pages_count'))\n logging.log(LOG_LEVEL_OUTPUT_INFO, '* revisions_count: %d',\n get_counter_metric(result, 'revisions_count'))\n logging.log(LOG_LEVEL_OUTPUT_INFO, '* very_long_page_histories_count: %d',\n get_counter_metric(result, 'very_long_page_histories_count'))\n revisions_per_page_distr = get_distributions_metric(\n result, 'revisions_per_page_distr')\n logging.log(LOG_LEVEL_OUTPUT_INFO, '* revisions_per_page_distr.mean: %d',\n revisions_per_page_distr.mean)\n logging.log(LOG_LEVEL_OUTPUT_INFO, '* revisions_per_page_distr.sum: %d',\n revisions_per_page_distr.sum)\n cumulative_page_rev_size_distr = get_distributions_metric(\n result, 'cumulative_page_rev_size_distr')\n logging.log(LOG_LEVEL_OUTPUT_INFO,\n '* cumulative_page_rev_size_distr.mean: %d',\n cumulative_page_rev_size_distr.mean)\n logging.log(LOG_LEVEL_OUTPUT_INFO, '* cumulative_page_rev_size_distr.sum: %d',\n cumulative_page_rev_size_distr.sum)","def printReport(self):\n\t\tself.app.printflush('Fetched: ' + str(self.fetched_count), self.app.IGNORE_EXIT_FLAG)\n\t\tself.app.printflush('Processes: ' + str(self.processes), self.app.IGNORE_EXIT_FLAG)\n\t\tself.app.printflush('Updated: ' + str(self.updated_count), self.app.IGNORE_EXIT_FLAG)\n\t\tself.app.printflush('Average page load time: ' + str(self.average_time), self.app.IGNORE_EXIT_FLAG)\n\t\tself.app.printflush('Returned with code: ' + repr(self.code_statistics), self.app.IGNORE_EXIT_FLAG)\n\t\tself.app.printflush('Closing Processes... ', self.app.IGNORE_EXIT_FLAG)","def _log_performance(i, train_perf, mean_pol_perf, best_perf, results_path,\n tb_logger):\n # tb_logger.add_scalars('performance', {\n # 'train_perf': train_perf,\n # 'eval_perf': mean_pol_perf,\n # 'best_train_perf': best_perf,\n # }, global_step=i)\n\n with open(results_path, 'a') as results_file:\n if i == 0:\n headings = ['Iter', 'Sampled Pol', 'Mean Pol', 'Best (Sampled)']\n headings = ' | '.join(headings)\n results_file.write(headings + '\\n')\n print('Timestamp' + (' ' * 17) + ' | ' + headings)\n\n log = ['{:<4d}', '{:>11.2f}', '{:>8.2f}', '{:>14.2f}']\n log = ' '.join(log).format(i, train_perf, mean_pol_perf, best_perf)\n results_file.write(log + '\\n')\n print('{:<26s} {}'.format(\n timer.asctime(timer.localtime(timer.time())), log))","def dataStats(self):\n print (\"Performing statistical analysis of the data\")\n # stuff to do","def _compute_statistics(self):\n # log to file\n output_dir = self.params['output_dir']\n if not os.path.exists(output_dir):\n os.mkdir(output_dir)\n output_dir = os.path.join(output_dir, 'statistics.log')\n log = logging.getLogger('tensorflow')\n handle = logging.FileHandler(output_dir)\n log.addHandler(handle)\n\n # FLOPS\n encoder_flops, decoder_flops = 0, 0\n encoder_count, decoder_count = 0, 0\n graph = tf.get_default_graph()\n for operation in graph.get_operations():\n flops = ops.get_stats_for_node_def(graph, operation.node_def,\n 'flops').value\n if flops is None:\n continue\n if operation.name.startswith('model/encoder'):\n # encoder\n encoder_flops += flops\n encoder_count += 1\n tf.logging.info('encoder operation %s : %d', operation.name, flops)\n elif operation.name.startswith('model/decoder'):\n # decoder\n decoder_flops += flops\n decoder_count += 1\n tf.logging.info('decoder operation %s : %d', operation.name, flops)\n else:\n # gradient\n pass\n tf.logging.info('flops of %d encoder tensor: %d',\n encoder_count, encoder_flops)\n tf.logging.info('flops of %d decoder tensor: %d',\n decoder_count, decoder_flops)\n tf.logging.info('flops of total %d tensor: %d',\n encoder_count + decoder_count,\n encoder_flops + decoder_flops)\n # parameters\n encoder_parameters, decoder_parameters = 0, 0\n encoder_count, decoder_count = 0, 0\n for var in tf.trainable_variables():\n parameters = np.prod(var.get_shape().as_list())\n if var.name.startswith('model/encoder'):\n # encoder\n encoder_parameters += parameters\n encoder_count += 1\n tf.logging.info('encoder variable %s : %d', var.name, parameters)\n elif var.name.startswith('model/decoder'):\n # decoder\n decoder_parameters += parameters\n decoder_count += 1\n tf.logging.info('decoder variable %s : %d', var.name, parameters)\n\n tf.logging.info('parameters of %d encoder tensor: %d',\n encoder_count, encoder_parameters)\n tf.logging.info('parameters of %d decoder tensor: %d',\n decoder_count, decoder_parameters)\n tf.logging.info('parameters of total %d tensor: %d',\n encoder_count + decoder_count,\n encoder_parameters + decoder_parameters)\n # disable log to file\n log.removeHandler(handle)","def print_testvals(self):\n print(self.use_planck)","def display_results(self):\n print(f'AI player results - Wins: {self._wins}, Draws: {self._draws}, Loss: {self._losses}')\n print(f'Total win percentage {(self._wins / self._count) * 100}', end='\\n\\n')\n\n with open(os.path.join(os.path.dirname(__file__), 'ai-results.json'), 'w') as f:\n output_dict = {str(k): v for k, v in self._state_values.items()}\n json.dump(output_dict, f, indent=4, sort_keys=True)","def logging(self, function):\n avg_nms_time_per_step = sum(self.nms_times)/len(self.nms_times)\n avg_total_time_per_step = sum(self.total_times)/len(self.total_times)\n\n avg_min_latency = [x[0] for x in self.inference_times]\n avg_max_latency = [x[1] for x in self.inference_times]\n avg_latency = [x[2] for x in self.inference_times]\n\n function(\"Inference stats: image size {}x{}, batches per step {}, batch size {}, {} steps\".format(\n self.cfg.model.image_size, self.cfg.model.image_size, self.cfg.ipuopts.batches_per_step, self.cfg.model.micro_batch_size, len(self.total_times)\n ))\n function(\"--------------------------------------------------\")\n function(\"Inference\")\n function(\"Average Min Latency per Batch: {:.3f} ms\".format(1000 * sum(avg_min_latency)/len(self.inference_times)))\n function(\"Average Max Latency per Batch: {:.3f} ms\".format(1000 * sum(avg_max_latency)/len(self.inference_times)))\n function(\"Average Latency per Batch: {:.3f} ms\".format(1000 * sum(avg_latency)/len(self.inference_times)))\n function(\"Average Inference Throughput: {:.3f} img/s\".format(sum(self.inference_throughputs)/len(self.inference_throughputs)))\n function(\"--------------------------------------------------\")\n # TODO remove the NMS and end-to-end time report once NMS is on device\n function(\"End-to-end\")\n function(\"Average NMS Latency per Batch: {:.3f} ms\".format(1000 * avg_nms_time_per_step/self.cfg.ipuopts.batches_per_step))\n function(\"Average End-to-end Latency per Batch: {:.3f} ms\".format(1000 * avg_total_time_per_step/self.cfg.ipuopts.batches_per_step))\n function(\"End-to-end Throughput: {:.3f} img/s\".format(sum(self.total_throughputs)/len(self.total_throughputs)))\n function(\"==================================================\")\n\n if self.cfg.eval.metrics:\n self.compute_and_print_eval_metrics()","def print_stats(self, session, x, y, keep_prob, feature_batch, label_batch, valid_features, valid_labels,\n cost, accuracy):\n cost = session.run(cost, feed_dict={x: feature_batch, y: label_batch, keep_prob: 1.0})\n train_accuracy = session.run(accuracy, feed_dict={x: feature_batch, y: label_batch, keep_prob: 1.0})\n valid_accuracy = session.run(accuracy, feed_dict={x: valid_features, y: valid_labels, keep_prob: 1.0})\n logger.info(\"loss:{}\".format(cost))\n logger.info(\"Train Accuracy:{}\".format(train_accuracy))\n logger.info(\"Validation Accuracy:{}\".format(valid_accuracy))","def generate_report(self, output_path):\n generated_on = str(datetime.datetime.now())\n checkpoint_name = self._get_checkpoint_name()\n dataset_name = Path(self._train_dataset_path).resolve().name\n train_features, train_labels = self.get_features_and_labels(\n self._read_dataset(self._train_dataset_path)\n )\n test_features, test_lables = self.get_features_and_labels(\n self._read_dataset(self._test_dataset_path)\n )\n training_accuracy = self.get_model_accuracy(\n train_features,\n train_labels\n )\n test_accuracy = self.get_model_accuracy(\n test_features,\n test_lables,\n )\n with open(output_path, 'a') as f:\n report = (\n \"*****************************************************\\n\"\n \"Report generated on: {}\\n\"\n \"Training dataset: {}\\n\"\n \"Model checkpoint: {}\\n\"\n \"---\\n\"\n \"Accuracy on training data: {}\\n\"\n \"Accuracy on testing data: {}\\n\"\n \"\\n\"\n ).format(\n generated_on,\n dataset_name,\n checkpoint_name,\n training_accuracy,\n test_accuracy,\n )\n f.writelines(report)","def print_brief_summary(self):\n print (\"Model {}\".format(self.modelName))\n print (\"Precision {}\".format(self.precision))\n print (\"Recall {}\".format(self.recall))\n print (\"f1 score {}\".format(self.f1))\n \n # work here\n print (\"\\nGold NER label counts:\")\n for ner in self.gold_cts.keys():\n print (\"{} : {} (tag{})\".format(self.gold_cts[ner], self.nerTags.ids_to_words([ner]), ner))\n print (\"\\nPredicted NER label counts:\")\n for ner in self.pred_cts.keys():\n print (\"{} : {} (tag{})\".format(self.pred_cts[ner], self.nerTags.ids_to_words([ner]), ner))","def create_summary_statistics(forward_accuracy, backward_accuracy, merged_accuracy):\n summary_statistics = open(f'summary_statistics.txt', 'a')\n summary_statistics.write(f'The forward model has an accuracy of: {forward_accuracy}\\n')\n summary_statistics.write(f'The backward model has an accuracy of: {backward_accuracy}\\n')\n summary_statistics.write(f'The merged model has an accuracy of: {merged_accuracy}\\n')\n summary_statistics.close()","def save(self,filename):\n f = open(filename,'w')\n f.write('Test results for %s v%s\\n' % (self.description,self.version))\n f.write('Series ran by %s\\n\\n' % self.person_name)\n for result in self.values():\n f.write('%-70s : %s\\n' % (result.id,result.outcome))\n if result.outcome != Result.PASS:\n for (kind, annotation) in result.annotations.items():\n f.write('%s:\\n%s\\n' % (kind, as_utf8(annotation)))\n f.write('\\n')\n f.write('\\n\\nPasses: %i\\n' % self.get_pass_count())\n f.write('Fails: %i\\n' % self.get_fail_count())\n f.write('Errors: %i\\n' % self.get_error_count())\n f.write('Untested: %i\\n' % self.get_untested_count())\n f.write('Skipped: %i\\n' % self.get_skipped_count())\n f.close()","def report(self, X, y):\n pred = self.model.predict(X)\n skplt.estimators.plot_feature_importances(\n self.model, x_tick_rotation=90)\n plt.show()\n skplt.metrics.plot_confusion_matrix(y, pred, normalize=True)\n plt.show()\n print(\"Classification report\")\n print(classification_report(y, pred))\n skplt.metrics.plot_precision_recall(y, self.model.predict_proba(X))\n plt.show()\n pd.DataFrame({\n \"predict_probability\": self.model.predict_proba(X)[:, 1],\n \"observed\": y\n }).hist(\n \"predict_probability\", by=\"observed\")\n plt.suptitle(\"Probability distribution by class\")\n plt.show()","def compute_and_print_eval_metrics(self):\n s = ('%20s' + '%12s' * 6) % ('Class', 'Images', 'Targets', 'P', 'R', 'mAP@.5', 'mAP@.5:.95')\n precision, recall, f1, mean_precision, mean_recall, map50, map = 0., 0., 0., 0., 0., 0., 0.\n ap = []\n eval_stats = [np.concatenate(x, 0) for x in zip(*self.eval_stats)]\n if len(eval_stats) and eval_stats[0].any():\n precision, recall, ap, f1, ap_class = ap_per_class(*eval_stats)\n precision, recall, ap50, ap = precision[:, 0], recall[:, 0], ap[:, 0], ap.mean(1)\n mean_precision, mean_recall, map50, map = precision.mean(), recall.mean(), ap50.mean(), ap.mean()\n nt = np.bincount(eval_stats[3].astype(np.int64), minlength=len(self.class_names)) # number of targets per class\n else:\n nt = np.zeros(1)\n\n pf = '%20s' + '%12.5g' * 6 # print format\n print(\"\\n EVALUTAION \\n\")\n print(s)\n print(pf % ('all', self.seen, nt.sum(), mean_precision, mean_recall, map50, map))\n if self.cfg.eval.verbose:\n for indx, cls in enumerate(ap_class):\n print(pf % (self.class_names[cls], self.seen, nt[cls], precision[indx], recall[indx], ap50[indx], ap[indx]))","def metrics(_):\r\n collector = BuildsCollector()\r\n build_metrics, headers = collector.get_metrics_table()\r\n print(tabulate(build_metrics, headers=headers))","def report(self):\n if isinstance(self.steps[-1][1], Regressor):\n return self.steps[-1][1].regression_report()\n else:\n return self.steps[-1][1].classification_report()","def final_eval(self):\n # Test performance - Load best model\n self.load_model(self.best_model_path, model_type='best')\n logging.info(\"Loading best model after epoch: %d\" %\n self.train_info['epoch'])\n\n perf_file = path.join(self.model_dir, \"perf.json\")\n if self.slurm_id:\n parent_dir = path.dirname(path.normpath(self.model_dir))\n perf_dir = path.join(parent_dir, \"perf\")\n if not path.exists(perf_dir):\n os.makedirs(perf_dir)\n perf_file = path.join(perf_dir, self.slurm_id + \".json\")\n\n output_dict = {'model_dir': self.model_dir}\n for key, val in vars(self.args).items():\n output_dict[key] = val\n\n for split in ['dev', 'test']:\n logging.info('\\n')\n logging.info('%s' % split.capitalize())\n result_dict = self.eval_model(split, final_eval=True)\n if split != 'test':\n logging.info('Calculated F1: %.3f' % result_dict['fscore'])\n\n output_dict[split] = result_dict\n\n json.dump(output_dict, open(perf_file, 'w'), indent=2)\n\n logging.info(\"Final performance summary at %s\" % perf_file)\n sys.stdout.flush()","def output_summary_stats(self):\n total_return = self.equity_curve['equity_curve'][-1]\n returns = self.equity_curve['returns']\n pnl = self.equity_curve['equity_curve']\n\n sharpe_ratio = create_sharpe_ratio(returns)\n drawdown, max_dd, dd_duration = create_drawdowns(pnl)\n self.equity_curve['drawdown'] = drawdown\n if len(dd_duration) == 1:\n dd_duration = dd_duration[0]\n\n stats = [(\"Total Return\", \"%0.2f%%\" % ((total_return - 1.0) * 100.0)),\n (\"Sharpe Ratio\", \"%0.2f\" % sharpe_ratio),\n (\"Max Drawdown\", \"%0.2f%%\" % (max_dd * 100.0)),\n (\"Drawdown Duration\", \"%s\" % dd_duration)]\n\n self.equity_curve.to_csv('equity.csv')\n self.positions.to_csv('positions.csv')\n self.prices.to_csv('prices.csv')\n\n return stats","def print_result(self):\n print(\"Final results: \")\n for i in range(1, len(self.agents) + 1):\n agent = self.agents[i-1]\n print(agent.name + \": {} wins\".format(self.results[agent.name]))","def print_metrics(mva, df_train, df_test,\n y_train, y_test,\n mva_response_train=None, mva_response_test=None,\n w_train=None, w_test=None):\n\n train_prediction = mva.predict(df_train)\n test_prediction = mva.predict(df_test)\n\n if mva_response_train is None:\n mva_response_train = classifiers.evaluate_mva(df_train, mva)\n if mva_response_test is None:\n mva_response_test = classifiers.evaluate_mva(df_test, mva)\n\n print(\"\\nClassification Reports:\")\n print(\"Test sample:\")\n print(classification_report(y_test, test_prediction,\n target_names=[\"background\", \"signal\"]))\n print(\"Training sample:\")\n print(classification_report(y_train, train_prediction,\n target_names=[\"background\", \"signal\"]))\n\n print(\"Confusion matrix:\")\n print(\"Test sample:\")\n print(confusion_matrix(y_test, test_prediction))\n print(\"Training sample:\")\n print(confusion_matrix(y_train, train_prediction))\n print()\n\n print(\"KS Test p-value:\")\n print(\"Signal:\")\n print(ks_2samp(mva_response_train[y_train == 1],\n mva_response_test[y_test == 1],\n None if w_train is None else w_train[y_train == 1],\n None if w_test is None else w_test[y_test == 1])[1])\n print(\"Background:\")\n print(ks_2samp(mva_response_train[y_train == 0],\n mva_response_test[y_test == 0],\n None if w_train is None else w_train[y_train == 0],\n None if w_test is None else w_test[y_test == 0])[1])\n print()\n\n # Try really hard to get the feature importances\n feature_importances = []\n try:\n feature_importances = mva.feature_importances_\n except AttributeError:\n pass\n try: # last step of a pipeline?\n feature_importances = mva.steps[-1][1].feature_importances_\n except AttributeError:\n pass\n try: # grid search?\n feature_importances = mva.best_estimator_.feature_importances_\n except AttributeError:\n pass\n try: # grid search last step of pipeline?\n feature_importances = \\\n mva.steps[-1][1].best_estimator_.feature_importances_\n except AttributeError:\n pass\n try: # grid search of a pipeline?\n feature_importances = \\\n mva.best_estimator_.steps[-1][0].feature_importances_\n except AttributeError:\n pass\n\n if len(feature_importances):\n print(\"Feature importance:\")\n for var, importance in sorted(\n zip(list(df_train), feature_importances),\n key=lambda x: x[1],\n reverse=True):\n print(\"{0:15} {1:.3E}\".format(var, importance))\n else:\n pass\n print()","def output_summary_stats(self):\n total_return = self.equity_curve['equity_curve'][-1]\n returns = self.equity_curve['returns']\n pnl = self.equity_curve['equity_curve']\n \n sharpe_ratio = create_sharpe_ratio(returns, periods=252*6.5*60)\n drawdown, max_dd, dd_duration = create_drawdowns(pnl)\n self.equity_curve['drawdown'] = drawdown\n \n stats = [(\"Total Return\", \"%0.2f%%\" % ((total_return - 1.0) * 100.0)), \n (\"Sharpe Ratio\", \"%0.2f\" % sharpe_ratio), \n (\"Max Drawdown\", \"%0.2f%%\" % (max_dd * 100.0)), \n (\"Drawdown Duration\", \"%d\" % dd_duration)]\n \n self.equity_curve.to_csv('equity.csv')\n \n return stats","def _to_string(self):\n self.results.print_results()\n self.results.print_comparison()"],"string":"[\n \"def report_performance(self):\\n performance = self.amygdala.visualize(self.timestep, \\n self.name, \\n self.log_dir)\\n print('Final performance is {0:.3}'.format(performance))\\n self.backup()\\n return performance\",\n \"def _output_performance(self):\\n self.portfolio.create_equity_curve_dataframe()\\n \\n print(\\\"Creating summary statistics...\\\")\\n stats = self.portfolio.output_summary_stats()\\n \\n print(\\\"Creating equity curve...\\\")\\n print(self.portfolio.equity_curve.tail(10))\\n pprint.pprint(stats)\\n \\n print(\\\"Signals: %s\\\" % self.signals)\\n print(\\\"Orders: %s\\\" % self.orders)\\n print(\\\"Fills: %s\\\" % self.fills)\",\n \"def print_performance_info(self):\\n pass\",\n \"def test(self):\\n statistics = self.__progress(self.testing, self.__val_fn)\\n print('Loss: {}'.format(statistics[0]))\\n print('Precision: {:.3%}'.format(statistics[1]))\\n print('Recall: {:.3%}'.format(statistics[2]))\\n print('Accuracy: {:.3%}'.format(statistics[3]))\\n self.report['test_loss'] = statistics[0]\\n self.report['test_precision'] = statistics[1]\\n self.report['test_recall'] = statistics[2]\\n self.report['test_accuracy'] = statistics[3]\",\n \"def print_statistics(self) -> None:\\n e = self.current_epoch\\n if len(self.loss_history[\\\"test_loss\\\"]) > 0:\\n template = 'Epoch: {} Training loss: {:.4f}, Test loss: {:.4f}'\\n print(template.format(e, self.loss_history[\\\"training_loss\\\"][-1],\\n self.loss_history[\\\"test_loss\\\"][-1]))\\n else:\\n template = 'Epoch: {} Training loss: {:.4f}'\\n print(template.format(e, self.loss_history[\\\"training_loss\\\"][-1]))\",\n \"def simulate_trading(self):\\n # Create the file output stream\\n posix_now = datetime.datetime.timestamp(datetime.datetime.now())\\n out_path = os.getcwd() + \\\"/OutputResults/backtest_{}\\\".format(posix_now)[:-7:] + \\\".csv\\\"\\n\\n out = open(out_path, \\\"w+\\\")\\n\\n spl = len(self.strat_params_list)\\n for i, sp in enumerate(self.strat_params_list): # http://book.pythontips.com/en/latest/enumerate.html\\n print(\\\"Strategy %s out of %s...\\\" % (i + 1, spl))\\n self._generate_trading_instances(sp)\\n self._run_backtest()\\n stats = self._output_performance()\\n pprint.pprint(stats)\\n\\n tot_ret = float(stats[0][1].replace(\\\"%\\\", \\\"\\\"))\\n cagr = float(stats[1][1].replace(\\\"%\\\", \\\"\\\"))\\n sharpe = float(stats[2][1])\\n max_dd = float(stats[3][1].replace(\\\"%\\\", \\\"\\\"))\\n dd_dur = int(stats[4][1])\\n\\n # This should be more general in future implementations...\\n out.write(\\n \\\"%s,%s,%s,%s,%s,%s,%s,%s\\\\n\\\" % (sp[\\\"ols_window\\\"], sp[\\\"zscore_high\\\"], sp[\\\"zscore_low\\\"],\\n tot_ret, cagr, sharpe, max_dd, dd_dur)\\n )\\n\\n out.close()\",\n \"def create_test_report(test_generator, test_history):\\n \\n df_res_labels = create_res_labels_df(test_generator, test_history)\\n \\n print_metric_to_console = False\\n lvls=['']\\n \\n metrics_dict = {}\\n \\n n_samples = df_res_labels.shape[0]\\n print('.'*50)\\n print('showing test metrics for {} samples'.format(n_samples))\\n print('`'*50)\\n \\n lvl_metrics_dict = {}\\n for lvl in lvls:\\n y_tr = df_res_labels['y_true' + lvl]\\n y_pre = df_res_labels['y_pred' + lvl] \\n \\n lvl_metrics_dict = {}\\n \\n # Macro / Micro Driven Metrics\\n for avg in ['macro', 'micro']:\\n \\n met_name = 'precision' + ('_'+ avg) \\n res = metrics.precision_score(y_tr, y_pre, average=avg)\\n lvl_metrics_dict[met_name] = res\\n \\n met_name = 'f1' + ('_'+ avg) \\n res = metrics.f1_score(y_tr, y_pre, average=avg)\\n lvl_metrics_dict[met_name] = res\\n \\n met_name = 'recall' + ('_'+ avg) \\n res = metrics.recall_score(y_tr, y_pre, average=avg)\\n lvl_metrics_dict[met_name] = res\\n \\n met_name = 'accuracy' \\n res = metrics.accuracy_score(y_tr, y_pre)\\n lvl_metrics_dict[met_name] = res\\n \\n metrics_dict[lvl] = lvl_metrics_dict\\n \\n df_test_results = pd.DataFrame(metrics_dict).sort_values(by=lvls, ascending=False)\\n df_test_results=df_test_results.reindex(columns=lvls)\\n \\n print(df_test_results)\\n print('- '*70)\\n \\n plot_confusion_matrix(df_res_labels)\\n \\n return df_res_labels\",\n \"def print_stats(self):\\n if self.df_avg is None:\\n self.collect_stats()\\n\\n print(\\\"Simulation Results\\\")\\n print(tabulate(self.df_avg, headers=\\\"keys\\\", showindex=False, tablefmt=\\\"fancy_grid\\\"))\\n print(\\\"FleetManager stats\\\")\\n print(tabulate(self.manager_df, headers=\\\"keys\\\", showindex=False, tablefmt=\\\"fancy_grid\\\"))\\n print(\\\"Customer stats\\\")\\n print(tabulate(self.customer_df, headers=\\\"keys\\\", showindex=False, tablefmt=\\\"fancy_grid\\\"))\\n print(\\\"Transport stats\\\")\\n print(tabulate(self.transport_df, headers=\\\"keys\\\", showindex=False, tablefmt=\\\"fancy_grid\\\"))\\n print(\\\"Station stats\\\")\\n print(tabulate(self.station_df, headers=\\\"keys\\\", showindex=False, tablefmt=\\\"fancy_grid\\\"))\",\n \"def print_results(self):\\n self.accuracy = round(accuracy_score(self.y_val, self.y_pred, 'weighted'), 4)\\n self.f1 = round(f1_score(self.y_val, self.y_pred, average='weighted'), 4)\\n self.precision = round(precision_score(self.y_val, self.y_pred, average='weighted'), 4)\\n\\n print(f'Results for {self.title}:')\\n print(f'{self.title} accuracy: {self.accuracy}')\\n print(f'{self.title} f-score: {self.f1}')\\n print(f'{self.title} precision: {self.precision}')\",\n \"def showWorstStats(self) :\\n Scenario.messageWorstStats()\\n self.showWorstGainWon()\\n self.showWorstBetUse()\\n self.showNbLevelLose()\",\n \"def log_results(best_model, model_name, max_features, train_score, test_score,\\n score_fp):\\n\\n # ensure the directorys where metrics are stored are created\\n if not os.path.exists(os.path.dirname(score_fp)):\\n os.makedirs(os.path.dirname(score_fp), exist_ok=True)\\n\\n st = datetime.fromtimestamp(time.time()).strftime('%Y-%m-%d %H:%M:%S')\\n with open(score_fp, 'a+') as f:\\n f.write(st + '\\\\n')\\n f.write('-' * 100 + '\\\\n')\\n f.write('Model Run: {}\\\\n\\\\n'.format(model_name))\\n f.write('Params: {}\\\\n\\\\n'.format(best_model.get_params())) \\n f.write('Max features: {}\\\\n\\\\n'.format(max_features))\\n f.write('Train Score: {}\\\\n\\\\n'.format(train_score))\\n f.write('Test Score: {}\\\\n\\\\n'.format(test_score))\",\n \"def final_strategy_test():\\r\\n print('-- Testing final_strategy --')\\r\\n print('Win rate:', compare_strategies(final_strategy))\",\n \"def run_test(self):\\n self.output_analytics = self.run_inference()\\n self.output_df = pd.DataFrame(self.output_analytics)\",\n \"def output_report(x, y, clf, test_flag=False):\\n\\n # Calculate recall at k time series\\n y, y_prob, y_pred, recall_at_k, avg_recall = calculate_recall_at_k_time_series(x, y, clf)\\n # Output confusion_matrix\\n print(\\\"Confusion matrix: \\\\n\\\", confusion_matrix(y, y_pred))\\n # Output recall_at_k\\n print(\\\"Recall at {}, {}, {}, {}, {}: \\\".format(5, 10, 20, 50, 100), end=' ')\\n for top in [5, 10, 20, 50, 100]:\\n if top == 100:\\n print(str(round(recall_at_k[top - 1], 2)) + \\\" accordingly\\\")\\n else:\\n print(\\\"{}, \\\".format(round(recall_at_k[top - 1], 2)), end='')\\n print(\\\"Average recalls over 100: \\\", round(avg_recall, 2))\\n if test_flag:\\n _, recall_at_k, _, _, _ = calculate_recall_at_k(y_prob[:, 1], y, k_max=y.shape[0])\\n print(\\n \\\"Positions of escalation flags: \\\", ([1] if recall_at_k[0] != 0 else []) +\\n [i + 1 for i in range(1, len(recall_at_k)) if\\n recall_at_k[i] != recall_at_k[i - 1]])\",\n \"def print_score(classifier,X_test,y_test):\\n print(\\\"Test results:\\\\n\\\")\\n print('Accuracy Score: {0:.4f}\\\\n'.format(accuracy_score(y_test,classifier.predict(X_test))))\\n print('Classification Report:\\\\n{}\\\\n'.format(classification_report(y_test,classifier.predict(X_test))))\\n print('Confusion Matrix:\\\\n{}\\\\n'.format(confusion_matrix(y_test,classifier.predict(X_test))))\",\n \"def summary(self):\\n print '%s Portfolio\\\\'s %s Strategy' % (self.portfolio.name, self.name)\\n print '-' * COL_DASH_WIDTH\\n\\n self.display_trades()\\n\\n for symbol in self.portfolio.assets.keys():\\n perf = self.performance[symbol]\\n\\n print '\\\\nSummary for %s from %s (first trade) to %s (last trade)' % (symbol, perf['start'], perf['end'])\\n print '.' * COL_DASH_WIDTH\\n print 'Summary:'\\n data = [[fmtn(perf['trades']), fmtn(perf['wins']), fmtn(perf['losses']), fmtn(perf['washes'])]]\\n print tabulate.tabulate(data, headers=['Total Trades', '# Wins', '# Losses', '# Washes'])\\n\\n print '\\\\nPerformance:'\\n data = [[\\n fmtn(perf['profit']), fmtn(perf['loss']), fmtn(perf['net_profit']),\\n fmtp(perf['profit_factor']), fmtp(perf['percent_profitable']), fmtn(perf['average_trade_net_profit'])\\n ]]\\n print tabulate.tabulate(data, headers=['Profit', 'Loss', 'Net Profit', 'Profit Factor', 'Percent Profitable', 'Average Net Profit per Trade'])\\n\\n print '\\\\nDrawdown:'\\n data = [[fmtn(perf['max_drawdown']), fmtn(perf['average_drawdown']), fmtn(perf['max_drawdown_days']), fmtn(perf['average_drawdown_days'])]]\\n print tabulate.tabulate(data, headers=['Max', 'Average', 'Max Days', 'Average Days'])\\n\\n print '\\\\nRisk:'\\n data = [[fmtn(perf['volatility_risk']), fmtn(perf['beta']), fmtn(perf['lower_partial_moment_risk']), fmtn(perf['t_r']), fmtn(perf['s_r'])]]\\n print tabulate.tabulate(data, headers=['Volatility', 'Beta', 'Lower Partial Moment', 'Treynor Ratio', 'Sharpe Ratio'])\",\n \"def print_scores(result_collector):\\n # print(\\\"\\\\n# Metric: Cohen's kappa\\\")\\n # result_collector.set_metric(['k_cohen', 'k'])\\n # result_collector.print_all_results()\\n print(\\\"\\\\n# Metric: Macro avg. F1\\\")\\n result_collector.set_metric([\\\"macro_avg\\\", \\\"fscore\\\"])\\n # result_collector.print_all_results()\\n result_collector.print_result_for_level(\\\"cc\\\")\\n result_collector.print_result_for_level(\\\"ro\\\", print_header=False)\\n result_collector.print_result_for_level(\\\"fu\\\", print_header=False)\\n result_collector.print_result_for_level(\\\"at\\\", print_header=False)\\n\\n # print(\\\"\\\\nMetric: Positive attachment F1\\\")\\n # result_collector.set_metric(['classwise', '1', 'fscore'])\\n # result_collector.print_result_for_level('at')\\n print(\\\"\\\\n# Metric: Labelled attachment score\\\")\\n result_collector.set_metric([\\\"accuracy\\\"])\\n result_collector.print_result_for_level(\\\"lat\\\")\",\n \"def print_stats(self):\\n if self.n_iter % 5 != 0:\\n return\\n\\n s_iter = \\\"%7i - \\\" % self.n_iter\\n s_stat = ' || '.join([\\n '{}: {:7.4f}'.format(k, np.mean(v)) for k, v in self.stats.items()\\n if type(v) is list and len(v) > 0\\n ])\\n for k in self.stats.keys():\\n if type(self.stats[k]) is list:\\n del self.stats[k][:]\\n\\n # transformer learning rate\\n # learning rates\\n s_lr = \\\" - \\\"\\n for k, v in self.optimizers.items():\\n s_lr = s_lr + (\\\" - %s LR: \\\" % k) + \\\" / \\\".join(\\n \\\"{:.4e}\\\".format(group['lr']) for group in v.param_groups)\\n\\n # processing speed\\n new_time = time.time()\\n diff = new_time - self.last_time\\n s_speed = \\\"{:7.2f} sent/s - {:8.2f} words/s - \\\".format(\\n self.stats['processed_s'] * 1.0 / diff,\\n self.stats['processed_w'] * 1.0 / diff\\n )\\n self.stats['processed_s'] = 0\\n self.stats['processed_w'] = 0\\n self.last_time = new_time\\n\\n # log speed + stats + learning rate\\n logger.info(s_iter + s_speed + s_stat + s_lr)\",\n \"def test_print_results(self):\\n calculated = super().predict_and_print()\\n self.assertEqual(calculated, EXP_PRINT_OUTPUT_BASE.format(.18, .1, 0.186, self.test_model.model.train_time) +\\n \\\"Max tree max_depth: 1\\\\n\\\"\\n \\\"Number of n_estimators: 1\\\\n\\\"\\n \\\"Impurity method: entropy\\\\n\\\")\",\n \"def sprint_statistics(\\n self,\\n dataset_name: str,\\n scoring_functions: List[autoPyTorchMetric],\\n metric: autoPyTorchMetric\\n ) -> str:\\n search_results = self.get_search_results(scoring_functions, metric)\\n success_status = (StatusType.SUCCESS, StatusType.DONOTADVANCE)\\n sio = io.StringIO()\\n sio.write(\\\"autoPyTorch results:\\\\n\\\")\\n sio.write(f\\\"\\\\tDataset name: {dataset_name}\\\\n\\\")\\n sio.write(f\\\"\\\\tOptimisation Metric: {metric}\\\\n\\\")\\n\\n num_runs = len(search_results.status_types)\\n num_success = sum([s in success_status for s in search_results.status_types])\\n num_crash = sum([s == StatusType.CRASHED for s in search_results.status_types])\\n num_timeout = sum([s == StatusType.TIMEOUT for s in search_results.status_types])\\n num_memout = sum([s == StatusType.MEMOUT for s in search_results.status_types])\\n\\n if num_success > 0:\\n best_score = metric._sign * np.max(metric._sign * search_results.opt_scores)\\n sio.write(f\\\"\\\\tBest validation score: {best_score}\\\\n\\\")\\n\\n sio.write(f\\\"\\\\tNumber of target algorithm runs: {num_runs}\\\\n\\\")\\n sio.write(f\\\"\\\\tNumber of successful target algorithm runs: {num_success}\\\\n\\\")\\n sio.write(f\\\"\\\\tNumber of crashed target algorithm runs: {num_crash}\\\\n\\\")\\n sio.write(f\\\"\\\\tNumber of target algorithms that exceeded the time \\\"\\n f\\\"limit: {num_timeout}\\\\n\\\")\\n sio.write(f\\\"\\\\tNumber of target algorithms that exceeded the memory \\\"\\n f\\\"limit: {num_memout}\\\\n\\\")\\n\\n return sio.getvalue()\",\n \"def output_test():\\n\\toutput_comparison_page(TEST_EVENT_LIST, TEST_COMPARISON_PAGE_FILEPATH)\",\n \"def print_outcomes(sim_output, strategy_name):\\n\\n # mean and confidence interval text of game reward\\n reward_mean_CI_text = Format.format_estimate_interval(\\n estimate=sim_output.get_ave_reward(),\\n interval=sim_output.get_CI_reward(alpha=P.alpha),\\n deci=1)\\n\\n # print game reward statistics\\n print(strategy_name)\\n print(\\\" Estimate of the mean game reward and {:.{prec}%} confidence interval:\\\".format(1 - P.alpha, prec=0),\\n reward_mean_CI_text)\",\n \"def showBestStats(self) :\\n Scenario.messageBestStats()\\n self.showBestStatLevelReached()\\n self.showNbCoupFindFirstAttempt()\\n self.showBestGainWon()\\n self.showBestBetUse()\\n self.showNbLevelWon()\",\n \"def show_stats(self, output_type='count'):\\n if not self._stats:\\n raise TypeError(\\\"self._stats is not defined. Try running run_parser first!\\\")\\n self._stats.print_spec(output_type)\",\n \"def _t_test_results(self):\\n t, df, p = self.api.m.math_utils.welchs_t_test(\\n self.lkgr.values, self.fkbr.values)\\n lines = [\\n 'LKGR values: %r' % self.lkgr.values,\\n 'FKBR values: %r' % self.fkbr.values,\\n 't-statistic: %r' % t,\\n 'deg. of freedom: %r' % df,\\n 'p-value: %r' % p,\\n 'Confidence score: %r' % (100 * (1 - p))\\n ]\\n return '\\\\n'.join(lines)\",\n \"def statistics_on_test(self, predicted_results, result):\\n # Print confusion matrix and mean average precision score\\n predicted_results_binary = self.predicted_results_to_binary(predicted_results)\\n print(\\\"\\\\nConfusion matrix : \\\")\\n print(confusion_matrix(result, predicted_results_binary))\\n print(\\\"\\\\nAverage precision score : \\\", average_precision_score(result, predicted_results_binary))\",\n \"def dump(self):\\n if self.logger is None:\\n return\\n Ys = self.get_ys()\\n vals = []\\n for i in range(min(20, len(Ys))):\\n vals.append(round(Ys[i],2)) \\n dt = dtime(self.t0) \\n \\n message = '{0} {1} {2} {3} {4:.6f} {5:.2f} {6:.2f} {7!s} {8!s}'.format(\\n dt, int(self.count_evals.value / dt), self.count_runs.value, self.count_evals.value, \\\\\\n self.best_y.value, self.get_y_mean(), self.get_y_standard_dev(), vals, self.best_x[:])\\n self.logger.info(message)\",\n \"def main(output_file):\\n with open(output_file, 'w+') as fl:\\n poor_perf_stats = pstats.Stats('poor_perf.log', stream=fl)\\n good_perf_stats = pstats.Stats('good_perf.log', stream=fl)\\n\\n poor_perf_stats.sort_stats('cumtime')\\n\\n fl.write('--------------------------------------------\\\\n')\\n fl.write('POOR PERFORMANCE STATS\\\\n')\\n fl.write(f\\\"Time: {poor_perf_stats.total_tt}\\\\n\\\")\\n fl.write(f\\\"Function Calls: {poor_perf_stats.total_calls}\\\\n\\\")\\n fl.write(f\\\"Top cumulative times\\\\n\\\")\\n poor_perf_stats.print_stats(20)\\n\\n fl.write('--------------------------------------------\\\\n')\\n fl.write('GOOD PERFORMANCE STATS\\\\n')\\n fl.write(f\\\"Time: {good_perf_stats.total_tt}\\\\n\\\")\\n fl.write(f\\\"Function Calls: {good_perf_stats.total_calls}\\\\n\\\")\\n fl.write(f\\\"Top 20 cumulative times\\\\n\\\")\\n good_perf_stats.print_stats(20)\",\n \"def printReport(self): \\n \\n print('Distribution: ', self._distribution_type)\\n print('Distribution Type: ', str(self._measure_type).replace('MeasureType.','')) \\n print('Type Detection Match: ', str(self._measure_type_match))\\n print('MLE: ', str(self._mle))\\n print('Goodness of Fit: ', str(self._gof)) \\n print('Goodness of Fit Pass: ', str(self._pass)) \\n print('Overall Score: ', str(self._score)) \\n print('-------------')\",\n \"def print_evaluations(ytrue, ypred, model):\\n\\n print(f'How does model {model} score:')\\n print(f'The accuracy of the model is: {round(accuracy_score(ytrue, ypred), 3)}')\\n print(f'The precision of the model is: {round(precision_score(ytrue, ypred, pos_label=\\\"bastille_\\\" ), 3)}')\\n print(f'The recall of the model is: {round(recall_score(ytrue, ypred, pos_label=\\\"bastille_\\\"), 3)}')\\n print(f'The f1-score of the model is: {round(f1_score(ytrue, ypred, pos_label=\\\"bastille_\\\"), 3)}')\",\n \"def get_report(X_train, y_train, X_test, y_test, model, output):\\n os = SMOTE(random_state=0)\\n os_data_X, os_data_y = os.fit_sample(X_train, y_train)\\n os_data_X = pd.DataFrame(data=os_data_X, columns=X_train.columns)\\n\\n # tune hyper parameters and fit model\\n hyperparameters = {\\n 'C': np.logspace(-4, 4, 20)\\n }\\n clf = GridSearchCV(model, hyperparameters, cv=5,\\n verbose=0, scoring=make_scorer(recall_score))\\n best_model = clf.fit(os_data_X, os_data_y)\\n\\n # measure accuracies\\n train_predictions = best_model.predict(X_train)\\n train_accuracy = accuracy_score(y_train, train_predictions)\\n test_predictions = best_model.predict(X_test)\\n test_accuracy = accuracy_score(y_test, test_predictions)\\n test_recall = recall_score(y_test, test_predictions)\\n test_precision = precision_score(y_test, test_predictions)\\n auc_score = roc_auc_score(y_test, best_model.predict_proba(X_test)[:, 1])\\n if (output == \\\"results_baseline\\\"):\\n accuracies_output_df['baseline'] = [\\n test_accuracy, train_accuracy, test_recall, test_precision, auc_score]\\n else:\\n accuracies_output_df['alternate model'] = [\\n test_accuracy, train_accuracy, test_recall, test_precision, auc_score]\\n\\n # plot and report confusion matrix\\n plot_confusion_matrix(best_model, X_test, y_test,\\n display_labels=['No Default', 'Default'])\\n report = classification_report(y_test, test_predictions, output_dict=True)\\n report_df = pd.DataFrame(report)\\n report_df = report_df.rename(columns={'0': 'Non Default',\\n '1': 'Default'})\\n report_df.index.name = \\\"measurment\\\"\\n report_df.to_csv(f'./{output}/classification_report.csv')\\n plt.savefig(f'./{output}/confusion_matrix.png')\\n plt.clf()\\n\\n # compute and save roc curve\\n fpr, tpr, thresholds = roc_curve(\\n y_test, best_model.predict_proba(X_test)[:, 1])\\n plt.plot(fpr, tpr)\\n plt.title('ROC report')\\n plt.plot((0, 1), (0, 1), '--k')\\n plt.xlabel('false positive rate')\\n plt.ylabel('true positive rate')\\n plt.savefig(f'./{output}/roc.png')\",\n \"def output_metrics(self):\\n print('')\\n for key in sorted(self.metrics):\\n print('{}:'.format(key), end='')\\n for k, v in self.metrics[key].items():\\n if type(v[-1]) is list:\\n print('\\\\t' + k + ': ' + ''.join('{:5.3f} '.format(vs) for vs in v[-1]), end='')\\n else:\\n print('\\\\t{}: {:5.3f}'.format(k, v[-1]), end='')\\n print('\\\\n', end='')\",\n \"def print_results(self) -> None:\\n print(\\\"=\\\" * 70, file=sys.stderr)\\n total = 0.0\\n max_points = 0.0\\n for problem in self.problems:\\n total += problem.run_tests()\\n max_points += problem.max_grade\\n print(f\\\"Total Grade: {total}/{max_points}\\\", file=sys.stderr)\",\n \"def print_metric(self):\\r\\n print(f'\\\\n\\\\n{self.sort} metric of size {self.n}')\\r\\n print(f'algorithm: {self.algo}')\\r\\n print(f'number of comparisons: {self.comps}')\\r\\n print(f'number of exchanges: {self.exs}')\\r\\n print(f'regression equation for comparisons: {self.comp_eq}')\\r\\n print(f'regression equation for exchanges: {self.ex_eq}')\\r\\n print(f'presorted data: {self.predata}')\\r\\n print(f'postsorted data: {self.postdata}')\",\n \"def print_results(self, regressor=False):\\n if regressor:\\n for i, model in enumerate(self.list_of_models):\\n print \\\"Model: \\\", model\\n print \\\"R2 score: \\\", self.r2_scores[i]\\n print \\\"MSE: \\\", self.mse_scores[i]\\n else:\\n for i, model in enumerate(self.list_of_models):\\n print \\\"Model: \\\", model\\n print \\\"F1 score: \\\", self.f1_scores[i]\\n print \\\"recall score: \\\", self.recall_scores[i]\\n print \\\"precision score: \\\", self.precision_scores[i]\\n print \\\"accuracy score: \\\", self.accuracy_scores[i]\",\n \"def print_tests_results(self):\\n\\n for test in self.test_report:\\n for detail in test:\\n print detail + ': ', test[detail]\",\n \"def log_test_statistics(self, data, statistics, predictions):\\n\\n independent_list = []\\n for var in self.model_arguments()[\\\"INDEPENDENT\\\"]:\\n independent_list.append(var[\\\"VARIABLE\\\"])\\n dependent_var = self.model_arguments()[\\\"DEPENDENT\\\"][\\\"VARIABLE\\\"]\\n\\n self.log.info(\\\"Dependent (Target) Variable: %s\\\", dependent_var)\\n for var in independent_list:\\n self.log.info(\\\"Independent (Input) Variable(s): %s\\\", var)\\n\\n self.log.info(\\\"Training Epochs: %d\\\", self.model_epochs())\\n\\n self.log.info(\\\"Test Compounds %s R-squared (R^2): %f\\\", dependent_var, statistics[\\\"R2\\\"])\\n self.log.info(\\\"Test Compounds %s Mean Unsigned Error (MUE): %f\\\", dependent_var, statistics[\\\"MUE\\\"])\\n self.log.info(\\\"Test Compounds %s RMS Error: %f\\\", dependent_var, statistics[\\\"RMSE\\\"])\\n\\n self.log.info(\\\"Test Compounds Kendall's Rank Coefficient (tau): %f, p-value: %f\\\",\\n statistics[\\\"kendall\\\"][\\\"tau\\\"], statistics[\\\"kendall\\\"][\\\"p-value\\\"])\\n self.log.info(\\\"Test Compounds Spearman Coefficient (rho): %f, p-value: %f\\\",\\n statistics[\\\"spearman\\\"][\\\"rho\\\"], statistics[\\\"spearman\\\"][\\\"p-value\\\"])\\n self.log.info(\\\"Test Compounds %s Mean Unsigned Error (MUE): %f\\\", dependent_var, statistics[\\\"MUE\\\"])\\n self.log.info(\\\"Test Compounds Results\\\")\\n self.log.info(\\\"ID, Tested Rank, Pred. Rank, Tested %s, Pred. %s\\\", dependent_var, dependent_var)\\n self.log.info(\\\"======================================================\\\")\\n\\n predict_list = self.model_prediction_records(data, predictions, statistics)\\n\\n for idx in range(len(predict_list)):\\n line = \\\"{:10s} {:4.1f} {:4.1f} {:10.4f} {:10.4f}\\\".format(predict_list[idx][0],\\n predict_list[idx][1],\\n predict_list[idx][2],\\n predict_list[idx][3],\\n predict_list[idx][4])\\n self.log.info(line)\",\n \"def print_score(y_actual, y_pred, measure):\\n print(\\\"\\\\t\\\\tWeighted Average Scores Over Each Output Class\\\\n\\\")\\n print(\\\"\\\\t\\\\tPrecision\\\\tRecall\\\\t\\\\tF1_Score\\\")\\n for column_name, column in y_actual.iteritems():\\n report = classification_report(y_actual[column_name], y_pred[column_name], output_dict=True )\\n prec = report[measure]['precision']\\n recall = report[measure]['recall']\\n f1 = report[measure]['f1-score']\\n print(\\\"%20.2f %15.2f % 15.2f\\\" % (prec, recall, f1) + \\\"\\\\t\\\\t\\\" + column_name )\",\n \"def analyze_end_performance(self) -> None:\\n self.logger.info(f\\\"###### FINAL RESULTS FOR EACH GAME###### \\\\n\\\")\\n total_results = [0, 0, 0, 0]\\n for game in self.instance_predictions:\\n self.logger.info(f\\\"Simulated game {game.home_team.name} vs {game.away_team.name} \\\"\\n f\\\"{len(self.instance_predictions[game])} times.\\\")\\n confusion_matrix = [0, 0, 0, 0]\\n for predicted_winner in self.instance_predictions[game]:\\n actual_winner = game.home_team if game.home_team.scores.get(\\n GamePeriod.TOTAL) > game.away_team.scores.get(GamePeriod.TOTAL) else game.away_team\\n actual_home_team_won = True if actual_winner == game.home_team else False\\n predicted_home_team_won = True if predicted_winner == game.home_team else False\\n confusion_matrix = recursive_build_confusion_matrix(confusion_matrix, actual_home_team_won,\\n predicted_home_team_won)\\n total_results[0] += confusion_matrix[0]\\n total_results[1] += confusion_matrix[1]\\n total_results[2] += confusion_matrix[2]\\n total_results[3] += confusion_matrix[3]\\n\\n for k, v in analyze_confusion_matrix(confusion_matrix, len(self.instance_predictions[game])).items():\\n self.logger.info(f\\\"{k} : {v}\\\")\\n self.logger.info(\\\"\\\\n###### FINAL RESULTS FOR ALL GAMES###### \\\\n\\\")\\n final_stats = analyze_confusion_matrix(total_results, sum(total_results))\\n for k, v in final_stats.items():\\n self.logger.info(f\\\"{k} : {v}\\\")\\n self.write_stats_to_json(final_stats, self.outfile)\",\n \"def print_output(self):\\n print(\\\"Reference score: \\\" + str(self.PotTax_reference.sum().TFI))\\n print(\\\"Intervention score: \\\" + str(self.PotTax_intervention.sum().TFI))\\n return\",\n \"def logging_loop(self, num_gpus):\\n # Launch the test worker to get performance metrics\\n self.test_worker = self_play.SelfPlay.options(\\n num_cpus=0, num_gpus=num_gpus,\\n ).remote(\\n self.checkpoint,\\n self.Game,\\n self.config,\\n self.config.seed + self.config.num_workers,\\n )\\n self.test_worker.continuous_self_play.remote(\\n self.shared_storage_worker, None, True\\n )\\n\\n # Write everything in TensorBoard\\n writer = SummaryWriter(self.config.results_path)\\n\\n print(\\n \\\"\\\\nTraining...\\\\nRun tensorboard --logdir ./results and go to http://localhost:6006/ to see in real time the training performance.\\\\n\\\"\\n )\\n\\n # Save hyperparameters to TensorBoard\\n hp_table = [\\n f\\\"| {key} | {value} |\\\" for key, value in self.config.__dict__.items()\\n ]\\n writer.add_text(\\n \\\"Hyperparameters\\\",\\n \\\"| Parameter | Value |\\\\n|-------|-------|\\\\n\\\" + \\\"\\\\n\\\".join(hp_table),\\n )\\n # Save model representation\\n writer.add_text(\\n \\\"Model summary\\\", self.summary,\\n )\\n # Loop for updating the training performance\\n counter = 0\\n keys = [\\n \\\"total_reward\\\",\\n \\\"wormzero_reward\\\",\\n \\\"opponent_reward\\\",\\n \\\"episode_length\\\",\\n \\\"mean_value\\\",\\n \\\"training_step\\\",\\n \\\"lr\\\",\\n \\\"total_loss\\\",\\n \\\"value_loss\\\",\\n \\\"policy_loss\\\",\\n \\\"num_played_games\\\",\\n \\\"num_played_steps\\\",\\n \\\"num_reanalysed_games\\\",\\n ]\\n info = ray.get(self.shared_storage_worker.get_info.remote(keys))\\n try:\\n while info[\\\"training_step\\\"] < self.config.training_steps:\\n info = ray.get(self.shared_storage_worker.get_info.remote(keys))\\n writer.add_scalar(\\n \\\"1.Total_reward/1.Total_reward\\\", info[\\\"total_reward\\\"], counter,\\n )\\n writer.add_scalar(\\n \\\"1.Total_reward/2.Mean_value\\\", info[\\\"mean_value\\\"], counter,\\n )\\n writer.add_scalar(\\n \\\"1.Total_reward/3.Episode_length\\\", info[\\\"episode_length\\\"], counter,\\n )\\n writer.add_scalar(\\n \\\"1.Total_reward/4.WormZero_reward\\\", info[\\\"wormzero_reward\\\"], counter,\\n )\\n writer.add_scalar(\\n \\\"1.Total_reward/5.Opponent_reward\\\",\\n info[\\\"opponent_reward\\\"],\\n counter,\\n )\\n writer.add_scalar(\\n \\\"2.Workers/1.Self_played_games\\\", info[\\\"num_played_games\\\"], counter,\\n )\\n writer.add_scalar(\\n \\\"2.Workers/2.Training_steps\\\", info[\\\"training_step\\\"], counter\\n )\\n writer.add_scalar(\\n \\\"2.Workers/3.Self_played_steps\\\", info[\\\"num_played_steps\\\"], counter\\n )\\n writer.add_scalar(\\n \\\"2.Workers/4.Reanalysed_games\\\",\\n info[\\\"num_reanalysed_games\\\"],\\n counter,\\n )\\n writer.add_scalar(\\n \\\"2.Workers/5.Training_steps_per_self_played_step_ratio\\\",\\n info[\\\"training_step\\\"] / max(1, info[\\\"num_played_steps\\\"]),\\n counter,\\n )\\n writer.add_scalar(\\\"2.Workers/6.Learning_rate\\\", info[\\\"lr\\\"], counter)\\n writer.add_scalar(\\n \\\"3.Loss/1.Total_weighted_loss\\\", info[\\\"total_loss\\\"], counter\\n )\\n writer.add_scalar(\\\"3.Loss/Value_loss\\\", info[\\\"value_loss\\\"], counter)\\n writer.add_scalar(\\\"3.Loss/Policy_loss\\\", info[\\\"policy_loss\\\"], counter)\\n print(\\n f'Last test reward: {info[\\\"total_reward\\\"]:.2f}. Training step: {info[\\\"training_step\\\"]}/{self.config.training_steps}. Played games: {info[\\\"num_played_games\\\"]}. Loss: {info[\\\"total_loss\\\"]:.2f}',\\n end=\\\"\\\\r\\\",\\n )\\n counter += 1\\n time.sleep(0.5)\\n except KeyboardInterrupt:\\n pass\\n\\n self.terminate_workers()\\n\\n if self.config.save_model:\\n # Persist replay buffer to disk\\n print(\\\"\\\\n\\\\nPersisting replay buffer games to disk...\\\")\\n pickle.dump(\\n {\\n \\\"buffer\\\": self.replay_buffer,\\n \\\"num_played_games\\\": self.checkpoint[\\\"num_played_games\\\"],\\n \\\"num_played_steps\\\": self.checkpoint[\\\"num_played_steps\\\"],\\n \\\"num_reanalysed_games\\\": self.checkpoint[\\\"num_reanalysed_games\\\"],\\n },\\n open(os.path.join(self.config.results_path, \\\"replay_buffer.pkl\\\"), \\\"wb\\\"),\\n )\",\n \"def show_score(clf, X_test, y_test):\\n y_pred = predict(clf, X_test)\\n print metrics.classification_report(y_test.astype(np.int), y_pred)\",\n \"def log_evaluation(tester, name, description):\\r\\n\\tfor dataset, output in tester.preds.items():\\r\\n\\t\\tresults = pandas.DataFrame.from_dict(output)\\r\\n\\t\\tpath = os.path.join(\\r\\n\\t\\t\\tEXPERIMENT_PATH, tester.config[\\\"name\\\"] + '-' + dataset)\\r\\n\\t\\twith open(path + \\\".csv\\\", \\\"w\\\") as f:\\r\\n\\t\\t\\tresults.to_csv(f, sep=\\\"\\\\t\\\", encoding='utf-8',\\r\\n\\t\\t\\t\\tfloat_format='%.3f', index=False)\",\n \"def test_execution_profiling(self):\\n self._test_reports_helper({\\\"--profile-execution\\\": \\\"\\\"}, [\\\"report.txt\\\"])\",\n \"def _process_results(self):\\n self.portfolio.create_backtest_result_dataframe()\\n stats = self._show_stats()\\n return stats\",\n \"def advancedStats():\",\n \"def print_results(self, f1_score, precision_score, recall_score):\\n print(\\\"Algorithm: %s\\\" % self.name)\\n self.print_score(f1_score, \\\"F1 Score\\\")\\n self.print_score(precision_score, \\\"Precision Score\\\")\\n self.print_score(recall_score, \\\"Recall Score\\\")\",\n \"def compute_log_metrics(pipe: Pipeline,\\n x_test: pd.core.frame.DataFrame,\\n y_test: np.ndarray,\\n y_test_onehot: np.ndarray):\\n test_prob = pipe.predict_proba(x_test)\\n test_pred = pipe.predict(x_test)\\n\\n acc = metrics.accuracy_score(y_test, test_pred)\\n precision = metrics.precision_score(y_test, test_pred, average='macro')\\n recall = metrics.recall_score(y_test, test_pred, average='macro')\\n f1_score = metrics.f1_score(y_test, test_pred, average='macro')\\n roc_auc = metrics.roc_auc_score(y_test_onehot,\\n test_prob,\\n average='macro',\\n multi_class='ovr')\\n avg_prc = metrics.average_precision_score(y_test_onehot,\\n test_prob,\\n average='macro')\\n print(\\\"\\\\nEVALUATION\\\\n\\\"\\n f\\\"\\\\tAccuracy = {acc:.4f}\\\\n\\\"\\n f\\\"\\\\tPrecision (macro) = {precision:.4f}\\\\n\\\"\\n f\\\"\\\\tRecall (macro) = {recall:.4f}\\\\n\\\"\\n f\\\"\\\\tF1 score (macro) = {f1_score:.4f}\\\\n\\\"\\n f\\\"\\\\tROC AUC (macro) = {roc_auc:.4f}\\\\n\\\"\\n f\\\"\\\\tAverage precision (macro) = {avg_prc:.4f}\\\")\\n\\n # Bedrock Logger: captures model metrics\\n bdrk.log_metrics(\\n {\\n \\\"Accuracy\\\": acc,\\n \\\"Precision (macro)\\\": precision,\\n \\\"Recall (macro)\\\": recall,\\n \\\"F1 Score (macro)\\\": f1_score,\\n \\\"ROC AUC (macro)\\\": roc_auc,\\n \\\"Avg precision (macro)\\\": avg_prc,\\n }\\n )\\n # `log_chart_data` assumes binary classification\\n # For multiclass labels, we can use a \\\"micro-average\\\" by\\n # quantifying score on all classes jointly\\n # See https://scikit-learn.org/stable/auto_examples/model_selection/plot_precision_recall.html # noqa: E501\\n # This will allow us to use the same `log_chart_data` method\\n bdrk.log_binary_classifier_metrics(\\n y_test_onehot.ravel().astype(int).tolist(), # list of int\\n test_prob.ravel().astype(float).tolist() # list of float\\n )\\n\\n return test_prob, test_pred\",\n \"def save_performances(self):\\r\\n nb_datasets = len(self.results)\\r\\n resu = [[] for k in range(nb_datasets)]\\r\\n\\r\\n # fetch results\\r\\n for k in range(nb_datasets):\\r\\n best = np.argmax(self.results[k]['mean_test_score'])\\r\\n resu[k].append(('score', self.results[k]['mean_test_score'][best]))\\r\\n resu[k] = resu[k] + list(self.results[k]['params'][best].items())\\r\\n\\r\\n # write results in csv\\r\\n for k, resu in enumerate(resu):\\r\\n with open('results/final_results_{}.csv'.format(k), 'a') as file:\\r\\n writer = csv.writer(file)\\r\\n writer.writerow(resu)\",\n \"def print_statistics(self):\\n print 'Ran %s iterations in %0.3f seconds\\\\n' % (\\n self.iterations, self.elapsed_time)\\n\\n print 'Overall Equity'\\n for index in range(len(self.holdem_ranges)):\\n range_short_form = '%r' % self.holdem_ranges[index]\\n print 'P%s) %-15s %0.3f' % (\\n index,\\n range_short_form,\\n float(self.win_stats.get(index, 0))/self.iterations)\\n print '\\\\n'\\n print 'Hand distribution for each player'\\n for stats in self.player_stats:\\n stats.print_report()\",\n \"def print_results():\\n now_time = time.time()\\n diff_time_in_sec = now_time - start_time\\n generated_per_second = total / diff_time_in_sec\\n generated_per_hour = 3600 * generated_per_second\\n saved_per_second = success / diff_time_in_sec\\n saved_per_hour = 3600 * saved_per_second\\n\\n os.system('cls' if os.name == 'nt' else 'clear')\\n print(f\\\"{'Generated:' : <16}{total : <12}\\\")\\n print(f\\\"{'New graphs:' : <16}{success : <12}\\\")\\n print(f\\\"{'Success rate:' : <16}{round((success / total) * 100, 3) : <7} %\\\")\\n print(f\\\"{'Speed:' : <16}{round(generated_per_hour) : <7} graphs/h\\\")\\n print(f\\\"{'Save speed:' : <16}{round(saved_per_hour) : <7} graphs/h\\\")\",\n \"def show_current_scattering_statistics(self, out=sys.stdout):\\n print(\\\"\\\", file=out)\\n print(\\\"Model and map statistics:\\\", file=out)\\n print(\\\" mean mFo map height @ carbon: %s\\\" % format_value(\\\"%.2f\\\",\\n flex.max(self.carbon_fo_values)), file=out)\\n if (self.calpha_mean_two_fofc > 0):\\n print(\\\" mean 2mFo-DFc map height @ C-alpha: %s\\\" % format_value(\\n \\\"%.2f\\\", self.calpha_mean_two_fofc), file=out)\\n print(\\\" mean B-factor: %s\\\" % format_value(\\\"%.2f\\\", self.b_mean_all), file=out)\\n if (self.b_mean_calpha > 0):\\n print(\\\" mean C-alpha B-factor: %s\\\" % format_value(\\\"%.2f\\\",\\n self.b_mean_calpha), file=out)\\n print(\\\" mean water B-factor: %s\\\" % format_value(\\\"%.2f\\\",\\n self.b_mean_hoh), file=out)\\n n_water_fofc_peaks = 0\\n n_water_anom_peaks = 0\\n water_sel = self.water_selection()\\n print(\\\" %d water molecules\\\" % len(water_sel), file=out)\\n for i_seq in water_sel :\\n map_stats = self.map_stats(i_seq)\\n if (map_stats.fofc >= 3.0):\\n n_water_fofc_peaks += 1\\n if (map_stats.anom is not None) and (map_stats.anom >= 3.0):\\n n_water_anom_peaks += 1\\n print(\\\" %d waters have mFo-DFc map >= 3.0 sigma\\\" % \\\\\\n n_water_fofc_peaks, file=out)\\n if (self.anomalous_flag):\\n print(\\\" %d waters have anomalous map >= 3.0 sigma\\\" % \\\\\\n n_water_anom_peaks, file=out)\\n print(\\\"\\\", file=out)\",\n \"def stats(self):\",\n \"def stats(self):\\n pass\",\n \"def print_stats(session, feature_batch, label_batch, cost, accuracy, x, y, keep_prob, valid_features, valid_labels):\\n result_cost = session.run(cost, feed_dict={x:feature_batch, y:label_batch, keep_prob: 1.0})\\n result_accuracy = session.run(accuracy, feed_dict={x:valid_features, y:valid_labels, keep_prob: 1.0})\\n print(\\\"Cost: {0:.2f} Accuracy: {1:.2f}% \\\" .format(result_cost,result_accuracy*100), end='')\\n return result_cost, result_accuracy\",\n \"def print_outcomes(MultipleGameSets, strategy_name):\\n\\n # mean and confidence interval text of patient survival time\\n reward_mean_PI_text = Format.format_estimate_interval(\\n estimate=MultipleGameSets.get_mean_total_reward(),\\n interval=MultipleGameSets.get_PI_total_reward(alpha=P.ALPHA),\\n deci=1)\\n\\n # print survival time statistics\\n print(strategy_name)\\n print(\\\" Estimate of mean rewards for gamblers and {:.{prec}%} prediction interval:\\\".format(1 - P.ALPHA, prec=0),\\n reward_mean_PI_text)\",\n \"def export_results(self):\\n problemIDs = list(set([result.problemID for result in self.results]))\\n configIDs = list(set([result.configID for result in self.results]))\\n\\n labels = []\\n labels.extend(TestResults._fields)\\n labels.extend(SizeMetrics._fields) \\n # Remove unused columns\\n labels.remove(\\\"size_metrics\\\")\\n labels.remove(\\\"problemID\\\")\\n labels.remove(\\\"configID\\\")\\n\\n # output = pd.Panel(items=labels, major_axis=problemIDs, minor_axis=configIDs)\\n multiindex = pd.MultiIndex.from_product([problemIDs, configIDs], names=[\\\"problems\\\", \\\"configs\\\"])\\n\\n output = pd.DataFrame(index=multiindex, columns=labels)\\n output.columns.names = [\\\"stats\\\"]\\n\\n for result in self.results:\\n problemID = result.problemID\\n configID = result.configID\\n for label in [label for label in TestResults._fields if label in labels]:\\n output.loc[(problemID, configID), label] = getattr(result, label)\\n for label in [label for label in SizeMetrics._fields if label in labels]:\\n output.loc[(problemID, configID), label] = getattr(result.size_metrics, label)\\n\\n # Compute Statistics\\n output.fillna(value=np.nan, inplace=True)\\n output.sort_index(inplace=True)\\n try:\\n TestFramework.compute_mosek_error(output, \\\"opt_val\\\", \\\"mosek_config\\\")\\n except (KeyError): # pragma: no cover\\n print(\\\"TestFramework.compute_mosek_error: 'mosek_config' or 'opt_val' field not found.\\\")\\n try:\\n TestFramework.compute_performance(output, \\\"solve_time\\\")\\n except (KeyError): # pragma: no cover\\n print(\\\"TestFramework.compute_performance: 'solve_time' field not found.\\\")\\n return output\",\n \"def log_output_data(self):\\r\\n with tf.name_scope('model_output'):\\r\\n for i in range(self.action_handler.get_number_actions()):\\r\\n variable_name = str(self.action_handler.action_list_names[i])\\r\\n tf.summary.histogram(variable_name + '_output', self.actor_last_row_layer[i])\",\n \"def __str__(self):\\n outs = str(self.cluster_subspace).split(\\\"\\\\n\\\")[:6]\\n\\n if self.regression_data is not None:\\n # This might need to be redefined to take \\\"expectation\\\" using measure\\n feature_avg = np.average(self.feature_matrix, axis=0)\\n feature_std = np.std(self.feature_matrix, axis=0)\\n outs += [\\n f\\\"Regression Data : estimator={self.regression_data.estimator_name}\\\",\\n f\\\" module={self.regression_data.module}\\\",\\n f\\\" parameters={self.regression_data.parameters}\\\",\\n f\\\"Target Property : \\\"\\n f\\\"mean={np.mean(self.regression_data.property_vector):0.4f} \\\"\\n f\\\"std={np.std(self.regression_data.property_vector):0.4f}\\\",\\n ]\\n fit_var = sum(\\n self._subspace.function_total_multiplicities[1:] * self.eci[1:] ** 2\\n )\\n outs += [\\n f\\\"ECI-based Property : mean={self.eci[0]:0.4f}\\\"\\n f\\\" std={np.sqrt(fit_var):0.4f}\\\",\\n \\\"Fit Summary\\\",\\n ]\\n\\n for i, term in enumerate(self._subspace.external_terms):\\n outs.append(f\\\"{repr(term)}={self.coefs[len(self.eci) + i]:0.3f}\\\")\\n\\n if self.regression_data is not None:\\n outs += [\\n \\\" ---------------------------------------------------------------------\\\"\\n \\\"-------------------------------\\\",\\n \\\" | ID Orbit ID Degree Cluster Diameter ECI Feature AVG\\\"\\n \\\" Feature STD ECI * STD |\\\",\\n f\\\" | 0 0 0 NA \\\"\\n f\\\"{self.eci[0]:^7.3f}{feature_avg[0]:^15.3f}\\\"\\n f\\\"{feature_std[0]:^15.3f}{feature_std[0] * self.eci[0]:^13.3f}|\\\",\\n ]\\n else:\\n outs += [\\n \\\" ---------------------------------------------------------\\\",\\n \\\" | ID Orbit ID Degree Cluster Diameter ECI |\\\",\\n f\\\" | 0 0 0 NA \\\"\\n f\\\"{self.eci[0]:^7.3f} |\\\",\\n ]\\n\\n for degree, orbits in self.cluster_subspace.orbits_by_size.items():\\n for orbit in orbits:\\n for i, bits in enumerate(orbit.bit_combos):\\n line = (\\n f\\\" |{orbit.bit_id + i:^6}{orbit.id:^12}{degree:^10}\\\"\\n f\\\"{orbit.base_cluster.diameter:^20.4f}\\\"\\n f\\\"{self.eci[orbit.bit_id + i]:^7.3f}\\\"\\n )\\n if self.regression_data is not None:\\n line += (\\n f\\\"{feature_avg[orbit.bit_id + i]:^15.3f}\\\"\\n f\\\"{feature_std[orbit.bit_id + i]:^15.3f}\\\"\\n f\\\"{feature_std[orbit.bit_id + i] * self.eci[orbit.bit_id + i]:^13.3f}\\\" # noqa\\n )\\n line += \\\"|\\\"\\n outs.append(line)\\n outs.append(\\\" \\\" + (len(outs[-1]) - 1) * \\\"-\\\")\\n return \\\"\\\\n\\\".join(outs)\",\n \"def stats(self):\\n raise NotImplementedError(\\\"Must implement in frontend subclass.\\\")\",\n \"def print_outcomes(multi_cohort, strategy_name):\\n\\n # mean and confidence interval text of rewards\\n survival_mean_PI_text = Format.format_estimate_interval(\\n estimate=multi_cohort.get_mean_total_reward(),\\n interval=multi_cohort.get_PI_total_reward(alpha=0.05),\\n deci=1)\\n\\n # print reward statistics\\n print(strategy_name)\\n print(\\\"Estimate of mean rewards ($) and {:.{prec}%} prediction interval:\\\".format(1 - 0.05, prec=0),\\n survival_mean_PI_text)\",\n \"def print_gesture_score(self):\\n performance_time = time.strptime(self.performance_title[:19], '%Y-%m-%dT%H-%M-%S')\\n plot_title = time.strftime('%Y-%m-%dT%H-%M-%S', performance_time) + \\\"-gesture-plot\\\"\\n generate_posthoc_gesture_score.plot_gesture_only_score(plot_title, self.gestures)\\n print(\\\"Saved gesture plot: \\\" + plot_title)\",\n \"def performance_analytics(self):\\n return pf.create_returns_tear_sheet(self.port_rets, return_fig=True)\",\n \"def log_best_performer(self) -> None:\\n best = self.get_highest_accuracy()\\n self.logger.info(f\\\"\\\\n\\\\nThe model with the highest accuracy {best[0]} has the following characteristics: \\\\n\\\")\\n for k, v in best[1].items():\\n if k != 'best_performer':\\n self.logger.info(f\\\"{k} : {v}\\\")\\n else:\\n self.logger.info(f\\\"Best Accuracy: {v['Accuracy']}\\\")\\n self.logger.info(\\\"Features used: \\\")\\n for f in v['Labels']:\\n self.logger.info(f)\\n for nw, w in v['Vars'].items():\\n self.logger.info(f\\\"{nw}: {w}\\\")\",\n \"def print_scores(self):\\n ### FILL IN ###\",\n \"def log_training_results(engine: Engine):\\n train_evaluator.run(self.train_dl)\\n metrics: Dict[str, float] = train_evaluator.state.metrics\\n avg_accuracy: float = metrics['accuracy']\\n avg_bce: float = metrics['bce']\\n pbar.log_message(\\n f'Training Results - Epoch: {engine.state.epoch} Avg accuracy: {avg_accuracy:.4f} Avg loss: {avg_bce:.4f}')\",\n \"def measure_test(self):\\n return self.execute(Sgp40I2cCmdExecuteSelfTest())\",\n \"def __holdout_test(self):\\n\\t\\tprint(\\\"\\\\t\\\\tHoldout Validation Results:\\\")\\n\\n\\t\\tprint(\\\"\\\\t\\\\t\\\\tAccuracy: \\\", analysis.accuracy(self.y_pred, self.y_test))\\n\\t\\tprint(\\\"\\\\t\\\\t\\\\tPrecision: \\\", analysis.precision(self.y_pred, self.y_test))\\n\\t\\tprint(\\\"\\\\t\\\\t\\\\tSpecificity: \\\", analysis.specificity(self.y_pred, self.y_test))\\n\\t\\tprint(\\\"\\\\t\\\\t\\\\tRecall: \\\", analysis.recall(self.y_pred, self.y_test))\",\n \"def print_stats(session, feature_batch, label_batch, cost, accuracy):\\n # TODO: Implement Function\\n cst = session.run(cost, feed_dict={x:feature_batch, y:label_batch, keep_prob:1.0})\\n acc = session.run(accuracy, feed_dict={x:valid_features, y:valid_labels, keep_prob:1.0}) \\n print(\\\"cost:{:.3f};accuracy:{:.3f} <br>\\\".format(cst,acc))\",\n \"def summary(self, test_type='t-test'):\\n summary = f'Results for running {self.cv_method} evaluation for {self.method} '\\n summary += f'on {self.n_model} models:\\\\n\\\\n'\\n name_length = max([max(len(m.name) for m in self.models) + 1, 6])\\n means = self.get_means()\\n sems = self.get_sem()\\n if means is None:\\n means = np.nan * np.ones(self.n_model)\\n if sems is None:\\n sems = np.nan * np.ones(self.n_model)\\n try:\\n p_zero = self.test_zero(test_type=test_type)\\n p_noise = self.test_noise(test_type=test_type)\\n except ValueError:\\n p_zero = np.nan * np.ones(self.n_model)\\n p_noise = np.nan * np.ones(self.n_model)\\n # header of the results table\\n summary += 'Model' + (' ' * (name_length - 5))\\n summary += '| Eval \\\\u00B1 SEM |'\\n summary += ' p (against 0) |'\\n summary += ' p (against NC) |\\\\n'\\n summary += '-' * (name_length + 51)\\n summary += '\\\\n'\\n for i, m in enumerate(self.models):\\n summary += m.name + (' ' * (name_length - len(m.name)))\\n summary += f'| {means[i]: 5.3f} \\\\u00B1 {sems[i]:4.3f} |'\\n if p_zero[i] < 0.001:\\n summary += ' < 0.001 |'\\n else:\\n summary += f'{p_zero[i]:>13.3f} |'\\n if p_noise[i] < 0.001:\\n summary += ' < 0.001 |'\\n else:\\n summary += f'{p_noise[i]:>14.3f} |'\\n summary += '\\\\n'\\n summary += '\\\\n'\\n if self.cv_method == 'crossvalidation':\\n summary += 'No p-values available as crossvalidation provides no variance estimate'\\n elif test_type == 't-test':\\n summary += 'p-values are based on uncorrected t-tests'\\n elif test_type == 'bootstrap':\\n summary += 'p-values are based on percentiles of the bootstrap samples'\\n elif test_type == 'ranksum':\\n summary += 'p-values are based on ranksum tests'\\n return summary\",\n \"def log_inference(tester, name, description):\\r\\n\\tfor dataset, output in tester.preds.items():\\r\\n\\t\\tresults = pandas.DataFrame.from_dict(output)\\r\\n\\t\\tpath = os.path.join(\\r\\n\\t\\t\\tEXPERIMENT_PATH, tester.config[\\\"name\\\"] + '-' + dataset)\\r\\n\\t\\twith open(path + \\\".csv\\\", \\\"w\\\") as f:\\r\\n\\t\\t\\tresults.to_csv(f, sep=\\\"\\\\t\\\", encoding='utf-8', \\r\\n\\t\\t\\t\\tfloat_format='%.3f', index=False)\\r\\n\\r\\n\\t\\twith open(path + \\\"-predictions.csv\\\", \\\"w\\\") as f:\\r\\n\\t\\t\\tresults[[\\\"tag\\\", \\\"y_hat\\\"]].to_csv(\\r\\n\\t\\t\\t\\tf, index=False, float_format='%.3f', header=False)\",\n \"def _generate_report(self):\\n total_duration = 0.0\\n total_nb_tests = 0\\n total_nb_success = 0\\n nb_modules = 0\\n payload = []\\n\\n res_table = prettytable.PrettyTable(\\n padding_width=2,\\n field_names=['Module', 'Duration', 'nb. Test Run', 'Success'])\\n res_table.align['Module'] = \\\"l\\\"\\n res_table.align['Duration'] = \\\"r\\\"\\n res_table.align['Success'] = \\\"r\\\"\\n\\n # for each scenario we draw a row for the table\\n for item in self.summary:\\n if item['task_status'] is True:\\n nb_modules += 1\\n total_duration += item['overall_duration']\\n total_nb_tests += item['nb_tests']\\n total_nb_success += item['nb_success']\\n try:\\n success_avg = 100 * item['nb_success'] / item['nb_tests']\\n except ZeroDivisionError:\\n success_avg = 0\\n success_str = f\\\"{success_avg:0.2f}%\\\"\\n duration_str = time.strftime(\\\"%H:%M:%S\\\",\\n time.gmtime(item['overall_duration']))\\n res_table.add_row([item['test_name'], duration_str,\\n item['nb_tests'], success_str])\\n payload.append({'module': item['test_name'],\\n 'details': {'duration': item['overall_duration'],\\n 'nb tests': item['nb_tests'],\\n 'success rate': success_str,\\n 'success': item['success'],\\n 'failures': item['failures']}})\\n\\n total_duration_str = time.strftime(\\\"%H:%M:%S\\\",\\n time.gmtime(total_duration))\\n try:\\n self.result = 100 * total_nb_success / total_nb_tests\\n except ZeroDivisionError:\\n self.result = 100\\n success_rate = f\\\"{self.result:0.2f}\\\"\\n success_rate_str = str(success_rate) + '%'\\n res_table.add_row([\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"])\\n res_table.add_row([\\\"TOTAL:\\\", total_duration_str, total_nb_tests,\\n success_rate_str])\\n\\n LOGGER.info(\\\"Rally Summary Report:\\\\n\\\\n%s\\\\n\\\", res_table.get_string())\\n LOGGER.info(\\\"Rally '%s' success_rate is %s%% in %s/%s modules\\\",\\n self.case_name, success_rate, nb_modules,\\n len(self.summary))\\n self.details['summary'] = {'duration': total_duration,\\n 'nb tests': total_nb_tests,\\n 'nb success': success_rate}\\n self.details[\\\"modules\\\"] = payload\",\n \"def print_results(self):\\n pass\",\n \"def show_all(self):\\n self.explained_variance_score()\\n self.max_error()\\n self.mean_absolute_error()\\n self.mean_squared_error()\\n self.median_absolute_error()\\n self.r2_score()\\n self.mean_poisson_deviance()\\n self.mean_gamma_deviance()\\n self.feature_importance()\\n self.learning_curve()\",\n \"def write_output(self) -> None:\\n self.home.round(2).to_csv(var.indicators_base_cumsum + \\\"home_\\\" + str(self.year) + \\\".csv\\\")\\n self.away.round(2).to_csv(var.indicators_base_cumsum + \\\"away_\\\" + str(self.year) + \\\".csv\\\")\",\n \"def dump(self):\\n fmt='%20s:%10.4fs%6.1f%%'\\n print('\\\\n----------------TIME MANAGER PROFILE----------------\\\\n\\\\n')\\n total_t=time.time()-self.tic0\\n for rec in self.record:\\n print(fmt % (rec[0],rec[1],100.0*rec[1]/total_t))\\n print(fmt % ('TOTAL ELAPSED TIME', total_t, 100.0))\\n print('\\\\n----------------TIME MANAGER PROFILE----------------\\\\n\\\\n')\",\n \"def eval_perf_total(model, X_train, y_train, X_test, y_test):\\n\\n y_hat_train = model.predict(X_train)\\n y_hat_test = model.predict(X_test)\\n \\n train_mae = metrics.mean_absolute_error(y_train, y_hat_train)\\n train_mse = metrics.mean_squared_error(y_train, y_hat_train)\\n train_rmse = np.sqrt(metrics.mean_squared_error(y_train, y_hat_train))\\n train_r = metrics.r2_score(y_train, y_hat_train)\\n\\n print('Evaluating Performance on Training Data:\\\\n')\\n print(f' Train Mean Absolute Error: {train_mae:,.2f}')\\n print(f' Train Mean Squared Error: {train_mse:,.2f}\\\\n')\\n print(f'Train Root Mean Squared Error: {train_rmse:,.2f}')\\n print(f'Train R-Square Value: {round(train_r,2)}')\\n\\n print('\\\\n'+'---'*25+'\\\\n')\\n\\n test_mae = metrics.mean_absolute_error(y_test, y_hat_test)\\n test_mse = metrics.mean_squared_error(y_test, y_hat_test)\\n test_rmse = np.sqrt(metrics.mean_squared_error(y_test, y_hat_test))\\n test_r = metrics.r2_score(y_test, y_hat_test)\\n\\n print('Evaluating Performance on Testing Data:\\\\n')\\n print(f' Test Mean Absolute Error: {test_mae:,.2f}')\\n print(f' Test Mean Squared Error: {test_mse:,.2f}\\\\n')\\n print(f'Test Root Mean Squared Error: {test_rmse:,.2f}')\\n print(f'Test R-Square Value: {round(test_r,2)}')\",\n \"def print_metrics(result):\\n logging.log(LOG_LEVEL_OUTPUT_INFO,\\n '------------------------------------------------')\\n logging.log(LOG_LEVEL_OUTPUT_INFO, ' KEY METRICS: ')\\n logging.log(LOG_LEVEL_OUTPUT_INFO,\\n '------------------------------------------------')\\n logging.log(LOG_LEVEL_OUTPUT_INFO, '* pages_count: %d',\\n get_counter_metric(result, 'pages_count'))\\n logging.log(LOG_LEVEL_OUTPUT_INFO, '* revisions_count: %d',\\n get_counter_metric(result, 'revisions_count'))\\n logging.log(LOG_LEVEL_OUTPUT_INFO, '* very_long_page_histories_count: %d',\\n get_counter_metric(result, 'very_long_page_histories_count'))\\n revisions_per_page_distr = get_distributions_metric(\\n result, 'revisions_per_page_distr')\\n logging.log(LOG_LEVEL_OUTPUT_INFO, '* revisions_per_page_distr.mean: %d',\\n revisions_per_page_distr.mean)\\n logging.log(LOG_LEVEL_OUTPUT_INFO, '* revisions_per_page_distr.sum: %d',\\n revisions_per_page_distr.sum)\\n cumulative_page_rev_size_distr = get_distributions_metric(\\n result, 'cumulative_page_rev_size_distr')\\n logging.log(LOG_LEVEL_OUTPUT_INFO,\\n '* cumulative_page_rev_size_distr.mean: %d',\\n cumulative_page_rev_size_distr.mean)\\n logging.log(LOG_LEVEL_OUTPUT_INFO, '* cumulative_page_rev_size_distr.sum: %d',\\n cumulative_page_rev_size_distr.sum)\",\n \"def printReport(self):\\n\\t\\tself.app.printflush('Fetched: ' + str(self.fetched_count), self.app.IGNORE_EXIT_FLAG)\\n\\t\\tself.app.printflush('Processes: ' + str(self.processes), self.app.IGNORE_EXIT_FLAG)\\n\\t\\tself.app.printflush('Updated: ' + str(self.updated_count), self.app.IGNORE_EXIT_FLAG)\\n\\t\\tself.app.printflush('Average page load time: ' + str(self.average_time), self.app.IGNORE_EXIT_FLAG)\\n\\t\\tself.app.printflush('Returned with code: ' + repr(self.code_statistics), self.app.IGNORE_EXIT_FLAG)\\n\\t\\tself.app.printflush('Closing Processes... ', self.app.IGNORE_EXIT_FLAG)\",\n \"def _log_performance(i, train_perf, mean_pol_perf, best_perf, results_path,\\n tb_logger):\\n # tb_logger.add_scalars('performance', {\\n # 'train_perf': train_perf,\\n # 'eval_perf': mean_pol_perf,\\n # 'best_train_perf': best_perf,\\n # }, global_step=i)\\n\\n with open(results_path, 'a') as results_file:\\n if i == 0:\\n headings = ['Iter', 'Sampled Pol', 'Mean Pol', 'Best (Sampled)']\\n headings = ' | '.join(headings)\\n results_file.write(headings + '\\\\n')\\n print('Timestamp' + (' ' * 17) + ' | ' + headings)\\n\\n log = ['{:<4d}', '{:>11.2f}', '{:>8.2f}', '{:>14.2f}']\\n log = ' '.join(log).format(i, train_perf, mean_pol_perf, best_perf)\\n results_file.write(log + '\\\\n')\\n print('{:<26s} {}'.format(\\n timer.asctime(timer.localtime(timer.time())), log))\",\n \"def dataStats(self):\\n print (\\\"Performing statistical analysis of the data\\\")\\n # stuff to do\",\n \"def _compute_statistics(self):\\n # log to file\\n output_dir = self.params['output_dir']\\n if not os.path.exists(output_dir):\\n os.mkdir(output_dir)\\n output_dir = os.path.join(output_dir, 'statistics.log')\\n log = logging.getLogger('tensorflow')\\n handle = logging.FileHandler(output_dir)\\n log.addHandler(handle)\\n\\n # FLOPS\\n encoder_flops, decoder_flops = 0, 0\\n encoder_count, decoder_count = 0, 0\\n graph = tf.get_default_graph()\\n for operation in graph.get_operations():\\n flops = ops.get_stats_for_node_def(graph, operation.node_def,\\n 'flops').value\\n if flops is None:\\n continue\\n if operation.name.startswith('model/encoder'):\\n # encoder\\n encoder_flops += flops\\n encoder_count += 1\\n tf.logging.info('encoder operation %s : %d', operation.name, flops)\\n elif operation.name.startswith('model/decoder'):\\n # decoder\\n decoder_flops += flops\\n decoder_count += 1\\n tf.logging.info('decoder operation %s : %d', operation.name, flops)\\n else:\\n # gradient\\n pass\\n tf.logging.info('flops of %d encoder tensor: %d',\\n encoder_count, encoder_flops)\\n tf.logging.info('flops of %d decoder tensor: %d',\\n decoder_count, decoder_flops)\\n tf.logging.info('flops of total %d tensor: %d',\\n encoder_count + decoder_count,\\n encoder_flops + decoder_flops)\\n # parameters\\n encoder_parameters, decoder_parameters = 0, 0\\n encoder_count, decoder_count = 0, 0\\n for var in tf.trainable_variables():\\n parameters = np.prod(var.get_shape().as_list())\\n if var.name.startswith('model/encoder'):\\n # encoder\\n encoder_parameters += parameters\\n encoder_count += 1\\n tf.logging.info('encoder variable %s : %d', var.name, parameters)\\n elif var.name.startswith('model/decoder'):\\n # decoder\\n decoder_parameters += parameters\\n decoder_count += 1\\n tf.logging.info('decoder variable %s : %d', var.name, parameters)\\n\\n tf.logging.info('parameters of %d encoder tensor: %d',\\n encoder_count, encoder_parameters)\\n tf.logging.info('parameters of %d decoder tensor: %d',\\n decoder_count, decoder_parameters)\\n tf.logging.info('parameters of total %d tensor: %d',\\n encoder_count + decoder_count,\\n encoder_parameters + decoder_parameters)\\n # disable log to file\\n log.removeHandler(handle)\",\n \"def print_testvals(self):\\n print(self.use_planck)\",\n \"def display_results(self):\\n print(f'AI player results - Wins: {self._wins}, Draws: {self._draws}, Loss: {self._losses}')\\n print(f'Total win percentage {(self._wins / self._count) * 100}', end='\\\\n\\\\n')\\n\\n with open(os.path.join(os.path.dirname(__file__), 'ai-results.json'), 'w') as f:\\n output_dict = {str(k): v for k, v in self._state_values.items()}\\n json.dump(output_dict, f, indent=4, sort_keys=True)\",\n \"def logging(self, function):\\n avg_nms_time_per_step = sum(self.nms_times)/len(self.nms_times)\\n avg_total_time_per_step = sum(self.total_times)/len(self.total_times)\\n\\n avg_min_latency = [x[0] for x in self.inference_times]\\n avg_max_latency = [x[1] for x in self.inference_times]\\n avg_latency = [x[2] for x in self.inference_times]\\n\\n function(\\\"Inference stats: image size {}x{}, batches per step {}, batch size {}, {} steps\\\".format(\\n self.cfg.model.image_size, self.cfg.model.image_size, self.cfg.ipuopts.batches_per_step, self.cfg.model.micro_batch_size, len(self.total_times)\\n ))\\n function(\\\"--------------------------------------------------\\\")\\n function(\\\"Inference\\\")\\n function(\\\"Average Min Latency per Batch: {:.3f} ms\\\".format(1000 * sum(avg_min_latency)/len(self.inference_times)))\\n function(\\\"Average Max Latency per Batch: {:.3f} ms\\\".format(1000 * sum(avg_max_latency)/len(self.inference_times)))\\n function(\\\"Average Latency per Batch: {:.3f} ms\\\".format(1000 * sum(avg_latency)/len(self.inference_times)))\\n function(\\\"Average Inference Throughput: {:.3f} img/s\\\".format(sum(self.inference_throughputs)/len(self.inference_throughputs)))\\n function(\\\"--------------------------------------------------\\\")\\n # TODO remove the NMS and end-to-end time report once NMS is on device\\n function(\\\"End-to-end\\\")\\n function(\\\"Average NMS Latency per Batch: {:.3f} ms\\\".format(1000 * avg_nms_time_per_step/self.cfg.ipuopts.batches_per_step))\\n function(\\\"Average End-to-end Latency per Batch: {:.3f} ms\\\".format(1000 * avg_total_time_per_step/self.cfg.ipuopts.batches_per_step))\\n function(\\\"End-to-end Throughput: {:.3f} img/s\\\".format(sum(self.total_throughputs)/len(self.total_throughputs)))\\n function(\\\"==================================================\\\")\\n\\n if self.cfg.eval.metrics:\\n self.compute_and_print_eval_metrics()\",\n \"def print_stats(self, session, x, y, keep_prob, feature_batch, label_batch, valid_features, valid_labels,\\n cost, accuracy):\\n cost = session.run(cost, feed_dict={x: feature_batch, y: label_batch, keep_prob: 1.0})\\n train_accuracy = session.run(accuracy, feed_dict={x: feature_batch, y: label_batch, keep_prob: 1.0})\\n valid_accuracy = session.run(accuracy, feed_dict={x: valid_features, y: valid_labels, keep_prob: 1.0})\\n logger.info(\\\"loss:{}\\\".format(cost))\\n logger.info(\\\"Train Accuracy:{}\\\".format(train_accuracy))\\n logger.info(\\\"Validation Accuracy:{}\\\".format(valid_accuracy))\",\n \"def generate_report(self, output_path):\\n generated_on = str(datetime.datetime.now())\\n checkpoint_name = self._get_checkpoint_name()\\n dataset_name = Path(self._train_dataset_path).resolve().name\\n train_features, train_labels = self.get_features_and_labels(\\n self._read_dataset(self._train_dataset_path)\\n )\\n test_features, test_lables = self.get_features_and_labels(\\n self._read_dataset(self._test_dataset_path)\\n )\\n training_accuracy = self.get_model_accuracy(\\n train_features,\\n train_labels\\n )\\n test_accuracy = self.get_model_accuracy(\\n test_features,\\n test_lables,\\n )\\n with open(output_path, 'a') as f:\\n report = (\\n \\\"*****************************************************\\\\n\\\"\\n \\\"Report generated on: {}\\\\n\\\"\\n \\\"Training dataset: {}\\\\n\\\"\\n \\\"Model checkpoint: {}\\\\n\\\"\\n \\\"---\\\\n\\\"\\n \\\"Accuracy on training data: {}\\\\n\\\"\\n \\\"Accuracy on testing data: {}\\\\n\\\"\\n \\\"\\\\n\\\"\\n ).format(\\n generated_on,\\n dataset_name,\\n checkpoint_name,\\n training_accuracy,\\n test_accuracy,\\n )\\n f.writelines(report)\",\n \"def print_brief_summary(self):\\n print (\\\"Model {}\\\".format(self.modelName))\\n print (\\\"Precision {}\\\".format(self.precision))\\n print (\\\"Recall {}\\\".format(self.recall))\\n print (\\\"f1 score {}\\\".format(self.f1))\\n \\n # work here\\n print (\\\"\\\\nGold NER label counts:\\\")\\n for ner in self.gold_cts.keys():\\n print (\\\"{} : {} (tag{})\\\".format(self.gold_cts[ner], self.nerTags.ids_to_words([ner]), ner))\\n print (\\\"\\\\nPredicted NER label counts:\\\")\\n for ner in self.pred_cts.keys():\\n print (\\\"{} : {} (tag{})\\\".format(self.pred_cts[ner], self.nerTags.ids_to_words([ner]), ner))\",\n \"def create_summary_statistics(forward_accuracy, backward_accuracy, merged_accuracy):\\n summary_statistics = open(f'summary_statistics.txt', 'a')\\n summary_statistics.write(f'The forward model has an accuracy of: {forward_accuracy}\\\\n')\\n summary_statistics.write(f'The backward model has an accuracy of: {backward_accuracy}\\\\n')\\n summary_statistics.write(f'The merged model has an accuracy of: {merged_accuracy}\\\\n')\\n summary_statistics.close()\",\n \"def save(self,filename):\\n f = open(filename,'w')\\n f.write('Test results for %s v%s\\\\n' % (self.description,self.version))\\n f.write('Series ran by %s\\\\n\\\\n' % self.person_name)\\n for result in self.values():\\n f.write('%-70s : %s\\\\n' % (result.id,result.outcome))\\n if result.outcome != Result.PASS:\\n for (kind, annotation) in result.annotations.items():\\n f.write('%s:\\\\n%s\\\\n' % (kind, as_utf8(annotation)))\\n f.write('\\\\n')\\n f.write('\\\\n\\\\nPasses: %i\\\\n' % self.get_pass_count())\\n f.write('Fails: %i\\\\n' % self.get_fail_count())\\n f.write('Errors: %i\\\\n' % self.get_error_count())\\n f.write('Untested: %i\\\\n' % self.get_untested_count())\\n f.write('Skipped: %i\\\\n' % self.get_skipped_count())\\n f.close()\",\n \"def report(self, X, y):\\n pred = self.model.predict(X)\\n skplt.estimators.plot_feature_importances(\\n self.model, x_tick_rotation=90)\\n plt.show()\\n skplt.metrics.plot_confusion_matrix(y, pred, normalize=True)\\n plt.show()\\n print(\\\"Classification report\\\")\\n print(classification_report(y, pred))\\n skplt.metrics.plot_precision_recall(y, self.model.predict_proba(X))\\n plt.show()\\n pd.DataFrame({\\n \\\"predict_probability\\\": self.model.predict_proba(X)[:, 1],\\n \\\"observed\\\": y\\n }).hist(\\n \\\"predict_probability\\\", by=\\\"observed\\\")\\n plt.suptitle(\\\"Probability distribution by class\\\")\\n plt.show()\",\n \"def compute_and_print_eval_metrics(self):\\n s = ('%20s' + '%12s' * 6) % ('Class', 'Images', 'Targets', 'P', 'R', 'mAP@.5', 'mAP@.5:.95')\\n precision, recall, f1, mean_precision, mean_recall, map50, map = 0., 0., 0., 0., 0., 0., 0.\\n ap = []\\n eval_stats = [np.concatenate(x, 0) for x in zip(*self.eval_stats)]\\n if len(eval_stats) and eval_stats[0].any():\\n precision, recall, ap, f1, ap_class = ap_per_class(*eval_stats)\\n precision, recall, ap50, ap = precision[:, 0], recall[:, 0], ap[:, 0], ap.mean(1)\\n mean_precision, mean_recall, map50, map = precision.mean(), recall.mean(), ap50.mean(), ap.mean()\\n nt = np.bincount(eval_stats[3].astype(np.int64), minlength=len(self.class_names)) # number of targets per class\\n else:\\n nt = np.zeros(1)\\n\\n pf = '%20s' + '%12.5g' * 6 # print format\\n print(\\\"\\\\n EVALUTAION \\\\n\\\")\\n print(s)\\n print(pf % ('all', self.seen, nt.sum(), mean_precision, mean_recall, map50, map))\\n if self.cfg.eval.verbose:\\n for indx, cls in enumerate(ap_class):\\n print(pf % (self.class_names[cls], self.seen, nt[cls], precision[indx], recall[indx], ap50[indx], ap[indx]))\",\n \"def metrics(_):\\r\\n collector = BuildsCollector()\\r\\n build_metrics, headers = collector.get_metrics_table()\\r\\n print(tabulate(build_metrics, headers=headers))\",\n \"def report(self):\\n if isinstance(self.steps[-1][1], Regressor):\\n return self.steps[-1][1].regression_report()\\n else:\\n return self.steps[-1][1].classification_report()\",\n \"def final_eval(self):\\n # Test performance - Load best model\\n self.load_model(self.best_model_path, model_type='best')\\n logging.info(\\\"Loading best model after epoch: %d\\\" %\\n self.train_info['epoch'])\\n\\n perf_file = path.join(self.model_dir, \\\"perf.json\\\")\\n if self.slurm_id:\\n parent_dir = path.dirname(path.normpath(self.model_dir))\\n perf_dir = path.join(parent_dir, \\\"perf\\\")\\n if not path.exists(perf_dir):\\n os.makedirs(perf_dir)\\n perf_file = path.join(perf_dir, self.slurm_id + \\\".json\\\")\\n\\n output_dict = {'model_dir': self.model_dir}\\n for key, val in vars(self.args).items():\\n output_dict[key] = val\\n\\n for split in ['dev', 'test']:\\n logging.info('\\\\n')\\n logging.info('%s' % split.capitalize())\\n result_dict = self.eval_model(split, final_eval=True)\\n if split != 'test':\\n logging.info('Calculated F1: %.3f' % result_dict['fscore'])\\n\\n output_dict[split] = result_dict\\n\\n json.dump(output_dict, open(perf_file, 'w'), indent=2)\\n\\n logging.info(\\\"Final performance summary at %s\\\" % perf_file)\\n sys.stdout.flush()\",\n \"def output_summary_stats(self):\\n total_return = self.equity_curve['equity_curve'][-1]\\n returns = self.equity_curve['returns']\\n pnl = self.equity_curve['equity_curve']\\n\\n sharpe_ratio = create_sharpe_ratio(returns)\\n drawdown, max_dd, dd_duration = create_drawdowns(pnl)\\n self.equity_curve['drawdown'] = drawdown\\n if len(dd_duration) == 1:\\n dd_duration = dd_duration[0]\\n\\n stats = [(\\\"Total Return\\\", \\\"%0.2f%%\\\" % ((total_return - 1.0) * 100.0)),\\n (\\\"Sharpe Ratio\\\", \\\"%0.2f\\\" % sharpe_ratio),\\n (\\\"Max Drawdown\\\", \\\"%0.2f%%\\\" % (max_dd * 100.0)),\\n (\\\"Drawdown Duration\\\", \\\"%s\\\" % dd_duration)]\\n\\n self.equity_curve.to_csv('equity.csv')\\n self.positions.to_csv('positions.csv')\\n self.prices.to_csv('prices.csv')\\n\\n return stats\",\n \"def print_result(self):\\n print(\\\"Final results: \\\")\\n for i in range(1, len(self.agents) + 1):\\n agent = self.agents[i-1]\\n print(agent.name + \\\": {} wins\\\".format(self.results[agent.name]))\",\n \"def print_metrics(mva, df_train, df_test,\\n y_train, y_test,\\n mva_response_train=None, mva_response_test=None,\\n w_train=None, w_test=None):\\n\\n train_prediction = mva.predict(df_train)\\n test_prediction = mva.predict(df_test)\\n\\n if mva_response_train is None:\\n mva_response_train = classifiers.evaluate_mva(df_train, mva)\\n if mva_response_test is None:\\n mva_response_test = classifiers.evaluate_mva(df_test, mva)\\n\\n print(\\\"\\\\nClassification Reports:\\\")\\n print(\\\"Test sample:\\\")\\n print(classification_report(y_test, test_prediction,\\n target_names=[\\\"background\\\", \\\"signal\\\"]))\\n print(\\\"Training sample:\\\")\\n print(classification_report(y_train, train_prediction,\\n target_names=[\\\"background\\\", \\\"signal\\\"]))\\n\\n print(\\\"Confusion matrix:\\\")\\n print(\\\"Test sample:\\\")\\n print(confusion_matrix(y_test, test_prediction))\\n print(\\\"Training sample:\\\")\\n print(confusion_matrix(y_train, train_prediction))\\n print()\\n\\n print(\\\"KS Test p-value:\\\")\\n print(\\\"Signal:\\\")\\n print(ks_2samp(mva_response_train[y_train == 1],\\n mva_response_test[y_test == 1],\\n None if w_train is None else w_train[y_train == 1],\\n None if w_test is None else w_test[y_test == 1])[1])\\n print(\\\"Background:\\\")\\n print(ks_2samp(mva_response_train[y_train == 0],\\n mva_response_test[y_test == 0],\\n None if w_train is None else w_train[y_train == 0],\\n None if w_test is None else w_test[y_test == 0])[1])\\n print()\\n\\n # Try really hard to get the feature importances\\n feature_importances = []\\n try:\\n feature_importances = mva.feature_importances_\\n except AttributeError:\\n pass\\n try: # last step of a pipeline?\\n feature_importances = mva.steps[-1][1].feature_importances_\\n except AttributeError:\\n pass\\n try: # grid search?\\n feature_importances = mva.best_estimator_.feature_importances_\\n except AttributeError:\\n pass\\n try: # grid search last step of pipeline?\\n feature_importances = \\\\\\n mva.steps[-1][1].best_estimator_.feature_importances_\\n except AttributeError:\\n pass\\n try: # grid search of a pipeline?\\n feature_importances = \\\\\\n mva.best_estimator_.steps[-1][0].feature_importances_\\n except AttributeError:\\n pass\\n\\n if len(feature_importances):\\n print(\\\"Feature importance:\\\")\\n for var, importance in sorted(\\n zip(list(df_train), feature_importances),\\n key=lambda x: x[1],\\n reverse=True):\\n print(\\\"{0:15} {1:.3E}\\\".format(var, importance))\\n else:\\n pass\\n print()\",\n \"def output_summary_stats(self):\\n total_return = self.equity_curve['equity_curve'][-1]\\n returns = self.equity_curve['returns']\\n pnl = self.equity_curve['equity_curve']\\n \\n sharpe_ratio = create_sharpe_ratio(returns, periods=252*6.5*60)\\n drawdown, max_dd, dd_duration = create_drawdowns(pnl)\\n self.equity_curve['drawdown'] = drawdown\\n \\n stats = [(\\\"Total Return\\\", \\\"%0.2f%%\\\" % ((total_return - 1.0) * 100.0)), \\n (\\\"Sharpe Ratio\\\", \\\"%0.2f\\\" % sharpe_ratio), \\n (\\\"Max Drawdown\\\", \\\"%0.2f%%\\\" % (max_dd * 100.0)), \\n (\\\"Drawdown Duration\\\", \\\"%d\\\" % dd_duration)]\\n \\n self.equity_curve.to_csv('equity.csv')\\n \\n return stats\",\n \"def _to_string(self):\\n self.results.print_results()\\n self.results.print_comparison()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6337549","0.6247714","0.62319505","0.62179327","0.6136802","0.61355233","0.5989832","0.5975646","0.59036446","0.5874888","0.5862709","0.584473","0.58438706","0.5822467","0.57994264","0.5798621","0.5797651","0.5784963","0.5765707","0.576413","0.5757759","0.575347","0.5734205","0.5733494","0.5715078","0.5704774","0.5703272","0.5691885","0.5684237","0.56605446","0.56533206","0.5648091","0.56466657","0.56384873","0.56327873","0.5627172","0.5615363","0.5609663","0.5605509","0.56004995","0.55898803","0.55854875","0.55853444","0.55805117","0.5578627","0.55770695","0.55672646","0.55539274","0.5543054","0.5535254","0.5533878","0.55319536","0.5525059","0.5523055","0.55212766","0.55157053","0.55059475","0.55045015","0.5492635","0.549215","0.54720426","0.54672074","0.545174","0.5442855","0.54427296","0.54419523","0.54380935","0.54347515","0.54292107","0.5421573","0.5411197","0.54110014","0.5399409","0.53988504","0.5395406","0.5393698","0.5389109","0.538819","0.5383764","0.5375778","0.537517","0.53661275","0.5365158","0.53629327","0.53629214","0.5362884","0.53618956","0.5356923","0.5347859","0.53441066","0.5342774","0.53407395","0.53391063","0.53365","0.533438","0.533167","0.5330862","0.5330664","0.53166157","0.53122145"],"string":"[\n \"0.6337549\",\n \"0.6247714\",\n \"0.62319505\",\n \"0.62179327\",\n \"0.6136802\",\n \"0.61355233\",\n \"0.5989832\",\n \"0.5975646\",\n \"0.59036446\",\n \"0.5874888\",\n \"0.5862709\",\n \"0.584473\",\n \"0.58438706\",\n \"0.5822467\",\n \"0.57994264\",\n \"0.5798621\",\n \"0.5797651\",\n \"0.5784963\",\n \"0.5765707\",\n \"0.576413\",\n \"0.5757759\",\n \"0.575347\",\n \"0.5734205\",\n \"0.5733494\",\n \"0.5715078\",\n \"0.5704774\",\n \"0.5703272\",\n \"0.5691885\",\n \"0.5684237\",\n \"0.56605446\",\n \"0.56533206\",\n \"0.5648091\",\n \"0.56466657\",\n \"0.56384873\",\n \"0.56327873\",\n \"0.5627172\",\n \"0.5615363\",\n \"0.5609663\",\n \"0.5605509\",\n \"0.56004995\",\n \"0.55898803\",\n \"0.55854875\",\n \"0.55853444\",\n \"0.55805117\",\n \"0.5578627\",\n \"0.55770695\",\n \"0.55672646\",\n \"0.55539274\",\n \"0.5543054\",\n \"0.5535254\",\n \"0.5533878\",\n \"0.55319536\",\n \"0.5525059\",\n \"0.5523055\",\n \"0.55212766\",\n \"0.55157053\",\n \"0.55059475\",\n \"0.55045015\",\n \"0.5492635\",\n \"0.549215\",\n \"0.54720426\",\n \"0.54672074\",\n \"0.545174\",\n \"0.5442855\",\n \"0.54427296\",\n \"0.54419523\",\n \"0.54380935\",\n \"0.54347515\",\n \"0.54292107\",\n \"0.5421573\",\n \"0.5411197\",\n \"0.54110014\",\n \"0.5399409\",\n \"0.53988504\",\n \"0.5395406\",\n \"0.5393698\",\n \"0.5389109\",\n \"0.538819\",\n \"0.5383764\",\n \"0.5375778\",\n \"0.537517\",\n \"0.53661275\",\n \"0.5365158\",\n \"0.53629327\",\n \"0.53629214\",\n \"0.5362884\",\n \"0.53618956\",\n \"0.5356923\",\n \"0.5347859\",\n \"0.53441066\",\n \"0.5342774\",\n \"0.53407395\",\n \"0.53391063\",\n \"0.53365\",\n \"0.533438\",\n \"0.533167\",\n \"0.5330862\",\n \"0.5330664\",\n \"0.53166157\",\n \"0.53122145\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6336608"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":14370,"cells":{"query":{"kind":"string","value":"Simulates the backtest and outputs portfolio performance. Loops over all variants of strategy parameters of a space generated by a cartesian product of hyperparameter values. Generates new instances of all the data handlers, event queues, and portfolio objects upon each iteration, in order to ensure a \"clean slate\" for each trading instance on every simulation. The parameter combinations and their performance metrics are stored in an output CSV file, which will subsequently be used to plot performance characteristics."},"document":{"kind":"string","value":"def simulate_trading(self):\n # Create the file output stream\n posix_now = datetime.datetime.timestamp(datetime.datetime.now())\n out_path = os.getcwd() + \"/OutputResults/backtest_{}\".format(posix_now)[:-7:] + \".csv\"\n\n out = open(out_path, \"w+\")\n\n spl = len(self.strat_params_list)\n for i, sp in enumerate(self.strat_params_list): # http://book.pythontips.com/en/latest/enumerate.html\n print(\"Strategy %s out of %s...\" % (i + 1, spl))\n self._generate_trading_instances(sp)\n self._run_backtest()\n stats = self._output_performance()\n pprint.pprint(stats)\n\n tot_ret = float(stats[0][1].replace(\"%\", \"\"))\n cagr = float(stats[1][1].replace(\"%\", \"\"))\n sharpe = float(stats[2][1])\n max_dd = float(stats[3][1].replace(\"%\", \"\"))\n dd_dur = int(stats[4][1])\n\n # This should be more general in future implementations...\n out.write(\n \"%s,%s,%s,%s,%s,%s,%s,%s\\n\" % (sp[\"ols_window\"], sp[\"zscore_high\"], sp[\"zscore_low\"],\n tot_ret, cagr, sharpe, max_dd, dd_dur)\n )\n\n out.close()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def parameter_optimization(self):\n out = open(self.csv_dir + self.strategy_id + '_gridsearch.csv', \"w\")\n spl = len(self.para_list)\n for i, sp in enumerate(self.para_list):\n print(\"Strategy %s out of %s...\" % (i + 1, spl))\n self._generate_trading_instances(sp)\n self._run_backtest()\n stats = self.portfolio.get_statistics()\n tot_profit = float(stats[0][1])\n sharpe = float(stats[1][1])\n max_dd = float(stats[2][1])\n win_rate = float(stats[7][1].replace(\"%\", \"\"))\n profit_factor = float(stats[8][1])\n\n out.write(\n \"%s,%s,%s,%s,%s,%s,%s\\n\" %\n (sp[\"takeprofit\"], sp[\"period\"], tot_profit, sharpe, max_dd, win_rate, profit_factor)\n )\n out.close()","def run():\n\n for simulation in range(0, N_SIMULATIONS):\n\n # Set up environment and agent\n e = Environment() # create environment (also adds some dummy traffic)\n a = e.create_agent(LearningAgent) # create agent\n e.set_primary_agent(a, enforce_deadline=True) # specify agent to track\n # NOTE: You can set enforce_deadline=False while debugging to allow longer trials\n # TODO: Change later enforce_deadline=True\n\n # Now simulate it\n sim = Simulator(e, update_delay=0.001, display=False) # create simulator (uses pygame when display=True, if available)\n # NOTE: To speed up simulation, reduce update_delay and/or set display=False\n\n sim.run(n_trials=N_TRIALS) # run for a specified number of trials\n # NOTE: To quit midway, press Esc or close pygame window, or hit Ctrl+C on the command-line\n\n if simulation == N_SIMULATIONS - 1:\n\n with open('results.csv', 'a') as csvfile:\n fieldnames = ['alpha', 'gamma', 'epsilon', 'success_rate', 'last_failure']\n writer = csv.DictWriter(csvfile, fieldnames=fieldnames)\n\n for index in range(0,len(simulation_rates)):\n writer.writerow({\n 'alpha': get_simulation_params(0)[0],\n 'gamma': get_simulation_params(0)[1],\n 'epsilon': get_simulation_params(0)[2],\n 'success_rate': simulation_rates[index],\n 'last_failure': last_errors[index]})\n\n\n if N_SIMULATIONS > 1: #multiple simulation AND last simulation\n\n plt.figure(1)\n\n plt.subplot(211)\n plt.plot(simulation_rates)\n plt.title('Success Rate/Simulation')\n plt.xlabel('# Simulation')\n plt.ylabel('Success Rate')\n\n plt.subplot(212)\n plt.plot(last_errors)\n plt.title('Last failed trial per simulation')\n plt.xlabel('# Simulation')\n plt.ylabel('Last failed trial')\n\n plt.show()","def target_portfolio_simulation(num_of_years=30, trials=100, method='normal'):\n print(\"Running method target_portfolio_simulation()\")\n\n sim_fia_cv = pd.DataFrame(index=range(num_of_years + 1))\n\n sim_base_total = pd.DataFrame(index=range(num_of_years + 1))\n sim_base_income = pd.DataFrame(index=range(num_of_years + 1))\n\n sim_port_total = pd.DataFrame(index=range(num_of_years + 1))\n sim_port_income = pd.DataFrame(index=range(num_of_years + 1))\n\n read_income_inputs = pd.read_excel(src + \"portfolio_information.xlsx\", sheet_name='income_model_inputs',\n index_col=[0])\n\n read_returns_est = pd.read_excel(src + \"portfolio_information.xlsx\", sheet_name='income_assets_returns_estimates',\n index_col=[0])\n\n read_asset_weights = pd.read_excel(src + \"portfolio_information.xlsx\", sheet_name='asset_weights',\n index_col=[0])\n\n # read_asset_weights.drop(read_asset_weights.index[-1], axis=0, inplace=True)\n\n # read random returns for simulation\n read_normal = pd.read_csv(src + 'median_returns_unsorted.csv', index_col=[0], parse_dates=True)\n cols = [read_normal.columns[c].split('_')[1] for c in np.arange(len(read_normal.columns))]\n read_normal.rename(columns=dict(zip(list(read_normal.columns), cols)), inplace=True)\n\n read_small = pd.read_csv(src + 'median_returns_smallest.csv', index_col=[0], parse_dates=True)\n read_small.rename(columns=dict(zip(list(read_small.columns), cols)), inplace=True)\n\n read_large = pd.read_csv(src + 'median_returns_largest.csv', index_col=[0], parse_dates=True)\n read_large.rename(columns=dict(zip(list(read_large.columns), cols)), inplace=True)\n\n assets_col_names = list(read_normal.columns)\n\n tickers = list(read_asset_weights.index)\n wts = np.array(read_asset_weights.loc[:, 'base'])\n\n def asset_median_returns(data, ticker):\n return data.filter(regex=ticker).median(axis=1)\n\n # dataframe for unsorted returns (normal)\n median_returns_normal = read_normal.copy()\n median_returns_normal.loc[:, 'portfolio_return'] = median_returns_normal.dot(wts)\n median_normal_fia = pd.DataFrame({'FIA': asset_median_returns(read_normal, 'FIA')})\n\n # dataframe for smallest to largest returns\n median_returns_smallest = read_small.copy()\n median_returns_smallest.loc[:, 'portfolio_return'] = median_returns_smallest.dot(wts)\n median_smallest_fia = pd.DataFrame({'FIA': asset_median_returns(read_small, 'FIA')})\n\n # dataframe for largest to smallest returns\n median_returns_largest = read_large.copy()\n median_returns_largest.loc[:, 'portfolio_return'] = median_returns_largest.dot(wts)\n median_largest_fia = pd.DataFrame({'FIA': asset_median_returns(read_large, 'FIA')})\n\n years = list(range(0, num_of_years + 1))\n income_cols = ['year', 'strategy_term', 'index_returns', 'term_ret', 'term_ret_with_par', 'term_annualize',\n 'ann_net_spread', 'term_ret_netspr', 'high_inc_benefit_base', 'rider_fee', 'eoy_income',\n 'contract_value']\n\n term = int(read_income_inputs.loc['term', 'inputs'])\n fia_ret = read_returns_est.loc[read_returns_est.index[-1], 'Annualized Returns']\n fia_risk = read_returns_est.loc[read_returns_est.index[-1], 'Annualized Risk']\n par_rate = float(read_income_inputs.loc['par_rate', 'inputs'])\n spread = float(read_income_inputs.loc['spread', 'inputs'])\n bonus_term = int(read_income_inputs.loc['bonus_term', 'inputs'])\n premium = float(read_income_inputs.loc['premium', 'inputs'])\n income_bonus = float(read_income_inputs.loc['income_bonus', 'inputs'])\n\n income_starts = int(read_income_inputs.loc['start_income_years', 'inputs'])\n income_growth = float(read_income_inputs.loc['income_growth', 'inputs'])\n rider_fee = float(read_income_inputs.loc['rider_fee', 'inputs'])\n inc_payout_factor = float(read_income_inputs.loc['income_payout_factor', 'inputs'])\n contract_bonus = float(read_income_inputs.loc['contract_bonus', 'inputs'])\n social = float(read_income_inputs.loc['social', 'inputs'])\n inflation = float(read_income_inputs.loc['inflation', 'inputs'])\n wtd_cpn_yield = float(read_income_inputs.loc['wtd_coupon_yld', 'inputs'])\n life_expectancy = int(read_income_inputs.loc['life_expectancy_age', 'inputs'])\n clients_age = int(read_income_inputs.loc['clients_age', 'inputs'])\n\n # ---------------INCOME MODEL--------------------------------------------\n runs = 0\n returns_dict = {}\n asset_dict = {}\n fia_dict = {}\n\n income_df = pd.DataFrame(index=years, columns=income_cols)\n income_df.loc[:, 'year'] = years\n income_df.loc[:, 'strategy_term'] = income_df.loc[:, 'year'] % term\n income_df.loc[:, 'strategy_term'] = income_df['strategy_term'].apply(lambda x: 1 if x == 0 else 0)\n\n if method == 'normal':\n income_df.loc[:, 'index_returns'] = read_normal.loc[:, 'FIA']\n\n elif method == 'smallest':\n income_df.loc[:, 'index_returns'] = read_small.loc[:, 'FIA']\n\n else:\n income_df.loc[:, 'index_returns'] = read_large.loc[:, 'FIA']\n\n # income_df.loc[:, 'index_returns'] = np.random.normal(fia_ret, fia_risk, size=(len(years), 1))\n\n cumprod = (1. + income_df['index_returns']).rolling(window=term).agg(lambda x: x.prod()) - 1\n income_df.loc[:, 'term_ret'] = np.where(income_df.loc[:, 'strategy_term'] == 1, cumprod, 0)\n income_df.loc[:, 'term_ret_with_par'] = income_df.loc[:, 'term_ret'] * par_rate\n income_df.loc[:, 'term_annualize'] = income_df.loc[:, 'term_ret_with_par'].apply(\n lambda x: (1 + x) ** (1 / term) - 1)\n income_df.loc[:, 'ann_net_spread'] = income_df.loc[:, 'term_annualize'] - spread\n income_df.loc[:, 'ann_net_spread'] = np.where(income_df.loc[:, 'strategy_term'] == 1,\n income_df.loc[:, 'ann_net_spread'], 0)\n income_df.loc[:, 'term_ret_netspr'] = income_df.loc[:, 'ann_net_spread'].apply(lambda x: (1 + x) ** term - 1)\n\n for counter in years:\n if counter == 0:\n income_df.loc[counter, 'high_inc_benefit_base'] = premium * (1 + income_bonus)\n\n elif counter <= min(bonus_term, income_starts):\n income_df.loc[counter, 'high_inc_benefit_base'] = income_df.loc[counter - 1, 'high_inc_benefit_base'] * \\\n (1 + income_growth)\n else:\n income_df.loc[counter, 'high_inc_benefit_base'] = income_df.loc[counter - 1, 'high_inc_benefit_base']\n\n income_df.loc[:, 'rider_fee'] = income_df.loc[:, 'high_inc_benefit_base'] * rider_fee\n income_df.loc[:, 'eoy_income'] = np.where(income_df.loc[:, 'year'] > income_starts,\n income_df.loc[:, 'high_inc_benefit_base'] * inc_payout_factor, 0)\n\n for counter in years:\n if counter == 0:\n income_df.loc[counter, 'contract_value'] = premium * (1 + contract_bonus)\n\n elif income_df.loc[counter, 'strategy_term'] == 1:\n x1 = income_df.loc[counter - 1, 'contract_value'] - income_df.loc[counter, 'rider_fee']\n x2 = (x1 * (1 + income_df.loc[counter, 'term_ret_netspr'])) - income_df.loc[counter, 'eoy_income']\n income_df.loc[counter, 'contract_value'] = x2\n\n else:\n x1 = income_df.loc[counter - 1, 'contract_value'] - income_df.loc[counter, 'rider_fee'] - \\\n income_df.loc[counter, 'eoy_income']\n\n income_df.loc[counter, 'contract_value'] = x1\n\n # variable stores the income number that is used in the base and fia portfolio calcs.\n\n income_from_fia = income_df.loc[income_df.index[-1], 'eoy_income']\n\n income_df.loc[:, 'contract_value'] = income_df.loc[:, 'contract_value'].apply(lambda x: 0 if x <= 0 else x)\n\n sim_fia_cv.loc[:, str(runs)] = income_df.loc[:, 'contract_value']\n\n # --------------------BASE MODEL---------------------------------------------\n base_wts = read_asset_weights.loc[:, 'base']\n base_assets = list(base_wts.index)\n base_weights = list(base_wts.values)\n base_returns = list(read_returns_est.loc[:, 'Annualized Returns'].values)\n base_std = list(read_returns_est.loc[:, 'Annualized Risk'].values)\n\n base_investment = float(read_income_inputs.loc['risky_assets', 'Base'])\n adv_fees = float(read_income_inputs.loc['advisor_fees', 'Base'])\n\n # -------------------required income----------------------------------\n req_annual_income = float(read_income_inputs.loc['annual_income', 'inputs'])\n income_needed = req_annual_income - social\n income_net_fia_income = max(0, income_needed - income_from_fia)\n cpn_income_base = base_investment * wtd_cpn_yield\n\n # ----------------------RANDOM RETURNS--------------------------\n r_cols = base_assets\n boy_value = ['bv_{}'.format(name) for name in base_assets]\n eoy_value = ['ev_{}'.format(name) for name in base_assets]\n\n random_returns = pd.DataFrame(index=income_df.index, columns=r_cols)\n\n for c in range(len(r_cols)):\n ret = np.random.normal(base_returns[c], base_std[c], size=(len(random_returns.index), 1))\n\n if method == 'smallest':\n random_returns = read_small.copy()\n\n elif method == 'largest':\n random_returns = read_large.copy()\n\n else:\n random_returns = read_normal.copy()\n\n base_df = random_returns.copy()\n fia_portfolio_df = random_returns.copy()\n port_investment = float(read_income_inputs.loc['risky_assets', 'FIA'])\n cpn_income_port = port_investment * wtd_cpn_yield\n\n # -------------BASE PORTFOLIO----------------------------\n for name in boy_value:\n base_df.loc[:, name] = 0.0\n\n for counter in years:\n period_returns = list(random_returns.loc[counter, :])\n if counter == 0:\n\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment for c in range(len(boy_value))]\n\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\n base_df.loc[counter, 'total_net_fees'] = 0.0\n base_df.loc[counter, 'income'] = 0.0\n base_investment = base_df.loc[counter, boy_value].sum()\n\n elif (counter > 0) and (counter < income_starts):\n\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment * (1 + period_returns[c])\n for c in range(len(boy_value))]\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\n base_df.loc[counter, 'adv_fees'] = base_df.loc[counter, 'total'] * adv_fees\n base_df.loc[counter, 'total_net_fees'] = base_df.loc[counter, 'total'] - base_df.loc[\n counter, 'adv_fees']\n\n # --coupon payment is invested back into the risky portfolio until the income is withdrawn----\n base_investment = base_df.loc[counter, 'total_net_fees'] + cpn_income_base\n\n else:\n\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment * (1 + period_returns[c])\n for c in range(len(boy_value))]\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\n base_df.loc[counter, 'adv_fees'] = base_df.loc[counter, 'total'] * adv_fees\n\n # ---req. income is adjusted for inflation from the second year of withdrawal. Reinvestment of coupon\n # stops from the year income starts. Req. income is reduced by the coupon payments\n\n if counter == income_starts:\n\n income_needed = req_annual_income - social\n base_df.loc[counter, 'income'] = income_needed - cpn_income_base\n income_needed = req_annual_income\n\n else:\n income_needed = income_needed * (1 + inflation) - social\n base_df.loc[counter, 'income'] = income_needed - cpn_income_base\n income_needed = income_needed + social\n\n base_df.loc[counter, 'total_net_fees'] = base_df.loc[counter, 'total'] - \\\n base_df.loc[counter, 'adv_fees'] - \\\n base_df.loc[counter, 'income']\n\n base_investment = base_df.loc[counter, 'total_net_fees']\n\n base_df.loc[:, 'adj_total'] = base_df.loc[:, 'total_net_fees'].apply(lambda x: x if x > 0 else 0)\n sim_base_total.loc[:, 's_{}'.format(str(runs))] = base_df.loc[:, 'total_net_fees']\n sim_base_income.loc[:, 's_{}'.format(str(runs))] = base_df.loc[:, 'income']\n\n # ----------------------------FIA PORTFOLIO----------------------------------------------\n for name in boy_value:\n fia_portfolio_df.loc[:, name] = 0.0\n\n for counter in years:\n period_returns = list(random_returns.loc[counter, :])\n if counter == 0:\n\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment\n for c in range(len(boy_value))]\n fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\n fia_portfolio_df.loc[counter, 'total_net_fees'] = 0.0\n fia_portfolio_df.loc[counter, 'income'] = 0.0\n port_investment = fia_portfolio_df.loc[counter, boy_value].sum()\n\n elif (counter > 0) and (counter < income_starts):\n\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment * (1 + period_returns[c])\n for c in range(len(boy_value))]\n fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\n fia_portfolio_df.loc[counter, 'adv_fees'] = fia_portfolio_df.loc[counter, 'total'] * adv_fees\n fia_portfolio_df.loc[counter, 'total_net_fees'] = fia_portfolio_df.loc[counter, 'total'] - \\\n fia_portfolio_df.loc[counter, 'adv_fees']\n\n port_investment = fia_portfolio_df.loc[counter, 'total_net_fees'] + cpn_income_port\n\n else:\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment * (1 + period_returns[c])\n for c in range(len(boy_value))]\n fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\n fia_portfolio_df.loc[counter, 'adv_fees'] = fia_portfolio_df.loc[counter, 'total'] * adv_fees\n\n # ---req. income is adjusted for inflation from the second year of withdrawal. Reinvestment of coupon\n # stops from the year income starts. Req. income is reduced by the coupon payments\n\n if counter == income_starts:\n\n income_needed = req_annual_income - social\n income_net_fia_income = max(0, income_needed - income_from_fia)\n fia_portfolio_df.loc[counter, 'income'] = max(0, income_net_fia_income - cpn_income_port)\n income_needed = req_annual_income\n\n else:\n income_needed = income_needed * (1 + inflation) - social\n income_net_fia_income = max(0, income_needed - income_from_fia)\n fia_portfolio_df.loc[counter, 'income'] = max(0, income_net_fia_income - cpn_income_port)\n income_needed = income_needed + social\n\n fia_portfolio_df.loc[counter, 'total_net_fees'] = fia_portfolio_df.loc[counter, 'total'] - \\\n fia_portfolio_df.loc[counter, 'adv_fees'] - \\\n fia_portfolio_df.loc[counter, 'income']\n\n port_investment = fia_portfolio_df.loc[counter, 'total_net_fees']\n\n sim_port_total.loc[:, 's_{}'.format(str(runs))] = fia_portfolio_df.loc[:, 'total_net_fees'] + \\\n income_df.loc[:, 'contract_value']\n\n sim_port_income.loc[:, 's_{}'.format(str(runs))] = fia_portfolio_df.loc[:, 'income']\n\n fia_portfolio_df.loc[:, 'adj_total'] = fia_portfolio_df.loc[:, 'total_net_fees'].apply(\n lambda x: x if x > 0 else 0)\n\n # ---------income breakdown for Base portfolio----------------------------------\n base_df.to_csv(src + 'base_port_detail.csv')\n sim_base_total.to_csv(src + 'base_ending_values.csv')\n income_breakdown_base = pd.DataFrame(sim_base_total.quantile(0.5, axis=1))\n income_breakdown_base.loc[:, 'income_from_portfolio'] = sim_base_income.quantile(0.5, axis=1)\n income_breakdown_base.loc[:, 'fia_income'] = 0.0\n income_breakdown_base.loc[:, 'social_security_income'] = social\n income_breakdown_base.loc[:, 'coupon_income'] = cpn_income_base\n\n income_breakdown_base.rename(columns={income_breakdown_base.columns[0]: 'portfolio_ending_value'}, inplace=True)\n income_breakdown_base.loc[:, 'income_from_portfolio'][\n income_breakdown_base.loc[:, 'portfolio_ending_value'] <= 0] = 0\n income_breakdown_base.loc[:, 'total_income'] = income_breakdown_base.loc[:, income_breakdown_base.columns[1:]].sum(\n axis=1)\n\n # ------------Block Ends-------------------------------------------------------------\n\n # ---------income breakdown for FIA portfolio----------------------------------\n fia_portfolio_df.to_csv(src + 'fia_port_detail.csv')\n sim_port_total.to_csv(src + 'fiaport_ending_values.csv')\n\n income_breakdown_port = pd.DataFrame(sim_port_total.quantile(0.5, axis=1))\n income_breakdown_port.loc[:, 'income_from_portfolio'] = sim_port_income.quantile(0.5, axis=1)\n income_breakdown_port.loc[:, 'fia_income'] = income_from_fia\n income_breakdown_port.loc[:, 'social_security_income'] = social\n income_breakdown_port.loc[:, 'coupon_income'] = cpn_income_port\n\n income_breakdown_port.rename(columns={income_breakdown_port.columns[0]: 'portfolio_ending_value'}, inplace=True)\n income_breakdown_port.loc[:, 'income_from_portfolio'][\n income_breakdown_port.loc[:, 'portfolio_ending_value'] <= 0] = 0\n income_breakdown_port.loc[:, 'total_income'] = income_breakdown_port.loc[:, income_breakdown_port.columns[1:]].sum(\n axis=1)\n\n # ------------Block Ends-------------------------------------------------------------\n q_cut = [0.0, 0.05, 0.25, 0.5, 0.75, 0.95, 1.0]\n sim_base_income[sim_base_total < income_needed] = 0.0\n\n sim_port_income[sim_port_total < income_net_fia_income] = 0\n\n sim_port_income = sim_port_income + income_from_fia\n\n # base_quantile = sim_base_total.loc[sim_base_total.index[-1]].quantile([0.05, 0.25, 0.50, 0.75, 0.90])\n\n # port_quantile = sim_port_total.loc[sim_port_total.index[-1]].quantile([0.05, 0.25, 0.50, 0.75, 0.90])\n\n base_quantile = sim_base_total.loc[sim_base_total.index[-1]].quantile(q_cut)\n\n port_quantile = sim_port_total.loc[sim_port_total.index[-1]].quantile(q_cut)\n\n # q_cut = [0.0, .05, 0.25, 0.5, 0.75, 0.95, 1.0]\n cols = ['Min', '5th', '25th', '50th', '75th', '90th', 'Max']\n\n # ----drop year 0--------\n sim_base_total = sim_base_total[1:]\n sim_port_total = sim_port_total[1:]\n\n # ----------------quantile analysis for base terminal value--------------------------\n base_qcut = pd.DataFrame(index=sim_base_total.index, columns=cols)\n for c in range(len(cols)):\n base_qcut.loc[:, cols[c]] = sim_base_total.quantile(q_cut[c], axis=1)\n\n base_qcut.clip(lower=0, inplace=True)\n\n # ----------------------quantile analysis for base income----------------------------\n base_income_qcut = pd.DataFrame(index=sim_base_income.index, columns=cols)\n for c in range(len(cols)):\n base_income_qcut.loc[:, cols[c]] = sim_base_income.quantile(q_cut[c], axis=1)\n\n # ----Remove NaN's prior to the income start years------------\n # base_income_qcut = base_income_qcut.loc[income_starts:]\n\n # -------------quantile analysis for portfolio terminal value ----------------\n port_qcut = pd.DataFrame(index=sim_port_total.index, columns=cols)\n for c in range(len(cols)):\n port_qcut.loc[:, cols[c]] = sim_port_total.quantile(q_cut[c], axis=1)\n\n port_qcut.clip(lower=0, inplace=True)\n\n # ---------------quantile analysis for portfolio income----------------------------\n port_income_qcut = pd.DataFrame(index=sim_port_income.index, columns=cols)\n for c in range(len(cols)):\n port_income_qcut.loc[:, cols[c]] = sim_port_income.quantile(q_cut[c], axis=1)\n\n # ----Remove NaN's prior to the income start years------------\n # port_income_qcut = port_income_qcut.loc[income_starts:]\n\n # ----------probability ending value will be less than 0 at the end of the horizon -----------------------\n base_legacy_risk = (sim_base_total.loc[sim_base_total.index[life_expectancy - clients_age]] < 0).sum() / (\n trials + 1)\n port_legacy_risk = (sim_port_total.loc[sim_port_total.index[life_expectancy - clients_age]] < 0).sum() / (\n trials + 1)\n\n legacy_risk = pd.DataFrame([base_legacy_risk, port_legacy_risk,\n 'Prob. of portfolio value less than 0 at the end of the expected life'],\n index=['base', 'fia_portfolio', 'Notes'],\n columns=['Ruin Probability'])\n\n # -----------Year-wise probability of ending value greater than 0 -----------------\n base_psuccess = sim_base_total.apply(lambda x: x > 0).sum(axis=1) / (trials + 1)\n port_psuccess = sim_port_total.apply(lambda x: x > 0).sum(axis=1) / (trials + 1)\n\n # -----------------------WRITING FILES TO EXCEL ---------------------------\n col_names = ['50th', 'age', 'comment']\n writer = pd.ExcelWriter(src + method + '_simulated_income_summary.xlsx', engine='xlsxwriter')\n read_income_inputs.to_excel(writer, sheet_name='inputs_for_income')\n\n read_returns_est.to_excel(writer, sheet_name='asset_returns_estimates')\n\n age_index = list(range(clients_age + 1, clients_age + len(base_qcut) + 1))\n base_qcut.loc[:, 'age'] = age_index\n base_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n base_qcut.loc[income_starts:, col_names].to_excel(writer, sheet_name='base_ending_value_quantiles')\n\n base_income_qcut = base_income_qcut.loc[1:, :]\n base_income_qcut.loc[:, 'age'] = age_index\n base_income_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n base_income_qcut.loc[income_starts:, col_names].to_excel(writer, sheet_name='base_income_quantiles')\n\n port_qcut.loc[:, 'age'] = age_index\n port_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n port_qcut.loc[income_starts:, col_names].to_excel(writer, sheet_name='fia_port_ending_value_quantiles')\n\n port_income_qcut = port_income_qcut.loc[1:, :]\n port_income_qcut.loc[:, 'age'] = age_index\n port_income_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n # port_income_qcut.loc[:, 'ending_contract_value'] = sim_fia_cv\n port_income_qcut.loc[income_starts:, col_names].to_excel(writer, sheet_name='fia_port_income_quantiles')\n\n # prob_success_df = pd.concat([base_psuccess, port_psuccess], axis=1)\n # prob_success_df.rename(columns={prob_success_df.columns[0]: 'prob(ending_value>0)_base',\n # prob_success_df.columns[1]: 'prob(ending_value>0)_port'}, inplace=True)\n\n # prob_success_df.loc[:, 'age'] = age_index\n # prob_success_df.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n # prob_success_df.to_excel(writer, sheet_name='success_probability')\n\n income_breakdown_base = income_breakdown_base.loc[1:, :]\n income_breakdown_base.loc[:, 'age'] = age_index\n income_breakdown_base.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n income_breakdown_base.loc[income_starts:, :].to_excel(writer, sheet_name='base_income_breakdown_median')\n\n income_breakdown_port = income_breakdown_port.loc[1:, :]\n income_breakdown_port.loc[:, 'age'] = age_index\n income_breakdown_port.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n income_breakdown_port.loc[:, 'ending_contract_value'] = income_df.loc[:, 'contract_value']\n income_breakdown_port.loc[income_starts:, :].to_excel(writer, sheet_name='fia_income_breakdown_median')\n\n if method == 'normal':\n # median_returns_normal.loc[:, 'fia_median_returns'] = median_normal_fia\n median_returns_normal.to_excel(writer, sheet_name='gr_port_median_normal')\n\n elif method == 'smallest':\n # median_returns_smallest.loc[:, 'fia_median_returns'] = median_smallest_fia\n median_returns_smallest.to_excel(writer, sheet_name='gr_port_median_asc')\n\n else:\n # median_returns_largest.loc[:, 'fia_median_returns'] = median_largest_fia\n median_returns_largest.to_excel(writer, sheet_name='gr_port_median_desc')\n\n terminal_val = pd.read_csv(src + 'terminal_values.csv', index_col=[0])\n ending_val = pd.read_csv(src + 'ending_values.csv', index_col=[0])\n ending_val_ror = pd.read_csv(src + 'ending_values_ror.csv', index_col=[0])\n\n terminal_val.to_excel(writer, sheet_name='terminal_values')\n ending_val.to_excel(writer, sheet_name='port_ending_values')\n ending_val_ror.to_excel(writer, sheet_name='port_annual_growth')\n\n writer.save()\n\n # -----------------Plotting charts--------------------------------------------\n base_qcut.loc[income_starts:].plot(grid=True, title='Quantile Terminal Value - Base Portfolio')\n plt.savefig(src + \"quantile_terminal_base.png\")\n plt.close('all')\n\n base_income_qcut.plot(grid=True, title='Quantile Income - Base Portfolio')\n plt.savefig(src + \"quantile_income_base.png\")\n plt.close('all')\n\n base_psuccess.plot(grid=True, title='Probability of Success (Portfolio Ending Value > 0) - Base Portfolio')\n plt.savefig(src + \"success_probabilty_base.png\")\n plt.close('all')\n\n (1 - base_psuccess).plot(grid=True, title='Probability of Ruin (Portfolio Ending Value < 0) - Base Portfolio')\n plt.savefig(src + \"ruin_probability_base.png\")\n plt.close('all')\n\n port_qcut.loc[income_starts:].plot(grid=True, title='Quantile Terminal Value - FIA Portfolio')\n plt.savefig(src + \"quantile_terminal_fia.png\")\n plt.close('all')\n\n port_income_qcut.plot(grid=True, title='Quantile Income - FIA Portfolio')\n plt.savefig(src + \"quantile_income_fia.png\")\n plt.close('all')\n\n port_psuccess.plot(grid=True, title='Probability of Success (Portfolio Ending Value > 0) - FIA Portfolio')\n plt.savefig(src + \"success_probabilty_fia.png\")\n plt.close('all')\n\n (1 - port_psuccess).plot(grid=True, title='Probability of Ruin (Portfolio Ending Value < 0) - FIA Portfolio')\n plt.savefig(src + \"ruin_probability_fia.png\")\n plt.close('all')\n\n print(\"simulation completed for {}\".format(method))","def simulate_trading(self):\n self._generate_trading_instances()\n self._run_backtest()\n self.portfolio.output_equity()\n res=self.portfolio.get_statistics()\n self.plot.plot_equity()\n return res","def run_simulator(simulation_parameters, spectrum_portfolio,\n ant_heights, site_radii, modulation_and_coding_lut, costs):\n\n unprojected_point = {\n 'type': 'Feature',\n 'geometry': {\n 'type': 'Point',\n 'coordinates': (-0.07496, 51.42411),\n },\n 'properties': {\n 'site_id': 'Crystal Palace Radio Tower'\n }\n }\n\n unprojected_crs = 'epsg:4326'\n projected_crs = 'epsg:3857'\n\n environments =[\n 'urban',\n 'suburban',\n 'rural'\n ]\n\n for environment in environments:\n for site_radius in site_radii[environment]:\n\n print('--working on {}: {}'.format(environment, site_radius))\n\n transmitter, interfering_transmitters, site_area, interfering_site_areas = \\\n produce_sites_and_site_areas(\n unprojected_point['geometry']['coordinates'],\n site_radius,\n unprojected_crs,\n projected_crs\n )\n\n receivers = generate_receivers(site_area, SIMULATION_PARAMETERS, 1)\n\n for frequency, bandwidth, generation in spectrum_portfolio:\n for ant_height in ant_heights:\n\n MANAGER = SimulationManager(\n transmitter, interfering_transmitters, receivers,\n site_area, SIMULATION_PARAMETERS\n )\n\n results = MANAGER.estimate_link_budget(\n frequency, bandwidth, generation, ant_height,\n environment,\n MODULATION_AND_CODING_LUT,\n SIMULATION_PARAMETERS\n )\n\n folder = os.path.join(BASE_PATH, '..', 'results', 'full_tables')\n filename = 'full_capacity_lut_{}_{}_{}_{}.csv'.format(\n environment, site_radius, frequency, ant_height)\n\n write_full_results(results, environment, site_radius,\n frequency, bandwidth, generation, ant_height,\n folder, filename, simulation_parameters)\n\n average_site_results = obtain_average_values(\n results, simulation_parameters\n )\n\n results_directory = os.path.join(BASE_PATH, '..', 'results')\n\n write_frequency_lookup_table(average_site_results, environment,\n site_radius, frequency, bandwidth, generation,\n ant_height, results_directory,\n 'average_capacity_lut.csv',\n simulation_parameters\n )\n\n if frequency == spectrum_portfolio[0][0]:\n\n percentile_site_results = obtain_percentile_values(\n results, simulation_parameters\n )\n\n percentile_site_results = calculate_costs(\n percentile_site_results, costs, simulation_parameters,\n site_radius, environment\n )\n\n write_cost_lookup_table(percentile_site_results, environment,\n site_radius, frequency, bandwidth, generation,\n ant_height, results_directory,\n 'percentile_{}_capacity_lut.csv'.format(\n simulation_parameters['percentile']),\n simulation_parameters\n )\n\n ## write out as shapes, if desired, for debugging purposes\n geojson_receivers = convert_results_geojson(results)\n\n write_shapefile(\n geojson_receivers, os.path.join(results_directory, 'shapes'),\n 'receivers_{}.shp'.format(site_radius),\n projected_crs\n )\n\n write_shapefile(\n transmitter, os.path.join(results_directory, 'shapes'),\n 'transmitter_{}.shp'.format(site_radius),\n projected_crs\n )\n\n write_shapefile(\n site_area, os.path.join(results_directory, 'shapes'),\n 'site_area_{}.shp'.format(site_radius),\n projected_crs\n )\n\n write_shapefile(\n interfering_transmitters, os.path.join(results_directory, 'shapes'),\n 'interfering_transmitters_{}.shp'.format(site_radius),\n projected_crs\n )\n\n write_shapefile(\n interfering_site_areas, os.path.join(results_directory, 'shapes'),\n 'interfering_site_areas_{}.shp'.format(site_radius),\n projected_crs\n )","def simulation_using_historical_returns(num_of_years=30, trials=100, method='normal'):\n\n print(\"Running method simulation_using_historical_returns()\")\n\n sim_fia_cv = pd.DataFrame(index=range(num_of_years + 1))\n\n sim_base_total = pd.DataFrame(index=range(num_of_years + 1))\n sim_base_income = pd.DataFrame(index=range(num_of_years + 1))\n\n sim_port_total = pd.DataFrame(index=range(num_of_years + 1))\n sim_port_income = pd.DataFrame(index=range(num_of_years + 1))\n\n read_income_inputs = pd.read_excel(src + \"portfolio_information.xlsx\", sheet_name='income_model_inputs',\n index_col=[0])\n\n read_returns_est = pd.read_excel(src + \"portfolio_information.xlsx\", sheet_name='income_assets_returns_estimates',\n index_col=[0])\n\n read_asset_weights = pd.read_excel(src + \"portfolio_information.xlsx\", sheet_name='asset_weights',\n index_col=[0])\n\n # read the base portfolio from accumulation model\n fia_name = read_income_inputs.loc['fia_name', 'inputs']\n path = src + fia_name + '/'\n file_name = fia_name + '_portfolio.csv'\n\n # base_port = pd.read_csv(path + 'base_portfolio.csv', index_col=[0], parse_dates=True)\n\n base_port = pd.read_csv(path + file_name, index_col=[0], parse_dates=True)\n\n ls1 = list(read_asset_weights.index[:-2])\n ls2 = ['Cash', fia_name, 'Total']\n combined_ls = ls1 + ls2\n base_port = base_port.loc[:, combined_ls]\n base_port.loc[:, 'port_return'] = base_port['Total'].pct_change().fillna(0)\n base_port.drop('Total', axis=1, inplace=True)\n yearly_base_port = base_port.groupby(by=base_port.index.year).apply(sum)\n\n # --Simulate only for first 20 years of returns--------\n first_n_years = int(read_income_inputs.loc['simulate_for_first_n_years', 'inputs'])\n yearly_base_port = yearly_base_port.loc[yearly_base_port.index[-(first_n_years + 1):], :]\n\n # --------Unsorted returns ---------------------\n read_normal = yearly_base_port.copy()\n read_normal.iloc[0] = 0.0\n read_normal.to_csv(src + 'base_port_ann_ret_unsorted.csv')\n read_normal.reset_index(drop=True, inplace=True)\n\n # ---make a copy to add to the consolidated summary file----\n save_normal = read_normal.copy()\n read_normal.drop('port_return', axis=1, inplace=True)\n read_normal.rename(columns={fia_name: 'FIA'}, inplace=True)\n\n # - Shift returns by one year and assign 0 returns to all assets in teh year 0, the year of initial investment.\n # read_normal = read_normal.shift().fillna(0)\n read_normal.to_csv(src + 'historical_port_return_unsorted.csv')\n\n # ----Best to worst returns----\n read_large = yearly_base_port.copy()\n\n # --Logic to sort the last 20 years of returns and assign 0 to first year, the year of investment\n read_large.iloc[0] = 1000.0\n read_large = read_large.sort_values(by=['port_return'], ascending=False)\n # --assign 0 to first year, the year of investment\n read_large.iloc[0] = 0.0\n read_large.to_csv(src + 'base_port_ann_ret_desc.csv')\n read_large.reset_index(drop=True, inplace=True)\n # read_large = read_large.shift().fillna(0)\n\n # ---make a copy to add to the consolidated summary file---\n save_desc = read_large.copy()\n read_large.drop('port_return', axis=1, inplace=True)\n read_large.rename(columns={fia_name: 'FIA'}, inplace=True)\n read_large.to_csv(src + 'historical_port_return_desc.csv')\n\n # ------Worst to Best Returns------------\n read_small = yearly_base_port.copy()\n read_small.iloc[0] = -1000.0\n read_small = read_small.sort_values(by=['port_return'], ascending=True)\n read_small.iloc[0] = 0.0\n read_small.to_csv(src + 'base_port_ann_ret_asc.csv')\n read_small.reset_index(drop=True, inplace=True)\n # read_small = read_small.shift().fillna(0)\n\n # ---make a copy to add to the consolidated summary file---\n save_asc = read_small.copy()\n read_small.drop('port_return', axis=1, inplace=True)\n read_small.rename(columns={fia_name: 'FIA'}, inplace=True)\n read_small.to_csv(src + 'historical_port_return_asc.csv')\n\n # read_asset_weights.drop(read_asset_weights.index[-1], axis=0, inplace=True)\n\n # read random returns for simulation\n # read_normal = pd.read_csv(src + 'median_returns_unsorted.csv', index_col=[0], parse_dates=True)\n # cols = [read_normal.columns[c].split('_')[1] for c in np.arange(len(read_normal.columns))]\n # read_normal.rename(columns=dict(zip(list(read_normal.columns), cols)), inplace=True)\n #\n # read_small = pd.read_csv(src + 'median_returns_smallest.csv', index_col=[0], parse_dates=True)\n # read_small.rename(columns=dict(zip(list(read_small.columns), cols)), inplace=True)\n #\n # read_large = pd.read_csv(src + 'median_returns_largest.csv', index_col=[0], parse_dates=True)\n # read_large.rename(columns=dict(zip(list(read_large.columns), cols)), inplace=True)\n\n assets_col_names = list(read_normal.columns)\n\n tickers = list(read_asset_weights.index)\n wts = np.array(read_asset_weights.loc[:, 'base'])\n\n def asset_median_returns(data, ticker):\n return data.filter(regex=ticker).median(axis=1)\n\n # dataframe for unsorted returns (normal)\n median_returns_normal = read_normal.copy()\n median_returns_normal.loc[:, 'portfolio_return'] = median_returns_normal.dot(wts)\n # median_returns_normal.rename(columns={fia_name: 'FIA'}, inplace=True)\n # median_normal_fia = pd.DataFrame({'FIA': asset_median_returns(read_normal, 'FIA')})\n\n # dataframe for smallest to largest returns\n median_returns_smallest = read_small.copy()\n median_returns_smallest.loc[:, 'portfolio_return'] = median_returns_smallest.dot(wts)\n # median_returns_smallest.rename(columns={fia_name: 'FIA'}, inplace=True)\n # median_smallest_fia = pd.DataFrame({'FIA': asset_median_returns(read_small, 'FIA')})\n\n # dataframe for largest to smallest returns\n median_returns_largest = read_large.copy()\n median_returns_largest.loc[:, 'portfolio_return'] = median_returns_largest.dot(wts)\n # median_returns_largest.rename(columns={fia_name: 'FIA'}, inplace=True)\n # median_largest_fia = pd.DataFrame({'FIA': asset_median_returns(read_large, 'FIA')})\n\n # years = list(range(0, num_of_years + 1))\n years = np.arange(len(read_normal))\n income_cols = ['year', 'strategy_term', 'index_returns', 'term_ret', 'term_ret_with_par', 'term_annualize',\n 'ann_net_spread', 'term_ret_netspr', 'high_inc_benefit_base', 'rider_fee', 'eoy_income',\n 'contract_value']\n\n term = int(read_income_inputs.loc['term', 'inputs'])\n fia_ret = read_returns_est.loc[read_returns_est.index[-1], 'Annualized Returns']\n fia_risk = read_returns_est.loc[read_returns_est.index[-1], 'Annualized Risk']\n par_rate = float(read_income_inputs.loc['par_rate', 'inputs'])\n spread = float(read_income_inputs.loc['spread', 'inputs'])\n bonus_term = int(read_income_inputs.loc['bonus_term', 'inputs'])\n premium = float(read_income_inputs.loc['premium', 'inputs'])\n income_bonus = float(read_income_inputs.loc['income_bonus', 'inputs'])\n\n income_starts = int(read_income_inputs.loc['start_income_years', 'inputs'])\n # --Income start right away, not deferred ----\n income_starts = 0\n income_growth = float(read_income_inputs.loc['income_growth', 'inputs'])\n rider_fee = float(read_income_inputs.loc['rider_fee', 'inputs'])\n inc_payout_factor = float(read_income_inputs.loc['income_payout_factor', 'inputs'])\n contract_bonus = float(read_income_inputs.loc['contract_bonus', 'inputs'])\n social = float(read_income_inputs.loc['social', 'inputs'])\n inflation = float(read_income_inputs.loc['inflation', 'inputs'])\n wtd_cpn_yield = float(read_income_inputs.loc['wtd_coupon_yld', 'inputs'])\n life_expectancy = int(read_income_inputs.loc['life_expectancy_age', 'inputs'])\n clients_age = int(read_income_inputs.loc['clients_age', 'inputs'])\n\n # --------------------------------------INCOME MODEL--------------------------------------------\n runs = 0\n returns_dict = {}\n asset_dict = {}\n fia_dict = {}\n\n income_df = pd.DataFrame(index=years, columns=income_cols)\n income_df.loc[:, 'year'] = years\n income_df.loc[:, 'strategy_term'] = income_df.loc[:, 'year'] % term\n income_df.loc[:, 'strategy_term'] = income_df['strategy_term'].apply(lambda x: 1 if x == 0 else 0)\n\n if method == 'normal':\n income_df.loc[:, 'index_returns'] = read_normal.loc[:, 'FIA']\n\n elif method == 'smallest':\n income_df.loc[:, 'index_returns'] = read_small.loc[:, 'FIA']\n\n else:\n income_df.loc[:, 'index_returns'] = read_large.loc[:, 'FIA']\n\n # income_df.loc[:, 'index_returns'] = np.random.normal(fia_ret, fia_risk, size=(len(years), 1))\n\n cumprod = (1. + income_df['index_returns']).rolling(window=term).agg(lambda x: x.prod()) - 1\n income_df.loc[:, 'term_ret'] = np.where(income_df.loc[:, 'strategy_term'] == 1, cumprod, 0)\n income_df.loc[:, 'term_ret_with_par'] = income_df.loc[:, 'term_ret'] * par_rate\n income_df.loc[:, 'term_annualize'] = income_df.loc[:, 'term_ret_with_par'].apply(\n lambda x: (1 + x) ** (1 / term) - 1)\n income_df.loc[:, 'ann_net_spread'] = income_df.loc[:, 'term_annualize'] - spread\n income_df.loc[:, 'ann_net_spread'] = np.where(income_df.loc[:, 'strategy_term'] == 1,\n income_df.loc[:, 'ann_net_spread'], 0)\n income_df.loc[:, 'term_ret_netspr'] = income_df.loc[:, 'ann_net_spread'].apply(lambda x: (1 + x) ** term - 1)\n\n for counter in years:\n if counter == 0:\n income_df.loc[counter, 'high_inc_benefit_base'] = premium * (1 + income_bonus)\n\n elif counter <= min(bonus_term, income_starts):\n income_df.loc[counter, 'high_inc_benefit_base'] = income_df.loc[counter - 1, 'high_inc_benefit_base'] * \\\n (1 + income_growth)\n else:\n income_df.loc[counter, 'high_inc_benefit_base'] = income_df.loc[counter - 1, 'high_inc_benefit_base']\n\n income_df.loc[:, 'rider_fee'] = income_df.loc[:, 'high_inc_benefit_base'] * rider_fee\n income_df.loc[:, 'eoy_income'] = np.where(income_df.loc[:, 'year'] > income_starts,\n income_df.loc[:, 'high_inc_benefit_base'] * inc_payout_factor, 0)\n\n for counter in years:\n if counter == 0:\n income_df.loc[counter, 'contract_value'] = premium * (1 + contract_bonus)\n\n elif income_df.loc[counter, 'strategy_term'] == 1:\n x1 = income_df.loc[counter - 1, 'contract_value'] - income_df.loc[counter, 'rider_fee']\n x2 = (x1 * (1 + income_df.loc[counter, 'term_ret_netspr'])) - income_df.loc[counter, 'eoy_income']\n income_df.loc[counter, 'contract_value'] = x2\n\n else:\n x1 = income_df.loc[counter - 1, 'contract_value'] - income_df.loc[counter, 'rider_fee'] - \\\n income_df.loc[counter, 'eoy_income']\n\n income_df.loc[counter, 'contract_value'] = x1\n\n # variable stores the income number that is used in the base and fia portfolio calcs.\n\n income_from_fia = income_df.loc[income_df.index[-1], 'eoy_income']\n\n income_df.loc[:, 'contract_value'] = income_df.loc[:, 'contract_value'].apply(lambda x: 0 if x <= 0 else x)\n\n sim_fia_cv.loc[:, str(runs)] = income_df.loc[:, 'contract_value']\n\n # ---------------------------------BASE MODEL---------------------------------------------\n\n base_wts = read_asset_weights.loc[:, 'base']\n base_assets = list(base_wts.index)\n base_weights = list(base_wts.values)\n base_returns = list(read_returns_est.loc[:, 'Annualized Returns'].values)\n base_std = list(read_returns_est.loc[:, 'Annualized Risk'].values)\n\n base_investment = float(read_income_inputs.loc['risky_assets', 'Base'])\n adv_fees = float(read_income_inputs.loc['advisor_fees', 'Base'])\n\n # -------------------------------------required income--------------------------------\n req_annual_income = float(read_income_inputs.loc['annual_income', 'inputs'])\n income_needed = req_annual_income - social\n income_net_fia_income = max(0, income_needed - income_from_fia)\n cpn_income_base = base_investment * wtd_cpn_yield\n\n # --------------------------------------------RANDOM RETURNS-----------------------------------\n r_cols = base_assets\n boy_value = ['bv_{}'.format(name) for name in base_assets]\n eoy_value = ['ev_{}'.format(name) for name in base_assets]\n\n random_returns = pd.DataFrame(index=income_df.index, columns=r_cols)\n\n for c in range(len(r_cols)):\n ret = np.random.normal(base_returns[c], base_std[c], size=(len(random_returns.index), 1))\n\n if method == 'smallest':\n random_returns = read_small.copy()\n\n elif method == 'largest':\n random_returns = read_large.copy()\n\n else:\n random_returns = read_normal.copy()\n\n base_df = random_returns.copy()\n fia_portfolio_df = random_returns.copy()\n port_investment = float(read_income_inputs.loc['risky_assets', 'FIA'])\n cpn_income_port = port_investment * wtd_cpn_yield\n\n # -------------------------------------BASE PORTFOLIO----------------------------\n for name in boy_value:\n base_df.loc[:, name] = 0.0\n\n for counter in years:\n period_returns = list(random_returns.loc[counter, :])\n if counter == 0:\n\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment for c in range(len(boy_value))]\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\n base_df.loc[counter, 'total_net_fees'] = 0.0\n base_df.loc[counter, 'income'] = 0.0\n base_investment = base_df.loc[counter, boy_value].sum()\n\n elif (counter > 0) and (counter < income_starts):\n\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment * (1 + period_returns[c])\n for c in range(len(boy_value))]\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\n base_df.loc[counter, 'adv_fees'] = base_df.loc[counter, 'total'] * adv_fees\n base_df.loc[counter, 'total_net_fees'] = base_df.loc[counter, 'total'] - base_df.loc[\n counter, 'adv_fees']\n\n # --coupon payment is invested back into the risky portfolio until the income is withdrawn----\n base_investment = base_df.loc[counter, 'total_net_fees'] + cpn_income_base\n\n else:\n\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment * (1 + period_returns[c])\n for c in range(len(boy_value))]\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\n base_df.loc[counter, 'adv_fees'] = base_df.loc[counter, 'total'] * adv_fees\n\n # ---req. income is adjusted for inflation from the second year of withdrawal. Reinvestment of coupon\n # stops from the year income starts. Req. income is reduced by the coupon payments\n\n if counter == income_starts:\n\n income_needed = req_annual_income - social\n base_df.loc[counter, 'income'] = income_needed - cpn_income_base\n income_needed = req_annual_income\n\n else:\n income_needed = income_needed * (1 + inflation) - social\n base_df.loc[counter, 'income'] = income_needed - cpn_income_base\n income_needed = income_needed + social\n\n base_df.loc[counter, 'total_net_fees'] = base_df.loc[counter, 'total'] - \\\n base_df.loc[counter, 'adv_fees'] - \\\n base_df.loc[counter, 'income']\n\n base_investment = base_df.loc[counter, 'total_net_fees']\n\n base_df.loc[:, 'adj_total'] = base_df.loc[:, 'total_net_fees'].apply(lambda x: x if x > 0 else 0)\n sim_base_total.loc[:, 's_{}'.format(str(runs))] = base_df.loc[:, 'total_net_fees']\n sim_base_income.loc[:, 's_{}'.format(str(runs))] = base_df.loc[:, 'income']\n\n # -------------------required income----------------------------------\n req_annual_income = float(read_income_inputs.loc['annual_income', 'inputs'])\n income_needed = req_annual_income - social\n # income_net_fia_income = max(0, income_needed - income_from_fia)\n cpn_income_base = base_investment * wtd_cpn_yield\n\n # ----------------------------FIA PORTFOLIO----------------------------------------------\n for name in boy_value:\n fia_portfolio_df.loc[:, name] = 0.0\n\n for counter in years:\n period_returns = list(random_returns.loc[counter, :])\n if counter == 0:\n\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment\n for c in range(len(boy_value))]\n fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\n fia_portfolio_df.loc[counter, 'total_net_fees'] = 0.0\n fia_portfolio_df.loc[counter, 'income'] = 0.0\n port_investment = fia_portfolio_df.loc[counter, boy_value].sum()\n\n elif (counter > 0) and (counter < income_starts):\n\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment * (1 + period_returns[c])\n for c in range(len(boy_value))]\n fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\n fia_portfolio_df.loc[counter, 'adv_fees'] = fia_portfolio_df.loc[counter, 'total'] * adv_fees\n fia_portfolio_df.loc[counter, 'total_net_fees'] = fia_portfolio_df.loc[counter, 'total'] - \\\n fia_portfolio_df.loc[counter, 'adv_fees']\n\n port_investment = fia_portfolio_df.loc[counter, 'total_net_fees'] + cpn_income_port\n\n else:\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment * (1 + period_returns[c])\n for c in range(len(boy_value))]\n fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\n fia_portfolio_df.loc[counter, 'adv_fees'] = fia_portfolio_df.loc[counter, 'total'] * adv_fees\n\n # ---req. income is adjusted for inflation from the second year of withdrawal. Reinvestment of coupon\n # stops from the year income starts. Req. income is reduced by the coupon payments\n\n if counter == income_starts:\n\n income_needed = req_annual_income - social\n income_net_fia_income = max(0, income_needed - income_from_fia)\n fia_portfolio_df.loc[counter, 'income'] = max(0, income_net_fia_income - cpn_income_port)\n income_needed = req_annual_income\n\n else:\n income_needed = income_needed * (1 + inflation) - social\n income_net_fia_income = max(0, income_needed - income_from_fia)\n fia_portfolio_df.loc[counter, 'income'] = max(0, income_net_fia_income - cpn_income_port)\n income_needed = income_needed + social\n\n fia_portfolio_df.loc[counter, 'total_net_fees'] = fia_portfolio_df.loc[counter, 'total'] - \\\n fia_portfolio_df.loc[counter, 'adv_fees'] - \\\n fia_portfolio_df.loc[counter, 'income']\n\n port_investment = fia_portfolio_df.loc[counter, 'total_net_fees']\n\n sim_port_total.loc[:, 's_{}'.format(str(runs))] = fia_portfolio_df.loc[:, 'total_net_fees'] + \\\n income_df.loc[:, 'contract_value']\n\n sim_port_income.loc[:, 's_{}'.format(str(runs))] = fia_portfolio_df.loc[:, 'income']\n\n fia_portfolio_df.loc[:, 'adj_total'] = fia_portfolio_df.loc[:, 'total_net_fees'].apply(\n lambda x: x if x > 0 else 0)\n\n # -------------------------------------income breakdown for Base portfolio----------------------------------\n base_df.to_csv(src + method + '_base_port_detail.csv')\n sim_base_total.to_csv(src + 'base_ending_values.csv')\n income_breakdown_base = pd.DataFrame(sim_base_total.quantile(0.5, axis=1))\n income_breakdown_base.loc[:, 'income_from_portfolio'] = sim_base_income.quantile(0.5, axis=1)\n income_breakdown_base.loc[:, 'fia_income'] = 0.0\n income_breakdown_base.loc[:, 'social_security_income'] = social\n income_breakdown_base.loc[:, 'coupon_income'] = cpn_income_base\n\n income_breakdown_base.rename(columns={income_breakdown_base.columns[0]: 'portfolio_ending_value'}, inplace=True)\n income_breakdown_base.loc[:, 'income_from_portfolio'][\n income_breakdown_base.loc[:, 'portfolio_ending_value'] <= 0] = 0\n income_breakdown_base.loc[:, 'total_income'] = income_breakdown_base.loc[:, income_breakdown_base.columns[1:]].sum(\n axis=1)\n\n # ------------------------------------Block Ends-------------------------------------------------------------\n\n # ------------------------------income breakdown for FIA portfolio----------------------------------\n fia_portfolio_df.loc[:, 'contract_value'] = income_df.loc[:, 'contract_value']\n fia_portfolio_df.to_csv(src + method + '_fia_port_detail.csv')\n sim_port_total.to_csv(src + 'fiaport_ending_values.csv')\n\n income_breakdown_port = pd.DataFrame(sim_port_total.quantile(0.5, axis=1))\n income_breakdown_port.loc[:, 'income_from_portfolio'] = sim_port_income.quantile(0.5, axis=1)\n income_breakdown_port.loc[:, 'fia_income'] = income_from_fia\n income_breakdown_port.loc[:, 'social_security_income'] = social\n income_breakdown_port.loc[:, 'coupon_income'] = cpn_income_port\n\n income_breakdown_port.rename(columns={income_breakdown_port.columns[0]: 'portfolio_ending_value'}, inplace=True)\n income_breakdown_port.loc[:, 'income_from_portfolio'][\n income_breakdown_port.loc[:, 'portfolio_ending_value'] <= 0] = 0\n income_breakdown_port.loc[:, 'total_income'] = income_breakdown_port.loc[:, income_breakdown_port.columns[1:]].sum(\n axis=1)\n\n # ------------------------------------Block Ends-------------------------------------------------------------\n q_cut = [0.0, .05, 0.25, 0.5, 0.75, 0.95, 1.0]\n sim_base_income[sim_base_total < income_needed] = 0.0\n\n sim_port_income[sim_port_total < income_net_fia_income] = 0\n\n sim_port_income = sim_port_income + income_from_fia\n\n base_quantile = sim_base_total.loc[sim_base_total.index[-1]].quantile(q_cut)\n\n port_quantile = sim_port_total.loc[sim_port_total.index[-1]].quantile(q_cut)\n\n cols = ['Min', '5th', '25th', '50th', '75th', '90th', 'Max']\n\n # ----------------------------------drop year 0---------------------------------\n sim_base_total = sim_base_total[1:]\n sim_port_total = sim_port_total[1:]\n\n # --------------------------------quantile analysis for base terminal value-----------------------------\n base_qcut = pd.DataFrame(index=sim_base_total.index, columns=cols)\n for c in range(len(cols)):\n base_qcut.loc[:, cols[c]] = sim_base_total.quantile(q_cut[c], axis=1)\n\n base_qcut.clip(lower=0, inplace=True)\n\n # ----------------------------------quantile analysis for base income----------------------------\n base_income_qcut = pd.DataFrame(index=sim_base_income.index, columns=cols)\n for c in range(len(cols)):\n base_income_qcut.loc[:, cols[c]] = sim_base_income.quantile(q_cut[c], axis=1)\n\n # ----Remove NaN's prior to the income start years------------\n # base_income_qcut = base_income_qcut.loc[income_starts:]\n\n # -------------quantile analysis for portfolio terminal value ---------------\n\n port_qcut = pd.DataFrame(index=sim_port_total.index, columns=cols)\n for c in range(len(cols)):\n port_qcut.loc[:, cols[c]] = sim_port_total.quantile(q_cut[c], axis=1)\n\n port_qcut.clip(lower=0, inplace=True)\n\n # ---------------quantile analysis for portfolio income----------------------------\n port_income_qcut = pd.DataFrame(index=sim_port_income.index, columns=cols)\n for c in range(len(cols)):\n port_income_qcut.loc[:, cols[c]] = sim_port_income.quantile(q_cut[c], axis=1)\n\n # ----Remove NaN's prior to the income start years------------\n # port_income_qcut = port_income_qcut.loc[income_starts:]\n\n # ----------probability ending value will be less than 0 at the end of the horizon -------------------------\n\n base_legacy_risk = (sim_base_total.loc[sim_base_total.index[life_expectancy - clients_age]] < 0).sum() / (\n trials + 1)\n port_legacy_risk = (sim_port_total.loc[sim_port_total.index[life_expectancy - clients_age]] < 0).sum() / (\n trials + 1)\n\n legacy_risk = pd.DataFrame([base_legacy_risk, port_legacy_risk,\n 'Prob. of portfolio value less than 0 at the end of the expected life'],\n index=['base', 'fia_portfolio', 'Notes'],\n columns=['Ruin Probability'])\n\n # -----------Year-wise probability of ending value greater than 0 -----------------\n base_psuccess = sim_base_total.apply(lambda x: x > 0).sum(axis=1) / (trials + 1)\n port_psuccess = sim_port_total.apply(lambda x: x > 0).sum(axis=1) / (trials + 1)\n\n # -----------------------WRITING FILES TO EXCEL ---------------------------\n col_names = ['50th', 'age', 'comment']\n writer = pd.ExcelWriter(src + method + '_historical_simulated_income_summary.xlsx', engine='xlsxwriter')\n read_income_inputs.to_excel(writer, sheet_name='inputs_for_income')\n\n read_returns_est.to_excel(writer, sheet_name='asset_returns_estimates')\n\n age_index = list(range(clients_age + 1, clients_age + len(base_qcut) + 1))\n base_qcut.loc[:, 'age'] = age_index\n base_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n base_qcut.loc[income_starts:, col_names].to_excel(writer, sheet_name='base_ending_value_quantiles')\n\n base_income_qcut = base_income_qcut.loc[1:, :]\n base_income_qcut.loc[:, 'age'] = age_index\n base_income_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n base_income_qcut.loc[income_starts:, col_names].to_excel(writer, sheet_name='base_income_quantiles')\n\n port_qcut.loc[:, 'age'] = age_index\n port_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n port_qcut.loc[income_starts:, col_names].to_excel(writer, sheet_name='fia_port_ending_value_quantiles')\n\n port_income_qcut = port_income_qcut.loc[1:, :]\n port_income_qcut.loc[:, 'age'] = age_index\n port_income_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n port_income_qcut.loc[income_starts:, col_names].to_excel(writer, sheet_name='fia_port_income_quantiles')\n\n # prob_success_df = pd.concat([base_psuccess, port_psuccess], axis=1)\n # prob_success_df.rename(columns={prob_success_df.columns[0]: 'prob(ending_value>0)_base',\n # prob_success_df.columns[1]: 'prob(ending_value>0)_port'}, inplace=True)\n\n # prob_success_df.loc[:, 'age'] = age_index\n # prob_success_df.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n # prob_success_df.to_excel(writer, sheet_name='success_probability')\n\n income_breakdown_base = income_breakdown_base.loc[1:, :]\n income_breakdown_base.loc[:, 'age'] = age_index\n income_breakdown_base.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n income_breakdown_base.loc[income_starts:, :].to_excel(writer, sheet_name='base_income_breakdown_median')\n\n income_breakdown_port = income_breakdown_port.loc[1:, :]\n income_breakdown_port.loc[:, 'age'] = age_index\n income_breakdown_port.loc[:, 'ending contract value'] = income_df.loc[:, 'contract_value'].fillna(0)\n income_breakdown_port.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n\n income_breakdown_port.loc[income_starts:, :].to_excel(writer, sheet_name='fia_income_breakdown_median')\n\n if method == 'normal':\n # median_returns_normal.loc[:, 'fia_median_returns'] = median_normal_fia\n # median_returns_normal.to_excel(writer, sheet_name='unsorted_port_return')\n save_normal.to_excel(writer, sheet_name='port_return_unsorted')\n\n elif method == 'smallest':\n # median_returns_smallest.loc[:, 'fia_median_returns'] = median_smallest_fia\n # median_returns_smallest.to_excel(writer, sheet_name='port_return_asc')\n save_asc.to_excel(writer, sheet_name='port_return_asc')\n\n else:\n # median_returns_largest.loc[:, 'fia_median_returns'] = median_largest_fia\n # median_returns_largest.to_excel(writer, sheet_name='port_return_desc')\n save_desc.to_excel(writer, sheet_name='port_return_desc')\n\n # terminal_val = pd.read_csv(src + 'terminal_values.csv', index_col=[0])\n # ending_val = pd.read_csv(src + 'ending_values.csv', index_col=[0])\n # ending_val_ror = pd.read_csv(src + 'ending_values_ror.csv', index_col=[0])\n\n # terminal_val.to_excel(writer, sheet_name='terminal_values')\n # ending_val.to_excel(writer, sheet_name='port_ending_values')\n # ending_val_ror.to_excel(writer, sheet_name='port_annual_growth')\n\n writer.save()\n\n # -----------------Plotting charts--------------------------------------------\n # base_qcut.loc[income_starts:].plot(grid=True, title='Quantile Terminal Value - Base Portfolio')\n # plt.savefig(src + \"quantile_terminal_base.png\")\n # plt.close('all')\n #\n # base_income_qcut.plot(grid=True, title='Quantile Income - Base Portfolio')\n # plt.savefig(src + \"quantile_income_base.png\")\n # plt.close('all')\n #\n # base_psuccess.plot(grid=True, title='Probability of Success (Portfolio Ending Value > 0) - Base Portfolio')\n # plt.savefig(src + \"success_probabilty_base.png\")\n # plt.close('all')\n #\n # (1 - base_psuccess).plot(grid=True, title='Probability of Ruin (Portfolio Ending Value < 0) - Base Portfolio')\n # plt.savefig(src + \"ruin_probability_base.png\")\n # plt.close('all')\n #\n # port_qcut.loc[income_starts:].plot(grid=True, title='Quantile Terminal Value - FIA Portfolio')\n # plt.savefig(src + \"quantile_terminal_fia.png\")\n # plt.close('all')\n #\n # port_income_qcut.plot(grid=True, title='Quantile Income - FIA Portfolio')\n # plt.savefig(src + \"quantile_income_fia.png\")\n # plt.close('all')\n #\n # port_psuccess.plot(grid=True, title='Probability of Success (Portfolio Ending Value > 0) - FIA Portfolio')\n # plt.savefig(src + \"success_probabilty_fia.png\")\n # plt.close('all')\n #\n # (1 - port_psuccess).plot(grid=True, title='Probability of Ruin (Portfolio Ending Value < 0) - FIA Portfolio')\n # plt.savefig(src + \"ruin_probability_fia.png\")\n # plt.close('all')\n\n print(\"simulation completed for {}\".format(method))","def _output_performance(self):\n self.portfolio.create_equity_curve_dataframe()\n \n print(\"Creating summary statistics...\")\n stats = self.portfolio.output_summary_stats()\n \n print(\"Creating equity curve...\")\n print(self.portfolio.equity_curve.tail(10))\n pprint.pprint(stats)\n \n print(\"Signals: %s\" % self.signals)\n print(\"Orders: %s\" % self.orders)\n print(\"Fills: %s\" % self.fills)","def _output_performance(self):\n self.portfolio.create_equity_curve_dataframe()\n\n print(\"Creating summary statistics...\")\n stats = self.portfolio.output_summary_stats()\n\n print(\"Creating equity curve...\")\n print(self.portfolio.equity_curve.tail(10))\n pprint.pprint(stats)\n\n print(\"Signals: %s\" % self.signals)\n print(\"Orders: %s\" % self.orders)\n print(\"Fills: %s\" % self.fills)","def _generate_trading_instances(self, strategy_params_dict):\n print(\"Creating DataHandler, Strategy, Portfolio, and ExecutionHandler for\")\n print(\"strategy parameter list: %s...\" % strategy_params_dict)\n\n # Set internal data members equal to the classes we passed in earlier, along with necessary parameters.\n # https://softwareengineering.stackexchange.com/questions/131403/what-is-the-name-of-in-python/131415\n self.data_handler = self.data_handler_class(self.events, self.csv_dir, self.symbol_list)\n self.strategy = self.strategy_class(self.data_handler, self.events, **strategy_params_dict)\n self.portfolio = self.portfolio_class(self.data_handler, self.events, self.start_date, self.initial_capital)\n self.execution_handler = self.execution_handler_class(self.events) # The Event Queue sent to ExecutionHandler","def generate_portfolio(S_0, params):\n\tpublic_client = gdax.PublicClient()\n\tallvar = []\n\tsumvar = 0\n\tfor coin in params:\n\t\ttheta = coin[0]\n\t\tv = theta[0]\n\t\tT = coin[1]\n\t\tprod_id = coin[2]\n\t\t# Get the current value of the coin, i.e. how much you bought\n\t\tname = prod_id + '-USD'\n\t\tstats = public_client.get_product_24hr_stats(name)\n\t\tvalue = (float(stats['high']) + float(stats['low']))/2\n\t\tallvar.append([prod_id, value, v])\n\t\tsumvar += v\n\tpriority = sorted(allvar, key=lambda i: i[2])\n\tportfolio = []\n\tfor i in priority:\n\t\tinvestment = S_0*i[2]/sumvar\n\t\tcurrency = investment/i[1]\n\t\tportfolio.append((i[0], currency, investment)) # id, investment, currency\n\tprint(\"\\nYour suggested investments are: \\n\")\n\tfor coin in portfolio:\n\t\tprint(\"%s: %s for %s USD\" % (coin[0], coin[1], coin[2]))\n\t# Prompt to save the portfolio\n\tdone = False\n\twhile done != True:\n\t\tinp = input(\"\\nWould you like to save this portfolio? (y/n)\t\")\n\t\ttry:\n\t\t\tif inp.lower() == 'y':\n\t\t\t\tpublic_client = gdax.PublicClient()\n\t\t\t\tcurrent_date = np.datetime64(public_client.get_time().get(\"iso\").split('T')[0])\n\t\t\t\t# Save the file\n\t\t\t\twith open(\"portfolios/%s.txt\" % (current_date), \"w\") as f:\n\t\t\t\t\tfor coin in portfolio:\n\t\t\t\t\t\tf.write(str(coin[0]) + ', ' + str(coin[1]) + ', ' + str(coin[2]) + '\\n')\n\t\t\t\tprint(\"Portfolio saved. Exiting.\\n\")\n\t\t\t\tdone = True\n\t\t\tif inp.lower() == 'n':\n\t\t\t\tprint(\"Program complete. Exiting.\\n\")\n\t\t\t\tdone = True\n\t\texcept ValueError:\n\t\t\tprint(\"Your input could not be interpreted.\")","def simulate_trading(self):\n self._run_backtest()\n self._output_performance()","def generate_portfolio_data(self):\n self.__load_portfolio_historical_prices()\n self.__populate_historical_trade_data()\n self.__calculate_portfolio_returns()\n self.__calculate_portfolio_performance()","def _generate_trading_instances(self):\n print(\n \"Initizalization...\"\n )\n\n self.data_handler = self.data_handler_cls(self.events, self.csv_dir, self.symbol_list, self.start_date,\n self.end_date)\n self.portfolio = self.portfolio_cls(self.data_handler, self.events, self.initial_capital, self.start_date,\n self.strategy_id, self.spread, self.commission,self.csv_dir)\n self.strategy = self.strategy_cls(self.data_handler, self.events, self.portfolio, self.spread, self.commission)\n self.plot = self.plot_cls(self.csv_dir, self.portfolio, self.strategy_id)","def main(ft_setups, ft_strategies):\n\n num_procs = 16\n\n # initialize level parameters\n level_params = dict()\n level_params['restol'] = 1e-09\n\n # initialize step parameters\n step_params = dict()\n step_params['maxiter'] = 50\n\n # initialize space transfer parameters\n space_transfer_params = dict()\n space_transfer_params['finter'] = True\n space_transfer_params['rorder'] = 2\n space_transfer_params['iorder'] = 6\n\n # initialize sweeper parameters\n sweeper_params = dict()\n sweeper_params['quad_type'] = 'RADAU-RIGHT'\n sweeper_params['num_nodes'] = [3]\n\n # initialize controller parameters\n controller_params = dict()\n controller_params['logger_level'] = 30\n\n for setup in ft_setups:\n if setup == 'HEAT':\n # initialize problem parameters\n problem_params = dict()\n problem_params['nu'] = 0.5\n problem_params['freq'] = 1\n problem_params['nvars'] = [255, 127]\n problem_params['bc'] = 'dirichlet-zero'\n\n level_params['dt'] = 0.5\n\n space_transfer_params['periodic'] = False\n\n # fill description dictionary for easy step instantiation\n description = dict()\n description['problem_class'] = heatNd_forced # pass problem class\n description['problem_params'] = problem_params # pass problem parameters\n description['sweeper_class'] = imex_1st_order # pass sweeper (see part B)\n description['sweeper_params'] = sweeper_params # pass sweeper parameters\n description['level_params'] = level_params # pass level parameters\n description['step_params'] = step_params # pass step parameters\n description['space_transfer_class'] = mesh_to_mesh # pass spatial transfer class\n description['space_transfer_params'] = space_transfer_params # pass paramters for spatial transfer\n\n # setup parameters \"in time\"\n t0 = 0.0\n Tend = 8.0\n\n elif setup == 'ADVECTION':\n # initialize problem parameters\n problem_params = dict()\n problem_params['c'] = 1.0\n problem_params['nvars'] = [256, 128]\n problem_params['freq'] = 2\n problem_params['order'] = 2\n problem_params['bc'] = 'periodic' # boundary conditions\n\n level_params['dt'] = 0.125\n\n space_transfer_params['periodic'] = True\n\n # fill description dictionary for easy step instantiation\n description = dict()\n description['problem_class'] = advectionNd # pass problem class\n description['problem_params'] = problem_params # pass problem parameters\n description['sweeper_class'] = generic_implicit # pass sweeper (see part B)\n description['sweeper_params'] = sweeper_params # pass sweeper parameters\n description['level_params'] = level_params # pass level parameters\n description['step_params'] = step_params # pass step parameters\n description['space_transfer_class'] = mesh_to_mesh # pass spatial transfer class\n description['space_transfer_params'] = space_transfer_params # pass paramters for spatial transfer\n\n # setup parameters \"in time\"\n t0 = 0.0\n Tend = 2.0\n\n else:\n raise NotImplementedError('setup not implemented')\n\n # do a reference run without any faults to see how things would look like (and to get maxiter/ref_niter)\n ft.strategy = 'NOFAULT'\n\n controller = controller_nonMPI_hard_faults(\n num_procs=num_procs, controller_params=controller_params, description=description\n )\n\n # get initial values on finest level\n P = controller.MS[0].levels[0].prob\n uinit = P.u_exact(t0)\n\n # call main function to get things done...\n uend, stats = controller.run(u0=uinit, t0=t0, Tend=Tend)\n\n # stats magic: get iteration counts to find maxiter/niter\n sortedlist_stats = get_sorted(stats, level=-1, type='niter', sortby='process')\n ref_niter = max([item[1] for item in sortedlist_stats])\n\n print('Will sweep over %i steps and %i iterations now...' % (num_procs, ref_niter))\n\n # loop over all strategies\n for strategy in ft_strategies:\n ft_iter = range(1, ref_niter + 1)\n ft_step = range(0, num_procs)\n\n print('------------------------------------------ working on strategy ', strategy)\n\n iter_count = np.zeros((len(ft_step), len(ft_iter)))\n\n # loop over all steps\n xcnt = -1\n for step in ft_step:\n xcnt += 1\n\n # loop over all iterations\n ycnt = -1\n for iter in ft_iter:\n ycnt += 1\n\n ft.hard_step = step\n ft.hard_iter = iter\n ft.strategy = strategy\n\n # call main function to get things done...\n uend, stats = controller.run(u0=uinit, t0=t0, Tend=Tend)\n\n # stats magic: get iteration counts to find maxiter/niter\n sortedlist_stats = get_sorted(stats, level=-1, type='niter', sortby='process')\n niter = max([item[1] for item in sortedlist_stats])\n iter_count[xcnt, ycnt] = niter\n\n print(iter_count)\n\n np.savez(\n 'data/' + setup + '_results_hf_' + strategy,\n iter_count=iter_count,\n description=description,\n ft_step=ft_step,\n ft_iter=ft_iter,\n )","def run(sim_attr_generator):\n#TODO: clean\n#TODO: integrate analyses\n def analyze_and_save(simulation,simulation_attributes):\n#? Ugly conf file analyses integration.\n if simulation_attributes.analyses and Args.output_file != None:\n verbose_print(\"Saving analyses for {0}.\".format(simulation_attributes.id_name),2)\n results = analyze_datas(\n simulation.result,\n simulation_attributes.analyses\n )\n plotables = ana_results_to_plotables(\n results,\n simulation_attributes.analyses\n )\n#TODO error handling for save\n analysis_save_dm(\n results,\n plotables,\n simulation_attributes.analyses,\n simulation_attributes.id_name\n )\n\n def save_simulation(simulation,simulation_attributes):\n if not simulation_attributes.analyses and Args.output_file != None:\n verbose_print(\"Saving simulation datas of {0}.\".format(\n simulation_attributes.id_name\n ),2) \n try:\n np.save(\n simulation_attributes.id_name,\n simulation.result\n )\n except:\n raise EnvironmentError(\"Can't save data to {}.\".format(\n simulation_attributes.id_name\n ))\n\n verbose_print(\"Starting simulation run.\",1)\n for i,simulation_attributes in enumerate(sim_attr_generator):\n verbose_print(\"Starting simulation number {0}: {1}\".format(\n i,\n simulation_attributes.id_name\n ),2)\n simulation = Simulation(\n SimulationVariables(simulation_attributes)\n )\n simulation.start()\n save_simulation(simulation,simulation_attributes)\n analyze_and_save(simulation,simulation_attributes)","def evolve(self, max_iter=40, warm_start=False, sigma_bound=None, info=True, cross_method=1, par_count=3,\n max_distance=2.0, archive_iter=30, max_archive_size=200):\n if sigma_bound is None:\n sigma_bound = [0.01, 0.2]\n\n if not warm_start:\n self.archive = []\n else:\n self.archive = self.archive.tolist()\n\n # outer loop\n for q in range(0, archive_iter):\n # create initial generation with strategy parameters\n init_gen = np.empty(shape=(self.gen_size, self.num_variables))\n init_sigma = np.empty(shape=(self.gen_size, self.num_variables))\n for i in range(0, self.num_variables):\n init_gen[:, i] = np.random.uniform(self.lower_bound[i], self.upper_bound[i], self.gen_size)\n init_sigma[:, i] = np.random.uniform(sigma_bound[0] * self.total_bound[i],\n sigma_bound[1] * self.total_bound[i], self.gen_size)\n\n self.gen = np.copy(init_gen)\n self.sigma = np.copy(init_sigma)\n\n if info:\n msg = \" Archive Iteration: {}/{}\".format(q+1, archive_iter)\n print(msg)\n self.gen = np.copy(init_gen)\n self.sigma = np.copy(init_sigma)\n\n # inner loop\n for k in range(0, max_iter):\n\n if info:\n if k == max_iter - 1:\n msg = \"Generation {}/{}\\n\".format(k+1, max_iter)\n else:\n msg = \"Generation {}/{}\".format(k+1, max_iter)\n sys.stdout.write(\"\\r\"+msg)\n\n # random selection based off number of parents\n selected = []\n for i in range(0, par_count):\n selected.append(np.random.choice(range(0, self.gen_size), self.gen_size, replace=False))\n selected = np.asarray(selected).T\n ch_val = []\n ch_sigma = []\n # reproduce from parents\n for i in range(0, self.gen_size):\n c_v, c_s = self.__reproduction(self.gen[selected[i]], self.sigma[selected[i]], cross_method)\n ch_val.append(c_v)\n ch_sigma.append(c_s)\n\n ch_val = np.asarray(ch_val)\n ch_sigma = np.asarray(ch_sigma)\n # check bounds of offspring\n for i in range(0, self.gen_size):\n for j in range(0, self.num_variables):\n if ch_val[i][j] > self.domain[0][j]:\n ch_val[i][j] = self.domain[0][j]\n ch_sigma[i][j] *= .90\n elif ch_val[i][j] < self.domain[1][j]:\n ch_val[i][j] = self.domain[1][j]\n ch_sigma[i][j] *= .90\n\n # combine offspring with parents\n parents_offspring_val = np.vstack((self.gen, ch_val))\n parents_offspring_sigma = np.vstack((self.sigma, ch_sigma))\n ind = list(range(0, 2 * self.gen_size))\n rel_fit = []\n # compute relative fitness\n for i in range(0, 2 * self.gen_size):\n tourn = np.random.choice(ind[0:i] + ind[(i + 1):], self.tourn_size)\n rel_fit.append(self.__pareto_dominance(parents_offspring_val[i], parents_offspring_val[tourn]))\n\n if len(self.archive) != 0: # if archive is not emtpy\n # loop over each member in archive\n for arch in self.archive:\n for i in range(0, 2 * self.gen_size):\n # calculate euclidean distance\n dist = np.linalg.norm(arch - parents_offspring_val[i])\n # if their distance is small -> they're too close,\n # larger the max distance the more spread out solutions become\n if dist <= max_distance:\n # penalize fitness down to 25%\n rel_fit[i] *= 0.25\n\n # sort individuals based on relative fitness\n rel_fit = np.asarray(rel_fit)\n sorted_ind = np.asarray(ind)[np.argsort(-rel_fit)]\n\n # use elitism to take the best half from pooled\n # parents and offspring\n self.gen = parents_offspring_val[sorted_ind[0:self.gen_size]]\n self.sigma = parents_offspring_sigma[sorted_ind[0:self.gen_size]]\n\n fits = self.fitness_functions(self.gen)\n # update archive\n if len(self.archive) !=0:\n # loop over every individual in surviving generation\n for j in range(0, self.gen_size):\n # indices of members in archive that are dominated by\n # the current individual\n ind_del = []\n f_arch = self.fitness_functions(np.asarray(self.archive))\n dominated = False\n for i in range(0, len(self.archive)):\n # individual strongly dominates member of archive\n if np.sum(f_arch[i] > fits[j]) == len(fits[j]):\n ind_del.append(i)\n # individual is strongly dominated by member\n elif np.sum(f_arch[i] < fits[j]) == len(fits[j]):\n dominated = True\n if len(ind_del) != 0:\n # delete members that are strongly dominated\n for index in sorted(ind_del, reverse=True):\n del self.archive[index]\n if not dominated: # weakly dominates members of archive\n if len(self.archive) <= max_archive_size:\n self.archive.append(self.gen[j])\n else: # archive is full\n temp = np.vstack((self.gen[j], self.archive))\n dist = self.__distance(temp)\n index = np.argmin(dist)\n if index != 0:\n del self.archive[index-1]\n self.archive.append(self.gen[j])\n\n dist = pdist(np.asarray(self.archive))\n #dist = self.__distance(np.asarray(self.archive))\n dist_min = np.min(dist)\n dist_max = np.max(dist)\n dist_median = np.median(dist)\n dist_std = np.std(dist)\n if info:\n msg = \" Archive Information:\\n\" \\\n \" Distance - Min: {}, Max: {}, Median: {}, Std: {}\\n\" \\\n \" Archive Size: {}\".format(dist_min, dist_max, dist_median,dist_std, len(self.archive))\n print(msg)\n\n else: # if first iteration of outer loop, just add best solution from gen\n self.archive.append(self.gen[0])\n self.archive = np.asarray(self.archive)","def run(self, num_iterations = 50, **kwargs):\n \n #setup system\n self.cost_calculator = t.CostCalculator(self.suppliers_allcards, self.all_ensembles_dict)\n bounds = np.array(self.cost_calculator.ensemble_sizes) - 1\n #define cost functions\n cost_func = lambda p: sum(self.cost_calculator.get_cost(p))\n #create model\n self.model = ga(cost_func, bounds, **kwargs)\n \n fitness_list = [];\n \n for i in range(num_iterations):\n #Update\n f = next(self.model)\n #get fitness values\n fitness_list.append(f[0])\n #Output\n print('\\r(%d/%d) '%(i+1,num_iterations), end = '')\n print('top ensemble fitness: %1.1f '%f[0], end = '')\n \n print('\\nDone')\n self.solution = self.cost_calculator.decode_arrangement(self.model.get_solution())","def portfolio_simulations_using_target_returns(num_of_years=30, trials=100):\n print(\"Running portfolio_simulations_using_target_returns() method\")\n sim_fia_cv = pd.DataFrame(index=range(num_of_years + 1))\n\n sim_base_total = pd.DataFrame(index=range(num_of_years + 1))\n sim_base_income = pd.DataFrame(index=range(num_of_years + 1))\n\n sim_port_total = pd.DataFrame(index=range(num_of_years + 1))\n sim_port_income = pd.DataFrame(index=range(num_of_years + 1))\n\n read_income_inputs = pd.read_excel(src + \"portfolio_information.xlsx\", sheet_name='income_model_inputs',\n index_col=[0])\n\n read_returns_est = pd.read_excel(src + \"portfolio_information.xlsx\", sheet_name='income_assets_returns_estimates',\n index_col=[0])\n\n read_asset_weights = pd.read_excel(src + \"portfolio_information.xlsx\", sheet_name='asset_weights',\n index_col=[0])\n\n # read random returns for simulation\n read_normal = pd.read_csv(src + 'median_returns_unsorted.csv', index_col=[0], parse_dates=True)\n cols = [read_normal.columns[c].split('_')[1] for c in np.arange(len(read_normal.columns))]\n read_normal.rename(columns=dict(zip(list(read_normal.columns), cols)), inplace=True)\n idx = list(read_normal.index)\n\n runs = 0\n while runs <= trials:\n # --------Shuffling the path of random returns from the median portfolio\n np.random.shuffle(idx)\n read_normal = read_normal.set_index([idx])\n read_normal = read_normal.sort_index()\n\n # assets_col_names = list(read_normal.columns)\n # tickers = list(read_asset_weights.index)\n wts = np.array(read_asset_weights.loc[:, 'base'])\n\n def asset_median_returns(data, ticker):\n return data.filter(regex=ticker).median(axis=1)\n\n # dataframe for unsorted returns (normal)\n median_returns_normal = read_normal.copy()\n median_returns_normal.loc[:, 'portfolio_return'] = median_returns_normal.dot(wts)\n median_normal_fia = pd.DataFrame({'FIA': asset_median_returns(read_normal, 'FIA')})\n\n years = list(range(0, num_of_years + 1))\n income_cols = ['year', 'strategy_term', 'index_returns', 'term_ret', 'term_ret_with_par', 'term_annualize',\n 'ann_net_spread', 'term_ret_netspr', 'high_inc_benefit_base', 'rider_fee', 'eoy_income',\n 'contract_value']\n\n term = int(read_income_inputs.loc['term', 'inputs'])\n fia_ret = read_returns_est.loc[read_returns_est.index[-1], 'Annualized Returns']\n fia_risk = read_returns_est.loc[read_returns_est.index[-1], 'Annualized Risk']\n par_rate = float(read_income_inputs.loc['par_rate', 'inputs'])\n spread = float(read_income_inputs.loc['spread', 'inputs'])\n bonus_term = int(read_income_inputs.loc['bonus_term', 'inputs'])\n premium = float(read_income_inputs.loc['premium', 'inputs'])\n income_bonus = float(read_income_inputs.loc['income_bonus', 'inputs'])\n\n income_starts = int(read_income_inputs.loc['start_income_years', 'inputs'])\n income_growth = float(read_income_inputs.loc['income_growth', 'inputs'])\n rider_fee = float(read_income_inputs.loc['rider_fee', 'inputs'])\n inc_payout_factor = float(read_income_inputs.loc['income_payout_factor', 'inputs'])\n contract_bonus = float(read_income_inputs.loc['contract_bonus', 'inputs'])\n social = float(read_income_inputs.loc['social', 'inputs'])\n inflation = float(read_income_inputs.loc['inflation', 'inputs'])\n wtd_cpn_yield = float(read_income_inputs.loc['wtd_coupon_yld', 'inputs'])\n life_expectancy = int(read_income_inputs.loc['life_expectancy_age', 'inputs'])\n clients_age = int(read_income_inputs.loc['clients_age', 'inputs'])\n\n # ---------------INCOME MODEL--------------------------------------------\n # runs = 0\n returns_dict = {}\n asset_dict = {}\n fia_dict = {}\n\n income_df = pd.DataFrame(index=years, columns=income_cols)\n income_df.loc[:, 'year'] = years\n income_df.loc[:, 'strategy_term'] = income_df.loc[:, 'year'] % term\n income_df.loc[:, 'strategy_term'] = income_df['strategy_term'].apply(lambda x: 1 if x == 0 else 0)\n\n income_df.loc[:, 'index_returns'] = read_normal.loc[:, 'FIA']\n\n cumprod = (1. + income_df['index_returns']).rolling(window=term).agg(lambda x: x.prod()) - 1\n income_df.loc[:, 'term_ret'] = np.where(income_df.loc[:, 'strategy_term'] == 1, cumprod, 0)\n income_df.loc[:, 'term_ret_with_par'] = income_df.loc[:, 'term_ret'] * par_rate\n income_df.loc[:, 'term_annualize'] = income_df.loc[:, 'term_ret_with_par'].apply(\n lambda x: (1 + x) ** (1 / term) - 1)\n income_df.loc[:, 'ann_net_spread'] = income_df.loc[:, 'term_annualize'] - spread\n income_df.loc[:, 'ann_net_spread'] = np.where(income_df.loc[:, 'strategy_term'] == 1,\n income_df.loc[:, 'ann_net_spread'], 0)\n income_df.loc[:, 'term_ret_netspr'] = income_df.loc[:, 'ann_net_spread'].apply(lambda x: (1 + x) ** term - 1)\n\n for counter in years:\n if counter == 0:\n income_df.loc[counter, 'high_inc_benefit_base'] = premium * (1 + income_bonus)\n\n elif counter <= min(bonus_term, income_starts):\n income_df.loc[counter, 'high_inc_benefit_base'] = income_df.loc[counter - 1, 'high_inc_benefit_base'] * \\\n (1 + income_growth)\n else:\n income_df.loc[counter, 'high_inc_benefit_base'] = income_df.loc[counter - 1, 'high_inc_benefit_base']\n\n income_df.loc[:, 'rider_fee'] = income_df.loc[:, 'high_inc_benefit_base'] * rider_fee\n income_df.loc[:, 'eoy_income'] = np.where(income_df.loc[:, 'year'] > income_starts,\n income_df.loc[:, 'high_inc_benefit_base'] * inc_payout_factor, 0)\n\n for counter in years:\n if counter == 0:\n income_df.loc[counter, 'contract_value'] = premium * (1 + contract_bonus)\n\n elif income_df.loc[counter, 'strategy_term'] == 1:\n x1 = income_df.loc[counter - 1, 'contract_value'] - income_df.loc[counter, 'rider_fee']\n x2 = (x1 * (1 + income_df.loc[counter, 'term_ret_netspr'])) - income_df.loc[counter, 'eoy_income']\n income_df.loc[counter, 'contract_value'] = x2\n\n else:\n x1 = income_df.loc[counter - 1, 'contract_value'] - income_df.loc[counter, 'rider_fee'] - \\\n income_df.loc[counter, 'eoy_income']\n\n income_df.loc[counter, 'contract_value'] = x1\n\n # variable stores the income number that is used in the base and fia portfolio calcs.\n\n income_from_fia = income_df.loc[income_df.index[-1], 'eoy_income']\n\n income_df.loc[:, 'contract_value'] = income_df.loc[:, 'contract_value'].apply(lambda x: 0 if x <= 0 else x)\n\n sim_fia_cv.loc[:, str(runs)] = income_df.loc[:, 'contract_value']\n\n # --------------------BASE MODEL---------------------------------------------\n\n base_wts = read_asset_weights.loc[:, 'base']\n base_assets = list(base_wts.index)\n base_weights = list(base_wts.values)\n base_returns = list(read_returns_est.loc[:, 'Annualized Returns'].values)\n base_std = list(read_returns_est.loc[:, 'Annualized Risk'].values)\n\n base_investment = float(read_income_inputs.loc['risky_assets', 'Base'])\n adv_fees = float(read_income_inputs.loc['advisor_fees', 'Base'])\n\n # -------------------required income----------------------------------\n req_annual_income = float(read_income_inputs.loc['annual_income', 'inputs'])\n income_needed = req_annual_income - social\n income_net_fia_income = max(0, income_needed - income_from_fia)\n cpn_income_base = base_investment * wtd_cpn_yield\n\n # ----------------------RANDOM RETURNS--------------------------\n r_cols = base_assets\n boy_value = ['bv_{}'.format(name) for name in base_assets]\n eoy_value = ['ev_{}'.format(name) for name in base_assets]\n\n random_returns = pd.DataFrame(index=income_df.index, columns=r_cols)\n\n random_returns = read_normal.copy()\n\n base_df = random_returns.copy()\n fia_portfolio_df = random_returns.copy()\n port_investment = float(read_income_inputs.loc['risky_assets', 'FIA'])\n cpn_income_port = port_investment * wtd_cpn_yield\n\n # -------------BASE PORTFOLIO----------------------------\n for name in boy_value:\n base_df.loc[:, name] = 0.0\n\n for counter in years:\n period_returns = list(random_returns.loc[counter, :])\n if counter == 0:\n\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment for c in range(len(boy_value))]\n\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\n base_df.loc[counter, 'total_net_fees'] = 0.0\n base_df.loc[counter, 'income'] = 0.0\n base_investment = base_df.loc[counter, boy_value].sum()\n\n elif (counter > 0) and (counter < income_starts):\n\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment * (1 + period_returns[c])\n for c in range(len(boy_value))]\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\n base_df.loc[counter, 'adv_fees'] = base_df.loc[counter, 'total'] * adv_fees\n base_df.loc[counter, 'total_net_fees'] = base_df.loc[counter, 'total'] - base_df.loc[\n counter, 'adv_fees']\n\n # --coupon payment is invested back into the risky portfolio until the income is withdrawn----\n base_investment = base_df.loc[counter, 'total_net_fees'] + cpn_income_base\n\n else:\n\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment * (1 + period_returns[c])\n for c in range(len(boy_value))]\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\n base_df.loc[counter, 'adv_fees'] = base_df.loc[counter, 'total'] * adv_fees\n\n # ---req. income is adjusted for inflation from the second year of withdrawal. Reinvestment of coupon\n # stops from the year income starts. Req. income is reduced by the coupon payments\n\n if counter == income_starts:\n\n income_needed = req_annual_income - social\n base_df.loc[counter, 'income'] = income_needed - cpn_income_base\n income_needed = req_annual_income\n\n else:\n income_needed = income_needed * (1 + inflation) - social\n base_df.loc[counter, 'income'] = income_needed - cpn_income_base\n income_needed = income_needed + social\n\n base_df.loc[counter, 'total_net_fees'] = base_df.loc[counter, 'total'] - \\\n base_df.loc[counter, 'adv_fees'] - \\\n base_df.loc[counter, 'income']\n\n base_investment = base_df.loc[counter, 'total_net_fees']\n\n base_df.loc[:, 'adj_total'] = base_df.loc[:, 'total_net_fees'].apply(lambda x: x if x > 0 else 0)\n sim_base_total.loc[:, 's_{}'.format(str(runs))] = base_df.loc[:, 'total_net_fees']\n sim_base_income.loc[:, 's_{}'.format(str(runs))] = base_df.loc[:, 'income']\n\n # ----------------------------FIA PORTFOLIO----------------------------------------------\n for name in boy_value:\n fia_portfolio_df.loc[:, name] = 0.0\n\n for counter in years:\n period_returns = list(random_returns.loc[counter, :])\n if counter == 0:\n\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment\n for c in range(len(boy_value))]\n fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\n fia_portfolio_df.loc[counter, 'total_net_fees'] = 0.0\n fia_portfolio_df.loc[counter, 'income'] = 0.0\n port_investment = fia_portfolio_df.loc[counter, boy_value].sum()\n\n elif (counter > 0) and (counter < income_starts):\n\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment * (1 + period_returns[c])\n for c in range(len(boy_value))]\n fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\n fia_portfolio_df.loc[counter, 'adv_fees'] = fia_portfolio_df.loc[counter, 'total'] * adv_fees\n fia_portfolio_df.loc[counter, 'total_net_fees'] = fia_portfolio_df.loc[counter, 'total'] - \\\n fia_portfolio_df.loc[counter, 'adv_fees']\n\n port_investment = fia_portfolio_df.loc[counter, 'total_net_fees'] + cpn_income_port\n\n else:\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment * (1 + period_returns[c])\n for c in range(len(boy_value))]\n fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\n fia_portfolio_df.loc[counter, 'adv_fees'] = fia_portfolio_df.loc[counter, 'total'] * adv_fees\n\n # ---req. income is adjusted for inflation from the second year of withdrawal. Reinvestment of coupon\n # stops from the year income starts. Req. income is reduced by the coupon payments\n\n if counter == income_starts:\n\n income_needed = req_annual_income - social\n income_net_fia_income = max(0, income_needed - income_from_fia)\n fia_portfolio_df.loc[counter, 'income'] = max(0, income_net_fia_income - cpn_income_port)\n income_needed = req_annual_income\n\n else:\n income_needed = income_needed * (1 + inflation) - social\n income_net_fia_income = max(0, income_needed - income_from_fia)\n fia_portfolio_df.loc[counter, 'income'] = max(0, income_net_fia_income - cpn_income_port)\n income_needed = income_needed + social\n\n fia_portfolio_df.loc[counter, 'total_net_fees'] = fia_portfolio_df.loc[counter, 'total'] - \\\n fia_portfolio_df.loc[counter, 'adv_fees'] - \\\n fia_portfolio_df.loc[counter, 'income']\n\n port_investment = fia_portfolio_df.loc[counter, 'total_net_fees']\n\n sim_port_total.loc[:, 's_{}'.format(str(runs))] = fia_portfolio_df.loc[:, 'total_net_fees'] + \\\n income_df.loc[:, 'contract_value']\n\n sim_port_income.loc[:, 's_{}'.format(str(runs))] = fia_portfolio_df.loc[:, 'income']\n\n fia_portfolio_df.loc[:, 'adj_total'] = fia_portfolio_df.loc[:, 'total_net_fees'].apply(\n lambda x: x if x > 0 else 0)\n\n runs = runs + 1\n\n # ----% of trials ending value at expected life is less than 0\n inflation_factor = (1 + annual_inflation) ** (life_expectancy - clients_age - income_starts)\n required_income_horizon = income_needed * inflation_factor\n required_income_horizon_net_fia = required_income_horizon - income_from_fia\n\n # prob_success_base = (sim_base_total.iloc[life_expectancy - clients_age] < 0).sum() / trials\n # prob_success_fia_port = (sim_port_total.iloc[life_expectancy - clients_age] < 0).sum() / trials\n\n prob_failure_base = (sim_base_total.iloc[life_expectancy - clients_age] < required_income_horizon).sum() / trials\n prob_failure_fia_port = (sim_port_total.iloc[life_expectancy - clients_age] < required_income_horizon_net_fia) \\\n .sum() / trials\n\n # ----Calculate at total average lifetime income for base portfolio----\n base_inc = sim_base_income.copy()\n base_inc = base_inc.cumsum()\n avg_income_base = base_inc.iloc[life_expectancy - clients_age].mean()\n\n # ----Calculate at total average lifetime income for FIA portfolio----\n port_inc = sim_port_income.copy()\n port_inc = port_inc.cumsum()\n avg_income_port = port_inc.iloc[life_expectancy - clients_age].mean()\n avg_income_port = avg_income_port + (income_from_fia * (life_expectancy - clients_age))\n\n # ---------income breakdown for Base portfolio----------------------------------\n base_df.to_csv(src + 'base_port_detail.csv')\n sim_base_total.to_csv(src + 'base_ending_values.csv')\n income_breakdown_base = pd.DataFrame(sim_base_total.quantile(0.5, axis=1))\n income_breakdown_base.loc[:, 'income_from_portfolio'] = sim_base_income.quantile(0.5, axis=1)\n income_breakdown_base.loc[:, 'fia_income'] = 0.0\n income_breakdown_base.loc[:, 'social_security_income'] = social\n income_breakdown_base.loc[:, 'coupon_income'] = cpn_income_base\n\n income_breakdown_base.rename(columns={income_breakdown_base.columns[0]: 'portfolio_ending_value'}, inplace=True)\n income_breakdown_base.loc[:, 'income_from_portfolio'][\n income_breakdown_base.loc[:, 'portfolio_ending_value'] <= 0] = 0\n income_breakdown_base.loc[:, 'total_income'] = income_breakdown_base.loc[:, income_breakdown_base.columns[1:]].sum(\n axis=1)\n\n # ------------Block Ends-------------------------------------------------------------\n\n # ---------income breakdown for FIA portfolio----------------------------------\n fia_portfolio_df.to_csv(src + 'fia_port_detail.csv')\n sim_port_total.to_csv(src + 'fiaport_ending_values.csv')\n\n income_breakdown_port = pd.DataFrame(sim_port_total.quantile(0.5, axis=1))\n income_breakdown_port.loc[:, 'income_from_portfolio'] = sim_port_income.quantile(0.5, axis=1)\n income_breakdown_port.loc[:, 'fia_income'] = income_from_fia\n income_breakdown_port.loc[:, 'social_security_income'] = social\n income_breakdown_port.loc[:, 'coupon_income'] = cpn_income_port\n\n income_breakdown_port.rename(columns={income_breakdown_port.columns[0]: 'portfolio_ending_value'}, inplace=True)\n income_breakdown_port.loc[:, 'income_from_portfolio'][\n income_breakdown_port.loc[:, 'portfolio_ending_value'] <= 0] = 0\n income_breakdown_port.loc[:, 'total_income'] = income_breakdown_port.loc[:, income_breakdown_port.columns[1:]].sum(\n axis=1)\n\n # ------------Block Ends-------------------------------------------------------------\n q_cut = [0.0, 0.05, 0.25, 0.5, 0.75, 0.95, 1.0]\n sim_base_income[sim_base_total < income_needed] = 0.0\n\n sim_port_income[sim_port_total < income_net_fia_income] = 0\n\n sim_port_income = sim_port_income + income_from_fia\n\n # base_quantile = sim_base_total.loc[sim_base_total.index[-1]].quantile([0.05, 0.25, 0.50, 0.75, 0.90])\n #\n # port_quantile = sim_port_total.loc[sim_port_total.index[-1]].quantile([0.05, 0.25, 0.50, 0.75, 0.90])\n\n base_quantile = sim_base_total.loc[sim_base_total.index[-1]].quantile(q_cut)\n\n port_quantile = sim_port_total.loc[sim_port_total.index[-1]].quantile(q_cut)\n\n # q_cut = [0.0, .05, 0.25, 0.5, 0.75, 0.95, 1.0]\n cols = ['Min', '5th', '25th', '50th', '75th', '90th', 'Max']\n\n # ----------------------------drop year 0--------------------------------\n sim_base_total = sim_base_total[1:]\n sim_port_total = sim_port_total[1:]\n\n # ---------quantile analysis for base terminal value-----------------------------\n base_qcut = pd.DataFrame(index=sim_base_total.index, columns=cols)\n for c in range(len(cols)):\n base_qcut.loc[:, cols[c]] = sim_base_total.quantile(q_cut[c], axis=1)\n\n base_qcut.clip(lower=0, inplace=True)\n\n # ---------quantile analysis for base income----------------------------\n base_income_qcut = pd.DataFrame(index=sim_base_income.index, columns=cols)\n for c in range(len(cols)):\n base_income_qcut.loc[:, cols[c]] = sim_base_income.quantile(q_cut[c], axis=1)\n\n # ----Remove NaN's prior to the income start years------------\n # base_income_qcut = base_income_qcut.loc[income_starts:]\n\n # -------------quantile analysis for portfolio terminal value ---------------\n\n port_qcut = pd.DataFrame(index=sim_port_total.index, columns=cols)\n for c in range(len(cols)):\n port_qcut.loc[:, cols[c]] = sim_port_total.quantile(q_cut[c], axis=1)\n\n port_qcut.clip(lower=0, inplace=True)\n\n # ---------------quantile analysis for portfolio income----------------------------\n port_income_qcut = pd.DataFrame(index=sim_port_income.index, columns=cols)\n for c in range(len(cols)):\n port_income_qcut.loc[:, cols[c]] = sim_port_income.quantile(q_cut[c], axis=1)\n\n # ----Remove NaN's prior to the income start years------------\n # port_income_qcut = port_income_qcut.loc[income_starts:]\n\n # ----------probability ending value will be less than 0 at the end of the horizon -------------------------\n\n base_legacy_risk = (sim_base_total.loc[sim_base_total.index[life_expectancy - clients_age]] < 0).sum() / (\n trials + 1)\n port_legacy_risk = (sim_port_total.loc[sim_port_total.index[life_expectancy - clients_age]] < 0).sum() / (\n trials + 1)\n\n legacy_risk = pd.DataFrame([base_legacy_risk, port_legacy_risk,\n 'Prob. of portfolio value less than 0 at the end of the expected life'],\n index=['base', 'fia_portfolio', 'Notes'],\n columns=['Ruin Probability'])\n\n # -----------Year-wise probability of ending value greater than 0 -----------------\n base_psuccess = sim_base_total.apply(lambda x: x > 0).sum(axis=1) / (trials + 1)\n port_psuccess = sim_port_total.apply(lambda x: x > 0).sum(axis=1) / (trials + 1)\n\n # -----------------------WRITING FILES TO EXCEL ---------------------------\n writer = pd.ExcelWriter(src + 'simulated_portfolios_summary.xlsx', engine='xlsxwriter')\n sim_base_total.to_excel(writer, sheet_name='base_ending_value')\n sim_port_total.to_excel(writer, sheet_name='fiaport_ending_value')\n base_qcut.to_excel(writer, sheet_name='base_quantile_ending')\n base_income_qcut.to_excel(writer, sheet_name='base_quantile_income')\n port_qcut.to_excel(writer, sheet_name='port_quantile_ending')\n port_income_qcut.to_excel(writer, sheet_name='port_quantile_income')\n\n sucess_df = pd.concat([base_psuccess, port_psuccess], axis=1)\n sucess_df.rename(columns={sucess_df.columns[0]: 'Base', sucess_df.columns[1]: 'Fia_Port'}, inplace=True)\n\n base_mean = sim_base_total[sim_base_total <= 0].isnull().sum().mean()\n port_mean = sim_port_total[sim_port_total <= 0].isnull().sum().mean()\n\n base_median = sim_base_total[sim_base_total <= 0].isnull().sum().median()\n port_median = sim_port_total[sim_port_total <= 0].isnull().sum().median()\n\n stats_df = pd.DataFrame([[base_mean, port_mean, 'Average years portfolio ending value > 0, out of N trials'],\n [base_median, port_median, 'Ending Value >0, 50% of the time']],\n index=['Mean years', 'Median years'], columns=['Base', 'fia_port', 'Comment'])\n\n # ---Average of terminal values at the end of horizon from N Trials\n stats_df.loc['Average Portfolio', 'Base'] = sim_base_total.iloc[-1].mean() + clients_age\n stats_df.loc['Average Portfolio', 'fia_port'] = sim_port_total.iloc[-1].mean() + clients_age\n stats_df.loc['Average Portfolio', 'Comment'] = \"Average of terminal values at the end of analysis period\" \\\n \" from N Trials\"\n\n # ----Median of terminal values at the end of horizon from N Trials\n stats_df.loc['Median Portfolio', 'Base'] = sim_base_total.iloc[-1].median() + clients_age\n stats_df.loc['Median Portfolio', 'fia_port'] = sim_port_total.iloc[-1].median() + clients_age\n stats_df.loc['Median Portfolio', 'Comment'] = \"Median of terminal values at the end of analysis period \" \\\n \"from N Trials\"\n\n # ---Average of terminal values at the end of Actuarial life from N Trials Base Portfolio\n stats_df.loc['Average Portfolio (end of expected_life)', 'Base'] = sim_base_total.iloc[\n life_expectancy - clients_age].mean()\n\n # ----Median of terminal values at the end of horizon from N Trials Base Portfolio\n stats_df.loc['Median Portfolio (end of expected_life)', 'Base'] = sim_base_total.iloc[\n life_expectancy - clients_age].median()\n\n # ---Average of terminal values at the end of Actuarial life from N Trials - FIA portfolio\n stats_df.loc['Average Portfolio (end of expected_life)', 'fia_port'] = sim_port_total.iloc[\n life_expectancy - clients_age].mean()\n stats_df.loc['Average Portfolio (end of expected_life)', 'Comment'] = \"Average of terminal values at the end of \" \\\n \"Actuarial life from N Trials\"\n\n # ----Median of terminal values at the end of horizon from N Trials - FIA Portfolio\n stats_df.loc['Median Portfolio (end of expected_life)', 'fia_port'] = sim_port_total.iloc[\n life_expectancy - clients_age].median()\n stats_df.loc['Median Portfolio (end of expected_life)', 'Comment'] = \"Median of terminal values at the end of \" \\\n \"horizon from N Trials\"\n\n stats_df.loc['% Success(value>0 at the end of expected_life)', 'Base'] = 1 - prob_failure_base\n stats_df.loc['% Success(value>0 at the end of expected_life)', 'fia_port'] = 1 - prob_failure_fia_port\n stats_df.loc['% Success(value>0 at the end of expected_life)', 'Comment'] = \"% of N trials yearly ending value \" \\\n \"greater than 0\"\n\n # -----Mininum of N portfolios terminal value at the end of Actuarial Age\n stats_df.loc['Minimum Value', 'Base'] = sim_base_total.iloc[life_expectancy - clients_age].min()\n stats_df.loc['Minimum Value', 'fia_port'] = sim_port_total.iloc[life_expectancy - clients_age].min()\n stats_df.loc['Minimum Value', 'Comment'] = \"Mininum of N portfolios terminal value at the end of Actuarial Age\"\n\n # -----Maxinum of N portfolios terminal value at the end of Actuarial Age\n stats_df.loc['Maximum Value', 'Base'] = sim_base_total.iloc[life_expectancy - clients_age].max()\n stats_df.loc['Maximum Value', 'fia_port'] = sim_port_total.iloc[life_expectancy - clients_age].max()\n stats_df.loc['Maximum Value', 'Comment'] = \"Maxinum of N portfolios terminal value at the end of Actuarial Age\"\n\n # -----Average income from N portfolios at the ned of Actuarial Age\n stats_df.loc['Avg Income (lifetime)', 'Base'] = avg_income_base\n stats_df.loc['Avg Income (lifetime)', 'fia_port'] = avg_income_port\n stats_df.loc['Avg Income (lifetime)', 'Comment'] = \"Average income from N portfolios at the end of Actuarial Age\"\n\n sucess_df.to_excel(writer, sheet_name='yearly_success_rates')\n stats_df.to_excel(writer, sheet_name='Stats')\n\n writer.save()\n\n print(\"simulation completed.\")","def simulation(cython: bool = False, parallel: bool = False):\n # Create the population\n cfg = Config()\n cfg.game.duration = 200\n cfg.update()\n pop = Population(\n name=\"delete_me\",\n config=cfg,\n folder_name=\"test_performance\",\n use_backup=True,\n overwrite=True, # Every iteration, create a new population from scratch\n )\n \n # Perform the simulations of experiment3\n train_games, _ = get_game_ids(experiment_id=3)\n if cython:\n multi_env = MultiEnvironmentCy(game_config=cfg, pop_config=pop.config)\n else:\n multi_env = MultiEnvironment(game_config=cfg, pop_config=pop.config)\n multi_env.set_games(train_games, noise=True)\n \n # Prepare the generation's reporters for the generation\n pop.reporters.start_generation(gen=pop.generation, logger=pop.log)\n \n # Fetch the dictionary of genomes\n genomes = list(iteritems(pop.population))\n \n if parallel:\n # Initialize the evaluation-pool\n pool = mp.Pool(mp.cpu_count())\n manager = mp.Manager()\n return_dict = manager.dict()\n \n for genome in genomes:\n pool.apply_async(func=multi_env.eval_genome, args=(genome, return_dict))\n pool.close() # Close the pool\n pool.join() # Postpone continuation until everything is finished\n else:\n return_dict = dict()\n for genome in genomes:\n multi_env.eval_genome(genome, return_dict)\n \n path = f\"population_backup/storage/test_performance/\"\n if os.path.exists(path):\n shutil.rmtree(path)","def after_run(self):\n # Calculate the performance of the strategy and portfolio\n self.portfolio.calc_stats()\n self.calc_performance()\n\n return self","def simulate_trajectories(kav):\n print \"Simulating \"+str(kav)\n wt_trajectories = []\n avp_trajectories = []\n vip_trajectories = []\n for tn in range(100):\n # get random initial condition\n # initial phases\n init_conditions_AV = [single_osc.lc(wt_T*np.random.rand()) \n for i in range(AVPcells+VIPcells)]\n init_conditions_NAV = [single_osc.lc(wt_T*np.random.rand())[:-1]\n for i in range(NAVcells)]\n y0_random = np.hstack(init_conditions_AV+init_conditions_NAV)\n\n # do the simulation\n model = GonzeModelManyCells(param, kav=kav, \n initial_values=y0_random)\n wt_trajectories.append(model.run(show_labels=False, seed=0))\n\n # avp bmalko\n avp_model = GonzeModelManyCells(param, bmalko='AVP', kav=kav, \n initial_values=y0_random)\n avp_trajectories.append(avp_model.run(show_labels=False, seed=0))\n\n # vip bmalko\n vip_model = GonzeModelManyCells(param, bmalko='VIP', kav=kav, \n initial_values=y0_random)\n vip_trajectories.append(vip_model.run(show_labels=False, seed=0))\n\n # save results\n with open(\"Data/params/wt_\"+str(kav)+\".pickle\", \"wb\") as output_file:\n pickle.dump(wt_trajectories, output_file)\n with open(\"Data/params/avp_\"+str(kav)+\".pickle\", \"wb\") as output_file:\n pickle.dump(avp_trajectories, output_file)\n with open(\"Data/params/vip_\"+str(kav)+\".pickle\", \"wb\") as output_file:\n pickle.dump(vip_trajectories, output_file)\n\n return {'wt': wt_trajectories,\n 'avp': avp_trajectories,\n 'vip': vip_trajectories}","def _generate_trading_instances(self, sp):\n print(\n \"Initialization...\"\n )\n self.data_handler = self.data_handler_cls(self.events, self.csv_dir, self.symbol_list, self.start_date,\n self.end_date)\n self.portfolio = self.portfolio_cls(self.data_handler, self.events, self.initial_capital, self.start_date,\n self.strategy_id, self.spread, self.commission,self.csv_dir)\n self.strategy = self.strategy_cls(self.data_handler, self.events, self.portfolio, self.spread, self.commission,\n sp)\n self.plot = self.plot_cls(self.csv_dir, self.portfolio, self.strategy_id)","def _main():\n ############### STOCK INPUT DATA #############\n \n StockTicker = [\"TEA\",\"POP\",\"ALE\",\"GIN\",\"JOE\"]\n StockType = [\"Com\",\"Com\",\"Com\",\"Pref\",\"Com\"] #($)\n TickerPrice = [2,1.5,3,1,1.8] #(USD)\n LastDividend = [0,0.08,0.23,0.08,0.13] #(GBP)\n FixedDividend = [0,0,0,0.02,0]\n ParValue = [1,1,0.6,1,2.5] #(GBP)\n\n \n ############# GET STOCK PARAMETERS ###########\n \n Get_StockParameters(len(StockTicker),StockTicker,StockType,TickerPrice,LastDividend,FixedDividend,ParValue)\n \n \n ############# PERFORM A TRADING FOR EACH STOCK ###########\n \n\n pathdir = \"./DataFiles/StockTrde_\"\n \n for i in range(0,len(StockTicker)):\n \n #Check if the data file exists\n datafile = pathdir+\"{}.csv\".format(i+1)\n if os.path.isfile(datafile) == False :\n Get_HistoricalStockData(i,datafile,datetime.datetime(2015, 1, 1),datetime.datetime(2016, 1, 1))\n\n # Get the Strategy\n Get_Trade(StockTicker[i],datafile)\n \n ############# PLOT THE TRADING ###########\n \n Plot_StockTrade(StockTicker[i])","def run(outputfile, graph_each, graph_avg, iterations):\n # runs auction the requested number of times\n bids = simulate_auction(iterations)\n \n # export results to csv format\n csvfile = open(outputfile, 'w', newline='')\n writer = csv.writer(csvfile, dialect='excel')\n writer.writerow([\"Bidder\", \"BidValue\", \"BidWeight\", \"CreationTimestep\", \"TimestepPaid\"])\n\n for bid in bids:\n entry = [bid.bidder, bid.value, bid.weight, bid.creation_timestep, bid.payment_timestep]\n writer.writerow(entry)\n\n # display data visualisation if --graphEach flag is used on cli\n if graph_each:\n plt.title('Auction simulation results')\n plt.xlabel('Simulation Timestep')\n plt.ylabel('Price Paid per Transaction by Agent')\n\n # for each unique bidder\n for bidder in list(set([bid.bidder for bid in bids])):\n x = []\n y = []\n\n for bid in bids:\n if bidder == bid.bidder:\n x.append(bid.payment_timestep)\n y.append(bid.value)\n \n # add results for current bidder to graph plot\n plt.scatter(x, y, label=bidder, alpha=0.5, s=2)\n\n plt.legend(loc='best')\n plt.show()\n\n # display data visualisation if --graphAvg flag is used on cli\n if graph_avg:\n plt.title('Auction simulation results')\n plt.xlabel('Simulation Timestep')\n plt.ylabel('Average Price Paid per Transaction by Block')\n\n # unique timesteps in the history\n x = list(set(bid.creation_timestep for bid in bids))\n y = []\n\n import statistics\n for timestep in x:\n bids_accepted = [bid.value for bid in bids if bid.creation_timestep == timestep]\n y.append(statistics.mean(bids_accepted))\n\n plt.plot(x, y, label=\"Avg Gas Price Paid\")\n plt.legend(loc='best')\n plt.show()","def run_experiment(x_loops=15, max_steps=0, display_on=True, max_fps=10,\n garden_size=8, tako_number=1, pop_max=30, max_width=1800,\n max_height=900, collect_data=True, export_all=False,\n rand_nets=False, max_gen = 505, genetic_mode=\"Plain\",\n learning_on=False, seeds=None, garden_mode=\"Diverse Static\",\n family_detection=None, family_mod=0, record_inbreeding=True,\n inbreed_lim = 1.1, hla_genes=0, binary_health=0,\n carrier_percentage=40, two_envs=False, diff_envs=False,\n migration_rate=0, phen_pref=False, filename=\"\"): \n #round width/height down to nearest multiple of 50 if need be\n if max_width % 50 != 0:\n max_width = max_width - (max_width % 50)\n if max_height % 50 != 0:\n max_height = max_height - (max_height % 50)\n\n i = 0\n #create csv files if they don't already exist\n if collect_data or export_all:\n if filename == \"\":\n filename = str(int(time.time())) + \".csv\"\n elif len(filename) < 4:\n filename = filename + \".csv\"\n elif filename[-4:] != \".csv\":\n filename = filename + \".csv\"\n\n if not os.path.exists(\"Data\"):\n os.makedirs(\"Data\")\n\n if collect_data:\n if not os.path.exists(os.path.join(\"Data\", filename)):\n with open(os.path.join(\"Data\", filename), 'a', newline='') as\\\n csvfile:\n writ = csv.writer(csvfile)\n writ.writerow(['iteration', 'env #', 'ID', 'parent1',\n 'parent2', 'age', 'generation', '# children',\n 'mating attempts', 'accum pain',\n 'cause of death', 'timestep', 'mutations',\n 'parent_degree', 'parent_genoverlap',\n '# disorders',\n 'health a', 'health b', 'preference'])\n else:\n with open(os.path.join(\"Data\", filename), newline='') as\\\n csvfile:\n reader = csv.DictReader(csvfile)\n row = None\n for row in reader: pass\n if row != None:\n i = int(row[\"iteration\"]) + 1\n\n if export_all:\n h = make_headers()\n f = os.path.join('Data', (filename[:-4] + ' gene data.csv'))\n if not os.path.exists(f):\n with open(f, 'a') as file:\n writ = csv.writer(file)\n writ.writerow(h)\n\n tako.rand_nets = rand_nets\n tako.family_mod = family_mod\n tako.family_detection = family_detection\n gt.family_detection = family_detection\n tako.record_inbreeding = record_inbreeding\n tako.inbreed_lim = inbreed_lim\n tako.hla_genes = hla_genes\n tako.binary_health = binary_health\n tako.carrier_percentage = carrier_percentage\n tako.phen_pref = phen_pref\n gt.phen_pref = phen_pref\n \n loop_limit = x_loops\n if loop_limit < 1:\n loop_limit = 1\n\n if seeds == None:\n seeds = [None for i in range(x_loops)]\n\n while loop_limit > 0:\n #check if seeds is long enough\n if len(seeds) < loop_limit + i:\n for j in range(loop_limit + i - len(seeds)):\n seeds.append(seeds[j])\n if seeds[0] != None:\n tako.set_seed(seeds[i])\n g = garden_game(garden_size, tako_number, pop_max, max_width,\n max_height, display_on, max_fps, learning_on,\n genetic_mode, rand_nets, garden_mode, filename,\n export_all, family_mod, family_detection,\n two_envs, diff_envs, migration_rate,\n seeds[i])\n if display_on:\n main_window = g\n main_window.main_loop(max_steps, max_gen, display_on,\n collect_data, garden_mode, i)\n else:\n g.main_loop(max_steps, max_gen, display_on, collect_data,\n garden_mode, i)\n loop_limit -= 1\n i += 1","def run_genetic_algorithm(bayes_params):\n\n print('Running genetic algorithm')\n\n # Unpacks parameters (unfortunately can't feed dataframe (or series or\n # array) data into a function with hyperopt, so am having to pickle the\n # parameters not being optimised with hyperopt\n params_file = '{}/Program_input/Input_params.pkl'.format(\n bayes_params['workingdirectory']\n )\n with open(params_file, 'rb') as f:\n fixed_params = pickle.load(f)\n if not type(fixed_params) in [dict, OrderedDict]:\n raise TypeError('Data in {} is not a pickled dictionary'.format(params_file))\n params = {**bayes_params, **fixed_params}\n\n # Records sequences and their fitnesses after each generation\n with open('{}/Program_output/Sequence_track.txt'.format(\n bayes_params['workingdirectory']), 'w') as f:\n f.write('Tracking GA optimisation progress\\n')\n\n ga_calcs = run_ga_calcs(params)\n\n # Defines whether sequences are compared by their raw or rank propensities.\n # Since BUDE scores and frequency values have to be compared by their rank\n # values, have made the decision to also compare propensity values by their\n # rankings.\n \"\"\"\n if params['matingpopmethod'] in ['fittest', 'roulettewheel']:\n raw_or_rank = 'raw'\n elif params['matingpopmethod'] in ['rankroulettewheel']:\n raw_or_rank = 'rank'\n \"\"\"\n raw_or_rank = 'rank'\n\n # Calculates propensity and/or BUDE energy of input structure\n with open('{}/Program_output/Sequence_track.txt'.format(\n bayes_params['workingdirectory']), 'a') as f:\n f.write('Input structure\\n')\n\n if params['fitnessscoremethod'] == 'alternate':\n (network_propensity_scores, network_frequency_scores\n ) = ga_calcs.measure_fitness_propensity(params['initialnetwork'])\n\n with open('{}/Program_output/Sequence_track.txt'.format(\n bayes_params['workingdirectory']), 'a') as f:\n f.write('network_id, sequence, propensity_score, frequency_score,'\n ' BUDE energy, clashscore\\n')\n for network, G in params['initialnetwork'].items():\n sequence = ''.join([G.nodes()[node]['aa_id'] for node in G.nodes()])\n propensity = network_propensity_scores[network]\n frequency = network_frequency_scores[network]\n f.write('{}, {}, {}, {}, {}, {}\\n'.format(\n network, sequence, propensity, frequency,\n params['inputpdbenergy'], params['inputpdbclash']\n ))\n f.write('\\n')\n\n if params['fitnessscoremethod'] == 'propensity':\n (network_propensity_scores, network_frequency_scores\n ) = ga_calcs.measure_fitness_propensity(params['initialnetwork'])\n\n with open('{}/Program_output/Sequence_track.txt'.format(\n bayes_params['workingdirectory']), 'a') as f:\n f.write('network_id, sequence, propensity_score, frequency_score\\n')\n for network, G in params['initialnetwork'].items():\n sequence = ''.join([G.nodes()[node]['aa_id'] for node in G.nodes()])\n propensity = network_propensity_scores[network]\n frequency = network_frequency_scores[network]\n f.write('{}, {}, {}, {}\\n'.format(\n network, sequence, propensity, frequency\n ))\n f.write('\\n')\n\n elif params['fitnessscoremethod'] == 'allatom':\n network_energies = ga_calcs.measure_fitness_allatom(params['initialnetwork'])\n\n with open('{}/Program_output/Sequence_track.txt'.format(\n bayes_params['workingdirectory']), 'a') as f:\n f.write('network_id, sequence, BUDE energy\\n')\n for network, G in params['initialnetwork'].items():\n sequence = ''.join([G.nodes()[node]['aa_id'] for node in G.nodes()])\n energy = network_energies[network]\n f.write('{}, {}, {}\\n'.format(network, sequence, energy))\n f.write('\\n')\n\n elif params['fitnessscoremethod'] == 'molprobity':\n network_clashes = ga_calcs.measure_fitness_clashscore(params['initialnetwork'])\n\n with open('{}/Program_output/Sequence_track.txt'.format(\n bayes_params['workingdirectory']), 'a') as f:\n f.write('network_id, sequence, clashscore\\n')\n for network, G in params['initialnetwork'].items():\n sequence = ''.join([G.nodes()[node]['aa_id'] for node in G.nodes()])\n clashscore = network_clashes[network]\n f.write('{}, {}, {}\\n'.format(network, sequence, clashscore))\n f.write('\\n')\n\n # Runs GA cycles\n gen = params['startgen']\n while gen < params['stopgen']:\n gen += 1\n print('Generation {}'.format(gen))\n with open('{}/Program_output/Sequence_track.txt'.format(\n bayes_params['workingdirectory']), 'a') as f:\n f.write('\\n\\n\\n\\n\\nGeneration {}\\n'.format(gen))\n\n\n all_networks_list = [params['sequencesdict']]\n pop_sizes = [params['populationsize']]\n\n for index, networks_dict in enumerate(all_networks_list):\n # Measures fitness of sequences in starting population.\n if (\n (params['fitnessscoremethod'] == 'propensity')\n or\n (params['fitnessscoremethod'] == 'alternate' and gen % 2 == 1)\n ):\n (network_propensity_scores, network_frequency_scores\n ) = ga_calcs.measure_fitness_propensity(networks_dict)\n network_fitness_scores = ga_calcs.combine_prop_and_freq_scores(\n network_propensity_scores, network_frequency_scores, raw_or_rank\n )\n\n # Records sequences output from this generation and their\n # associated fitnesses\n with open('{}/Program_output/Sequence_track.txt'.format(\n bayes_params['workingdirectory']), 'a') as f:\n f.write('network, sequence, propensity, frequency, probability\\n')\n for network, G in networks_dict.items():\n sequence = ''.join([G.nodes()[node]['aa_id'] for node in G.nodes()])\n propensity = network_propensity_scores[network]\n frequency = network_frequency_scores[network]\n probability = network_fitness_scores[network]\n f.write('{}, {}, {}, {}, {}\\n'.format(\n network, sequence, propensity, frequency, probability\n ))\n f.write('Total: {}, {}, {}'.format(\n sum(network_propensity_scores.values()),\n sum(network_frequency_scores.values()),\n sum(network_fitness_scores.values())\n ))\n f.write('\\n')\n elif (\n (params['fitnessscoremethod'] == 'allatom')\n or\n (params['fitnessscoremethod'] == 'alternate' and gen % 4 == 2)\n ):\n # Runs BUDE energy scoring on parallel processors\n network_energies = ga_calcs.measure_fitness_allatom(networks_dict)\n (network_fitness_scores\n ) = ga_calcs.convert_energies_to_probabilities(network_energies)\n\n # Records sequences output from this generation and their\n # associated fitnesses\n with open('{}/Program_output/Sequence_track.txt'.format(\n bayes_params['workingdirectory']), 'a') as f:\n f.write('network, sequence, BUDE score, probability\\n')\n for network, G in networks_dict.items():\n sequence = ''.join([G.nodes()[node]['aa_id'] for node in G.nodes()])\n energy = network_energies[network]\n probability = network_fitness_scores[network]\n f.write('{}, {}, {}, {}\\n'.format(\n network, sequence, energy, probability\n ))\n f.write('Total: {}, {}'.format(\n sum(network_energies.values()),\n sum(network_fitness_scores.values())\n ))\n f.write('\\n')\n\n elif (\n (params['fitnessscoremethod'] == 'molprobity')\n or\n (params['fitnessscoremethod'] == 'alternate' and gen % 4 == 0)\n ):\n # Runs MolProbity scoring on parallel processors\n network_clashes = ga_calcs.measure_fitness_clashscore(networks_dict)\n (network_fitness_scores\n ) = ga_calcs.convert_clashscores_to_probabilities(network_clashes)\n\n # Records sequences output from this generation and their\n # associated fitnesses\n with open('{}/Program_output/Sequence_track.txt'.format(\n bayes_params['workingdirectory']), 'a') as f:\n f.write('network, sequence, clashscore, probability\\n')\n for network, G in networks_dict.items():\n sequence = ''.join([G.nodes()[node]['aa_id'] for node in G.nodes()])\n clash = network_clashes[network]\n probability = network_fitness_scores[network]\n f.write('{}, {}, {}, {}\\n'.format(\n network, sequence, clash, probability\n ))\n f.write('Total: {}, {}'.format(\n sum(network_clashes.values()),\n sum(network_fitness_scores.values())\n ))\n f.write('\\n')\n\n # Selects subpopulation for mating\n if params['matingpopmethod'] == 'fittest':\n mating_pop_dict = ga_calcs.create_mat_pop_fittest(\n networks_dict, network_fitness_scores, pop_sizes[index],\n params['unfitfraction']\n )\n elif params['matingpopmethod'] in ['roulettewheel', 'rankroulettewheel']:\n mating_pop_dict = ga_calcs.create_mat_pop_roulette_wheel(\n networks_dict, network_fitness_scores, pop_sizes[index], params['']\n )\n\n # Performs crossover of parent sequences to generate child sequences\n if params['crossovermethod'] == 'uniform':\n crossover_pop_dict = ga_calcs.uniform_crossover(mating_pop_dict)\n elif params['crossovermethod'] == 'segmented':\n crossover_pop_dict = ga_calcs.segmented_crossover(mating_pop_dict)\n\n # Mutates child sequences\n if params['mutationmethod'] == 'swap':\n mutated_pop_dict = ga_calcs.swap_mutate(crossover_pop_dict)\n elif params['mutationmethod'] == 'scramble':\n mutated_pop_dict = ga_calcs.scramble_mutate(crossover_pop_dict)\n\n # Combines parent and child sequences into single generation\n merged_networks_dict = ga_calcs.add_children_to_parents(\n mutated_pop_dict, mating_pop_dict\n )\n\n random_order = [n for n in range(len(merged_networks_dict))]\n random.shuffle(random_order)\n shuffled_merged_networks_dict = OrderedDict(\n {list(merged_networks_dict.keys())[n]:\n list(merged_networks_dict.values())[n] for n in random_order}\n )\n params['sequencesdict'] = shuffled_merged_networks_dict\n\n # Calculates fitness of output sequences and filters population to maintain\n # the fittest 50%, plus sums the probabilities of the retained sequences and\n # returns this value (to be minimised with hyperopt)\n summed_fitness = 0\n\n with open('{}/Program_output/Sequence_track.txt'.format(\n bayes_params['workingdirectory']), 'a') as f:\n f.write('\\n\\n\\n\\n\\nOutput generation\\n')\n\n if params['fitnessscoremethod'] != 'allatom':\n (network_propensity_scores, network_frequency_scores\n ) = ga_calcs.measure_fitness_propensity(params['sequencesdict'])\n network_fitness_scores = ga_calcs.combine_prop_and_freq_scores(\n network_propensity_scores, network_frequency_scores, raw_or_rank\n )\n elif params['fitnessscoremethod'] == 'allatom':\n network_energies = ga_calcs.measure_fitness_allatom(params['sequencesdict'])\n (network_fitness_scores\n ) = ga_calcs.convert_energies_to_probabilities(network_energies)\n\n # Records sequences output from this generation and their associated\n # fitnesses\n with open('{}/Program_output/Sequence_track.txt'.format(\n bayes_params['workingdirectory']), 'a') as f:\n if params['fitnessscoremethod'] != 'allatom':\n f.write('network, sequence, propensity, frequency\\n')\n elif params['fitnessscoremethod'] == 'allatom':\n f.write('network, sequence, BUDE score\\n')\n for network, G in params['sequencesdict'].items():\n sequence = ''.join([G.nodes()[node]['aa_id'] for node in G.nodes()])\n if params['fitnessscoremethod'] != 'allatom':\n propensity = network_propensity_scores[network]\n frequency = network_frequency_scores[network]\n f.write('{}, {}, {}, {}\\n'.format(\n network, sequence, propensity, frequency\n ))\n elif params['fitnessscoremethod'] == 'allatom':\n energy = network_energies[network]\n f.write('{}, {}, {}\\n'.format(network, sequence, energy))\n if params['fitnessscoremethod'] != 'allatom':\n f.write('Total: {}, {}'.format(\n sum(network_propensity_scores.values()),\n sum(network_frequency_scores.values())\n ))\n elif params['fitnessscoremethod'] == 'allatom':\n f.write('Total: {}'.format(sum(network_energies.values())))\n f.write('\\n')\n\n params['sequencesdict'] = ga_calcs.create_mat_pop_fittest(\n params['sequencesdict'], network_fitness_scores,\n params['populationsize'], unfit_fraction=0\n )\n\n for network in params['sequencesdict'].keys():\n # Higher propensity is more likely, so add because output from\n # measure_fitness_propensity is sum of -log(propensity) values, and\n # hyperopt minimises output score\n # Can't combine propensity and frequency scores without first converting\n # to a probability, so for calculating output combined fitness can only\n # use combined propensity scores to rank the structures\n if params['fitnessscoremethod'] != 'allatom':\n summed_fitness += network_propensity_scores[network]\n # Lower score is more likely, so add because hyperopt minimises output\n # score\n elif params['fitnessscoremethod'] == 'allatom':\n summed_fitness += network_energies[network]\n\n with open('{}/Program_output/GA_output_sequences_dict.pkl'.format(\n bayes_params['workingdirectory']), 'wb') as f:\n pickle.dump(params['sequencesdict'], f)\n\n print(summed_fitness)\n\n return summed_fitness","def run():\n if am_i_root():\n\n print(\"*** initializing...\")\n\n # Print parameters\n print(\"N_DIMS = \" + str(N_DIMS))\n print(\"LAMBDA_OVER_DX = \" + str(LAMBDA_OVER_DX))\n print(\"R_DT = \" + str(R_DT))\n print(\"MU0_POISSON = \" + str(MU0_POISSON))\n print(\"NORM_POISSON = \" + NORM_POISSON)\n print(\"N_GRID = \" + str(N_GRID))\n print(\"N_HITS = \" + str(N_HITS))\n print(\"POLICY = \" + str(POLICY))\n if POLICY == -1:\n print(\"MODEL_PATH = \" + str(MODEL_PATH))\n else:\n print(\"STEPS_AHEAD = \" + str(STEPS_AHEAD))\n print(\"EPSILON = \" + str(EPSILON))\n print(\"STOP_t = \" + str(STOP_t))\n print(\"STOP_p = \" + str(STOP_p))\n print(\"N_PARALLEL = \" + str(N_PARALLEL))\n print(\"WITH_MPI = \" + str(WITH_MPI))\n print(\"ADAPTIVE_N_RUNS = \" + str(ADAPTIVE_N_RUNS))\n print(\"REL_TOL = \" + str(REL_TOL))\n print(\"MAX_N_RUNS = \" + str(MAX_N_RUNS))\n print(\"N_RUNS(input) = \" + str(N_RUNS))\n sys.stdout.flush()\n\n # Perform runs\n if am_i_root():\n print(\"*** generating episodes...\")\n\n N_runs = N_RUNS\n if ADAPTIVE_N_RUNS or WITH_MPI:\n N_runs = int(N_PARALLEL * (np.ceil(N_runs / N_PARALLEL))) # make it multiple of N_PARALLEL\n if am_i_root():\n print(\"N_RUNS(current) = \" + str(N_runs))\n sys.stdout.flush()\n\n N_runso = 0\n\n if WITH_MPI:\n cdf_t_tot_loc = np.zeros(LEN_CDF_T, dtype=float)\n cdf_h_tot_loc = np.zeros(LEN_CDF_H, dtype=float)\n mean_t_loc = np.nan * np.ones(MAX_N_RUNS // N_PARALLEL, dtype=float)\n failed_loc = - np.ones(MAX_N_RUNS // N_PARALLEL, dtype=float)\n else:\n cdf_t_tot = np.zeros(LEN_CDF_T, dtype=float)\n cdf_h_tot = np.zeros(LEN_CDF_H, dtype=float)\n mean_t_episodes = np.nan * np.ones(MAX_N_RUNS, dtype=float)\n failed_episodes = - np.ones(MAX_N_RUNS, dtype=float)\n\n while True:\n if WITH_MPI: # MPI\n if N_runs % N_PARALLEL != 0:\n raise Exception(\"N_runs must be multiple of N_PARALLEL with MPI\")\n COMM.Barrier()\n # Decomposition\n Nepisodes = N_runs // N_PARALLEL\n episode_list = range(N_runso + ME, N_runs, N_PARALLEL)\n # Run episodes and reduce locally\n ind = N_runso // N_PARALLEL\n for episode in episode_list:\n cdf_t, cdf_h, mean_t_loc[ind], failed_loc[ind] = Worker(episode)\n cdf_t_tot_loc += cdf_t\n cdf_h_tot_loc += cdf_h\n ind += 1\n\n # Reduce globally the mean_t and failed\n mean_t_episodes = np.empty([N_runs], dtype=float)\n failed_episodes = np.empty([N_runs], dtype=float)\n COMM.Barrier()\n COMM.Allgather([mean_t_loc[:ind], Nepisodes, MPI.DOUBLE], [mean_t_episodes, Nepisodes, MPI.DOUBLE])\n COMM.Allgather([failed_loc[:ind], Nepisodes, MPI.DOUBLE], [failed_episodes, Nepisodes, MPI.DOUBLE])\n COMM.Barrier()\n elif N_PARALLEL > 1: # multiprocessing\n # Run episodes in parallel\n pool = multiprocessing.Pool(N_PARALLEL)\n result = pool.map(Worker, range(N_runso, N_runs))\n pool.close()\n pool.join()\n # Reduce\n ind = N_runso\n for cdf_t, cdf_h, mean_t, failed in result:\n cdf_t_tot += cdf_t\n cdf_h_tot += cdf_h\n mean_t_episodes[ind] = mean_t\n failed_episodes[ind] = failed\n ind += 1\n elif N_PARALLEL == 1: # sequential\n ind = N_runso\n for episode in range(N_runso, N_runs):\n cdf_t, cdf_h, mean_t, failed = Worker(episode)\n cdf_t_tot += cdf_t\n cdf_h_tot += cdf_h\n mean_t_episodes[ind] = mean_t\n failed_episodes[ind] = failed\n ind += 1\n else:\n raise Exception(\"Problem with N_PARALLEL: must be an int >= 1\")\n\n # estimate of the error\n mean_ep = np.mean(mean_t_episodes[:N_runs])\n sigma_ep = np.std(mean_t_episodes[:N_runs])\n std_error_mean = sigma_ep / np.sqrt(N_runs)\n rel_std_error_mean = std_error_mean / mean_ep\n\n # break clause\n if not ADAPTIVE_N_RUNS:\n break\n else:\n if rel_std_error_mean < REL_TOL:\n break\n elif N_runs >= MAX_N_RUNS:\n break\n else:\n N_runso = N_runs\n N_runs = int(np.ceil(1.05 * (sigma_ep / mean_ep / REL_TOL) ** 2))\n N_runs = min(N_runs, MAX_N_RUNS)\n N_runs = int(N_PARALLEL * (np.ceil(N_runs / N_PARALLEL))) # make it multiple of N_PARALLEL\n if am_i_root():\n print(\"N_RUNS(current) = \" + str(N_runs))\n sys.stdout.flush()\n\n if am_i_root():\n print(\"N_RUNS(performed) = \" + str(N_runs))\n sys.stdout.flush()\n\n # Reduce\n if am_i_root():\n print(\"*** post-processing...\")\n if WITH_MPI:\n # locally\n cdf_t_tot_loc /= N_runs\n cdf_h_tot_loc /= N_runs\n # Reduce globally\n cdf_t_tot = np.empty([LEN_CDF_T], dtype=float)\n cdf_h_tot = np.empty([LEN_CDF_H], dtype=float)\n COMM.Barrier()\n COMM.Allreduce(cdf_t_tot_loc, cdf_t_tot, op=MPI.SUM)\n COMM.Allreduce(cdf_h_tot_loc, cdf_h_tot, op=MPI.SUM)\n COMM.Barrier()\n else:\n cdf_t_tot /= N_runs\n cdf_h_tot /= N_runs\n mean_t_episodes = mean_t_episodes[:N_runs]\n failed_episodes = failed_episodes[:N_runs]\n\n # Further post-processing, save and plot\n if am_i_root():\n\n # from cdf to pdf\n pdf_t_tot = cdf_to_pdf(cdf_t_tot)\n pdf_h_tot = cdf_to_pdf(cdf_h_tot)\n\n # compute stats of number of steps and number of hits\n t_bins = np.arange(BIN_START_T, BIN_END_T, BIN_SIZE_T) + 0.5 * BIN_SIZE_T\n mean_t, sigma_t, skew_t, kurt_t, p_found = stats_from_pdf(t_bins, pdf_t_tot)\n p25_t, p50_t, p75_t, p90_t, p95_t, p99_t, _ = stats_from_cdf(t_bins, cdf_t_tot)\n\n h_bins = np.arange(BIN_START_H, BIN_END_H, BIN_SIZE_H) + 0.5 * BIN_SIZE_H\n mean_h, sigma_h, skew_h, kurt_h, _ = stats_from_pdf(h_bins, pdf_h_tot)\n p25_h, p50_h, p75_h, p90_h, p95_h, p99_h, _ = stats_from_cdf(h_bins, cdf_h_tot)\n\n print(\"probability that the source is never found : %.10f\" % (1.0 - p_found, ))\n print(\"mean number of steps to find the source : %.3f +/- %.3f\" % (mean_t, 1.96 * std_error_mean))\n print(\"number of steps to find the source with 50 %% probability: %.3f\" % p50_t)\n print(\"number of steps to find the source with 99 %% probability: %.3f\" % p99_t)\n nb_failed = np.sum(failed_episodes)\n if np.any(failed_episodes < 0):\n nb_failed = -1\n print(\"problem while recording failures\")\n else:\n print(\"number of failed episodes : %d / %d (%f %%)\"\n % (nb_failed, N_runs, nb_failed / N_runs * 100))\n sys.stdout.flush()\n\n # save all parameters to txt file\n inputs = {\n \"N_DIMS\": N_DIMS,\n \"LAMBDA_OVER_DX\": LAMBDA_OVER_DX,\n \"R_DT\": R_DT,\n \"MU0_POISSON\": MU0_POISSON,\n \"NORM_POISSON\": NORM_POISSON,\n \"N_GRID\": N_GRID,\n \"N_HITS\": N_HITS,\n \"POLICY\": POLICY,\n \"STEPS_AHEAD\": STEPS_AHEAD,\n \"MODEL_PATH\": MODEL_PATH,\n \"STOP_t\": STOP_t,\n \"STOP_p\": STOP_p,\n \"ADAPTIVE_N_RUNS\": ADAPTIVE_N_RUNS,\n \"REL_TOL\": REL_TOL,\n \"MAX_N_RUNS\": MAX_N_RUNS,\n \"N_RUNS_PERFORMED\": N_runs,\n \"BIN_START_T\": BIN_START_T,\n \"BIN_END_T\": BIN_END_T,\n \"BIN_SIZE_T\": BIN_SIZE_T,\n \"BIN_START_H\": BIN_START_H,\n \"BIN_END_H\": BIN_END_H,\n \"BIN_SIZE_H\": BIN_SIZE_H,\n \"EPSILON\": EPSILON,\n }\n param_txt_file = os.path.join(DIR_OUTPUTS, str(RUN_NAME + \"_parameters\" + \".txt\"))\n with open(param_txt_file, 'w') as out:\n for key, val in inputs.items():\n print(key + \" = \" + str(val), file=out)\n\n # save stats\n stats_file = os.path.join(DIR_OUTPUTS, str(RUN_NAME + \"_statistics\" + \".txt\"))\n with open(stats_file, \"w\") as sfile:\n sfile.write(\"p_not_found\\t%+.4e\\n\" % (1 - p_found,))\n for varname in \\\n ('mean_t', 'sigma_t', 'skew_t', 'kurt_t', 'p25_t', 'p50_t', 'p75_t', 'p90_t', 'p95_t', 'p99_t'):\n sfile.write(\"%s\\t\\t%+.4e\\n\" % (varname, locals()[varname]))\n for varname in \\\n ('mean_h', 'sigma_h', 'skew_h', 'kurt_h', 'p25_h', 'p50_h', 'p75_h', 'p90_h', 'p95_h', 'p99_h'):\n sfile.write(\"%s\\t\\t%+.4e\\n\" % (varname, locals()[varname]))\n\n # save CDF of number of steps\n table_file = os.path.join(DIR_OUTPUTS, str(RUN_NAME + \"_table_CDF_nsteps\" + \".npy\"))\n np.save(table_file, np.vstack((t_bins, cdf_t_tot)))\n\n # save CDF of number of hits\n table_file = os.path.join(DIR_OUTPUTS, str(RUN_NAME + \"_table_CDF_nhits\" + \".npy\"))\n np.save(table_file, np.vstack((h_bins, cdf_h_tot)))\n\n # create and save figures\n if POLICY == -1:\n specifics = \"MODEL = \" + os.path.basename(MODEL_PATH)\n else:\n specifics = \"STEPS_AHEAD = \" + str(STEPS_AHEAD)\n subtitle = (\n \"N_DIMS = \"\n + str(N_DIMS)\n + \", \"\n + \"LAMBDA_OVER_DX = \"\n + str(LAMBDA_OVER_DX)\n + \", \"\n + \"R_DT = \"\n + str(R_DT)\n + \", \"\n + \"POLICY = \"\n + str(POLICY)\n + \", \"\n + specifics\n + \", \"\n + \"N_GRID = \"\n + str(N_GRID)\n + \", \"\n + \"N_HITS = \"\n + str(N_HITS)\n + \", \"\n + \"N_RUNS = \"\n + str(N_runs)\n + \"\\n\"\n )\n\n # plot PDF(nsteps), CDF(nsteps), PDF(nhits), CDF(nhits)\n fig, ax = plt.subplots(2, 2, figsize=(12, 10))\n plt.subplots_adjust(left=0.08, bottom=0.06, right=0.96, top=0.92, hspace=0.35, wspace=0.30)\n kwargs = {'xycoords': 'axes fraction', 'fontsize': 8, 'ha': \"right\"}\n for row, varname in enumerate([\"number of steps\", \"number of hits\"]):\n if varname == \"number of steps\":\n bins = t_bins\n cdf_tot = cdf_t_tot\n pdf_tot = pdf_t_tot\n mean = mean_t\n sigma = sigma_t\n skew = skew_t\n kurt = kurt_t\n p50 = p50_t\n p75 = p75_t\n p90 = p90_t\n p99 = p99_t\n filesuffix = 'nsteps'\n color = \"tab:blue\"\n else:\n bins = h_bins\n cdf_tot = cdf_h_tot\n pdf_tot = pdf_h_tot\n mean = mean_h\n sigma = sigma_h\n skew = skew_h\n kurt = kurt_h\n p50 = p50_h\n p75 = p75_h\n p90 = p90_h\n p99 = p99_h\n filesuffix = 'nhits'\n color = \"tab:orange\"\n max_x = bins[np.nonzero(pdf_tot)[0][-1]]\n for col, fct in enumerate([\"PDF\", \"CDF\"]):\n if fct == \"PDF\":\n ydata = pdf_tot\n ylim = (0.0, 1.02 * np.max(pdf_tot))\n elif fct == \"CDF\":\n ydata = cdf_tot\n ylim = (0.0, 1.0)\n\n ax[row, col].plot(bins, ydata, \"-o\", color=color, markersize=2, linewidth=1)\n ax[row, col].set_title(fct + \" of \" + varname)\n ax[row, col].set_xlabel(varname + \" to find the source\")\n ax[row, col].set_xlim((0, max_x))\n ax[row, col].set_ylim(ylim)\n\n if fct == \"PDF\":\n ax[row, col].annotate(\"p_not_found = \" + \"{:.3e}\".format(1.0 - p_found), xy=(0.98, 0.60), **kwargs)\n ax[row, col].annotate(\"mean = \" + \"{:.3e}\".format(mean), xy=(0.98, 0.56), **kwargs)\n ax[row, col].annotate(\"std = \" + \"{:.3e}\".format(sigma), xy=(0.98, 0.52), **kwargs)\n ax[row, col].annotate(\"skew = \" + \"{:.3e}\".format(skew), xy=(0.98, 0.48), **kwargs)\n ax[row, col].annotate(\"ex. kurt = \" + \"{:.3e}\".format(kurt), xy=(0.98, 0.44), **kwargs)\n elif fct == \"CDF\":\n ax[row, col].annotate(\"p_not_found = \" + \"{:.3e}\".format(1.0 - p_found), xy=(0.98, 0.60), **kwargs)\n ax[row, col].annotate(\"P50 = \" + \"{:.3e}\".format(p50), xy=(0.98, 0.56), **kwargs)\n ax[row, col].annotate(\"P75 = \" + \"{:.3e}\".format(p75), xy=(0.98, 0.52), **kwargs)\n ax[row, col].annotate(\"P90 = \" + \"{:.3e}\".format(p90), xy=(0.98, 0.48), **kwargs)\n ax[row, col].annotate(\"P99 = \" + \"{:.3e}\".format(p99), xy=(0.98, 0.44), **kwargs)\n plt.grid(False)\n plt.figtext(0.5, 0.985, subtitle, fontsize=7, ha=\"center\", va=\"top\")\n plt.draw()\n figure_file = os.path.join(DIR_OUTPUTS, str(RUN_NAME + \"_figure_distributions.pdf\"))\n fig.savefig(figure_file)\n plt.close(fig)\n\n # plot mean nb steps vs number of episodes\n number_episodes = range(1, N_runs + 1)\n cum_mean_t_episodes = np.cumsum(mean_t_episodes) / number_episodes\n if N_runs >= 100:\n number_episodes = number_episodes[20:]\n cum_mean_t_episodes = cum_mean_t_episodes[20:]\n fig, ax = plt.subplots()\n ax.plot(number_episodes, cum_mean_t_episodes, color=\"r\")\n ax.set_title(\"Convergence of the mean number of steps\")\n ax.set_xlabel(\"number of episodes\")\n ax.set_ylabel(\"mean number of steps\")\n plt.figtext(0.5, 0.985, subtitle, fontsize=5, ha=\"center\", va=\"top\")\n plt.grid(False)\n plt.draw()\n figure_file = os.path.join(DIR_OUTPUTS, str(RUN_NAME + \"_figure_convergence.pdf\"))\n fig.savefig(figure_file)\n plt.close(fig)\n\n # save monitoring information (concatenate episodes files)\n monitoring_episodes_file = os.path.join(DIR_OUTPUTS, str(RUN_NAME + \"_monitoring_episodes.txt\"))\n filenames = [os.path.join(DIR_TMP, str(\"monitoring_episode_\" + str(episode) + \".txt\")) for episode in range(N_runs)]\n with open(monitoring_episodes_file, \"w\") as mfile:\n mfile.write(\"# episode\\thit_init\\tstop_flag\\tboundary_flag\\t\"\n \"p_not_found\\t\\tmean_nsteps\\t\\ttime_elapsed(sec)\\n\")\n for fname in filenames:\n if os.path.isfile(fname):\n with open(fname) as infile:\n mfile.write(infile.read())\n os.remove(fname)\n else:\n print(\"Unexpected: Missing episode file: \" + str(fname))\n\n # clean up tmp dirs\n if len(os.listdir(DIR_TMP)) != 0:\n print(\"Unexpected: The directory '\" + DIR_TMP\n + \"' is not removed, because it should be empty but is not.\")\n else:\n os.rmdir(DIR_TMP)\n if len(os.listdir(PARENT_DIR_TMP)) == 0:\n os.rmdir(PARENT_DIR_TMP)\n\n # summary\n monitoring_file = os.path.join(DIR_OUTPUTS, str(RUN_NAME + \"_monitoring_summary\" + \".txt\"))\n with open(monitoring_file, \"w\") as mfile:\n mfile.write(\"*** initial hit ***\\n\")\n first_hit = np.loadtxt(monitoring_episodes_file, usecols=1, dtype='int')\n hit_max = np.max(first_hit)\n hit_hist, _ = np.histogram(first_hit, bins=np.arange(0.5, hit_max + 1.5), density=True)\n for h in range(1, hit_max + 1):\n mfile.write(\"hit=%1d: %6.2f %% \\n\" % (h, hit_hist[h - 1] * 100))\n\n mfile.write(\"\\n*** stats convergence ***\\n\")\n mfile.write(\"number of episodes simulated : %d\\n\" % N_runs)\n mfile.write(\"standard error of the mean (estimate): %.4e = %5.2f %%\\n\"\n % (std_error_mean, rel_std_error_mean * 100))\n\n stopping_reason = np.loadtxt(monitoring_episodes_file, usecols=2, dtype='int')\n stop_max = np.max(stopping_reason)\n stopping_hist, _ = np.histogram(stopping_reason, bins=np.arange(0.5, stop_max + 1.5), density=True)\n mfile.write(\"\\n*** reason for stopping (1 is success, anything else is failure) ***\\n\")\n for stop in range(1, stop_max + 1):\n mfile.write(\"stop=%1d: %6.2f %% \\n\" % (stop, stopping_hist[stop - 1] * 100))\n\n mfile.write(\"\\n*** probability that the source is not found at the end of the episodes ***\\n\")\n p_not_found = np.loadtxt(monitoring_episodes_file, usecols=4)\n p_gtr_stop = p_not_found[p_not_found > STOP_p]\n p_not_found_max = np.max(p_not_found)\n mfile.write(\"criteria (STOP_p): %.5e\\n\" % STOP_p)\n mfile.write(\"max(p) : %.5e\\n\" % p_not_found_max)\n mfile.write(\"number of episodes where p > STOP_p: %7d (%8.4f %%)\\n\"\n % (len(p_gtr_stop), len(p_gtr_stop) / N_runs * 100))\n\n near_boundaries = np.loadtxt(monitoring_episodes_file, usecols=3, dtype='int')\n near_boundaries = np.count_nonzero(near_boundaries)\n mfile.write(\"\\n*** agent near boundaries ***\\n\")\n mfile.write(\"number of episodes where it happened: %7d (%8.4f %%)\\n\"\n % (near_boundaries, near_boundaries / N_runs * 100))\n\n episode_elapsed = np.loadtxt(monitoring_episodes_file, usecols=5)\n mfile.write(\"\\n*** computational cost per episode ***\\n\")\n mfile.write(\"avg elapsed seconds per episode: %.5e\\n\" % (np.mean(episode_elapsed)))\n mfile.write(\"max elapsed seconds per episode: %.5e\\n\" % (np.max(episode_elapsed)))\n\n elapsed_time_0 = (time.monotonic() - start_time_0) / 3600.0\n mfile.write(\"\\n*** computational cost ***\\n\")\n mfile.write(\"N_PARALLEL = %d\\n\" % N_PARALLEL)\n mfile.write(\"total elapsed hours : %.5e\\n\" % elapsed_time_0)\n mfile.write(\"cost in hours = total elapsed time * N_PARALLEL: %.5e\\n\" % (elapsed_time_0 * N_PARALLEL))\n\n print(\">>> Results saved in the directory: \" + DIR_OUTPUTS)\n\n sys.stdout.flush()","def _generate_trading_instances(self):\n print(\"Creating DataHandler, Strategy, Portfolio, and ExecutionHandler for\")\n\n # Set internal data members equal to the classes we passed in earlier, along with necessary parameters.\n # https://softwareengineering.stackexchange.com/questions/131403/what-is-the-name-of-in-python/131415\n self.data_handler = self.data_handler_class(self.events, self.csv_dir, self.symbol_list)\n self.strategy = self.strategy_class(self.data_handler, self.events)\n self.portfolio = self.portfolio_class(self.data_handler, self.events, self.start_date, self.initial_capital)\n self.execution_handler = self.execution_handler_class(self.events) # The Event Queue sent to ExecutionHandler","def run_and_evaluate():\n tsp_problems = read_all_problems()\n # Empty list of metrics\n results = []\n for problem in tqdm.tqdm(tsp_problems):\n # As random factors are involved repeat experiments a couple of times\n best_routes_base = []\n best_routes_af = []\n best_routes_ms = []\n base_times = []\n af_times = []\n ms_times = []\n for i in range(10):\n # Base solution\n start_time = timeit.default_timer()\n best_route_base = solve_tsp_basic(problem)\n base_time = timeit.default_timer() - start_time\n best_routes_base.append(Fitness(route=best_route_base).route_distance())\n base_times.append(base_time)\n\n # AF clustering solution\n start_time = timeit.default_timer()\n best_route_af = solve_tsp_affinity_propagation(problem)\n af_time = timeit.default_timer() - start_time\n best_routes_af.append(Fitness(route=best_route_af).route_distance())\n af_times.append(af_time)\n\n # MS solution\n start_time = timeit.default_timer()\n best_route_ms = solve_mean_shift(problem)\n ms_time = timeit.default_timer() - start_time\n best_routes_ms.append(Fitness(route=best_route_ms).route_distance())\n ms_times.append(ms_time)\n\n results.append(\n {\n \"problem name\": problem.name,\n \"optimal solution\": find_route_optimal_route_length(problem),\n \"baseline tour length\": mean(best_routes_base),\n \"af clustering tour length\": mean(best_routes_af),\n \"ms clustering tour length\": mean(best_routes_ms),\n \"baseline algorithm time\": mean(base_times),\n \"af clustering algorithm time\": mean(af_times),\n \"ms clustering algorithm time\": mean(ms_times),\n }\n )\n # Create dataframe and safe results\n df = pd.DataFrame(results)\n df.to_csv(\"results.csv\", index=False)\n return df","def main():\n run_simulation(spectral=False, ml=False, num_procs=1)\n run_simulation(spectral=True, ml=False, num_procs=1)\n run_simulation(spectral=False, ml=True, num_procs=1)\n run_simulation(spectral=True, ml=True, num_procs=1)\n run_simulation(spectral=False, ml=True, num_procs=10)\n run_simulation(spectral=True, ml=True, num_procs=10)","def runSim(self):\n if self.verbose:\n print(\"Running Simulation, This may take a while\")\n self.makeXData(float(self.pretime))\n pool = Pool(processes=len(self.powers))\n jobs = []\n self.gem_pair = []\n self.electron = []\n self.hole = []\n self.filled = []\n self.signal = []\n self.gsignal = []\n self.ehsignal = []\n self.gloss = []\n self.tloss = []\n self.qk = []\n for power, pulse in zip(self.powers, self.pulses):\n inputs = [power, pulse, self.steps, self.trap, self.tolerance,\n self.EHdecay, self.Etrap, self.FHloss, self.Gdecay,\n self.G2decay, self.G3decay, self.GHdecay, self.Gescape,\n self.Gform, self.G3loss, self.Keq, self.trackQ,\n self.verbose]\n jobs.append(pool.apply_async(powerRun, inputs))\n for job in jobs:\n gem_pair, electron, hole, filled, signal, gsignal, ehsignal, gloss, tloss, qk = job.get()\n self.signal.append(signal * self.scalar / self.step)\n self.gsignal.append(gsignal * self.scalar / self.step)\n self.ehsignal.append(ehsignal * self.scalar / self.step)\n self.gloss.append(gloss * self.scalar / self.step)\n self.tloss.append(tloss * self.scalar / self.step)\n self.gem_pair.append(gem_pair)\n self.electron.append(electron)\n self.hole.append(hole)\n self.filled.append(filled)\n self.qk.append(qk)\n pool.close()","def run(self, niter, calc_moments=True, save_last_param=None, verbose=True,\n return_analytics=False, seed=None):\n if niter < 1:\n if verbose:\n print(\"Nothing to do here as provided arg. `niter` is {}\" \\\n .format(niter))\n # return with desired args\n out = [self.INFO_OK]\n if calc_moments:\n out.append((None, None))\n if return_analytics:\n out.append((None, None, None))\n return out if len(out) > 1 else out[0]\n\n # Get seeds for sampling in workers for each iteration\n if isinstance(seed, np.random.RandomState):\n rng = seed\n else:\n rng = np.random.RandomState(seed=seed)\n seeds = rng.randint(0, pystan_max_uint, size=(niter, self.K))\n\n # Localise some instance variables\n # Mean and cov of the posterior approximation\n S = self.S\n m = self.m\n # Natural parameters of the approximation\n Q = self.Q\n r = self.r\n # Natural site parameters\n Qi = self.Qi\n ri = self.ri\n # Natural site proposal parameters\n Qi2 = self.Qi2\n ri2 = self.ri2\n # Site parameter updates\n dQi = self.dQi\n dri = self.dri\n\n # Array for positive definitness checking of each cavity distribution\n posdefs = np.empty(self.K, dtype=bool)\n\n if calc_moments:\n # Allocate memory for results\n m_phi_s = np.zeros((niter, self.dphi))\n cov_phi_s = np.zeros((niter, self.dphi, self.dphi))\n\n # monitor sampling times, mean stepsizes, and max rhats, and other times\n stimes = np.zeros(niter)\n msteps = np.zeros(niter)\n mrhats = np.zeros(niter)\n othertimes = np.zeros(niter)\n\n # Iterate niter rounds\n for cur_iter in range(niter):\n self.iter += 1\n\n # Tilted distributions (parallelisable)\n # -------------------------------------\n\n if verbose:\n print(\n \"Iter {} starting. Process tilted distributions\"\n .format(self.iter)\n )\n for k in range(self.K):\n if verbose:\n sys.stdout.write(\"\\r site {}\".format(k+1)+' '*10+'\\b'*9)\n # Force flush here as it is not done automatically\n sys.stdout.flush()\n # Process the site\n if save_last_param:\n posdefs[k] = self.workers[k].tilted(\n dQi[:,:,k],\n dri[:,k],\n save_samples = save_last_param,\n seed = seeds[cur_iter, k]\n )\n else:\n posdefs[k] = self.workers[k].tilted(\n dQi[:,:,k],\n dri[:,k],\n seed = seeds[cur_iter, k]\n )\n if verbose and not posdefs[k]:\n sys.stdout.write(\"fail\\n\")\n if verbose:\n if np.all(posdefs):\n print(\"\\rAll sites ok\")\n elif np.any(posdefs):\n print(\"\\rSome sites failed and are not updated\")\n else:\n print(\"\\rEvery site failed\")\n if not np.any(posdefs):\n # all sites failed, return with desired args\n out = [self.INFO_ALL_SITES_FAIL]\n if calc_moments:\n out.append((m_phi_s, cov_phi_s))\n if return_analytics:\n out.append((stimes, msteps, mrhats, othertimes))\n return out if len(out) > 1 else out[0]\n\n # Store max sampling time\n stimes[cur_iter] = max([w.last_time for w in self.workers])\n msteps[cur_iter] = max([w.last_msteps for w in self.workers])\n mrhats[cur_iter] = max([w.last_mrhat for w in self.workers])\n\n if verbose:\n print(\n \"Sampling done, max sampling time {}\"\n .format(stimes[cur_iter])\n )\n\n # measure elapsed time for othertimes\n start_othertime = time.time()\n\n # Update global approx\n # --------------------\n\n # Initial dampig factor\n df = self.df0(self.iter)\n if verbose:\n print(\"Iter {}, starting df {:.3g}\".format(self.iter, df))\n fail_printline_pos = False\n fail_printline_cov = False\n # Fail flag for pos.def enforcing\n failed_force_pos_def = False\n while True:\n # Try to update the global posterior approximation\n\n # These 4 lines could be run in parallel also\n np.add(Qi, np.multiply(df, dQi, out=Qi2), out=Qi2)\n np.add(ri, np.multiply(df, dri, out=ri2), out=ri2)\n np.add(Qi2.sum(2, out=Q), self.Q0, out=Q)\n np.add(ri2.sum(1, out=r), self.r0, out=r)\n\n # Check for positive definiteness\n cho_Q = S\n np.copyto(cho_Q, Q)\n try:\n linalg.cho_factor(cho_Q, overwrite_a=True)\n except linalg.LinAlgError:\n # Not positive definite -> reduce damping factor\n df *= self.df_decay\n if verbose:\n fail_printline_pos = True\n sys.stdout.write(\n \"\\rNon pos. def. posterior cov, \" +\n \"reducing df to {:.3}\".format(df) +\n \" \"*5 + \"\\b\"*5\n )\n sys.stdout.flush()\n if self.iter == 1:\n if verbose:\n print(\"\\nInvalid prior.\")\n # return with desired args\n out = [self.INFO_INVALID_PRIOR]\n if calc_moments:\n out.append((m_phi_s, cov_phi_s))\n if return_analytics:\n out.append((stimes, msteps, mrhats, othertimes))\n return out if len(out) > 1 else out[0]\n if df < self.df_treshold:\n if verbose:\n print(\"\\nDamping factor reached minimum.\")\n df = self.df0(self.iter)\n np.add(Qi, np.multiply(df, dQi, out=Qi2), out=Qi2)\n np.add(ri, np.multiply(df, dri, out=ri2), out=ri2)\n if failed_force_pos_def:\n if verbose:\n print(\"Failed to force pos_def global.\")\n # return with desired args\n out = [self.INFO_DF_TRESHOLD_REACHED_CAVITY]\n if calc_moments:\n out.append((m_phi_s, cov_phi_s))\n if return_analytics:\n out.append((stimes, msteps, mrhats, othertimes))\n return out if len(out) > 1 else out[0]\n failed_force_pos_def = True\n # Try to fix by forcing improper sites to proper\n posdefs.fill(0)\n for k in range(self.K):\n # Set min eigenvalue to MIN_EIG by adding to the\n # diagonal if it is smaller than MIN_EIG_TRESHOLD\n min_eig = linalg.eigvalsh(\n Qi2[:,:,k], eigvals=(0,0))[0]\n if min_eig < self.MIN_EIG_TRESHOLD:\n Qi[:,:,k].flat[::self.dphi+1] += (\n self.MIN_EIG - min_eig)\n posdefs[k] = 1\n if verbose:\n print(\"Force sites {} pos_def.\".format(\n np.nonzero(posdefs)[0]))\n continue\n\n # Cavity distributions (parallelisable)\n # -------------------------------------\n # Check positive definitness for each cavity distribution\n for k in range(self.K):\n posdefs[k] = \\\n self.workers[k].cavity(Q, r, Qi2[:,:,k], ri2[:,k])\n # Early stopping criterion (when in serial)\n if not posdefs[k]:\n break\n\n if np.all(posdefs):\n # All cavity distributions are positive definite.\n # Accept step (switch Qi-Qi2 and ri-ri2)\n temp = Qi\n Qi = Qi2\n Qi2 = temp\n temp = ri\n ri = ri2\n ri2 = temp\n self.Qi = Qi\n self.Qi2 = Qi2\n self.ri = ri\n self.ri2 = ri2\n break\n\n else:\n # Not all cavity distributions are positive definite ...\n # reduce the damping factor\n df *= self.df_decay\n if verbose:\n if fail_printline_pos:\n fail_printline_pos = False\n print()\n fail_printline_cov = True\n sys.stdout.write(\n \"\\rNon pos. def. cavity, \" +\n \"(first encountered in site {}), \"\n .format(np.nonzero(~posdefs)[0][0]) +\n \"reducing df to {:.3}\".format(df) +\n \" \"*5 + \"\\b\"*5\n )\n sys.stdout.flush()\n if df < self.df_treshold:\n if verbose:\n print(\"\\nDamping factor reached minimum.\")\n df = self.df0(self.iter)\n np.add(Qi, np.multiply(df, dQi, out=Qi2), out=Qi2)\n np.add(ri, np.multiply(df, dri, out=ri2), out=ri2)\n if failed_force_pos_def:\n if verbose:\n print(\"Failed to force pos_def cavities.\")\n # return with desired args\n out = [self.INFO_DF_TRESHOLD_REACHED_CAVITY]\n if calc_moments:\n out.append((m_phi_s, cov_phi_s))\n if return_analytics:\n out.append((stimes, msteps, mrhats, othertimes))\n return out if len(out) > 1 else out[0]\n failed_force_pos_def = True\n # Try to fix by forcing improper sites to proper\n posdefs.fill(0)\n for k in range(self.K):\n # Set min eigenvalue to MIN_EIG by adding to the\n # diagonal if it is smaller than MIN_EIG_TRESHOLD\n min_eig = linalg.eigvalsh(\n Qi2[:,:,k], eigvals=(0,0))[0]\n if min_eig < self.MIN_EIG_TRESHOLD:\n Qi[:,:,k].flat[::self.dphi+1] += (\n self.MIN_EIG - min_eig)\n posdefs[k] = 1\n if verbose:\n print(\"Force sites {} pos_def.\".format(\n np.nonzero(posdefs)[0]))\n if verbose and (fail_printline_pos or fail_printline_cov):\n print()\n\n if calc_moments:\n # Invert Q (chol was already calculated)\n # N.B. The following inversion could be done while\n # parallel jobs are running, thus saving time.\n invert_normal_params(cho_Q, r, out_A='in-place', out_b=m,\n cho_form=True)\n # Store the approximation moments\n np.copyto(m_phi_s[cur_iter], m)\n np.copyto(cov_phi_s[cur_iter], S.T)\n if verbose:\n print(\n \"Mean and std of phi[0]: {:.3}, {:.3}\"\n .format(\n m_phi_s[cur_iter,0],\n np.sqrt(cov_phi_s[cur_iter,0,0])\n )\n )\n\n # measure total time - tilted time\n othertimes[cur_iter] = time.time() - start_othertime\n\n if verbose:\n print(\"Iter {} done.\".format(self.iter))\n\n if verbose:\n print(\n \"{} iterations done\\nTotal limiting sampling time: {}\"\n .format(niter, stimes.sum())\n )\n\n # return with desired args\n out = [self.INFO_OK]\n if calc_moments:\n out.append((m_phi_s, cov_phi_s))\n if return_analytics:\n out.append((stimes, msteps, mrhats, othertimes))\n return tuple(out) if len(out) > 1 else out[0]","def run(runs, params):\n trade_hist = []\n final_states = []\n final_rewards = []\n for i in np.arange(runs):\n env = MarketEnv(*params)\n for step in np.arange(env.end_time):\n total_returns = env.get_returns()\n actions = [agent.act([c]) for agent, c in zip(env.agents, total_returns)]\n print(f'ACTIONS: {actions}')\n state, done, rewards, info = env.step(actions)\n trade_hist.append(env.trade_history)\n final_states.append(state)\n final_rewards.append(rewards)\n return trade_hist, final_states, final_rewards","def runSCRO():\n\n dataset = None\n if EXPERIMENT == 0:\n print \"EXPERIMENT WITH MNIST DATASET\"\n dataset = mnist_data_builder()\n # elif EXPERIMENT == 1:\n # print \"EXPERIMENT WITH ...\"\n # dataset = my_call()\n else:\n print \"EXPERIMENT NOT IMPLEMENTED!\"\n\n print \"Starting keras executor\"\n ke = KerasExecutor(dataset, TEST_SIZE, METRICS, EARLY_STOPPING_PATIENCE_KERAS, LOSS)\n\n config_data = open('parametersGenetic.json')\n configuration = Configuration(config_data)\n\n ##############################\n # Initialisation\n ##############################\n\n reef = initialisation(Rsize=RSIZE, config=configuration, n_global_in=deepcopy(ke.n_in), n_global_out=ke.n_out,\n ke=ke)\n # Population is already evaluated in the initialisation function\n\n history = []\n\n max_fitness_ever = 0.0\n generations_with_no_improvement = 0\n\n ID_EXECUTION = str(time.time())\n\n output_file = open(\"EXECUTION_\" + ID_EXECUTION + \".csv\", \"w\")\n output_file.write(\"fitness_mean_validation,fitness_std_validation,fitness_max_validation,fitness_min_validation,\"\n \"fitness_mean_train,fitness_std_train,fitness_max_train,fitness_min_train,\"\n \"fitness_mean_test,fitness_std_test,fitness_max_test,fitness_min_test,\"\n \"count_evaluations,individuals_depredated,ratio_reef,time_generation,reef\\n\")\n\n output_file_population = open(\"EXECUTION_\" + ID_EXECUTION + \"_REEF_EVOLUTION.csv\", \"w\")\n\n output_file_population.write(\n \"generation,pos_in_reef,accuracy_validation,number_layers,accuracy_training,accuracy_test\\n\")\n\n output_file_individuals = open(\"EXECUTION_\" + str(ID_EXECUTION) + \"_INDIVIDUALS.txt\", \"w\")\n\n ##############################\n # Loop\n ##############################\n for i in range(MAX_GENERATIONS_SCRO):\n\n start_time = time.time()\n\n print colored(\"GENERATION: \" + str(i), \"red\")\n pool = []\n\n if len(filter(lambda w: w is not None, reef)) == 0:\n output_file.write(\"ALL REEF IS NONE!\")\n print colored(\"ALL REEF IS NONE!, BREAKING EVOLUTION!\", \"red\")\n break\n\n # 1 Asexual reproduction\n asexual_new_individual = deepcopy(asexual_reproduction(reef, configuration))\n if asexual_new_individual is not None:\n pool = pool + [asexual_new_individual]\n\n # 2 Sexual reproduction\n sexual_new_individuals = sexual_reproduction(reef, configuration)\n pool = pool + sexual_new_individuals\n\n # 3 Larvae settlement\n print colored(\"STARTING EVALUATION. INDIVIDUALS TO EVALUATE: \" + str(len(pool)), \"red\")\n pool, count_evaluations = eval_population(pool, ke)\n\n # print \"POOL EVALUATED: \"\n # for ind_pool in pool:\n # print \"IND: \" + str(ind_pool.fitness[\"accuracy_validation\"])\n\n reef, settled = larvae_settlement(reef, pool)\n\n # 4 Depredation\n # Todo remove returns same object\n reef, individuals_depredated = depredation(reef)\n\n # History\n\n fitness = fitness_mean_std(reef)\n\n fitness_mean_validation = fitness[\"validation\"][\"mean\"]\n fitness_std_validation = fitness[\"validation\"][\"std\"]\n fitness_max_validation = fitness[\"validation\"][\"max\"]\n fitness_min_validation = fitness[\"validation\"][\"min\"]\n\n fitness_mean_train = fitness[\"train\"][\"mean\"]\n fitness_std_train = fitness[\"train\"][\"std\"]\n fitness_max_train = fitness[\"train\"][\"max\"]\n fitness_min_train = fitness[\"train\"][\"min\"]\n\n fitness_mean_test = fitness[\"test\"][\"mean\"]\n fitness_std_test = fitness[\"test\"][\"std\"]\n fitness_max_test = fitness[\"test\"][\"max\"]\n fitness_min_test = fitness[\"test\"][\"min\"]\n\n if fitness_max_validation > max_fitness_ever:\n max_fitness_ever = fitness_max_validation\n generations_with_no_improvement = 0\n else:\n generations_with_no_improvement += 1\n\n finish_time = time.time()\n\n time_generation = finish_time - start_time\n\n positions_free = len(filter(lambda w: w is not None, reef))\n positions_total = len(reef)\n\n history.append([fitness_mean_validation, fitness_std_validation, fitness_max_validation, fitness_min_validation,\n fitness_mean_train, fitness_std_train, fitness_max_train, fitness_min_train,\n fitness_mean_test, fitness_std_test, fitness_max_test, fitness_min_test,\n count_evaluations, individuals_depredated,\n str(positions_free) + \"/\" + str(positions_total), time_generation, deepcopy(reef)])\n\n output_file.write(str(fitness_mean_validation) + \",\" +\n str(fitness_std_validation) + \",\" +\n str(fitness_max_validation) + \",\" +\n str(fitness_min_validation) + \",\" +\n\n str(fitness_mean_train) + \",\" +\n str(fitness_std_train) + \",\" +\n str(fitness_max_train) + \",\" +\n str(fitness_min_train) + \",\" +\n\n str(fitness_mean_test) + \",\" +\n str(fitness_std_test) + \",\" +\n str(fitness_max_test) + \",\" +\n str(fitness_min_test) + \",\" +\n\n str(count_evaluations) + \",\" +\n str(individuals_depredated) + \",\" +\n str(positions_free) + \"/\" + str(positions_total) + \",\" +\n str(time_generation) + \",\" +\n str(reef) + \"\\n\")\n\n # PRINTING THE STATUS OF THE REEF and INDIVIDUALS DEFINITION\n output_file_individuals.write(\"GENERATION: \" + str(i) + \" - POSITION IN REEF: \" + str(position_reef) + \"\\n\")\n\n for position_reef in range(len(reef)):\n\n # Controlar nones\n if reef[position_reef] is not None:\n output_file_population.write(\",\".join([str(i), str(position_reef),\n str(reef[position_reef].fitness[\"accuracy_validation\"]),\n str(reef[position_reef].fitness[\"number_layers\"]),\n str(reef[position_reef].fitness[\"accuracy_training\"]),\n str(reef[position_reef].fitness[\"accuracy_test\"])]) + \"\\n\")\n\n # PRINTING INDIVIDUALS:\n output_file_individuals.write(reef[position_reef].toString())\n\n\n output_file_individuals.write(\"----------------------------------------------------------\\n\")\n output_file_individuals.write(\"----------------------------------------------------------\\n\")\n output_file_individuals.write(\"----------------------------------------------------------\\n\\n\\n\")\n \n\n # GENERATION,INDIVIDUAL_POSITION,\n\n print colored(\n str(fitness_mean) + \",\" + str(fitness_std) + \",\" + str(fitness_max) + \",\" + str(fitness_min) + \",\" + str(\n count_evaluations) + \",\" + str(individuals_depredated) + \",\" +\n str(positions_free) + \"/\" + str(positions_total) + \",\" + str(time_generation), 'yellow')\n\n if generations_with_no_improvement >= MAX_GENERATIONS_SCRO:\n print colored(\"Stop criterion reached! \" + str(generations_with_no_improvement) + \"generations with no \"\n \"improvement!\", \"red\")\n break\n\n output_file.close()\n output_file_population.close()\n output_file_individuals.close()","def Dynamic_Analysis(InputMethod=Get_Input, TestMode=False, PrintMode=True, ReportMode=True):\n OutputFile = None\n CSVFile = None\n try:\n Simulation_Title=\"Padulles-Amphlett\"\n if PrintMode==True:\n print(\"###########\")\n print(Simulation_Title+\"-Model Simulation\")\n print(\"###########\")\n OutputParamsKeys = list(OutputParams.keys())\n OutputParamsKeys.sort()\n Output_Dict = dict(zip(OutputParamsKeys, [None] * len(OutputParamsKeys)))\n if not TestMode:\n Input_Dict = InputMethod(InputParams,params_default=Defaults)\n else:\n Input_Dict = InputMethod\n Input_Dict = filter_default(input_dict=Input_Dict, params_default=Defaults)\n Input_Dict = filter_lambda(Input_Dict)\n if PrintMode==True:\n print(\"Analyzing . . .\")\n Name = Input_Dict[\"Name\"]\n if ReportMode==True:\n OutputFile = Output_Init(Input_Dict,Simulation_Title,Name)\n CSVFile = CSV_Init(OutputParamsKeys,OutputParams,Simulation_Title,Name)\n HTMLFile = HTML_Init(Simulation_Title, Name)\n IEndMax = Input_Dict[\"JMax\"] * Input_Dict[\"A\"]\n IEnd = min(IEndMax, Input_Dict[\"i-stop\"])\n IEnd = Input_Dict[\"i-stop\"]\n IStep = Input_Dict[\"i-step\"]\n Precision = get_precision(IStep)\n i = Input_Dict[\"i-start\"]\n I_List = []\n Power_List = []\n Vstack_List = []\n Kr=Kr_Calc(Input_Dict[\"N0\"])\n qH2=qH2_Calc(Input_Dict[\"qMethanol\"],Input_Dict[\"CV\"],Input_Dict[\"t1\"],Input_Dict[\"t2\"])\n qO2=qO2_Calc(qH2,Input_Dict[\"rho\"])\n while i < IEnd:\n try:\n I_List.append(i)\n Output_Dict[\"PO2\"] = PO2_Calc(Input_Dict[\"KO2\"], Input_Dict[\"tO2\"], Kr, i, qO2)\n Output_Dict[\"PH2\"] = PH2_Calc(Input_Dict[\"KH2\"], Input_Dict[\"tH2\"], Kr, i, qH2)\n Output_Dict[\"Eta Activation\"] = Eta_Act_Calc(Input_Dict[\"T\"], Output_Dict[\"PO2\"], Output_Dict[\"PH2\"], i,\n Input_Dict[\"A\"])\n Output_Dict[\"Eta Ohmic\"] = Eta_Ohmic_Calc(i, Input_Dict[\"l\"], Input_Dict[\"A\"], Input_Dict[\"T\"],\n Input_Dict[\"lambda\"], R_elec=Input_Dict[\"R\"])\n Output_Dict[\"Eta Concentration\"] = Eta_Conc_Calc(i, Input_Dict[\"A\"], Input_Dict[\"B\"],\n Input_Dict[\"JMax\"])\n Output_Dict[\"Loss\"] = Loss_Calc(Output_Dict[\"Eta Activation\"], Output_Dict[\"Eta Ohmic\"],\n Output_Dict[\"Eta Concentration\"])\n Output_Dict[\"PH2O\"]=PH2O_Calc(Input_Dict[\"KH2O\"],Input_Dict[\"tH2O\"],Kr,i,qH2)\n Output_Dict[\"E\"]=Enernst_Calc(Input_Dict[\"E0\"],Input_Dict[\"N0\"],Input_Dict[\"T\"],Output_Dict[\"PH2\"],Output_Dict[\"PO2\"],Output_Dict[\"PH2O\"])\n Output_Dict[\"FC Voltage\"]=Vcell_Calc(Output_Dict[\"E\"], Output_Dict[\"Loss\"],Input_Dict[\"N0\"])\n Vstack_List.append(Output_Dict[\"FC Voltage\"])\n Output_Dict[\"FC Efficiency\"] = Efficiency_Calc(Output_Dict[\"FC Voltage\"],Input_Dict[\"N0\"])\n Output_Dict[\"FC Power\"] = Power_Calc(Output_Dict[\"FC Voltage\"], i)\n Power_List.append(Output_Dict[\"FC Power\"])\n if ReportMode==True:\n Output_Save(OutputParamsKeys, Output_Dict,OutputParams, i, OutputFile,PrintMode)\n CSV_Save(OutputParamsKeys, Output_Dict, i, CSVFile)\n i = rounder(i + IStep, Precision)\n except Exception as e:\n print(str(e))\n i = rounder(i + IStep, Precision)\n if ReportMode==True:\n Output_Save(OutputParamsKeys, Output_Dict, OutputParams, i, OutputFile,PrintMode)\n CSV_Save(OutputParamsKeys, Output_Dict, i, CSVFile)\n if ReportMode==True:\n HTML_Chart(x=str(I_List), y=str(Power_List), color='rgba(255,99,132,1)', x_label=\"I(A)\", y_label=\"P(W)\",\n chart_name=\"FC-Power\", size=\"600px\", file=HTMLFile)\n HTML_Chart(x=str(I_List), y=str(Vstack_List), color='rgba(99,100,255,1)', x_label=\"I(A)\", y_label=\"V(V)\",\n chart_name=\"FC-Voltage\", size=\"600px\", file=HTMLFile)\n HTML_Input_Table(Input_Dict=Input_Dict, Input_Params=InputParams, file=HTMLFile)\n HTML_End(HTMLFile)\n OutputFile.close()\n CSVFile.close()\n HTMLFile.close()\n if PrintMode==True:\n print(\"Done!\")\n if not TestMode:\n if PrintMode==True:\n print(\"Result In -->\" + os.path.join(os.getcwd(),Simulation_Title))\n else:\n return {\"P\": Power_List, \"I\": I_List, \"V\": Vstack_List}\n except Exception:\n print(\"[Error] Padulles-Amphlett Dynamic Simulation Failed!(Check Your Inputs)\")","def run():\n\n # Set up environment and agent\n e = Environment() # create environment (also adds some dummy traffic)\n a = e.create_agent(LearningAgent) # create agent\n e.set_primary_agent(a, enforce_deadline=False) # set agent to track\n\n # Now simulate it\n sim = Simulator(e, update_delay=0.0) # reduce update_delay to speed up simulation\n sim.run(n_trials=num_of_experiments) # press Esc or close pygame window to quit\n \n pd.Series(a.success).to_pickle('success_' + exp_id + '.pickle')\n a.Q_table.to_pickle('qtable_' + exp_id + '.pickle')\n pd.Series(a.q_delta_avg).to_pickle('convergence_' + exp_id + '.pickle')\n pd.Series(a.t_total).to_pickle('steps_' + exp_id + '.pickle')","def backtesting_engine(symbol, strategy, fromdate, todate, args=None):\n\n # Backtesting Engine\n cerebro = bt.Cerebro()\n\n # Add a Strategy if no Data Required for the model\n if args is None:\n cerebro.addstrategy(strategy)\n # If the Strategy requires a Model and therefore data\n elif args is not None:\n cerebro.addstrategy(strategy, args)\n\n # Retrieve Data from Alpaca\n data = bt.feeds.YahooFinanceData(\n dataname=symbol,\n fromdate=fromdate, # datetime.date(2015, 1, 1)\n todate=todate, # datetime.datetime(2016, 1, 1)\n reverse=False\n )\n\n # Add Data to Backtesting Engine\n cerebro.adddata(data)\n\n # Set Initial Portfolio Value\n cerebro.broker.setcash(100000.0)\n\n # Add Analysis Tools\n cerebro.addanalyzer(bt.analyzers.SharpeRatio, _name='sharpe')\n cerebro.addanalyzer(bt.analyzers.Returns, _name='returns')\n cerebro.addanalyzer(bt.analyzers.SQN, _name='sqn')\n cerebro.addanalyzer(bt.analyzers.DrawDown, _name='drawdown')\n cerebro.addanalyzer(bt.analyzers.PositionsValue, _name='posval')\n cerebro.addanalyzer(bt.analyzers.PyFolio, _name='pyfolio')\n\n # Starting Portfolio Value\n print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())\n\n # Run the Backtesting Engine\n backtest = cerebro.run()\n\n # Print Analysis and Final Portfolio Value\n print(\n 'Final Portfolio Value: %.2f' % cerebro.broker.getvalue()\n )\n print(\n 'Return: ', backtest[0].analyzers.returns.get_analysis()\n )\n print(\n 'Sharpe Ratio: ', backtest[0].analyzers.sharpe.get_analysis()\n )\n print(\n 'System Quality Number: ', backtest[0].analyzers.sqn.get_analysis()\n )\n print(\n 'Drawdown: ', backtest[0].analyzers.drawdown.get_analysis()\n )\n print(\n 'Active Position Value: ', backtest[0].analyzers.posval.get_analysis()\n )\n print(\n 'Pyfolio: ', backtest[0].analyzers.pyfolio.get_analysis()\n )\n\n # Print Analysis and Final Portfolio Value\n pyfoliozer = backtest[0].analyzers.getbyname('pyfolio')\n returns, positions, transactions, gross_lev = pyfoliozer.get_pf_items()\n\n # See if we can add regular FB data to compare against returns of algo\n pf.create_full_tear_sheet(\n returns, positions=positions, transactions=transactions\n )","def _generate_all_charts(spec, input_data):\n\n def _generate_chart(_, data_q, graph):\n \"\"\"Generates the chart.\n \"\"\"\n\n logs = list()\n\n logging.info(\" Generating the chart '{0}' ...\".\n format(graph.get(\"title\", \"\")))\n logs.append((\"INFO\", \" Generating the chart '{0}' ...\".\n format(graph.get(\"title\", \"\"))))\n\n job_name = graph[\"data\"].keys()[0]\n\n csv_tbl = list()\n res = list()\n\n # Transform the data\n logs.append((\"INFO\", \" Creating the data set for the {0} '{1}'.\".\n format(graph.get(\"type\", \"\"), graph.get(\"title\", \"\"))))\n data = input_data.filter_data(graph, continue_on_error=True)\n if data is None:\n logging.error(\"No data.\")\n return\n\n chart_data = dict()\n chart_tags = dict()\n for job, job_data in data.iteritems():\n if job != job_name:\n continue\n for index, bld in job_data.items():\n for test_name, test in bld.items():\n if chart_data.get(test_name, None) is None:\n chart_data[test_name] = OrderedDict()\n try:\n chart_data[test_name][int(index)] = \\\n test[\"result\"][\"receive-rate\"]\n chart_tags[test_name] = test.get(\"tags\", None)\n except (KeyError, TypeError):\n pass\n\n # Add items to the csv table:\n for tst_name, tst_data in chart_data.items():\n tst_lst = list()\n for bld in builds_dict[job_name]:\n itm = tst_data.get(int(bld), '')\n if not isinstance(itm, str):\n itm = itm.avg\n tst_lst.append(str(itm))\n csv_tbl.append(\"{0},\".format(tst_name) + \",\".join(tst_lst) + '\\n')\n\n # Generate traces:\n traces = list()\n index = 0\n groups = graph.get(\"groups\", None)\n visibility = list()\n\n if groups:\n for group in groups:\n visible = list()\n for tag in group:\n for test_name, test_data in chart_data.items():\n if not test_data:\n logs.append((\"WARNING\",\n \"No data for the test '{0}'\".\n format(test_name)))\n continue\n if tag in chart_tags[test_name]:\n message = \"index: {index}, test: {test}\".format(\n index=index, test=test_name)\n test_name = test_name.split('.')[-1]\n try:\n trace, rslt = _generate_trending_traces(\n test_data,\n job_name=job_name,\n build_info=build_info,\n name='-'.join(test_name.split('-')[2:-1]),\n color=COLORS[index])\n except IndexError:\n message = \"Out of colors: {}\".format(message)\n logs.append((\"ERROR\", message))\n logging.error(message)\n index += 1\n continue\n traces.extend(trace)\n visible.extend([True for _ in range(len(trace))])\n res.append(rslt)\n index += 1\n break\n visibility.append(visible)\n else:\n for test_name, test_data in chart_data.items():\n if not test_data:\n logs.append((\"WARNING\", \"No data for the test '{0}'\".\n format(test_name)))\n continue\n message = \"index: {index}, test: {test}\".format(\n index=index, test=test_name)\n test_name = test_name.split('.')[-1]\n try:\n trace, rslt = _generate_trending_traces(\n test_data,\n job_name=job_name,\n build_info=build_info,\n name='-'.join(test_name.split('-')[2:-1]),\n color=COLORS[index])\n except IndexError:\n message = \"Out of colors: {}\".format(message)\n logs.append((\"ERROR\", message))\n logging.error(message)\n index += 1\n continue\n traces.extend(trace)\n res.append(rslt)\n index += 1\n\n if traces:\n # Generate the chart:\n try:\n layout = deepcopy(graph[\"layout\"])\n except KeyError as err:\n logging.error(\"Finished with error: No layout defined\")\n logging.error(repr(err))\n return\n if groups:\n show = list()\n for i in range(len(visibility)):\n visible = list()\n for r in range(len(visibility)):\n for _ in range(len(visibility[r])):\n visible.append(i == r)\n show.append(visible)\n\n buttons = list()\n buttons.append(dict(\n label=\"All\",\n method=\"update\",\n args=[{\"visible\": [True for _ in range(len(show[0]))]}, ]\n ))\n for i in range(len(groups)):\n try:\n label = graph[\"group-names\"][i]\n except (IndexError, KeyError):\n label = \"Group {num}\".format(num=i + 1)\n buttons.append(dict(\n label=label,\n method=\"update\",\n args=[{\"visible\": show[i]}, ]\n ))\n\n layout['updatemenus'] = list([\n dict(\n active=0,\n type=\"dropdown\",\n direction=\"down\",\n xanchor=\"left\",\n yanchor=\"bottom\",\n x=-0.12,\n y=1.0,\n buttons=buttons\n )\n ])\n\n name_file = \"{0}-{1}{2}\".format(spec.cpta[\"output-file\"],\n graph[\"output-file-name\"],\n spec.cpta[\"output-file-type\"])\n\n logs.append((\"INFO\", \" Writing the file '{0}' ...\".\n format(name_file)))\n plpl = plgo.Figure(data=traces, layout=layout)\n try:\n ploff.plot(plpl, show_link=False, auto_open=False,\n filename=name_file)\n except plerr.PlotlyEmptyDataError:\n logs.append((\"WARNING\", \"No data for the plot. Skipped.\"))\n\n data_out = {\n \"job_name\": job_name,\n \"csv_table\": csv_tbl,\n \"results\": res,\n \"logs\": logs\n }\n data_q.put(data_out)\n\n builds_dict = dict()\n for job in spec.input[\"builds\"].keys():\n if builds_dict.get(job, None) is None:\n builds_dict[job] = list()\n for build in spec.input[\"builds\"][job]:\n status = build[\"status\"]\n if status != \"failed\" and status != \"not found\" and \\\n status != \"removed\":\n builds_dict[job].append(str(build[\"build\"]))\n\n # Create \"build ID\": \"date\" dict:\n build_info = dict()\n tb_tbl = spec.environment.get(\"testbeds\", None)\n for job_name, job_data in builds_dict.items():\n if build_info.get(job_name, None) is None:\n build_info[job_name] = OrderedDict()\n for build in job_data:\n testbed = \"\"\n tb_ip = input_data.metadata(job_name, build).get(\"testbed\", \"\")\n if tb_ip and tb_tbl:\n testbed = tb_tbl.get(tb_ip, \"\")\n build_info[job_name][build] = (\n input_data.metadata(job_name, build).get(\"generated\", \"\"),\n input_data.metadata(job_name, build).get(\"version\", \"\"),\n testbed\n )\n\n work_queue = multiprocessing.JoinableQueue()\n manager = multiprocessing.Manager()\n data_queue = manager.Queue()\n cpus = multiprocessing.cpu_count()\n\n workers = list()\n for cpu in range(cpus):\n worker = Worker(work_queue,\n data_queue,\n _generate_chart)\n worker.daemon = True\n worker.start()\n workers.append(worker)\n os.system(\"taskset -p -c {0} {1} > /dev/null 2>&1\".\n format(cpu, worker.pid))\n\n for chart in spec.cpta[\"plots\"]:\n work_queue.put((chart, ))\n work_queue.join()\n\n anomaly_classifications = list()\n\n # Create the header:\n csv_tables = dict()\n for job_name in builds_dict.keys():\n if csv_tables.get(job_name, None) is None:\n csv_tables[job_name] = list()\n header = \"Build Number:,\" + \",\".join(builds_dict[job_name]) + '\\n'\n csv_tables[job_name].append(header)\n build_dates = [x[0] for x in build_info[job_name].values()]\n header = \"Build Date:,\" + \",\".join(build_dates) + '\\n'\n csv_tables[job_name].append(header)\n versions = [x[1] for x in build_info[job_name].values()]\n header = \"Version:,\" + \",\".join(versions) + '\\n'\n csv_tables[job_name].append(header)\n\n while not data_queue.empty():\n result = data_queue.get()\n\n anomaly_classifications.extend(result[\"results\"])\n csv_tables[result[\"job_name\"]].extend(result[\"csv_table\"])\n\n for item in result[\"logs\"]:\n if item[0] == \"INFO\":\n logging.info(item[1])\n elif item[0] == \"ERROR\":\n logging.error(item[1])\n elif item[0] == \"DEBUG\":\n logging.debug(item[1])\n elif item[0] == \"CRITICAL\":\n logging.critical(item[1])\n elif item[0] == \"WARNING\":\n logging.warning(item[1])\n\n del data_queue\n\n # Terminate all workers\n for worker in workers:\n worker.terminate()\n worker.join()\n\n # Write the tables:\n for job_name, csv_table in csv_tables.items():\n file_name = spec.cpta[\"output-file\"] + \"-\" + job_name + \"-trending\"\n with open(\"{0}.csv\".format(file_name), 'w') as file_handler:\n file_handler.writelines(csv_table)\n\n txt_table = None\n with open(\"{0}.csv\".format(file_name), 'rb') as csv_file:\n csv_content = csv.reader(csv_file, delimiter=',', quotechar='\"')\n line_nr = 0\n for row in csv_content:\n if txt_table is None:\n txt_table = prettytable.PrettyTable(row)\n else:\n if line_nr > 1:\n for idx, item in enumerate(row):\n try:\n row[idx] = str(round(float(item) / 1000000, 2))\n except ValueError:\n pass\n try:\n txt_table.add_row(row)\n except Exception as err:\n logging.warning(\"Error occurred while generating TXT \"\n \"table:\\n{0}\".format(err))\n line_nr += 1\n txt_table.align[\"Build Number:\"] = \"l\"\n with open(\"{0}.txt\".format(file_name), \"w\") as txt_file:\n txt_file.write(str(txt_table))\n\n # Evaluate result:\n if anomaly_classifications:\n result = \"PASS\"\n for classification in anomaly_classifications:\n if classification == \"regression\" or classification == \"outlier\":\n result = \"FAIL\"\n break\n else:\n result = \"FAIL\"\n\n logging.info(\"Partial results: {0}\".format(anomaly_classifications))\n logging.info(\"Result: {0}\".format(result))\n\n return result","def run_grid_search():\n\n best_score = 0\n best_learning_rate = 0\n best_discount_rate = 0\n best_initial_q_hat = 0\n trial_results = []\n number_of_trials = 30\n # TODO These ought to be done with numpy.arange but I don't have that package installed at the moment\n for learning_rate_raw in range(5, 50, 5):\n for discount_rate_raw in range(5, 20, 5):\n for initial_q_hat in range(0, 10, 1):\n learning_rate = learning_rate_raw * 0.01\n discount_rate = discount_rate_raw * 0.05\n # Set up environment and agent\n e = Environment() # create environment (also adds some dummy traffic)\n a = e.create_agent(LearningAgent, learning_rate, discount_rate, initial_q_hat) # create agent\n e.set_primary_agent(a, enforce_deadline=True) # specify agent to track\n # NOTE: You can set enforce_deadline=False while debugging to allow longer trials\n\n # Now simulate it\n sim = Simulator(e, update_delay=0, display=False) # create simulator (uses pygame when display=True, if available)\n # NOTE: To speed up simulation, reduce update_delay and/or set display=False\n\n sim.run(n_trials=number_of_trials) # run for a specified number of trials\n # NOTE: To quit midway, press Esc or close pygame window, or hit Ctrl+C on the command-line\n score = a.get_score()\n if score > best_score:\n best_score = score\n best_learning_rate = learning_rate\n best_discount_rate = discount_rate\n best_initial_q_hat = initial_q_hat\n trial_results.append((learning_rate, discount_rate, initial_q_hat, score, a.get_proportion_of_states_visited(), len(a.get_failed_trials())/ float(number_of_trials)))\n print \"Gridsearch finished, best learning rate: %.2f, best discount rate: %.2f, best initial q hat %i\" % (best_learning_rate, best_discount_rate, best_initial_q_hat)\n\n with open('gridsearch_results.csv', 'wb') as csvfile:\n spamwriter = csv.writer(csvfile, delimiter=';', quotechar='|', quoting=csv.QUOTE_MINIMAL)\n spamwriter.writerow(('learning rate', 'discount factor', 'initial q-hat value', 'score', 'states visited', 'failed trials'))\n for result in trial_results:\n spamwriter.writerow(result)","def simulate(self, x='volatility', y='expected_return', iters=1000, weight_bound=(0,1), leverage=1, ret_format='df',\n file_path=None, x_var=None, y_var=None):\n if y_var is None:\n y_var = dict()\n if x_var is None:\n x_var = dict()\n\n x_val = np.zeros(iters)\n y_val = np.zeros(iters)\n weight_vals = np.zeros(shape=(iters, len(self.assets)))\n individual_bound = ConstGen.construct_weight_bound(self.ret_vec.shape[0], (0,1), weight_bound)\n\n for it in range(iters):\n temp_weights = ConstGen.gen_random_weight(self.ret_vec.shape[0], individual_bound, leverage)\n weight_vals[it] = temp_weights\n x_val[it] = self.metric_creator.method_dict[x](temp_weights, **x_var)\n y_val[it] = self.metric_creator.method_dict[y](temp_weights, **y_var)\n\n if ret_format == 'sns': # Change to plt, fig format\n fig, ax = plt.subplots(figsize=(18, 12));\n ax = sns.scatterplot(x_val, y_val);\n ax.set_title(f\"{x} VS {y}\")\n plt.xlim(x_val.min(), x_val.max());\n plt.ylim(y_val.min(), y_val.max());\n plt.xlabel(x);\n plt.ylabel(y);\n if file_path:\n plt.savefig(file_path)\n plt.show()\n else:\n res_df = pd.DataFrame(columns=[x] + [y], data=np.concatenate([x_val.reshape(1, -1), y_val.reshape(1, -1)]).T)\n res_df = pd.concat([res_df, pd.DataFrame(columns=self.assets, data=weight_vals)], axis=1)\n if ret_format == 'plotly':\n return px.scatter(res_df, x=x, y=y, title=f\"{x} vs {y}\")\n elif ret_format == \"df\":\n return res_df\n else:\n raise FormatException(\"\"\"Return Format must be sns, plotly, df\"\"\")","def main(out_path, runs, repeats = 128, workers = 0, local = False):\n\n logger.info(\"simulating %i runs\", len(runs))\n\n get_run_data = borg.util.memoize(borg.storage.RunData.from_bundle)\n\n def yield_jobs():\n for run in runs:\n all_data = get_run_data(run[\"bundle\"])\n validation = sklearn.cross_validation.ShuffleSplit(len(all_data), repeats, test_fraction = 0.2, indices = False)\n\n if run[\"portfolio_name\"] == \"-\":\n makers = map(borg.experiments.simulate_runs.SolverMaker, all_data.solver_names)\n else:\n makers = [borg.experiments.simulate_runs.PortfolioMaker(run[\"portfolio_name\"])]\n\n max_instances = len(all_data) * 0.8\n\n for (train_mask, test_mask) in validation:\n for instances in map(int, map(round, numpy.r_[10.0:max_instances:32j])):\n for maker in makers:\n yield (\n simulate_run,\n [\n run,\n maker,\n all_data,\n train_mask,\n test_mask,\n instances,\n run[\"independent\"],\n run[\"mixture\"],\n ],\n )\n\n with borg.util.openz(out_path, \"wb\") as out_file:\n writer = csv.writer(out_file)\n\n writer.writerow([\"description\", \"solver\", \"instances\", \"successes\", \"mean_time\", \"median_time\"])\n\n for (_, row) in condor.do(yield_jobs(), workers, local):\n writer.writerow(row)\n\n out_file.flush()","def evol_run(self, pkg_setup):\n\n params = specs.ODict()\n params[(\"tune\", \"soma\", \"Ra\")] = [100.0 * 0.5, 100 * 1.5]\n\n amps = [0.0, 0.65] # amplitudes\n times = [100, 200] # start times\n dur = 50 # ms\n targetRates = [0.0, 81.0]\n\n # initial cfg set up\n initCfg = {} # specs.ODict()\n initCfg[\"duration\"] = 200 * len(amps)\n initCfg[(\"hParams\", \"celsius\")] = 37\n\n initCfg[\"savePickle\"] = True\n initCfg[\"saveJson\"] = False\n initCfg[\"saveDataInclude\"] = [\"simConfig\", \"netParams\", \"net\", \"simData\"]\n\n initCfg[(\"IClamp1\", \"pop\")] = \"ITS4\"\n initCfg[(\"IClamp1\", \"amp\")] = amps\n initCfg[(\"IClamp1\", \"start\")] = times\n initCfg[(\"IClamp1\", \"dur\")] = 100\n\n initCfg[(\"analysis\", \"plotfI\", \"amps\")] = amps\n initCfg[(\"analysis\", \"plotfI\", \"times\")] = times\n initCfg[(\"analysis\", \"plotfI\", \"dur\")] = dur\n initCfg[(\"analysis\", \"plotfI\", \"targetRates\")] = targetRates\n\n for k, v in params.items():\n initCfg[k] = v[0] # initialize params in cfg so they can be modified\n\n # fitness function\n fitnessFuncArgs = {}\n fitnessFuncArgs[\"targetRates\"] = targetRates\n\n def fitnessFunc(simData, **kwargs):\n targetRates = kwargs[\"targetRates\"]\n\n diffRates = [abs(x - t) for x, t in zip(simData[\"fI\"], targetRates)]\n fitness = np.mean(diffRates)\n\n print(\" Candidate rates: \", simData[\"fI\"])\n print(\" Target rates: \", targetRates)\n print(\" Difference: \", diffRates)\n\n return fitness\n\n # create Batch object with paramaters to modify, and specifying files to use\n b = Batch(cfgFile='src/cfg.py', netParamsFile='src/netParams.py', params=params, initCfg=initCfg)\n\n # Set output folder, grid method (all param combinations), and run configuration\n b.batchLabel = \"ITS4_evol\"\n b.saveFolder = \"/tmp/\" + b.batchLabel\n b.method = \"evol\"\n b.seed = 0\n b.runCfg = {\"type\": \"mpi_bulletin\", \"script\": \"init.py\"}\n b.evolCfg = {\n \"evolAlgorithm\": \"custom\",\n \"fitnessFunc\": fitnessFunc, # fitness expression (should read simData)\n \"fitnessFuncArgs\": fitnessFuncArgs,\n \"pop_size\": 2,\n \"num_elites\": 1, # keep this number of parents for next generation if they are fitter than children\n \"mutation_rate\": 0.4,\n \"crossover\": 0.5,\n \"maximize\": False, # maximize fitness function?\n \"max_generations\": 1,\n \"time_sleep\": 0.25, # wait this time before checking again if sim is completed (for each generation)\n \"maxiter_wait\": 20, # max number of times to check if sim is completed (for each generation)\n \"defaultFitness\": 1000, # set fitness value in case simulation time is over\n }\n # Run batch simulations\n b.run()","def run_back_test(self, algorithm):\n for date in self.dates:\n info = dict()\n info['cash'] = self.cash['cash'][date]\n info['date'] = date\n original_order = Order()\n for stock in self.stocks:\n original_order.order(stock, self.stock_data[stock].position['Position'][date])\n make_order = algorithm(self.get_back_dataframe(date), original_order, info)\n\n for stock in make_order.orders:\n self.order_stock(stock, make_order.orders[stock], date)","def genOptimiseAll(startPar, stopPar, stepPar, dbCol, paramOption):\n print \"optimisation of parameter '{0}'\".format(paramOption)\n\n query = \"select stockname, excludedattributes, bestnu from {0} \"\n \"where active=true and excludedattributes is not NULL and \"\n \"excludedattributes<>'' and {1} is NULL order by stockname asc\".format(common.dataminestocksViewName, dbCol)\n\n conn = common.getdbcon()\n cur = conn.cursor()\n curUp = conn.cursor()\n cur.execute(query)\n curStep = stepPar\n curStart = startPar\n curStop = stopPar\n\n for row in cur:\n stockname = row[0]\n dataExtract = common.extractData(stockname)\n\n for i in range(0, 4):\n print \"Trying: step {0}. curStart = {1}, curStop = {2}, curStep = {3}\".format(i, curStart, curStop, curStep)\n (optParam, corr) = optimiseOne(paramOption, row, dataExtract, curStart, curStop, curStep)\n curStart = optParam - curStep # 1 step earlier\n curStep = curStep / 10 # 10 times finer\n\n curUp.execute(\"update {0} set {4}={1}, bestCorrelation={2} where stockname='{3}'\".\n format(common.dataminestocksViewName, curStart, corr, stockname, dbCol))\n\n conn.commit()\n conn.close()\n print \"optimisation finished\"","def test_run_beta_diversity_through_plots_parallel(self):\r\n run_beta_diversity_through_plots(\r\n self.test_data['biom'][0],\r\n self.test_data['map'][0],\r\n self.test_out,\r\n call_commands_serially,\r\n self.params,\r\n self.qiime_config,\r\n tree_fp=self.test_data['tree'][0],\r\n parallel=True,\r\n status_update_callback=no_status_updates)\r\n\r\n unweighted_unifrac_dm_fp = join(\r\n self.test_out,\r\n 'unweighted_unifrac_dm.txt')\r\n weighted_unifrac_dm_fp = join(self.test_out, 'weighted_unifrac_dm.txt')\r\n unweighted_unifrac_pc_fp = join(\r\n self.test_out,\r\n 'unweighted_unifrac_pc.txt')\r\n weighted_unifrac_pc_fp = join(self.test_out, 'weighted_unifrac_pc.txt')\r\n weighted_unifrac_html_fp = join(self.test_out,\r\n 'weighted_unifrac_emperor_pcoa_plot', 'index.html')\r\n\r\n # check for expected relations between values in the unweighted unifrac\r\n # distance matrix\r\n dm = parse_distmat_to_dict(open(unweighted_unifrac_dm_fp))\r\n self.assertTrue(dm['f1']['f2'] < dm['f1']['p1'],\r\n \"Distance between pair of fecal samples is larger than distance\"\r\n \" between fecal and palm sample (unweighted unifrac).\")\r\n self.assertEqual(dm['f1']['f1'], 0)\r\n # check for expected relations between values in the weighted unifrac\r\n # distance matrix\r\n dm = parse_distmat_to_dict(open(weighted_unifrac_dm_fp))\r\n self.assertTrue(dm['f1']['f2'] < dm['f1']['p1'],\r\n \"Distance between pair of fecal samples is larger than distance\"\r\n \" between fecal and palm sample (unweighted unifrac).\")\r\n self.assertEqual(dm['f1']['f1'], 0)\r\n\r\n # check that final output files have non-zero size\r\n self.assertTrue(getsize(unweighted_unifrac_pc_fp) > 0)\r\n self.assertTrue(getsize(weighted_unifrac_pc_fp) > 0)\r\n self.assertTrue(getsize(weighted_unifrac_html_fp) > 0)\r\n\r\n # Check that the log file is created and has size > 0\r\n log_fp = glob(join(self.test_out, 'log*.txt'))[0]\r\n self.assertTrue(getsize(log_fp) > 0)","def main():\n\n # Hypothesis:\n # The `impact` encapsulates the volatility, stability and overall\n # fluctuation of the market; in particular, movements that would\n # affect one's portfolio, e.g. unexpected (i.e. not predicted)\n # increases or drops in prices.\n # For the StrategyLearner should directly affect the learned\n # policy, particularly, in terms of willingness to take risks by\n # betting on the behavior of the market.\n # This can be translated into three metrics:\n # - Number of entries:\n # These should be reduced as market impact increases which\n # shows the learning agent being more cautious about its bets\n # - Cumulative return:\n # Directly related to the point mentioned above, as market\n # impact increases and the agent's willingness to take risks\n # decreaes, so is the overall performance of the strategy\n # - Training episodes:\n # This applies specifically to the Q-Learning agent, but it\n # is interesting to see how as the market impact increases,\n # the number of complete training episodes (i.e. a complete\n # pass on the trading data) is not affected. One would think\n # that the agent would converge faster when the impact is\n # large as it would quickly realize that the most optimal\n # strategy is to not do anything. However, impact does not\n # affect the rate of convergence, but rather the strategy\n # that the agent converges to\n\n # Set the seed for reproducibility\n random.seed(1481090000)\n\n # Experiment parameters\n symbol = 'JPM'\n # In-sample: January 1, 2008 to December 31 2009\n start_date = dt.datetime(2008, 1, 1)\n end_date = dt.datetime(2009, 12, 31)\n starting_value = 100000\n commission = 0.0\n # Values to use to evaluate the effect of the impact\n impact_values = [0.0, 0.005, 0.01, 0.05, 0.1, 0.25, 0.5, 1.0]\n\n all_entries = []\n all_returns = []\n all_episodes = []\n\n for impact in impact_values:\n log.info(\"Evaluating the effect of impact=%s\", impact)\n strategy_learner = StrategyLearner(verbose=False, impact=impact)\n\n log.info(\"Training StrategyLearner\")\n strategy_learner.addEvidence(\n symbol=symbol,\n sd=start_date,\n ed=end_date,\n sv=starting_value\n )\n\n log.info(\"Querying StrategyLearner to generate trades\")\n trades = strategy_learner.testPolicy(\n symbol=symbol,\n sd=start_date,\n ed=end_date,\n sv=starting_value\n )\n\n log.info(\"Transforming StrategyLearner trades into marketsim orders\")\n orders = _convert_trades_to_marketisim_orders(symbol, trades)\n\n log.info(\"Computing portfolio values for %d orders\", orders.shape[0])\n port_vals = compute_portvals(\n orders,\n start_val=starting_value,\n commission=commission,\n impact=impact\n )\n\n cumulative_return = _compute_cumulative_return(port_vals)\n\n all_entries.append(strategy_learner.metadata['entries'])\n all_returns.append(cumulative_return)\n all_episodes.append(strategy_learner.metadata['training_episodes'])\n\n _plot_and_save_number_of_entries_per_impact_value(impact_values, all_entries)\n _plot_and_save_number_of_episodes_per_impact_value(impact_values, all_episodes)\n _plot_and_save_cumulative_return_per_impact_value(impact_values, all_returns)","def main(train_stock, val_stock, window_size, batch_size, ep_count,\n strategy=\"t-dqn\", model_name=\"model_debug\", pretrained=False,\n debug=False):\n agent = Agent(window_size, strategy=strategy, pretrained=pretrained, model_name=model_name)\n \n train_data = get_stock_data(train_stock)\n val_data = get_stock_data(val_stock)\n\n initial_offset = val_data[1] - val_data[0]\n\n for episode in range(1, ep_count + 1):\n train_result = train_model(agent, episode, train_data, ep_count=ep_count,\n batch_size=batch_size, window_size=window_size)\n val_result, _ = evaluate_model(agent, val_data, window_size, debug)\n show_train_result(train_result, val_result, initial_offset)","def generate(self):\n \n ##Dictionary of put values\n self.put = {}\n ##Dictionary of call values\n self.call = {}\n \n bottom = self.range[0]\n top = self.range[2]\n iter = self.range[1]\n \n if self.meth == 'bi':\n for E in range(bottom,top+iter,iter):\n solver = bi.binomial_euro(S=self.S,E=E,r=self.r,M=400,sigma=self.sigma,method='higham',T=self.T,opt='put')\n self.put[E] = solver.solve()\n del solver\n for E in range(bottom,top+iter,iter):\n solver = bi.binomial_euro(S=self.S,E=E,r=self.r,M=400,sigma=self.sigma,method='higham',T=self.T,opt='call')\n self.call[E] = solver.solve()\n del solver\n elif self.meth == 'mc':\n for E in range(bottom,top+iter,iter):\n solver = mc.mcfast_euro(S=self.S,E=E,r=self.r,M=20000,sigma=self.sigma,T=self.T,opt='put')\n self.put[E] = solver.solve()\n del solver\n for E in range(bottom,top+iter,iter):\n solver = mc.mcfast_euro(S=self.S,E=E,r=self.r,M=20000,sigma=self.sigma,T=self.T,opt='call')\n self.call[E] = solver.solve()\n del solver\n elif self.meth == 'all':\n pass","def run(self):\n\n # Initialization assumptions\n z = self.draw_normal_initial()\n gradient = self.cv_gradient_initial(z)\n gradient[np.isnan(gradient)] = 0\n variance = np.power(gradient, 2) \n final_parameters = self.current_parameters()\n final_samples = 1\n\n # Create optimizer\n if self.optimizer == 'ADAM':\n self.optim = ADAM(final_parameters, variance, self.learning_rate, 0.9, 0.999)\n elif self.optimizer == 'RMSProp':\n self.optim = RMSProp(final_parameters, variance, self.learning_rate, 0.99)\n\n # Record elbo\n if self.record_elbo is True:\n elbo_records = np.zeros(self.iterations)\n else:\n elbo_records = None\n\n for i in range(self.iterations):\n x = self.draw_normal()\n gradient = self.cv_gradient(x)\n gradient[np.isnan(gradient)] = 0\n self.change_parameters(self.optim.update(gradient))\n\n if self.printer is True:\n self.print_progress(i, self.optim.parameters[::2])\n\n # Construct final parameters using final 10% of samples\n if i > self.iterations-round(self.iterations/10):\n final_samples += 1\n final_parameters = final_parameters+self.optim.parameters\n\n if self.record_elbo is True:\n elbo_records[i] = self.get_elbo(self.optim.parameters[::2])\n\n final_parameters = final_parameters/float(final_samples)\n self.change_parameters(final_parameters)\n final_means = np.array([final_parameters[el] for el in range(len(final_parameters)) if el%2==0])\n final_ses = np.array([final_parameters[el] for el in range(len(final_parameters)) if el%2!=0])\n if not self.quiet_progress:\n print(\"\")\n print(\"Final model ELBO is \" + str(-self.neg_posterior(final_means)-self.create_normal_logq(final_means)))\n return self.q, final_means, final_ses, elbo_records","def backtest_portfolio(self):\n self.rank=dict()\n self.accuracy=dict()\n portfolio = dict()\n \n for algo in self.algos:\n portfolio[algo]=pd.DataFrame(index=self.positions.index)\n self.pos_diff=dict()\n self.pos_diff[algo] = self.positions[algo].diff()\n \n portfolio[algo]['price_diff'] = self.bars['Close']-self.bars['Open']\n #portfolio['price_diff'][0:5] = 0.0\n portfolio[algo]['profit'] = self.positions[algo] * portfolio[algo]['price_diff']\n portfolio[algo]['total'] = self.initial_capital + portfolio[algo]['profit'].cumsum()\n portfolio[algo]['returns'] = portfolio[algo]['total'].pct_change()\n d=np.array(portfolio[algo]['profit']).copy()\n d[d>0]=1\n d[d<0]=0\n d[np.array(self.positions[algo])==0]=1\n for i in np.arange(1,len(d)+1):\n c=float(sum(d[0:i]))/(i)\n d[i-1]=c\n portfolio[algo]['accuracy']=d\n self.rank[algo]=float(portfolio[algo]['total'][-1] - portfolio[algo]['total'][0])\n self.returns=portfolio\n c=np.array(self.returns[algo]['profit'])\n c[c>0]=1\n c[c<0]=0\n c[np.array(self.positions[algo])==0]=1\n accuracy=round(float(c.sum())/len(c),2)*self.rank[algo]\n self.accuracy[algo]=accuracy\n #self.ranking= sorted(self.rank.items(), key=operator.itemgetter(1), reverse=True)\n self.ranking= sorted(self.accuracy.items(), key=operator.itemgetter(1))\n self.ready=True\n return (portfolio, self.rank, self.ranking)","def run_experiment(out_root, num_procs, trials_per_setting, **kwargs):\n # TODO: UPDATE DOCS!\n param_dicts = product_of_dict_lists(kwargs)\n out_file = (\n out_root\n + \"-\".join(\n [\n \"{}={}\".format(key, value[0])\n for key, value in kwargs.items()\n if len(kwargs[key]) == 1\n ]\n )\n + \".csv\"\n )\n\n # build list of parameters\n parameters = []\n for param_idx in range(len(param_dicts)):\n params = param_dicts[param_idx]\n params[\"param_idx\"] = param_idx\n for trial_idx in range(trials_per_setting):\n t_params = dict(params) # copy so that can vary trial number\n t_params[\"trial_idx\"] = trial_idx\n parameters.append(t_params)\n # parameters = [params for _ in range(trials_per_setting)]\n # send work to pool, wrapped in a progress bar\n procs = Pool(num_procs)\n data = pd.concat(\n list(\n tqdm.tqdm(procs.imap(run_trial_from_kw, parameters), total=len(parameters))\n ),\n ignore_index=True,\n )\n # write output\n data.to_csv(out_file)","def run_performance():\n # Create a Struct data instance from config\n inputs = Struct(config)\n inputs.throttle = throttle\n # Get oxidizer properties at the given temperature\n n2o = n2o_properties(inputs.ox.T_tank)\n # Our integration variables are oxidizer mass and liquid oxidizer volume\n Mox = n2o.rho_l*(inputs.ox.liquid_V) + n2o.rho_g*(inputs.ox.tank_V-inputs.ox.liquid_V)\n if inputs.options.output_on:\n print(\"Initial oxidizer mass: {} kg.\".format(Mox))\n\n start = time.perf_counter() # Start timer for integration\n\n time, record = integration(inputs) # Time = time for integration, record = output data\n F_thrust = record.F_thrust\n p_cc = record.p_cc\n p_oxtank = record.p_oxtank\n p_oxpresstank = record.p_oxpresstank\n p_fueltank = record.p_fueltank\n p_fuelpresstank = record.p_fuelpresstank\n p_oxmanifold = record.p_oxmanifold\n T_oxtank = record.T_oxtank\n T_cc = record.T_cc\n area_core = record.area_core\n OF = record.OF_i\n gamma_ex = record.gamma_ex\n m_dot_ox = record.m_dot_ox\n m_dot_fuel = record.m_dot_fuel\n p_crit = record.p_crit\n m_dot_ox_crit = record.m_dot_ox_crit\n M_e = record.M_e\n p_exit = record.p_exit\n p_shock = record.p_shock\n\n time_elapsed = start-time.perf_counter() # Stop the timer and print elapsed time\n if inputs.options.output_on:\n print(\"Time elapsed for this timestep: {} sec.\".format(time_elapsed))","def run_strategy(strategy_class, start_years: List[int], num_years: int, output_csv_prefix: Optional[str]):\n\n display = [['Years', 'End value (real)', 'End value (nominal)', 'Gain', 'Inflation']]\n for year in start_years:\n # Create the market and investment account.\n market = Market(snp_yearly, year, year_to_inflation)\n account = InvestmentAccount(STARTING_CASH, market)\n\n # Create the Strategy instance.\n # (We instantiate a Strategy for each start date that we're going to simulate).\n strategy = strategy_class()\n\n if output_csv_prefix is not None:\n strategy.set_output_csv_prefix(output_csv_prefix + f'_for_year_{year}.csv')\n\n years_range = f'{year} - {year + num_years}'\n\n # Run the strategy.\n status = strategy.go(account, market, year, num_years)\n if status[0]:\n inflation = get_inflation_percentage(year, num_years)\n\n # For reporting of the outcome, convert nominal dollars to real (starting year) dollars.\n net_worth_real = nominal_to_real(account.net_worth, year, num_years)\n \n # Did we gain or lose?\n remainder_percent = (net_worth_real - STARTING_CASH) * 100 / STARTING_CASH\n\n if remainder_percent > 0:\n gainloss = [f'{remainder_percent:.2f}%']\n else:\n gainloss = [colored(f'{remainder_percent:.2f}%', 'red')]\n\n display += [[years_range, f'${net_worth_real:,.0f}', f'${account.net_worth:,.0f}'] + gainloss + [f'{inflation*100:.2f}%']]\n else:\n display += [[years_range, status[1], '', '', '', '']]\n\n # Debug output. \n logger.info(tabulate(display, headers='firstrow'))\n if output_csv_prefix is not None:\n stripped = [[remove_ansi_codes(i) for i in line] for line in display]\n write_table_to_csv(stripped, output_csv_prefix + '_summary.csv')","def run(self):\n\n # Initialization assumptions\n z = self.draw_normal_initial()\n gradient = self.cv_gradient_initial(z)\n gradient[np.isnan(gradient)] = 0\n variance = np.power(gradient, 2) \n final_parameters = self.current_parameters()\n final_samples = 1\n\n # Create optimizer\n if self.optimizer == 'ADAM':\n self.optim = ADAM(final_parameters, variance, self.learning_rate, 0.9, 0.999)\n elif self.optimizer == 'RMSProp':\n self.optim = RMSProp(final_parameters, variance, self.learning_rate, 0.99)\n\n # Record elbo\n if self.record_elbo is True:\n elbo_records = np.zeros(self.iterations)\n else:\n elbo_records = None\n\n for i in range(self.iterations):\n x = self.draw_normal()\n gradient = self.cv_gradient(x)\n gradient[np.isnan(gradient)] = 0\n self.change_parameters(self.optim.update(gradient))\n\n if self.printer is True:\n self.print_progress(i, self.optim.parameters[::2])\n\n # Construct final parameters using final 10% of samples\n if i > self.iterations-round(self.iterations/10):\n final_samples += 1\n final_parameters = final_parameters+self.optim.parameters\n\n if self.record_elbo is True:\n elbo_records[i] = self.get_elbo(self.optim.parameters[::2])\n\n final_parameters = final_parameters/float(final_samples)\n self.change_parameters(final_parameters)\n final_means = np.array([final_parameters[el] for el in range(len(final_parameters)) if el%2==0])\n final_ses = np.array([final_parameters[el] for el in range(len(final_parameters)) if el%2!=0])\n if not self.quiet_progress:\n print(\"\")\n print(\"Final model ELBO is \" + str(-self.full_neg_posterior(final_means)-self.create_normal_logq(final_means)))\n return self.q, final_means, final_ses, elbo_records","def main(self, args):\n for plot in args.plots:\n if plot == 'no_plot':\n break\n print \"plotting\", plot\n\n fig = self.plot_figure(plot)\n\n fformat = '{plot}_{index}.{ext}'\n fname = fformat.format(plot=plot, index=self.index, ext='png')\n fpath = os.path.join(plot_dir, fname)\n fig.savefig(fpath)\n\n if args.distributions == 'all':\n distributions = ['Uf', 'Wf', 'uf_abs',\n 'vorticity', 'vertical_shear']\n else:\n distributions = args.distributions\n for dist in distributions:\n range = self.properties[dist]['range']\n name = self.properties[dist]['name']\n print \"plotting distribution\", dist, name\n fig = self.plot_distribution(getattr(self, dist), range, name)\n\n fformat = 'distribution_{q}_{index}.{ext}'\n fname = fformat.format(q=dist, index=self.index, ext='png')\n fpath = os.path.join(plot_dir, fname)\n fig.savefig(fpath)\n\n if args.funcs:\n for func in args.funcs:\n print \"multiprocessing\", func\n f = getattr(self, 'plot_' + func)\n f()","def run_script(input_dir, output_dir):\n\n \"\"\"+++++++++++++++++++++++++++++++++++\"\"\"\n \"\"\"Step 1. load dataset \"\"\"\n print(\"loading data ......\")\n print(\"+++++++Read the surface shape data+++++++\")\n shape_file_name = input_dir + \"aligned_shapes.mat\"\n mat = loadmat(shape_file_name)\n y_design = mat['aligned_shape']\n n, l, m = y_design.shape\n print(\"The dimension of shape matrix is \" + str(y_design.shape))\n print(\"+++++++Read the sphere coordinate data+++++++\")\n template_file_name = input_dir + \"template.mat\"\n mat = loadmat(template_file_name)\n coord_mat = mat['template']\n # d = coord_mat.shape[1]\n print(\"+++++++Read the design matrix+++++++\")\n design_data_file_name = input_dir + \"design_data.txt\"\n design_data = np.loadtxt(design_data_file_name)\n # read the covariate type\n var_type_file_name = input_dir + \"var_type.txt\"\n var_type = np.loadtxt(var_type_file_name)\n print(\"+++++++Construct the design matrix: normalization+++++++\")\n x_design = read_x(design_data, var_type)\n p = x_design.shape[1]\n print(\"The dimension of design matrix is \" + str(x_design.shape))\n\n \"\"\"+++++++++++++++++++++++++++++++++++\"\"\"\n \"\"\"Step 2. Statistical analysis: including (1) smoothing and (2) hypothesis testing\"\"\"\n gpvals, lpvals_fdr, clu_pvals, efit_beta, efity_design, efit_eta = mfsda.run_stats(y_design, coord_mat, design_data, var_type)\n\n \"\"\"+++++++++++++++++++++++++++++++++++\"\"\"\n \"\"\"Step3. Save all the results\"\"\"\n gpvals_file_name = output_dir + \"global_pvalue.txt\"\n np.savetxt(gpvals_file_name, gpvals)\n lpvals_fdr_file_name = output_dir + \"local_pvalue_fdr.txt\"\n np.savetxt(lpvals_fdr_file_name, lpvals_fdr)\n clu_pvals_file_name = output_dir + \"cluster_pvalue.txt\"\n np.savetxt(clu_pvals_file_name, clu_pvals)","def compute(self):\n self.find_n()\n\n # call hotspot field plots\n for scenario in self.scenarios:\n fields_dict = {}\n ancestor_files = []\n for filename in io.get_all_ancestor_files(self.cfg,\n pattern='hotspot_*.nc'):\n key = os.path.basename(os.path.dirname(filename))\n splitname = os.path.basename(filename).split(\"_\")\n if key.split(\"_\")[-1] == scenario:\n fields_dict[(\n f\"{splitname[-1].split('.nc')[0]}_\"\n f\"{splitname[1]}_{key}\")] = iris.load_cube(filename)\n ancestor_files.append(filename)\n fields_dict[\"scenario\"] = scenario\n fields_dict[\"ancestors\"] = ancestor_files\n self.hotspot_fields_plot(fields_dict)\n\n # call scatter plots\n for season in self.seasons:\n timeseries_dict = {\"large_scale\": {}, \"regional\": {}}\n for region, value in timeseries_dict.items():\n for filename in io.get_all_ancestor_files(\n self.cfg,\n pattern=f'rolling_mean_{region}_{season}.nc'):\n value[os.path.basename(os.path.dirname(filename))] = (\n iris.load_cube(filename))\n value[os.path.basename(\n os.path.dirname(filename))] = (filename)\n for var_combination in self.var_combinations:\n self.timeseries_scatter_plot(deepcopy(timeseries_dict), season,\n var_combination)","def run_all():\n db = DBInterface()\n year = Config.get_property(\"league_year\")\n session = Session(bind=db.engine)\n\n scraper.scrape_all(db, session, year)\n session.commit()\n\n bets.predict_all(db, session)\n session.commit()\n session.close()","def _generate_trading_instances(self, start_date, end_date, instruments, params):\n configuration = self.configuration\n configuration.start_date = start_date\n configuration.end_date = end_date\n configuration.instruments = instruments\n\n logger.info(\"Creating DataHandler, Strategy, Portfolio and ExecutionHandler\")\n logger.info(\"Start date: %s\" % start_date)\n logger.info(\"End date: %s\" % end_date)\n logger.info(\"Instrument(s): %s...\" % instruments)\n logger.info(\"Params: %s...\" % params)\n\n self.data_handler = self.data_handler_cls(self.events, configuration)\n self.strategy = self.strategy_cls(self.data_handler, self.events, configuration, **params)\n self.portfolio = self.portfolio_cls(self.data_handler, self.events, configuration)\n self.execution_handler = self.execution_handler_cls(self.data_handler, self.events, configuration)","def before_trading_start(context, data):\n factors = pipeline_output('ff_example')\n\n # get the data we're going to use\n returns = factors['returns']\n mkt_cap = factors.sort_values(['market_cap'], ascending=True)\n be_me = factors.sort_values(['be_me'], ascending=True)\n\n # to compose the six portfolios, split our universe into portions\n half = int(len(mkt_cap)*0.5)\n small_caps = mkt_cap[:half]\n big_caps = mkt_cap[half:]\n \n thirty = int(len(be_me)*0.3)\n seventy = int(len(be_me)*0.7)\n growth = be_me[:thirty]\n neutral = be_me[thirty:seventy]\n value = be_me[seventy:]\n\n # now use the portions to construct the portfolios.\n # note: these portfolios are just lists (indices) of equities\n small_value = small_caps.index.intersection(value.index)\n small_neutral = small_caps.index.intersection(neutral.index)\n small_growth = small_caps.index.intersection(growth.index)\n \n big_value = big_caps.index.intersection(value.index)\n big_neutral = big_caps.index.intersection(neutral.index)\n big_growth = big_caps.index.intersection(growth.index)\n\n # take the mean to get the portfolio return, assuming uniform\n # allocation to its constituent equities.\n sv = returns[small_value].mean()\n sn = returns[small_neutral].mean()\n sg = returns[small_growth].mean()\n \n bv = returns[big_value].mean()\n bn = returns[big_neutral].mean()\n bg = returns[big_growth].mean()\n\n # computing SMB\n context.smb = (sv + sn + sg)/3 - (bv + bn + bg)/3\n\n # computing HML\n context.hml = (sv + bv)/2 - (sg + bg)/2","def run(self):\n self.timestamp['start'] = datetime.datetime.now().strftime('%Y%m%dT%H%M%S')\n\n for point in self._prepare_grid():\n graph = self._prepare_graph(**point)\n env = self._prepare_env(graph, **point)\n log = self._prepare_logger(graph, env, **point)\n\n try:\n env.run(until=self.runtime)\n except Exception as e:\n print(e)\n log.close()\n\n # self.timestamp[grid.hash_grid_point(point)].append(datetime.datetime.now().strftime('%Y%m%dT%H%M%S'))\n\n self.timestamp['end'] = datetime.datetime.now().strftime('%Y%m%dT%H%M%S')","def test_classical_processing_multiple_tapes(self, execute_kwargs):\n dev = qml.device(\"default.qubit\", wires=2)\n params = jax.numpy.array([0.3, 0.2])\n\n def cost_fn(x):\n with qml.queuing.AnnotatedQueue() as q1:\n qml.Hadamard(0)\n qml.RY(x[0], wires=[0])\n qml.CNOT(wires=[0, 1])\n qml.expval(qml.PauliZ(0))\n\n tape1 = qml.tape.QuantumScript.from_queue(q1)\n\n with qml.queuing.AnnotatedQueue() as q2:\n qml.Hadamard(0)\n qml.CRX(2 * x[0] * x[1], wires=[0, 1])\n qml.RX(2 * x[1], wires=[1])\n qml.expval(qml.PauliZ(0))\n\n tape2 = qml.tape.QuantumScript.from_queue(q2)\n\n result = execute(tapes=[tape1, tape2], device=dev, **execute_kwargs)\n return result[0] + result[1] - 7 * result[1]\n\n res = jax.jit(jax.grad(cost_fn))(params)\n assert res.shape == (2,)","def run_all(prob: str='', algo: str='', timelimit: int=3600):\n if prob:\n prob = prob.upper()\n if algo:\n algo = algo.lower()\n probs = (prob,) if prob in PROBS else PROBS\n algos = (algo,) if algo in ALGOS else ALGOS\n\n if not os.path.exists(DAT_PATH):\n raise Exception(\"{} directory does not exist.\".format(DAT_PATH))\n\n if not os.path.exists(RES_PATH):\n os.makedirs(RES_PATH)\n\n for prob in probs:\n path = DAT_PATH + prob\n inss = [f for f in os.listdir(path) if os.path.isfile(os.path.join(path, f))]\n inss.sort()\n for algo in algos:\n wfile = \"{}rst_{}_{}.csv\".format(RES_PATH, prob, algo)\n with open (wfile, 'w') as f:\n f.write(\"instance,objective value,time to find best soltuion\")\n if algo == 'bc':\n f.write(\",lower bound\")\n if algo[:3] == 'ids':\n f.write(\",number of total iterations,number of iterations to find best soltuion\")\n f.write(\"\\n\")\n for ins in inss:\n mod = Model(problem=prob, filename=os.path.join(path, ins))\n mod.solve(algorithm=algo, timelimit=timelimit)\n with open (wfile, 'a') as f:\n f.write(\"{},{},{:.2f}\".format(ins,mod.objval, mod.ttb))\n if algo == 'bc':\n f.write(\",{}\".format(mod.lb))\n if algo[:3] == 'ids':\n f.write(\",{},{}\".format(mod.num_ite, mod.bes_ite))\n f.write(\"\\n\")","def run(self):\n is_spsa = True\n is_steep_descent = False\n is_rprop = False\n\n k = 0\n theta = self.theta0\n\n while True:\n k = k + 1\n\n self.iter = k\n print(f'starting iter {k} ...')\n\n if self.constraints is not None:\n theta = self.constraints(theta)\n\n print('current param:')\n for name, value in utils.true_param(theta).items():\n print(f' {name}: {value[\"value\"]}')\n\n c_k = self.c / (k ** self.gamma)\n a_k = self.a / ((k + self.A) ** self.alpha)\n\n # print(f' ck: {c_k:0.5f}')\n # print(f' ak: {a_k:0.5f}')\n\n # Run the engine match here to get the gradient\n print('Run engine match ...')\n gradient = self.approximate_gradient(theta, c_k, k)\n\n # For SPSA we update with a small step (theta = theta - a_k * gradient)\n if is_spsa:\n theta = utils.linear_combinaison(1.0, theta, -a_k, gradient)\n logging.info(f'{__file__} > theta from spsa: {theta}')\n # print(f'new param after application of gradient:')\n # for n, v in theta.items():\n # print(f' {n}: {int(v[\"value\"] * v[\"factor\"])}')\n\n # For steepest descent we update via a constant small step in the gradient direction\n elif is_steep_descent:\n mu = -0.01 / max(1.0, utils.norm2(gradient))\n theta = utils.linear_combinaison(1.0, theta, mu, gradient)\n\n # For RPROP, we update with information about the sign of the gradients\n elif is_rprop:\n theta = utils.linear_combinaison(1.0, theta, -0.01, self.rprop(theta, gradient))\n\n # Apply parameter limits\n theta = utils.apply_limits(theta)\n logging.info(f'{__file__} > theta with limits: {theta}')\n # print(f'new param after application of limits:')\n # for n, v in theta.items():\n # print(f' {n}: {int(v[\"value\"] * v[\"factor\"])}')\n\n # We then move to the point which gives the best average of goal\n (avg_goal, avg_theta) = self.average_best_evals(30)\n logging.info(f'{__file__} > avg_theta from average_best_evals: {avg_theta}')\n\n theta = utils.linear_combinaison(0.98, theta, 0.02, avg_theta)\n logging.info(f'{__file__} > theta with avg_theta: {theta}')\n # print(f'new param after application of best average param:')\n # for n, v in theta.items():\n # print(f' {n}: {int(v[\"value\"] * v[\"factor\"])}')\n\n # Apply parameter limits\n theta = utils.apply_limits(theta) # This is the best param.\n logging.info(f'{__file__} > best param: {theta}')\n # print(f'new param after application of limits:')\n # for n, v in theta.items():\n # print(f' {n}: {int(v[\"value\"] * v[\"factor\"])}')\n\n # Log best param values\n for kv, vv in theta.items():\n logging.info(f'<best> iter: {k}, param: {kv}, value: {int(vv[\"value\"]*vv[\"factor\"])}')\n print('best param:')\n for n, v in theta.items():\n print(f' {n}: {int(v[\"value\"] * v[\"factor\"])}')\n\n mean_all_goal, _ = self.average_evaluations(30)\n print(f'mean all goal: {mean_all_goal}')\n\n mean_best_goal, _ = self.average_best_evals(30)\n print(f'mean best goal: {mean_best_goal}')\n\n # Save data in csv for plotting.\n plot_data = {}\n plot_data.update({'iter': k})\n plot_data.update({'meanbestgoal': mean_best_goal})\n plot_data.update({'meanallgoal': mean_all_goal})\n plot_theta = utils.true_param(theta)\n for name, value in plot_theta.items():\n plot_data.update({name: value[\"value\"]})\n\n with open(self.plot_data_file, 'a') as f:\n cnt = 0\n for name, value in plot_data.items():\n cnt += 1\n if cnt == len(plot_data):\n f.write(f'{value}\\n')\n else:\n f.write(f'{value},')\n\n print(f'done iter {k} / {self.max_iter}')\n logging.info(f'{__file__} > done iter {k} / {self.max_iter}')\n print('=========================================')\n\n # Stopping rule 1: Average goal and iteration meet the\n # stop_all_mean_goal and stop_min_iter criteria.\n if k >= self.stop_min_iter and mean_all_goal <= self.stop_all_mean_goal:\n print('Stop opimization due to good average all goal!')\n break\n\n # Stopping rule 2: Average best goal and iteration meet the\n # stop_best_mean_goal and stop_min_iter criteria.\n if k >= self.stop_min_iter and mean_best_goal <= self.stop_best_mean_goal:\n print('Stop opimization due to good average best goal!')\n break\n\n # Stopping rule 3: Max iteration is reached.\n if k >= self.max_iter:\n print('Stop opimization due to max iteration!')\n break\n\n return utils.true_param(theta)","def _evaluate_performance__static_winners(self):\n # | - _evaluate_performance__\n\n # | - class attributes #################################################\n AL = self\n al_gen = self.al_gen\n verbose = self.verbose\n seed_ids = self.seed_ids\n acquisition_bin = self.acquisition_bin\n completed_ids = self.completed_ids\n CandidateSpace = self.CandidateSpace\n RegressionModel = self.RegressionModel\n DuplicateFinder = self.DuplicateFinder\n al_gen_dict = self.al_gen_dict\n\n stop_mode = self.stop_mode\n stop_num_generations = self.stop_num_generations\n\n index_acq_gen_dict = self.index_acq_gen_dict\n #__| #################################################################\n\n # #####################################################################\n mode = \"lowest_N\" # 'lowest_N' or 'lowest_perc'\n\n N_ids = 10\n lowest_perc = 5\n\n # Number of consecutive generations that the Nth best systems must\n # remain static\n M_gens = 3\n # #####################################################################\n\n if mode == \"lowest_perc\":\n num_candidates = CandidateSpace.FingerPrints.df_pre.shape[0]\n N_ids = int(num_candidates * (lowest_perc * 0.01))\n\n gen_keys = list(AL.al_gen_dict.keys())\n\n if len(gen_keys) > M_gens:\n latest_M_keys = gen_keys[-(M_gens + 1):]\n last_gen_key = gen_keys[-1]\n\n al_gen_dict_subset_i = dict(zip(\n latest_M_keys,\n [AL.al_gen_dict.get(i, None) for i in latest_M_keys]))\n\n indices_list = []\n iterator = enumerate(al_gen_dict_subset_i.items())\n for i_cnt, (gen_i, AL_i) in iterator:\n model_i = AL_i.model\n\n model_i = AL.add_main_Y_to_model(\n model_i, plot_dft_instead_of_pred=True)\n model_i = model_i[(model_i[\"duplicate\"] == False)]\n model_i = model_i.sort_values(\"Y_main\")\n\n indices_i = model_i.index.tolist()\n\n indices_list.append(indices_i)\n\n if i_cnt >= M_gens:\n indices_i = indices_list[i_cnt][0:N_ids]\n ids_static_list = []\n for j in range(M_gens):\n indices_j = indices_list[i_cnt - (j + 1)][0:N_ids]\n ids_static = indices_j == indices_i\n ids_static_list.append(ids_static)\n\n ids_are_static = all(ids_static_list)\n\n self.performance__static_winners[last_gen_key] = ids_are_static\n #__|","def copy_generate_median_portfolio_from_simulation(num_of_years=30, trials=100, method='normal'):\n sim_fia_cv = pd.DataFrame(index=range(num_of_years + 1))\n\n sim_base_total = pd.DataFrame(index=range(num_of_years + 1))\n sim_base_income = pd.DataFrame(index=range(num_of_years + 1))\n\n sim_port_total = pd.DataFrame(index=range(num_of_years + 1))\n sim_port_income = pd.DataFrame(index=range(num_of_years + 1))\n\n # read_income_inputs = pd.read_csv(src + \"income_model_inputs.csv\", index_col='Items')\n read_income_inputs = pd.read_excel(src + \"portfolio_information.xlsx\", sheet_name='income_model_inputs',\n index_col=[0])\n\n # read_returns_est = pd.read_csv(src + \"income_assets_returns_estimates.csv\", index_col='Symbol')\n read_returns_est = pd.read_excel(src + \"portfolio_information.xlsx\", sheet_name='income_assets_returns_estimates',\n index_col=[0])\n\n # read_returns_est.drop(['BM', read_returns_est.index[-1]], axis=0, inplace=True)\n # read_portfolio_inputs = pd.read_csv(src + \"income_portfolio_inputs.csv\", index_col='Items')\n\n # read_asset_weights = pd.read_csv(src + \"asset_weights.csv\", index_col='Asset')\n read_asset_weights = pd.read_excel(src + \"portfolio_information.xlsx\", sheet_name='asset_weights',\n index_col=[0])\n\n all_assets = list(read_asset_weights.index)\n read_asset_weights.drop(read_asset_weights.index[-1], axis=0, inplace=True)\n\n # read random returns for simulation\n read_normal = pd.read_csv(src + 'sort_normal.csv', index_col=[0], parse_dates=True)\n\n assets_col_names = list(read_normal.columns)\n\n tickers = list(read_asset_weights.index)\n wts = np.array(read_asset_weights.loc[:, 'base'])\n\n def asset_median_returns(data, ticker):\n return data.filter(regex=ticker).median(axis=1)\n\n # dataframe for unsorted returns (normal)\n median_returns_normal = pd.DataFrame({t: asset_median_returns(read_normal, t) for t in tickers})\n median_returns_normal.loc[:, 'portfolio_return'] = median_returns_normal.dot(wts)\n median_normal_fia = pd.DataFrame({'FIA': asset_median_returns(read_normal, 'r_FIA')})\n\n years = list(range(0, num_of_years + 1))\n income_cols = ['year', 'strategy_term', 'index_returns', 'term_ret', 'term_ret_with_par', 'term_annualize',\n 'ann_net_spread', 'term_ret_netspr', 'high_inc_benefit_base', 'rider_fee', 'eoy_income',\n 'contract_value']\n\n term = int(read_income_inputs.loc['term', 'inputs'])\n fia_ret = read_returns_est.loc[read_returns_est.index[-1], 'Annualized Returns']\n fia_risk = read_returns_est.loc[read_returns_est.index[-1], 'Annualized Risk']\n par_rate = float(read_income_inputs.loc['par_rate', 'inputs'])\n spread = float(read_income_inputs.loc['spread', 'inputs'])\n bonus_term = int(read_income_inputs.loc['bonus_term', 'inputs'])\n premium = float(read_income_inputs.loc['premium', 'inputs'])\n income_bonus = float(read_income_inputs.loc['income_bonus', 'inputs'])\n\n income_starts = int(read_income_inputs.loc['start_income_years', 'inputs'])\n income_growth = float(read_income_inputs.loc['income_growth', 'inputs'])\n rider_fee = float(read_income_inputs.loc['rider_fee', 'inputs'])\n inc_payout_factor = float(read_income_inputs.loc['income_payout_factor', 'inputs'])\n contract_bonus = float(read_income_inputs.loc['contract_bonus', 'inputs'])\n social = float(read_income_inputs.loc['social', 'inputs'])\n inflation = float(read_income_inputs.loc['inflation', 'inputs'])\n wtd_cpn_yield = float(read_income_inputs.loc['wtd_coupon_yld', 'inputs'])\n life_expectancy = int(read_income_inputs.loc['life_expectancy_age', 'inputs'])\n clients_age = int(read_income_inputs.loc['clients_age', 'inputs'])\n\n # # ---------------INCOME MODEL--------------------------------------------\n runs = 0\n returns_dict = {}\n asset_dict = {}\n fia_dict = {}\n while runs <= trials:\n print(runs)\n\n # --------------------BASE MODEL---------------------------------------------\n\n base_wts = read_asset_weights.loc[:, 'base']\n base_assets = list(base_wts.index)\n base_weights = list(base_wts.values)\n base_returns = list(read_returns_est.loc[:, 'Annualized Returns'].values)\n base_std = list(read_returns_est.loc[:, 'Annualized Risk'].values)\n\n base_investment = float(read_income_inputs.loc['risky_assets', 'Base'])\n adv_fees = float(read_income_inputs.loc['advisor_fees', 'Base'])\n\n # ----------------------RANDOM RETURNS--------------------------\n r_cols = ['r_{}'.format(name) for name in base_assets]\n boy_value = ['bv_{}'.format(name) for name in base_assets]\n eoy_value = ['ev_{}'.format(name) for name in base_assets]\n\n random_returns = pd.DataFrame(index=years, columns=r_cols)\n #\n # for c in range(len(r_cols)):\n # ret = np.random.normal(base_returns[c], base_std[c], size=(len(random_returns.index), 1))\n\n this_run_cols = ['{}_{}'.format(cname, str(runs)) for cname in r_cols]\n random_returns = read_normal.loc[:, this_run_cols]\n base_df = random_returns.copy()\n\n # -------------BASE PORTFOLIO----------------------------\n for name in boy_value:\n base_df.loc[:, name] = 0.0\n\n for counter in years:\n period_returns = list(random_returns.loc[counter, :])\n if counter == 0:\n\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment for c in range(len(boy_value))]\n\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\n\n base_investment = base_df.loc[counter, boy_value].sum()\n\n elif (counter > 0) and (counter < income_starts):\n\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment * (1 + period_returns[c])\n for c in range(len(boy_value))]\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\n\n # base_df.loc[counter, 'total_net_fees'] = base_df.loc[counter, 'total']\n\n # --coupon payment is invested back into the risky portfolio until the income is withdrawn----\n base_investment = base_df.loc[counter, 'total']\n\n else:\n\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment * (1 + period_returns[c])\n for c in range(len(boy_value))]\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\n\n base_investment = base_df.loc[counter, 'total']\n\n base_df.loc[:, 'adj_total'] = base_df.loc[:, 'total'].apply(lambda x: x if x > 0 else 0)\n\n asset_dict.update({'{}_{}'.format('s', str(runs)): base_df.loc[:, 'total']})\n # asset_dict.update({'{}_{}'.format('s', str(runs)): ret.flatten()})\n runs += 1\n\n asset_values = pd.DataFrame(asset_dict)\n assets_ror = asset_values.pct_change().fillna(0)\n terminal_values = asset_values.iloc[-1]\n terminal_df = pd.DataFrame(columns=['Terminal Value'])\n terminal_df['Terminal Value'] = terminal_values\n\n median_index = np.where(asset_values == np.median(np.array(terminal_values)))\n median_index = median_index[1][0]\n m_cols = ['{}_{}_{}'.format('r', r, str(median_index)) for r in all_assets]\n median_portfolio_asset_returns = read_normal.loc[:, m_cols]\n median_portfolio_asset_returns.loc[:, 'port_ending_values'] = asset_values.loc[:, '{}_{}'.format('s', median_index)]\n median_portfolio_asset_returns.loc[:, 'ror'] = median_portfolio_asset_returns[\n 'port_ending_values'].pct_change().fillna(0)\n desc_sort = median_portfolio_asset_returns.sort_values(by=median_portfolio_asset_returns.columns[-1],\n ascending=False)\n asc_sort = median_portfolio_asset_returns.sort_values(by=median_portfolio_asset_returns.columns[-1],\n ascending=True)\n unsorted_df = median_portfolio_asset_returns\n unsorted_df.drop(unsorted_df.columns[-2:], axis=1, inplace=True)\n desc_sort.drop(desc_sort.columns[-2:], axis=1, inplace=True)\n asc_sort.drop(asc_sort.columns[-2:], axis=1, inplace=True)\n\n # reset index and drop existing index\n desc_sort.reset_index(drop=True, inplace=True)\n asc_sort.reset_index(drop=True, inplace=True)\n\n unsorted_df.to_csv(src + 'median_returns_unsorted.csv')\n desc_sort.to_csv(src + 'median_returns_largest.csv')\n asc_sort.to_csv(src + 'median_returns_smallest.csv')\n asset_values.to_csv(src + 'ending_values.csv')\n assets_ror.to_csv(src + 'ending_values_ror.csv')\n terminal_df.to_csv(src + 'terminal_values.csv')\n print(\"simulation completed....\")","def main():\n # Data processing and parsing\n mp_df = pd.read_csv(\"melting_points.csv\")\n bp_df = pd.read_csv(\"boiling_points.csv\")\n data_processing(mp_df)\n data_processing(bp_df)\n # Plots scatterplots of our variables with the boiling and melting point\n plot_scatter('Mass', 'T_exp', mp_df, 'Molecular Weight', 'Melting Point')\n plot_scatter('Mass', 'T_exp', bp_df, 'Molecular Weight', 'Boiling Point')\n plot_scatter('Atom_counts', 'T_exp', mp_df, 'Number of Atoms',\n 'Melting Point')\n \n # Creates models and prints statistics using simply mass and atom counts\n simple_parameters = ['Mass', 'Atom_counts', 'T_exp']\n model = ml_df(mp_df, simple_parameters, 0.2)\n plot_T(model, simple_parameters, \"simple_melting.png\", 'Melting Point')\n slope_average(mp_df, simple_parameters, 0.2, 100)\n model2 = ml_df(bp_df, simple_parameters, 0.2)\n plot_T(model2, simple_parameters, 'simple_boiling.png', 'Boiling Point')\n slope_average(bp_df, simple_parameters, 0.2, 100)\n # Creates models and prints statistics using more variables\n complex_parameters = [\"Mass\", \"Atom_counts\", \"C\", \"H\", \"Acid\", \"Alcohol\",\n \"Unsaturation\", \"T_exp\"]\n model3 = ml_df(mp_df, complex_parameters, 0.2)\n plot_T(model3, complex_parameters, \"complex_melting.png\", 'Melting Point')\n slope_average(mp_df, complex_parameters, 0.2, 100)\n model4 = ml_df(bp_df, complex_parameters, 0.2)\n plot_T(model4, complex_parameters, \"complex_boiling.png\", 'Boiling Point')\n slope_average(bp_df, complex_parameters, 0.2, 100)\n # Creates models and prints statistics for our model using all available variables\n complex_parameters2 = [\"Mass\", \"Atom_counts\", \"C\", \"H\", 'O', 'N', 'F',\n 'Cl', 'Br', 'I', 'S', 'Si', 'Halide', \"Acid\", \"Alcohol\",\n \"Unsaturation\", \"T_exp\"]\n model = ml_df(mp_df, complex_parameters2, 0.2)\n plot_T(model, complex_parameters2, \"more_complex_melting.png\",\n 'Melting Point')\n slope_average(mp_df, complex_parameters2, 0.2, 100)\n # Tries a few different regressor models\n slope_average(mp_df, complex_parameters2, 0.2, 100, LinearRegression())\n slope_average(bp_df, complex_parameters2, 0.2, 100, RandomForestRegressor())","def build_and_run_(spec):\n opt = spec['opt']\n print \"pool starting \", opt\n\n # lenght of simulation \n tf = float(opt.get('tf', 100))\n\n # model # coupling function # connectivity \n simargs = {}\n for mod, key in [(models, 'model'), \n (connectivity, 'connectivity'),\n (coupling, 'coupling')]:\n simargs[key] = build_sim_part(mod, opt[key])\n\n # noise # integrator \n optint = opt['integrator']\n if 'noise' in optint:\n optint['noise'] = build_sim_part(noise, optint['noise'])\n simargs['integrator'] = build_sim_part(integrators, optint)\n\n # monitors \n if not type(opt['monitors']) in (list,):\n opt['monitors'] = [opt['monitors']]\n simargs['monitors'] = []\n for mon in opt['monitors']:\n simargs['monitors'].append(build_sim_part(monitors, mon))\n\n # stimulus \n # NotImplemented\n\n # simulator \n sim = simulator.Simulator(**simargs)\n sim.configure()\n\n # TODO open HDF5 first, figure out correct sizes, etc\n\n # loop, writing data to h5\n ts = [[] for _ in opt['monitors']]\n ys = [[] for _ in opt['monitors']]\n for i, all_monitor_data in enumerate(sim(tf)):\n for j, mondata in enumerate(all_monitor_data):\n if not mondata is None:\n t, y = mondata\n ts[j].append(t)\n ys[j].append(y)\n\n # write data to hdf5 file\n path = os.path.abspath(opt.get('wd', './'))\n h5fname = os.path.join(path, \"tvb_%s.h5\" % (spec['md5sum'], ))\n h5 = h5py.File(h5fname, 'w')\n\n for i, (mon, (t, y)) in enumerate(zip(simargs['monitors'], zip(ts, ys))):\n mname = \"mon_%d_%s\" % (i, mon.__class__.__name__)\n g = h5.create_group(mname)\n g.create_dataset('ts', data=t)\n g.create_dataset('ys', data=y)\n\n h5.close()\n\n # return filename\n print \"pool finished\", opt\n return h5fname","def portfolio_performance(returns,weights):\r\n print('Calculating Portfolio Performance')\r\n # returns=target_asset_port_data_attributes['component_returns']\r\n # weights =target_asset_port_data_attributes['effective_weights']\r\n\r\n component_returns= returns\r\n compnent_weights = pd.DataFrame(data=np.nan,index= component_returns.index,columns=component_returns.columns)\r\n compnent_weights.loc[weights.index,:] = weights\r\n\r\n portfolio_dates = component_returns.index\r\n components = component_returns.columns\r\n\r\n # pre-allocate\r\n BoP_df = pd.DataFrame(data=np.nan,index=portfolio_dates,columns=components)\r\n EoP_df = pd.DataFrame(data=np.nan,index=portfolio_dates,columns=components)\r\n PnL_df = pd.DataFrame(data=np.nan,index=portfolio_dates,columns=components)\r\n portfolio_BoP = pd.DataFrame(data=np.nan,index=portfolio_dates,columns=['Portfolio BoP'])\r\n portfolio_EoP = pd.DataFrame(data=np.nan,index=portfolio_dates,columns=['Portfolio EoP'])\r\n portfolio_PnL = pd.DataFrame(data=np.nan,index=portfolio_dates,columns=['Portfolio PnL'])\r\n \r\n portfolio_index = pd.DataFrame(data=np.nan,index=portfolio_dates,columns=['Index'])\r\n previous_index_value = np.int64(1)\r\n\r\n pre_date = portfolio_dates[0]\r\n # set BoP to start weights\r\n for date,row in component_returns.iterrows():\r\n # print(date)\r\n # 1st date\r\n if date == portfolio_dates[0]:\r\n BoP_df.loc[date] = compnent_weights.iloc[0,:]\r\n EoP_df.loc[date] = BoP_df.loc[date] * (1+component_returns.loc[date])\r\n PnL_df.loc[date] = EoP_df.loc[date].subtract(BoP_df.loc[date])\r\n\r\n portfolio_BoP.loc[date] = BoP_df.loc[date].sum()\r\n portfolio_EoP.loc[date] = EoP_df.loc[date].sum()\r\n portfolio_PnL.loc[date] = PnL_df.loc[date].sum()\r\n\r\n portfolio_index.loc[date] = np.nansum([previous_index_value,portfolio_PnL.loc[date].values])\r\n previous_index_value = portfolio_index.loc[date]\r\n pre_date = date\r\n\r\n # after first date\r\n else:\r\n BoP_df.loc[date] = EoP_df.loc[pre_date]\r\n # weights override\r\n if date in compnent_weights.index:\r\n none_NaN_index = ~compnent_weights.loc[date].isnull()\r\n if not compnent_weights.loc[date][none_NaN_index].empty:\r\n tmp_sum = BoP_df.loc[date].sum()\r\n BoP_df.loc[date][none_NaN_index.values] = (compnent_weights.loc[date][none_NaN_index.values].values)*tmp_sum\r\n\r\n \r\n EoP_df.loc[date] = BoP_df.loc[date] * (1+component_returns.loc[date])\r\n PnL_df.loc[date] = EoP_df.loc[date].subtract(BoP_df.loc[date])\r\n\r\n portfolio_BoP.loc[date] = BoP_df.loc[date].sum()\r\n portfolio_EoP.loc[date] = EoP_df.loc[date].sum()\r\n portfolio_PnL.loc[date] = PnL_df.loc[date].sum()\r\n \r\n portfolio_index.loc[date] = np.nansum([previous_index_value,portfolio_PnL.loc[date].values])\r\n previous_index_value = portfolio_index.loc[date]\r\n pre_date = date\r\n\r\n\r\n portfolio_returns = portfolio_index.pct_change(1) \r\n portfolio_returns.columns = ['Returns']\r\n\r\n portfolio_index\r\n perf = portfolio_index.calc_stats()\r\n \r\n output = pd.Series(data = [perf,PnL_df,portfolio_index,portfolio_BoP,portfolio_EoP,BoP_df], index=['Portfolio Perf','Component PnL','portfolio_index','portfolio_BoP','portfolio_EoP','BoP_df'])\r\n return output","def run():\n trials = 100\n\n multipliers = [0.25, 0.3, 0.35, 0.5, 0.75, 1, 1.25, 1.45, 1.5, 1.55, 1.6] # Coefficients for learning rate\n\n mean_penalty = []\n median_penalty = []\n std_penalty = []\n\n mean_trial_time = []\n median_trial_time = []\n std_trial_time = []\n\n mean_success_rate = []\n median_success_rate = []\n std_success_rate = []\n\n for m in multipliers:\n all_penalties = [] # All penalties from trail sets\n all_average_trial_time = []\n all_success_rates = []\n\n for i in range(0, 20):\n # print \"Trial set:\", i\n # Set up environment and agent\n e = Environment() # create environment (also adds some dummy traffic)\n agent = e.create_agent(LearnerAgent) # create agent\n agent.mult = m\n e.set_primary_agent(agent, enforce_deadline=True) # specify agent to track\n\n # Now simulate it\n sim = Simulator(e, update_delay=0.0, display=False) # create simulator (uses pygame when display=True, if available)\n\n sim.run(n_trials=trials) # run for a specified number of trials\n\n all_penalties.append(agent.all_trails_penalties)\n all_average_trial_time.append(agent.time/float(trials))\n all_success_rates.append(float(trials-agent.aborted_trials)/trials)\n\n mean_penalty.append(np.mean(all_penalties))\n median_penalty.append(np.median(all_penalties))\n std_penalty.append(np.std(all_penalties))\n\n mean_trial_time.append(np.mean(all_average_trial_time))\n median_trial_time.append(np.median(all_average_trial_time))\n std_trial_time.append(np.std(all_average_trial_time))\n\n mean_success_rate.append(np.mean(all_success_rates))\n median_success_rate.append(np.median(all_success_rates))\n std_success_rate.append(np.std(all_success_rates))\n\n for i in range(0, len(multipliers)):\n print \"\"\n print \"Multiplier:\", multipliers[i]\n print \"\"\n print \"Mean penalty per {} trials:\".format(trials), mean_penalty[i]\n print \"Median penalty per {} trials:\".format(trials), median_penalty[i]\n print \"Std.Dev. penalty per {} trials:\".format(trials), std_penalty[i]\n\n print \"\"\n print \"Mean trial time:\", mean_trial_time[i]\n print \"Median trial time:\", median_trial_time[i]\n print \"Std.Dev. trial time:\", std_trial_time[i]\n\n print \"\"\n print \"Mean success rate per {} trials:\".format(trials), mean_success_rate[i]\n print \"Median success rate per {} trials:\".format(trials), median_success_rate[i]\n print \"Std.Dev. success rate per {} trials:\".format(trials), std_success_rate[i]","def __init__(\n self, problem_results: ProblemResults, demand_instruments: Array, supply_instruments: Array,\n inverse_covariance_matrix: Array, expected_xi_jacobian: Array, expected_omega_jacobian: Array,\n expected_prices: Array, expected_shares: Array, start_time: float, end_time: float, draws: int,\n iteration_stats: Sequence[Mapping[Hashable, SolverStats]]) -> None:\n self.problem_results = problem_results\n self.demand_instruments = demand_instruments\n self.supply_instruments = supply_instruments\n self.inverse_covariance_matrix = inverse_covariance_matrix\n self.expected_xi_by_theta_jacobian = expected_xi_jacobian\n self.expected_omega_by_theta_jacobian = expected_omega_jacobian\n self.expected_prices = expected_prices\n self.expected_shares = expected_shares\n self.computation_time = end_time - start_time\n self.draws = draws\n unique_market_ids = problem_results.problem.unique_market_ids\n self.fp_converged = np.array(\n [[m[t].converged if m else True for m in iteration_stats] for t in unique_market_ids], dtype=np.bool_\n )\n self.fp_iterations = np.array(\n [[m[t].iterations if m else 0 for m in iteration_stats] for t in unique_market_ids], dtype=np.int64\n )\n self.contraction_evaluations = np.array(\n [[m[t].evaluations if m else 0 for m in iteration_stats] for t in unique_market_ids], dtype=np.int64\n )\n\n # construct default supply and demand shifter formulations\n self.supply_shifter_formulation = self.demand_shifter_formulation = None\n if self.problem_results.problem.K3 > 0:\n assert self.problem_results.problem.product_formulations[0] is not None\n assert self.problem_results.problem.product_formulations[2] is not None\n\n X1_terms = self.problem_results.problem.product_formulations[0]._terms\n X3_terms = self.problem_results.problem.product_formulations[2]._terms\n X1_expressions = self.problem_results.problem.product_formulations[0]._expressions\n X3_expressions = self.problem_results.problem.product_formulations[2]._expressions\n\n supply_shifters = set()\n for term, expression in zip(X3_terms, X3_expressions):\n if all(str(s) != 'shares' for s in expression.free_symbols) and term.name() != 'Intercept':\n supply_shifters.add(term.name())\n\n demand_shifters = set()\n for term, expression in zip(X1_terms, X1_expressions):\n if all(str(s) != 'prices' for s in expression.free_symbols) and term.name() != 'Intercept':\n demand_shifters.add(term.name())\n\n if supply_shifters - demand_shifters:\n supply_shifter_formula = ' + '.join(sorted(supply_shifters - demand_shifters))\n self.supply_shifter_formulation = Formulation(f'{supply_shifter_formula} - 1')\n\n if demand_shifters - supply_shifters:\n demand_shifter_formula = ' + '.join(sorted(demand_shifters - supply_shifters))\n self.demand_shifter_formulation = Formulation(f'{demand_shifter_formula} - 1')","def start_trading(self):\n backtest_result_dir = os.path.join(self.result_dir, str(self.backtest_date))\n os.mkdir(backtest_result_dir)\n backtest_scores_path = os.path.join(backtest_result_dir, 'scores.csv')\n # all_backtest_scores_exists = os.path.isfile(self.all_backtest_scores_path)\n\n last_backtest_scores = open(self.last_backtest_scores_path, \"w\")\n backtest_scores = open(backtest_scores_path, \"w\")\n\n\n fieldnames = [ 'Backtest Name', 'Backtest Date', 'Strategy', 'Start Date', 'End Date', 'Instrument(s)', 'Params'] + \\\n ['Number of signals', 'Number of orders', 'Number of trades', 'Total USD Return', 'Total BTC Return',\n 'Sharpe Ratio', 'BTC Sharpe Ratio', 'Max Drawdown', 'BTC Max Drawdown', 'Drawdown Duration', 'BTC Drawdown Duration',\n 'Monthly BTC Return', 'Yearly BTC Return', 'Avg. winning trade', 'Median duration', 'Avg. losing trade', 'Median returns winning', 'Largest losing trade',\n 'Gross loss', 'Largest winning trade', 'Avg duration', 'Avg returns losing', 'Median returns losing', 'Profit factor',\n 'Winning round trips', 'Percent profitable', 'Total profit', 'Shortest duration', 'Median returns all round trips',\n 'Losing round trips', 'Longest duration', 'Avg returns all round trips', 'Gross profit', 'Avg returns winning',\n 'Total number of round trips', 'Ratio Avg. Win:Avg. Loss', 'Avg. trade net profit', 'Even round trips',\n 'Configuration Filename', 'Leverage']\n\n try:\n with last_backtest_scores as a, backtest_scores as b:\n writer_a = csv.DictWriter(a, fieldnames=fieldnames)\n writer_b = csv.DictWriter(b, fieldnames=fieldnames)\n writer_a.writeheader()\n writer_b.writeheader()\n\n # if not all_backtest_scores_exists:\n # writer_c.writeheader()\n\n for i, (start, end) in enumerate(zip(self.start_dates, self.end_dates)):\n for j, instruments in enumerate(self.instrument_list):\n for k, params in enumerate(self.strategy_params):\n num_backtest = i * (self.num_instruments * self.num_params + 1) + j * (self.num_params + 1) + k + 1\n print(\"Strategy %s out of %s\" % (num_backtest, self.num_backtests))\n self._generate_trading_instances(start, end, instruments, params)\n self._run()\n stats = self._process_results()\n\n general_stats = stats['general']\n pnl_stats = stats['pnl']['All trades'].to_dict()\n summary_stats = stats['summary']['All trades'].to_dict()\n duration_stats = stats['duration']['All trades'].to_dict()\n return_stats = stats['returns']['All trades'].to_dict()\n params_value = '/'.join([ '{}:{}'.format(item[0], item[1]) for item in params.items() ])\n\n row = {\n 'Backtest Name': self.backtest_name,\n 'Backtest Date': self.backtest_date,\n 'Strategy': self.strategy.strategy_name,\n 'Start Date': start,\n 'End Date': end,\n 'Instrument(s)': format_instrument_list(instruments),\n 'Params': params_value,\n 'Number of signals': self.signals,\n 'Number of orders': self.orders,\n 'Number of trades': self.fills,\n **general_stats,\n **pnl_stats,\n **summary_stats,\n **duration_stats,\n **return_stats,\n 'Configuration Filename': self.configuration_filename,\n 'Leverage': self.default_leverage\n }\n\n writer_a.writerow(row)\n writer_b.writerow(row)\n\n except IOError:\n print('I/O Error')\n\n all_scores_csv = pd.concat([ pd.read_csv(self.all_backtest_scores_path), pd.read_csv(backtest_scores_path)])\n all_scores_csv.to_csv(self.all_backtest_scores_path, columns=fieldnames, index=False, encoding='utf-8-sig')\n\n self._open_results_in_excel()","def grid_evaluation(param_list_one, param_list_two, param_eval, n_trials=16, \n aggr_method=np.mean, save_dir='data/', file_name='grid evaluation',\n save_to_disk=True, save_each=1000, chunksize=1.):\n \n \n if not list(param_list_two): # If `param_list_two` is empty\n params = param_list_one\n grid_shape = (len(param_list_one),)\n is_really_grid = False\n \n else:\n params = list(itertools.product(param_list_one, param_list_two))\n grid_shape = (len(param_list_one), len(param_list_two))\n is_really_grid = True\n \n def grid_fun(point): # Function to compute for each grid point\n \n trial_out = np.nan * np.ones((n_trials,))\n \n for i in np.arange(n_trials):\n \n if is_really_grid:\n trial_out[i] = param_eval(point[0], point[1])\n else: # If `param_list_two` is empty\n trial_out[i] = param_eval(point)\n \n return aggr_method(trial_out)\n \n n_grid_pts = len(params)\n \n # Recording procedure\n def record_experiment(grid):\n now = datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n save_path = save_dir + now + ' ' + file_name + '.pkl'\n experiment = {\n 'date': now,\n 'rows': param_list_one,\n 'cols': param_list_two,\n 'n_trials': n_trials,\n 'grid': np.reshape(grid, grid_shape),\n 'path': save_path\n }\n if save_to_disk:\n utils.save_obj(experiment, save_path)\n return experiment\n \n # Set a pool of workers\n nb_workers = min(mp.cpu_count(), 24)\n print('Working with {} processes.'.format(nb_workers))\n pool = mp.Pool(nb_workers)\n \n # Iterate `grid_fun` across workers\n it = pool.imap(grid_fun, params, chunksize=chunksize)\n grid = np.nan * np.ones((n_grid_pts,))\n\n for idx, val in enumerate(tqdm(it, total=n_grid_pts)):\n grid[idx] = val\n \n # Make sure that we save after each couple of iterations\n if (idx >= save_each) and (idx % save_each == 0): \n experiment = record_experiment(grid)\n \n # Close pool\n pool.close()\n pool.join()\n \n experiment = record_experiment(grid)\n \n return experiment","def __init__(self):\n if cons.is_boa == True:\n algorithm = \"XCS/BOA\"\n else:\n algorithm = \"XCS\"\n print(algorithm + \": Running \" + str( cons.multiple_runs ) + \" times\")\n #Global Parameters-------------------------------------------------------------------------------------\n self.population = None # The rule population (the 'solution/model' evolved by XCS)\n self.learn_track = None # Output file that will store tracking information during learning\n #self.previousState = None # states received from environment from last time step\n\n for count in range( cons.multiple_runs ):\n print(\"Time \" + str( count ) + \": Initializing Algorithm...\")\n #-------------------------------------------------------\n # POPULATION REBOOT - Begin XCS learning from an existing saved rule population\n #-------------------------------------------------------\n if cons.do_population_reboot:\n self.populationReboot( count )\n #-------------------------------------------------------\n # NORMAL XCS - Run XCS from scratch on given data\n #-------------------------------------------------------\n else:\n try:\n self.learn_track = open(cons.out_file+str(count)+'_LearnTrack.txt','w')\n except Exception as inst:\n print(type(inst))\n print(inst.args)\n print(inst)\n print('cannot open', cons.out_file+str(count)+'_LearnTrack.txt')\n raise\n else:\n self.learn_track.write(\"Explore_Iteration\\tMacroPopSize\\tMicroPopSize\\tAccuracy_Estimate\\tAveGenerality\\tExpRules\\tTime(min)\\n\")\n\n # Instantiate Population---------\n self.population = ClassifierSet()\n self.explore_iter = 0\n self.tracked_results = [0] * cons.tracking_frequency\n self.exploit_iters = 0\n\n #Run the XCS/BOA algorithm--------------------------------------------------------------------------\n self.boa = None\n bcons.setCycleTime( int( cons.N/2 ) )\n self.run_XCS_BOA( count )","def run_simulation(**kwargs):\n print(\"executing run_simulation() in file\", __file__)\n print(\"got the dictionary kwargs =\", kwargs)\n\n # HERE is where you would usually run your simulation (e.g. DMRG).\n # simulate some heavy calculations:\n for i in range(30):\n print(\"step \", i, flush=True) # (remove `flush=True` for Python 2)\n # the flush=True makes the output appear immediately\n time.sleep(5)\n\n results = {'kwargs': kwargs, 'example_data': np.random.random((2, 2))}\n\n output_filename = kwargs['output_filename']\n print(\"save results to \", output_filename)\n with open(output_filename, 'wb') as f:\n pickle.dump(results, f)","def run(self,step=2,\n sizePop=100,\n infoFields=['migrate_to','fitness'],\n recombination_rate = 0.00375,\n migration_rate = 0.01,\n mutation_rate = [0.00000001],\n subPopNames = ['x','y','z','w'],\n alleleNames = ['A','B'],\n s1 = 0.1,\n burnin=50,\n **kargs):\n\n self.reset()\n pop=sim.Population(size=[sizePop]*self.numPop, loci=self.loci, lociPos=list(range(self.dist, (self.dist*self.loci)+1,self.dist)), subPopNames=subPopNames, infoFields=infoFields)\n \n simu = sim.Simulator(pop)\n print(\"The simulation has started\")\n t1 = time.time()\n\n\n mutate_snps=range(0,50)+range(51,101)\n\n # define the initialization of each loci based the beta distribution where a and b parameters are allele frequencies from noncoding human regions\n snps=[0.14, 0.11, 0.17, 0.11, 0.32, 0.33, 0.21, 0.11, 0.11, 0.28, 0.11, 0.12, 0.8, 0.66, 0.74, 0.68, 0.66, 0.77, 0.77, 0.76, 0.77, 0.74, 0.72, 0.11, 0.73, 0.72, 0.72, 0.72, 0.54, 0.17, 0.78, 0.64, 0.78, 0.2, 0.24, 0.25, 0.78, 0.66, 0.2, 0.14, 0.75, 0.16, 0.72, 0.18, 0.77, 0.42, 0.34, 0.7, 0.17, 0.14, 0.2, 0.46, 0.13, 0.26, 0.16, 0.13, 0.14, 0.24, 0.18, 0.36, 0.71, 0.27, 0.28, 0.25, 0.25, 0.3, 0.19, 0.14, 0.16, 0.3, 0.39, 0.16, 0.24, 0.32, 0.11, 0.18, 0.48, 0.31, 0.21, 0.15, 0.34, 0.71, 0.33, 0.18, 0.71, 0.13, 0.23, 0.2, 0.22, 0.23, 0.16, 0.23, 0.23, 0.22, 0.24, 0.82, 0.36, 0.37, 0.72, 0.16, 0.14]\n self.initFreq=[]\n\n \n for i in range(len(snps)):\n alpha=float(4*sizePop*migration_rate*snps[i])\n bhta=float(4*sizePop*migration_rate*(1-snps[i])) \n p=numpy.random.beta(alpha,bhta)\n while (p>=0.9 or p<=0.1):\n p=numpy.random.beta(alpha,bhta)\n \n print \" SNP {snp} with alpha {alpha}, bhta {bhta} and frequency {p}\".format(snp=i, alpha=alpha, bhta=bhta, p=p)\n self.initFreq.append(p)\n\n simu.evolve(\n \n initOps=[sim.InitGenotype(freq=[self.initFreq[i], 1-self.initFreq[i]], loci=i) for i in range(len(snps))],\n \n\n # initialize the sex and select the 50 loci (parents)\n preOps = [sim.InitSex(maleProp=0.5,at=[0]),\n\n # initialize the genotype of locus 50 at generation 0 (in the beginning of the simulation)\n sim.PyOperator(self.genotypeBegin,at=[0]),\n \n # Wait 50 generations for the system to reach equilibrium\n # Then, change the the genotype of locus 50 at generation 50 by inserting a single copy of allele 0 in one individual \n sim.PyOperator(self.genotypeAfter,at=[50]),\n\n # function that carries out the selection proccess\n sim.MaSelector(loci=50,wildtype=0,fitness=[1+s1, 1+s1/2, 1],begin=50, end=-1,subPops=1)],\n\n # recombination\n matingScheme=sim.RandomMating(ops=[\n sim.Recombinator(rates=recombination_rate)]),\n \n # mutation and migration of offsprings\n postOps = [\n\n \n sim.SNPMutator(u=mutation_rate,loci=mutate_snps),\n \n # call function to calculate Fst and check for equilibrium state\n sim.PyOperator(self.calcFst,step=step),\n\n #migration\n # Here we define an island model, but this can easily be changed.\n # For more information about the migration models, please look in the documentation of SimuPOP here http://simupop.sourceforge.net/manual_svn/build/userGuide_ch7_sec3.html\n sim.Migrator(sim.utils.migrIslandRates(migration_rate,self.numPop)),\n \n # call function to save the allele frequencies\n sim.PyOperator(self.checkAlleles, step=step, param = subPopNames),\n \n \n # check if locus 50 is lost due to genetic drift. If yes, we terminate the simulation\n sim.Stat(alleleFreq=50,step=step,subPops=1,begin=50,end=-1),\n sim.TerminateIf('alleleFreq[50][0] == 0',step=step,begin=50,end=-1),\n \n # check the progress of the simulation\n sim.PyEval('\"Gen: %d\" % gen',step=step),\n sim.PyOutput('\\n',step=step),\n \n ],\n gen=self.Gen\n \n )\n \n \n t2 = time.time()\n print \"simulation took\", t2-t1, \"seconds.\"","def main():\n\n global time\n global MAX_ITER\n global output_dt\n\n # ==========================================================================\n # Initialize\n # - Returns a dictionary of dictionaries, which are the parameters from the\n # configuration file (by section) and from the command line (listed as\n # section 'CommandLine')\n params = init()\n\n # ==========================================================================\n # Evolution loop\n\n prev_write = -1\n\n for n_iter in xrange(MAX_ITER):\n\n # Exceeded maximum time\n if time >= tmax:\n break\n\n # Unexpected exit? (e.g. like \"touch .dump_restart\")\n # TODO\n\n # Write output\n do_write = False\n if output_dt > 0:\n curr_write = int(math.floor(time / output_dt))\n if curr_write > prev_write:\n do_write = True\n prev_write = curr_write\n if do_write: # to allow for a condition based on step number\n Grid.write_data(n_iter)\n\n # Boundary condition fill\n Grid.fill_boundary_conditions()\n\n # Compute step size\n dt = compute_time_step()\n\n # Evolve a single step\n Hydro.one_step(dt)\n\n # Update time\n time = time + dt\n\n # End evolution loop\n\n # ==========================================================================\n # Finalize\n\n # Write final output\n Grid.write_data(n_iter+1)\n finalize()","def test(self, num_decisions=1000):\n all_grid_states = []\n all_grid_rewards = []\n\n all_grid_states_cluster = []\n all_surrounding_states_cluster = []\n\n all_system_rewards = []\n all_system_states = []\n all_system_states_cluster = []\n\n all_actions = []\n all_dissipation = []\n\n os.system(\"mkdir \" + self.folder_name + \"Test/\")\n filename = self.folder_name + \"Test/\" + \"TEST.h5\"\n self.system.reset_context(filename)\n tag = \"_test_init\"\n self.system.run_decorrelation(20)\n\n grid_dist, surrounding_dist, _, _, _, _ = self.system.get_state_reward()\n state = self._get_state(grid_dist, surrounding_dist)\n\n all_dissipation.append(self.system.get_dissipation())\n for i in range(num_decisions):\n action_index = self._get_action(state, episode=10000)\n tag = \"_test_\" + str(i)\n actions = [self.all_actions[i] for i in action_index]\n all_actions.append(actions)\n self.system.update_action(actions)\n system_states, system_rewards, system_states_cluster = self.system.run_step(\n is_detailed=True, tag=tag)\n\n grid_dist, surrounding_dist, grid_reward, surrounding_reward, grid_states_cluster, surrounding_states_cluster = self.system.get_state_reward()\n\n # The \"grid states\" and dissipation are recorded at the end of a decision\n # Dissipation here is total entropy production (not epr)\n # Actions are recorded at the beginning of the decision\n\n state = self._get_state(grid_dist, surrounding_dist)\n reward = self._get_reward(grid_reward, surrounding_reward)\n\n all_grid_states.append(state)\n all_grid_rewards.append(reward)\n\n all_grid_states_cluster.append(grid_states_cluster)\n all_surrounding_states_cluster.append(surrounding_states_cluster)\n\n all_dissipation.append(self.system.get_dissipation())\n\n # The \"System States\" are recorded every 0.25 seconds. Excludes 0th second\n all_system_states.append(system_states)\n # Just to have a 1D array use extend\n all_system_rewards.append(system_rewards)\n all_system_states_cluster.append(system_states_cluster)\n\n if (i % 100 == 99):\n np.save(self.folder_name + \"grid_rewards\",\n np.array(all_grid_rewards))\n np.save(self.folder_name + \"grid_states\",\n np.array(all_grid_states, dtype=object))\n np.save(self.folder_name + \"grid_states_cluster\",\n np.array(all_grid_states_cluster, dtype=object))\n\n np.save(self.folder_name + \"surrounding_states_cluster\",\n np.array(all_surrounding_states_cluster))\n\n np.save(self.folder_name + \"actions\", np.array(all_actions))\n\n np.save(self.folder_name + \"dissipation\",\n np.array(all_dissipation))\n np.save(self.folder_name + \"system_states\",\n np.array(all_system_states))\n np.save(self.folder_name + \"system_states_cluster\",\n np.array(all_system_states_cluster))\n np.save(self.folder_name + \"system_rewards\",\n np.array(all_system_rewards))","def main(args, base_dir):\n for i in range(args.n_training):\n # value of the next seed\n seed = args.seed + i\n\n # The time when the current experiment started.\n now = strftime(\"%Y-%m-%d-%H:%M:%S\")\n\n # Create a save directory folder (if it doesn't exist).\n if args.log_dir is not None:\n dir_name = args.log_dir\n else:\n dir_name = os.path.join(base_dir, '{}/{}'.format(\n args.env_name, now))\n ensure_dir(dir_name)\n\n # Get the policy class.\n if args.alg == \"TD3\":\n from hbaselines.multiagent.td3 import MultiFeedForwardPolicy\n elif args.alg == \"SAC\":\n from hbaselines.multiagent.sac import MultiFeedForwardPolicy\n elif args.alg == \"PPO\":\n from hbaselines.multiagent.ppo import MultiFeedForwardPolicy\n elif args.alg == \"TRPO\":\n from hbaselines.multiagent.trpo import MultiFeedForwardPolicy\n else:\n raise ValueError(\"Unknown algorithm: {}\".format(args.alg))\n\n # Get the hyperparameters.\n hp = get_hyperparameters(args, MultiFeedForwardPolicy)\n\n # add the seed for logging purposes\n params_with_extra = hp.copy()\n params_with_extra['seed'] = seed\n params_with_extra['env_name'] = args.env_name\n params_with_extra['policy_name'] = \"MultiFeedForwardPolicy\"\n params_with_extra['algorithm'] = args.alg\n params_with_extra['date/time'] = now\n\n # Add the hyperparameters to the folder.\n with open(os.path.join(dir_name, 'hyperparameters.json'), 'w') as f:\n json.dump(params_with_extra, f, sort_keys=True, indent=4)\n\n run_exp(\n env=args.env_name,\n policy=MultiFeedForwardPolicy,\n hp=hp,\n dir_name=dir_name,\n evaluate=args.evaluate,\n seed=seed,\n eval_interval=args.eval_interval,\n log_interval=args.log_interval,\n save_interval=args.save_interval,\n initial_exploration_steps=args.initial_exploration_steps,\n ckpt_path=args.ckpt_path,\n )","def BayesPaperStats(maxIters, numRuns):\n\n comm = MPI.COMM_WORLD\n rank = comm.Get_rank()\n\n assert comm.Get_size() == numRuns, \"Please ensure there is one process running per run i.e \" + str(numRuns) + \" processes.\"\n \n problemBounds = {\"Bfield\": choco.uniform(10, 1300), \"T\": choco.uniform(50, 230), \"Btheta\": choco.uniform(0, 90), \"Etheta\": choco.uniform(0, 90)}\n\n # The target for each algorithm. This was determined by using the values in the literature, so there is clearly some deviation either due to the detuning or computation.\n globalFoM = 1.033\n\n if rank == 0:\n timeList = []\n iterationList = []\n\n # Set up the database for the chocolate optimiser.\n connection = choco.SQLiteConnection(\"sqlite:///bayes_paper_\" + str(rank) + \"_db.db\")\n\n # Define which solver will be used.\n solver = choco.Bayes(connection, problemBounds, utility_function = \"ei\", n_bootstrap = int(np.ceil(maxIters/10)), clear_db = True)\n\n # Clear the database. TODO: To do this?\n connection.clear()\n\n # Start timing.\n startTime = time.time()\n timeElapsed = None\n iterationSuccess = None\n\n # Start optimisation.\n for iteration in range(maxIters):\n\n # Make one suggestion.\n try:\n token, nextParams = solver.next()\n except:\n print(\"Error suggesting a new point. Here are the last set of parameters sampled:\")\n print(str(nextParams))\n print(\"Iteration number: \" + str(iteration))\n continue\n\n # Check what FoM this gives. Go negative as this is a minimisation routine.\n fEval = abs(FitnessPaper(**nextParams))\n\n # Update best FoM.\n if fEval >= globalFoM:\n # The algorithm has managed to surpass or equal the paper value.\n iterationSuccess = iteration\n timeElapsed = time.time() - startTime\n \n if rank == 0:\n iterationList.append(iterationSuccess)\n timeList.append(timeElapsed)\n\n break\n \n # Tell the optimiser about the result.\n solver.update(token, fEval)\n\n # Run complete. Send results to main process. Tags are unique identifiers.\n if rank != 0:\n comm.send(timeElapsed, dest = 0, tag = 1)\n comm.send(iterationSuccess, dest = 0, tag = 2)\n\n # Wait for all the processes to end.\n comm.Barrier()\n\n if rank == 0:\n # Aggregate the data.\n for process in range(comm.Get_size() - 1):\n # Get the data.\n individualTime = None\n individualTime = comm.recv(individualTime, source = process + 1, tag = 1)\n\n individualIter = None\n individualIter = comm.recv(individualIter, source = process + 1, tag = 2)\n\n if individualIter is not None:\n # Both values must therefore be non-null.\n iterationList.append(individualIter)\n timeList.append(individualTime)\n\n avgRuntime = np.average(timeList)\n avgIters = np.average(iterationList)\n try:\n\n fastestTime = np.min(timeList)\n\n except ValueError:\n \n # List is empty.\n fastestTime = float('NaN')\n\n numSuccess = len(iterationList)\n successRate = numSuccess/numRuns\n\n print(\"Bayesian optimisation paper testing complete! Here are the stats:\")\n print(\"Number of successful runs: \" + str(numSuccess) + \" (Success rate of \" + str(successRate) + \")\")\n print(\"Average iterations required for success: \" + str(avgIters))\n print(\"Average time required for success: \" + str(avgRuntime))\n print(\"Fastest convergence time: \" + str(fastestTime))\n print(\"------------------------------------------------------------------------------------------------------------------\")\n \n return","def eval_calc(self):\n\n # Define the GmfGetter\n\n #for args_tag in range(len(self.args)-1):\n # Looping over all source models (Note: the last attribute in self.args is a monitor - so skipping it)\n \n from openquake.calculators import getters\n from openquake.baselib import general\n from openquake.hazardlib import const, calc, gsim\n from openquake.commands import dbserver as cdbs\n if self.vtag >= 12:\n from openquake.hazardlib.const import StdDev\n if self.vtag >= 12:\n from openquake.commonlib import datastore\n else:\n from openquake.baselib import datastore\n\n cur_getter = getters.GmfGetter(self.args[0][0], calc.filters.SourceFilter(\n self.dstore['sitecol'], self.dstore['oqparam'].maximum_distance), \n self.calculator.param['oqparam'], self.calculator.param['amplifier'], \n self.calculator.param['sec_perils'])\n\n # Evaluate each computer\n print('FetchOpenQuake: Evaluting ground motion models.')\n for computer in cur_getter.gen_computers(self.mon):\n # Looping over rupture(s) in the current realization\n sids = computer.sids\n #print('eval_calc: site ID sids = ')\n #print(sids)\n eids_by_rlz = computer.ebrupture.get_eids_by_rlz(\n cur_getter.rlzs_by_gsim)\n mag = computer.ebrupture.rupture.mag\n im_list = []\n data = general.AccumDict(accum=[])\n cur_T = self.event_info['IntensityMeasure'].get('Periods', None)\n for cur_gs, rlzs in cur_getter.rlzs_by_gsim.items():\n # Looping over GMPE(s)\n #print('eval_calc: cur_gs = ')\n #print(cur_gs)\n num_events = sum(len(eids_by_rlz[rlz]) for rlz in rlzs)\n if num_events == 0: # it may happen\n continue\n # NB: the trick for performance is to keep the call to\n # .compute outside of the loop over the realizations;\n # it is better to have few calls producing big arrays\n tmpMean = []\n tmpstdtot = []\n tmpstdinter = []\n tmpstdintra = []\n if self.vtag >= 12:\n mean_stds_all = computer.cmaker.get_mean_stds([computer.ctx], StdDev.EVENT)[0]\n for imti, imt in enumerate(computer.imts): \n # Looping over IM(s)\n #print('eval_calc: imt = ', imt)\n if str(imt) in ['PGA', 'PGV', 'PGD']:\n cur_T = [0.0]\n im_list.append(str(imt))\n imTag = 'ln' + str(imt)\n else:\n if 'SA' not in im_list:\n im_list.append('SA')\n imTag = 'lnSA'\n if isinstance(cur_gs, gsim.multi.MultiGMPE):\n gs = cur_gs[str(imt)] # MultiGMPE\n else:\n gs = cur_gs # regular GMPE\n try:\n if self.vtag >= 12:\n mean_stds = mean_stds_all[:, imti]\n num_sids = len(computer.sids)\n num_stds = len(mean_stds)\n if num_stds == 1:\n # no standard deviation is available\n # for truncation_level = 0 there is only mean, no stds\n if computer.correlation_model:\n raise ValueError('truncation_level=0 requires '\n 'no correlation model')\n mean = mean_stds[0]\n stddev_intra = 0\n stddev_inter = 0\n stddev_total = 0\n if imti == 0:\n tmpMean = mean\n tmpstdinter = np.concatenate((tmpstdinter, stddev_inter), axis=1)\n tmpstdintra = np.concatenate((tmpstdintra, stddev_intra), axis=1)\n tmpstdtot = stddev_total\n else:\n tmpMean = np.concatenate((tmpMean, mean), axis=0)\n tmpstdinter = np.concatenate((tmpstdinter, stddev_inter), axis=1)\n tmpstdintra = np.concatenate((tmpstdintra, stddev_intra), axis=1)\n tmpstdtot = np.concatenate((tmpstdtot, stddev_total), axis=0)\n elif num_stds == 2:\n # If the GSIM provides only total standard deviation, we need\n # to compute mean and total standard deviation at the sites\n # of interest.\n # In this case, we also assume no correlation model is used.\n # By default, we evaluate stddev_inter as the stddev_total\n\n if self.correlation_model:\n raise CorrelationButNoInterIntraStdDevs(\n self.correlation_model, gsim)\n\n mean, stddev_total = mean_stds\n stddev_total = stddev_total.reshape(stddev_total.shape + (1, ))\n mean = mean.reshape(mean.shape + (1, ))\n stddev_inter = stddev_total\n stddev_intra = 0\n if imti == 0:\n tmpMean = mean\n tmpstdinter = np.concatenate((tmpstdinter, stddev_inter), axis=1)\n tmpstdintra = np.concatenate((tmpstdintra, stddev_intra), axis=1)\n tmpstdtot = stddev_total\n else:\n tmpMean = np.concatenate((tmpMean, mean), axis=0)\n tmpstdinter = np.concatenate((tmpstdinter, stddev_inter), axis=1)\n tmpstdintra = np.concatenate((tmpstdintra, stddev_intra), axis=1)\n tmpstdtot = np.concatenate((tmpstdtot, stddev_total), axis=0)\n else:\n mean, stddev_inter, stddev_intra = mean_stds\n stddev_intra = stddev_intra.reshape(stddev_intra.shape + (1, ))\n stddev_inter = stddev_inter.reshape(stddev_inter.shape + (1, ))\n mean = mean.reshape(mean.shape + (1, ))\n if imti == 0:\n tmpMean = mean\n tmpstdinter = stddev_inter\n tmpstdintra = stddev_intra\n tmpstdtot = np.sqrt(stddev_inter * stddev_inter + stddev_intra * stddev_intra)\n else:\n tmpMean = np.concatenate((tmpMean, mean), axis=1)\n tmpstdinter = np.concatenate((tmpstdinter, stddev_inter), axis=1)\n tmpstdintra = np.concatenate((tmpstdintra, stddev_intra), axis=1)\n tmpstdtot = np.concatenate((tmpstdtot,np.sqrt(stddev_inter * stddev_inter + stddev_intra * stddev_intra)), axis=1)\n\n elif self.vtag == 11:\n # v11\n dctx = computer.dctx.roundup(\n cur_gs.minimum_distance)\n if computer.distribution is None:\n if computer.correlation_model:\n raise ValueError('truncation_level=0 requires '\n 'no correlation model')\n mean, _stddevs = cur_gs.get_mean_and_stddevs(\n computer.sctx, computer.rctx, dctx, imt, stddev_types=[])\n num_sids = len(computer.sids)\n if cur_gs.DEFINED_FOR_STANDARD_DEVIATION_TYPES == {const.StdDev.TOTAL}:\n # If the GSIM provides only total standard deviation, we need\n # to compute mean and total standard deviation at the sites\n # of interest.\n # In this case, we also assume no correlation model is used.\n if computer.correlation_model:\n raise CorrelationButNoInterIntraStdDevs(\n computer.correlation_model, cur_gs)\n\n mean, [stddev_total] = cur_gs.get_mean_and_stddevs(\n computer.sctx, computer.rctx, dctx, imt, [const.StdDev.TOTAL])\n stddev_total = stddev_total.reshape(\n stddev_total.shape + (1, ))\n mean = mean.reshape(mean.shape + (1, ))\n if imti == 0:\n tmpMean = mean\n tmpstdtot = stddev_total\n else:\n tmpMean = np.concatenate((tmpMean, mean), axis=0)\n tmpstdtot = np.concatenate((tmpstdtot, stddev_total), axis=0)\n else:\n mean, [stddev_inter, stddev_intra] = cur_gs.get_mean_and_stddevs(\n computer.sctx, computer.rctx, dctx, imt,\n [const.StdDev.INTER_EVENT, const.StdDev.INTRA_EVENT])\n stddev_intra = stddev_intra.reshape(\n stddev_intra.shape + (1, ))\n stddev_inter = stddev_inter.reshape(\n stddev_inter.shape + (1, ))\n mean = mean.reshape(mean.shape + (1, ))\n\n if imti == 0:\n tmpMean = mean\n tmpstdinter = stddev_inter\n tmpstdintra = stddev_intra\n tmpstdtot = np.sqrt(stddev_inter * stddev_inter + stddev_intra * stddev_intra)\n else:\n tmpMean = np.concatenate((tmpMean, mean), axis=1)\n tmpstdinter = np.concatenate((tmpstdinter, stddev_inter), axis=1)\n tmpstdintra = np.concatenate((tmpstdintra, stddev_intra), axis=1)\n tmpstdtot = np.concatenate((tmpstdtot,np.sqrt(stddev_inter * stddev_inter + stddev_intra * stddev_intra)), axis=1)\n\n else:\n # v10\n dctx = computer.dctx.roundup(\n cur_gs.minimum_distance)\n if computer.truncation_level == 0:\n if computer.correlation_model:\n raise ValueError('truncation_level=0 requires '\n 'no correlation model')\n mean, _stddevs = cur_gs.get_mean_and_stddevs(\n computer.sctx, computer.rctx, dctx, imt, stddev_types=[])\n num_sids = len(computer.sids)\n if cur_gs.DEFINED_FOR_STANDARD_DEVIATION_TYPES == {const.StdDev.TOTAL}:\n # If the GSIM provides only total standard deviation, we need\n # to compute mean and total standard deviation at the sites\n # of interest.\n # In this case, we also assume no correlation model is used.\n if computer.correlation_model:\n raise CorrelationButNoInterIntraStdDevs(\n computer.correlation_model, cur_gs)\n\n mean, [stddev_total] = cur_gs.get_mean_and_stddevs(\n computer.sctx, computer.rctx, dctx, imt, [const.StdDev.TOTAL])\n stddev_total = stddev_total.reshape(\n stddev_total.shape + (1, ))\n mean = mean.reshape(mean.shape + (1, ))\n if imti == 0:\n tmpMean = mean\n tmpstdtot = stddev_total\n else:\n tmpMean = np.concatenate((tmpMean, mean), axis=0)\n tmpstdtot = np.concatenate((tmpstdtot, stddev_total), axis=0)\n else:\n mean, [stddev_inter, stddev_intra] = cur_gs.get_mean_and_stddevs(\n computer.sctx, computer.rctx, dctx, imt,\n [const.StdDev.INTER_EVENT, const.StdDev.INTRA_EVENT])\n stddev_intra = stddev_intra.reshape(\n stddev_intra.shape + (1, ))\n stddev_inter = stddev_inter.reshape(\n stddev_inter.shape + (1, ))\n mean = mean.reshape(mean.shape + (1, ))\n\n if imti == 0:\n tmpMean = mean\n tmpstdinter = stddev_inter\n tmpstdintra = stddev_intra\n tmpstdtot = np.sqrt(stddev_inter * stddev_inter + stddev_intra * stddev_intra)\n else:\n tmpMean = np.concatenate((tmpMean, mean), axis=1)\n tmpstdinter = np.concatenate((tmpstdinter, stddev_inter), axis=1)\n tmpstdintra = np.concatenate((tmpstdintra, stddev_intra), axis=1)\n tmpstdtot = np.concatenate((tmpstdtot,np.sqrt(stddev_inter * stddev_inter + stddev_intra * stddev_intra)), axis=1)\n\n except Exception as exc:\n raise RuntimeError(\n '(%s, %s, source_id=%r) %s: %s' %\n (gs, imt, computer.source_id.decode('utf8'),\n exc.__class__.__name__, exc)\n ).with_traceback(exc.__traceback__)\n\n # initialize\n # NOTE: needs to be extended for gmpe logic tree\n gm_collector = []\n # collect data\n for k in range(tmpMean.shape[0]):\n imResult = {}\n if len(tmpMean):\n imResult.update({'Mean': [float(x) for x in tmpMean[k].tolist()]})\n if len(tmpstdtot):\n imResult.update({'TotalStdDev': [float(x) for x in tmpstdtot[k].tolist()]})\n if len(tmpstdinter):\n imResult.update({'InterEvStdDev': [float(x) for x in tmpstdinter[k].tolist()]})\n if len(tmpstdintra):\n imResult.update({'IntraEvStdDev': [float(x) for x in tmpstdintra[k].tolist()]})\n gm_collector.append({imTag: imResult})\n #print(gm_collector)\n \n # close datastore instance\n self.calculator.datastore.close()\n \n # stop dbserver\n if self.vtag >= 11:\n cdbs.main('stop')\n else:\n cdbs.dbserver('stop')\n \n # terminate the subprocess\n if self.prc:\n self.prc.kill()\n\n # copy calc hdf file\n if self.vtag >= 11:\n calc_id = datastore.get_last_calc_id()\n path = os.path.join(datastore.get_datadir(), 'calc_%d.hdf5' % calc_id)\n else:\n path = os.path.join(datastore.get_datadir(), 'calc_%d.hdf5' % self.calc_id)\n\n if self.dir_info:\n dir_output = self.dir_info['Output']\n try:\n shutil.copy2(path, dir_output)\n print('FetchOpenQuake: calc hdf file saved.')\n except:\n print('FetchOpenQuake: failed to copy calc hdf file.')\n\n # Final results\n res = {'Magnitude': mag,\n 'Periods': cur_T,\n 'IM': im_list,\n 'GroundMotions': gm_collector}\n \n # return\n return res","def _optimization_loop(self, iteration=0):\n self.logger.print_optimization_header()\n\n while iteration < self.iterations:\n try:\n self._execute_experiment()\n except RepeatedExperimentError:\n # G.debug_(F'Skipping repeated Experiment: {_ex!s}\\n')\n if len(self.similar_experiments) + len(self.tested_keys) >= self.search_space_size:\n G.log_(f\"Hyperparameter search space has been exhausted\")\n break\n self.skipped_iterations += 1\n continue\n except StopIteration:\n if len(self.similar_experiments) + len(self.tested_keys) >= self.search_space_size:\n G.log_(f\"Hyperparameter search space has been exhausted\")\n break\n # G.debug_(f'Re-initializing hyperparameter grid after testing {len(self.tested_keys)} keys')\n self._set_hyperparameter_space()\n continue\n\n self.logger.print_result(\n self.current_hyperparameters_list,\n self.current_score,\n experiment_id=self.current_experiment.experiment_id,\n )\n\n if (\n (self.best_experiment is None) # First evaluation\n or (self.do_maximize and (self.best_score < self.current_score)) # New best max\n or (not self.do_maximize and (self.best_score > self.current_score)) # New best min\n ):\n self.best_experiment = self.current_experiment.experiment_id\n self.best_score = self.current_score\n\n iteration += 1","def main():\n\n convergence_rates_e1 = pd.DataFrame(index=['Mean', 'St.D'])\n convergence_rates_e4 = pd.DataFrame(index=['Mean', 'St.D'])\n success_rates = pd.DataFrame(index=['1e-1', '1e-2', '1e-3', '1e-4', '1e-5'])\n peak_ratios = pd.DataFrame(index=['1e-1', '1e-2', '1e-3', '1e-4', '1e-5'])\n\n for function in range(1, 21):\n results = pickle.load(open('results/benchmarking_result_{}.pkl'.format(function), 'rb'))\n\n col_name = 'F{}'.format(function)\n index = function-1\n\n convergence_rates_e1.insert(index, col_name, results.ConvergenceRates[0])\n convergence_rates_e4.insert(index, col_name, results.ConvergenceRates[3])\n\n success_rates.insert(index, col_name, results.SuccessRate)\n peak_ratios.insert(index, col_name, results.PeakRatio)\n\n for i in results.SimulationSwarms:\n\n x_axis = [k for k, _ in i.items()]\n y_axis = [v for _, v in i.items()]\n\n plt.subplot(4, 5, function) # subplot indexes from 1\n plt.plot(x_axis, y_axis, 'k-')\n\n plt.savefig('results/nmmso_benchmark.png')\n plt.show()\n\n pd.set_option('display.max_columns', None) # make sure all columns are printed below\n print(convergence_rates_e1)\n print(convergence_rates_e4)\n print(success_rates)\n print(peak_ratios)\n\n # Table V from Fieldsend et al.\n table4 = pd.Series([\n peak_ratios.stack().median(),\n peak_ratios.stack().mean(),\n peak_ratios.stack().std()\n ])\n print(table4)\n\n # do we want to reproduce Fig. 3","def performFEA(params, aquifer, size, timestep, t1endtime):\n\n # Initialize parameters\n Hpdf = params[0]\n φpdf = params[1]\n Kpdf = params[2]\n ctinvpdf = 1/params[3]\n Qpdf = params[4]\n cspdf = params[5]\n\n # Calculate total number of time steps\n t1 = round(t1endtime / timestep)\n timeperiod = timestep * np.linspace(0, 2*t1, 2*t1+1)\n\n # Initialize boundary conditions\n rw = aquifer.rw #0.1\n rmax = aquifer.rmax #1000\n mu = aquifer.mu #0.31e-3\n elems = 25\n\n # Construct empty containers\n pmatrixwell = np.zeros([size, 2*t1+1])\n Tmatrixwell = np.zeros([size, 2*t1+1])\n\n # Run forward model with finite element method\n for index in range(size):\n pmatrixwell[index, :], Tmatrixwell[index, :] = main(aquifer=aquifer, degree=2, btype=\"spline\", elems=elems, rw=rw, rmax=rmax, H=Hpdf[index], mu=mu,\n φ=φpdf[index], ctinv=ctinvpdf[index], k_int=Kpdf[index], Q=Qpdf[index], timestep=timestep,\n t1endtime=t1endtime)\n\n # save array after each timestep for each run, export matrix from main()\n # save seperate runs in csv file, use mean from each timestep, plot 95% CI with seaborn\n # with open('pmatrix.npy', 'wb') as f:\n # np.save(f, pmatrixwell)\n\n # np.savetxt('data.csv', (col1_array, col2_array, col3_array), delimiter=',')\n\n return pmatrixwell, Tmatrixwell","def run_experiments(size, approach):\n\n # Create folder (if needed) where to store query evaluation logs and raw results\n if not os.path.isdir(f\"{path_reports}/eval-query-{current_run}\"):\n shell(f\"mkdir -p {path_reports}/eval-query-{current_run}\")\n\n # Create folder (if needed) where to store store evaluation logs and raw results\n if not os.path.isdir(f\"{path_reports}/eval-store-{current_run}\"):\n shell(f\"mkdir -p {path_reports}/eval-store-{current_run}\")\n\n # Helper function computing the path of a file/folder for the given query evaluation iteration \n def query_path(iteration, filename=None):\n folder = size[\"id\"] + \"_\" + str(iteration)\n return path_reports + \"/eval-query-\" + current_run + \"/\" + folder + (\"/\" + filename if filename != None else \"\") \n\n # Helper function computing the path of a file/folder for the given store evaluation test / cpu setting\n def store_path(test, cpu, filename=None):\n folder = size[\"id\"] + \"_\" + approach[\"id\"] + \"_\" + test + (\"_\" + cpu[\"id\"] if cpu != None else \"\")\n return path_reports + \"/eval-store-\" + current_run + \"/\" + folder + (\"/\" + filename if filename != None else \"\")\n \n # Determine whether partial traces and named graphs are supported\n partial = approach[\"supports_partial\"]\n graphs = approach[\"supports_graphs\"]\n \n # Skip setting if all data is available (check for presence of log files - delete them to repeat test)\n may_skip = (not sp_enable or not partial or not graphs or os.path.isfile(store_path(\"sp\", None, \"eval.log\")))\n may_skip = may_skip and (not sf_enable or os.path.isfile(store_path(\"sf\", None, \"eval.log\")))\n if query_enable and approach[\"id\"] == query_approach_id:\n for i in range(0, query_num_iterations):\n may_skip = may_skip and os.path.isfile(query_path(i, \"eval.log\"))\n for cpu in cpus:\n may_skip = may_skip and (not pp_enable or not partial or not graphs or os.path.isfile(store_path(\"pp\", cpu, \"eval.log\")))\n may_skip = may_skip and (not pf_enable or os.path.isfile(store_path(\"pf\", cpu, \"eval.log\")))\n if may_skip:\n return\n\n # Delete (if needed) and extract again the repository from its .tar.xz file, so to work on a clean repository (at the end of this test, the repository is no more clean)\n path_dump = path_data + \"/\" + size[\"id\"] + \"_\" + approach[\"id\"] + \".tar.lz\"\n path_repo = path_repos + \"/\" + size[\"id\"] + \"_\" + approach[\"id\"]\n if not os.path.isfile(path_dump):\n log(f\"Missing required file {path_dump}\")\n sys.exit()\n if os.path.isdir(path_repo):\n shell(f\"rm -Rf {path_repo}\")\n shell(f\"{cmd_plzip} -kdc {path_dump} | tar xf - -C {path_repos}\")\n\n # Locate the repository URL\n repo_url = f\"http://localhost:{server_port}/repositories/promo\"\n \n # Query test (if enabled)\n if query_enable and approach[\"id\"] == query_approach_id:\n for i in range(0, query_num_iterations):\n if not os.path.isfile(query_path(i, \"eval.log\")):\n shell(f\"mkdir -p {query_path(i)}\")\n shell(f\"taskset -a {query_taskset} {cmd_graphdb} start {path_repo}\")\n shell(f\"taskset -a {query_taskset} {cmd_mokip} eval-query -w -u {repo_url} -q {path_queries} -r {query_path(i)} \" +\n f\"| tee {query_path(i, 'eval.log')}\")\n shell(f\"taskset -a {query_taskset} {cmd_graphdb} stop {path_repo}\")\n\n # Sequential Partial test (to assess store times per trace and their components)\n if sp_enable and partial and graphs and not os.path.isfile(store_path(\"sp\", None, \"eval.log\")):\n shell(f\"mkdir -p {store_path('sp', None)}\")\n shell(f\"taskset -a {sp_taskset} {cmd_graphdb} start {path_repo}\")\n shell(f\"taskset -a {sp_taskset} {cmd_mokip} eval-store -d {path_data}/traces_pp.jsonl.gz \" + \n f\"-u {repo_url} -i {approach['inference']} -U REPLACE_GRAPH_PROTOCOL \" + \n f\"-o {path_ontology} --trace-namespace '{namespace_trace}' --graph-namespace '{namespace_graph}' \" +\n f\"-T {timeout} -r {store_path('sp', None)} -t 1 -w 50 -p -D \" + \n f\"| tee {store_path('sp', None, 'eval.log')}\")\n shell(f\"taskset -a {sp_taskset} {cmd_graphdb} stop {path_repo}\")\n\n # Parallel Partial (to assess throughput, varying # of CPU cores)\n for cpu in cpus:\n if pp_enable and partial and graphs and not os.path.isfile(store_path(\"pp\", cpu, \"eval.log\")):\n shell(f\"mkdir -p {store_path('pp', cpu)}\")\n shell(f\"taskset -a {cpu['taskset']} {cmd_graphdb} start {path_repo}\")\n shell(f\"taskset -a {cpu['taskset']} {cmd_mokip} eval-store -d {path_data}/traces_pp.jsonl.gz \" +\n f\"-u {repo_url} -i {approach['inference']} -U REPLACE_GRAPH_PROTOCOL \" + \n f\"-o {path_ontology} --trace-namespace '{namespace_trace}' --graph-namespace '{namespace_graph}' \" + \n f\"-T {timeout} -r {store_path('pp', cpu)} -t {max(2, cpu['num_threads'])} -w 50 -p -D \" + \n f\"| tee {store_path('pp', cpu, 'eval.log')}\")\n shell(f\"taskset -a {cpu['taskset']} {cmd_graphdb} stop {path_repo}\")\n\n # Sequential Full test (to assess store times per trace and their components)\n if sf_enable and not os.path.isfile(store_path(\"sf\", None, \"eval.log\")):\n shell(f\"mkdir -p {store_path('sf', None)}\")\n shell(f\"taskset -a {sf_taskset} {cmd_graphdb} start {path_repo}\")\n shell(f\"taskset -a {sf_taskset} {cmd_mokip} eval-store -d {path_data}/traces_sf.jsonl.gz \" + \n f\"-u {repo_url} -i {approach['inference']} -U {'APPEND' if graphs else 'APPEND_DEFAULT_GRAPH'} \" +\n f\"-o {path_ontology} --trace-namespace '{namespace_trace}' --graph-namespace '{namespace_graph}' \" +\n f\"-T {timeout} -r {store_path('sf', None)} -t 1 -w 50 \" + \n f\"| tee {store_path('sf', None, 'eval.log')}\")\n shell(f\"taskset -a {sf_taskset} {cmd_graphdb} stop {path_repo}\")\n\n # Parallel Full (to assess throughput where data is also deleted, varying # of CPU cores)\n for cpu in cpus:\n if pf_enable and not os.path.isfile(store_path(\"pf\", cpu, \"eval.log\")):\n update = \"APPEND\" if graphs else \"APPEND_DEFAULT_GRAPH\"\n shell(f\"mkdir -p {store_path('pf', cpu)}\")\n shell(f\"taskset -a {cpu['taskset']} {cmd_graphdb} start {path_repo}\")\n shell(f\"taskset -a {cpu['taskset']} {cmd_mokip} eval-store -d {path_data}/traces_pf_{cpu['id']}.jsonl.gz \" +\n f\"-u {repo_url} -i {approach['inference']} -U {'APPEND' if graphs else 'APPEND_DEFAULT_GRAPH'} \" + \n f\"-o {path_ontology} --trace-namespace '{namespace_trace}' --graph-namespace '{namespace_graph}' \" +\n f\"-T {timeout} -r {store_path('pf', cpu)} -t {max(2, cpu['num_threads'])} -w 50 \" + \n f\"| tee {store_path('pf', cpu, 'eval.log')}\")\n shell(f\"taskset -a {cpu['taskset']} {cmd_graphdb} stop {path_repo}\")\n\n # Drop the repository (both to save space and since it is not clean anymore)\n shell(f\"rm -Rf {path_repo}\")","def one_experiment():\n\n # set the name of the experiment\n now = datetime.datetime.now()\n experiment_id = str(now.day) + \"_\" + str(now.month) + \"_\" + str(now.hour) + \".\" + str(now.minute)\n experiment_name = 'overfit_' + str(experiment_id)\n\n # define if you want to use preprocessed data from file\n use_prep_data = False\n if use_prep_data:\n set_params(preproc_data_id='16_5_10.16.47')\n\n # define the changing parameter and its value\n changing_param_name = 'class_weights'\n changing_param_value = [{0: 15, 1: 85}]\n # {0:15, 1:85}]#, {0:4, 1:100}, {0:3, 1:100}, {0:2, 1:100}, {0:1, 1:100}] #[{0:1, 1:1}, {0:15, 1:85}]#\n\n features_to_use = ['user', 'countries', 'session', 'format', 'token']\n # set constant parameters\n set_params(use_word_emb=1)\n set_params(epochs=40)\n set_params(features_to_use=features_to_use)\n\n # save constant parameters to a new \"experiment_..\" filgithx+P@2ub\n save_constant_parameters(experiment_name, changing_param_name)\n\n # run experiment for every parameter value\n for value in changing_param_value:\n process = psutil.Process(os.getpid())\n print(\"-----MEMORY before starting experiment ------\", int(process.memory_info().rss/(8*10**3)), \"KB\")\n\n # update the parameter value\n set_params(class_weights_1=value)\n\n # update the model_id for this new model\n now = datetime.datetime.now()\n new_model_id = str(now.day) + \"_\" + str(now.month) + \"_\" + str(now.hour) + \".\" + str(now.minute) + \".\" + str(now.second)\n\n set_params(model_id=new_model_id)\n\n # evaluate the new model and save the results in the experiment file\n oneExperiment = Process(target=run_experiment, args=(experiment_name,\n new_model_id, changing_param_name, value,))\n oneExperiment.start()\n oneExperiment.join()","def test_sales_forecasting_multiprocessing(self):\n with TemporaryDirectory() as temp_dir:\n self.run_command(\n (\n \"python3 main.py --use-synthetic-data --epochs 1\"\n f\" --mov-mean-window 0 --log-dir {temp_dir}\"\n \" --multiprocessing\"\n ),\n working_path,\n [\n \"Begin training loop\", \"Training:\", r\"epoch:\\s+1\",\n \"Validation:\", \"Best RMSPE|no valid RMSPE results\"\n ]\n )","def run():\n \n start_time = time.time()\n \n args = parse_args_plotting()\n config = ConfigParser()\n config.read(args.config_file)\n \n # initialize the OP class object\n OPs = initialize_plot_options(config)\n \n # which plot\n plot_settings = {}\n burnin = config.getint('plotting', 'burnin', fallback=0)\n plot_astr = config.getboolean('plotting', 'Astrometry_orbits_plot', fallback=False)\n plot_astr_pred = config.getboolean('plotting', 'Astrometric_prediction_plot', fallback=False)\n plot_rv_full = config.getboolean('plotting', 'RV_orbits_plot', fallback=False)\n plot_rv = config.getboolean('plotting', 'RV_plot', fallback=False)\n plot_rel_sep = config.getboolean('plotting', 'Relative_separation_plot', fallback=False)\n plot_position_angle = config.getboolean('plotting', 'Position_angle_plot', fallback=False)\n plot_proper_motions = config.getboolean('plotting', 'Proper_motion_plot', fallback=False)\n plot_corner = config.getboolean('plotting', 'Corner_plot', fallback=False)\n save_params = config.getboolean('save_results', 'save_params', fallback=True)\n checkconv = config.getboolean('plotting', 'check_convergence', fallback=False)\n \n if checkconv:\n OPs.plot_chains()\n if plot_astr:\n OPs.astrometry()\n if plot_astr_pred:\n OPs.astrometric_prediction_plot()\n if plot_rv_full:\n OPs.RV_fullorbit()\n if plot_rv:\n OPs.RV()\n if plot_rel_sep:\n OPs.relsep()\n if plot_position_angle:\n OPs.PA()\n if plot_proper_motions:\n OPs.proper_motions()\n if plot_corner:\n OPs.plot_corner()\n if save_params:\n OPs.save_data()","def run_and_store(self):\n # Initialization assumptions\n z = self.draw_normal_initial()\n gradient = self.cv_gradient_initial(z)\n gradient[np.isnan(gradient)] = 0\n variance = np.power(gradient,2) \n final_parameters = self.current_parameters()\n final_samples = 1\n\n # Create optimizer\n if self.optimizer == 'ADAM':\n self.optim = ADAM(final_parameters, variance, self.learning_rate, 0.9, 0.999)\n elif self.optimizer == 'RMSProp':\n self.optim = RMSProp(final_parameters, variance, self.learning_rate, 0.99)\n\n # Stored updates\n stored_means = np.zeros((self.iterations,len(final_parameters)/2))\n stored_predictive_likelihood = np.zeros(self.iterations)\n\n # Record elbo\n if self.record_elbo is True:\n elbo_records = np.zeros(self.iterations)\n else:\n elbo_records = None\n\n for i in range(self.iterations):\n gradient = self.cv_gradient(self.draw_normal())\n gradient[np.isnan(gradient)] = 0\n new_parameters = self.optim.update(gradient)\n self.change_parameters(new_parameters)\n\n stored_means[i] = self.optim.parameters[::2]\n stored_predictive_likelihood[i] = self.neg_posterior(stored_means[i])\n\n if self.printer is True:\n self.print_progress(i,self.optim.parameters[::2])\n\n # Construct final parameters using final 10% of samples\n if i > self.iterations-round(self.iterations/10):\n final_samples += 1\n final_parameters = final_parameters+self.optim.parameters\n\n if self.record_elbo is True:\n elbo_records[i] = self.get_elbo(self.optim.parameters[::2])\n\n final_parameters = final_parameters/float(final_samples)\n self.change_parameters(final_parameters)\n final_means = np.array([final_parameters[el] for el in range(len(final_parameters)) if el%2==0])\n final_ses = np.array([final_parameters[el] for el in range(len(final_parameters)) if el%2!=0])\n\n if not self.quiet_progress:\n print(\"\")\n print(\"Final model ELBO is \" + str(-self.neg_posterior(final_means)-self.create_normal_logq(final_means)))\n return self.q, final_means, final_ses, stored_means, stored_predictive_likelihood, elbo_records","def run_and_store(self):\n # Initialization assumptions\n z = self.draw_normal_initial()\n gradient = self.cv_gradient_initial(z)\n gradient[np.isnan(gradient)] = 0\n variance = np.power(gradient,2) \n final_parameters = self.current_parameters()\n final_samples = 1\n\n # Create optimizer\n if self.optimizer == 'ADAM':\n self.optim = ADAM(final_parameters, variance, self.learning_rate, 0.9, 0.999)\n elif self.optimizer == 'RMSProp':\n self.optim = RMSProp(final_parameters, variance, self.learning_rate, 0.99)\n\n # Stored updates\n stored_means = np.zeros((self.iterations,len(final_parameters)/2))\n stored_predictive_likelihood = np.zeros(self.iterations)\n\n # Record elbo\n if self.record_elbo is True:\n elbo_records = np.zeros(self.iterations)\n else:\n elbo_records = None\n\n for i in range(self.iterations):\n gradient = self.cv_gradient(self.draw_normal())\n gradient[np.isnan(gradient)] = 0\n new_parameters = self.optim.update(gradient)\n self.change_parameters(new_parameters)\n\n stored_means[i] = self.optim.parameters[::2]\n stored_predictive_likelihood[i] = self.neg_posterior(stored_means[i])\n\n if self.printer is True:\n self.print_progress(i,self.optim.parameters[::2])\n\n # Construct final parameters using final 10% of samples\n if i > self.iterations-round(self.iterations/10):\n final_samples += 1\n final_parameters = final_parameters+self.optim.parameters\n\n if self.record_elbo is True:\n elbo_records[i] = self.get_elbo(self.optim.parameters[::2])\n\n final_parameters = final_parameters/float(final_samples)\n self.change_parameters(final_parameters)\n final_means = np.array([final_parameters[el] for el in range(len(final_parameters)) if el%2==0])\n final_ses = np.array([final_parameters[el] for el in range(len(final_parameters)) if el%2!=0])\n\n if not self.quiet_progress:\n print(\"\")\n print(\"Final model ELBO is \" + str(-self.full_neg_posterior(final_means)-self.create_normal_logq(final_means)))\n return self.q, final_means, final_ses, stored_means, stored_predictive_likelihood, elbo_records","def main():\n # Load data and pre-process it\n path = \"data2.csv\"\n features, labels, df = load_data(path)\n\n # Learning rates - including our own\n learning_rates = [0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1, 5, 10, 0.75]\n\n # Num iterations\n num_iterations = [100 for i in range(9)] + [1000]\n\n # Keep track of all final weights for different learning rates\n lines = []\n\n # Compute weights for each learning rate\n for rate, num_iters in zip(learning_rates, num_iterations):\n\n # Get weights from gradient descent and add to weights list\n weights = gradient_descent(features, labels, rate, num_iters)\n lines.append([rate, num_iters] + weights)\n\n # Now write 'lines' to file\n with open('results2.csv', \"w\") as out_file:\n for line in lines:\n out_file.write(\"{}, {}, {}, {}, {} \\n\".format(line[0], line[1],\n line[2], line[3], line[4]))\n out_file.close()\n\n # Select which weights to use for plotting\n index = -1\n\n\n plot_db.visualize_3d(df, lin_reg_weights=lines[index][2:],\n feat1='norm_x1', feat2='norm_x2', labels='label',\n xlim=(-1, 1), ylim=(-1, 1), zlim=(0, 3),\n alpha=learning_rates[index], xlabel='age',\n ylabel='weight', zlabel='height',\n title='')","def generate_trajectories():\n\n setup_timestamp_logging()\n\n logger = logging.getLogger()\n\n substance = Substance.from_components('C(C(C(C(C(F)(F)Br)(F)F)(F)F)(F)F)(C(C(C(F)(F)F)(F)F)(F)F)(F)F')\n\n logger.info('Building system.')\n\n build_system = BuildSmirnoffSystem('build_system')\n build_system.coordinate_file_path = 'coords.pdb'\n build_system.substance = substance\n build_system.force_field_path = 'smirnoff99Frosst-1.1.0.offxml'\n build_system.execute('', None)\n\n logger.info('System built.')\n\n production_simulation = RunOpenMMSimulation(f'production_simulation')\n production_simulation.steps_per_iteration = 500\n production_simulation.output_frequency = 1\n production_simulation.timestep = 2.0 * unit.femtosecond\n production_simulation.thermodynamic_state = ThermodynamicState(temperature=298.15*unit.kelvin,\n pressure=1.0*unit.atmosphere)\n production_simulation.input_coordinate_file = 'coords.pdb'\n production_simulation.system_path = 'system.xml'\n\n compute_resources = ComputeResources(number_of_threads=4)\n\n logger.info(f'Simulation started.')\n production_simulation_schema = production_simulation.schema\n production_simulation.execute('', compute_resources)\n production_simulation.schema = production_simulation_schema\n logger.info(f'Simulation finished.')","def run(params, payoff):\n\n # Canvas for drawing.\n pylab.figure(1, figsize = (12, 6))\n pylab.ion()\n pylab.draw()\n\n trait_matrix = []\n\n # Replay of an experiment.\n if os.path.isfile(\"results.pkl\"):\n fh = open(\"results.pkl\", \"r\")\n pickle.load(fh) \n population = pickle.load(fh)\n fh.close()\n for time_step in range(0, params[\"generations\"] / \\\n params[\"report_freq\"] + 1):\n print(\"Generation %d of %d\" %(time_step * params[\"report_freq\"], \n params[\"generations\"]))\n traits = []\n for p in population:\n traits.append(p.get_trait_list()[time_step])\n plot_trait_distribution(params, trait_matrix, traits)\n plot_trait_histogram(params, traits)\n \n # New experiment.\n else:\n start = 0\n \n # Create a population.\n population = [Player(i) for i in range(0, params[\"population\"])]\n\n # Create a network.\n net = network.build_network(population, params[\"network_topology\"], \n params[\"network_params\"])\n \n # Seed the random number generator for the experiment.\n random.seed(params[\"seed\"])\n \n # Assign a trait for each individual in the population. \n for p in population:\n p.inherit_trait(params[\"init_trait\"])\n p.commit_inheritance()\n\n # Create a dynamics module based on network (complete or other) \n # type and update rule selected. \n dynamics = dynamics_module(params[\"network_topology\"], \n params[\"update_rule\"])(net, params, payoff)\n\n # The dynamics.\n for time_step in range(0, params[\"generations\"]):\n\n # Pre interaction.\n dynamics.pre_interaction()\n\n # Plot results at report_freq.\n if time_step % params[\"report_freq\"] == 0:\n print(\"Generation %d of %d\" %(time_step, params[\"generations\"]))\n traits = []\n for p in population:\n traits.append(p.get_trait())\n plot_trait_distribution(params, trait_matrix, traits)\n plot_trait_histogram(params, traits)\n\n # Interact.\n for count in range(0, params[\"population\"]):\n dynamics.interact()\n\n # Post interaction.\n dynamics.post_interaction()\n \n # Update.\n for count in range(0, params[\"population\"]):\n dynamics.update()\n\n # Post update; commit trait inheritance.\n dynamics.post_update()\n\n # Keep the final plot window around until the user shuts it.\n pylab.show()\n pylab.close(1)","def backtest(self, strategy): \n \n # load data\n data = self.import_data(currency_ids = np.unique(self.signals.currency_id),\n start_date = self.start_date,\n end_date = self.end_date)\n \n # add cash and columns for currencies\n data[\"capital\"] = 0\n data[\"actions\"] = \"\" # should become a dictW \n data[\"description\"] = \"\"\n data[\"capital\"].iloc[0] = self.initial_capital\n data[\"worth_usd\"] = 0\n \n for currency_id in np.unique(self.signals.currency_id):\n data[currency_id + '_position'] = 0\n \n # a sell or buy can influence subsequent positions, so calculate iteratively\n for observation in range(1, len(data.index)):\n \n date = data.index[observation]\n \n # investment this period is zero\n investment_capital_period = 0\n \n # amount of currency_ids initially same as last period\n for currency_id in np.unique(self.signals.currency_id):\n data[currency_id + '_position'].iloc[observation] = data[currency_id + '_position'].iloc[observation-1] \n \n # at each point, compute size of each position (cash and currencies), and record actions\n if(data.index[observation] in self.signals.index):\n \n action_df = pd.DataFrame(columns=list([\"Currency\",\"NominalAmount\", \"CapitalAmount\"]))\n \n # could be multiple actions\n for index, action in self.signals.loc[date].iterrows(): \n currency_id = action['currency_id']\n signal = action['signal']\n \n # Buy\n if signal == 1:\n \n # buy for 10% currency_id\n investment_capital = data[\"capital\"].iloc[observation-1] * 0.10 \n\n # estimate how many coins\n investment_nominal = round(investment_capital / data[currency_id].iloc[observation])\n \n # calculate exact capital needed\n investment_capital_exact = investment_nominal * data[currency_id].iloc[observation]\n investment_capital_period = investment_capital_period + investment_capital_exact \n \n # change the amount of currency hold\n data[currency_id + '_position'].iloc[observation] = data[currency_id + '_position'].iloc[observation-1] + investment_nominal\n \n # report action by appending a Series to the (empty) dataframe\n action_df = action_df.append(pd.Series({\"Currency\": currency_id, \n \"NominalAmount\": investment_nominal, \n \"CapitalAmount\": investment_capital_exact}),ignore_index=True)\n \n # report description\n data[\"description\"].iloc[observation] = (data[\"actions\"].iloc[observation] + \"\\n Buy \" + \n str(investment_nominal) + \" \" + str(currency_id) + \n \" for \" + str(investment_capital_exact))\n \n # Sell\n if signal == -1:\n \n # sell currency_id for 10% of total capital\n investment_capital = data[\"capital\"].iloc[observation-1] * 0.10 \n \n # estimate how many coins\n investment_nominal = round(investment_capital / data[currency_id].iloc[observation])\n \n # calculate exact capital needed\n investment_capital_exact = investment_nominal * data[currency_id].iloc[observation]\n investment_capital_period = investment_capital_period - investment_capital_exact\n \n # change the amount of currency hold\n data[currency_id + '_position'].iloc[observation] = data[currency_id + '_position'].iloc[observation-1] - investment_nominal\n \n # report action\n action_df = action_df.append(pd.Series({\"Currency\": currency_id, \n \"NominalAmount\": investment_nominal, \n \"CapitalAmount\": investment_capital_exact}),ignore_index=True)\n \n # report description\n data[\"description\"].iloc[observation] = data[\"actions\"].iloc[observation] + \"Sell \" + str(investment_nominal) + \" \" + str(currency_id) + \" for \" + str(investment_capital_exact)\n \n # report actions\n data[\"actions\"].iloc[observation] = action_df.to_json()\n \n # calculate resulting cash capital\n data[\"capital\"].iloc[observation] = data[\"capital\"].iloc[observation-1] - investment_capital_period\n \n # calculate worth by capital (usd) and each currency * price\n data[\"worth_usd\"].iloc[observation] = data[\"capital\"].iloc[observation] \n return data","def main(**kwargs):\n flowsheet = Flowsheet(name='MB_Model') \n \n # Fix variables\n setInputs(flowsheet) \n\n ts = time.time() \n\n mb = flowsheet.MB_fuel\n \n # Initialize fuel reactor\n flowsheet.MB_fuel._initialize(outlvl=1,\n optarg={\"tol\" : 1e-8,\n \"max_cpu_time\" : 600,\n \"print_level\" : 5,\n \"halt_on_ampl_error\": 'yes'}) \n \n # Create a solver\n opt = SolverFactory('ipopt')\n opt.options = {'tol': 1e-8,\n 'linear_solver' : 'ma27',\n 'bound_push': 1e-8,\n 'max_cpu_time': 600,\n 'print_level': 5}\n \n results = opt.solve(flowsheet,tee=True,symbolic_solver_labels=False,\n keepfiles=False)\n\n #flowsheet.MB_fuel.Solid_In_M.fix(691.4)\n #flowsheet.MB_fuel.Gas_In_y['CO2'].fix(0.03999)\n #flowsheet.MB_fuel.Gas_In_y['H2O'].fix(0.00001)\n #flowsheet.MB_fuel.Gas_In_y['CH4'].fix(0.96)\n\n\n\n #results = opt.solve(flowsheet,tee=True,symbolic_solver_labels=False,\n # keepfiles=False)\n \n \n print(\"\\n\")\n print(\"----------------------------------------------------------\")\n print('Total simulation time: ', value(time.time() - ts), \" s\")\n print(\"----------------------------------------------------------\")\n\n \n # Print some variables \n #print_summary_fuel_reactor(flowsheet) \n\n # Plot some variables \n #results_plot_fuel_reactor(flowsheet) \n\n m = flowsheet.MB_fuel\n if 'Solid_M' in kwargs:\n m.Solid_In_M.fix(kwargs['Solid_M'])\n if 'Solid_T' in kwargs:\n m.Solid_In_Ts[t].fix(kwargs['Solid_T'])\n if 'Solid_x' in kwargs:\n m.Solid_In_x['Fe2O3'].fix(kwargs['Solid_x']['Fe2O3'])\n m.Solid_In_x['Fe3O4'].fix(kwargs['Solid_x']['Fe3O4'])\n m.Solid_In_x['Al2O3'].fix(kwargs['Solid_x']['Al2O3'])\n if 'Gas_F' in kwargs:\n m.Gas_In_F.fix(kwargs['Gas_F'])\n if 'Gas_P' in kwargs:\n m.Gas_In_P.fix(kwargs['Gas_P'])\n if 'Gas_T' in kwargs:\n m.Gas_In_T.fix(kwargs['Gas_T'])\n if 'Gas_y' in kwargs:\n m.Gas_In_y['CO2'].fix(kwargs['Gas_y']['CO2'])\n m.Gas_In_y['H2O'].fix(kwargs['Gas_y']['H2O'])\n m.Gas_In_y['CH4'].fix(kwargs['Gas_y']['CH4'])\n\n results = opt.solve(flowsheet, tee=True)\n\n with open('ss_fs.txt','w') as f:\n flowsheet.display(ostream=f)\n\n dt_Gflux_CO2 = []\n dt_Gflux_H2O = []\n dt_Gflux_CH4 = []\n dt_Sflux_Fe2O3 = []\n dt_Sflux_Fe3O4 = []\n dt_Sflux_Al2O3 = []\n dt_Ctrans_CO2 = []\n dt_Ctrans_H2O = []\n dt_Ctrans_CH4 = []\n dt_qtrans_Fe2O3 = []\n dt_qtrans_Fe3O4 = []\n dt_qtrans_Al2O3 = []\n dt_Ghflux = []\n dt_Ts = []\n dt_TgGS = []\n dt_TsGS = []\n dt_vg = []\n dt_vs = []\n\n# for z in mb.z.get_finite_elements():\n# if z != mb.z.first() and z != mb.z.last():\n#\n# dt_Gflux_CO2.append( (mb.Cg[z,'CO2'].value-mb.Cg[prev,'CO2'].value)/\\\n# (mb.G_flux[z,'CO2'].value-mb.G_flux[prev,'CO2'].value) \\\n# *(z-prev)*mb.eps.value*mb.L.value /(z-prev))\n#\n# dt_Gflux_H2O.append( (mb.Cg[z,'H2O'].value-mb.Cg[prev,'H2O'].value)/\\\n# (mb.G_flux[z,'H2O'].value-mb.G_flux[prev,'H2O'].value) \\\n# *(z-prev)*mb.eps.value*mb.L.value /(z-prev))\n#\n# dt_Gflux_CH4.append( (mb.Cg[z,'CH4'].value-mb.Cg[prev,'CH4'].value)/\\\n# (mb.G_flux[z,'CH4'].value-mb.G_flux[prev,'CH4'].value) \\\n# *(z-prev)*mb.eps.value*mb.L.value /(z-prev))\n#\n# dt_Ctrans_CO2.append( (mb.Cg[z,'CO2'].value-mb.Cg[prev,'CO2'].value)/\\\n# (mb.Ctrans[z,'CO2'].value)* \\\n# #-mv.Ctrans[prev,'CO2'].value)*\\\n# mb.eps.value/(1-mb.eps.value) /(z-prev))\n#\n# dt_Ctrans_H2O.append( (mb.Cg[z,'H2O'].value-mb.Cg[prev,'H2O'].value)/\\\n# (mb.Ctrans[z,'H2O'].value)* \\\n# #-mv.Ctrans[prev,'H2O'].value)*\\\n# mb.eps.value/(1-mb.eps.value) /(z-prev))\n#\n# dt_Ctrans_CH4.append( (mb.Cg[z,'CH4'].value-mb.Cg[prev,'CH4'].value)/\\\n# (mb.Ctrans[z,'CH4'].value)* \\\n# #-mv.Ctrans[prev,'CH4'].value)*\\\n# mb.eps.value/(1-mb.eps.value) /(z-prev))\n#\n# dt_Sflux_Fe2O3.append( (mb.q[z,'Fe2O3'].value-mb.q[prev,'Fe2O3'].value)/\\\n# (mb.S_flux[z,'Fe2O3'].value-mb.S_flux[prev,'Fe2O3'].value)*\\\n# (z-prev)/(1-mb.eps.value)*mb.L.value /(z-prev))\n#\n# dt_Sflux_Fe3O4.append( (mb.q[z,'Fe3O4'].value-mb.q[prev,'Fe3O4'].value)/\\\n# (mb.S_flux[z,'Fe3O4'].value-mb.S_flux[prev,'Fe3O4'].value)*\\\n# (z-prev)/(1-mb.eps.value)*mb.L.value /(z-prev))\n#\n# dt_Sflux_Al2O3.append( (mb.q[z,'Al2O3'].value-mb.q[prev,'Al2O3'].value)/\\\n# (mb.S_flux[z,'Al2O3'].value-mb.S_flux[prev,'Al2O3'].value)*\\\n# (z-prev)/(1-mb.eps.value)*mb.L.value /(z-prev))\n#\n# dt_qtrans_Fe2O3.append( (mb.q[z,'Fe2O3'].value-mb.q[prev,'Fe2O3'].value)/\\\n# (mb.qtrans[z,'Fe2O3'].value )/(z-prev)) \n# #-mb.qtrans[prev,'Fe2O3'].value) )\n#\n# dt_qtrans_Fe3O4.append( (mb.q[z,'Fe3O4'].value-mb.q[prev,'Fe3O4'].value)/\\\n# (mb.qtrans[z,'Fe3O4'].value )/(z-prev)) \n# #-mb.qtrans[prev,'Fe3O4'].value) )\n#\n# dt_qtrans_Al2O3.append( (mb.q[z,'Fe3O4'].value-mb.q[prev,'Fe3O4'].value)/\\\n# (mb.qtrans[z,'Fe3O4'].value )/(z-prev)) \n# #-mb.qtrans[prev,'Fe3O4'].value) )\n#\n# dt_Ghflux.append( (mb.Tg[z].value-mb.Tg[prev].value)/\\\n# (mb.Gh_flux[z].value-mb.Gh_flux[prev].value)* (z-prev)* mb.eps.value*\\\n# mb.L.value* mb.rho_vap[z].value* mb.cp_gas[z].value /(z-prev)) \n#\n# dt_Ts.append( (z-prev)*(1-mb.eps.value)*mb.L.value/mb.vs.value /(z-prev))\n#\n# dt_TgGS.append( (mb.Tg[z].value - mb.Tg[prev].value)/\\\n# mb.Tg_GS[z].value* mb.eps.value* mb.rho_vap[z].value* mb.cp_gas[z].value \n# /(z-prev))\n# \n# dt_TsGS.append( (mb.Ts[z].value - mb.Ts[prev].value)/\\\n# mb.Tg_GS[z].value* (1-mb.eps.value)* mb.rho_sol.value* mb.cp_sol[z].value*1e-3 \n# /(z-prev))\n# \n# dt_vg.append( mb.L.value*(z-prev)/mb.vg[z].value /(z-prev))\n# \n# dt_vs.append( mb.L.value*(z-prev)/mb.vs.value /(z-prev))\n#\n# prev = z\n#\n# with open('dt.txt','w') as f:\n# f.write('dt_Gflux_CO2\\t')\n# for t in dt_Gflux_CO2:\n# f.write('%1.3f'%t +'\\t')\n#\n# f.write('\\ndt_Gflux_H2O\\t')\n# for t in dt_Gflux_H2O:\n# f.write('%1.3f'%t +'\\t')\n#\n# f.write('\\ndt_Gflux_CH4\\t') \n# for t in dt_Gflux_CH4:\n# f.write('%1.3f'%t +'\\t')\n#\n# f.write('\\ndt_Sflux_Fe2O3\\t') \n# for t in dt_Sflux_Fe2O3:\n# f.write('%1.3f'%t +'\\t')\n#\n# f.write('\\ndt_Sflux_Fe3O4\\t') \n# for t in dt_Sflux_Fe3O4:\n# f.write('%1.3f'%t +'\\t')\n#\n# f.write('\\ndt_Sflux_Al2O3\\t') \n# for t in dt_Sflux_Al2O3:\n# f.write('%1.3f'%t +'\\t')\n#\n# f.write('\\ndt_Ctrans_CO2\\t') \n# for t in dt_Ctrans_CO2:\n# f.write('%1.3f'%t +'\\t')\n# \n# f.write('\\ndt_Ctrans_H2O\\t') \n# for t in dt_Ctrans_H2O:\n# f.write('%1.3f'%t +'\\t')\n#\n# f.write('\\ndt_Ctrans_CH4\\t') \n# for t in dt_Ctrans_CH4:\n# f.write('%1.3f'%t +'\\t')\n#\n# f.write('\\ndt_qtrans_Fe2O3\\t') \n# for t in dt_qtrans_Fe2O3:\n# f.write('%1.3f'%t +'\\t')\n#\n# f.write('\\ndt_qtrans_Fe3O4\\t') \n# for t in dt_qtrans_Fe3O4:\n# f.write('%1.3f'%t +'\\t')\n#\n# f.write('\\ndt_qtrans_Al2O3\\t') \n# for t in dt_qtrans_Al2O3:\n# f.write('%1.3f'%t +'\\t')\n#\n# f.write('\\ndt_Ghflux\\t') \n# for t in dt_Ghflux:\n# f.write('%1.3f'%t +'\\t')\n#\n# f.write('\\ndt_Ts\\t\\t') \n# for t in dt_Ts:\n# f.write('%1.3f'%t +'\\t')\n#\n# f.write('\\ndt_TgGS\\t\\t') \n# for t in dt_TgGS:\n# f.write('%1.3f'%t +'\\t')\n#\n# f.write('\\ndt_TsGS\\t\\t') \n# for t in dt_TsGS:\n# f.write('%1.3f'%t +'\\t')\n#\n# f.write('\\ndt_vg\\t\\t') \n# for t in dt_vg:\n# f.write('%1.3f'%t +'\\t')\n#\n# f.write('\\ndt_vs\\t\\t') \n# for t in dt_vs:\n# f.write('%1.3f'%t +'\\t')\n\n\n # Store the flowsheet \n return flowsheet","def main():\n parser = argparse.ArgumentParser(\n description=\"Generate bar plots comparing Matlab solvers to RLSO\"\n )\n parser.add_argument(\n \"--path\", type=str, default=None, help=\"the path to saved testing data for RLSO\"\n )\n parser.add_argument(\n \"--trials\",\n type=int,\n default=1000,\n help=\"Number of test problems to use defaults to 1000\",\n )\n parser.add_argument(\n \"--savestring\",\n type=str,\n default=None,\n help=\"Name to save our plot as. Defaults to random int .png\",\n )\n parser.add_argument(\n \"--titlestring\",\n type=str,\n default=None,\n help=\"String to append to our plot title. Defaults to None\",\n )\n parser.add_argument(\n \"--excludeinter\",\n type=bool,\n default=True,\n help=\"Exclude interior point method form testing. Reasons Convex only, and cannot set starting point. Default True\",\n )\n\n args = parser.parse_args()\n\n if args.path is None:\n print(\"Path to pickle data needed!\")\n return\n\n pickle_savepath = args.path\n with open(pickle_savepath, \"rb\") as pickle_file:\n pickle_tuple = pickle.load(pickle_file)\n\n complex_dic = pickle_tuple[4]\n lastbest = unravel_c_dic_lastbest(complex_dic)\n Abx0 = generate_Abx0(complex_dic, lastbest)\n\n if args.trials is None:\n trials = 1000\n else:\n trials = args.trials\n if args.savestring is None:\n save_string = str(np.random.randint(low=1e15, high=9e15)) + \".png\"\n else:\n save_string = args.savestring\n\n start = time.time()\n print(f\"start time {start}\")\n\n solutions = []\n for tuples in Abx0[:trials]:\n solutions.append(quad_solve(tuples, printout=False))\n end = time.time()\n print(f\"Matlab run time: {end - start}\")\n\n rlso = []\n active = []\n inter = []\n trust = []\n for [x, y, z, w] in solutions:\n rlso.append(x)\n active.append(y)\n inter.append(z)\n trust.append(w)\n title_string = args.titlestring\n plot_box(\n rlso,\n active,\n inter,\n trust,\n title_string,\n save_string,\n exclude_inter=args.excludeinter,\n )\n\n return","def run_all_models(self):\n #self.process_nitrate()\n try:\n sur_df = self.store.get('/said/{}/iv'.format(self.site['id']))\n con_df = self.store.get('/said/{}/qwdata'.format(self.site['id']))\n\n except KeyError:\n print('site {} not found'.format(site['name']))\n\n\n #determine start and end for plots\n start_date, end_date = get_time_limit(sur_df, con_df)\n\n #update start and end according to user\n user_start = self.site.get('start')\n user_end = self.site.get('end')\n\n if user_start:\n start_date = pd.to_datetime(user_start)\n\n if user_end:\n end_date = pd.to_datetime(user_end)\n\n\n #plot_ssc(ssc_model, filename='plots/{}_ssc.png'.format(site['name']),\n # start_date=start_date, end_date=end_date)\n\n #append the model results to summary\n #summary_table= summary_table.append(model_row_summary(ssc_model))\n\n for directory in ['model_data','report']:\n try:\n os.stat(directory)\n except:\n os.mkdir(directory)\n\n #pp_model_list = import pdb; pdb.set_trace()[\n # ['log(PP)',['log(Turb_HACH)']],\n # ['log(PP)',['log(Turb_YSI)']]\n #]\n\n #self.run_model(pp_model_list, 'PP')\n\n no3_model_list = [\n ['Nitrate',['NitrateSurr']],\n ]\n self.run_model(no3_model_list, 'Nitrate')\n\n ssc_model_list = [\n ['log(SSC)',['log(Turb_HACH)']],\n ['log(SSC)',['log(Turb_YSI)']]\n ]\n self.run_model(ssc_model_list, 'SSC')\n\n tp_model_list = [\n ['log(TP)',['log(OrthoP)','log(Turb_HACH)']],\n ['log(TP)',['log(OrthoP)','log(Turb_YSI)']],\n ['log(TP)',['log(Turb_HACH)']],\n ['log(TP)',['log(Turb_YSI)']]\n ]\n self.run_model(tp_model_list, 'TP')\n\n #write ssc model report\n #reportfile = 'report/{}_ssc_report.txt'.format(site['name'])\n #with open(reportfile, 'w') as f:\n # f.write(ssc_model.get_model_report().as_text())\n #summary_table= summary_table.append(model_row_summary(p_model1))\n #summary_table= summary_table.append(model_row_summary(p_model2))\n #plot_model(ssc_model, filename='plots/{}_ssc_model.png'.format(site['name']))\n #plot_phos(p_model1, p_model2, filename='plots/{}_tp.png'.format(site['name']),\n # start_date=start_date, end_date=end_date)\n #plot_model(p_model1, filename='plots/{}_orthoP_model.png'.format(site['name']))\n #\n ## try to plot phosphate\n #try:\n # phos_plot(con_data, sur_data, filename='plots/{}_p.png'.format(site['name']), title=site['name'],\n # return_model=True)\n #except:\n # print('phospate plot didnt work')\n #\n self.summary_table.to_csv('report/{}_model_summary.csv'.format(self.site['name']),\n index=False)","def main(rand,mu,lamb,cxpb,mutpb,ngen,param):\n \n random.seed(rand)\n NGEN = ngen\n MU = mu\n LAMBDA = lamb\n CXPB = cxpb\n MUTPB = mutpb\n \n # Used for printing the results. It is the parameter that is changed one run from another\n if param==\"rand\" or param==\"optimal\":\n list_results=[rand]\n elif param==\"mu\":\n list_results=[mu]\n elif param==\"lamb\":\n list_results=[lamb]\n elif param==\"cross\":\n list_results=[cxpb]\n elif param==\"mutate\":\n list_results=[mutpb]\n elif param==\"ngen\":\n list_results=[ngen]\n elif param==\"original\":\n list_results=[0]\n \n # Initialization of the objects for the GA\n pop = toolbox.population(n=MU)\n hof = tools.ParetoFront()\n stats = tools.Statistics(lambda ind: ind.fitness.values)\n stats.register(\"avg\", np.mean, axis=0)\n stats.register(\"std\", np.std, axis=0)\n stats.register(\"min\", np.min, axis=0)\n stats.register(\"max\", np.max, axis=0)\n\n # Run of the GA\n p,logbook=algorithms.eaMuPlusLambda(pop, toolbox, MU, LAMBDA, CXPB, MUTPB, NGEN, stats,\n halloffame=hof,verbose=0)\n with open(results_path+param+'_logbook.csv', 'a',newline='') as f:\n w = csv.DictWriter(f, logbook[0].keys())\n w.writeheader()\n for el in logbook:\n w.writerow(el)\n w.writerow({})\n \n # Takes the max fitness of the population from all of the runs\n max_fit=0\n max_gen=0\n for elt in logbook:\n if elt['max'][0]>max_fit:\n max_fit=elt['max'][0]\n max_gen=elt['gen']\n list_results.append(max_fit)\n list_results.append(max_gen)\n \n #TODO\n# for ind in hof:\n# dist = numpy.linalg.norm(a-b)\n\n print (\"{0} {1} {2} {3}\".format(round(list_results[1],3),round(list_results[2],3),round(list_results[0],3),hof[0]))\n current_out_writer.writerow([list_results[0],list_results[1],list_results[2],hof[0]])\n \n return pop, stats, hof","def calc_performance(self):\n for symbol in self.portfolio.assets.keys():\n\n # Total the Performance of all the trades\n start = self.portfolio.trades[symbol].index[0]\n end = self.portfolio.trades[symbol].index[-1]\n trades = len(self.record[symbol])\n profit = self.record[symbol]['profit'].sum()\n loss = self.record[symbol]['loss'].sum()\n # Total or average the trade info for all the trades\n try:\n wins = len(self.record[symbol].groupby('win/loose').groups['w'])\n except (ValueError, KeyError):\n wins = 0\n try:\n losses = len(self.record[symbol].groupby('win/loose').groups['l'])\n except (ValueError, KeyError):\n losses = 0\n try:\n washes = len(self.record[symbol].groupby('win/loose').groups['-'])\n except (ValueError, KeyError):\n washes = 0\n max_drawdown = self.record[symbol]['drawdown'].max()\n average_drawdown = self.record[symbol]['drawdown'].mean()\n max_drawdown_time = self.record[symbol]['drawdown days'].max()\n average_drawdown_time = self.record[symbol]['drawdown days'].mean()\n # Average the risk and market comparisons for all trades\n vol_risk = self.record[symbol]['volatility'].mean()\n beta = self.record[symbol]['beta'].mean()\n lpm_risk = self.record[symbol]['lpm'].mean()\n e_r = self.record[symbol]['expected_return'].mean()\n # Calculate Risk measures\n treynor_ratio = (e_r - self.risk_free_return) / beta\n sharpe_ratio = (e_r - self.risk_free_return) / vol_risk\n # Package up the data for each symbol\n self.performance[symbol] = {\n 'start': start,\n 'end': end,\n 'trades': trades,\n 'wins': wins,\n 'losses': losses,\n 'washes': washes,\n 'profit': profit,\n 'loss': loss,\n 'net_profit': profit - loss,\n 'profit_factor': profit / loss if loss != 0 else 1.0,\n 'percent_profitable': wins / trades if trades != 0 else 0.0,\n 'average_trade_net_profit' : (profit - loss) / trades if trades != 0 else 0.0,\n 'max_drawdown' : max_drawdown,\n 'average_drawdown' : average_drawdown,\n 'max_drawdown_days' : max_drawdown_time,\n 'average_drawdown_days' : average_drawdown_time,\n 'volatility_risk' : vol_risk,\n 'beta' : beta,\n 'lower_partial_moment_risk' : lpm_risk,\n 't_r' : treynor_ratio,\n 's_r' : sharpe_ratio\n }\n\n return self","def run_simulation(conf):\r\n\r\n\r\n # Validate cofiguration\r\n conf = validate_conf(conf)\r\n\r\n # Define function to run and storage path\r\n newpath = create_path(conf)\r\n\r\n # Define trajectory and time per state\r\n nr_states = 4\r\n traj = [list(range(nr_states))]\r\n T_list = [[conf.time_per_state for _ in range(nr_states)]]\r\n\r\n # Set parameters\r\n params, gamma, eta, lambda_var = parameters_linear_track_value(nr_states,\r\n conf.time_per_state,\r\n conf.alpha,\r\n conf.A_pre,\r\n )\r\n\r\n params['trajectories'] = traj\r\n params = update_params(conf.mode, params, T_list, gamma, eta)\r\n\r\n # Define the initial weights from CA3 to CA1\r\n T_mat = np.array([\r\n [0,1,0,0,0],\r\n [0,0,1,0,0],\r\n [0,0,0,1,0],\r\n [0,0,0,0,1],\r\n [0,0,0,0,1],\r\n ])\r\n M_theor = np.linalg.inv(np.eye(nr_states)-gamma*T_mat[:-1, :-1])\r\n wini = np.tile(np.expand_dims(np.expand_dims(M_theor,2),3),[1,1,params['N_pre_tot'],params['N_post']])\r\n params['wini'] = wini\r\n\r\n # Define the reward vector\r\n reward_vec = np.array([0,0,0,1])\r\n params['reward_vec'] = reward_vec\r\n\r\n # calculate theoretical value\r\n v_theor = np.matmul(M_theor, reward_vec)\r\n print(f'Theoretical Values: {list(v_theor)}')\r\n\r\n # Run spiking network\r\n params['step'] = params['step']*10\r\n reward_neuron_activity = run_value_function(**params)\r\n\r\n\r\n #############################\r\n # STORING AND PLOTTING\r\n #############################\r\n\r\n if conf.store_data:\r\n pickle.dump(reward_neuron_activity, open(newpath + '/rebuttal_value_function.pkl','wb'))\r\n\r\n rates = np.mean(np.sum(reward_neuron_activity, axis=2)/conf.time_per_state*1000, axis=1)\r\n\r\n # fs = 16\r\n\r\n fig = plt.figure()\r\n plt.bar(list(range(1,nr_states+1)), rates)\r\n if conf.store_data:\r\n fig.savefig(newpath+'/fig_value_function.eps',bbox_inches='tight',format='eps')\r\n\r\n plt.figure()\r\n for i in range(4):\r\n x = reward_neuron_activity[i]\r\n plt.plot(np.max(x,axis=0)*(i+1), '.', markersize=15)"],"string":"[\n \"def parameter_optimization(self):\\n out = open(self.csv_dir + self.strategy_id + '_gridsearch.csv', \\\"w\\\")\\n spl = len(self.para_list)\\n for i, sp in enumerate(self.para_list):\\n print(\\\"Strategy %s out of %s...\\\" % (i + 1, spl))\\n self._generate_trading_instances(sp)\\n self._run_backtest()\\n stats = self.portfolio.get_statistics()\\n tot_profit = float(stats[0][1])\\n sharpe = float(stats[1][1])\\n max_dd = float(stats[2][1])\\n win_rate = float(stats[7][1].replace(\\\"%\\\", \\\"\\\"))\\n profit_factor = float(stats[8][1])\\n\\n out.write(\\n \\\"%s,%s,%s,%s,%s,%s,%s\\\\n\\\" %\\n (sp[\\\"takeprofit\\\"], sp[\\\"period\\\"], tot_profit, sharpe, max_dd, win_rate, profit_factor)\\n )\\n out.close()\",\n \"def run():\\n\\n for simulation in range(0, N_SIMULATIONS):\\n\\n # Set up environment and agent\\n e = Environment() # create environment (also adds some dummy traffic)\\n a = e.create_agent(LearningAgent) # create agent\\n e.set_primary_agent(a, enforce_deadline=True) # specify agent to track\\n # NOTE: You can set enforce_deadline=False while debugging to allow longer trials\\n # TODO: Change later enforce_deadline=True\\n\\n # Now simulate it\\n sim = Simulator(e, update_delay=0.001, display=False) # create simulator (uses pygame when display=True, if available)\\n # NOTE: To speed up simulation, reduce update_delay and/or set display=False\\n\\n sim.run(n_trials=N_TRIALS) # run for a specified number of trials\\n # NOTE: To quit midway, press Esc or close pygame window, or hit Ctrl+C on the command-line\\n\\n if simulation == N_SIMULATIONS - 1:\\n\\n with open('results.csv', 'a') as csvfile:\\n fieldnames = ['alpha', 'gamma', 'epsilon', 'success_rate', 'last_failure']\\n writer = csv.DictWriter(csvfile, fieldnames=fieldnames)\\n\\n for index in range(0,len(simulation_rates)):\\n writer.writerow({\\n 'alpha': get_simulation_params(0)[0],\\n 'gamma': get_simulation_params(0)[1],\\n 'epsilon': get_simulation_params(0)[2],\\n 'success_rate': simulation_rates[index],\\n 'last_failure': last_errors[index]})\\n\\n\\n if N_SIMULATIONS > 1: #multiple simulation AND last simulation\\n\\n plt.figure(1)\\n\\n plt.subplot(211)\\n plt.plot(simulation_rates)\\n plt.title('Success Rate/Simulation')\\n plt.xlabel('# Simulation')\\n plt.ylabel('Success Rate')\\n\\n plt.subplot(212)\\n plt.plot(last_errors)\\n plt.title('Last failed trial per simulation')\\n plt.xlabel('# Simulation')\\n plt.ylabel('Last failed trial')\\n\\n plt.show()\",\n \"def target_portfolio_simulation(num_of_years=30, trials=100, method='normal'):\\n print(\\\"Running method target_portfolio_simulation()\\\")\\n\\n sim_fia_cv = pd.DataFrame(index=range(num_of_years + 1))\\n\\n sim_base_total = pd.DataFrame(index=range(num_of_years + 1))\\n sim_base_income = pd.DataFrame(index=range(num_of_years + 1))\\n\\n sim_port_total = pd.DataFrame(index=range(num_of_years + 1))\\n sim_port_income = pd.DataFrame(index=range(num_of_years + 1))\\n\\n read_income_inputs = pd.read_excel(src + \\\"portfolio_information.xlsx\\\", sheet_name='income_model_inputs',\\n index_col=[0])\\n\\n read_returns_est = pd.read_excel(src + \\\"portfolio_information.xlsx\\\", sheet_name='income_assets_returns_estimates',\\n index_col=[0])\\n\\n read_asset_weights = pd.read_excel(src + \\\"portfolio_information.xlsx\\\", sheet_name='asset_weights',\\n index_col=[0])\\n\\n # read_asset_weights.drop(read_asset_weights.index[-1], axis=0, inplace=True)\\n\\n # read random returns for simulation\\n read_normal = pd.read_csv(src + 'median_returns_unsorted.csv', index_col=[0], parse_dates=True)\\n cols = [read_normal.columns[c].split('_')[1] for c in np.arange(len(read_normal.columns))]\\n read_normal.rename(columns=dict(zip(list(read_normal.columns), cols)), inplace=True)\\n\\n read_small = pd.read_csv(src + 'median_returns_smallest.csv', index_col=[0], parse_dates=True)\\n read_small.rename(columns=dict(zip(list(read_small.columns), cols)), inplace=True)\\n\\n read_large = pd.read_csv(src + 'median_returns_largest.csv', index_col=[0], parse_dates=True)\\n read_large.rename(columns=dict(zip(list(read_large.columns), cols)), inplace=True)\\n\\n assets_col_names = list(read_normal.columns)\\n\\n tickers = list(read_asset_weights.index)\\n wts = np.array(read_asset_weights.loc[:, 'base'])\\n\\n def asset_median_returns(data, ticker):\\n return data.filter(regex=ticker).median(axis=1)\\n\\n # dataframe for unsorted returns (normal)\\n median_returns_normal = read_normal.copy()\\n median_returns_normal.loc[:, 'portfolio_return'] = median_returns_normal.dot(wts)\\n median_normal_fia = pd.DataFrame({'FIA': asset_median_returns(read_normal, 'FIA')})\\n\\n # dataframe for smallest to largest returns\\n median_returns_smallest = read_small.copy()\\n median_returns_smallest.loc[:, 'portfolio_return'] = median_returns_smallest.dot(wts)\\n median_smallest_fia = pd.DataFrame({'FIA': asset_median_returns(read_small, 'FIA')})\\n\\n # dataframe for largest to smallest returns\\n median_returns_largest = read_large.copy()\\n median_returns_largest.loc[:, 'portfolio_return'] = median_returns_largest.dot(wts)\\n median_largest_fia = pd.DataFrame({'FIA': asset_median_returns(read_large, 'FIA')})\\n\\n years = list(range(0, num_of_years + 1))\\n income_cols = ['year', 'strategy_term', 'index_returns', 'term_ret', 'term_ret_with_par', 'term_annualize',\\n 'ann_net_spread', 'term_ret_netspr', 'high_inc_benefit_base', 'rider_fee', 'eoy_income',\\n 'contract_value']\\n\\n term = int(read_income_inputs.loc['term', 'inputs'])\\n fia_ret = read_returns_est.loc[read_returns_est.index[-1], 'Annualized Returns']\\n fia_risk = read_returns_est.loc[read_returns_est.index[-1], 'Annualized Risk']\\n par_rate = float(read_income_inputs.loc['par_rate', 'inputs'])\\n spread = float(read_income_inputs.loc['spread', 'inputs'])\\n bonus_term = int(read_income_inputs.loc['bonus_term', 'inputs'])\\n premium = float(read_income_inputs.loc['premium', 'inputs'])\\n income_bonus = float(read_income_inputs.loc['income_bonus', 'inputs'])\\n\\n income_starts = int(read_income_inputs.loc['start_income_years', 'inputs'])\\n income_growth = float(read_income_inputs.loc['income_growth', 'inputs'])\\n rider_fee = float(read_income_inputs.loc['rider_fee', 'inputs'])\\n inc_payout_factor = float(read_income_inputs.loc['income_payout_factor', 'inputs'])\\n contract_bonus = float(read_income_inputs.loc['contract_bonus', 'inputs'])\\n social = float(read_income_inputs.loc['social', 'inputs'])\\n inflation = float(read_income_inputs.loc['inflation', 'inputs'])\\n wtd_cpn_yield = float(read_income_inputs.loc['wtd_coupon_yld', 'inputs'])\\n life_expectancy = int(read_income_inputs.loc['life_expectancy_age', 'inputs'])\\n clients_age = int(read_income_inputs.loc['clients_age', 'inputs'])\\n\\n # ---------------INCOME MODEL--------------------------------------------\\n runs = 0\\n returns_dict = {}\\n asset_dict = {}\\n fia_dict = {}\\n\\n income_df = pd.DataFrame(index=years, columns=income_cols)\\n income_df.loc[:, 'year'] = years\\n income_df.loc[:, 'strategy_term'] = income_df.loc[:, 'year'] % term\\n income_df.loc[:, 'strategy_term'] = income_df['strategy_term'].apply(lambda x: 1 if x == 0 else 0)\\n\\n if method == 'normal':\\n income_df.loc[:, 'index_returns'] = read_normal.loc[:, 'FIA']\\n\\n elif method == 'smallest':\\n income_df.loc[:, 'index_returns'] = read_small.loc[:, 'FIA']\\n\\n else:\\n income_df.loc[:, 'index_returns'] = read_large.loc[:, 'FIA']\\n\\n # income_df.loc[:, 'index_returns'] = np.random.normal(fia_ret, fia_risk, size=(len(years), 1))\\n\\n cumprod = (1. + income_df['index_returns']).rolling(window=term).agg(lambda x: x.prod()) - 1\\n income_df.loc[:, 'term_ret'] = np.where(income_df.loc[:, 'strategy_term'] == 1, cumprod, 0)\\n income_df.loc[:, 'term_ret_with_par'] = income_df.loc[:, 'term_ret'] * par_rate\\n income_df.loc[:, 'term_annualize'] = income_df.loc[:, 'term_ret_with_par'].apply(\\n lambda x: (1 + x) ** (1 / term) - 1)\\n income_df.loc[:, 'ann_net_spread'] = income_df.loc[:, 'term_annualize'] - spread\\n income_df.loc[:, 'ann_net_spread'] = np.where(income_df.loc[:, 'strategy_term'] == 1,\\n income_df.loc[:, 'ann_net_spread'], 0)\\n income_df.loc[:, 'term_ret_netspr'] = income_df.loc[:, 'ann_net_spread'].apply(lambda x: (1 + x) ** term - 1)\\n\\n for counter in years:\\n if counter == 0:\\n income_df.loc[counter, 'high_inc_benefit_base'] = premium * (1 + income_bonus)\\n\\n elif counter <= min(bonus_term, income_starts):\\n income_df.loc[counter, 'high_inc_benefit_base'] = income_df.loc[counter - 1, 'high_inc_benefit_base'] * \\\\\\n (1 + income_growth)\\n else:\\n income_df.loc[counter, 'high_inc_benefit_base'] = income_df.loc[counter - 1, 'high_inc_benefit_base']\\n\\n income_df.loc[:, 'rider_fee'] = income_df.loc[:, 'high_inc_benefit_base'] * rider_fee\\n income_df.loc[:, 'eoy_income'] = np.where(income_df.loc[:, 'year'] > income_starts,\\n income_df.loc[:, 'high_inc_benefit_base'] * inc_payout_factor, 0)\\n\\n for counter in years:\\n if counter == 0:\\n income_df.loc[counter, 'contract_value'] = premium * (1 + contract_bonus)\\n\\n elif income_df.loc[counter, 'strategy_term'] == 1:\\n x1 = income_df.loc[counter - 1, 'contract_value'] - income_df.loc[counter, 'rider_fee']\\n x2 = (x1 * (1 + income_df.loc[counter, 'term_ret_netspr'])) - income_df.loc[counter, 'eoy_income']\\n income_df.loc[counter, 'contract_value'] = x2\\n\\n else:\\n x1 = income_df.loc[counter - 1, 'contract_value'] - income_df.loc[counter, 'rider_fee'] - \\\\\\n income_df.loc[counter, 'eoy_income']\\n\\n income_df.loc[counter, 'contract_value'] = x1\\n\\n # variable stores the income number that is used in the base and fia portfolio calcs.\\n\\n income_from_fia = income_df.loc[income_df.index[-1], 'eoy_income']\\n\\n income_df.loc[:, 'contract_value'] = income_df.loc[:, 'contract_value'].apply(lambda x: 0 if x <= 0 else x)\\n\\n sim_fia_cv.loc[:, str(runs)] = income_df.loc[:, 'contract_value']\\n\\n # --------------------BASE MODEL---------------------------------------------\\n base_wts = read_asset_weights.loc[:, 'base']\\n base_assets = list(base_wts.index)\\n base_weights = list(base_wts.values)\\n base_returns = list(read_returns_est.loc[:, 'Annualized Returns'].values)\\n base_std = list(read_returns_est.loc[:, 'Annualized Risk'].values)\\n\\n base_investment = float(read_income_inputs.loc['risky_assets', 'Base'])\\n adv_fees = float(read_income_inputs.loc['advisor_fees', 'Base'])\\n\\n # -------------------required income----------------------------------\\n req_annual_income = float(read_income_inputs.loc['annual_income', 'inputs'])\\n income_needed = req_annual_income - social\\n income_net_fia_income = max(0, income_needed - income_from_fia)\\n cpn_income_base = base_investment * wtd_cpn_yield\\n\\n # ----------------------RANDOM RETURNS--------------------------\\n r_cols = base_assets\\n boy_value = ['bv_{}'.format(name) for name in base_assets]\\n eoy_value = ['ev_{}'.format(name) for name in base_assets]\\n\\n random_returns = pd.DataFrame(index=income_df.index, columns=r_cols)\\n\\n for c in range(len(r_cols)):\\n ret = np.random.normal(base_returns[c], base_std[c], size=(len(random_returns.index), 1))\\n\\n if method == 'smallest':\\n random_returns = read_small.copy()\\n\\n elif method == 'largest':\\n random_returns = read_large.copy()\\n\\n else:\\n random_returns = read_normal.copy()\\n\\n base_df = random_returns.copy()\\n fia_portfolio_df = random_returns.copy()\\n port_investment = float(read_income_inputs.loc['risky_assets', 'FIA'])\\n cpn_income_port = port_investment * wtd_cpn_yield\\n\\n # -------------BASE PORTFOLIO----------------------------\\n for name in boy_value:\\n base_df.loc[:, name] = 0.0\\n\\n for counter in years:\\n period_returns = list(random_returns.loc[counter, :])\\n if counter == 0:\\n\\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment for c in range(len(boy_value))]\\n\\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\\n base_df.loc[counter, 'total_net_fees'] = 0.0\\n base_df.loc[counter, 'income'] = 0.0\\n base_investment = base_df.loc[counter, boy_value].sum()\\n\\n elif (counter > 0) and (counter < income_starts):\\n\\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment * (1 + period_returns[c])\\n for c in range(len(boy_value))]\\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\\n base_df.loc[counter, 'adv_fees'] = base_df.loc[counter, 'total'] * adv_fees\\n base_df.loc[counter, 'total_net_fees'] = base_df.loc[counter, 'total'] - base_df.loc[\\n counter, 'adv_fees']\\n\\n # --coupon payment is invested back into the risky portfolio until the income is withdrawn----\\n base_investment = base_df.loc[counter, 'total_net_fees'] + cpn_income_base\\n\\n else:\\n\\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment * (1 + period_returns[c])\\n for c in range(len(boy_value))]\\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\\n base_df.loc[counter, 'adv_fees'] = base_df.loc[counter, 'total'] * adv_fees\\n\\n # ---req. income is adjusted for inflation from the second year of withdrawal. Reinvestment of coupon\\n # stops from the year income starts. Req. income is reduced by the coupon payments\\n\\n if counter == income_starts:\\n\\n income_needed = req_annual_income - social\\n base_df.loc[counter, 'income'] = income_needed - cpn_income_base\\n income_needed = req_annual_income\\n\\n else:\\n income_needed = income_needed * (1 + inflation) - social\\n base_df.loc[counter, 'income'] = income_needed - cpn_income_base\\n income_needed = income_needed + social\\n\\n base_df.loc[counter, 'total_net_fees'] = base_df.loc[counter, 'total'] - \\\\\\n base_df.loc[counter, 'adv_fees'] - \\\\\\n base_df.loc[counter, 'income']\\n\\n base_investment = base_df.loc[counter, 'total_net_fees']\\n\\n base_df.loc[:, 'adj_total'] = base_df.loc[:, 'total_net_fees'].apply(lambda x: x if x > 0 else 0)\\n sim_base_total.loc[:, 's_{}'.format(str(runs))] = base_df.loc[:, 'total_net_fees']\\n sim_base_income.loc[:, 's_{}'.format(str(runs))] = base_df.loc[:, 'income']\\n\\n # ----------------------------FIA PORTFOLIO----------------------------------------------\\n for name in boy_value:\\n fia_portfolio_df.loc[:, name] = 0.0\\n\\n for counter in years:\\n period_returns = list(random_returns.loc[counter, :])\\n if counter == 0:\\n\\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment\\n for c in range(len(boy_value))]\\n fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\\n fia_portfolio_df.loc[counter, 'total_net_fees'] = 0.0\\n fia_portfolio_df.loc[counter, 'income'] = 0.0\\n port_investment = fia_portfolio_df.loc[counter, boy_value].sum()\\n\\n elif (counter > 0) and (counter < income_starts):\\n\\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment * (1 + period_returns[c])\\n for c in range(len(boy_value))]\\n fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\\n fia_portfolio_df.loc[counter, 'adv_fees'] = fia_portfolio_df.loc[counter, 'total'] * adv_fees\\n fia_portfolio_df.loc[counter, 'total_net_fees'] = fia_portfolio_df.loc[counter, 'total'] - \\\\\\n fia_portfolio_df.loc[counter, 'adv_fees']\\n\\n port_investment = fia_portfolio_df.loc[counter, 'total_net_fees'] + cpn_income_port\\n\\n else:\\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment * (1 + period_returns[c])\\n for c in range(len(boy_value))]\\n fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\\n fia_portfolio_df.loc[counter, 'adv_fees'] = fia_portfolio_df.loc[counter, 'total'] * adv_fees\\n\\n # ---req. income is adjusted for inflation from the second year of withdrawal. Reinvestment of coupon\\n # stops from the year income starts. Req. income is reduced by the coupon payments\\n\\n if counter == income_starts:\\n\\n income_needed = req_annual_income - social\\n income_net_fia_income = max(0, income_needed - income_from_fia)\\n fia_portfolio_df.loc[counter, 'income'] = max(0, income_net_fia_income - cpn_income_port)\\n income_needed = req_annual_income\\n\\n else:\\n income_needed = income_needed * (1 + inflation) - social\\n income_net_fia_income = max(0, income_needed - income_from_fia)\\n fia_portfolio_df.loc[counter, 'income'] = max(0, income_net_fia_income - cpn_income_port)\\n income_needed = income_needed + social\\n\\n fia_portfolio_df.loc[counter, 'total_net_fees'] = fia_portfolio_df.loc[counter, 'total'] - \\\\\\n fia_portfolio_df.loc[counter, 'adv_fees'] - \\\\\\n fia_portfolio_df.loc[counter, 'income']\\n\\n port_investment = fia_portfolio_df.loc[counter, 'total_net_fees']\\n\\n sim_port_total.loc[:, 's_{}'.format(str(runs))] = fia_portfolio_df.loc[:, 'total_net_fees'] + \\\\\\n income_df.loc[:, 'contract_value']\\n\\n sim_port_income.loc[:, 's_{}'.format(str(runs))] = fia_portfolio_df.loc[:, 'income']\\n\\n fia_portfolio_df.loc[:, 'adj_total'] = fia_portfolio_df.loc[:, 'total_net_fees'].apply(\\n lambda x: x if x > 0 else 0)\\n\\n # ---------income breakdown for Base portfolio----------------------------------\\n base_df.to_csv(src + 'base_port_detail.csv')\\n sim_base_total.to_csv(src + 'base_ending_values.csv')\\n income_breakdown_base = pd.DataFrame(sim_base_total.quantile(0.5, axis=1))\\n income_breakdown_base.loc[:, 'income_from_portfolio'] = sim_base_income.quantile(0.5, axis=1)\\n income_breakdown_base.loc[:, 'fia_income'] = 0.0\\n income_breakdown_base.loc[:, 'social_security_income'] = social\\n income_breakdown_base.loc[:, 'coupon_income'] = cpn_income_base\\n\\n income_breakdown_base.rename(columns={income_breakdown_base.columns[0]: 'portfolio_ending_value'}, inplace=True)\\n income_breakdown_base.loc[:, 'income_from_portfolio'][\\n income_breakdown_base.loc[:, 'portfolio_ending_value'] <= 0] = 0\\n income_breakdown_base.loc[:, 'total_income'] = income_breakdown_base.loc[:, income_breakdown_base.columns[1:]].sum(\\n axis=1)\\n\\n # ------------Block Ends-------------------------------------------------------------\\n\\n # ---------income breakdown for FIA portfolio----------------------------------\\n fia_portfolio_df.to_csv(src + 'fia_port_detail.csv')\\n sim_port_total.to_csv(src + 'fiaport_ending_values.csv')\\n\\n income_breakdown_port = pd.DataFrame(sim_port_total.quantile(0.5, axis=1))\\n income_breakdown_port.loc[:, 'income_from_portfolio'] = sim_port_income.quantile(0.5, axis=1)\\n income_breakdown_port.loc[:, 'fia_income'] = income_from_fia\\n income_breakdown_port.loc[:, 'social_security_income'] = social\\n income_breakdown_port.loc[:, 'coupon_income'] = cpn_income_port\\n\\n income_breakdown_port.rename(columns={income_breakdown_port.columns[0]: 'portfolio_ending_value'}, inplace=True)\\n income_breakdown_port.loc[:, 'income_from_portfolio'][\\n income_breakdown_port.loc[:, 'portfolio_ending_value'] <= 0] = 0\\n income_breakdown_port.loc[:, 'total_income'] = income_breakdown_port.loc[:, income_breakdown_port.columns[1:]].sum(\\n axis=1)\\n\\n # ------------Block Ends-------------------------------------------------------------\\n q_cut = [0.0, 0.05, 0.25, 0.5, 0.75, 0.95, 1.0]\\n sim_base_income[sim_base_total < income_needed] = 0.0\\n\\n sim_port_income[sim_port_total < income_net_fia_income] = 0\\n\\n sim_port_income = sim_port_income + income_from_fia\\n\\n # base_quantile = sim_base_total.loc[sim_base_total.index[-1]].quantile([0.05, 0.25, 0.50, 0.75, 0.90])\\n\\n # port_quantile = sim_port_total.loc[sim_port_total.index[-1]].quantile([0.05, 0.25, 0.50, 0.75, 0.90])\\n\\n base_quantile = sim_base_total.loc[sim_base_total.index[-1]].quantile(q_cut)\\n\\n port_quantile = sim_port_total.loc[sim_port_total.index[-1]].quantile(q_cut)\\n\\n # q_cut = [0.0, .05, 0.25, 0.5, 0.75, 0.95, 1.0]\\n cols = ['Min', '5th', '25th', '50th', '75th', '90th', 'Max']\\n\\n # ----drop year 0--------\\n sim_base_total = sim_base_total[1:]\\n sim_port_total = sim_port_total[1:]\\n\\n # ----------------quantile analysis for base terminal value--------------------------\\n base_qcut = pd.DataFrame(index=sim_base_total.index, columns=cols)\\n for c in range(len(cols)):\\n base_qcut.loc[:, cols[c]] = sim_base_total.quantile(q_cut[c], axis=1)\\n\\n base_qcut.clip(lower=0, inplace=True)\\n\\n # ----------------------quantile analysis for base income----------------------------\\n base_income_qcut = pd.DataFrame(index=sim_base_income.index, columns=cols)\\n for c in range(len(cols)):\\n base_income_qcut.loc[:, cols[c]] = sim_base_income.quantile(q_cut[c], axis=1)\\n\\n # ----Remove NaN's prior to the income start years------------\\n # base_income_qcut = base_income_qcut.loc[income_starts:]\\n\\n # -------------quantile analysis for portfolio terminal value ----------------\\n port_qcut = pd.DataFrame(index=sim_port_total.index, columns=cols)\\n for c in range(len(cols)):\\n port_qcut.loc[:, cols[c]] = sim_port_total.quantile(q_cut[c], axis=1)\\n\\n port_qcut.clip(lower=0, inplace=True)\\n\\n # ---------------quantile analysis for portfolio income----------------------------\\n port_income_qcut = pd.DataFrame(index=sim_port_income.index, columns=cols)\\n for c in range(len(cols)):\\n port_income_qcut.loc[:, cols[c]] = sim_port_income.quantile(q_cut[c], axis=1)\\n\\n # ----Remove NaN's prior to the income start years------------\\n # port_income_qcut = port_income_qcut.loc[income_starts:]\\n\\n # ----------probability ending value will be less than 0 at the end of the horizon -----------------------\\n base_legacy_risk = (sim_base_total.loc[sim_base_total.index[life_expectancy - clients_age]] < 0).sum() / (\\n trials + 1)\\n port_legacy_risk = (sim_port_total.loc[sim_port_total.index[life_expectancy - clients_age]] < 0).sum() / (\\n trials + 1)\\n\\n legacy_risk = pd.DataFrame([base_legacy_risk, port_legacy_risk,\\n 'Prob. of portfolio value less than 0 at the end of the expected life'],\\n index=['base', 'fia_portfolio', 'Notes'],\\n columns=['Ruin Probability'])\\n\\n # -----------Year-wise probability of ending value greater than 0 -----------------\\n base_psuccess = sim_base_total.apply(lambda x: x > 0).sum(axis=1) / (trials + 1)\\n port_psuccess = sim_port_total.apply(lambda x: x > 0).sum(axis=1) / (trials + 1)\\n\\n # -----------------------WRITING FILES TO EXCEL ---------------------------\\n col_names = ['50th', 'age', 'comment']\\n writer = pd.ExcelWriter(src + method + '_simulated_income_summary.xlsx', engine='xlsxwriter')\\n read_income_inputs.to_excel(writer, sheet_name='inputs_for_income')\\n\\n read_returns_est.to_excel(writer, sheet_name='asset_returns_estimates')\\n\\n age_index = list(range(clients_age + 1, clients_age + len(base_qcut) + 1))\\n base_qcut.loc[:, 'age'] = age_index\\n base_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n base_qcut.loc[income_starts:, col_names].to_excel(writer, sheet_name='base_ending_value_quantiles')\\n\\n base_income_qcut = base_income_qcut.loc[1:, :]\\n base_income_qcut.loc[:, 'age'] = age_index\\n base_income_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n base_income_qcut.loc[income_starts:, col_names].to_excel(writer, sheet_name='base_income_quantiles')\\n\\n port_qcut.loc[:, 'age'] = age_index\\n port_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n port_qcut.loc[income_starts:, col_names].to_excel(writer, sheet_name='fia_port_ending_value_quantiles')\\n\\n port_income_qcut = port_income_qcut.loc[1:, :]\\n port_income_qcut.loc[:, 'age'] = age_index\\n port_income_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n # port_income_qcut.loc[:, 'ending_contract_value'] = sim_fia_cv\\n port_income_qcut.loc[income_starts:, col_names].to_excel(writer, sheet_name='fia_port_income_quantiles')\\n\\n # prob_success_df = pd.concat([base_psuccess, port_psuccess], axis=1)\\n # prob_success_df.rename(columns={prob_success_df.columns[0]: 'prob(ending_value>0)_base',\\n # prob_success_df.columns[1]: 'prob(ending_value>0)_port'}, inplace=True)\\n\\n # prob_success_df.loc[:, 'age'] = age_index\\n # prob_success_df.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n # prob_success_df.to_excel(writer, sheet_name='success_probability')\\n\\n income_breakdown_base = income_breakdown_base.loc[1:, :]\\n income_breakdown_base.loc[:, 'age'] = age_index\\n income_breakdown_base.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n income_breakdown_base.loc[income_starts:, :].to_excel(writer, sheet_name='base_income_breakdown_median')\\n\\n income_breakdown_port = income_breakdown_port.loc[1:, :]\\n income_breakdown_port.loc[:, 'age'] = age_index\\n income_breakdown_port.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n income_breakdown_port.loc[:, 'ending_contract_value'] = income_df.loc[:, 'contract_value']\\n income_breakdown_port.loc[income_starts:, :].to_excel(writer, sheet_name='fia_income_breakdown_median')\\n\\n if method == 'normal':\\n # median_returns_normal.loc[:, 'fia_median_returns'] = median_normal_fia\\n median_returns_normal.to_excel(writer, sheet_name='gr_port_median_normal')\\n\\n elif method == 'smallest':\\n # median_returns_smallest.loc[:, 'fia_median_returns'] = median_smallest_fia\\n median_returns_smallest.to_excel(writer, sheet_name='gr_port_median_asc')\\n\\n else:\\n # median_returns_largest.loc[:, 'fia_median_returns'] = median_largest_fia\\n median_returns_largest.to_excel(writer, sheet_name='gr_port_median_desc')\\n\\n terminal_val = pd.read_csv(src + 'terminal_values.csv', index_col=[0])\\n ending_val = pd.read_csv(src + 'ending_values.csv', index_col=[0])\\n ending_val_ror = pd.read_csv(src + 'ending_values_ror.csv', index_col=[0])\\n\\n terminal_val.to_excel(writer, sheet_name='terminal_values')\\n ending_val.to_excel(writer, sheet_name='port_ending_values')\\n ending_val_ror.to_excel(writer, sheet_name='port_annual_growth')\\n\\n writer.save()\\n\\n # -----------------Plotting charts--------------------------------------------\\n base_qcut.loc[income_starts:].plot(grid=True, title='Quantile Terminal Value - Base Portfolio')\\n plt.savefig(src + \\\"quantile_terminal_base.png\\\")\\n plt.close('all')\\n\\n base_income_qcut.plot(grid=True, title='Quantile Income - Base Portfolio')\\n plt.savefig(src + \\\"quantile_income_base.png\\\")\\n plt.close('all')\\n\\n base_psuccess.plot(grid=True, title='Probability of Success (Portfolio Ending Value > 0) - Base Portfolio')\\n plt.savefig(src + \\\"success_probabilty_base.png\\\")\\n plt.close('all')\\n\\n (1 - base_psuccess).plot(grid=True, title='Probability of Ruin (Portfolio Ending Value < 0) - Base Portfolio')\\n plt.savefig(src + \\\"ruin_probability_base.png\\\")\\n plt.close('all')\\n\\n port_qcut.loc[income_starts:].plot(grid=True, title='Quantile Terminal Value - FIA Portfolio')\\n plt.savefig(src + \\\"quantile_terminal_fia.png\\\")\\n plt.close('all')\\n\\n port_income_qcut.plot(grid=True, title='Quantile Income - FIA Portfolio')\\n plt.savefig(src + \\\"quantile_income_fia.png\\\")\\n plt.close('all')\\n\\n port_psuccess.plot(grid=True, title='Probability of Success (Portfolio Ending Value > 0) - FIA Portfolio')\\n plt.savefig(src + \\\"success_probabilty_fia.png\\\")\\n plt.close('all')\\n\\n (1 - port_psuccess).plot(grid=True, title='Probability of Ruin (Portfolio Ending Value < 0) - FIA Portfolio')\\n plt.savefig(src + \\\"ruin_probability_fia.png\\\")\\n plt.close('all')\\n\\n print(\\\"simulation completed for {}\\\".format(method))\",\n \"def simulate_trading(self):\\n self._generate_trading_instances()\\n self._run_backtest()\\n self.portfolio.output_equity()\\n res=self.portfolio.get_statistics()\\n self.plot.plot_equity()\\n return res\",\n \"def run_simulator(simulation_parameters, spectrum_portfolio,\\n ant_heights, site_radii, modulation_and_coding_lut, costs):\\n\\n unprojected_point = {\\n 'type': 'Feature',\\n 'geometry': {\\n 'type': 'Point',\\n 'coordinates': (-0.07496, 51.42411),\\n },\\n 'properties': {\\n 'site_id': 'Crystal Palace Radio Tower'\\n }\\n }\\n\\n unprojected_crs = 'epsg:4326'\\n projected_crs = 'epsg:3857'\\n\\n environments =[\\n 'urban',\\n 'suburban',\\n 'rural'\\n ]\\n\\n for environment in environments:\\n for site_radius in site_radii[environment]:\\n\\n print('--working on {}: {}'.format(environment, site_radius))\\n\\n transmitter, interfering_transmitters, site_area, interfering_site_areas = \\\\\\n produce_sites_and_site_areas(\\n unprojected_point['geometry']['coordinates'],\\n site_radius,\\n unprojected_crs,\\n projected_crs\\n )\\n\\n receivers = generate_receivers(site_area, SIMULATION_PARAMETERS, 1)\\n\\n for frequency, bandwidth, generation in spectrum_portfolio:\\n for ant_height in ant_heights:\\n\\n MANAGER = SimulationManager(\\n transmitter, interfering_transmitters, receivers,\\n site_area, SIMULATION_PARAMETERS\\n )\\n\\n results = MANAGER.estimate_link_budget(\\n frequency, bandwidth, generation, ant_height,\\n environment,\\n MODULATION_AND_CODING_LUT,\\n SIMULATION_PARAMETERS\\n )\\n\\n folder = os.path.join(BASE_PATH, '..', 'results', 'full_tables')\\n filename = 'full_capacity_lut_{}_{}_{}_{}.csv'.format(\\n environment, site_radius, frequency, ant_height)\\n\\n write_full_results(results, environment, site_radius,\\n frequency, bandwidth, generation, ant_height,\\n folder, filename, simulation_parameters)\\n\\n average_site_results = obtain_average_values(\\n results, simulation_parameters\\n )\\n\\n results_directory = os.path.join(BASE_PATH, '..', 'results')\\n\\n write_frequency_lookup_table(average_site_results, environment,\\n site_radius, frequency, bandwidth, generation,\\n ant_height, results_directory,\\n 'average_capacity_lut.csv',\\n simulation_parameters\\n )\\n\\n if frequency == spectrum_portfolio[0][0]:\\n\\n percentile_site_results = obtain_percentile_values(\\n results, simulation_parameters\\n )\\n\\n percentile_site_results = calculate_costs(\\n percentile_site_results, costs, simulation_parameters,\\n site_radius, environment\\n )\\n\\n write_cost_lookup_table(percentile_site_results, environment,\\n site_radius, frequency, bandwidth, generation,\\n ant_height, results_directory,\\n 'percentile_{}_capacity_lut.csv'.format(\\n simulation_parameters['percentile']),\\n simulation_parameters\\n )\\n\\n ## write out as shapes, if desired, for debugging purposes\\n geojson_receivers = convert_results_geojson(results)\\n\\n write_shapefile(\\n geojson_receivers, os.path.join(results_directory, 'shapes'),\\n 'receivers_{}.shp'.format(site_radius),\\n projected_crs\\n )\\n\\n write_shapefile(\\n transmitter, os.path.join(results_directory, 'shapes'),\\n 'transmitter_{}.shp'.format(site_radius),\\n projected_crs\\n )\\n\\n write_shapefile(\\n site_area, os.path.join(results_directory, 'shapes'),\\n 'site_area_{}.shp'.format(site_radius),\\n projected_crs\\n )\\n\\n write_shapefile(\\n interfering_transmitters, os.path.join(results_directory, 'shapes'),\\n 'interfering_transmitters_{}.shp'.format(site_radius),\\n projected_crs\\n )\\n\\n write_shapefile(\\n interfering_site_areas, os.path.join(results_directory, 'shapes'),\\n 'interfering_site_areas_{}.shp'.format(site_radius),\\n projected_crs\\n )\",\n \"def simulation_using_historical_returns(num_of_years=30, trials=100, method='normal'):\\n\\n print(\\\"Running method simulation_using_historical_returns()\\\")\\n\\n sim_fia_cv = pd.DataFrame(index=range(num_of_years + 1))\\n\\n sim_base_total = pd.DataFrame(index=range(num_of_years + 1))\\n sim_base_income = pd.DataFrame(index=range(num_of_years + 1))\\n\\n sim_port_total = pd.DataFrame(index=range(num_of_years + 1))\\n sim_port_income = pd.DataFrame(index=range(num_of_years + 1))\\n\\n read_income_inputs = pd.read_excel(src + \\\"portfolio_information.xlsx\\\", sheet_name='income_model_inputs',\\n index_col=[0])\\n\\n read_returns_est = pd.read_excel(src + \\\"portfolio_information.xlsx\\\", sheet_name='income_assets_returns_estimates',\\n index_col=[0])\\n\\n read_asset_weights = pd.read_excel(src + \\\"portfolio_information.xlsx\\\", sheet_name='asset_weights',\\n index_col=[0])\\n\\n # read the base portfolio from accumulation model\\n fia_name = read_income_inputs.loc['fia_name', 'inputs']\\n path = src + fia_name + '/'\\n file_name = fia_name + '_portfolio.csv'\\n\\n # base_port = pd.read_csv(path + 'base_portfolio.csv', index_col=[0], parse_dates=True)\\n\\n base_port = pd.read_csv(path + file_name, index_col=[0], parse_dates=True)\\n\\n ls1 = list(read_asset_weights.index[:-2])\\n ls2 = ['Cash', fia_name, 'Total']\\n combined_ls = ls1 + ls2\\n base_port = base_port.loc[:, combined_ls]\\n base_port.loc[:, 'port_return'] = base_port['Total'].pct_change().fillna(0)\\n base_port.drop('Total', axis=1, inplace=True)\\n yearly_base_port = base_port.groupby(by=base_port.index.year).apply(sum)\\n\\n # --Simulate only for first 20 years of returns--------\\n first_n_years = int(read_income_inputs.loc['simulate_for_first_n_years', 'inputs'])\\n yearly_base_port = yearly_base_port.loc[yearly_base_port.index[-(first_n_years + 1):], :]\\n\\n # --------Unsorted returns ---------------------\\n read_normal = yearly_base_port.copy()\\n read_normal.iloc[0] = 0.0\\n read_normal.to_csv(src + 'base_port_ann_ret_unsorted.csv')\\n read_normal.reset_index(drop=True, inplace=True)\\n\\n # ---make a copy to add to the consolidated summary file----\\n save_normal = read_normal.copy()\\n read_normal.drop('port_return', axis=1, inplace=True)\\n read_normal.rename(columns={fia_name: 'FIA'}, inplace=True)\\n\\n # - Shift returns by one year and assign 0 returns to all assets in teh year 0, the year of initial investment.\\n # read_normal = read_normal.shift().fillna(0)\\n read_normal.to_csv(src + 'historical_port_return_unsorted.csv')\\n\\n # ----Best to worst returns----\\n read_large = yearly_base_port.copy()\\n\\n # --Logic to sort the last 20 years of returns and assign 0 to first year, the year of investment\\n read_large.iloc[0] = 1000.0\\n read_large = read_large.sort_values(by=['port_return'], ascending=False)\\n # --assign 0 to first year, the year of investment\\n read_large.iloc[0] = 0.0\\n read_large.to_csv(src + 'base_port_ann_ret_desc.csv')\\n read_large.reset_index(drop=True, inplace=True)\\n # read_large = read_large.shift().fillna(0)\\n\\n # ---make a copy to add to the consolidated summary file---\\n save_desc = read_large.copy()\\n read_large.drop('port_return', axis=1, inplace=True)\\n read_large.rename(columns={fia_name: 'FIA'}, inplace=True)\\n read_large.to_csv(src + 'historical_port_return_desc.csv')\\n\\n # ------Worst to Best Returns------------\\n read_small = yearly_base_port.copy()\\n read_small.iloc[0] = -1000.0\\n read_small = read_small.sort_values(by=['port_return'], ascending=True)\\n read_small.iloc[0] = 0.0\\n read_small.to_csv(src + 'base_port_ann_ret_asc.csv')\\n read_small.reset_index(drop=True, inplace=True)\\n # read_small = read_small.shift().fillna(0)\\n\\n # ---make a copy to add to the consolidated summary file---\\n save_asc = read_small.copy()\\n read_small.drop('port_return', axis=1, inplace=True)\\n read_small.rename(columns={fia_name: 'FIA'}, inplace=True)\\n read_small.to_csv(src + 'historical_port_return_asc.csv')\\n\\n # read_asset_weights.drop(read_asset_weights.index[-1], axis=0, inplace=True)\\n\\n # read random returns for simulation\\n # read_normal = pd.read_csv(src + 'median_returns_unsorted.csv', index_col=[0], parse_dates=True)\\n # cols = [read_normal.columns[c].split('_')[1] for c in np.arange(len(read_normal.columns))]\\n # read_normal.rename(columns=dict(zip(list(read_normal.columns), cols)), inplace=True)\\n #\\n # read_small = pd.read_csv(src + 'median_returns_smallest.csv', index_col=[0], parse_dates=True)\\n # read_small.rename(columns=dict(zip(list(read_small.columns), cols)), inplace=True)\\n #\\n # read_large = pd.read_csv(src + 'median_returns_largest.csv', index_col=[0], parse_dates=True)\\n # read_large.rename(columns=dict(zip(list(read_large.columns), cols)), inplace=True)\\n\\n assets_col_names = list(read_normal.columns)\\n\\n tickers = list(read_asset_weights.index)\\n wts = np.array(read_asset_weights.loc[:, 'base'])\\n\\n def asset_median_returns(data, ticker):\\n return data.filter(regex=ticker).median(axis=1)\\n\\n # dataframe for unsorted returns (normal)\\n median_returns_normal = read_normal.copy()\\n median_returns_normal.loc[:, 'portfolio_return'] = median_returns_normal.dot(wts)\\n # median_returns_normal.rename(columns={fia_name: 'FIA'}, inplace=True)\\n # median_normal_fia = pd.DataFrame({'FIA': asset_median_returns(read_normal, 'FIA')})\\n\\n # dataframe for smallest to largest returns\\n median_returns_smallest = read_small.copy()\\n median_returns_smallest.loc[:, 'portfolio_return'] = median_returns_smallest.dot(wts)\\n # median_returns_smallest.rename(columns={fia_name: 'FIA'}, inplace=True)\\n # median_smallest_fia = pd.DataFrame({'FIA': asset_median_returns(read_small, 'FIA')})\\n\\n # dataframe for largest to smallest returns\\n median_returns_largest = read_large.copy()\\n median_returns_largest.loc[:, 'portfolio_return'] = median_returns_largest.dot(wts)\\n # median_returns_largest.rename(columns={fia_name: 'FIA'}, inplace=True)\\n # median_largest_fia = pd.DataFrame({'FIA': asset_median_returns(read_large, 'FIA')})\\n\\n # years = list(range(0, num_of_years + 1))\\n years = np.arange(len(read_normal))\\n income_cols = ['year', 'strategy_term', 'index_returns', 'term_ret', 'term_ret_with_par', 'term_annualize',\\n 'ann_net_spread', 'term_ret_netspr', 'high_inc_benefit_base', 'rider_fee', 'eoy_income',\\n 'contract_value']\\n\\n term = int(read_income_inputs.loc['term', 'inputs'])\\n fia_ret = read_returns_est.loc[read_returns_est.index[-1], 'Annualized Returns']\\n fia_risk = read_returns_est.loc[read_returns_est.index[-1], 'Annualized Risk']\\n par_rate = float(read_income_inputs.loc['par_rate', 'inputs'])\\n spread = float(read_income_inputs.loc['spread', 'inputs'])\\n bonus_term = int(read_income_inputs.loc['bonus_term', 'inputs'])\\n premium = float(read_income_inputs.loc['premium', 'inputs'])\\n income_bonus = float(read_income_inputs.loc['income_bonus', 'inputs'])\\n\\n income_starts = int(read_income_inputs.loc['start_income_years', 'inputs'])\\n # --Income start right away, not deferred ----\\n income_starts = 0\\n income_growth = float(read_income_inputs.loc['income_growth', 'inputs'])\\n rider_fee = float(read_income_inputs.loc['rider_fee', 'inputs'])\\n inc_payout_factor = float(read_income_inputs.loc['income_payout_factor', 'inputs'])\\n contract_bonus = float(read_income_inputs.loc['contract_bonus', 'inputs'])\\n social = float(read_income_inputs.loc['social', 'inputs'])\\n inflation = float(read_income_inputs.loc['inflation', 'inputs'])\\n wtd_cpn_yield = float(read_income_inputs.loc['wtd_coupon_yld', 'inputs'])\\n life_expectancy = int(read_income_inputs.loc['life_expectancy_age', 'inputs'])\\n clients_age = int(read_income_inputs.loc['clients_age', 'inputs'])\\n\\n # --------------------------------------INCOME MODEL--------------------------------------------\\n runs = 0\\n returns_dict = {}\\n asset_dict = {}\\n fia_dict = {}\\n\\n income_df = pd.DataFrame(index=years, columns=income_cols)\\n income_df.loc[:, 'year'] = years\\n income_df.loc[:, 'strategy_term'] = income_df.loc[:, 'year'] % term\\n income_df.loc[:, 'strategy_term'] = income_df['strategy_term'].apply(lambda x: 1 if x == 0 else 0)\\n\\n if method == 'normal':\\n income_df.loc[:, 'index_returns'] = read_normal.loc[:, 'FIA']\\n\\n elif method == 'smallest':\\n income_df.loc[:, 'index_returns'] = read_small.loc[:, 'FIA']\\n\\n else:\\n income_df.loc[:, 'index_returns'] = read_large.loc[:, 'FIA']\\n\\n # income_df.loc[:, 'index_returns'] = np.random.normal(fia_ret, fia_risk, size=(len(years), 1))\\n\\n cumprod = (1. + income_df['index_returns']).rolling(window=term).agg(lambda x: x.prod()) - 1\\n income_df.loc[:, 'term_ret'] = np.where(income_df.loc[:, 'strategy_term'] == 1, cumprod, 0)\\n income_df.loc[:, 'term_ret_with_par'] = income_df.loc[:, 'term_ret'] * par_rate\\n income_df.loc[:, 'term_annualize'] = income_df.loc[:, 'term_ret_with_par'].apply(\\n lambda x: (1 + x) ** (1 / term) - 1)\\n income_df.loc[:, 'ann_net_spread'] = income_df.loc[:, 'term_annualize'] - spread\\n income_df.loc[:, 'ann_net_spread'] = np.where(income_df.loc[:, 'strategy_term'] == 1,\\n income_df.loc[:, 'ann_net_spread'], 0)\\n income_df.loc[:, 'term_ret_netspr'] = income_df.loc[:, 'ann_net_spread'].apply(lambda x: (1 + x) ** term - 1)\\n\\n for counter in years:\\n if counter == 0:\\n income_df.loc[counter, 'high_inc_benefit_base'] = premium * (1 + income_bonus)\\n\\n elif counter <= min(bonus_term, income_starts):\\n income_df.loc[counter, 'high_inc_benefit_base'] = income_df.loc[counter - 1, 'high_inc_benefit_base'] * \\\\\\n (1 + income_growth)\\n else:\\n income_df.loc[counter, 'high_inc_benefit_base'] = income_df.loc[counter - 1, 'high_inc_benefit_base']\\n\\n income_df.loc[:, 'rider_fee'] = income_df.loc[:, 'high_inc_benefit_base'] * rider_fee\\n income_df.loc[:, 'eoy_income'] = np.where(income_df.loc[:, 'year'] > income_starts,\\n income_df.loc[:, 'high_inc_benefit_base'] * inc_payout_factor, 0)\\n\\n for counter in years:\\n if counter == 0:\\n income_df.loc[counter, 'contract_value'] = premium * (1 + contract_bonus)\\n\\n elif income_df.loc[counter, 'strategy_term'] == 1:\\n x1 = income_df.loc[counter - 1, 'contract_value'] - income_df.loc[counter, 'rider_fee']\\n x2 = (x1 * (1 + income_df.loc[counter, 'term_ret_netspr'])) - income_df.loc[counter, 'eoy_income']\\n income_df.loc[counter, 'contract_value'] = x2\\n\\n else:\\n x1 = income_df.loc[counter - 1, 'contract_value'] - income_df.loc[counter, 'rider_fee'] - \\\\\\n income_df.loc[counter, 'eoy_income']\\n\\n income_df.loc[counter, 'contract_value'] = x1\\n\\n # variable stores the income number that is used in the base and fia portfolio calcs.\\n\\n income_from_fia = income_df.loc[income_df.index[-1], 'eoy_income']\\n\\n income_df.loc[:, 'contract_value'] = income_df.loc[:, 'contract_value'].apply(lambda x: 0 if x <= 0 else x)\\n\\n sim_fia_cv.loc[:, str(runs)] = income_df.loc[:, 'contract_value']\\n\\n # ---------------------------------BASE MODEL---------------------------------------------\\n\\n base_wts = read_asset_weights.loc[:, 'base']\\n base_assets = list(base_wts.index)\\n base_weights = list(base_wts.values)\\n base_returns = list(read_returns_est.loc[:, 'Annualized Returns'].values)\\n base_std = list(read_returns_est.loc[:, 'Annualized Risk'].values)\\n\\n base_investment = float(read_income_inputs.loc['risky_assets', 'Base'])\\n adv_fees = float(read_income_inputs.loc['advisor_fees', 'Base'])\\n\\n # -------------------------------------required income--------------------------------\\n req_annual_income = float(read_income_inputs.loc['annual_income', 'inputs'])\\n income_needed = req_annual_income - social\\n income_net_fia_income = max(0, income_needed - income_from_fia)\\n cpn_income_base = base_investment * wtd_cpn_yield\\n\\n # --------------------------------------------RANDOM RETURNS-----------------------------------\\n r_cols = base_assets\\n boy_value = ['bv_{}'.format(name) for name in base_assets]\\n eoy_value = ['ev_{}'.format(name) for name in base_assets]\\n\\n random_returns = pd.DataFrame(index=income_df.index, columns=r_cols)\\n\\n for c in range(len(r_cols)):\\n ret = np.random.normal(base_returns[c], base_std[c], size=(len(random_returns.index), 1))\\n\\n if method == 'smallest':\\n random_returns = read_small.copy()\\n\\n elif method == 'largest':\\n random_returns = read_large.copy()\\n\\n else:\\n random_returns = read_normal.copy()\\n\\n base_df = random_returns.copy()\\n fia_portfolio_df = random_returns.copy()\\n port_investment = float(read_income_inputs.loc['risky_assets', 'FIA'])\\n cpn_income_port = port_investment * wtd_cpn_yield\\n\\n # -------------------------------------BASE PORTFOLIO----------------------------\\n for name in boy_value:\\n base_df.loc[:, name] = 0.0\\n\\n for counter in years:\\n period_returns = list(random_returns.loc[counter, :])\\n if counter == 0:\\n\\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment for c in range(len(boy_value))]\\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\\n base_df.loc[counter, 'total_net_fees'] = 0.0\\n base_df.loc[counter, 'income'] = 0.0\\n base_investment = base_df.loc[counter, boy_value].sum()\\n\\n elif (counter > 0) and (counter < income_starts):\\n\\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment * (1 + period_returns[c])\\n for c in range(len(boy_value))]\\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\\n base_df.loc[counter, 'adv_fees'] = base_df.loc[counter, 'total'] * adv_fees\\n base_df.loc[counter, 'total_net_fees'] = base_df.loc[counter, 'total'] - base_df.loc[\\n counter, 'adv_fees']\\n\\n # --coupon payment is invested back into the risky portfolio until the income is withdrawn----\\n base_investment = base_df.loc[counter, 'total_net_fees'] + cpn_income_base\\n\\n else:\\n\\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment * (1 + period_returns[c])\\n for c in range(len(boy_value))]\\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\\n base_df.loc[counter, 'adv_fees'] = base_df.loc[counter, 'total'] * adv_fees\\n\\n # ---req. income is adjusted for inflation from the second year of withdrawal. Reinvestment of coupon\\n # stops from the year income starts. Req. income is reduced by the coupon payments\\n\\n if counter == income_starts:\\n\\n income_needed = req_annual_income - social\\n base_df.loc[counter, 'income'] = income_needed - cpn_income_base\\n income_needed = req_annual_income\\n\\n else:\\n income_needed = income_needed * (1 + inflation) - social\\n base_df.loc[counter, 'income'] = income_needed - cpn_income_base\\n income_needed = income_needed + social\\n\\n base_df.loc[counter, 'total_net_fees'] = base_df.loc[counter, 'total'] - \\\\\\n base_df.loc[counter, 'adv_fees'] - \\\\\\n base_df.loc[counter, 'income']\\n\\n base_investment = base_df.loc[counter, 'total_net_fees']\\n\\n base_df.loc[:, 'adj_total'] = base_df.loc[:, 'total_net_fees'].apply(lambda x: x if x > 0 else 0)\\n sim_base_total.loc[:, 's_{}'.format(str(runs))] = base_df.loc[:, 'total_net_fees']\\n sim_base_income.loc[:, 's_{}'.format(str(runs))] = base_df.loc[:, 'income']\\n\\n # -------------------required income----------------------------------\\n req_annual_income = float(read_income_inputs.loc['annual_income', 'inputs'])\\n income_needed = req_annual_income - social\\n # income_net_fia_income = max(0, income_needed - income_from_fia)\\n cpn_income_base = base_investment * wtd_cpn_yield\\n\\n # ----------------------------FIA PORTFOLIO----------------------------------------------\\n for name in boy_value:\\n fia_portfolio_df.loc[:, name] = 0.0\\n\\n for counter in years:\\n period_returns = list(random_returns.loc[counter, :])\\n if counter == 0:\\n\\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment\\n for c in range(len(boy_value))]\\n fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\\n fia_portfolio_df.loc[counter, 'total_net_fees'] = 0.0\\n fia_portfolio_df.loc[counter, 'income'] = 0.0\\n port_investment = fia_portfolio_df.loc[counter, boy_value].sum()\\n\\n elif (counter > 0) and (counter < income_starts):\\n\\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment * (1 + period_returns[c])\\n for c in range(len(boy_value))]\\n fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\\n fia_portfolio_df.loc[counter, 'adv_fees'] = fia_portfolio_df.loc[counter, 'total'] * adv_fees\\n fia_portfolio_df.loc[counter, 'total_net_fees'] = fia_portfolio_df.loc[counter, 'total'] - \\\\\\n fia_portfolio_df.loc[counter, 'adv_fees']\\n\\n port_investment = fia_portfolio_df.loc[counter, 'total_net_fees'] + cpn_income_port\\n\\n else:\\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment * (1 + period_returns[c])\\n for c in range(len(boy_value))]\\n fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\\n fia_portfolio_df.loc[counter, 'adv_fees'] = fia_portfolio_df.loc[counter, 'total'] * adv_fees\\n\\n # ---req. income is adjusted for inflation from the second year of withdrawal. Reinvestment of coupon\\n # stops from the year income starts. Req. income is reduced by the coupon payments\\n\\n if counter == income_starts:\\n\\n income_needed = req_annual_income - social\\n income_net_fia_income = max(0, income_needed - income_from_fia)\\n fia_portfolio_df.loc[counter, 'income'] = max(0, income_net_fia_income - cpn_income_port)\\n income_needed = req_annual_income\\n\\n else:\\n income_needed = income_needed * (1 + inflation) - social\\n income_net_fia_income = max(0, income_needed - income_from_fia)\\n fia_portfolio_df.loc[counter, 'income'] = max(0, income_net_fia_income - cpn_income_port)\\n income_needed = income_needed + social\\n\\n fia_portfolio_df.loc[counter, 'total_net_fees'] = fia_portfolio_df.loc[counter, 'total'] - \\\\\\n fia_portfolio_df.loc[counter, 'adv_fees'] - \\\\\\n fia_portfolio_df.loc[counter, 'income']\\n\\n port_investment = fia_portfolio_df.loc[counter, 'total_net_fees']\\n\\n sim_port_total.loc[:, 's_{}'.format(str(runs))] = fia_portfolio_df.loc[:, 'total_net_fees'] + \\\\\\n income_df.loc[:, 'contract_value']\\n\\n sim_port_income.loc[:, 's_{}'.format(str(runs))] = fia_portfolio_df.loc[:, 'income']\\n\\n fia_portfolio_df.loc[:, 'adj_total'] = fia_portfolio_df.loc[:, 'total_net_fees'].apply(\\n lambda x: x if x > 0 else 0)\\n\\n # -------------------------------------income breakdown for Base portfolio----------------------------------\\n base_df.to_csv(src + method + '_base_port_detail.csv')\\n sim_base_total.to_csv(src + 'base_ending_values.csv')\\n income_breakdown_base = pd.DataFrame(sim_base_total.quantile(0.5, axis=1))\\n income_breakdown_base.loc[:, 'income_from_portfolio'] = sim_base_income.quantile(0.5, axis=1)\\n income_breakdown_base.loc[:, 'fia_income'] = 0.0\\n income_breakdown_base.loc[:, 'social_security_income'] = social\\n income_breakdown_base.loc[:, 'coupon_income'] = cpn_income_base\\n\\n income_breakdown_base.rename(columns={income_breakdown_base.columns[0]: 'portfolio_ending_value'}, inplace=True)\\n income_breakdown_base.loc[:, 'income_from_portfolio'][\\n income_breakdown_base.loc[:, 'portfolio_ending_value'] <= 0] = 0\\n income_breakdown_base.loc[:, 'total_income'] = income_breakdown_base.loc[:, income_breakdown_base.columns[1:]].sum(\\n axis=1)\\n\\n # ------------------------------------Block Ends-------------------------------------------------------------\\n\\n # ------------------------------income breakdown for FIA portfolio----------------------------------\\n fia_portfolio_df.loc[:, 'contract_value'] = income_df.loc[:, 'contract_value']\\n fia_portfolio_df.to_csv(src + method + '_fia_port_detail.csv')\\n sim_port_total.to_csv(src + 'fiaport_ending_values.csv')\\n\\n income_breakdown_port = pd.DataFrame(sim_port_total.quantile(0.5, axis=1))\\n income_breakdown_port.loc[:, 'income_from_portfolio'] = sim_port_income.quantile(0.5, axis=1)\\n income_breakdown_port.loc[:, 'fia_income'] = income_from_fia\\n income_breakdown_port.loc[:, 'social_security_income'] = social\\n income_breakdown_port.loc[:, 'coupon_income'] = cpn_income_port\\n\\n income_breakdown_port.rename(columns={income_breakdown_port.columns[0]: 'portfolio_ending_value'}, inplace=True)\\n income_breakdown_port.loc[:, 'income_from_portfolio'][\\n income_breakdown_port.loc[:, 'portfolio_ending_value'] <= 0] = 0\\n income_breakdown_port.loc[:, 'total_income'] = income_breakdown_port.loc[:, income_breakdown_port.columns[1:]].sum(\\n axis=1)\\n\\n # ------------------------------------Block Ends-------------------------------------------------------------\\n q_cut = [0.0, .05, 0.25, 0.5, 0.75, 0.95, 1.0]\\n sim_base_income[sim_base_total < income_needed] = 0.0\\n\\n sim_port_income[sim_port_total < income_net_fia_income] = 0\\n\\n sim_port_income = sim_port_income + income_from_fia\\n\\n base_quantile = sim_base_total.loc[sim_base_total.index[-1]].quantile(q_cut)\\n\\n port_quantile = sim_port_total.loc[sim_port_total.index[-1]].quantile(q_cut)\\n\\n cols = ['Min', '5th', '25th', '50th', '75th', '90th', 'Max']\\n\\n # ----------------------------------drop year 0---------------------------------\\n sim_base_total = sim_base_total[1:]\\n sim_port_total = sim_port_total[1:]\\n\\n # --------------------------------quantile analysis for base terminal value-----------------------------\\n base_qcut = pd.DataFrame(index=sim_base_total.index, columns=cols)\\n for c in range(len(cols)):\\n base_qcut.loc[:, cols[c]] = sim_base_total.quantile(q_cut[c], axis=1)\\n\\n base_qcut.clip(lower=0, inplace=True)\\n\\n # ----------------------------------quantile analysis for base income----------------------------\\n base_income_qcut = pd.DataFrame(index=sim_base_income.index, columns=cols)\\n for c in range(len(cols)):\\n base_income_qcut.loc[:, cols[c]] = sim_base_income.quantile(q_cut[c], axis=1)\\n\\n # ----Remove NaN's prior to the income start years------------\\n # base_income_qcut = base_income_qcut.loc[income_starts:]\\n\\n # -------------quantile analysis for portfolio terminal value ---------------\\n\\n port_qcut = pd.DataFrame(index=sim_port_total.index, columns=cols)\\n for c in range(len(cols)):\\n port_qcut.loc[:, cols[c]] = sim_port_total.quantile(q_cut[c], axis=1)\\n\\n port_qcut.clip(lower=0, inplace=True)\\n\\n # ---------------quantile analysis for portfolio income----------------------------\\n port_income_qcut = pd.DataFrame(index=sim_port_income.index, columns=cols)\\n for c in range(len(cols)):\\n port_income_qcut.loc[:, cols[c]] = sim_port_income.quantile(q_cut[c], axis=1)\\n\\n # ----Remove NaN's prior to the income start years------------\\n # port_income_qcut = port_income_qcut.loc[income_starts:]\\n\\n # ----------probability ending value will be less than 0 at the end of the horizon -------------------------\\n\\n base_legacy_risk = (sim_base_total.loc[sim_base_total.index[life_expectancy - clients_age]] < 0).sum() / (\\n trials + 1)\\n port_legacy_risk = (sim_port_total.loc[sim_port_total.index[life_expectancy - clients_age]] < 0).sum() / (\\n trials + 1)\\n\\n legacy_risk = pd.DataFrame([base_legacy_risk, port_legacy_risk,\\n 'Prob. of portfolio value less than 0 at the end of the expected life'],\\n index=['base', 'fia_portfolio', 'Notes'],\\n columns=['Ruin Probability'])\\n\\n # -----------Year-wise probability of ending value greater than 0 -----------------\\n base_psuccess = sim_base_total.apply(lambda x: x > 0).sum(axis=1) / (trials + 1)\\n port_psuccess = sim_port_total.apply(lambda x: x > 0).sum(axis=1) / (trials + 1)\\n\\n # -----------------------WRITING FILES TO EXCEL ---------------------------\\n col_names = ['50th', 'age', 'comment']\\n writer = pd.ExcelWriter(src + method + '_historical_simulated_income_summary.xlsx', engine='xlsxwriter')\\n read_income_inputs.to_excel(writer, sheet_name='inputs_for_income')\\n\\n read_returns_est.to_excel(writer, sheet_name='asset_returns_estimates')\\n\\n age_index = list(range(clients_age + 1, clients_age + len(base_qcut) + 1))\\n base_qcut.loc[:, 'age'] = age_index\\n base_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n base_qcut.loc[income_starts:, col_names].to_excel(writer, sheet_name='base_ending_value_quantiles')\\n\\n base_income_qcut = base_income_qcut.loc[1:, :]\\n base_income_qcut.loc[:, 'age'] = age_index\\n base_income_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n base_income_qcut.loc[income_starts:, col_names].to_excel(writer, sheet_name='base_income_quantiles')\\n\\n port_qcut.loc[:, 'age'] = age_index\\n port_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n port_qcut.loc[income_starts:, col_names].to_excel(writer, sheet_name='fia_port_ending_value_quantiles')\\n\\n port_income_qcut = port_income_qcut.loc[1:, :]\\n port_income_qcut.loc[:, 'age'] = age_index\\n port_income_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n port_income_qcut.loc[income_starts:, col_names].to_excel(writer, sheet_name='fia_port_income_quantiles')\\n\\n # prob_success_df = pd.concat([base_psuccess, port_psuccess], axis=1)\\n # prob_success_df.rename(columns={prob_success_df.columns[0]: 'prob(ending_value>0)_base',\\n # prob_success_df.columns[1]: 'prob(ending_value>0)_port'}, inplace=True)\\n\\n # prob_success_df.loc[:, 'age'] = age_index\\n # prob_success_df.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n # prob_success_df.to_excel(writer, sheet_name='success_probability')\\n\\n income_breakdown_base = income_breakdown_base.loc[1:, :]\\n income_breakdown_base.loc[:, 'age'] = age_index\\n income_breakdown_base.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n income_breakdown_base.loc[income_starts:, :].to_excel(writer, sheet_name='base_income_breakdown_median')\\n\\n income_breakdown_port = income_breakdown_port.loc[1:, :]\\n income_breakdown_port.loc[:, 'age'] = age_index\\n income_breakdown_port.loc[:, 'ending contract value'] = income_df.loc[:, 'contract_value'].fillna(0)\\n income_breakdown_port.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n\\n income_breakdown_port.loc[income_starts:, :].to_excel(writer, sheet_name='fia_income_breakdown_median')\\n\\n if method == 'normal':\\n # median_returns_normal.loc[:, 'fia_median_returns'] = median_normal_fia\\n # median_returns_normal.to_excel(writer, sheet_name='unsorted_port_return')\\n save_normal.to_excel(writer, sheet_name='port_return_unsorted')\\n\\n elif method == 'smallest':\\n # median_returns_smallest.loc[:, 'fia_median_returns'] = median_smallest_fia\\n # median_returns_smallest.to_excel(writer, sheet_name='port_return_asc')\\n save_asc.to_excel(writer, sheet_name='port_return_asc')\\n\\n else:\\n # median_returns_largest.loc[:, 'fia_median_returns'] = median_largest_fia\\n # median_returns_largest.to_excel(writer, sheet_name='port_return_desc')\\n save_desc.to_excel(writer, sheet_name='port_return_desc')\\n\\n # terminal_val = pd.read_csv(src + 'terminal_values.csv', index_col=[0])\\n # ending_val = pd.read_csv(src + 'ending_values.csv', index_col=[0])\\n # ending_val_ror = pd.read_csv(src + 'ending_values_ror.csv', index_col=[0])\\n\\n # terminal_val.to_excel(writer, sheet_name='terminal_values')\\n # ending_val.to_excel(writer, sheet_name='port_ending_values')\\n # ending_val_ror.to_excel(writer, sheet_name='port_annual_growth')\\n\\n writer.save()\\n\\n # -----------------Plotting charts--------------------------------------------\\n # base_qcut.loc[income_starts:].plot(grid=True, title='Quantile Terminal Value - Base Portfolio')\\n # plt.savefig(src + \\\"quantile_terminal_base.png\\\")\\n # plt.close('all')\\n #\\n # base_income_qcut.plot(grid=True, title='Quantile Income - Base Portfolio')\\n # plt.savefig(src + \\\"quantile_income_base.png\\\")\\n # plt.close('all')\\n #\\n # base_psuccess.plot(grid=True, title='Probability of Success (Portfolio Ending Value > 0) - Base Portfolio')\\n # plt.savefig(src + \\\"success_probabilty_base.png\\\")\\n # plt.close('all')\\n #\\n # (1 - base_psuccess).plot(grid=True, title='Probability of Ruin (Portfolio Ending Value < 0) - Base Portfolio')\\n # plt.savefig(src + \\\"ruin_probability_base.png\\\")\\n # plt.close('all')\\n #\\n # port_qcut.loc[income_starts:].plot(grid=True, title='Quantile Terminal Value - FIA Portfolio')\\n # plt.savefig(src + \\\"quantile_terminal_fia.png\\\")\\n # plt.close('all')\\n #\\n # port_income_qcut.plot(grid=True, title='Quantile Income - FIA Portfolio')\\n # plt.savefig(src + \\\"quantile_income_fia.png\\\")\\n # plt.close('all')\\n #\\n # port_psuccess.plot(grid=True, title='Probability of Success (Portfolio Ending Value > 0) - FIA Portfolio')\\n # plt.savefig(src + \\\"success_probabilty_fia.png\\\")\\n # plt.close('all')\\n #\\n # (1 - port_psuccess).plot(grid=True, title='Probability of Ruin (Portfolio Ending Value < 0) - FIA Portfolio')\\n # plt.savefig(src + \\\"ruin_probability_fia.png\\\")\\n # plt.close('all')\\n\\n print(\\\"simulation completed for {}\\\".format(method))\",\n \"def _output_performance(self):\\n self.portfolio.create_equity_curve_dataframe()\\n \\n print(\\\"Creating summary statistics...\\\")\\n stats = self.portfolio.output_summary_stats()\\n \\n print(\\\"Creating equity curve...\\\")\\n print(self.portfolio.equity_curve.tail(10))\\n pprint.pprint(stats)\\n \\n print(\\\"Signals: %s\\\" % self.signals)\\n print(\\\"Orders: %s\\\" % self.orders)\\n print(\\\"Fills: %s\\\" % self.fills)\",\n \"def _output_performance(self):\\n self.portfolio.create_equity_curve_dataframe()\\n\\n print(\\\"Creating summary statistics...\\\")\\n stats = self.portfolio.output_summary_stats()\\n\\n print(\\\"Creating equity curve...\\\")\\n print(self.portfolio.equity_curve.tail(10))\\n pprint.pprint(stats)\\n\\n print(\\\"Signals: %s\\\" % self.signals)\\n print(\\\"Orders: %s\\\" % self.orders)\\n print(\\\"Fills: %s\\\" % self.fills)\",\n \"def _generate_trading_instances(self, strategy_params_dict):\\n print(\\\"Creating DataHandler, Strategy, Portfolio, and ExecutionHandler for\\\")\\n print(\\\"strategy parameter list: %s...\\\" % strategy_params_dict)\\n\\n # Set internal data members equal to the classes we passed in earlier, along with necessary parameters.\\n # https://softwareengineering.stackexchange.com/questions/131403/what-is-the-name-of-in-python/131415\\n self.data_handler = self.data_handler_class(self.events, self.csv_dir, self.symbol_list)\\n self.strategy = self.strategy_class(self.data_handler, self.events, **strategy_params_dict)\\n self.portfolio = self.portfolio_class(self.data_handler, self.events, self.start_date, self.initial_capital)\\n self.execution_handler = self.execution_handler_class(self.events) # The Event Queue sent to ExecutionHandler\",\n \"def generate_portfolio(S_0, params):\\n\\tpublic_client = gdax.PublicClient()\\n\\tallvar = []\\n\\tsumvar = 0\\n\\tfor coin in params:\\n\\t\\ttheta = coin[0]\\n\\t\\tv = theta[0]\\n\\t\\tT = coin[1]\\n\\t\\tprod_id = coin[2]\\n\\t\\t# Get the current value of the coin, i.e. how much you bought\\n\\t\\tname = prod_id + '-USD'\\n\\t\\tstats = public_client.get_product_24hr_stats(name)\\n\\t\\tvalue = (float(stats['high']) + float(stats['low']))/2\\n\\t\\tallvar.append([prod_id, value, v])\\n\\t\\tsumvar += v\\n\\tpriority = sorted(allvar, key=lambda i: i[2])\\n\\tportfolio = []\\n\\tfor i in priority:\\n\\t\\tinvestment = S_0*i[2]/sumvar\\n\\t\\tcurrency = investment/i[1]\\n\\t\\tportfolio.append((i[0], currency, investment)) # id, investment, currency\\n\\tprint(\\\"\\\\nYour suggested investments are: \\\\n\\\")\\n\\tfor coin in portfolio:\\n\\t\\tprint(\\\"%s: %s for %s USD\\\" % (coin[0], coin[1], coin[2]))\\n\\t# Prompt to save the portfolio\\n\\tdone = False\\n\\twhile done != True:\\n\\t\\tinp = input(\\\"\\\\nWould you like to save this portfolio? (y/n)\\t\\\")\\n\\t\\ttry:\\n\\t\\t\\tif inp.lower() == 'y':\\n\\t\\t\\t\\tpublic_client = gdax.PublicClient()\\n\\t\\t\\t\\tcurrent_date = np.datetime64(public_client.get_time().get(\\\"iso\\\").split('T')[0])\\n\\t\\t\\t\\t# Save the file\\n\\t\\t\\t\\twith open(\\\"portfolios/%s.txt\\\" % (current_date), \\\"w\\\") as f:\\n\\t\\t\\t\\t\\tfor coin in portfolio:\\n\\t\\t\\t\\t\\t\\tf.write(str(coin[0]) + ', ' + str(coin[1]) + ', ' + str(coin[2]) + '\\\\n')\\n\\t\\t\\t\\tprint(\\\"Portfolio saved. Exiting.\\\\n\\\")\\n\\t\\t\\t\\tdone = True\\n\\t\\t\\tif inp.lower() == 'n':\\n\\t\\t\\t\\tprint(\\\"Program complete. Exiting.\\\\n\\\")\\n\\t\\t\\t\\tdone = True\\n\\t\\texcept ValueError:\\n\\t\\t\\tprint(\\\"Your input could not be interpreted.\\\")\",\n \"def simulate_trading(self):\\n self._run_backtest()\\n self._output_performance()\",\n \"def generate_portfolio_data(self):\\n self.__load_portfolio_historical_prices()\\n self.__populate_historical_trade_data()\\n self.__calculate_portfolio_returns()\\n self.__calculate_portfolio_performance()\",\n \"def _generate_trading_instances(self):\\n print(\\n \\\"Initizalization...\\\"\\n )\\n\\n self.data_handler = self.data_handler_cls(self.events, self.csv_dir, self.symbol_list, self.start_date,\\n self.end_date)\\n self.portfolio = self.portfolio_cls(self.data_handler, self.events, self.initial_capital, self.start_date,\\n self.strategy_id, self.spread, self.commission,self.csv_dir)\\n self.strategy = self.strategy_cls(self.data_handler, self.events, self.portfolio, self.spread, self.commission)\\n self.plot = self.plot_cls(self.csv_dir, self.portfolio, self.strategy_id)\",\n \"def main(ft_setups, ft_strategies):\\n\\n num_procs = 16\\n\\n # initialize level parameters\\n level_params = dict()\\n level_params['restol'] = 1e-09\\n\\n # initialize step parameters\\n step_params = dict()\\n step_params['maxiter'] = 50\\n\\n # initialize space transfer parameters\\n space_transfer_params = dict()\\n space_transfer_params['finter'] = True\\n space_transfer_params['rorder'] = 2\\n space_transfer_params['iorder'] = 6\\n\\n # initialize sweeper parameters\\n sweeper_params = dict()\\n sweeper_params['quad_type'] = 'RADAU-RIGHT'\\n sweeper_params['num_nodes'] = [3]\\n\\n # initialize controller parameters\\n controller_params = dict()\\n controller_params['logger_level'] = 30\\n\\n for setup in ft_setups:\\n if setup == 'HEAT':\\n # initialize problem parameters\\n problem_params = dict()\\n problem_params['nu'] = 0.5\\n problem_params['freq'] = 1\\n problem_params['nvars'] = [255, 127]\\n problem_params['bc'] = 'dirichlet-zero'\\n\\n level_params['dt'] = 0.5\\n\\n space_transfer_params['periodic'] = False\\n\\n # fill description dictionary for easy step instantiation\\n description = dict()\\n description['problem_class'] = heatNd_forced # pass problem class\\n description['problem_params'] = problem_params # pass problem parameters\\n description['sweeper_class'] = imex_1st_order # pass sweeper (see part B)\\n description['sweeper_params'] = sweeper_params # pass sweeper parameters\\n description['level_params'] = level_params # pass level parameters\\n description['step_params'] = step_params # pass step parameters\\n description['space_transfer_class'] = mesh_to_mesh # pass spatial transfer class\\n description['space_transfer_params'] = space_transfer_params # pass paramters for spatial transfer\\n\\n # setup parameters \\\"in time\\\"\\n t0 = 0.0\\n Tend = 8.0\\n\\n elif setup == 'ADVECTION':\\n # initialize problem parameters\\n problem_params = dict()\\n problem_params['c'] = 1.0\\n problem_params['nvars'] = [256, 128]\\n problem_params['freq'] = 2\\n problem_params['order'] = 2\\n problem_params['bc'] = 'periodic' # boundary conditions\\n\\n level_params['dt'] = 0.125\\n\\n space_transfer_params['periodic'] = True\\n\\n # fill description dictionary for easy step instantiation\\n description = dict()\\n description['problem_class'] = advectionNd # pass problem class\\n description['problem_params'] = problem_params # pass problem parameters\\n description['sweeper_class'] = generic_implicit # pass sweeper (see part B)\\n description['sweeper_params'] = sweeper_params # pass sweeper parameters\\n description['level_params'] = level_params # pass level parameters\\n description['step_params'] = step_params # pass step parameters\\n description['space_transfer_class'] = mesh_to_mesh # pass spatial transfer class\\n description['space_transfer_params'] = space_transfer_params # pass paramters for spatial transfer\\n\\n # setup parameters \\\"in time\\\"\\n t0 = 0.0\\n Tend = 2.0\\n\\n else:\\n raise NotImplementedError('setup not implemented')\\n\\n # do a reference run without any faults to see how things would look like (and to get maxiter/ref_niter)\\n ft.strategy = 'NOFAULT'\\n\\n controller = controller_nonMPI_hard_faults(\\n num_procs=num_procs, controller_params=controller_params, description=description\\n )\\n\\n # get initial values on finest level\\n P = controller.MS[0].levels[0].prob\\n uinit = P.u_exact(t0)\\n\\n # call main function to get things done...\\n uend, stats = controller.run(u0=uinit, t0=t0, Tend=Tend)\\n\\n # stats magic: get iteration counts to find maxiter/niter\\n sortedlist_stats = get_sorted(stats, level=-1, type='niter', sortby='process')\\n ref_niter = max([item[1] for item in sortedlist_stats])\\n\\n print('Will sweep over %i steps and %i iterations now...' % (num_procs, ref_niter))\\n\\n # loop over all strategies\\n for strategy in ft_strategies:\\n ft_iter = range(1, ref_niter + 1)\\n ft_step = range(0, num_procs)\\n\\n print('------------------------------------------ working on strategy ', strategy)\\n\\n iter_count = np.zeros((len(ft_step), len(ft_iter)))\\n\\n # loop over all steps\\n xcnt = -1\\n for step in ft_step:\\n xcnt += 1\\n\\n # loop over all iterations\\n ycnt = -1\\n for iter in ft_iter:\\n ycnt += 1\\n\\n ft.hard_step = step\\n ft.hard_iter = iter\\n ft.strategy = strategy\\n\\n # call main function to get things done...\\n uend, stats = controller.run(u0=uinit, t0=t0, Tend=Tend)\\n\\n # stats magic: get iteration counts to find maxiter/niter\\n sortedlist_stats = get_sorted(stats, level=-1, type='niter', sortby='process')\\n niter = max([item[1] for item in sortedlist_stats])\\n iter_count[xcnt, ycnt] = niter\\n\\n print(iter_count)\\n\\n np.savez(\\n 'data/' + setup + '_results_hf_' + strategy,\\n iter_count=iter_count,\\n description=description,\\n ft_step=ft_step,\\n ft_iter=ft_iter,\\n )\",\n \"def run(sim_attr_generator):\\n#TODO: clean\\n#TODO: integrate analyses\\n def analyze_and_save(simulation,simulation_attributes):\\n#? Ugly conf file analyses integration.\\n if simulation_attributes.analyses and Args.output_file != None:\\n verbose_print(\\\"Saving analyses for {0}.\\\".format(simulation_attributes.id_name),2)\\n results = analyze_datas(\\n simulation.result,\\n simulation_attributes.analyses\\n )\\n plotables = ana_results_to_plotables(\\n results,\\n simulation_attributes.analyses\\n )\\n#TODO error handling for save\\n analysis_save_dm(\\n results,\\n plotables,\\n simulation_attributes.analyses,\\n simulation_attributes.id_name\\n )\\n\\n def save_simulation(simulation,simulation_attributes):\\n if not simulation_attributes.analyses and Args.output_file != None:\\n verbose_print(\\\"Saving simulation datas of {0}.\\\".format(\\n simulation_attributes.id_name\\n ),2) \\n try:\\n np.save(\\n simulation_attributes.id_name,\\n simulation.result\\n )\\n except:\\n raise EnvironmentError(\\\"Can't save data to {}.\\\".format(\\n simulation_attributes.id_name\\n ))\\n\\n verbose_print(\\\"Starting simulation run.\\\",1)\\n for i,simulation_attributes in enumerate(sim_attr_generator):\\n verbose_print(\\\"Starting simulation number {0}: {1}\\\".format(\\n i,\\n simulation_attributes.id_name\\n ),2)\\n simulation = Simulation(\\n SimulationVariables(simulation_attributes)\\n )\\n simulation.start()\\n save_simulation(simulation,simulation_attributes)\\n analyze_and_save(simulation,simulation_attributes)\",\n \"def evolve(self, max_iter=40, warm_start=False, sigma_bound=None, info=True, cross_method=1, par_count=3,\\n max_distance=2.0, archive_iter=30, max_archive_size=200):\\n if sigma_bound is None:\\n sigma_bound = [0.01, 0.2]\\n\\n if not warm_start:\\n self.archive = []\\n else:\\n self.archive = self.archive.tolist()\\n\\n # outer loop\\n for q in range(0, archive_iter):\\n # create initial generation with strategy parameters\\n init_gen = np.empty(shape=(self.gen_size, self.num_variables))\\n init_sigma = np.empty(shape=(self.gen_size, self.num_variables))\\n for i in range(0, self.num_variables):\\n init_gen[:, i] = np.random.uniform(self.lower_bound[i], self.upper_bound[i], self.gen_size)\\n init_sigma[:, i] = np.random.uniform(sigma_bound[0] * self.total_bound[i],\\n sigma_bound[1] * self.total_bound[i], self.gen_size)\\n\\n self.gen = np.copy(init_gen)\\n self.sigma = np.copy(init_sigma)\\n\\n if info:\\n msg = \\\" Archive Iteration: {}/{}\\\".format(q+1, archive_iter)\\n print(msg)\\n self.gen = np.copy(init_gen)\\n self.sigma = np.copy(init_sigma)\\n\\n # inner loop\\n for k in range(0, max_iter):\\n\\n if info:\\n if k == max_iter - 1:\\n msg = \\\"Generation {}/{}\\\\n\\\".format(k+1, max_iter)\\n else:\\n msg = \\\"Generation {}/{}\\\".format(k+1, max_iter)\\n sys.stdout.write(\\\"\\\\r\\\"+msg)\\n\\n # random selection based off number of parents\\n selected = []\\n for i in range(0, par_count):\\n selected.append(np.random.choice(range(0, self.gen_size), self.gen_size, replace=False))\\n selected = np.asarray(selected).T\\n ch_val = []\\n ch_sigma = []\\n # reproduce from parents\\n for i in range(0, self.gen_size):\\n c_v, c_s = self.__reproduction(self.gen[selected[i]], self.sigma[selected[i]], cross_method)\\n ch_val.append(c_v)\\n ch_sigma.append(c_s)\\n\\n ch_val = np.asarray(ch_val)\\n ch_sigma = np.asarray(ch_sigma)\\n # check bounds of offspring\\n for i in range(0, self.gen_size):\\n for j in range(0, self.num_variables):\\n if ch_val[i][j] > self.domain[0][j]:\\n ch_val[i][j] = self.domain[0][j]\\n ch_sigma[i][j] *= .90\\n elif ch_val[i][j] < self.domain[1][j]:\\n ch_val[i][j] = self.domain[1][j]\\n ch_sigma[i][j] *= .90\\n\\n # combine offspring with parents\\n parents_offspring_val = np.vstack((self.gen, ch_val))\\n parents_offspring_sigma = np.vstack((self.sigma, ch_sigma))\\n ind = list(range(0, 2 * self.gen_size))\\n rel_fit = []\\n # compute relative fitness\\n for i in range(0, 2 * self.gen_size):\\n tourn = np.random.choice(ind[0:i] + ind[(i + 1):], self.tourn_size)\\n rel_fit.append(self.__pareto_dominance(parents_offspring_val[i], parents_offspring_val[tourn]))\\n\\n if len(self.archive) != 0: # if archive is not emtpy\\n # loop over each member in archive\\n for arch in self.archive:\\n for i in range(0, 2 * self.gen_size):\\n # calculate euclidean distance\\n dist = np.linalg.norm(arch - parents_offspring_val[i])\\n # if their distance is small -> they're too close,\\n # larger the max distance the more spread out solutions become\\n if dist <= max_distance:\\n # penalize fitness down to 25%\\n rel_fit[i] *= 0.25\\n\\n # sort individuals based on relative fitness\\n rel_fit = np.asarray(rel_fit)\\n sorted_ind = np.asarray(ind)[np.argsort(-rel_fit)]\\n\\n # use elitism to take the best half from pooled\\n # parents and offspring\\n self.gen = parents_offspring_val[sorted_ind[0:self.gen_size]]\\n self.sigma = parents_offspring_sigma[sorted_ind[0:self.gen_size]]\\n\\n fits = self.fitness_functions(self.gen)\\n # update archive\\n if len(self.archive) !=0:\\n # loop over every individual in surviving generation\\n for j in range(0, self.gen_size):\\n # indices of members in archive that are dominated by\\n # the current individual\\n ind_del = []\\n f_arch = self.fitness_functions(np.asarray(self.archive))\\n dominated = False\\n for i in range(0, len(self.archive)):\\n # individual strongly dominates member of archive\\n if np.sum(f_arch[i] > fits[j]) == len(fits[j]):\\n ind_del.append(i)\\n # individual is strongly dominated by member\\n elif np.sum(f_arch[i] < fits[j]) == len(fits[j]):\\n dominated = True\\n if len(ind_del) != 0:\\n # delete members that are strongly dominated\\n for index in sorted(ind_del, reverse=True):\\n del self.archive[index]\\n if not dominated: # weakly dominates members of archive\\n if len(self.archive) <= max_archive_size:\\n self.archive.append(self.gen[j])\\n else: # archive is full\\n temp = np.vstack((self.gen[j], self.archive))\\n dist = self.__distance(temp)\\n index = np.argmin(dist)\\n if index != 0:\\n del self.archive[index-1]\\n self.archive.append(self.gen[j])\\n\\n dist = pdist(np.asarray(self.archive))\\n #dist = self.__distance(np.asarray(self.archive))\\n dist_min = np.min(dist)\\n dist_max = np.max(dist)\\n dist_median = np.median(dist)\\n dist_std = np.std(dist)\\n if info:\\n msg = \\\" Archive Information:\\\\n\\\" \\\\\\n \\\" Distance - Min: {}, Max: {}, Median: {}, Std: {}\\\\n\\\" \\\\\\n \\\" Archive Size: {}\\\".format(dist_min, dist_max, dist_median,dist_std, len(self.archive))\\n print(msg)\\n\\n else: # if first iteration of outer loop, just add best solution from gen\\n self.archive.append(self.gen[0])\\n self.archive = np.asarray(self.archive)\",\n \"def run(self, num_iterations = 50, **kwargs):\\n \\n #setup system\\n self.cost_calculator = t.CostCalculator(self.suppliers_allcards, self.all_ensembles_dict)\\n bounds = np.array(self.cost_calculator.ensemble_sizes) - 1\\n #define cost functions\\n cost_func = lambda p: sum(self.cost_calculator.get_cost(p))\\n #create model\\n self.model = ga(cost_func, bounds, **kwargs)\\n \\n fitness_list = [];\\n \\n for i in range(num_iterations):\\n #Update\\n f = next(self.model)\\n #get fitness values\\n fitness_list.append(f[0])\\n #Output\\n print('\\\\r(%d/%d) '%(i+1,num_iterations), end = '')\\n print('top ensemble fitness: %1.1f '%f[0], end = '')\\n \\n print('\\\\nDone')\\n self.solution = self.cost_calculator.decode_arrangement(self.model.get_solution())\",\n \"def portfolio_simulations_using_target_returns(num_of_years=30, trials=100):\\n print(\\\"Running portfolio_simulations_using_target_returns() method\\\")\\n sim_fia_cv = pd.DataFrame(index=range(num_of_years + 1))\\n\\n sim_base_total = pd.DataFrame(index=range(num_of_years + 1))\\n sim_base_income = pd.DataFrame(index=range(num_of_years + 1))\\n\\n sim_port_total = pd.DataFrame(index=range(num_of_years + 1))\\n sim_port_income = pd.DataFrame(index=range(num_of_years + 1))\\n\\n read_income_inputs = pd.read_excel(src + \\\"portfolio_information.xlsx\\\", sheet_name='income_model_inputs',\\n index_col=[0])\\n\\n read_returns_est = pd.read_excel(src + \\\"portfolio_information.xlsx\\\", sheet_name='income_assets_returns_estimates',\\n index_col=[0])\\n\\n read_asset_weights = pd.read_excel(src + \\\"portfolio_information.xlsx\\\", sheet_name='asset_weights',\\n index_col=[0])\\n\\n # read random returns for simulation\\n read_normal = pd.read_csv(src + 'median_returns_unsorted.csv', index_col=[0], parse_dates=True)\\n cols = [read_normal.columns[c].split('_')[1] for c in np.arange(len(read_normal.columns))]\\n read_normal.rename(columns=dict(zip(list(read_normal.columns), cols)), inplace=True)\\n idx = list(read_normal.index)\\n\\n runs = 0\\n while runs <= trials:\\n # --------Shuffling the path of random returns from the median portfolio\\n np.random.shuffle(idx)\\n read_normal = read_normal.set_index([idx])\\n read_normal = read_normal.sort_index()\\n\\n # assets_col_names = list(read_normal.columns)\\n # tickers = list(read_asset_weights.index)\\n wts = np.array(read_asset_weights.loc[:, 'base'])\\n\\n def asset_median_returns(data, ticker):\\n return data.filter(regex=ticker).median(axis=1)\\n\\n # dataframe for unsorted returns (normal)\\n median_returns_normal = read_normal.copy()\\n median_returns_normal.loc[:, 'portfolio_return'] = median_returns_normal.dot(wts)\\n median_normal_fia = pd.DataFrame({'FIA': asset_median_returns(read_normal, 'FIA')})\\n\\n years = list(range(0, num_of_years + 1))\\n income_cols = ['year', 'strategy_term', 'index_returns', 'term_ret', 'term_ret_with_par', 'term_annualize',\\n 'ann_net_spread', 'term_ret_netspr', 'high_inc_benefit_base', 'rider_fee', 'eoy_income',\\n 'contract_value']\\n\\n term = int(read_income_inputs.loc['term', 'inputs'])\\n fia_ret = read_returns_est.loc[read_returns_est.index[-1], 'Annualized Returns']\\n fia_risk = read_returns_est.loc[read_returns_est.index[-1], 'Annualized Risk']\\n par_rate = float(read_income_inputs.loc['par_rate', 'inputs'])\\n spread = float(read_income_inputs.loc['spread', 'inputs'])\\n bonus_term = int(read_income_inputs.loc['bonus_term', 'inputs'])\\n premium = float(read_income_inputs.loc['premium', 'inputs'])\\n income_bonus = float(read_income_inputs.loc['income_bonus', 'inputs'])\\n\\n income_starts = int(read_income_inputs.loc['start_income_years', 'inputs'])\\n income_growth = float(read_income_inputs.loc['income_growth', 'inputs'])\\n rider_fee = float(read_income_inputs.loc['rider_fee', 'inputs'])\\n inc_payout_factor = float(read_income_inputs.loc['income_payout_factor', 'inputs'])\\n contract_bonus = float(read_income_inputs.loc['contract_bonus', 'inputs'])\\n social = float(read_income_inputs.loc['social', 'inputs'])\\n inflation = float(read_income_inputs.loc['inflation', 'inputs'])\\n wtd_cpn_yield = float(read_income_inputs.loc['wtd_coupon_yld', 'inputs'])\\n life_expectancy = int(read_income_inputs.loc['life_expectancy_age', 'inputs'])\\n clients_age = int(read_income_inputs.loc['clients_age', 'inputs'])\\n\\n # ---------------INCOME MODEL--------------------------------------------\\n # runs = 0\\n returns_dict = {}\\n asset_dict = {}\\n fia_dict = {}\\n\\n income_df = pd.DataFrame(index=years, columns=income_cols)\\n income_df.loc[:, 'year'] = years\\n income_df.loc[:, 'strategy_term'] = income_df.loc[:, 'year'] % term\\n income_df.loc[:, 'strategy_term'] = income_df['strategy_term'].apply(lambda x: 1 if x == 0 else 0)\\n\\n income_df.loc[:, 'index_returns'] = read_normal.loc[:, 'FIA']\\n\\n cumprod = (1. + income_df['index_returns']).rolling(window=term).agg(lambda x: x.prod()) - 1\\n income_df.loc[:, 'term_ret'] = np.where(income_df.loc[:, 'strategy_term'] == 1, cumprod, 0)\\n income_df.loc[:, 'term_ret_with_par'] = income_df.loc[:, 'term_ret'] * par_rate\\n income_df.loc[:, 'term_annualize'] = income_df.loc[:, 'term_ret_with_par'].apply(\\n lambda x: (1 + x) ** (1 / term) - 1)\\n income_df.loc[:, 'ann_net_spread'] = income_df.loc[:, 'term_annualize'] - spread\\n income_df.loc[:, 'ann_net_spread'] = np.where(income_df.loc[:, 'strategy_term'] == 1,\\n income_df.loc[:, 'ann_net_spread'], 0)\\n income_df.loc[:, 'term_ret_netspr'] = income_df.loc[:, 'ann_net_spread'].apply(lambda x: (1 + x) ** term - 1)\\n\\n for counter in years:\\n if counter == 0:\\n income_df.loc[counter, 'high_inc_benefit_base'] = premium * (1 + income_bonus)\\n\\n elif counter <= min(bonus_term, income_starts):\\n income_df.loc[counter, 'high_inc_benefit_base'] = income_df.loc[counter - 1, 'high_inc_benefit_base'] * \\\\\\n (1 + income_growth)\\n else:\\n income_df.loc[counter, 'high_inc_benefit_base'] = income_df.loc[counter - 1, 'high_inc_benefit_base']\\n\\n income_df.loc[:, 'rider_fee'] = income_df.loc[:, 'high_inc_benefit_base'] * rider_fee\\n income_df.loc[:, 'eoy_income'] = np.where(income_df.loc[:, 'year'] > income_starts,\\n income_df.loc[:, 'high_inc_benefit_base'] * inc_payout_factor, 0)\\n\\n for counter in years:\\n if counter == 0:\\n income_df.loc[counter, 'contract_value'] = premium * (1 + contract_bonus)\\n\\n elif income_df.loc[counter, 'strategy_term'] == 1:\\n x1 = income_df.loc[counter - 1, 'contract_value'] - income_df.loc[counter, 'rider_fee']\\n x2 = (x1 * (1 + income_df.loc[counter, 'term_ret_netspr'])) - income_df.loc[counter, 'eoy_income']\\n income_df.loc[counter, 'contract_value'] = x2\\n\\n else:\\n x1 = income_df.loc[counter - 1, 'contract_value'] - income_df.loc[counter, 'rider_fee'] - \\\\\\n income_df.loc[counter, 'eoy_income']\\n\\n income_df.loc[counter, 'contract_value'] = x1\\n\\n # variable stores the income number that is used in the base and fia portfolio calcs.\\n\\n income_from_fia = income_df.loc[income_df.index[-1], 'eoy_income']\\n\\n income_df.loc[:, 'contract_value'] = income_df.loc[:, 'contract_value'].apply(lambda x: 0 if x <= 0 else x)\\n\\n sim_fia_cv.loc[:, str(runs)] = income_df.loc[:, 'contract_value']\\n\\n # --------------------BASE MODEL---------------------------------------------\\n\\n base_wts = read_asset_weights.loc[:, 'base']\\n base_assets = list(base_wts.index)\\n base_weights = list(base_wts.values)\\n base_returns = list(read_returns_est.loc[:, 'Annualized Returns'].values)\\n base_std = list(read_returns_est.loc[:, 'Annualized Risk'].values)\\n\\n base_investment = float(read_income_inputs.loc['risky_assets', 'Base'])\\n adv_fees = float(read_income_inputs.loc['advisor_fees', 'Base'])\\n\\n # -------------------required income----------------------------------\\n req_annual_income = float(read_income_inputs.loc['annual_income', 'inputs'])\\n income_needed = req_annual_income - social\\n income_net_fia_income = max(0, income_needed - income_from_fia)\\n cpn_income_base = base_investment * wtd_cpn_yield\\n\\n # ----------------------RANDOM RETURNS--------------------------\\n r_cols = base_assets\\n boy_value = ['bv_{}'.format(name) for name in base_assets]\\n eoy_value = ['ev_{}'.format(name) for name in base_assets]\\n\\n random_returns = pd.DataFrame(index=income_df.index, columns=r_cols)\\n\\n random_returns = read_normal.copy()\\n\\n base_df = random_returns.copy()\\n fia_portfolio_df = random_returns.copy()\\n port_investment = float(read_income_inputs.loc['risky_assets', 'FIA'])\\n cpn_income_port = port_investment * wtd_cpn_yield\\n\\n # -------------BASE PORTFOLIO----------------------------\\n for name in boy_value:\\n base_df.loc[:, name] = 0.0\\n\\n for counter in years:\\n period_returns = list(random_returns.loc[counter, :])\\n if counter == 0:\\n\\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment for c in range(len(boy_value))]\\n\\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\\n base_df.loc[counter, 'total_net_fees'] = 0.0\\n base_df.loc[counter, 'income'] = 0.0\\n base_investment = base_df.loc[counter, boy_value].sum()\\n\\n elif (counter > 0) and (counter < income_starts):\\n\\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment * (1 + period_returns[c])\\n for c in range(len(boy_value))]\\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\\n base_df.loc[counter, 'adv_fees'] = base_df.loc[counter, 'total'] * adv_fees\\n base_df.loc[counter, 'total_net_fees'] = base_df.loc[counter, 'total'] - base_df.loc[\\n counter, 'adv_fees']\\n\\n # --coupon payment is invested back into the risky portfolio until the income is withdrawn----\\n base_investment = base_df.loc[counter, 'total_net_fees'] + cpn_income_base\\n\\n else:\\n\\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment * (1 + period_returns[c])\\n for c in range(len(boy_value))]\\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\\n base_df.loc[counter, 'adv_fees'] = base_df.loc[counter, 'total'] * adv_fees\\n\\n # ---req. income is adjusted for inflation from the second year of withdrawal. Reinvestment of coupon\\n # stops from the year income starts. Req. income is reduced by the coupon payments\\n\\n if counter == income_starts:\\n\\n income_needed = req_annual_income - social\\n base_df.loc[counter, 'income'] = income_needed - cpn_income_base\\n income_needed = req_annual_income\\n\\n else:\\n income_needed = income_needed * (1 + inflation) - social\\n base_df.loc[counter, 'income'] = income_needed - cpn_income_base\\n income_needed = income_needed + social\\n\\n base_df.loc[counter, 'total_net_fees'] = base_df.loc[counter, 'total'] - \\\\\\n base_df.loc[counter, 'adv_fees'] - \\\\\\n base_df.loc[counter, 'income']\\n\\n base_investment = base_df.loc[counter, 'total_net_fees']\\n\\n base_df.loc[:, 'adj_total'] = base_df.loc[:, 'total_net_fees'].apply(lambda x: x if x > 0 else 0)\\n sim_base_total.loc[:, 's_{}'.format(str(runs))] = base_df.loc[:, 'total_net_fees']\\n sim_base_income.loc[:, 's_{}'.format(str(runs))] = base_df.loc[:, 'income']\\n\\n # ----------------------------FIA PORTFOLIO----------------------------------------------\\n for name in boy_value:\\n fia_portfolio_df.loc[:, name] = 0.0\\n\\n for counter in years:\\n period_returns = list(random_returns.loc[counter, :])\\n if counter == 0:\\n\\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment\\n for c in range(len(boy_value))]\\n fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\\n fia_portfolio_df.loc[counter, 'total_net_fees'] = 0.0\\n fia_portfolio_df.loc[counter, 'income'] = 0.0\\n port_investment = fia_portfolio_df.loc[counter, boy_value].sum()\\n\\n elif (counter > 0) and (counter < income_starts):\\n\\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment * (1 + period_returns[c])\\n for c in range(len(boy_value))]\\n fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\\n fia_portfolio_df.loc[counter, 'adv_fees'] = fia_portfolio_df.loc[counter, 'total'] * adv_fees\\n fia_portfolio_df.loc[counter, 'total_net_fees'] = fia_portfolio_df.loc[counter, 'total'] - \\\\\\n fia_portfolio_df.loc[counter, 'adv_fees']\\n\\n port_investment = fia_portfolio_df.loc[counter, 'total_net_fees'] + cpn_income_port\\n\\n else:\\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment * (1 + period_returns[c])\\n for c in range(len(boy_value))]\\n fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\\n fia_portfolio_df.loc[counter, 'adv_fees'] = fia_portfolio_df.loc[counter, 'total'] * adv_fees\\n\\n # ---req. income is adjusted for inflation from the second year of withdrawal. Reinvestment of coupon\\n # stops from the year income starts. Req. income is reduced by the coupon payments\\n\\n if counter == income_starts:\\n\\n income_needed = req_annual_income - social\\n income_net_fia_income = max(0, income_needed - income_from_fia)\\n fia_portfolio_df.loc[counter, 'income'] = max(0, income_net_fia_income - cpn_income_port)\\n income_needed = req_annual_income\\n\\n else:\\n income_needed = income_needed * (1 + inflation) - social\\n income_net_fia_income = max(0, income_needed - income_from_fia)\\n fia_portfolio_df.loc[counter, 'income'] = max(0, income_net_fia_income - cpn_income_port)\\n income_needed = income_needed + social\\n\\n fia_portfolio_df.loc[counter, 'total_net_fees'] = fia_portfolio_df.loc[counter, 'total'] - \\\\\\n fia_portfolio_df.loc[counter, 'adv_fees'] - \\\\\\n fia_portfolio_df.loc[counter, 'income']\\n\\n port_investment = fia_portfolio_df.loc[counter, 'total_net_fees']\\n\\n sim_port_total.loc[:, 's_{}'.format(str(runs))] = fia_portfolio_df.loc[:, 'total_net_fees'] + \\\\\\n income_df.loc[:, 'contract_value']\\n\\n sim_port_income.loc[:, 's_{}'.format(str(runs))] = fia_portfolio_df.loc[:, 'income']\\n\\n fia_portfolio_df.loc[:, 'adj_total'] = fia_portfolio_df.loc[:, 'total_net_fees'].apply(\\n lambda x: x if x > 0 else 0)\\n\\n runs = runs + 1\\n\\n # ----% of trials ending value at expected life is less than 0\\n inflation_factor = (1 + annual_inflation) ** (life_expectancy - clients_age - income_starts)\\n required_income_horizon = income_needed * inflation_factor\\n required_income_horizon_net_fia = required_income_horizon - income_from_fia\\n\\n # prob_success_base = (sim_base_total.iloc[life_expectancy - clients_age] < 0).sum() / trials\\n # prob_success_fia_port = (sim_port_total.iloc[life_expectancy - clients_age] < 0).sum() / trials\\n\\n prob_failure_base = (sim_base_total.iloc[life_expectancy - clients_age] < required_income_horizon).sum() / trials\\n prob_failure_fia_port = (sim_port_total.iloc[life_expectancy - clients_age] < required_income_horizon_net_fia) \\\\\\n .sum() / trials\\n\\n # ----Calculate at total average lifetime income for base portfolio----\\n base_inc = sim_base_income.copy()\\n base_inc = base_inc.cumsum()\\n avg_income_base = base_inc.iloc[life_expectancy - clients_age].mean()\\n\\n # ----Calculate at total average lifetime income for FIA portfolio----\\n port_inc = sim_port_income.copy()\\n port_inc = port_inc.cumsum()\\n avg_income_port = port_inc.iloc[life_expectancy - clients_age].mean()\\n avg_income_port = avg_income_port + (income_from_fia * (life_expectancy - clients_age))\\n\\n # ---------income breakdown for Base portfolio----------------------------------\\n base_df.to_csv(src + 'base_port_detail.csv')\\n sim_base_total.to_csv(src + 'base_ending_values.csv')\\n income_breakdown_base = pd.DataFrame(sim_base_total.quantile(0.5, axis=1))\\n income_breakdown_base.loc[:, 'income_from_portfolio'] = sim_base_income.quantile(0.5, axis=1)\\n income_breakdown_base.loc[:, 'fia_income'] = 0.0\\n income_breakdown_base.loc[:, 'social_security_income'] = social\\n income_breakdown_base.loc[:, 'coupon_income'] = cpn_income_base\\n\\n income_breakdown_base.rename(columns={income_breakdown_base.columns[0]: 'portfolio_ending_value'}, inplace=True)\\n income_breakdown_base.loc[:, 'income_from_portfolio'][\\n income_breakdown_base.loc[:, 'portfolio_ending_value'] <= 0] = 0\\n income_breakdown_base.loc[:, 'total_income'] = income_breakdown_base.loc[:, income_breakdown_base.columns[1:]].sum(\\n axis=1)\\n\\n # ------------Block Ends-------------------------------------------------------------\\n\\n # ---------income breakdown for FIA portfolio----------------------------------\\n fia_portfolio_df.to_csv(src + 'fia_port_detail.csv')\\n sim_port_total.to_csv(src + 'fiaport_ending_values.csv')\\n\\n income_breakdown_port = pd.DataFrame(sim_port_total.quantile(0.5, axis=1))\\n income_breakdown_port.loc[:, 'income_from_portfolio'] = sim_port_income.quantile(0.5, axis=1)\\n income_breakdown_port.loc[:, 'fia_income'] = income_from_fia\\n income_breakdown_port.loc[:, 'social_security_income'] = social\\n income_breakdown_port.loc[:, 'coupon_income'] = cpn_income_port\\n\\n income_breakdown_port.rename(columns={income_breakdown_port.columns[0]: 'portfolio_ending_value'}, inplace=True)\\n income_breakdown_port.loc[:, 'income_from_portfolio'][\\n income_breakdown_port.loc[:, 'portfolio_ending_value'] <= 0] = 0\\n income_breakdown_port.loc[:, 'total_income'] = income_breakdown_port.loc[:, income_breakdown_port.columns[1:]].sum(\\n axis=1)\\n\\n # ------------Block Ends-------------------------------------------------------------\\n q_cut = [0.0, 0.05, 0.25, 0.5, 0.75, 0.95, 1.0]\\n sim_base_income[sim_base_total < income_needed] = 0.0\\n\\n sim_port_income[sim_port_total < income_net_fia_income] = 0\\n\\n sim_port_income = sim_port_income + income_from_fia\\n\\n # base_quantile = sim_base_total.loc[sim_base_total.index[-1]].quantile([0.05, 0.25, 0.50, 0.75, 0.90])\\n #\\n # port_quantile = sim_port_total.loc[sim_port_total.index[-1]].quantile([0.05, 0.25, 0.50, 0.75, 0.90])\\n\\n base_quantile = sim_base_total.loc[sim_base_total.index[-1]].quantile(q_cut)\\n\\n port_quantile = sim_port_total.loc[sim_port_total.index[-1]].quantile(q_cut)\\n\\n # q_cut = [0.0, .05, 0.25, 0.5, 0.75, 0.95, 1.0]\\n cols = ['Min', '5th', '25th', '50th', '75th', '90th', 'Max']\\n\\n # ----------------------------drop year 0--------------------------------\\n sim_base_total = sim_base_total[1:]\\n sim_port_total = sim_port_total[1:]\\n\\n # ---------quantile analysis for base terminal value-----------------------------\\n base_qcut = pd.DataFrame(index=sim_base_total.index, columns=cols)\\n for c in range(len(cols)):\\n base_qcut.loc[:, cols[c]] = sim_base_total.quantile(q_cut[c], axis=1)\\n\\n base_qcut.clip(lower=0, inplace=True)\\n\\n # ---------quantile analysis for base income----------------------------\\n base_income_qcut = pd.DataFrame(index=sim_base_income.index, columns=cols)\\n for c in range(len(cols)):\\n base_income_qcut.loc[:, cols[c]] = sim_base_income.quantile(q_cut[c], axis=1)\\n\\n # ----Remove NaN's prior to the income start years------------\\n # base_income_qcut = base_income_qcut.loc[income_starts:]\\n\\n # -------------quantile analysis for portfolio terminal value ---------------\\n\\n port_qcut = pd.DataFrame(index=sim_port_total.index, columns=cols)\\n for c in range(len(cols)):\\n port_qcut.loc[:, cols[c]] = sim_port_total.quantile(q_cut[c], axis=1)\\n\\n port_qcut.clip(lower=0, inplace=True)\\n\\n # ---------------quantile analysis for portfolio income----------------------------\\n port_income_qcut = pd.DataFrame(index=sim_port_income.index, columns=cols)\\n for c in range(len(cols)):\\n port_income_qcut.loc[:, cols[c]] = sim_port_income.quantile(q_cut[c], axis=1)\\n\\n # ----Remove NaN's prior to the income start years------------\\n # port_income_qcut = port_income_qcut.loc[income_starts:]\\n\\n # ----------probability ending value will be less than 0 at the end of the horizon -------------------------\\n\\n base_legacy_risk = (sim_base_total.loc[sim_base_total.index[life_expectancy - clients_age]] < 0).sum() / (\\n trials + 1)\\n port_legacy_risk = (sim_port_total.loc[sim_port_total.index[life_expectancy - clients_age]] < 0).sum() / (\\n trials + 1)\\n\\n legacy_risk = pd.DataFrame([base_legacy_risk, port_legacy_risk,\\n 'Prob. of portfolio value less than 0 at the end of the expected life'],\\n index=['base', 'fia_portfolio', 'Notes'],\\n columns=['Ruin Probability'])\\n\\n # -----------Year-wise probability of ending value greater than 0 -----------------\\n base_psuccess = sim_base_total.apply(lambda x: x > 0).sum(axis=1) / (trials + 1)\\n port_psuccess = sim_port_total.apply(lambda x: x > 0).sum(axis=1) / (trials + 1)\\n\\n # -----------------------WRITING FILES TO EXCEL ---------------------------\\n writer = pd.ExcelWriter(src + 'simulated_portfolios_summary.xlsx', engine='xlsxwriter')\\n sim_base_total.to_excel(writer, sheet_name='base_ending_value')\\n sim_port_total.to_excel(writer, sheet_name='fiaport_ending_value')\\n base_qcut.to_excel(writer, sheet_name='base_quantile_ending')\\n base_income_qcut.to_excel(writer, sheet_name='base_quantile_income')\\n port_qcut.to_excel(writer, sheet_name='port_quantile_ending')\\n port_income_qcut.to_excel(writer, sheet_name='port_quantile_income')\\n\\n sucess_df = pd.concat([base_psuccess, port_psuccess], axis=1)\\n sucess_df.rename(columns={sucess_df.columns[0]: 'Base', sucess_df.columns[1]: 'Fia_Port'}, inplace=True)\\n\\n base_mean = sim_base_total[sim_base_total <= 0].isnull().sum().mean()\\n port_mean = sim_port_total[sim_port_total <= 0].isnull().sum().mean()\\n\\n base_median = sim_base_total[sim_base_total <= 0].isnull().sum().median()\\n port_median = sim_port_total[sim_port_total <= 0].isnull().sum().median()\\n\\n stats_df = pd.DataFrame([[base_mean, port_mean, 'Average years portfolio ending value > 0, out of N trials'],\\n [base_median, port_median, 'Ending Value >0, 50% of the time']],\\n index=['Mean years', 'Median years'], columns=['Base', 'fia_port', 'Comment'])\\n\\n # ---Average of terminal values at the end of horizon from N Trials\\n stats_df.loc['Average Portfolio', 'Base'] = sim_base_total.iloc[-1].mean() + clients_age\\n stats_df.loc['Average Portfolio', 'fia_port'] = sim_port_total.iloc[-1].mean() + clients_age\\n stats_df.loc['Average Portfolio', 'Comment'] = \\\"Average of terminal values at the end of analysis period\\\" \\\\\\n \\\" from N Trials\\\"\\n\\n # ----Median of terminal values at the end of horizon from N Trials\\n stats_df.loc['Median Portfolio', 'Base'] = sim_base_total.iloc[-1].median() + clients_age\\n stats_df.loc['Median Portfolio', 'fia_port'] = sim_port_total.iloc[-1].median() + clients_age\\n stats_df.loc['Median Portfolio', 'Comment'] = \\\"Median of terminal values at the end of analysis period \\\" \\\\\\n \\\"from N Trials\\\"\\n\\n # ---Average of terminal values at the end of Actuarial life from N Trials Base Portfolio\\n stats_df.loc['Average Portfolio (end of expected_life)', 'Base'] = sim_base_total.iloc[\\n life_expectancy - clients_age].mean()\\n\\n # ----Median of terminal values at the end of horizon from N Trials Base Portfolio\\n stats_df.loc['Median Portfolio (end of expected_life)', 'Base'] = sim_base_total.iloc[\\n life_expectancy - clients_age].median()\\n\\n # ---Average of terminal values at the end of Actuarial life from N Trials - FIA portfolio\\n stats_df.loc['Average Portfolio (end of expected_life)', 'fia_port'] = sim_port_total.iloc[\\n life_expectancy - clients_age].mean()\\n stats_df.loc['Average Portfolio (end of expected_life)', 'Comment'] = \\\"Average of terminal values at the end of \\\" \\\\\\n \\\"Actuarial life from N Trials\\\"\\n\\n # ----Median of terminal values at the end of horizon from N Trials - FIA Portfolio\\n stats_df.loc['Median Portfolio (end of expected_life)', 'fia_port'] = sim_port_total.iloc[\\n life_expectancy - clients_age].median()\\n stats_df.loc['Median Portfolio (end of expected_life)', 'Comment'] = \\\"Median of terminal values at the end of \\\" \\\\\\n \\\"horizon from N Trials\\\"\\n\\n stats_df.loc['% Success(value>0 at the end of expected_life)', 'Base'] = 1 - prob_failure_base\\n stats_df.loc['% Success(value>0 at the end of expected_life)', 'fia_port'] = 1 - prob_failure_fia_port\\n stats_df.loc['% Success(value>0 at the end of expected_life)', 'Comment'] = \\\"% of N trials yearly ending value \\\" \\\\\\n \\\"greater than 0\\\"\\n\\n # -----Mininum of N portfolios terminal value at the end of Actuarial Age\\n stats_df.loc['Minimum Value', 'Base'] = sim_base_total.iloc[life_expectancy - clients_age].min()\\n stats_df.loc['Minimum Value', 'fia_port'] = sim_port_total.iloc[life_expectancy - clients_age].min()\\n stats_df.loc['Minimum Value', 'Comment'] = \\\"Mininum of N portfolios terminal value at the end of Actuarial Age\\\"\\n\\n # -----Maxinum of N portfolios terminal value at the end of Actuarial Age\\n stats_df.loc['Maximum Value', 'Base'] = sim_base_total.iloc[life_expectancy - clients_age].max()\\n stats_df.loc['Maximum Value', 'fia_port'] = sim_port_total.iloc[life_expectancy - clients_age].max()\\n stats_df.loc['Maximum Value', 'Comment'] = \\\"Maxinum of N portfolios terminal value at the end of Actuarial Age\\\"\\n\\n # -----Average income from N portfolios at the ned of Actuarial Age\\n stats_df.loc['Avg Income (lifetime)', 'Base'] = avg_income_base\\n stats_df.loc['Avg Income (lifetime)', 'fia_port'] = avg_income_port\\n stats_df.loc['Avg Income (lifetime)', 'Comment'] = \\\"Average income from N portfolios at the end of Actuarial Age\\\"\\n\\n sucess_df.to_excel(writer, sheet_name='yearly_success_rates')\\n stats_df.to_excel(writer, sheet_name='Stats')\\n\\n writer.save()\\n\\n print(\\\"simulation completed.\\\")\",\n \"def simulation(cython: bool = False, parallel: bool = False):\\n # Create the population\\n cfg = Config()\\n cfg.game.duration = 200\\n cfg.update()\\n pop = Population(\\n name=\\\"delete_me\\\",\\n config=cfg,\\n folder_name=\\\"test_performance\\\",\\n use_backup=True,\\n overwrite=True, # Every iteration, create a new population from scratch\\n )\\n \\n # Perform the simulations of experiment3\\n train_games, _ = get_game_ids(experiment_id=3)\\n if cython:\\n multi_env = MultiEnvironmentCy(game_config=cfg, pop_config=pop.config)\\n else:\\n multi_env = MultiEnvironment(game_config=cfg, pop_config=pop.config)\\n multi_env.set_games(train_games, noise=True)\\n \\n # Prepare the generation's reporters for the generation\\n pop.reporters.start_generation(gen=pop.generation, logger=pop.log)\\n \\n # Fetch the dictionary of genomes\\n genomes = list(iteritems(pop.population))\\n \\n if parallel:\\n # Initialize the evaluation-pool\\n pool = mp.Pool(mp.cpu_count())\\n manager = mp.Manager()\\n return_dict = manager.dict()\\n \\n for genome in genomes:\\n pool.apply_async(func=multi_env.eval_genome, args=(genome, return_dict))\\n pool.close() # Close the pool\\n pool.join() # Postpone continuation until everything is finished\\n else:\\n return_dict = dict()\\n for genome in genomes:\\n multi_env.eval_genome(genome, return_dict)\\n \\n path = f\\\"population_backup/storage/test_performance/\\\"\\n if os.path.exists(path):\\n shutil.rmtree(path)\",\n \"def after_run(self):\\n # Calculate the performance of the strategy and portfolio\\n self.portfolio.calc_stats()\\n self.calc_performance()\\n\\n return self\",\n \"def simulate_trajectories(kav):\\n print \\\"Simulating \\\"+str(kav)\\n wt_trajectories = []\\n avp_trajectories = []\\n vip_trajectories = []\\n for tn in range(100):\\n # get random initial condition\\n # initial phases\\n init_conditions_AV = [single_osc.lc(wt_T*np.random.rand()) \\n for i in range(AVPcells+VIPcells)]\\n init_conditions_NAV = [single_osc.lc(wt_T*np.random.rand())[:-1]\\n for i in range(NAVcells)]\\n y0_random = np.hstack(init_conditions_AV+init_conditions_NAV)\\n\\n # do the simulation\\n model = GonzeModelManyCells(param, kav=kav, \\n initial_values=y0_random)\\n wt_trajectories.append(model.run(show_labels=False, seed=0))\\n\\n # avp bmalko\\n avp_model = GonzeModelManyCells(param, bmalko='AVP', kav=kav, \\n initial_values=y0_random)\\n avp_trajectories.append(avp_model.run(show_labels=False, seed=0))\\n\\n # vip bmalko\\n vip_model = GonzeModelManyCells(param, bmalko='VIP', kav=kav, \\n initial_values=y0_random)\\n vip_trajectories.append(vip_model.run(show_labels=False, seed=0))\\n\\n # save results\\n with open(\\\"Data/params/wt_\\\"+str(kav)+\\\".pickle\\\", \\\"wb\\\") as output_file:\\n pickle.dump(wt_trajectories, output_file)\\n with open(\\\"Data/params/avp_\\\"+str(kav)+\\\".pickle\\\", \\\"wb\\\") as output_file:\\n pickle.dump(avp_trajectories, output_file)\\n with open(\\\"Data/params/vip_\\\"+str(kav)+\\\".pickle\\\", \\\"wb\\\") as output_file:\\n pickle.dump(vip_trajectories, output_file)\\n\\n return {'wt': wt_trajectories,\\n 'avp': avp_trajectories,\\n 'vip': vip_trajectories}\",\n \"def _generate_trading_instances(self, sp):\\n print(\\n \\\"Initialization...\\\"\\n )\\n self.data_handler = self.data_handler_cls(self.events, self.csv_dir, self.symbol_list, self.start_date,\\n self.end_date)\\n self.portfolio = self.portfolio_cls(self.data_handler, self.events, self.initial_capital, self.start_date,\\n self.strategy_id, self.spread, self.commission,self.csv_dir)\\n self.strategy = self.strategy_cls(self.data_handler, self.events, self.portfolio, self.spread, self.commission,\\n sp)\\n self.plot = self.plot_cls(self.csv_dir, self.portfolio, self.strategy_id)\",\n \"def _main():\\n ############### STOCK INPUT DATA #############\\n \\n StockTicker = [\\\"TEA\\\",\\\"POP\\\",\\\"ALE\\\",\\\"GIN\\\",\\\"JOE\\\"]\\n StockType = [\\\"Com\\\",\\\"Com\\\",\\\"Com\\\",\\\"Pref\\\",\\\"Com\\\"] #($)\\n TickerPrice = [2,1.5,3,1,1.8] #(USD)\\n LastDividend = [0,0.08,0.23,0.08,0.13] #(GBP)\\n FixedDividend = [0,0,0,0.02,0]\\n ParValue = [1,1,0.6,1,2.5] #(GBP)\\n\\n \\n ############# GET STOCK PARAMETERS ###########\\n \\n Get_StockParameters(len(StockTicker),StockTicker,StockType,TickerPrice,LastDividend,FixedDividend,ParValue)\\n \\n \\n ############# PERFORM A TRADING FOR EACH STOCK ###########\\n \\n\\n pathdir = \\\"./DataFiles/StockTrde_\\\"\\n \\n for i in range(0,len(StockTicker)):\\n \\n #Check if the data file exists\\n datafile = pathdir+\\\"{}.csv\\\".format(i+1)\\n if os.path.isfile(datafile) == False :\\n Get_HistoricalStockData(i,datafile,datetime.datetime(2015, 1, 1),datetime.datetime(2016, 1, 1))\\n\\n # Get the Strategy\\n Get_Trade(StockTicker[i],datafile)\\n \\n ############# PLOT THE TRADING ###########\\n \\n Plot_StockTrade(StockTicker[i])\",\n \"def run(outputfile, graph_each, graph_avg, iterations):\\n # runs auction the requested number of times\\n bids = simulate_auction(iterations)\\n \\n # export results to csv format\\n csvfile = open(outputfile, 'w', newline='')\\n writer = csv.writer(csvfile, dialect='excel')\\n writer.writerow([\\\"Bidder\\\", \\\"BidValue\\\", \\\"BidWeight\\\", \\\"CreationTimestep\\\", \\\"TimestepPaid\\\"])\\n\\n for bid in bids:\\n entry = [bid.bidder, bid.value, bid.weight, bid.creation_timestep, bid.payment_timestep]\\n writer.writerow(entry)\\n\\n # display data visualisation if --graphEach flag is used on cli\\n if graph_each:\\n plt.title('Auction simulation results')\\n plt.xlabel('Simulation Timestep')\\n plt.ylabel('Price Paid per Transaction by Agent')\\n\\n # for each unique bidder\\n for bidder in list(set([bid.bidder for bid in bids])):\\n x = []\\n y = []\\n\\n for bid in bids:\\n if bidder == bid.bidder:\\n x.append(bid.payment_timestep)\\n y.append(bid.value)\\n \\n # add results for current bidder to graph plot\\n plt.scatter(x, y, label=bidder, alpha=0.5, s=2)\\n\\n plt.legend(loc='best')\\n plt.show()\\n\\n # display data visualisation if --graphAvg flag is used on cli\\n if graph_avg:\\n plt.title('Auction simulation results')\\n plt.xlabel('Simulation Timestep')\\n plt.ylabel('Average Price Paid per Transaction by Block')\\n\\n # unique timesteps in the history\\n x = list(set(bid.creation_timestep for bid in bids))\\n y = []\\n\\n import statistics\\n for timestep in x:\\n bids_accepted = [bid.value for bid in bids if bid.creation_timestep == timestep]\\n y.append(statistics.mean(bids_accepted))\\n\\n plt.plot(x, y, label=\\\"Avg Gas Price Paid\\\")\\n plt.legend(loc='best')\\n plt.show()\",\n \"def run_experiment(x_loops=15, max_steps=0, display_on=True, max_fps=10,\\n garden_size=8, tako_number=1, pop_max=30, max_width=1800,\\n max_height=900, collect_data=True, export_all=False,\\n rand_nets=False, max_gen = 505, genetic_mode=\\\"Plain\\\",\\n learning_on=False, seeds=None, garden_mode=\\\"Diverse Static\\\",\\n family_detection=None, family_mod=0, record_inbreeding=True,\\n inbreed_lim = 1.1, hla_genes=0, binary_health=0,\\n carrier_percentage=40, two_envs=False, diff_envs=False,\\n migration_rate=0, phen_pref=False, filename=\\\"\\\"): \\n #round width/height down to nearest multiple of 50 if need be\\n if max_width % 50 != 0:\\n max_width = max_width - (max_width % 50)\\n if max_height % 50 != 0:\\n max_height = max_height - (max_height % 50)\\n\\n i = 0\\n #create csv files if they don't already exist\\n if collect_data or export_all:\\n if filename == \\\"\\\":\\n filename = str(int(time.time())) + \\\".csv\\\"\\n elif len(filename) < 4:\\n filename = filename + \\\".csv\\\"\\n elif filename[-4:] != \\\".csv\\\":\\n filename = filename + \\\".csv\\\"\\n\\n if not os.path.exists(\\\"Data\\\"):\\n os.makedirs(\\\"Data\\\")\\n\\n if collect_data:\\n if not os.path.exists(os.path.join(\\\"Data\\\", filename)):\\n with open(os.path.join(\\\"Data\\\", filename), 'a', newline='') as\\\\\\n csvfile:\\n writ = csv.writer(csvfile)\\n writ.writerow(['iteration', 'env #', 'ID', 'parent1',\\n 'parent2', 'age', 'generation', '# children',\\n 'mating attempts', 'accum pain',\\n 'cause of death', 'timestep', 'mutations',\\n 'parent_degree', 'parent_genoverlap',\\n '# disorders',\\n 'health a', 'health b', 'preference'])\\n else:\\n with open(os.path.join(\\\"Data\\\", filename), newline='') as\\\\\\n csvfile:\\n reader = csv.DictReader(csvfile)\\n row = None\\n for row in reader: pass\\n if row != None:\\n i = int(row[\\\"iteration\\\"]) + 1\\n\\n if export_all:\\n h = make_headers()\\n f = os.path.join('Data', (filename[:-4] + ' gene data.csv'))\\n if not os.path.exists(f):\\n with open(f, 'a') as file:\\n writ = csv.writer(file)\\n writ.writerow(h)\\n\\n tako.rand_nets = rand_nets\\n tako.family_mod = family_mod\\n tako.family_detection = family_detection\\n gt.family_detection = family_detection\\n tako.record_inbreeding = record_inbreeding\\n tako.inbreed_lim = inbreed_lim\\n tako.hla_genes = hla_genes\\n tako.binary_health = binary_health\\n tako.carrier_percentage = carrier_percentage\\n tako.phen_pref = phen_pref\\n gt.phen_pref = phen_pref\\n \\n loop_limit = x_loops\\n if loop_limit < 1:\\n loop_limit = 1\\n\\n if seeds == None:\\n seeds = [None for i in range(x_loops)]\\n\\n while loop_limit > 0:\\n #check if seeds is long enough\\n if len(seeds) < loop_limit + i:\\n for j in range(loop_limit + i - len(seeds)):\\n seeds.append(seeds[j])\\n if seeds[0] != None:\\n tako.set_seed(seeds[i])\\n g = garden_game(garden_size, tako_number, pop_max, max_width,\\n max_height, display_on, max_fps, learning_on,\\n genetic_mode, rand_nets, garden_mode, filename,\\n export_all, family_mod, family_detection,\\n two_envs, diff_envs, migration_rate,\\n seeds[i])\\n if display_on:\\n main_window = g\\n main_window.main_loop(max_steps, max_gen, display_on,\\n collect_data, garden_mode, i)\\n else:\\n g.main_loop(max_steps, max_gen, display_on, collect_data,\\n garden_mode, i)\\n loop_limit -= 1\\n i += 1\",\n \"def run_genetic_algorithm(bayes_params):\\n\\n print('Running genetic algorithm')\\n\\n # Unpacks parameters (unfortunately can't feed dataframe (or series or\\n # array) data into a function with hyperopt, so am having to pickle the\\n # parameters not being optimised with hyperopt\\n params_file = '{}/Program_input/Input_params.pkl'.format(\\n bayes_params['workingdirectory']\\n )\\n with open(params_file, 'rb') as f:\\n fixed_params = pickle.load(f)\\n if not type(fixed_params) in [dict, OrderedDict]:\\n raise TypeError('Data in {} is not a pickled dictionary'.format(params_file))\\n params = {**bayes_params, **fixed_params}\\n\\n # Records sequences and their fitnesses after each generation\\n with open('{}/Program_output/Sequence_track.txt'.format(\\n bayes_params['workingdirectory']), 'w') as f:\\n f.write('Tracking GA optimisation progress\\\\n')\\n\\n ga_calcs = run_ga_calcs(params)\\n\\n # Defines whether sequences are compared by their raw or rank propensities.\\n # Since BUDE scores and frequency values have to be compared by their rank\\n # values, have made the decision to also compare propensity values by their\\n # rankings.\\n \\\"\\\"\\\"\\n if params['matingpopmethod'] in ['fittest', 'roulettewheel']:\\n raw_or_rank = 'raw'\\n elif params['matingpopmethod'] in ['rankroulettewheel']:\\n raw_or_rank = 'rank'\\n \\\"\\\"\\\"\\n raw_or_rank = 'rank'\\n\\n # Calculates propensity and/or BUDE energy of input structure\\n with open('{}/Program_output/Sequence_track.txt'.format(\\n bayes_params['workingdirectory']), 'a') as f:\\n f.write('Input structure\\\\n')\\n\\n if params['fitnessscoremethod'] == 'alternate':\\n (network_propensity_scores, network_frequency_scores\\n ) = ga_calcs.measure_fitness_propensity(params['initialnetwork'])\\n\\n with open('{}/Program_output/Sequence_track.txt'.format(\\n bayes_params['workingdirectory']), 'a') as f:\\n f.write('network_id, sequence, propensity_score, frequency_score,'\\n ' BUDE energy, clashscore\\\\n')\\n for network, G in params['initialnetwork'].items():\\n sequence = ''.join([G.nodes()[node]['aa_id'] for node in G.nodes()])\\n propensity = network_propensity_scores[network]\\n frequency = network_frequency_scores[network]\\n f.write('{}, {}, {}, {}, {}, {}\\\\n'.format(\\n network, sequence, propensity, frequency,\\n params['inputpdbenergy'], params['inputpdbclash']\\n ))\\n f.write('\\\\n')\\n\\n if params['fitnessscoremethod'] == 'propensity':\\n (network_propensity_scores, network_frequency_scores\\n ) = ga_calcs.measure_fitness_propensity(params['initialnetwork'])\\n\\n with open('{}/Program_output/Sequence_track.txt'.format(\\n bayes_params['workingdirectory']), 'a') as f:\\n f.write('network_id, sequence, propensity_score, frequency_score\\\\n')\\n for network, G in params['initialnetwork'].items():\\n sequence = ''.join([G.nodes()[node]['aa_id'] for node in G.nodes()])\\n propensity = network_propensity_scores[network]\\n frequency = network_frequency_scores[network]\\n f.write('{}, {}, {}, {}\\\\n'.format(\\n network, sequence, propensity, frequency\\n ))\\n f.write('\\\\n')\\n\\n elif params['fitnessscoremethod'] == 'allatom':\\n network_energies = ga_calcs.measure_fitness_allatom(params['initialnetwork'])\\n\\n with open('{}/Program_output/Sequence_track.txt'.format(\\n bayes_params['workingdirectory']), 'a') as f:\\n f.write('network_id, sequence, BUDE energy\\\\n')\\n for network, G in params['initialnetwork'].items():\\n sequence = ''.join([G.nodes()[node]['aa_id'] for node in G.nodes()])\\n energy = network_energies[network]\\n f.write('{}, {}, {}\\\\n'.format(network, sequence, energy))\\n f.write('\\\\n')\\n\\n elif params['fitnessscoremethod'] == 'molprobity':\\n network_clashes = ga_calcs.measure_fitness_clashscore(params['initialnetwork'])\\n\\n with open('{}/Program_output/Sequence_track.txt'.format(\\n bayes_params['workingdirectory']), 'a') as f:\\n f.write('network_id, sequence, clashscore\\\\n')\\n for network, G in params['initialnetwork'].items():\\n sequence = ''.join([G.nodes()[node]['aa_id'] for node in G.nodes()])\\n clashscore = network_clashes[network]\\n f.write('{}, {}, {}\\\\n'.format(network, sequence, clashscore))\\n f.write('\\\\n')\\n\\n # Runs GA cycles\\n gen = params['startgen']\\n while gen < params['stopgen']:\\n gen += 1\\n print('Generation {}'.format(gen))\\n with open('{}/Program_output/Sequence_track.txt'.format(\\n bayes_params['workingdirectory']), 'a') as f:\\n f.write('\\\\n\\\\n\\\\n\\\\n\\\\nGeneration {}\\\\n'.format(gen))\\n\\n\\n all_networks_list = [params['sequencesdict']]\\n pop_sizes = [params['populationsize']]\\n\\n for index, networks_dict in enumerate(all_networks_list):\\n # Measures fitness of sequences in starting population.\\n if (\\n (params['fitnessscoremethod'] == 'propensity')\\n or\\n (params['fitnessscoremethod'] == 'alternate' and gen % 2 == 1)\\n ):\\n (network_propensity_scores, network_frequency_scores\\n ) = ga_calcs.measure_fitness_propensity(networks_dict)\\n network_fitness_scores = ga_calcs.combine_prop_and_freq_scores(\\n network_propensity_scores, network_frequency_scores, raw_or_rank\\n )\\n\\n # Records sequences output from this generation and their\\n # associated fitnesses\\n with open('{}/Program_output/Sequence_track.txt'.format(\\n bayes_params['workingdirectory']), 'a') as f:\\n f.write('network, sequence, propensity, frequency, probability\\\\n')\\n for network, G in networks_dict.items():\\n sequence = ''.join([G.nodes()[node]['aa_id'] for node in G.nodes()])\\n propensity = network_propensity_scores[network]\\n frequency = network_frequency_scores[network]\\n probability = network_fitness_scores[network]\\n f.write('{}, {}, {}, {}, {}\\\\n'.format(\\n network, sequence, propensity, frequency, probability\\n ))\\n f.write('Total: {}, {}, {}'.format(\\n sum(network_propensity_scores.values()),\\n sum(network_frequency_scores.values()),\\n sum(network_fitness_scores.values())\\n ))\\n f.write('\\\\n')\\n elif (\\n (params['fitnessscoremethod'] == 'allatom')\\n or\\n (params['fitnessscoremethod'] == 'alternate' and gen % 4 == 2)\\n ):\\n # Runs BUDE energy scoring on parallel processors\\n network_energies = ga_calcs.measure_fitness_allatom(networks_dict)\\n (network_fitness_scores\\n ) = ga_calcs.convert_energies_to_probabilities(network_energies)\\n\\n # Records sequences output from this generation and their\\n # associated fitnesses\\n with open('{}/Program_output/Sequence_track.txt'.format(\\n bayes_params['workingdirectory']), 'a') as f:\\n f.write('network, sequence, BUDE score, probability\\\\n')\\n for network, G in networks_dict.items():\\n sequence = ''.join([G.nodes()[node]['aa_id'] for node in G.nodes()])\\n energy = network_energies[network]\\n probability = network_fitness_scores[network]\\n f.write('{}, {}, {}, {}\\\\n'.format(\\n network, sequence, energy, probability\\n ))\\n f.write('Total: {}, {}'.format(\\n sum(network_energies.values()),\\n sum(network_fitness_scores.values())\\n ))\\n f.write('\\\\n')\\n\\n elif (\\n (params['fitnessscoremethod'] == 'molprobity')\\n or\\n (params['fitnessscoremethod'] == 'alternate' and gen % 4 == 0)\\n ):\\n # Runs MolProbity scoring on parallel processors\\n network_clashes = ga_calcs.measure_fitness_clashscore(networks_dict)\\n (network_fitness_scores\\n ) = ga_calcs.convert_clashscores_to_probabilities(network_clashes)\\n\\n # Records sequences output from this generation and their\\n # associated fitnesses\\n with open('{}/Program_output/Sequence_track.txt'.format(\\n bayes_params['workingdirectory']), 'a') as f:\\n f.write('network, sequence, clashscore, probability\\\\n')\\n for network, G in networks_dict.items():\\n sequence = ''.join([G.nodes()[node]['aa_id'] for node in G.nodes()])\\n clash = network_clashes[network]\\n probability = network_fitness_scores[network]\\n f.write('{}, {}, {}, {}\\\\n'.format(\\n network, sequence, clash, probability\\n ))\\n f.write('Total: {}, {}'.format(\\n sum(network_clashes.values()),\\n sum(network_fitness_scores.values())\\n ))\\n f.write('\\\\n')\\n\\n # Selects subpopulation for mating\\n if params['matingpopmethod'] == 'fittest':\\n mating_pop_dict = ga_calcs.create_mat_pop_fittest(\\n networks_dict, network_fitness_scores, pop_sizes[index],\\n params['unfitfraction']\\n )\\n elif params['matingpopmethod'] in ['roulettewheel', 'rankroulettewheel']:\\n mating_pop_dict = ga_calcs.create_mat_pop_roulette_wheel(\\n networks_dict, network_fitness_scores, pop_sizes[index], params['']\\n )\\n\\n # Performs crossover of parent sequences to generate child sequences\\n if params['crossovermethod'] == 'uniform':\\n crossover_pop_dict = ga_calcs.uniform_crossover(mating_pop_dict)\\n elif params['crossovermethod'] == 'segmented':\\n crossover_pop_dict = ga_calcs.segmented_crossover(mating_pop_dict)\\n\\n # Mutates child sequences\\n if params['mutationmethod'] == 'swap':\\n mutated_pop_dict = ga_calcs.swap_mutate(crossover_pop_dict)\\n elif params['mutationmethod'] == 'scramble':\\n mutated_pop_dict = ga_calcs.scramble_mutate(crossover_pop_dict)\\n\\n # Combines parent and child sequences into single generation\\n merged_networks_dict = ga_calcs.add_children_to_parents(\\n mutated_pop_dict, mating_pop_dict\\n )\\n\\n random_order = [n for n in range(len(merged_networks_dict))]\\n random.shuffle(random_order)\\n shuffled_merged_networks_dict = OrderedDict(\\n {list(merged_networks_dict.keys())[n]:\\n list(merged_networks_dict.values())[n] for n in random_order}\\n )\\n params['sequencesdict'] = shuffled_merged_networks_dict\\n\\n # Calculates fitness of output sequences and filters population to maintain\\n # the fittest 50%, plus sums the probabilities of the retained sequences and\\n # returns this value (to be minimised with hyperopt)\\n summed_fitness = 0\\n\\n with open('{}/Program_output/Sequence_track.txt'.format(\\n bayes_params['workingdirectory']), 'a') as f:\\n f.write('\\\\n\\\\n\\\\n\\\\n\\\\nOutput generation\\\\n')\\n\\n if params['fitnessscoremethod'] != 'allatom':\\n (network_propensity_scores, network_frequency_scores\\n ) = ga_calcs.measure_fitness_propensity(params['sequencesdict'])\\n network_fitness_scores = ga_calcs.combine_prop_and_freq_scores(\\n network_propensity_scores, network_frequency_scores, raw_or_rank\\n )\\n elif params['fitnessscoremethod'] == 'allatom':\\n network_energies = ga_calcs.measure_fitness_allatom(params['sequencesdict'])\\n (network_fitness_scores\\n ) = ga_calcs.convert_energies_to_probabilities(network_energies)\\n\\n # Records sequences output from this generation and their associated\\n # fitnesses\\n with open('{}/Program_output/Sequence_track.txt'.format(\\n bayes_params['workingdirectory']), 'a') as f:\\n if params['fitnessscoremethod'] != 'allatom':\\n f.write('network, sequence, propensity, frequency\\\\n')\\n elif params['fitnessscoremethod'] == 'allatom':\\n f.write('network, sequence, BUDE score\\\\n')\\n for network, G in params['sequencesdict'].items():\\n sequence = ''.join([G.nodes()[node]['aa_id'] for node in G.nodes()])\\n if params['fitnessscoremethod'] != 'allatom':\\n propensity = network_propensity_scores[network]\\n frequency = network_frequency_scores[network]\\n f.write('{}, {}, {}, {}\\\\n'.format(\\n network, sequence, propensity, frequency\\n ))\\n elif params['fitnessscoremethod'] == 'allatom':\\n energy = network_energies[network]\\n f.write('{}, {}, {}\\\\n'.format(network, sequence, energy))\\n if params['fitnessscoremethod'] != 'allatom':\\n f.write('Total: {}, {}'.format(\\n sum(network_propensity_scores.values()),\\n sum(network_frequency_scores.values())\\n ))\\n elif params['fitnessscoremethod'] == 'allatom':\\n f.write('Total: {}'.format(sum(network_energies.values())))\\n f.write('\\\\n')\\n\\n params['sequencesdict'] = ga_calcs.create_mat_pop_fittest(\\n params['sequencesdict'], network_fitness_scores,\\n params['populationsize'], unfit_fraction=0\\n )\\n\\n for network in params['sequencesdict'].keys():\\n # Higher propensity is more likely, so add because output from\\n # measure_fitness_propensity is sum of -log(propensity) values, and\\n # hyperopt minimises output score\\n # Can't combine propensity and frequency scores without first converting\\n # to a probability, so for calculating output combined fitness can only\\n # use combined propensity scores to rank the structures\\n if params['fitnessscoremethod'] != 'allatom':\\n summed_fitness += network_propensity_scores[network]\\n # Lower score is more likely, so add because hyperopt minimises output\\n # score\\n elif params['fitnessscoremethod'] == 'allatom':\\n summed_fitness += network_energies[network]\\n\\n with open('{}/Program_output/GA_output_sequences_dict.pkl'.format(\\n bayes_params['workingdirectory']), 'wb') as f:\\n pickle.dump(params['sequencesdict'], f)\\n\\n print(summed_fitness)\\n\\n return summed_fitness\",\n \"def run():\\n if am_i_root():\\n\\n print(\\\"*** initializing...\\\")\\n\\n # Print parameters\\n print(\\\"N_DIMS = \\\" + str(N_DIMS))\\n print(\\\"LAMBDA_OVER_DX = \\\" + str(LAMBDA_OVER_DX))\\n print(\\\"R_DT = \\\" + str(R_DT))\\n print(\\\"MU0_POISSON = \\\" + str(MU0_POISSON))\\n print(\\\"NORM_POISSON = \\\" + NORM_POISSON)\\n print(\\\"N_GRID = \\\" + str(N_GRID))\\n print(\\\"N_HITS = \\\" + str(N_HITS))\\n print(\\\"POLICY = \\\" + str(POLICY))\\n if POLICY == -1:\\n print(\\\"MODEL_PATH = \\\" + str(MODEL_PATH))\\n else:\\n print(\\\"STEPS_AHEAD = \\\" + str(STEPS_AHEAD))\\n print(\\\"EPSILON = \\\" + str(EPSILON))\\n print(\\\"STOP_t = \\\" + str(STOP_t))\\n print(\\\"STOP_p = \\\" + str(STOP_p))\\n print(\\\"N_PARALLEL = \\\" + str(N_PARALLEL))\\n print(\\\"WITH_MPI = \\\" + str(WITH_MPI))\\n print(\\\"ADAPTIVE_N_RUNS = \\\" + str(ADAPTIVE_N_RUNS))\\n print(\\\"REL_TOL = \\\" + str(REL_TOL))\\n print(\\\"MAX_N_RUNS = \\\" + str(MAX_N_RUNS))\\n print(\\\"N_RUNS(input) = \\\" + str(N_RUNS))\\n sys.stdout.flush()\\n\\n # Perform runs\\n if am_i_root():\\n print(\\\"*** generating episodes...\\\")\\n\\n N_runs = N_RUNS\\n if ADAPTIVE_N_RUNS or WITH_MPI:\\n N_runs = int(N_PARALLEL * (np.ceil(N_runs / N_PARALLEL))) # make it multiple of N_PARALLEL\\n if am_i_root():\\n print(\\\"N_RUNS(current) = \\\" + str(N_runs))\\n sys.stdout.flush()\\n\\n N_runso = 0\\n\\n if WITH_MPI:\\n cdf_t_tot_loc = np.zeros(LEN_CDF_T, dtype=float)\\n cdf_h_tot_loc = np.zeros(LEN_CDF_H, dtype=float)\\n mean_t_loc = np.nan * np.ones(MAX_N_RUNS // N_PARALLEL, dtype=float)\\n failed_loc = - np.ones(MAX_N_RUNS // N_PARALLEL, dtype=float)\\n else:\\n cdf_t_tot = np.zeros(LEN_CDF_T, dtype=float)\\n cdf_h_tot = np.zeros(LEN_CDF_H, dtype=float)\\n mean_t_episodes = np.nan * np.ones(MAX_N_RUNS, dtype=float)\\n failed_episodes = - np.ones(MAX_N_RUNS, dtype=float)\\n\\n while True:\\n if WITH_MPI: # MPI\\n if N_runs % N_PARALLEL != 0:\\n raise Exception(\\\"N_runs must be multiple of N_PARALLEL with MPI\\\")\\n COMM.Barrier()\\n # Decomposition\\n Nepisodes = N_runs // N_PARALLEL\\n episode_list = range(N_runso + ME, N_runs, N_PARALLEL)\\n # Run episodes and reduce locally\\n ind = N_runso // N_PARALLEL\\n for episode in episode_list:\\n cdf_t, cdf_h, mean_t_loc[ind], failed_loc[ind] = Worker(episode)\\n cdf_t_tot_loc += cdf_t\\n cdf_h_tot_loc += cdf_h\\n ind += 1\\n\\n # Reduce globally the mean_t and failed\\n mean_t_episodes = np.empty([N_runs], dtype=float)\\n failed_episodes = np.empty([N_runs], dtype=float)\\n COMM.Barrier()\\n COMM.Allgather([mean_t_loc[:ind], Nepisodes, MPI.DOUBLE], [mean_t_episodes, Nepisodes, MPI.DOUBLE])\\n COMM.Allgather([failed_loc[:ind], Nepisodes, MPI.DOUBLE], [failed_episodes, Nepisodes, MPI.DOUBLE])\\n COMM.Barrier()\\n elif N_PARALLEL > 1: # multiprocessing\\n # Run episodes in parallel\\n pool = multiprocessing.Pool(N_PARALLEL)\\n result = pool.map(Worker, range(N_runso, N_runs))\\n pool.close()\\n pool.join()\\n # Reduce\\n ind = N_runso\\n for cdf_t, cdf_h, mean_t, failed in result:\\n cdf_t_tot += cdf_t\\n cdf_h_tot += cdf_h\\n mean_t_episodes[ind] = mean_t\\n failed_episodes[ind] = failed\\n ind += 1\\n elif N_PARALLEL == 1: # sequential\\n ind = N_runso\\n for episode in range(N_runso, N_runs):\\n cdf_t, cdf_h, mean_t, failed = Worker(episode)\\n cdf_t_tot += cdf_t\\n cdf_h_tot += cdf_h\\n mean_t_episodes[ind] = mean_t\\n failed_episodes[ind] = failed\\n ind += 1\\n else:\\n raise Exception(\\\"Problem with N_PARALLEL: must be an int >= 1\\\")\\n\\n # estimate of the error\\n mean_ep = np.mean(mean_t_episodes[:N_runs])\\n sigma_ep = np.std(mean_t_episodes[:N_runs])\\n std_error_mean = sigma_ep / np.sqrt(N_runs)\\n rel_std_error_mean = std_error_mean / mean_ep\\n\\n # break clause\\n if not ADAPTIVE_N_RUNS:\\n break\\n else:\\n if rel_std_error_mean < REL_TOL:\\n break\\n elif N_runs >= MAX_N_RUNS:\\n break\\n else:\\n N_runso = N_runs\\n N_runs = int(np.ceil(1.05 * (sigma_ep / mean_ep / REL_TOL) ** 2))\\n N_runs = min(N_runs, MAX_N_RUNS)\\n N_runs = int(N_PARALLEL * (np.ceil(N_runs / N_PARALLEL))) # make it multiple of N_PARALLEL\\n if am_i_root():\\n print(\\\"N_RUNS(current) = \\\" + str(N_runs))\\n sys.stdout.flush()\\n\\n if am_i_root():\\n print(\\\"N_RUNS(performed) = \\\" + str(N_runs))\\n sys.stdout.flush()\\n\\n # Reduce\\n if am_i_root():\\n print(\\\"*** post-processing...\\\")\\n if WITH_MPI:\\n # locally\\n cdf_t_tot_loc /= N_runs\\n cdf_h_tot_loc /= N_runs\\n # Reduce globally\\n cdf_t_tot = np.empty([LEN_CDF_T], dtype=float)\\n cdf_h_tot = np.empty([LEN_CDF_H], dtype=float)\\n COMM.Barrier()\\n COMM.Allreduce(cdf_t_tot_loc, cdf_t_tot, op=MPI.SUM)\\n COMM.Allreduce(cdf_h_tot_loc, cdf_h_tot, op=MPI.SUM)\\n COMM.Barrier()\\n else:\\n cdf_t_tot /= N_runs\\n cdf_h_tot /= N_runs\\n mean_t_episodes = mean_t_episodes[:N_runs]\\n failed_episodes = failed_episodes[:N_runs]\\n\\n # Further post-processing, save and plot\\n if am_i_root():\\n\\n # from cdf to pdf\\n pdf_t_tot = cdf_to_pdf(cdf_t_tot)\\n pdf_h_tot = cdf_to_pdf(cdf_h_tot)\\n\\n # compute stats of number of steps and number of hits\\n t_bins = np.arange(BIN_START_T, BIN_END_T, BIN_SIZE_T) + 0.5 * BIN_SIZE_T\\n mean_t, sigma_t, skew_t, kurt_t, p_found = stats_from_pdf(t_bins, pdf_t_tot)\\n p25_t, p50_t, p75_t, p90_t, p95_t, p99_t, _ = stats_from_cdf(t_bins, cdf_t_tot)\\n\\n h_bins = np.arange(BIN_START_H, BIN_END_H, BIN_SIZE_H) + 0.5 * BIN_SIZE_H\\n mean_h, sigma_h, skew_h, kurt_h, _ = stats_from_pdf(h_bins, pdf_h_tot)\\n p25_h, p50_h, p75_h, p90_h, p95_h, p99_h, _ = stats_from_cdf(h_bins, cdf_h_tot)\\n\\n print(\\\"probability that the source is never found : %.10f\\\" % (1.0 - p_found, ))\\n print(\\\"mean number of steps to find the source : %.3f +/- %.3f\\\" % (mean_t, 1.96 * std_error_mean))\\n print(\\\"number of steps to find the source with 50 %% probability: %.3f\\\" % p50_t)\\n print(\\\"number of steps to find the source with 99 %% probability: %.3f\\\" % p99_t)\\n nb_failed = np.sum(failed_episodes)\\n if np.any(failed_episodes < 0):\\n nb_failed = -1\\n print(\\\"problem while recording failures\\\")\\n else:\\n print(\\\"number of failed episodes : %d / %d (%f %%)\\\"\\n % (nb_failed, N_runs, nb_failed / N_runs * 100))\\n sys.stdout.flush()\\n\\n # save all parameters to txt file\\n inputs = {\\n \\\"N_DIMS\\\": N_DIMS,\\n \\\"LAMBDA_OVER_DX\\\": LAMBDA_OVER_DX,\\n \\\"R_DT\\\": R_DT,\\n \\\"MU0_POISSON\\\": MU0_POISSON,\\n \\\"NORM_POISSON\\\": NORM_POISSON,\\n \\\"N_GRID\\\": N_GRID,\\n \\\"N_HITS\\\": N_HITS,\\n \\\"POLICY\\\": POLICY,\\n \\\"STEPS_AHEAD\\\": STEPS_AHEAD,\\n \\\"MODEL_PATH\\\": MODEL_PATH,\\n \\\"STOP_t\\\": STOP_t,\\n \\\"STOP_p\\\": STOP_p,\\n \\\"ADAPTIVE_N_RUNS\\\": ADAPTIVE_N_RUNS,\\n \\\"REL_TOL\\\": REL_TOL,\\n \\\"MAX_N_RUNS\\\": MAX_N_RUNS,\\n \\\"N_RUNS_PERFORMED\\\": N_runs,\\n \\\"BIN_START_T\\\": BIN_START_T,\\n \\\"BIN_END_T\\\": BIN_END_T,\\n \\\"BIN_SIZE_T\\\": BIN_SIZE_T,\\n \\\"BIN_START_H\\\": BIN_START_H,\\n \\\"BIN_END_H\\\": BIN_END_H,\\n \\\"BIN_SIZE_H\\\": BIN_SIZE_H,\\n \\\"EPSILON\\\": EPSILON,\\n }\\n param_txt_file = os.path.join(DIR_OUTPUTS, str(RUN_NAME + \\\"_parameters\\\" + \\\".txt\\\"))\\n with open(param_txt_file, 'w') as out:\\n for key, val in inputs.items():\\n print(key + \\\" = \\\" + str(val), file=out)\\n\\n # save stats\\n stats_file = os.path.join(DIR_OUTPUTS, str(RUN_NAME + \\\"_statistics\\\" + \\\".txt\\\"))\\n with open(stats_file, \\\"w\\\") as sfile:\\n sfile.write(\\\"p_not_found\\\\t%+.4e\\\\n\\\" % (1 - p_found,))\\n for varname in \\\\\\n ('mean_t', 'sigma_t', 'skew_t', 'kurt_t', 'p25_t', 'p50_t', 'p75_t', 'p90_t', 'p95_t', 'p99_t'):\\n sfile.write(\\\"%s\\\\t\\\\t%+.4e\\\\n\\\" % (varname, locals()[varname]))\\n for varname in \\\\\\n ('mean_h', 'sigma_h', 'skew_h', 'kurt_h', 'p25_h', 'p50_h', 'p75_h', 'p90_h', 'p95_h', 'p99_h'):\\n sfile.write(\\\"%s\\\\t\\\\t%+.4e\\\\n\\\" % (varname, locals()[varname]))\\n\\n # save CDF of number of steps\\n table_file = os.path.join(DIR_OUTPUTS, str(RUN_NAME + \\\"_table_CDF_nsteps\\\" + \\\".npy\\\"))\\n np.save(table_file, np.vstack((t_bins, cdf_t_tot)))\\n\\n # save CDF of number of hits\\n table_file = os.path.join(DIR_OUTPUTS, str(RUN_NAME + \\\"_table_CDF_nhits\\\" + \\\".npy\\\"))\\n np.save(table_file, np.vstack((h_bins, cdf_h_tot)))\\n\\n # create and save figures\\n if POLICY == -1:\\n specifics = \\\"MODEL = \\\" + os.path.basename(MODEL_PATH)\\n else:\\n specifics = \\\"STEPS_AHEAD = \\\" + str(STEPS_AHEAD)\\n subtitle = (\\n \\\"N_DIMS = \\\"\\n + str(N_DIMS)\\n + \\\", \\\"\\n + \\\"LAMBDA_OVER_DX = \\\"\\n + str(LAMBDA_OVER_DX)\\n + \\\", \\\"\\n + \\\"R_DT = \\\"\\n + str(R_DT)\\n + \\\", \\\"\\n + \\\"POLICY = \\\"\\n + str(POLICY)\\n + \\\", \\\"\\n + specifics\\n + \\\", \\\"\\n + \\\"N_GRID = \\\"\\n + str(N_GRID)\\n + \\\", \\\"\\n + \\\"N_HITS = \\\"\\n + str(N_HITS)\\n + \\\", \\\"\\n + \\\"N_RUNS = \\\"\\n + str(N_runs)\\n + \\\"\\\\n\\\"\\n )\\n\\n # plot PDF(nsteps), CDF(nsteps), PDF(nhits), CDF(nhits)\\n fig, ax = plt.subplots(2, 2, figsize=(12, 10))\\n plt.subplots_adjust(left=0.08, bottom=0.06, right=0.96, top=0.92, hspace=0.35, wspace=0.30)\\n kwargs = {'xycoords': 'axes fraction', 'fontsize': 8, 'ha': \\\"right\\\"}\\n for row, varname in enumerate([\\\"number of steps\\\", \\\"number of hits\\\"]):\\n if varname == \\\"number of steps\\\":\\n bins = t_bins\\n cdf_tot = cdf_t_tot\\n pdf_tot = pdf_t_tot\\n mean = mean_t\\n sigma = sigma_t\\n skew = skew_t\\n kurt = kurt_t\\n p50 = p50_t\\n p75 = p75_t\\n p90 = p90_t\\n p99 = p99_t\\n filesuffix = 'nsteps'\\n color = \\\"tab:blue\\\"\\n else:\\n bins = h_bins\\n cdf_tot = cdf_h_tot\\n pdf_tot = pdf_h_tot\\n mean = mean_h\\n sigma = sigma_h\\n skew = skew_h\\n kurt = kurt_h\\n p50 = p50_h\\n p75 = p75_h\\n p90 = p90_h\\n p99 = p99_h\\n filesuffix = 'nhits'\\n color = \\\"tab:orange\\\"\\n max_x = bins[np.nonzero(pdf_tot)[0][-1]]\\n for col, fct in enumerate([\\\"PDF\\\", \\\"CDF\\\"]):\\n if fct == \\\"PDF\\\":\\n ydata = pdf_tot\\n ylim = (0.0, 1.02 * np.max(pdf_tot))\\n elif fct == \\\"CDF\\\":\\n ydata = cdf_tot\\n ylim = (0.0, 1.0)\\n\\n ax[row, col].plot(bins, ydata, \\\"-o\\\", color=color, markersize=2, linewidth=1)\\n ax[row, col].set_title(fct + \\\" of \\\" + varname)\\n ax[row, col].set_xlabel(varname + \\\" to find the source\\\")\\n ax[row, col].set_xlim((0, max_x))\\n ax[row, col].set_ylim(ylim)\\n\\n if fct == \\\"PDF\\\":\\n ax[row, col].annotate(\\\"p_not_found = \\\" + \\\"{:.3e}\\\".format(1.0 - p_found), xy=(0.98, 0.60), **kwargs)\\n ax[row, col].annotate(\\\"mean = \\\" + \\\"{:.3e}\\\".format(mean), xy=(0.98, 0.56), **kwargs)\\n ax[row, col].annotate(\\\"std = \\\" + \\\"{:.3e}\\\".format(sigma), xy=(0.98, 0.52), **kwargs)\\n ax[row, col].annotate(\\\"skew = \\\" + \\\"{:.3e}\\\".format(skew), xy=(0.98, 0.48), **kwargs)\\n ax[row, col].annotate(\\\"ex. kurt = \\\" + \\\"{:.3e}\\\".format(kurt), xy=(0.98, 0.44), **kwargs)\\n elif fct == \\\"CDF\\\":\\n ax[row, col].annotate(\\\"p_not_found = \\\" + \\\"{:.3e}\\\".format(1.0 - p_found), xy=(0.98, 0.60), **kwargs)\\n ax[row, col].annotate(\\\"P50 = \\\" + \\\"{:.3e}\\\".format(p50), xy=(0.98, 0.56), **kwargs)\\n ax[row, col].annotate(\\\"P75 = \\\" + \\\"{:.3e}\\\".format(p75), xy=(0.98, 0.52), **kwargs)\\n ax[row, col].annotate(\\\"P90 = \\\" + \\\"{:.3e}\\\".format(p90), xy=(0.98, 0.48), **kwargs)\\n ax[row, col].annotate(\\\"P99 = \\\" + \\\"{:.3e}\\\".format(p99), xy=(0.98, 0.44), **kwargs)\\n plt.grid(False)\\n plt.figtext(0.5, 0.985, subtitle, fontsize=7, ha=\\\"center\\\", va=\\\"top\\\")\\n plt.draw()\\n figure_file = os.path.join(DIR_OUTPUTS, str(RUN_NAME + \\\"_figure_distributions.pdf\\\"))\\n fig.savefig(figure_file)\\n plt.close(fig)\\n\\n # plot mean nb steps vs number of episodes\\n number_episodes = range(1, N_runs + 1)\\n cum_mean_t_episodes = np.cumsum(mean_t_episodes) / number_episodes\\n if N_runs >= 100:\\n number_episodes = number_episodes[20:]\\n cum_mean_t_episodes = cum_mean_t_episodes[20:]\\n fig, ax = plt.subplots()\\n ax.plot(number_episodes, cum_mean_t_episodes, color=\\\"r\\\")\\n ax.set_title(\\\"Convergence of the mean number of steps\\\")\\n ax.set_xlabel(\\\"number of episodes\\\")\\n ax.set_ylabel(\\\"mean number of steps\\\")\\n plt.figtext(0.5, 0.985, subtitle, fontsize=5, ha=\\\"center\\\", va=\\\"top\\\")\\n plt.grid(False)\\n plt.draw()\\n figure_file = os.path.join(DIR_OUTPUTS, str(RUN_NAME + \\\"_figure_convergence.pdf\\\"))\\n fig.savefig(figure_file)\\n plt.close(fig)\\n\\n # save monitoring information (concatenate episodes files)\\n monitoring_episodes_file = os.path.join(DIR_OUTPUTS, str(RUN_NAME + \\\"_monitoring_episodes.txt\\\"))\\n filenames = [os.path.join(DIR_TMP, str(\\\"monitoring_episode_\\\" + str(episode) + \\\".txt\\\")) for episode in range(N_runs)]\\n with open(monitoring_episodes_file, \\\"w\\\") as mfile:\\n mfile.write(\\\"# episode\\\\thit_init\\\\tstop_flag\\\\tboundary_flag\\\\t\\\"\\n \\\"p_not_found\\\\t\\\\tmean_nsteps\\\\t\\\\ttime_elapsed(sec)\\\\n\\\")\\n for fname in filenames:\\n if os.path.isfile(fname):\\n with open(fname) as infile:\\n mfile.write(infile.read())\\n os.remove(fname)\\n else:\\n print(\\\"Unexpected: Missing episode file: \\\" + str(fname))\\n\\n # clean up tmp dirs\\n if len(os.listdir(DIR_TMP)) != 0:\\n print(\\\"Unexpected: The directory '\\\" + DIR_TMP\\n + \\\"' is not removed, because it should be empty but is not.\\\")\\n else:\\n os.rmdir(DIR_TMP)\\n if len(os.listdir(PARENT_DIR_TMP)) == 0:\\n os.rmdir(PARENT_DIR_TMP)\\n\\n # summary\\n monitoring_file = os.path.join(DIR_OUTPUTS, str(RUN_NAME + \\\"_monitoring_summary\\\" + \\\".txt\\\"))\\n with open(monitoring_file, \\\"w\\\") as mfile:\\n mfile.write(\\\"*** initial hit ***\\\\n\\\")\\n first_hit = np.loadtxt(monitoring_episodes_file, usecols=1, dtype='int')\\n hit_max = np.max(first_hit)\\n hit_hist, _ = np.histogram(first_hit, bins=np.arange(0.5, hit_max + 1.5), density=True)\\n for h in range(1, hit_max + 1):\\n mfile.write(\\\"hit=%1d: %6.2f %% \\\\n\\\" % (h, hit_hist[h - 1] * 100))\\n\\n mfile.write(\\\"\\\\n*** stats convergence ***\\\\n\\\")\\n mfile.write(\\\"number of episodes simulated : %d\\\\n\\\" % N_runs)\\n mfile.write(\\\"standard error of the mean (estimate): %.4e = %5.2f %%\\\\n\\\"\\n % (std_error_mean, rel_std_error_mean * 100))\\n\\n stopping_reason = np.loadtxt(monitoring_episodes_file, usecols=2, dtype='int')\\n stop_max = np.max(stopping_reason)\\n stopping_hist, _ = np.histogram(stopping_reason, bins=np.arange(0.5, stop_max + 1.5), density=True)\\n mfile.write(\\\"\\\\n*** reason for stopping (1 is success, anything else is failure) ***\\\\n\\\")\\n for stop in range(1, stop_max + 1):\\n mfile.write(\\\"stop=%1d: %6.2f %% \\\\n\\\" % (stop, stopping_hist[stop - 1] * 100))\\n\\n mfile.write(\\\"\\\\n*** probability that the source is not found at the end of the episodes ***\\\\n\\\")\\n p_not_found = np.loadtxt(monitoring_episodes_file, usecols=4)\\n p_gtr_stop = p_not_found[p_not_found > STOP_p]\\n p_not_found_max = np.max(p_not_found)\\n mfile.write(\\\"criteria (STOP_p): %.5e\\\\n\\\" % STOP_p)\\n mfile.write(\\\"max(p) : %.5e\\\\n\\\" % p_not_found_max)\\n mfile.write(\\\"number of episodes where p > STOP_p: %7d (%8.4f %%)\\\\n\\\"\\n % (len(p_gtr_stop), len(p_gtr_stop) / N_runs * 100))\\n\\n near_boundaries = np.loadtxt(monitoring_episodes_file, usecols=3, dtype='int')\\n near_boundaries = np.count_nonzero(near_boundaries)\\n mfile.write(\\\"\\\\n*** agent near boundaries ***\\\\n\\\")\\n mfile.write(\\\"number of episodes where it happened: %7d (%8.4f %%)\\\\n\\\"\\n % (near_boundaries, near_boundaries / N_runs * 100))\\n\\n episode_elapsed = np.loadtxt(monitoring_episodes_file, usecols=5)\\n mfile.write(\\\"\\\\n*** computational cost per episode ***\\\\n\\\")\\n mfile.write(\\\"avg elapsed seconds per episode: %.5e\\\\n\\\" % (np.mean(episode_elapsed)))\\n mfile.write(\\\"max elapsed seconds per episode: %.5e\\\\n\\\" % (np.max(episode_elapsed)))\\n\\n elapsed_time_0 = (time.monotonic() - start_time_0) / 3600.0\\n mfile.write(\\\"\\\\n*** computational cost ***\\\\n\\\")\\n mfile.write(\\\"N_PARALLEL = %d\\\\n\\\" % N_PARALLEL)\\n mfile.write(\\\"total elapsed hours : %.5e\\\\n\\\" % elapsed_time_0)\\n mfile.write(\\\"cost in hours = total elapsed time * N_PARALLEL: %.5e\\\\n\\\" % (elapsed_time_0 * N_PARALLEL))\\n\\n print(\\\">>> Results saved in the directory: \\\" + DIR_OUTPUTS)\\n\\n sys.stdout.flush()\",\n \"def _generate_trading_instances(self):\\n print(\\\"Creating DataHandler, Strategy, Portfolio, and ExecutionHandler for\\\")\\n\\n # Set internal data members equal to the classes we passed in earlier, along with necessary parameters.\\n # https://softwareengineering.stackexchange.com/questions/131403/what-is-the-name-of-in-python/131415\\n self.data_handler = self.data_handler_class(self.events, self.csv_dir, self.symbol_list)\\n self.strategy = self.strategy_class(self.data_handler, self.events)\\n self.portfolio = self.portfolio_class(self.data_handler, self.events, self.start_date, self.initial_capital)\\n self.execution_handler = self.execution_handler_class(self.events) # The Event Queue sent to ExecutionHandler\",\n \"def run_and_evaluate():\\n tsp_problems = read_all_problems()\\n # Empty list of metrics\\n results = []\\n for problem in tqdm.tqdm(tsp_problems):\\n # As random factors are involved repeat experiments a couple of times\\n best_routes_base = []\\n best_routes_af = []\\n best_routes_ms = []\\n base_times = []\\n af_times = []\\n ms_times = []\\n for i in range(10):\\n # Base solution\\n start_time = timeit.default_timer()\\n best_route_base = solve_tsp_basic(problem)\\n base_time = timeit.default_timer() - start_time\\n best_routes_base.append(Fitness(route=best_route_base).route_distance())\\n base_times.append(base_time)\\n\\n # AF clustering solution\\n start_time = timeit.default_timer()\\n best_route_af = solve_tsp_affinity_propagation(problem)\\n af_time = timeit.default_timer() - start_time\\n best_routes_af.append(Fitness(route=best_route_af).route_distance())\\n af_times.append(af_time)\\n\\n # MS solution\\n start_time = timeit.default_timer()\\n best_route_ms = solve_mean_shift(problem)\\n ms_time = timeit.default_timer() - start_time\\n best_routes_ms.append(Fitness(route=best_route_ms).route_distance())\\n ms_times.append(ms_time)\\n\\n results.append(\\n {\\n \\\"problem name\\\": problem.name,\\n \\\"optimal solution\\\": find_route_optimal_route_length(problem),\\n \\\"baseline tour length\\\": mean(best_routes_base),\\n \\\"af clustering tour length\\\": mean(best_routes_af),\\n \\\"ms clustering tour length\\\": mean(best_routes_ms),\\n \\\"baseline algorithm time\\\": mean(base_times),\\n \\\"af clustering algorithm time\\\": mean(af_times),\\n \\\"ms clustering algorithm time\\\": mean(ms_times),\\n }\\n )\\n # Create dataframe and safe results\\n df = pd.DataFrame(results)\\n df.to_csv(\\\"results.csv\\\", index=False)\\n return df\",\n \"def main():\\n run_simulation(spectral=False, ml=False, num_procs=1)\\n run_simulation(spectral=True, ml=False, num_procs=1)\\n run_simulation(spectral=False, ml=True, num_procs=1)\\n run_simulation(spectral=True, ml=True, num_procs=1)\\n run_simulation(spectral=False, ml=True, num_procs=10)\\n run_simulation(spectral=True, ml=True, num_procs=10)\",\n \"def runSim(self):\\n if self.verbose:\\n print(\\\"Running Simulation, This may take a while\\\")\\n self.makeXData(float(self.pretime))\\n pool = Pool(processes=len(self.powers))\\n jobs = []\\n self.gem_pair = []\\n self.electron = []\\n self.hole = []\\n self.filled = []\\n self.signal = []\\n self.gsignal = []\\n self.ehsignal = []\\n self.gloss = []\\n self.tloss = []\\n self.qk = []\\n for power, pulse in zip(self.powers, self.pulses):\\n inputs = [power, pulse, self.steps, self.trap, self.tolerance,\\n self.EHdecay, self.Etrap, self.FHloss, self.Gdecay,\\n self.G2decay, self.G3decay, self.GHdecay, self.Gescape,\\n self.Gform, self.G3loss, self.Keq, self.trackQ,\\n self.verbose]\\n jobs.append(pool.apply_async(powerRun, inputs))\\n for job in jobs:\\n gem_pair, electron, hole, filled, signal, gsignal, ehsignal, gloss, tloss, qk = job.get()\\n self.signal.append(signal * self.scalar / self.step)\\n self.gsignal.append(gsignal * self.scalar / self.step)\\n self.ehsignal.append(ehsignal * self.scalar / self.step)\\n self.gloss.append(gloss * self.scalar / self.step)\\n self.tloss.append(tloss * self.scalar / self.step)\\n self.gem_pair.append(gem_pair)\\n self.electron.append(electron)\\n self.hole.append(hole)\\n self.filled.append(filled)\\n self.qk.append(qk)\\n pool.close()\",\n \"def run(self, niter, calc_moments=True, save_last_param=None, verbose=True,\\n return_analytics=False, seed=None):\\n if niter < 1:\\n if verbose:\\n print(\\\"Nothing to do here as provided arg. `niter` is {}\\\" \\\\\\n .format(niter))\\n # return with desired args\\n out = [self.INFO_OK]\\n if calc_moments:\\n out.append((None, None))\\n if return_analytics:\\n out.append((None, None, None))\\n return out if len(out) > 1 else out[0]\\n\\n # Get seeds for sampling in workers for each iteration\\n if isinstance(seed, np.random.RandomState):\\n rng = seed\\n else:\\n rng = np.random.RandomState(seed=seed)\\n seeds = rng.randint(0, pystan_max_uint, size=(niter, self.K))\\n\\n # Localise some instance variables\\n # Mean and cov of the posterior approximation\\n S = self.S\\n m = self.m\\n # Natural parameters of the approximation\\n Q = self.Q\\n r = self.r\\n # Natural site parameters\\n Qi = self.Qi\\n ri = self.ri\\n # Natural site proposal parameters\\n Qi2 = self.Qi2\\n ri2 = self.ri2\\n # Site parameter updates\\n dQi = self.dQi\\n dri = self.dri\\n\\n # Array for positive definitness checking of each cavity distribution\\n posdefs = np.empty(self.K, dtype=bool)\\n\\n if calc_moments:\\n # Allocate memory for results\\n m_phi_s = np.zeros((niter, self.dphi))\\n cov_phi_s = np.zeros((niter, self.dphi, self.dphi))\\n\\n # monitor sampling times, mean stepsizes, and max rhats, and other times\\n stimes = np.zeros(niter)\\n msteps = np.zeros(niter)\\n mrhats = np.zeros(niter)\\n othertimes = np.zeros(niter)\\n\\n # Iterate niter rounds\\n for cur_iter in range(niter):\\n self.iter += 1\\n\\n # Tilted distributions (parallelisable)\\n # -------------------------------------\\n\\n if verbose:\\n print(\\n \\\"Iter {} starting. Process tilted distributions\\\"\\n .format(self.iter)\\n )\\n for k in range(self.K):\\n if verbose:\\n sys.stdout.write(\\\"\\\\r site {}\\\".format(k+1)+' '*10+'\\\\b'*9)\\n # Force flush here as it is not done automatically\\n sys.stdout.flush()\\n # Process the site\\n if save_last_param:\\n posdefs[k] = self.workers[k].tilted(\\n dQi[:,:,k],\\n dri[:,k],\\n save_samples = save_last_param,\\n seed = seeds[cur_iter, k]\\n )\\n else:\\n posdefs[k] = self.workers[k].tilted(\\n dQi[:,:,k],\\n dri[:,k],\\n seed = seeds[cur_iter, k]\\n )\\n if verbose and not posdefs[k]:\\n sys.stdout.write(\\\"fail\\\\n\\\")\\n if verbose:\\n if np.all(posdefs):\\n print(\\\"\\\\rAll sites ok\\\")\\n elif np.any(posdefs):\\n print(\\\"\\\\rSome sites failed and are not updated\\\")\\n else:\\n print(\\\"\\\\rEvery site failed\\\")\\n if not np.any(posdefs):\\n # all sites failed, return with desired args\\n out = [self.INFO_ALL_SITES_FAIL]\\n if calc_moments:\\n out.append((m_phi_s, cov_phi_s))\\n if return_analytics:\\n out.append((stimes, msteps, mrhats, othertimes))\\n return out if len(out) > 1 else out[0]\\n\\n # Store max sampling time\\n stimes[cur_iter] = max([w.last_time for w in self.workers])\\n msteps[cur_iter] = max([w.last_msteps for w in self.workers])\\n mrhats[cur_iter] = max([w.last_mrhat for w in self.workers])\\n\\n if verbose:\\n print(\\n \\\"Sampling done, max sampling time {}\\\"\\n .format(stimes[cur_iter])\\n )\\n\\n # measure elapsed time for othertimes\\n start_othertime = time.time()\\n\\n # Update global approx\\n # --------------------\\n\\n # Initial dampig factor\\n df = self.df0(self.iter)\\n if verbose:\\n print(\\\"Iter {}, starting df {:.3g}\\\".format(self.iter, df))\\n fail_printline_pos = False\\n fail_printline_cov = False\\n # Fail flag for pos.def enforcing\\n failed_force_pos_def = False\\n while True:\\n # Try to update the global posterior approximation\\n\\n # These 4 lines could be run in parallel also\\n np.add(Qi, np.multiply(df, dQi, out=Qi2), out=Qi2)\\n np.add(ri, np.multiply(df, dri, out=ri2), out=ri2)\\n np.add(Qi2.sum(2, out=Q), self.Q0, out=Q)\\n np.add(ri2.sum(1, out=r), self.r0, out=r)\\n\\n # Check for positive definiteness\\n cho_Q = S\\n np.copyto(cho_Q, Q)\\n try:\\n linalg.cho_factor(cho_Q, overwrite_a=True)\\n except linalg.LinAlgError:\\n # Not positive definite -> reduce damping factor\\n df *= self.df_decay\\n if verbose:\\n fail_printline_pos = True\\n sys.stdout.write(\\n \\\"\\\\rNon pos. def. posterior cov, \\\" +\\n \\\"reducing df to {:.3}\\\".format(df) +\\n \\\" \\\"*5 + \\\"\\\\b\\\"*5\\n )\\n sys.stdout.flush()\\n if self.iter == 1:\\n if verbose:\\n print(\\\"\\\\nInvalid prior.\\\")\\n # return with desired args\\n out = [self.INFO_INVALID_PRIOR]\\n if calc_moments:\\n out.append((m_phi_s, cov_phi_s))\\n if return_analytics:\\n out.append((stimes, msteps, mrhats, othertimes))\\n return out if len(out) > 1 else out[0]\\n if df < self.df_treshold:\\n if verbose:\\n print(\\\"\\\\nDamping factor reached minimum.\\\")\\n df = self.df0(self.iter)\\n np.add(Qi, np.multiply(df, dQi, out=Qi2), out=Qi2)\\n np.add(ri, np.multiply(df, dri, out=ri2), out=ri2)\\n if failed_force_pos_def:\\n if verbose:\\n print(\\\"Failed to force pos_def global.\\\")\\n # return with desired args\\n out = [self.INFO_DF_TRESHOLD_REACHED_CAVITY]\\n if calc_moments:\\n out.append((m_phi_s, cov_phi_s))\\n if return_analytics:\\n out.append((stimes, msteps, mrhats, othertimes))\\n return out if len(out) > 1 else out[0]\\n failed_force_pos_def = True\\n # Try to fix by forcing improper sites to proper\\n posdefs.fill(0)\\n for k in range(self.K):\\n # Set min eigenvalue to MIN_EIG by adding to the\\n # diagonal if it is smaller than MIN_EIG_TRESHOLD\\n min_eig = linalg.eigvalsh(\\n Qi2[:,:,k], eigvals=(0,0))[0]\\n if min_eig < self.MIN_EIG_TRESHOLD:\\n Qi[:,:,k].flat[::self.dphi+1] += (\\n self.MIN_EIG - min_eig)\\n posdefs[k] = 1\\n if verbose:\\n print(\\\"Force sites {} pos_def.\\\".format(\\n np.nonzero(posdefs)[0]))\\n continue\\n\\n # Cavity distributions (parallelisable)\\n # -------------------------------------\\n # Check positive definitness for each cavity distribution\\n for k in range(self.K):\\n posdefs[k] = \\\\\\n self.workers[k].cavity(Q, r, Qi2[:,:,k], ri2[:,k])\\n # Early stopping criterion (when in serial)\\n if not posdefs[k]:\\n break\\n\\n if np.all(posdefs):\\n # All cavity distributions are positive definite.\\n # Accept step (switch Qi-Qi2 and ri-ri2)\\n temp = Qi\\n Qi = Qi2\\n Qi2 = temp\\n temp = ri\\n ri = ri2\\n ri2 = temp\\n self.Qi = Qi\\n self.Qi2 = Qi2\\n self.ri = ri\\n self.ri2 = ri2\\n break\\n\\n else:\\n # Not all cavity distributions are positive definite ...\\n # reduce the damping factor\\n df *= self.df_decay\\n if verbose:\\n if fail_printline_pos:\\n fail_printline_pos = False\\n print()\\n fail_printline_cov = True\\n sys.stdout.write(\\n \\\"\\\\rNon pos. def. cavity, \\\" +\\n \\\"(first encountered in site {}), \\\"\\n .format(np.nonzero(~posdefs)[0][0]) +\\n \\\"reducing df to {:.3}\\\".format(df) +\\n \\\" \\\"*5 + \\\"\\\\b\\\"*5\\n )\\n sys.stdout.flush()\\n if df < self.df_treshold:\\n if verbose:\\n print(\\\"\\\\nDamping factor reached minimum.\\\")\\n df = self.df0(self.iter)\\n np.add(Qi, np.multiply(df, dQi, out=Qi2), out=Qi2)\\n np.add(ri, np.multiply(df, dri, out=ri2), out=ri2)\\n if failed_force_pos_def:\\n if verbose:\\n print(\\\"Failed to force pos_def cavities.\\\")\\n # return with desired args\\n out = [self.INFO_DF_TRESHOLD_REACHED_CAVITY]\\n if calc_moments:\\n out.append((m_phi_s, cov_phi_s))\\n if return_analytics:\\n out.append((stimes, msteps, mrhats, othertimes))\\n return out if len(out) > 1 else out[0]\\n failed_force_pos_def = True\\n # Try to fix by forcing improper sites to proper\\n posdefs.fill(0)\\n for k in range(self.K):\\n # Set min eigenvalue to MIN_EIG by adding to the\\n # diagonal if it is smaller than MIN_EIG_TRESHOLD\\n min_eig = linalg.eigvalsh(\\n Qi2[:,:,k], eigvals=(0,0))[0]\\n if min_eig < self.MIN_EIG_TRESHOLD:\\n Qi[:,:,k].flat[::self.dphi+1] += (\\n self.MIN_EIG - min_eig)\\n posdefs[k] = 1\\n if verbose:\\n print(\\\"Force sites {} pos_def.\\\".format(\\n np.nonzero(posdefs)[0]))\\n if verbose and (fail_printline_pos or fail_printline_cov):\\n print()\\n\\n if calc_moments:\\n # Invert Q (chol was already calculated)\\n # N.B. The following inversion could be done while\\n # parallel jobs are running, thus saving time.\\n invert_normal_params(cho_Q, r, out_A='in-place', out_b=m,\\n cho_form=True)\\n # Store the approximation moments\\n np.copyto(m_phi_s[cur_iter], m)\\n np.copyto(cov_phi_s[cur_iter], S.T)\\n if verbose:\\n print(\\n \\\"Mean and std of phi[0]: {:.3}, {:.3}\\\"\\n .format(\\n m_phi_s[cur_iter,0],\\n np.sqrt(cov_phi_s[cur_iter,0,0])\\n )\\n )\\n\\n # measure total time - tilted time\\n othertimes[cur_iter] = time.time() - start_othertime\\n\\n if verbose:\\n print(\\\"Iter {} done.\\\".format(self.iter))\\n\\n if verbose:\\n print(\\n \\\"{} iterations done\\\\nTotal limiting sampling time: {}\\\"\\n .format(niter, stimes.sum())\\n )\\n\\n # return with desired args\\n out = [self.INFO_OK]\\n if calc_moments:\\n out.append((m_phi_s, cov_phi_s))\\n if return_analytics:\\n out.append((stimes, msteps, mrhats, othertimes))\\n return tuple(out) if len(out) > 1 else out[0]\",\n \"def run(runs, params):\\n trade_hist = []\\n final_states = []\\n final_rewards = []\\n for i in np.arange(runs):\\n env = MarketEnv(*params)\\n for step in np.arange(env.end_time):\\n total_returns = env.get_returns()\\n actions = [agent.act([c]) for agent, c in zip(env.agents, total_returns)]\\n print(f'ACTIONS: {actions}')\\n state, done, rewards, info = env.step(actions)\\n trade_hist.append(env.trade_history)\\n final_states.append(state)\\n final_rewards.append(rewards)\\n return trade_hist, final_states, final_rewards\",\n \"def runSCRO():\\n\\n dataset = None\\n if EXPERIMENT == 0:\\n print \\\"EXPERIMENT WITH MNIST DATASET\\\"\\n dataset = mnist_data_builder()\\n # elif EXPERIMENT == 1:\\n # print \\\"EXPERIMENT WITH ...\\\"\\n # dataset = my_call()\\n else:\\n print \\\"EXPERIMENT NOT IMPLEMENTED!\\\"\\n\\n print \\\"Starting keras executor\\\"\\n ke = KerasExecutor(dataset, TEST_SIZE, METRICS, EARLY_STOPPING_PATIENCE_KERAS, LOSS)\\n\\n config_data = open('parametersGenetic.json')\\n configuration = Configuration(config_data)\\n\\n ##############################\\n # Initialisation\\n ##############################\\n\\n reef = initialisation(Rsize=RSIZE, config=configuration, n_global_in=deepcopy(ke.n_in), n_global_out=ke.n_out,\\n ke=ke)\\n # Population is already evaluated in the initialisation function\\n\\n history = []\\n\\n max_fitness_ever = 0.0\\n generations_with_no_improvement = 0\\n\\n ID_EXECUTION = str(time.time())\\n\\n output_file = open(\\\"EXECUTION_\\\" + ID_EXECUTION + \\\".csv\\\", \\\"w\\\")\\n output_file.write(\\\"fitness_mean_validation,fitness_std_validation,fitness_max_validation,fitness_min_validation,\\\"\\n \\\"fitness_mean_train,fitness_std_train,fitness_max_train,fitness_min_train,\\\"\\n \\\"fitness_mean_test,fitness_std_test,fitness_max_test,fitness_min_test,\\\"\\n \\\"count_evaluations,individuals_depredated,ratio_reef,time_generation,reef\\\\n\\\")\\n\\n output_file_population = open(\\\"EXECUTION_\\\" + ID_EXECUTION + \\\"_REEF_EVOLUTION.csv\\\", \\\"w\\\")\\n\\n output_file_population.write(\\n \\\"generation,pos_in_reef,accuracy_validation,number_layers,accuracy_training,accuracy_test\\\\n\\\")\\n\\n output_file_individuals = open(\\\"EXECUTION_\\\" + str(ID_EXECUTION) + \\\"_INDIVIDUALS.txt\\\", \\\"w\\\")\\n\\n ##############################\\n # Loop\\n ##############################\\n for i in range(MAX_GENERATIONS_SCRO):\\n\\n start_time = time.time()\\n\\n print colored(\\\"GENERATION: \\\" + str(i), \\\"red\\\")\\n pool = []\\n\\n if len(filter(lambda w: w is not None, reef)) == 0:\\n output_file.write(\\\"ALL REEF IS NONE!\\\")\\n print colored(\\\"ALL REEF IS NONE!, BREAKING EVOLUTION!\\\", \\\"red\\\")\\n break\\n\\n # 1 Asexual reproduction\\n asexual_new_individual = deepcopy(asexual_reproduction(reef, configuration))\\n if asexual_new_individual is not None:\\n pool = pool + [asexual_new_individual]\\n\\n # 2 Sexual reproduction\\n sexual_new_individuals = sexual_reproduction(reef, configuration)\\n pool = pool + sexual_new_individuals\\n\\n # 3 Larvae settlement\\n print colored(\\\"STARTING EVALUATION. INDIVIDUALS TO EVALUATE: \\\" + str(len(pool)), \\\"red\\\")\\n pool, count_evaluations = eval_population(pool, ke)\\n\\n # print \\\"POOL EVALUATED: \\\"\\n # for ind_pool in pool:\\n # print \\\"IND: \\\" + str(ind_pool.fitness[\\\"accuracy_validation\\\"])\\n\\n reef, settled = larvae_settlement(reef, pool)\\n\\n # 4 Depredation\\n # Todo remove returns same object\\n reef, individuals_depredated = depredation(reef)\\n\\n # History\\n\\n fitness = fitness_mean_std(reef)\\n\\n fitness_mean_validation = fitness[\\\"validation\\\"][\\\"mean\\\"]\\n fitness_std_validation = fitness[\\\"validation\\\"][\\\"std\\\"]\\n fitness_max_validation = fitness[\\\"validation\\\"][\\\"max\\\"]\\n fitness_min_validation = fitness[\\\"validation\\\"][\\\"min\\\"]\\n\\n fitness_mean_train = fitness[\\\"train\\\"][\\\"mean\\\"]\\n fitness_std_train = fitness[\\\"train\\\"][\\\"std\\\"]\\n fitness_max_train = fitness[\\\"train\\\"][\\\"max\\\"]\\n fitness_min_train = fitness[\\\"train\\\"][\\\"min\\\"]\\n\\n fitness_mean_test = fitness[\\\"test\\\"][\\\"mean\\\"]\\n fitness_std_test = fitness[\\\"test\\\"][\\\"std\\\"]\\n fitness_max_test = fitness[\\\"test\\\"][\\\"max\\\"]\\n fitness_min_test = fitness[\\\"test\\\"][\\\"min\\\"]\\n\\n if fitness_max_validation > max_fitness_ever:\\n max_fitness_ever = fitness_max_validation\\n generations_with_no_improvement = 0\\n else:\\n generations_with_no_improvement += 1\\n\\n finish_time = time.time()\\n\\n time_generation = finish_time - start_time\\n\\n positions_free = len(filter(lambda w: w is not None, reef))\\n positions_total = len(reef)\\n\\n history.append([fitness_mean_validation, fitness_std_validation, fitness_max_validation, fitness_min_validation,\\n fitness_mean_train, fitness_std_train, fitness_max_train, fitness_min_train,\\n fitness_mean_test, fitness_std_test, fitness_max_test, fitness_min_test,\\n count_evaluations, individuals_depredated,\\n str(positions_free) + \\\"/\\\" + str(positions_total), time_generation, deepcopy(reef)])\\n\\n output_file.write(str(fitness_mean_validation) + \\\",\\\" +\\n str(fitness_std_validation) + \\\",\\\" +\\n str(fitness_max_validation) + \\\",\\\" +\\n str(fitness_min_validation) + \\\",\\\" +\\n\\n str(fitness_mean_train) + \\\",\\\" +\\n str(fitness_std_train) + \\\",\\\" +\\n str(fitness_max_train) + \\\",\\\" +\\n str(fitness_min_train) + \\\",\\\" +\\n\\n str(fitness_mean_test) + \\\",\\\" +\\n str(fitness_std_test) + \\\",\\\" +\\n str(fitness_max_test) + \\\",\\\" +\\n str(fitness_min_test) + \\\",\\\" +\\n\\n str(count_evaluations) + \\\",\\\" +\\n str(individuals_depredated) + \\\",\\\" +\\n str(positions_free) + \\\"/\\\" + str(positions_total) + \\\",\\\" +\\n str(time_generation) + \\\",\\\" +\\n str(reef) + \\\"\\\\n\\\")\\n\\n # PRINTING THE STATUS OF THE REEF and INDIVIDUALS DEFINITION\\n output_file_individuals.write(\\\"GENERATION: \\\" + str(i) + \\\" - POSITION IN REEF: \\\" + str(position_reef) + \\\"\\\\n\\\")\\n\\n for position_reef in range(len(reef)):\\n\\n # Controlar nones\\n if reef[position_reef] is not None:\\n output_file_population.write(\\\",\\\".join([str(i), str(position_reef),\\n str(reef[position_reef].fitness[\\\"accuracy_validation\\\"]),\\n str(reef[position_reef].fitness[\\\"number_layers\\\"]),\\n str(reef[position_reef].fitness[\\\"accuracy_training\\\"]),\\n str(reef[position_reef].fitness[\\\"accuracy_test\\\"])]) + \\\"\\\\n\\\")\\n\\n # PRINTING INDIVIDUALS:\\n output_file_individuals.write(reef[position_reef].toString())\\n\\n\\n output_file_individuals.write(\\\"----------------------------------------------------------\\\\n\\\")\\n output_file_individuals.write(\\\"----------------------------------------------------------\\\\n\\\")\\n output_file_individuals.write(\\\"----------------------------------------------------------\\\\n\\\\n\\\\n\\\")\\n \\n\\n # GENERATION,INDIVIDUAL_POSITION,\\n\\n print colored(\\n str(fitness_mean) + \\\",\\\" + str(fitness_std) + \\\",\\\" + str(fitness_max) + \\\",\\\" + str(fitness_min) + \\\",\\\" + str(\\n count_evaluations) + \\\",\\\" + str(individuals_depredated) + \\\",\\\" +\\n str(positions_free) + \\\"/\\\" + str(positions_total) + \\\",\\\" + str(time_generation), 'yellow')\\n\\n if generations_with_no_improvement >= MAX_GENERATIONS_SCRO:\\n print colored(\\\"Stop criterion reached! \\\" + str(generations_with_no_improvement) + \\\"generations with no \\\"\\n \\\"improvement!\\\", \\\"red\\\")\\n break\\n\\n output_file.close()\\n output_file_population.close()\\n output_file_individuals.close()\",\n \"def Dynamic_Analysis(InputMethod=Get_Input, TestMode=False, PrintMode=True, ReportMode=True):\\n OutputFile = None\\n CSVFile = None\\n try:\\n Simulation_Title=\\\"Padulles-Amphlett\\\"\\n if PrintMode==True:\\n print(\\\"###########\\\")\\n print(Simulation_Title+\\\"-Model Simulation\\\")\\n print(\\\"###########\\\")\\n OutputParamsKeys = list(OutputParams.keys())\\n OutputParamsKeys.sort()\\n Output_Dict = dict(zip(OutputParamsKeys, [None] * len(OutputParamsKeys)))\\n if not TestMode:\\n Input_Dict = InputMethod(InputParams,params_default=Defaults)\\n else:\\n Input_Dict = InputMethod\\n Input_Dict = filter_default(input_dict=Input_Dict, params_default=Defaults)\\n Input_Dict = filter_lambda(Input_Dict)\\n if PrintMode==True:\\n print(\\\"Analyzing . . .\\\")\\n Name = Input_Dict[\\\"Name\\\"]\\n if ReportMode==True:\\n OutputFile = Output_Init(Input_Dict,Simulation_Title,Name)\\n CSVFile = CSV_Init(OutputParamsKeys,OutputParams,Simulation_Title,Name)\\n HTMLFile = HTML_Init(Simulation_Title, Name)\\n IEndMax = Input_Dict[\\\"JMax\\\"] * Input_Dict[\\\"A\\\"]\\n IEnd = min(IEndMax, Input_Dict[\\\"i-stop\\\"])\\n IEnd = Input_Dict[\\\"i-stop\\\"]\\n IStep = Input_Dict[\\\"i-step\\\"]\\n Precision = get_precision(IStep)\\n i = Input_Dict[\\\"i-start\\\"]\\n I_List = []\\n Power_List = []\\n Vstack_List = []\\n Kr=Kr_Calc(Input_Dict[\\\"N0\\\"])\\n qH2=qH2_Calc(Input_Dict[\\\"qMethanol\\\"],Input_Dict[\\\"CV\\\"],Input_Dict[\\\"t1\\\"],Input_Dict[\\\"t2\\\"])\\n qO2=qO2_Calc(qH2,Input_Dict[\\\"rho\\\"])\\n while i < IEnd:\\n try:\\n I_List.append(i)\\n Output_Dict[\\\"PO2\\\"] = PO2_Calc(Input_Dict[\\\"KO2\\\"], Input_Dict[\\\"tO2\\\"], Kr, i, qO2)\\n Output_Dict[\\\"PH2\\\"] = PH2_Calc(Input_Dict[\\\"KH2\\\"], Input_Dict[\\\"tH2\\\"], Kr, i, qH2)\\n Output_Dict[\\\"Eta Activation\\\"] = Eta_Act_Calc(Input_Dict[\\\"T\\\"], Output_Dict[\\\"PO2\\\"], Output_Dict[\\\"PH2\\\"], i,\\n Input_Dict[\\\"A\\\"])\\n Output_Dict[\\\"Eta Ohmic\\\"] = Eta_Ohmic_Calc(i, Input_Dict[\\\"l\\\"], Input_Dict[\\\"A\\\"], Input_Dict[\\\"T\\\"],\\n Input_Dict[\\\"lambda\\\"], R_elec=Input_Dict[\\\"R\\\"])\\n Output_Dict[\\\"Eta Concentration\\\"] = Eta_Conc_Calc(i, Input_Dict[\\\"A\\\"], Input_Dict[\\\"B\\\"],\\n Input_Dict[\\\"JMax\\\"])\\n Output_Dict[\\\"Loss\\\"] = Loss_Calc(Output_Dict[\\\"Eta Activation\\\"], Output_Dict[\\\"Eta Ohmic\\\"],\\n Output_Dict[\\\"Eta Concentration\\\"])\\n Output_Dict[\\\"PH2O\\\"]=PH2O_Calc(Input_Dict[\\\"KH2O\\\"],Input_Dict[\\\"tH2O\\\"],Kr,i,qH2)\\n Output_Dict[\\\"E\\\"]=Enernst_Calc(Input_Dict[\\\"E0\\\"],Input_Dict[\\\"N0\\\"],Input_Dict[\\\"T\\\"],Output_Dict[\\\"PH2\\\"],Output_Dict[\\\"PO2\\\"],Output_Dict[\\\"PH2O\\\"])\\n Output_Dict[\\\"FC Voltage\\\"]=Vcell_Calc(Output_Dict[\\\"E\\\"], Output_Dict[\\\"Loss\\\"],Input_Dict[\\\"N0\\\"])\\n Vstack_List.append(Output_Dict[\\\"FC Voltage\\\"])\\n Output_Dict[\\\"FC Efficiency\\\"] = Efficiency_Calc(Output_Dict[\\\"FC Voltage\\\"],Input_Dict[\\\"N0\\\"])\\n Output_Dict[\\\"FC Power\\\"] = Power_Calc(Output_Dict[\\\"FC Voltage\\\"], i)\\n Power_List.append(Output_Dict[\\\"FC Power\\\"])\\n if ReportMode==True:\\n Output_Save(OutputParamsKeys, Output_Dict,OutputParams, i, OutputFile,PrintMode)\\n CSV_Save(OutputParamsKeys, Output_Dict, i, CSVFile)\\n i = rounder(i + IStep, Precision)\\n except Exception as e:\\n print(str(e))\\n i = rounder(i + IStep, Precision)\\n if ReportMode==True:\\n Output_Save(OutputParamsKeys, Output_Dict, OutputParams, i, OutputFile,PrintMode)\\n CSV_Save(OutputParamsKeys, Output_Dict, i, CSVFile)\\n if ReportMode==True:\\n HTML_Chart(x=str(I_List), y=str(Power_List), color='rgba(255,99,132,1)', x_label=\\\"I(A)\\\", y_label=\\\"P(W)\\\",\\n chart_name=\\\"FC-Power\\\", size=\\\"600px\\\", file=HTMLFile)\\n HTML_Chart(x=str(I_List), y=str(Vstack_List), color='rgba(99,100,255,1)', x_label=\\\"I(A)\\\", y_label=\\\"V(V)\\\",\\n chart_name=\\\"FC-Voltage\\\", size=\\\"600px\\\", file=HTMLFile)\\n HTML_Input_Table(Input_Dict=Input_Dict, Input_Params=InputParams, file=HTMLFile)\\n HTML_End(HTMLFile)\\n OutputFile.close()\\n CSVFile.close()\\n HTMLFile.close()\\n if PrintMode==True:\\n print(\\\"Done!\\\")\\n if not TestMode:\\n if PrintMode==True:\\n print(\\\"Result In -->\\\" + os.path.join(os.getcwd(),Simulation_Title))\\n else:\\n return {\\\"P\\\": Power_List, \\\"I\\\": I_List, \\\"V\\\": Vstack_List}\\n except Exception:\\n print(\\\"[Error] Padulles-Amphlett Dynamic Simulation Failed!(Check Your Inputs)\\\")\",\n \"def run():\\n\\n # Set up environment and agent\\n e = Environment() # create environment (also adds some dummy traffic)\\n a = e.create_agent(LearningAgent) # create agent\\n e.set_primary_agent(a, enforce_deadline=False) # set agent to track\\n\\n # Now simulate it\\n sim = Simulator(e, update_delay=0.0) # reduce update_delay to speed up simulation\\n sim.run(n_trials=num_of_experiments) # press Esc or close pygame window to quit\\n \\n pd.Series(a.success).to_pickle('success_' + exp_id + '.pickle')\\n a.Q_table.to_pickle('qtable_' + exp_id + '.pickle')\\n pd.Series(a.q_delta_avg).to_pickle('convergence_' + exp_id + '.pickle')\\n pd.Series(a.t_total).to_pickle('steps_' + exp_id + '.pickle')\",\n \"def backtesting_engine(symbol, strategy, fromdate, todate, args=None):\\n\\n # Backtesting Engine\\n cerebro = bt.Cerebro()\\n\\n # Add a Strategy if no Data Required for the model\\n if args is None:\\n cerebro.addstrategy(strategy)\\n # If the Strategy requires a Model and therefore data\\n elif args is not None:\\n cerebro.addstrategy(strategy, args)\\n\\n # Retrieve Data from Alpaca\\n data = bt.feeds.YahooFinanceData(\\n dataname=symbol,\\n fromdate=fromdate, # datetime.date(2015, 1, 1)\\n todate=todate, # datetime.datetime(2016, 1, 1)\\n reverse=False\\n )\\n\\n # Add Data to Backtesting Engine\\n cerebro.adddata(data)\\n\\n # Set Initial Portfolio Value\\n cerebro.broker.setcash(100000.0)\\n\\n # Add Analysis Tools\\n cerebro.addanalyzer(bt.analyzers.SharpeRatio, _name='sharpe')\\n cerebro.addanalyzer(bt.analyzers.Returns, _name='returns')\\n cerebro.addanalyzer(bt.analyzers.SQN, _name='sqn')\\n cerebro.addanalyzer(bt.analyzers.DrawDown, _name='drawdown')\\n cerebro.addanalyzer(bt.analyzers.PositionsValue, _name='posval')\\n cerebro.addanalyzer(bt.analyzers.PyFolio, _name='pyfolio')\\n\\n # Starting Portfolio Value\\n print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())\\n\\n # Run the Backtesting Engine\\n backtest = cerebro.run()\\n\\n # Print Analysis and Final Portfolio Value\\n print(\\n 'Final Portfolio Value: %.2f' % cerebro.broker.getvalue()\\n )\\n print(\\n 'Return: ', backtest[0].analyzers.returns.get_analysis()\\n )\\n print(\\n 'Sharpe Ratio: ', backtest[0].analyzers.sharpe.get_analysis()\\n )\\n print(\\n 'System Quality Number: ', backtest[0].analyzers.sqn.get_analysis()\\n )\\n print(\\n 'Drawdown: ', backtest[0].analyzers.drawdown.get_analysis()\\n )\\n print(\\n 'Active Position Value: ', backtest[0].analyzers.posval.get_analysis()\\n )\\n print(\\n 'Pyfolio: ', backtest[0].analyzers.pyfolio.get_analysis()\\n )\\n\\n # Print Analysis and Final Portfolio Value\\n pyfoliozer = backtest[0].analyzers.getbyname('pyfolio')\\n returns, positions, transactions, gross_lev = pyfoliozer.get_pf_items()\\n\\n # See if we can add regular FB data to compare against returns of algo\\n pf.create_full_tear_sheet(\\n returns, positions=positions, transactions=transactions\\n )\",\n \"def _generate_all_charts(spec, input_data):\\n\\n def _generate_chart(_, data_q, graph):\\n \\\"\\\"\\\"Generates the chart.\\n \\\"\\\"\\\"\\n\\n logs = list()\\n\\n logging.info(\\\" Generating the chart '{0}' ...\\\".\\n format(graph.get(\\\"title\\\", \\\"\\\")))\\n logs.append((\\\"INFO\\\", \\\" Generating the chart '{0}' ...\\\".\\n format(graph.get(\\\"title\\\", \\\"\\\"))))\\n\\n job_name = graph[\\\"data\\\"].keys()[0]\\n\\n csv_tbl = list()\\n res = list()\\n\\n # Transform the data\\n logs.append((\\\"INFO\\\", \\\" Creating the data set for the {0} '{1}'.\\\".\\n format(graph.get(\\\"type\\\", \\\"\\\"), graph.get(\\\"title\\\", \\\"\\\"))))\\n data = input_data.filter_data(graph, continue_on_error=True)\\n if data is None:\\n logging.error(\\\"No data.\\\")\\n return\\n\\n chart_data = dict()\\n chart_tags = dict()\\n for job, job_data in data.iteritems():\\n if job != job_name:\\n continue\\n for index, bld in job_data.items():\\n for test_name, test in bld.items():\\n if chart_data.get(test_name, None) is None:\\n chart_data[test_name] = OrderedDict()\\n try:\\n chart_data[test_name][int(index)] = \\\\\\n test[\\\"result\\\"][\\\"receive-rate\\\"]\\n chart_tags[test_name] = test.get(\\\"tags\\\", None)\\n except (KeyError, TypeError):\\n pass\\n\\n # Add items to the csv table:\\n for tst_name, tst_data in chart_data.items():\\n tst_lst = list()\\n for bld in builds_dict[job_name]:\\n itm = tst_data.get(int(bld), '')\\n if not isinstance(itm, str):\\n itm = itm.avg\\n tst_lst.append(str(itm))\\n csv_tbl.append(\\\"{0},\\\".format(tst_name) + \\\",\\\".join(tst_lst) + '\\\\n')\\n\\n # Generate traces:\\n traces = list()\\n index = 0\\n groups = graph.get(\\\"groups\\\", None)\\n visibility = list()\\n\\n if groups:\\n for group in groups:\\n visible = list()\\n for tag in group:\\n for test_name, test_data in chart_data.items():\\n if not test_data:\\n logs.append((\\\"WARNING\\\",\\n \\\"No data for the test '{0}'\\\".\\n format(test_name)))\\n continue\\n if tag in chart_tags[test_name]:\\n message = \\\"index: {index}, test: {test}\\\".format(\\n index=index, test=test_name)\\n test_name = test_name.split('.')[-1]\\n try:\\n trace, rslt = _generate_trending_traces(\\n test_data,\\n job_name=job_name,\\n build_info=build_info,\\n name='-'.join(test_name.split('-')[2:-1]),\\n color=COLORS[index])\\n except IndexError:\\n message = \\\"Out of colors: {}\\\".format(message)\\n logs.append((\\\"ERROR\\\", message))\\n logging.error(message)\\n index += 1\\n continue\\n traces.extend(trace)\\n visible.extend([True for _ in range(len(trace))])\\n res.append(rslt)\\n index += 1\\n break\\n visibility.append(visible)\\n else:\\n for test_name, test_data in chart_data.items():\\n if not test_data:\\n logs.append((\\\"WARNING\\\", \\\"No data for the test '{0}'\\\".\\n format(test_name)))\\n continue\\n message = \\\"index: {index}, test: {test}\\\".format(\\n index=index, test=test_name)\\n test_name = test_name.split('.')[-1]\\n try:\\n trace, rslt = _generate_trending_traces(\\n test_data,\\n job_name=job_name,\\n build_info=build_info,\\n name='-'.join(test_name.split('-')[2:-1]),\\n color=COLORS[index])\\n except IndexError:\\n message = \\\"Out of colors: {}\\\".format(message)\\n logs.append((\\\"ERROR\\\", message))\\n logging.error(message)\\n index += 1\\n continue\\n traces.extend(trace)\\n res.append(rslt)\\n index += 1\\n\\n if traces:\\n # Generate the chart:\\n try:\\n layout = deepcopy(graph[\\\"layout\\\"])\\n except KeyError as err:\\n logging.error(\\\"Finished with error: No layout defined\\\")\\n logging.error(repr(err))\\n return\\n if groups:\\n show = list()\\n for i in range(len(visibility)):\\n visible = list()\\n for r in range(len(visibility)):\\n for _ in range(len(visibility[r])):\\n visible.append(i == r)\\n show.append(visible)\\n\\n buttons = list()\\n buttons.append(dict(\\n label=\\\"All\\\",\\n method=\\\"update\\\",\\n args=[{\\\"visible\\\": [True for _ in range(len(show[0]))]}, ]\\n ))\\n for i in range(len(groups)):\\n try:\\n label = graph[\\\"group-names\\\"][i]\\n except (IndexError, KeyError):\\n label = \\\"Group {num}\\\".format(num=i + 1)\\n buttons.append(dict(\\n label=label,\\n method=\\\"update\\\",\\n args=[{\\\"visible\\\": show[i]}, ]\\n ))\\n\\n layout['updatemenus'] = list([\\n dict(\\n active=0,\\n type=\\\"dropdown\\\",\\n direction=\\\"down\\\",\\n xanchor=\\\"left\\\",\\n yanchor=\\\"bottom\\\",\\n x=-0.12,\\n y=1.0,\\n buttons=buttons\\n )\\n ])\\n\\n name_file = \\\"{0}-{1}{2}\\\".format(spec.cpta[\\\"output-file\\\"],\\n graph[\\\"output-file-name\\\"],\\n spec.cpta[\\\"output-file-type\\\"])\\n\\n logs.append((\\\"INFO\\\", \\\" Writing the file '{0}' ...\\\".\\n format(name_file)))\\n plpl = plgo.Figure(data=traces, layout=layout)\\n try:\\n ploff.plot(plpl, show_link=False, auto_open=False,\\n filename=name_file)\\n except plerr.PlotlyEmptyDataError:\\n logs.append((\\\"WARNING\\\", \\\"No data for the plot. Skipped.\\\"))\\n\\n data_out = {\\n \\\"job_name\\\": job_name,\\n \\\"csv_table\\\": csv_tbl,\\n \\\"results\\\": res,\\n \\\"logs\\\": logs\\n }\\n data_q.put(data_out)\\n\\n builds_dict = dict()\\n for job in spec.input[\\\"builds\\\"].keys():\\n if builds_dict.get(job, None) is None:\\n builds_dict[job] = list()\\n for build in spec.input[\\\"builds\\\"][job]:\\n status = build[\\\"status\\\"]\\n if status != \\\"failed\\\" and status != \\\"not found\\\" and \\\\\\n status != \\\"removed\\\":\\n builds_dict[job].append(str(build[\\\"build\\\"]))\\n\\n # Create \\\"build ID\\\": \\\"date\\\" dict:\\n build_info = dict()\\n tb_tbl = spec.environment.get(\\\"testbeds\\\", None)\\n for job_name, job_data in builds_dict.items():\\n if build_info.get(job_name, None) is None:\\n build_info[job_name] = OrderedDict()\\n for build in job_data:\\n testbed = \\\"\\\"\\n tb_ip = input_data.metadata(job_name, build).get(\\\"testbed\\\", \\\"\\\")\\n if tb_ip and tb_tbl:\\n testbed = tb_tbl.get(tb_ip, \\\"\\\")\\n build_info[job_name][build] = (\\n input_data.metadata(job_name, build).get(\\\"generated\\\", \\\"\\\"),\\n input_data.metadata(job_name, build).get(\\\"version\\\", \\\"\\\"),\\n testbed\\n )\\n\\n work_queue = multiprocessing.JoinableQueue()\\n manager = multiprocessing.Manager()\\n data_queue = manager.Queue()\\n cpus = multiprocessing.cpu_count()\\n\\n workers = list()\\n for cpu in range(cpus):\\n worker = Worker(work_queue,\\n data_queue,\\n _generate_chart)\\n worker.daemon = True\\n worker.start()\\n workers.append(worker)\\n os.system(\\\"taskset -p -c {0} {1} > /dev/null 2>&1\\\".\\n format(cpu, worker.pid))\\n\\n for chart in spec.cpta[\\\"plots\\\"]:\\n work_queue.put((chart, ))\\n work_queue.join()\\n\\n anomaly_classifications = list()\\n\\n # Create the header:\\n csv_tables = dict()\\n for job_name in builds_dict.keys():\\n if csv_tables.get(job_name, None) is None:\\n csv_tables[job_name] = list()\\n header = \\\"Build Number:,\\\" + \\\",\\\".join(builds_dict[job_name]) + '\\\\n'\\n csv_tables[job_name].append(header)\\n build_dates = [x[0] for x in build_info[job_name].values()]\\n header = \\\"Build Date:,\\\" + \\\",\\\".join(build_dates) + '\\\\n'\\n csv_tables[job_name].append(header)\\n versions = [x[1] for x in build_info[job_name].values()]\\n header = \\\"Version:,\\\" + \\\",\\\".join(versions) + '\\\\n'\\n csv_tables[job_name].append(header)\\n\\n while not data_queue.empty():\\n result = data_queue.get()\\n\\n anomaly_classifications.extend(result[\\\"results\\\"])\\n csv_tables[result[\\\"job_name\\\"]].extend(result[\\\"csv_table\\\"])\\n\\n for item in result[\\\"logs\\\"]:\\n if item[0] == \\\"INFO\\\":\\n logging.info(item[1])\\n elif item[0] == \\\"ERROR\\\":\\n logging.error(item[1])\\n elif item[0] == \\\"DEBUG\\\":\\n logging.debug(item[1])\\n elif item[0] == \\\"CRITICAL\\\":\\n logging.critical(item[1])\\n elif item[0] == \\\"WARNING\\\":\\n logging.warning(item[1])\\n\\n del data_queue\\n\\n # Terminate all workers\\n for worker in workers:\\n worker.terminate()\\n worker.join()\\n\\n # Write the tables:\\n for job_name, csv_table in csv_tables.items():\\n file_name = spec.cpta[\\\"output-file\\\"] + \\\"-\\\" + job_name + \\\"-trending\\\"\\n with open(\\\"{0}.csv\\\".format(file_name), 'w') as file_handler:\\n file_handler.writelines(csv_table)\\n\\n txt_table = None\\n with open(\\\"{0}.csv\\\".format(file_name), 'rb') as csv_file:\\n csv_content = csv.reader(csv_file, delimiter=',', quotechar='\\\"')\\n line_nr = 0\\n for row in csv_content:\\n if txt_table is None:\\n txt_table = prettytable.PrettyTable(row)\\n else:\\n if line_nr > 1:\\n for idx, item in enumerate(row):\\n try:\\n row[idx] = str(round(float(item) / 1000000, 2))\\n except ValueError:\\n pass\\n try:\\n txt_table.add_row(row)\\n except Exception as err:\\n logging.warning(\\\"Error occurred while generating TXT \\\"\\n \\\"table:\\\\n{0}\\\".format(err))\\n line_nr += 1\\n txt_table.align[\\\"Build Number:\\\"] = \\\"l\\\"\\n with open(\\\"{0}.txt\\\".format(file_name), \\\"w\\\") as txt_file:\\n txt_file.write(str(txt_table))\\n\\n # Evaluate result:\\n if anomaly_classifications:\\n result = \\\"PASS\\\"\\n for classification in anomaly_classifications:\\n if classification == \\\"regression\\\" or classification == \\\"outlier\\\":\\n result = \\\"FAIL\\\"\\n break\\n else:\\n result = \\\"FAIL\\\"\\n\\n logging.info(\\\"Partial results: {0}\\\".format(anomaly_classifications))\\n logging.info(\\\"Result: {0}\\\".format(result))\\n\\n return result\",\n \"def run_grid_search():\\n\\n best_score = 0\\n best_learning_rate = 0\\n best_discount_rate = 0\\n best_initial_q_hat = 0\\n trial_results = []\\n number_of_trials = 30\\n # TODO These ought to be done with numpy.arange but I don't have that package installed at the moment\\n for learning_rate_raw in range(5, 50, 5):\\n for discount_rate_raw in range(5, 20, 5):\\n for initial_q_hat in range(0, 10, 1):\\n learning_rate = learning_rate_raw * 0.01\\n discount_rate = discount_rate_raw * 0.05\\n # Set up environment and agent\\n e = Environment() # create environment (also adds some dummy traffic)\\n a = e.create_agent(LearningAgent, learning_rate, discount_rate, initial_q_hat) # create agent\\n e.set_primary_agent(a, enforce_deadline=True) # specify agent to track\\n # NOTE: You can set enforce_deadline=False while debugging to allow longer trials\\n\\n # Now simulate it\\n sim = Simulator(e, update_delay=0, display=False) # create simulator (uses pygame when display=True, if available)\\n # NOTE: To speed up simulation, reduce update_delay and/or set display=False\\n\\n sim.run(n_trials=number_of_trials) # run for a specified number of trials\\n # NOTE: To quit midway, press Esc or close pygame window, or hit Ctrl+C on the command-line\\n score = a.get_score()\\n if score > best_score:\\n best_score = score\\n best_learning_rate = learning_rate\\n best_discount_rate = discount_rate\\n best_initial_q_hat = initial_q_hat\\n trial_results.append((learning_rate, discount_rate, initial_q_hat, score, a.get_proportion_of_states_visited(), len(a.get_failed_trials())/ float(number_of_trials)))\\n print \\\"Gridsearch finished, best learning rate: %.2f, best discount rate: %.2f, best initial q hat %i\\\" % (best_learning_rate, best_discount_rate, best_initial_q_hat)\\n\\n with open('gridsearch_results.csv', 'wb') as csvfile:\\n spamwriter = csv.writer(csvfile, delimiter=';', quotechar='|', quoting=csv.QUOTE_MINIMAL)\\n spamwriter.writerow(('learning rate', 'discount factor', 'initial q-hat value', 'score', 'states visited', 'failed trials'))\\n for result in trial_results:\\n spamwriter.writerow(result)\",\n \"def simulate(self, x='volatility', y='expected_return', iters=1000, weight_bound=(0,1), leverage=1, ret_format='df',\\n file_path=None, x_var=None, y_var=None):\\n if y_var is None:\\n y_var = dict()\\n if x_var is None:\\n x_var = dict()\\n\\n x_val = np.zeros(iters)\\n y_val = np.zeros(iters)\\n weight_vals = np.zeros(shape=(iters, len(self.assets)))\\n individual_bound = ConstGen.construct_weight_bound(self.ret_vec.shape[0], (0,1), weight_bound)\\n\\n for it in range(iters):\\n temp_weights = ConstGen.gen_random_weight(self.ret_vec.shape[0], individual_bound, leverage)\\n weight_vals[it] = temp_weights\\n x_val[it] = self.metric_creator.method_dict[x](temp_weights, **x_var)\\n y_val[it] = self.metric_creator.method_dict[y](temp_weights, **y_var)\\n\\n if ret_format == 'sns': # Change to plt, fig format\\n fig, ax = plt.subplots(figsize=(18, 12));\\n ax = sns.scatterplot(x_val, y_val);\\n ax.set_title(f\\\"{x} VS {y}\\\")\\n plt.xlim(x_val.min(), x_val.max());\\n plt.ylim(y_val.min(), y_val.max());\\n plt.xlabel(x);\\n plt.ylabel(y);\\n if file_path:\\n plt.savefig(file_path)\\n plt.show()\\n else:\\n res_df = pd.DataFrame(columns=[x] + [y], data=np.concatenate([x_val.reshape(1, -1), y_val.reshape(1, -1)]).T)\\n res_df = pd.concat([res_df, pd.DataFrame(columns=self.assets, data=weight_vals)], axis=1)\\n if ret_format == 'plotly':\\n return px.scatter(res_df, x=x, y=y, title=f\\\"{x} vs {y}\\\")\\n elif ret_format == \\\"df\\\":\\n return res_df\\n else:\\n raise FormatException(\\\"\\\"\\\"Return Format must be sns, plotly, df\\\"\\\"\\\")\",\n \"def main(out_path, runs, repeats = 128, workers = 0, local = False):\\n\\n logger.info(\\\"simulating %i runs\\\", len(runs))\\n\\n get_run_data = borg.util.memoize(borg.storage.RunData.from_bundle)\\n\\n def yield_jobs():\\n for run in runs:\\n all_data = get_run_data(run[\\\"bundle\\\"])\\n validation = sklearn.cross_validation.ShuffleSplit(len(all_data), repeats, test_fraction = 0.2, indices = False)\\n\\n if run[\\\"portfolio_name\\\"] == \\\"-\\\":\\n makers = map(borg.experiments.simulate_runs.SolverMaker, all_data.solver_names)\\n else:\\n makers = [borg.experiments.simulate_runs.PortfolioMaker(run[\\\"portfolio_name\\\"])]\\n\\n max_instances = len(all_data) * 0.8\\n\\n for (train_mask, test_mask) in validation:\\n for instances in map(int, map(round, numpy.r_[10.0:max_instances:32j])):\\n for maker in makers:\\n yield (\\n simulate_run,\\n [\\n run,\\n maker,\\n all_data,\\n train_mask,\\n test_mask,\\n instances,\\n run[\\\"independent\\\"],\\n run[\\\"mixture\\\"],\\n ],\\n )\\n\\n with borg.util.openz(out_path, \\\"wb\\\") as out_file:\\n writer = csv.writer(out_file)\\n\\n writer.writerow([\\\"description\\\", \\\"solver\\\", \\\"instances\\\", \\\"successes\\\", \\\"mean_time\\\", \\\"median_time\\\"])\\n\\n for (_, row) in condor.do(yield_jobs(), workers, local):\\n writer.writerow(row)\\n\\n out_file.flush()\",\n \"def evol_run(self, pkg_setup):\\n\\n params = specs.ODict()\\n params[(\\\"tune\\\", \\\"soma\\\", \\\"Ra\\\")] = [100.0 * 0.5, 100 * 1.5]\\n\\n amps = [0.0, 0.65] # amplitudes\\n times = [100, 200] # start times\\n dur = 50 # ms\\n targetRates = [0.0, 81.0]\\n\\n # initial cfg set up\\n initCfg = {} # specs.ODict()\\n initCfg[\\\"duration\\\"] = 200 * len(amps)\\n initCfg[(\\\"hParams\\\", \\\"celsius\\\")] = 37\\n\\n initCfg[\\\"savePickle\\\"] = True\\n initCfg[\\\"saveJson\\\"] = False\\n initCfg[\\\"saveDataInclude\\\"] = [\\\"simConfig\\\", \\\"netParams\\\", \\\"net\\\", \\\"simData\\\"]\\n\\n initCfg[(\\\"IClamp1\\\", \\\"pop\\\")] = \\\"ITS4\\\"\\n initCfg[(\\\"IClamp1\\\", \\\"amp\\\")] = amps\\n initCfg[(\\\"IClamp1\\\", \\\"start\\\")] = times\\n initCfg[(\\\"IClamp1\\\", \\\"dur\\\")] = 100\\n\\n initCfg[(\\\"analysis\\\", \\\"plotfI\\\", \\\"amps\\\")] = amps\\n initCfg[(\\\"analysis\\\", \\\"plotfI\\\", \\\"times\\\")] = times\\n initCfg[(\\\"analysis\\\", \\\"plotfI\\\", \\\"dur\\\")] = dur\\n initCfg[(\\\"analysis\\\", \\\"plotfI\\\", \\\"targetRates\\\")] = targetRates\\n\\n for k, v in params.items():\\n initCfg[k] = v[0] # initialize params in cfg so they can be modified\\n\\n # fitness function\\n fitnessFuncArgs = {}\\n fitnessFuncArgs[\\\"targetRates\\\"] = targetRates\\n\\n def fitnessFunc(simData, **kwargs):\\n targetRates = kwargs[\\\"targetRates\\\"]\\n\\n diffRates = [abs(x - t) for x, t in zip(simData[\\\"fI\\\"], targetRates)]\\n fitness = np.mean(diffRates)\\n\\n print(\\\" Candidate rates: \\\", simData[\\\"fI\\\"])\\n print(\\\" Target rates: \\\", targetRates)\\n print(\\\" Difference: \\\", diffRates)\\n\\n return fitness\\n\\n # create Batch object with paramaters to modify, and specifying files to use\\n b = Batch(cfgFile='src/cfg.py', netParamsFile='src/netParams.py', params=params, initCfg=initCfg)\\n\\n # Set output folder, grid method (all param combinations), and run configuration\\n b.batchLabel = \\\"ITS4_evol\\\"\\n b.saveFolder = \\\"/tmp/\\\" + b.batchLabel\\n b.method = \\\"evol\\\"\\n b.seed = 0\\n b.runCfg = {\\\"type\\\": \\\"mpi_bulletin\\\", \\\"script\\\": \\\"init.py\\\"}\\n b.evolCfg = {\\n \\\"evolAlgorithm\\\": \\\"custom\\\",\\n \\\"fitnessFunc\\\": fitnessFunc, # fitness expression (should read simData)\\n \\\"fitnessFuncArgs\\\": fitnessFuncArgs,\\n \\\"pop_size\\\": 2,\\n \\\"num_elites\\\": 1, # keep this number of parents for next generation if they are fitter than children\\n \\\"mutation_rate\\\": 0.4,\\n \\\"crossover\\\": 0.5,\\n \\\"maximize\\\": False, # maximize fitness function?\\n \\\"max_generations\\\": 1,\\n \\\"time_sleep\\\": 0.25, # wait this time before checking again if sim is completed (for each generation)\\n \\\"maxiter_wait\\\": 20, # max number of times to check if sim is completed (for each generation)\\n \\\"defaultFitness\\\": 1000, # set fitness value in case simulation time is over\\n }\\n # Run batch simulations\\n b.run()\",\n \"def run_back_test(self, algorithm):\\n for date in self.dates:\\n info = dict()\\n info['cash'] = self.cash['cash'][date]\\n info['date'] = date\\n original_order = Order()\\n for stock in self.stocks:\\n original_order.order(stock, self.stock_data[stock].position['Position'][date])\\n make_order = algorithm(self.get_back_dataframe(date), original_order, info)\\n\\n for stock in make_order.orders:\\n self.order_stock(stock, make_order.orders[stock], date)\",\n \"def genOptimiseAll(startPar, stopPar, stepPar, dbCol, paramOption):\\n print \\\"optimisation of parameter '{0}'\\\".format(paramOption)\\n\\n query = \\\"select stockname, excludedattributes, bestnu from {0} \\\"\\n \\\"where active=true and excludedattributes is not NULL and \\\"\\n \\\"excludedattributes<>'' and {1} is NULL order by stockname asc\\\".format(common.dataminestocksViewName, dbCol)\\n\\n conn = common.getdbcon()\\n cur = conn.cursor()\\n curUp = conn.cursor()\\n cur.execute(query)\\n curStep = stepPar\\n curStart = startPar\\n curStop = stopPar\\n\\n for row in cur:\\n stockname = row[0]\\n dataExtract = common.extractData(stockname)\\n\\n for i in range(0, 4):\\n print \\\"Trying: step {0}. curStart = {1}, curStop = {2}, curStep = {3}\\\".format(i, curStart, curStop, curStep)\\n (optParam, corr) = optimiseOne(paramOption, row, dataExtract, curStart, curStop, curStep)\\n curStart = optParam - curStep # 1 step earlier\\n curStep = curStep / 10 # 10 times finer\\n\\n curUp.execute(\\\"update {0} set {4}={1}, bestCorrelation={2} where stockname='{3}'\\\".\\n format(common.dataminestocksViewName, curStart, corr, stockname, dbCol))\\n\\n conn.commit()\\n conn.close()\\n print \\\"optimisation finished\\\"\",\n \"def test_run_beta_diversity_through_plots_parallel(self):\\r\\n run_beta_diversity_through_plots(\\r\\n self.test_data['biom'][0],\\r\\n self.test_data['map'][0],\\r\\n self.test_out,\\r\\n call_commands_serially,\\r\\n self.params,\\r\\n self.qiime_config,\\r\\n tree_fp=self.test_data['tree'][0],\\r\\n parallel=True,\\r\\n status_update_callback=no_status_updates)\\r\\n\\r\\n unweighted_unifrac_dm_fp = join(\\r\\n self.test_out,\\r\\n 'unweighted_unifrac_dm.txt')\\r\\n weighted_unifrac_dm_fp = join(self.test_out, 'weighted_unifrac_dm.txt')\\r\\n unweighted_unifrac_pc_fp = join(\\r\\n self.test_out,\\r\\n 'unweighted_unifrac_pc.txt')\\r\\n weighted_unifrac_pc_fp = join(self.test_out, 'weighted_unifrac_pc.txt')\\r\\n weighted_unifrac_html_fp = join(self.test_out,\\r\\n 'weighted_unifrac_emperor_pcoa_plot', 'index.html')\\r\\n\\r\\n # check for expected relations between values in the unweighted unifrac\\r\\n # distance matrix\\r\\n dm = parse_distmat_to_dict(open(unweighted_unifrac_dm_fp))\\r\\n self.assertTrue(dm['f1']['f2'] < dm['f1']['p1'],\\r\\n \\\"Distance between pair of fecal samples is larger than distance\\\"\\r\\n \\\" between fecal and palm sample (unweighted unifrac).\\\")\\r\\n self.assertEqual(dm['f1']['f1'], 0)\\r\\n # check for expected relations between values in the weighted unifrac\\r\\n # distance matrix\\r\\n dm = parse_distmat_to_dict(open(weighted_unifrac_dm_fp))\\r\\n self.assertTrue(dm['f1']['f2'] < dm['f1']['p1'],\\r\\n \\\"Distance between pair of fecal samples is larger than distance\\\"\\r\\n \\\" between fecal and palm sample (unweighted unifrac).\\\")\\r\\n self.assertEqual(dm['f1']['f1'], 0)\\r\\n\\r\\n # check that final output files have non-zero size\\r\\n self.assertTrue(getsize(unweighted_unifrac_pc_fp) > 0)\\r\\n self.assertTrue(getsize(weighted_unifrac_pc_fp) > 0)\\r\\n self.assertTrue(getsize(weighted_unifrac_html_fp) > 0)\\r\\n\\r\\n # Check that the log file is created and has size > 0\\r\\n log_fp = glob(join(self.test_out, 'log*.txt'))[0]\\r\\n self.assertTrue(getsize(log_fp) > 0)\",\n \"def main():\\n\\n # Hypothesis:\\n # The `impact` encapsulates the volatility, stability and overall\\n # fluctuation of the market; in particular, movements that would\\n # affect one's portfolio, e.g. unexpected (i.e. not predicted)\\n # increases or drops in prices.\\n # For the StrategyLearner should directly affect the learned\\n # policy, particularly, in terms of willingness to take risks by\\n # betting on the behavior of the market.\\n # This can be translated into three metrics:\\n # - Number of entries:\\n # These should be reduced as market impact increases which\\n # shows the learning agent being more cautious about its bets\\n # - Cumulative return:\\n # Directly related to the point mentioned above, as market\\n # impact increases and the agent's willingness to take risks\\n # decreaes, so is the overall performance of the strategy\\n # - Training episodes:\\n # This applies specifically to the Q-Learning agent, but it\\n # is interesting to see how as the market impact increases,\\n # the number of complete training episodes (i.e. a complete\\n # pass on the trading data) is not affected. One would think\\n # that the agent would converge faster when the impact is\\n # large as it would quickly realize that the most optimal\\n # strategy is to not do anything. However, impact does not\\n # affect the rate of convergence, but rather the strategy\\n # that the agent converges to\\n\\n # Set the seed for reproducibility\\n random.seed(1481090000)\\n\\n # Experiment parameters\\n symbol = 'JPM'\\n # In-sample: January 1, 2008 to December 31 2009\\n start_date = dt.datetime(2008, 1, 1)\\n end_date = dt.datetime(2009, 12, 31)\\n starting_value = 100000\\n commission = 0.0\\n # Values to use to evaluate the effect of the impact\\n impact_values = [0.0, 0.005, 0.01, 0.05, 0.1, 0.25, 0.5, 1.0]\\n\\n all_entries = []\\n all_returns = []\\n all_episodes = []\\n\\n for impact in impact_values:\\n log.info(\\\"Evaluating the effect of impact=%s\\\", impact)\\n strategy_learner = StrategyLearner(verbose=False, impact=impact)\\n\\n log.info(\\\"Training StrategyLearner\\\")\\n strategy_learner.addEvidence(\\n symbol=symbol,\\n sd=start_date,\\n ed=end_date,\\n sv=starting_value\\n )\\n\\n log.info(\\\"Querying StrategyLearner to generate trades\\\")\\n trades = strategy_learner.testPolicy(\\n symbol=symbol,\\n sd=start_date,\\n ed=end_date,\\n sv=starting_value\\n )\\n\\n log.info(\\\"Transforming StrategyLearner trades into marketsim orders\\\")\\n orders = _convert_trades_to_marketisim_orders(symbol, trades)\\n\\n log.info(\\\"Computing portfolio values for %d orders\\\", orders.shape[0])\\n port_vals = compute_portvals(\\n orders,\\n start_val=starting_value,\\n commission=commission,\\n impact=impact\\n )\\n\\n cumulative_return = _compute_cumulative_return(port_vals)\\n\\n all_entries.append(strategy_learner.metadata['entries'])\\n all_returns.append(cumulative_return)\\n all_episodes.append(strategy_learner.metadata['training_episodes'])\\n\\n _plot_and_save_number_of_entries_per_impact_value(impact_values, all_entries)\\n _plot_and_save_number_of_episodes_per_impact_value(impact_values, all_episodes)\\n _plot_and_save_cumulative_return_per_impact_value(impact_values, all_returns)\",\n \"def main(train_stock, val_stock, window_size, batch_size, ep_count,\\n strategy=\\\"t-dqn\\\", model_name=\\\"model_debug\\\", pretrained=False,\\n debug=False):\\n agent = Agent(window_size, strategy=strategy, pretrained=pretrained, model_name=model_name)\\n \\n train_data = get_stock_data(train_stock)\\n val_data = get_stock_data(val_stock)\\n\\n initial_offset = val_data[1] - val_data[0]\\n\\n for episode in range(1, ep_count + 1):\\n train_result = train_model(agent, episode, train_data, ep_count=ep_count,\\n batch_size=batch_size, window_size=window_size)\\n val_result, _ = evaluate_model(agent, val_data, window_size, debug)\\n show_train_result(train_result, val_result, initial_offset)\",\n \"def generate(self):\\n \\n ##Dictionary of put values\\n self.put = {}\\n ##Dictionary of call values\\n self.call = {}\\n \\n bottom = self.range[0]\\n top = self.range[2]\\n iter = self.range[1]\\n \\n if self.meth == 'bi':\\n for E in range(bottom,top+iter,iter):\\n solver = bi.binomial_euro(S=self.S,E=E,r=self.r,M=400,sigma=self.sigma,method='higham',T=self.T,opt='put')\\n self.put[E] = solver.solve()\\n del solver\\n for E in range(bottom,top+iter,iter):\\n solver = bi.binomial_euro(S=self.S,E=E,r=self.r,M=400,sigma=self.sigma,method='higham',T=self.T,opt='call')\\n self.call[E] = solver.solve()\\n del solver\\n elif self.meth == 'mc':\\n for E in range(bottom,top+iter,iter):\\n solver = mc.mcfast_euro(S=self.S,E=E,r=self.r,M=20000,sigma=self.sigma,T=self.T,opt='put')\\n self.put[E] = solver.solve()\\n del solver\\n for E in range(bottom,top+iter,iter):\\n solver = mc.mcfast_euro(S=self.S,E=E,r=self.r,M=20000,sigma=self.sigma,T=self.T,opt='call')\\n self.call[E] = solver.solve()\\n del solver\\n elif self.meth == 'all':\\n pass\",\n \"def run(self):\\n\\n # Initialization assumptions\\n z = self.draw_normal_initial()\\n gradient = self.cv_gradient_initial(z)\\n gradient[np.isnan(gradient)] = 0\\n variance = np.power(gradient, 2) \\n final_parameters = self.current_parameters()\\n final_samples = 1\\n\\n # Create optimizer\\n if self.optimizer == 'ADAM':\\n self.optim = ADAM(final_parameters, variance, self.learning_rate, 0.9, 0.999)\\n elif self.optimizer == 'RMSProp':\\n self.optim = RMSProp(final_parameters, variance, self.learning_rate, 0.99)\\n\\n # Record elbo\\n if self.record_elbo is True:\\n elbo_records = np.zeros(self.iterations)\\n else:\\n elbo_records = None\\n\\n for i in range(self.iterations):\\n x = self.draw_normal()\\n gradient = self.cv_gradient(x)\\n gradient[np.isnan(gradient)] = 0\\n self.change_parameters(self.optim.update(gradient))\\n\\n if self.printer is True:\\n self.print_progress(i, self.optim.parameters[::2])\\n\\n # Construct final parameters using final 10% of samples\\n if i > self.iterations-round(self.iterations/10):\\n final_samples += 1\\n final_parameters = final_parameters+self.optim.parameters\\n\\n if self.record_elbo is True:\\n elbo_records[i] = self.get_elbo(self.optim.parameters[::2])\\n\\n final_parameters = final_parameters/float(final_samples)\\n self.change_parameters(final_parameters)\\n final_means = np.array([final_parameters[el] for el in range(len(final_parameters)) if el%2==0])\\n final_ses = np.array([final_parameters[el] for el in range(len(final_parameters)) if el%2!=0])\\n if not self.quiet_progress:\\n print(\\\"\\\")\\n print(\\\"Final model ELBO is \\\" + str(-self.neg_posterior(final_means)-self.create_normal_logq(final_means)))\\n return self.q, final_means, final_ses, elbo_records\",\n \"def backtest_portfolio(self):\\n self.rank=dict()\\n self.accuracy=dict()\\n portfolio = dict()\\n \\n for algo in self.algos:\\n portfolio[algo]=pd.DataFrame(index=self.positions.index)\\n self.pos_diff=dict()\\n self.pos_diff[algo] = self.positions[algo].diff()\\n \\n portfolio[algo]['price_diff'] = self.bars['Close']-self.bars['Open']\\n #portfolio['price_diff'][0:5] = 0.0\\n portfolio[algo]['profit'] = self.positions[algo] * portfolio[algo]['price_diff']\\n portfolio[algo]['total'] = self.initial_capital + portfolio[algo]['profit'].cumsum()\\n portfolio[algo]['returns'] = portfolio[algo]['total'].pct_change()\\n d=np.array(portfolio[algo]['profit']).copy()\\n d[d>0]=1\\n d[d<0]=0\\n d[np.array(self.positions[algo])==0]=1\\n for i in np.arange(1,len(d)+1):\\n c=float(sum(d[0:i]))/(i)\\n d[i-1]=c\\n portfolio[algo]['accuracy']=d\\n self.rank[algo]=float(portfolio[algo]['total'][-1] - portfolio[algo]['total'][0])\\n self.returns=portfolio\\n c=np.array(self.returns[algo]['profit'])\\n c[c>0]=1\\n c[c<0]=0\\n c[np.array(self.positions[algo])==0]=1\\n accuracy=round(float(c.sum())/len(c),2)*self.rank[algo]\\n self.accuracy[algo]=accuracy\\n #self.ranking= sorted(self.rank.items(), key=operator.itemgetter(1), reverse=True)\\n self.ranking= sorted(self.accuracy.items(), key=operator.itemgetter(1))\\n self.ready=True\\n return (portfolio, self.rank, self.ranking)\",\n \"def run_experiment(out_root, num_procs, trials_per_setting, **kwargs):\\n # TODO: UPDATE DOCS!\\n param_dicts = product_of_dict_lists(kwargs)\\n out_file = (\\n out_root\\n + \\\"-\\\".join(\\n [\\n \\\"{}={}\\\".format(key, value[0])\\n for key, value in kwargs.items()\\n if len(kwargs[key]) == 1\\n ]\\n )\\n + \\\".csv\\\"\\n )\\n\\n # build list of parameters\\n parameters = []\\n for param_idx in range(len(param_dicts)):\\n params = param_dicts[param_idx]\\n params[\\\"param_idx\\\"] = param_idx\\n for trial_idx in range(trials_per_setting):\\n t_params = dict(params) # copy so that can vary trial number\\n t_params[\\\"trial_idx\\\"] = trial_idx\\n parameters.append(t_params)\\n # parameters = [params for _ in range(trials_per_setting)]\\n # send work to pool, wrapped in a progress bar\\n procs = Pool(num_procs)\\n data = pd.concat(\\n list(\\n tqdm.tqdm(procs.imap(run_trial_from_kw, parameters), total=len(parameters))\\n ),\\n ignore_index=True,\\n )\\n # write output\\n data.to_csv(out_file)\",\n \"def run_performance():\\n # Create a Struct data instance from config\\n inputs = Struct(config)\\n inputs.throttle = throttle\\n # Get oxidizer properties at the given temperature\\n n2o = n2o_properties(inputs.ox.T_tank)\\n # Our integration variables are oxidizer mass and liquid oxidizer volume\\n Mox = n2o.rho_l*(inputs.ox.liquid_V) + n2o.rho_g*(inputs.ox.tank_V-inputs.ox.liquid_V)\\n if inputs.options.output_on:\\n print(\\\"Initial oxidizer mass: {} kg.\\\".format(Mox))\\n\\n start = time.perf_counter() # Start timer for integration\\n\\n time, record = integration(inputs) # Time = time for integration, record = output data\\n F_thrust = record.F_thrust\\n p_cc = record.p_cc\\n p_oxtank = record.p_oxtank\\n p_oxpresstank = record.p_oxpresstank\\n p_fueltank = record.p_fueltank\\n p_fuelpresstank = record.p_fuelpresstank\\n p_oxmanifold = record.p_oxmanifold\\n T_oxtank = record.T_oxtank\\n T_cc = record.T_cc\\n area_core = record.area_core\\n OF = record.OF_i\\n gamma_ex = record.gamma_ex\\n m_dot_ox = record.m_dot_ox\\n m_dot_fuel = record.m_dot_fuel\\n p_crit = record.p_crit\\n m_dot_ox_crit = record.m_dot_ox_crit\\n M_e = record.M_e\\n p_exit = record.p_exit\\n p_shock = record.p_shock\\n\\n time_elapsed = start-time.perf_counter() # Stop the timer and print elapsed time\\n if inputs.options.output_on:\\n print(\\\"Time elapsed for this timestep: {} sec.\\\".format(time_elapsed))\",\n \"def run_strategy(strategy_class, start_years: List[int], num_years: int, output_csv_prefix: Optional[str]):\\n\\n display = [['Years', 'End value (real)', 'End value (nominal)', 'Gain', 'Inflation']]\\n for year in start_years:\\n # Create the market and investment account.\\n market = Market(snp_yearly, year, year_to_inflation)\\n account = InvestmentAccount(STARTING_CASH, market)\\n\\n # Create the Strategy instance.\\n # (We instantiate a Strategy for each start date that we're going to simulate).\\n strategy = strategy_class()\\n\\n if output_csv_prefix is not None:\\n strategy.set_output_csv_prefix(output_csv_prefix + f'_for_year_{year}.csv')\\n\\n years_range = f'{year} - {year + num_years}'\\n\\n # Run the strategy.\\n status = strategy.go(account, market, year, num_years)\\n if status[0]:\\n inflation = get_inflation_percentage(year, num_years)\\n\\n # For reporting of the outcome, convert nominal dollars to real (starting year) dollars.\\n net_worth_real = nominal_to_real(account.net_worth, year, num_years)\\n \\n # Did we gain or lose?\\n remainder_percent = (net_worth_real - STARTING_CASH) * 100 / STARTING_CASH\\n\\n if remainder_percent > 0:\\n gainloss = [f'{remainder_percent:.2f}%']\\n else:\\n gainloss = [colored(f'{remainder_percent:.2f}%', 'red')]\\n\\n display += [[years_range, f'${net_worth_real:,.0f}', f'${account.net_worth:,.0f}'] + gainloss + [f'{inflation*100:.2f}%']]\\n else:\\n display += [[years_range, status[1], '', '', '', '']]\\n\\n # Debug output. \\n logger.info(tabulate(display, headers='firstrow'))\\n if output_csv_prefix is not None:\\n stripped = [[remove_ansi_codes(i) for i in line] for line in display]\\n write_table_to_csv(stripped, output_csv_prefix + '_summary.csv')\",\n \"def run(self):\\n\\n # Initialization assumptions\\n z = self.draw_normal_initial()\\n gradient = self.cv_gradient_initial(z)\\n gradient[np.isnan(gradient)] = 0\\n variance = np.power(gradient, 2) \\n final_parameters = self.current_parameters()\\n final_samples = 1\\n\\n # Create optimizer\\n if self.optimizer == 'ADAM':\\n self.optim = ADAM(final_parameters, variance, self.learning_rate, 0.9, 0.999)\\n elif self.optimizer == 'RMSProp':\\n self.optim = RMSProp(final_parameters, variance, self.learning_rate, 0.99)\\n\\n # Record elbo\\n if self.record_elbo is True:\\n elbo_records = np.zeros(self.iterations)\\n else:\\n elbo_records = None\\n\\n for i in range(self.iterations):\\n x = self.draw_normal()\\n gradient = self.cv_gradient(x)\\n gradient[np.isnan(gradient)] = 0\\n self.change_parameters(self.optim.update(gradient))\\n\\n if self.printer is True:\\n self.print_progress(i, self.optim.parameters[::2])\\n\\n # Construct final parameters using final 10% of samples\\n if i > self.iterations-round(self.iterations/10):\\n final_samples += 1\\n final_parameters = final_parameters+self.optim.parameters\\n\\n if self.record_elbo is True:\\n elbo_records[i] = self.get_elbo(self.optim.parameters[::2])\\n\\n final_parameters = final_parameters/float(final_samples)\\n self.change_parameters(final_parameters)\\n final_means = np.array([final_parameters[el] for el in range(len(final_parameters)) if el%2==0])\\n final_ses = np.array([final_parameters[el] for el in range(len(final_parameters)) if el%2!=0])\\n if not self.quiet_progress:\\n print(\\\"\\\")\\n print(\\\"Final model ELBO is \\\" + str(-self.full_neg_posterior(final_means)-self.create_normal_logq(final_means)))\\n return self.q, final_means, final_ses, elbo_records\",\n \"def main(self, args):\\n for plot in args.plots:\\n if plot == 'no_plot':\\n break\\n print \\\"plotting\\\", plot\\n\\n fig = self.plot_figure(plot)\\n\\n fformat = '{plot}_{index}.{ext}'\\n fname = fformat.format(plot=plot, index=self.index, ext='png')\\n fpath = os.path.join(plot_dir, fname)\\n fig.savefig(fpath)\\n\\n if args.distributions == 'all':\\n distributions = ['Uf', 'Wf', 'uf_abs',\\n 'vorticity', 'vertical_shear']\\n else:\\n distributions = args.distributions\\n for dist in distributions:\\n range = self.properties[dist]['range']\\n name = self.properties[dist]['name']\\n print \\\"plotting distribution\\\", dist, name\\n fig = self.plot_distribution(getattr(self, dist), range, name)\\n\\n fformat = 'distribution_{q}_{index}.{ext}'\\n fname = fformat.format(q=dist, index=self.index, ext='png')\\n fpath = os.path.join(plot_dir, fname)\\n fig.savefig(fpath)\\n\\n if args.funcs:\\n for func in args.funcs:\\n print \\\"multiprocessing\\\", func\\n f = getattr(self, 'plot_' + func)\\n f()\",\n \"def run_script(input_dir, output_dir):\\n\\n \\\"\\\"\\\"+++++++++++++++++++++++++++++++++++\\\"\\\"\\\"\\n \\\"\\\"\\\"Step 1. load dataset \\\"\\\"\\\"\\n print(\\\"loading data ......\\\")\\n print(\\\"+++++++Read the surface shape data+++++++\\\")\\n shape_file_name = input_dir + \\\"aligned_shapes.mat\\\"\\n mat = loadmat(shape_file_name)\\n y_design = mat['aligned_shape']\\n n, l, m = y_design.shape\\n print(\\\"The dimension of shape matrix is \\\" + str(y_design.shape))\\n print(\\\"+++++++Read the sphere coordinate data+++++++\\\")\\n template_file_name = input_dir + \\\"template.mat\\\"\\n mat = loadmat(template_file_name)\\n coord_mat = mat['template']\\n # d = coord_mat.shape[1]\\n print(\\\"+++++++Read the design matrix+++++++\\\")\\n design_data_file_name = input_dir + \\\"design_data.txt\\\"\\n design_data = np.loadtxt(design_data_file_name)\\n # read the covariate type\\n var_type_file_name = input_dir + \\\"var_type.txt\\\"\\n var_type = np.loadtxt(var_type_file_name)\\n print(\\\"+++++++Construct the design matrix: normalization+++++++\\\")\\n x_design = read_x(design_data, var_type)\\n p = x_design.shape[1]\\n print(\\\"The dimension of design matrix is \\\" + str(x_design.shape))\\n\\n \\\"\\\"\\\"+++++++++++++++++++++++++++++++++++\\\"\\\"\\\"\\n \\\"\\\"\\\"Step 2. Statistical analysis: including (1) smoothing and (2) hypothesis testing\\\"\\\"\\\"\\n gpvals, lpvals_fdr, clu_pvals, efit_beta, efity_design, efit_eta = mfsda.run_stats(y_design, coord_mat, design_data, var_type)\\n\\n \\\"\\\"\\\"+++++++++++++++++++++++++++++++++++\\\"\\\"\\\"\\n \\\"\\\"\\\"Step3. Save all the results\\\"\\\"\\\"\\n gpvals_file_name = output_dir + \\\"global_pvalue.txt\\\"\\n np.savetxt(gpvals_file_name, gpvals)\\n lpvals_fdr_file_name = output_dir + \\\"local_pvalue_fdr.txt\\\"\\n np.savetxt(lpvals_fdr_file_name, lpvals_fdr)\\n clu_pvals_file_name = output_dir + \\\"cluster_pvalue.txt\\\"\\n np.savetxt(clu_pvals_file_name, clu_pvals)\",\n \"def compute(self):\\n self.find_n()\\n\\n # call hotspot field plots\\n for scenario in self.scenarios:\\n fields_dict = {}\\n ancestor_files = []\\n for filename in io.get_all_ancestor_files(self.cfg,\\n pattern='hotspot_*.nc'):\\n key = os.path.basename(os.path.dirname(filename))\\n splitname = os.path.basename(filename).split(\\\"_\\\")\\n if key.split(\\\"_\\\")[-1] == scenario:\\n fields_dict[(\\n f\\\"{splitname[-1].split('.nc')[0]}_\\\"\\n f\\\"{splitname[1]}_{key}\\\")] = iris.load_cube(filename)\\n ancestor_files.append(filename)\\n fields_dict[\\\"scenario\\\"] = scenario\\n fields_dict[\\\"ancestors\\\"] = ancestor_files\\n self.hotspot_fields_plot(fields_dict)\\n\\n # call scatter plots\\n for season in self.seasons:\\n timeseries_dict = {\\\"large_scale\\\": {}, \\\"regional\\\": {}}\\n for region, value in timeseries_dict.items():\\n for filename in io.get_all_ancestor_files(\\n self.cfg,\\n pattern=f'rolling_mean_{region}_{season}.nc'):\\n value[os.path.basename(os.path.dirname(filename))] = (\\n iris.load_cube(filename))\\n value[os.path.basename(\\n os.path.dirname(filename))] = (filename)\\n for var_combination in self.var_combinations:\\n self.timeseries_scatter_plot(deepcopy(timeseries_dict), season,\\n var_combination)\",\n \"def run_all():\\n db = DBInterface()\\n year = Config.get_property(\\\"league_year\\\")\\n session = Session(bind=db.engine)\\n\\n scraper.scrape_all(db, session, year)\\n session.commit()\\n\\n bets.predict_all(db, session)\\n session.commit()\\n session.close()\",\n \"def _generate_trading_instances(self, start_date, end_date, instruments, params):\\n configuration = self.configuration\\n configuration.start_date = start_date\\n configuration.end_date = end_date\\n configuration.instruments = instruments\\n\\n logger.info(\\\"Creating DataHandler, Strategy, Portfolio and ExecutionHandler\\\")\\n logger.info(\\\"Start date: %s\\\" % start_date)\\n logger.info(\\\"End date: %s\\\" % end_date)\\n logger.info(\\\"Instrument(s): %s...\\\" % instruments)\\n logger.info(\\\"Params: %s...\\\" % params)\\n\\n self.data_handler = self.data_handler_cls(self.events, configuration)\\n self.strategy = self.strategy_cls(self.data_handler, self.events, configuration, **params)\\n self.portfolio = self.portfolio_cls(self.data_handler, self.events, configuration)\\n self.execution_handler = self.execution_handler_cls(self.data_handler, self.events, configuration)\",\n \"def before_trading_start(context, data):\\n factors = pipeline_output('ff_example')\\n\\n # get the data we're going to use\\n returns = factors['returns']\\n mkt_cap = factors.sort_values(['market_cap'], ascending=True)\\n be_me = factors.sort_values(['be_me'], ascending=True)\\n\\n # to compose the six portfolios, split our universe into portions\\n half = int(len(mkt_cap)*0.5)\\n small_caps = mkt_cap[:half]\\n big_caps = mkt_cap[half:]\\n \\n thirty = int(len(be_me)*0.3)\\n seventy = int(len(be_me)*0.7)\\n growth = be_me[:thirty]\\n neutral = be_me[thirty:seventy]\\n value = be_me[seventy:]\\n\\n # now use the portions to construct the portfolios.\\n # note: these portfolios are just lists (indices) of equities\\n small_value = small_caps.index.intersection(value.index)\\n small_neutral = small_caps.index.intersection(neutral.index)\\n small_growth = small_caps.index.intersection(growth.index)\\n \\n big_value = big_caps.index.intersection(value.index)\\n big_neutral = big_caps.index.intersection(neutral.index)\\n big_growth = big_caps.index.intersection(growth.index)\\n\\n # take the mean to get the portfolio return, assuming uniform\\n # allocation to its constituent equities.\\n sv = returns[small_value].mean()\\n sn = returns[small_neutral].mean()\\n sg = returns[small_growth].mean()\\n \\n bv = returns[big_value].mean()\\n bn = returns[big_neutral].mean()\\n bg = returns[big_growth].mean()\\n\\n # computing SMB\\n context.smb = (sv + sn + sg)/3 - (bv + bn + bg)/3\\n\\n # computing HML\\n context.hml = (sv + bv)/2 - (sg + bg)/2\",\n \"def run(self):\\n self.timestamp['start'] = datetime.datetime.now().strftime('%Y%m%dT%H%M%S')\\n\\n for point in self._prepare_grid():\\n graph = self._prepare_graph(**point)\\n env = self._prepare_env(graph, **point)\\n log = self._prepare_logger(graph, env, **point)\\n\\n try:\\n env.run(until=self.runtime)\\n except Exception as e:\\n print(e)\\n log.close()\\n\\n # self.timestamp[grid.hash_grid_point(point)].append(datetime.datetime.now().strftime('%Y%m%dT%H%M%S'))\\n\\n self.timestamp['end'] = datetime.datetime.now().strftime('%Y%m%dT%H%M%S')\",\n \"def test_classical_processing_multiple_tapes(self, execute_kwargs):\\n dev = qml.device(\\\"default.qubit\\\", wires=2)\\n params = jax.numpy.array([0.3, 0.2])\\n\\n def cost_fn(x):\\n with qml.queuing.AnnotatedQueue() as q1:\\n qml.Hadamard(0)\\n qml.RY(x[0], wires=[0])\\n qml.CNOT(wires=[0, 1])\\n qml.expval(qml.PauliZ(0))\\n\\n tape1 = qml.tape.QuantumScript.from_queue(q1)\\n\\n with qml.queuing.AnnotatedQueue() as q2:\\n qml.Hadamard(0)\\n qml.CRX(2 * x[0] * x[1], wires=[0, 1])\\n qml.RX(2 * x[1], wires=[1])\\n qml.expval(qml.PauliZ(0))\\n\\n tape2 = qml.tape.QuantumScript.from_queue(q2)\\n\\n result = execute(tapes=[tape1, tape2], device=dev, **execute_kwargs)\\n return result[0] + result[1] - 7 * result[1]\\n\\n res = jax.jit(jax.grad(cost_fn))(params)\\n assert res.shape == (2,)\",\n \"def run_all(prob: str='', algo: str='', timelimit: int=3600):\\n if prob:\\n prob = prob.upper()\\n if algo:\\n algo = algo.lower()\\n probs = (prob,) if prob in PROBS else PROBS\\n algos = (algo,) if algo in ALGOS else ALGOS\\n\\n if not os.path.exists(DAT_PATH):\\n raise Exception(\\\"{} directory does not exist.\\\".format(DAT_PATH))\\n\\n if not os.path.exists(RES_PATH):\\n os.makedirs(RES_PATH)\\n\\n for prob in probs:\\n path = DAT_PATH + prob\\n inss = [f for f in os.listdir(path) if os.path.isfile(os.path.join(path, f))]\\n inss.sort()\\n for algo in algos:\\n wfile = \\\"{}rst_{}_{}.csv\\\".format(RES_PATH, prob, algo)\\n with open (wfile, 'w') as f:\\n f.write(\\\"instance,objective value,time to find best soltuion\\\")\\n if algo == 'bc':\\n f.write(\\\",lower bound\\\")\\n if algo[:3] == 'ids':\\n f.write(\\\",number of total iterations,number of iterations to find best soltuion\\\")\\n f.write(\\\"\\\\n\\\")\\n for ins in inss:\\n mod = Model(problem=prob, filename=os.path.join(path, ins))\\n mod.solve(algorithm=algo, timelimit=timelimit)\\n with open (wfile, 'a') as f:\\n f.write(\\\"{},{},{:.2f}\\\".format(ins,mod.objval, mod.ttb))\\n if algo == 'bc':\\n f.write(\\\",{}\\\".format(mod.lb))\\n if algo[:3] == 'ids':\\n f.write(\\\",{},{}\\\".format(mod.num_ite, mod.bes_ite))\\n f.write(\\\"\\\\n\\\")\",\n \"def run(self):\\n is_spsa = True\\n is_steep_descent = False\\n is_rprop = False\\n\\n k = 0\\n theta = self.theta0\\n\\n while True:\\n k = k + 1\\n\\n self.iter = k\\n print(f'starting iter {k} ...')\\n\\n if self.constraints is not None:\\n theta = self.constraints(theta)\\n\\n print('current param:')\\n for name, value in utils.true_param(theta).items():\\n print(f' {name}: {value[\\\"value\\\"]}')\\n\\n c_k = self.c / (k ** self.gamma)\\n a_k = self.a / ((k + self.A) ** self.alpha)\\n\\n # print(f' ck: {c_k:0.5f}')\\n # print(f' ak: {a_k:0.5f}')\\n\\n # Run the engine match here to get the gradient\\n print('Run engine match ...')\\n gradient = self.approximate_gradient(theta, c_k, k)\\n\\n # For SPSA we update with a small step (theta = theta - a_k * gradient)\\n if is_spsa:\\n theta = utils.linear_combinaison(1.0, theta, -a_k, gradient)\\n logging.info(f'{__file__} > theta from spsa: {theta}')\\n # print(f'new param after application of gradient:')\\n # for n, v in theta.items():\\n # print(f' {n}: {int(v[\\\"value\\\"] * v[\\\"factor\\\"])}')\\n\\n # For steepest descent we update via a constant small step in the gradient direction\\n elif is_steep_descent:\\n mu = -0.01 / max(1.0, utils.norm2(gradient))\\n theta = utils.linear_combinaison(1.0, theta, mu, gradient)\\n\\n # For RPROP, we update with information about the sign of the gradients\\n elif is_rprop:\\n theta = utils.linear_combinaison(1.0, theta, -0.01, self.rprop(theta, gradient))\\n\\n # Apply parameter limits\\n theta = utils.apply_limits(theta)\\n logging.info(f'{__file__} > theta with limits: {theta}')\\n # print(f'new param after application of limits:')\\n # for n, v in theta.items():\\n # print(f' {n}: {int(v[\\\"value\\\"] * v[\\\"factor\\\"])}')\\n\\n # We then move to the point which gives the best average of goal\\n (avg_goal, avg_theta) = self.average_best_evals(30)\\n logging.info(f'{__file__} > avg_theta from average_best_evals: {avg_theta}')\\n\\n theta = utils.linear_combinaison(0.98, theta, 0.02, avg_theta)\\n logging.info(f'{__file__} > theta with avg_theta: {theta}')\\n # print(f'new param after application of best average param:')\\n # for n, v in theta.items():\\n # print(f' {n}: {int(v[\\\"value\\\"] * v[\\\"factor\\\"])}')\\n\\n # Apply parameter limits\\n theta = utils.apply_limits(theta) # This is the best param.\\n logging.info(f'{__file__} > best param: {theta}')\\n # print(f'new param after application of limits:')\\n # for n, v in theta.items():\\n # print(f' {n}: {int(v[\\\"value\\\"] * v[\\\"factor\\\"])}')\\n\\n # Log best param values\\n for kv, vv in theta.items():\\n logging.info(f'<best> iter: {k}, param: {kv}, value: {int(vv[\\\"value\\\"]*vv[\\\"factor\\\"])}')\\n print('best param:')\\n for n, v in theta.items():\\n print(f' {n}: {int(v[\\\"value\\\"] * v[\\\"factor\\\"])}')\\n\\n mean_all_goal, _ = self.average_evaluations(30)\\n print(f'mean all goal: {mean_all_goal}')\\n\\n mean_best_goal, _ = self.average_best_evals(30)\\n print(f'mean best goal: {mean_best_goal}')\\n\\n # Save data in csv for plotting.\\n plot_data = {}\\n plot_data.update({'iter': k})\\n plot_data.update({'meanbestgoal': mean_best_goal})\\n plot_data.update({'meanallgoal': mean_all_goal})\\n plot_theta = utils.true_param(theta)\\n for name, value in plot_theta.items():\\n plot_data.update({name: value[\\\"value\\\"]})\\n\\n with open(self.plot_data_file, 'a') as f:\\n cnt = 0\\n for name, value in plot_data.items():\\n cnt += 1\\n if cnt == len(plot_data):\\n f.write(f'{value}\\\\n')\\n else:\\n f.write(f'{value},')\\n\\n print(f'done iter {k} / {self.max_iter}')\\n logging.info(f'{__file__} > done iter {k} / {self.max_iter}')\\n print('=========================================')\\n\\n # Stopping rule 1: Average goal and iteration meet the\\n # stop_all_mean_goal and stop_min_iter criteria.\\n if k >= self.stop_min_iter and mean_all_goal <= self.stop_all_mean_goal:\\n print('Stop opimization due to good average all goal!')\\n break\\n\\n # Stopping rule 2: Average best goal and iteration meet the\\n # stop_best_mean_goal and stop_min_iter criteria.\\n if k >= self.stop_min_iter and mean_best_goal <= self.stop_best_mean_goal:\\n print('Stop opimization due to good average best goal!')\\n break\\n\\n # Stopping rule 3: Max iteration is reached.\\n if k >= self.max_iter:\\n print('Stop opimization due to max iteration!')\\n break\\n\\n return utils.true_param(theta)\",\n \"def _evaluate_performance__static_winners(self):\\n # | - _evaluate_performance__\\n\\n # | - class attributes #################################################\\n AL = self\\n al_gen = self.al_gen\\n verbose = self.verbose\\n seed_ids = self.seed_ids\\n acquisition_bin = self.acquisition_bin\\n completed_ids = self.completed_ids\\n CandidateSpace = self.CandidateSpace\\n RegressionModel = self.RegressionModel\\n DuplicateFinder = self.DuplicateFinder\\n al_gen_dict = self.al_gen_dict\\n\\n stop_mode = self.stop_mode\\n stop_num_generations = self.stop_num_generations\\n\\n index_acq_gen_dict = self.index_acq_gen_dict\\n #__| #################################################################\\n\\n # #####################################################################\\n mode = \\\"lowest_N\\\" # 'lowest_N' or 'lowest_perc'\\n\\n N_ids = 10\\n lowest_perc = 5\\n\\n # Number of consecutive generations that the Nth best systems must\\n # remain static\\n M_gens = 3\\n # #####################################################################\\n\\n if mode == \\\"lowest_perc\\\":\\n num_candidates = CandidateSpace.FingerPrints.df_pre.shape[0]\\n N_ids = int(num_candidates * (lowest_perc * 0.01))\\n\\n gen_keys = list(AL.al_gen_dict.keys())\\n\\n if len(gen_keys) > M_gens:\\n latest_M_keys = gen_keys[-(M_gens + 1):]\\n last_gen_key = gen_keys[-1]\\n\\n al_gen_dict_subset_i = dict(zip(\\n latest_M_keys,\\n [AL.al_gen_dict.get(i, None) for i in latest_M_keys]))\\n\\n indices_list = []\\n iterator = enumerate(al_gen_dict_subset_i.items())\\n for i_cnt, (gen_i, AL_i) in iterator:\\n model_i = AL_i.model\\n\\n model_i = AL.add_main_Y_to_model(\\n model_i, plot_dft_instead_of_pred=True)\\n model_i = model_i[(model_i[\\\"duplicate\\\"] == False)]\\n model_i = model_i.sort_values(\\\"Y_main\\\")\\n\\n indices_i = model_i.index.tolist()\\n\\n indices_list.append(indices_i)\\n\\n if i_cnt >= M_gens:\\n indices_i = indices_list[i_cnt][0:N_ids]\\n ids_static_list = []\\n for j in range(M_gens):\\n indices_j = indices_list[i_cnt - (j + 1)][0:N_ids]\\n ids_static = indices_j == indices_i\\n ids_static_list.append(ids_static)\\n\\n ids_are_static = all(ids_static_list)\\n\\n self.performance__static_winners[last_gen_key] = ids_are_static\\n #__|\",\n \"def copy_generate_median_portfolio_from_simulation(num_of_years=30, trials=100, method='normal'):\\n sim_fia_cv = pd.DataFrame(index=range(num_of_years + 1))\\n\\n sim_base_total = pd.DataFrame(index=range(num_of_years + 1))\\n sim_base_income = pd.DataFrame(index=range(num_of_years + 1))\\n\\n sim_port_total = pd.DataFrame(index=range(num_of_years + 1))\\n sim_port_income = pd.DataFrame(index=range(num_of_years + 1))\\n\\n # read_income_inputs = pd.read_csv(src + \\\"income_model_inputs.csv\\\", index_col='Items')\\n read_income_inputs = pd.read_excel(src + \\\"portfolio_information.xlsx\\\", sheet_name='income_model_inputs',\\n index_col=[0])\\n\\n # read_returns_est = pd.read_csv(src + \\\"income_assets_returns_estimates.csv\\\", index_col='Symbol')\\n read_returns_est = pd.read_excel(src + \\\"portfolio_information.xlsx\\\", sheet_name='income_assets_returns_estimates',\\n index_col=[0])\\n\\n # read_returns_est.drop(['BM', read_returns_est.index[-1]], axis=0, inplace=True)\\n # read_portfolio_inputs = pd.read_csv(src + \\\"income_portfolio_inputs.csv\\\", index_col='Items')\\n\\n # read_asset_weights = pd.read_csv(src + \\\"asset_weights.csv\\\", index_col='Asset')\\n read_asset_weights = pd.read_excel(src + \\\"portfolio_information.xlsx\\\", sheet_name='asset_weights',\\n index_col=[0])\\n\\n all_assets = list(read_asset_weights.index)\\n read_asset_weights.drop(read_asset_weights.index[-1], axis=0, inplace=True)\\n\\n # read random returns for simulation\\n read_normal = pd.read_csv(src + 'sort_normal.csv', index_col=[0], parse_dates=True)\\n\\n assets_col_names = list(read_normal.columns)\\n\\n tickers = list(read_asset_weights.index)\\n wts = np.array(read_asset_weights.loc[:, 'base'])\\n\\n def asset_median_returns(data, ticker):\\n return data.filter(regex=ticker).median(axis=1)\\n\\n # dataframe for unsorted returns (normal)\\n median_returns_normal = pd.DataFrame({t: asset_median_returns(read_normal, t) for t in tickers})\\n median_returns_normal.loc[:, 'portfolio_return'] = median_returns_normal.dot(wts)\\n median_normal_fia = pd.DataFrame({'FIA': asset_median_returns(read_normal, 'r_FIA')})\\n\\n years = list(range(0, num_of_years + 1))\\n income_cols = ['year', 'strategy_term', 'index_returns', 'term_ret', 'term_ret_with_par', 'term_annualize',\\n 'ann_net_spread', 'term_ret_netspr', 'high_inc_benefit_base', 'rider_fee', 'eoy_income',\\n 'contract_value']\\n\\n term = int(read_income_inputs.loc['term', 'inputs'])\\n fia_ret = read_returns_est.loc[read_returns_est.index[-1], 'Annualized Returns']\\n fia_risk = read_returns_est.loc[read_returns_est.index[-1], 'Annualized Risk']\\n par_rate = float(read_income_inputs.loc['par_rate', 'inputs'])\\n spread = float(read_income_inputs.loc['spread', 'inputs'])\\n bonus_term = int(read_income_inputs.loc['bonus_term', 'inputs'])\\n premium = float(read_income_inputs.loc['premium', 'inputs'])\\n income_bonus = float(read_income_inputs.loc['income_bonus', 'inputs'])\\n\\n income_starts = int(read_income_inputs.loc['start_income_years', 'inputs'])\\n income_growth = float(read_income_inputs.loc['income_growth', 'inputs'])\\n rider_fee = float(read_income_inputs.loc['rider_fee', 'inputs'])\\n inc_payout_factor = float(read_income_inputs.loc['income_payout_factor', 'inputs'])\\n contract_bonus = float(read_income_inputs.loc['contract_bonus', 'inputs'])\\n social = float(read_income_inputs.loc['social', 'inputs'])\\n inflation = float(read_income_inputs.loc['inflation', 'inputs'])\\n wtd_cpn_yield = float(read_income_inputs.loc['wtd_coupon_yld', 'inputs'])\\n life_expectancy = int(read_income_inputs.loc['life_expectancy_age', 'inputs'])\\n clients_age = int(read_income_inputs.loc['clients_age', 'inputs'])\\n\\n # # ---------------INCOME MODEL--------------------------------------------\\n runs = 0\\n returns_dict = {}\\n asset_dict = {}\\n fia_dict = {}\\n while runs <= trials:\\n print(runs)\\n\\n # --------------------BASE MODEL---------------------------------------------\\n\\n base_wts = read_asset_weights.loc[:, 'base']\\n base_assets = list(base_wts.index)\\n base_weights = list(base_wts.values)\\n base_returns = list(read_returns_est.loc[:, 'Annualized Returns'].values)\\n base_std = list(read_returns_est.loc[:, 'Annualized Risk'].values)\\n\\n base_investment = float(read_income_inputs.loc['risky_assets', 'Base'])\\n adv_fees = float(read_income_inputs.loc['advisor_fees', 'Base'])\\n\\n # ----------------------RANDOM RETURNS--------------------------\\n r_cols = ['r_{}'.format(name) for name in base_assets]\\n boy_value = ['bv_{}'.format(name) for name in base_assets]\\n eoy_value = ['ev_{}'.format(name) for name in base_assets]\\n\\n random_returns = pd.DataFrame(index=years, columns=r_cols)\\n #\\n # for c in range(len(r_cols)):\\n # ret = np.random.normal(base_returns[c], base_std[c], size=(len(random_returns.index), 1))\\n\\n this_run_cols = ['{}_{}'.format(cname, str(runs)) for cname in r_cols]\\n random_returns = read_normal.loc[:, this_run_cols]\\n base_df = random_returns.copy()\\n\\n # -------------BASE PORTFOLIO----------------------------\\n for name in boy_value:\\n base_df.loc[:, name] = 0.0\\n\\n for counter in years:\\n period_returns = list(random_returns.loc[counter, :])\\n if counter == 0:\\n\\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment for c in range(len(boy_value))]\\n\\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\\n\\n base_investment = base_df.loc[counter, boy_value].sum()\\n\\n elif (counter > 0) and (counter < income_starts):\\n\\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment * (1 + period_returns[c])\\n for c in range(len(boy_value))]\\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\\n\\n # base_df.loc[counter, 'total_net_fees'] = base_df.loc[counter, 'total']\\n\\n # --coupon payment is invested back into the risky portfolio until the income is withdrawn----\\n base_investment = base_df.loc[counter, 'total']\\n\\n else:\\n\\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment * (1 + period_returns[c])\\n for c in range(len(boy_value))]\\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\\n\\n base_investment = base_df.loc[counter, 'total']\\n\\n base_df.loc[:, 'adj_total'] = base_df.loc[:, 'total'].apply(lambda x: x if x > 0 else 0)\\n\\n asset_dict.update({'{}_{}'.format('s', str(runs)): base_df.loc[:, 'total']})\\n # asset_dict.update({'{}_{}'.format('s', str(runs)): ret.flatten()})\\n runs += 1\\n\\n asset_values = pd.DataFrame(asset_dict)\\n assets_ror = asset_values.pct_change().fillna(0)\\n terminal_values = asset_values.iloc[-1]\\n terminal_df = pd.DataFrame(columns=['Terminal Value'])\\n terminal_df['Terminal Value'] = terminal_values\\n\\n median_index = np.where(asset_values == np.median(np.array(terminal_values)))\\n median_index = median_index[1][0]\\n m_cols = ['{}_{}_{}'.format('r', r, str(median_index)) for r in all_assets]\\n median_portfolio_asset_returns = read_normal.loc[:, m_cols]\\n median_portfolio_asset_returns.loc[:, 'port_ending_values'] = asset_values.loc[:, '{}_{}'.format('s', median_index)]\\n median_portfolio_asset_returns.loc[:, 'ror'] = median_portfolio_asset_returns[\\n 'port_ending_values'].pct_change().fillna(0)\\n desc_sort = median_portfolio_asset_returns.sort_values(by=median_portfolio_asset_returns.columns[-1],\\n ascending=False)\\n asc_sort = median_portfolio_asset_returns.sort_values(by=median_portfolio_asset_returns.columns[-1],\\n ascending=True)\\n unsorted_df = median_portfolio_asset_returns\\n unsorted_df.drop(unsorted_df.columns[-2:], axis=1, inplace=True)\\n desc_sort.drop(desc_sort.columns[-2:], axis=1, inplace=True)\\n asc_sort.drop(asc_sort.columns[-2:], axis=1, inplace=True)\\n\\n # reset index and drop existing index\\n desc_sort.reset_index(drop=True, inplace=True)\\n asc_sort.reset_index(drop=True, inplace=True)\\n\\n unsorted_df.to_csv(src + 'median_returns_unsorted.csv')\\n desc_sort.to_csv(src + 'median_returns_largest.csv')\\n asc_sort.to_csv(src + 'median_returns_smallest.csv')\\n asset_values.to_csv(src + 'ending_values.csv')\\n assets_ror.to_csv(src + 'ending_values_ror.csv')\\n terminal_df.to_csv(src + 'terminal_values.csv')\\n print(\\\"simulation completed....\\\")\",\n \"def main():\\n # Data processing and parsing\\n mp_df = pd.read_csv(\\\"melting_points.csv\\\")\\n bp_df = pd.read_csv(\\\"boiling_points.csv\\\")\\n data_processing(mp_df)\\n data_processing(bp_df)\\n # Plots scatterplots of our variables with the boiling and melting point\\n plot_scatter('Mass', 'T_exp', mp_df, 'Molecular Weight', 'Melting Point')\\n plot_scatter('Mass', 'T_exp', bp_df, 'Molecular Weight', 'Boiling Point')\\n plot_scatter('Atom_counts', 'T_exp', mp_df, 'Number of Atoms',\\n 'Melting Point')\\n \\n # Creates models and prints statistics using simply mass and atom counts\\n simple_parameters = ['Mass', 'Atom_counts', 'T_exp']\\n model = ml_df(mp_df, simple_parameters, 0.2)\\n plot_T(model, simple_parameters, \\\"simple_melting.png\\\", 'Melting Point')\\n slope_average(mp_df, simple_parameters, 0.2, 100)\\n model2 = ml_df(bp_df, simple_parameters, 0.2)\\n plot_T(model2, simple_parameters, 'simple_boiling.png', 'Boiling Point')\\n slope_average(bp_df, simple_parameters, 0.2, 100)\\n # Creates models and prints statistics using more variables\\n complex_parameters = [\\\"Mass\\\", \\\"Atom_counts\\\", \\\"C\\\", \\\"H\\\", \\\"Acid\\\", \\\"Alcohol\\\",\\n \\\"Unsaturation\\\", \\\"T_exp\\\"]\\n model3 = ml_df(mp_df, complex_parameters, 0.2)\\n plot_T(model3, complex_parameters, \\\"complex_melting.png\\\", 'Melting Point')\\n slope_average(mp_df, complex_parameters, 0.2, 100)\\n model4 = ml_df(bp_df, complex_parameters, 0.2)\\n plot_T(model4, complex_parameters, \\\"complex_boiling.png\\\", 'Boiling Point')\\n slope_average(bp_df, complex_parameters, 0.2, 100)\\n # Creates models and prints statistics for our model using all available variables\\n complex_parameters2 = [\\\"Mass\\\", \\\"Atom_counts\\\", \\\"C\\\", \\\"H\\\", 'O', 'N', 'F',\\n 'Cl', 'Br', 'I', 'S', 'Si', 'Halide', \\\"Acid\\\", \\\"Alcohol\\\",\\n \\\"Unsaturation\\\", \\\"T_exp\\\"]\\n model = ml_df(mp_df, complex_parameters2, 0.2)\\n plot_T(model, complex_parameters2, \\\"more_complex_melting.png\\\",\\n 'Melting Point')\\n slope_average(mp_df, complex_parameters2, 0.2, 100)\\n # Tries a few different regressor models\\n slope_average(mp_df, complex_parameters2, 0.2, 100, LinearRegression())\\n slope_average(bp_df, complex_parameters2, 0.2, 100, RandomForestRegressor())\",\n \"def build_and_run_(spec):\\n opt = spec['opt']\\n print \\\"pool starting \\\", opt\\n\\n # lenght of simulation \\n tf = float(opt.get('tf', 100))\\n\\n # model # coupling function # connectivity \\n simargs = {}\\n for mod, key in [(models, 'model'), \\n (connectivity, 'connectivity'),\\n (coupling, 'coupling')]:\\n simargs[key] = build_sim_part(mod, opt[key])\\n\\n # noise # integrator \\n optint = opt['integrator']\\n if 'noise' in optint:\\n optint['noise'] = build_sim_part(noise, optint['noise'])\\n simargs['integrator'] = build_sim_part(integrators, optint)\\n\\n # monitors \\n if not type(opt['monitors']) in (list,):\\n opt['monitors'] = [opt['monitors']]\\n simargs['monitors'] = []\\n for mon in opt['monitors']:\\n simargs['monitors'].append(build_sim_part(monitors, mon))\\n\\n # stimulus \\n # NotImplemented\\n\\n # simulator \\n sim = simulator.Simulator(**simargs)\\n sim.configure()\\n\\n # TODO open HDF5 first, figure out correct sizes, etc\\n\\n # loop, writing data to h5\\n ts = [[] for _ in opt['monitors']]\\n ys = [[] for _ in opt['monitors']]\\n for i, all_monitor_data in enumerate(sim(tf)):\\n for j, mondata in enumerate(all_monitor_data):\\n if not mondata is None:\\n t, y = mondata\\n ts[j].append(t)\\n ys[j].append(y)\\n\\n # write data to hdf5 file\\n path = os.path.abspath(opt.get('wd', './'))\\n h5fname = os.path.join(path, \\\"tvb_%s.h5\\\" % (spec['md5sum'], ))\\n h5 = h5py.File(h5fname, 'w')\\n\\n for i, (mon, (t, y)) in enumerate(zip(simargs['monitors'], zip(ts, ys))):\\n mname = \\\"mon_%d_%s\\\" % (i, mon.__class__.__name__)\\n g = h5.create_group(mname)\\n g.create_dataset('ts', data=t)\\n g.create_dataset('ys', data=y)\\n\\n h5.close()\\n\\n # return filename\\n print \\\"pool finished\\\", opt\\n return h5fname\",\n \"def portfolio_performance(returns,weights):\\r\\n print('Calculating Portfolio Performance')\\r\\n # returns=target_asset_port_data_attributes['component_returns']\\r\\n # weights =target_asset_port_data_attributes['effective_weights']\\r\\n\\r\\n component_returns= returns\\r\\n compnent_weights = pd.DataFrame(data=np.nan,index= component_returns.index,columns=component_returns.columns)\\r\\n compnent_weights.loc[weights.index,:] = weights\\r\\n\\r\\n portfolio_dates = component_returns.index\\r\\n components = component_returns.columns\\r\\n\\r\\n # pre-allocate\\r\\n BoP_df = pd.DataFrame(data=np.nan,index=portfolio_dates,columns=components)\\r\\n EoP_df = pd.DataFrame(data=np.nan,index=portfolio_dates,columns=components)\\r\\n PnL_df = pd.DataFrame(data=np.nan,index=portfolio_dates,columns=components)\\r\\n portfolio_BoP = pd.DataFrame(data=np.nan,index=portfolio_dates,columns=['Portfolio BoP'])\\r\\n portfolio_EoP = pd.DataFrame(data=np.nan,index=portfolio_dates,columns=['Portfolio EoP'])\\r\\n portfolio_PnL = pd.DataFrame(data=np.nan,index=portfolio_dates,columns=['Portfolio PnL'])\\r\\n \\r\\n portfolio_index = pd.DataFrame(data=np.nan,index=portfolio_dates,columns=['Index'])\\r\\n previous_index_value = np.int64(1)\\r\\n\\r\\n pre_date = portfolio_dates[0]\\r\\n # set BoP to start weights\\r\\n for date,row in component_returns.iterrows():\\r\\n # print(date)\\r\\n # 1st date\\r\\n if date == portfolio_dates[0]:\\r\\n BoP_df.loc[date] = compnent_weights.iloc[0,:]\\r\\n EoP_df.loc[date] = BoP_df.loc[date] * (1+component_returns.loc[date])\\r\\n PnL_df.loc[date] = EoP_df.loc[date].subtract(BoP_df.loc[date])\\r\\n\\r\\n portfolio_BoP.loc[date] = BoP_df.loc[date].sum()\\r\\n portfolio_EoP.loc[date] = EoP_df.loc[date].sum()\\r\\n portfolio_PnL.loc[date] = PnL_df.loc[date].sum()\\r\\n\\r\\n portfolio_index.loc[date] = np.nansum([previous_index_value,portfolio_PnL.loc[date].values])\\r\\n previous_index_value = portfolio_index.loc[date]\\r\\n pre_date = date\\r\\n\\r\\n # after first date\\r\\n else:\\r\\n BoP_df.loc[date] = EoP_df.loc[pre_date]\\r\\n # weights override\\r\\n if date in compnent_weights.index:\\r\\n none_NaN_index = ~compnent_weights.loc[date].isnull()\\r\\n if not compnent_weights.loc[date][none_NaN_index].empty:\\r\\n tmp_sum = BoP_df.loc[date].sum()\\r\\n BoP_df.loc[date][none_NaN_index.values] = (compnent_weights.loc[date][none_NaN_index.values].values)*tmp_sum\\r\\n\\r\\n \\r\\n EoP_df.loc[date] = BoP_df.loc[date] * (1+component_returns.loc[date])\\r\\n PnL_df.loc[date] = EoP_df.loc[date].subtract(BoP_df.loc[date])\\r\\n\\r\\n portfolio_BoP.loc[date] = BoP_df.loc[date].sum()\\r\\n portfolio_EoP.loc[date] = EoP_df.loc[date].sum()\\r\\n portfolio_PnL.loc[date] = PnL_df.loc[date].sum()\\r\\n \\r\\n portfolio_index.loc[date] = np.nansum([previous_index_value,portfolio_PnL.loc[date].values])\\r\\n previous_index_value = portfolio_index.loc[date]\\r\\n pre_date = date\\r\\n\\r\\n\\r\\n portfolio_returns = portfolio_index.pct_change(1) \\r\\n portfolio_returns.columns = ['Returns']\\r\\n\\r\\n portfolio_index\\r\\n perf = portfolio_index.calc_stats()\\r\\n \\r\\n output = pd.Series(data = [perf,PnL_df,portfolio_index,portfolio_BoP,portfolio_EoP,BoP_df], index=['Portfolio Perf','Component PnL','portfolio_index','portfolio_BoP','portfolio_EoP','BoP_df'])\\r\\n return output\",\n \"def run():\\n trials = 100\\n\\n multipliers = [0.25, 0.3, 0.35, 0.5, 0.75, 1, 1.25, 1.45, 1.5, 1.55, 1.6] # Coefficients for learning rate\\n\\n mean_penalty = []\\n median_penalty = []\\n std_penalty = []\\n\\n mean_trial_time = []\\n median_trial_time = []\\n std_trial_time = []\\n\\n mean_success_rate = []\\n median_success_rate = []\\n std_success_rate = []\\n\\n for m in multipliers:\\n all_penalties = [] # All penalties from trail sets\\n all_average_trial_time = []\\n all_success_rates = []\\n\\n for i in range(0, 20):\\n # print \\\"Trial set:\\\", i\\n # Set up environment and agent\\n e = Environment() # create environment (also adds some dummy traffic)\\n agent = e.create_agent(LearnerAgent) # create agent\\n agent.mult = m\\n e.set_primary_agent(agent, enforce_deadline=True) # specify agent to track\\n\\n # Now simulate it\\n sim = Simulator(e, update_delay=0.0, display=False) # create simulator (uses pygame when display=True, if available)\\n\\n sim.run(n_trials=trials) # run for a specified number of trials\\n\\n all_penalties.append(agent.all_trails_penalties)\\n all_average_trial_time.append(agent.time/float(trials))\\n all_success_rates.append(float(trials-agent.aborted_trials)/trials)\\n\\n mean_penalty.append(np.mean(all_penalties))\\n median_penalty.append(np.median(all_penalties))\\n std_penalty.append(np.std(all_penalties))\\n\\n mean_trial_time.append(np.mean(all_average_trial_time))\\n median_trial_time.append(np.median(all_average_trial_time))\\n std_trial_time.append(np.std(all_average_trial_time))\\n\\n mean_success_rate.append(np.mean(all_success_rates))\\n median_success_rate.append(np.median(all_success_rates))\\n std_success_rate.append(np.std(all_success_rates))\\n\\n for i in range(0, len(multipliers)):\\n print \\\"\\\"\\n print \\\"Multiplier:\\\", multipliers[i]\\n print \\\"\\\"\\n print \\\"Mean penalty per {} trials:\\\".format(trials), mean_penalty[i]\\n print \\\"Median penalty per {} trials:\\\".format(trials), median_penalty[i]\\n print \\\"Std.Dev. penalty per {} trials:\\\".format(trials), std_penalty[i]\\n\\n print \\\"\\\"\\n print \\\"Mean trial time:\\\", mean_trial_time[i]\\n print \\\"Median trial time:\\\", median_trial_time[i]\\n print \\\"Std.Dev. trial time:\\\", std_trial_time[i]\\n\\n print \\\"\\\"\\n print \\\"Mean success rate per {} trials:\\\".format(trials), mean_success_rate[i]\\n print \\\"Median success rate per {} trials:\\\".format(trials), median_success_rate[i]\\n print \\\"Std.Dev. success rate per {} trials:\\\".format(trials), std_success_rate[i]\",\n \"def __init__(\\n self, problem_results: ProblemResults, demand_instruments: Array, supply_instruments: Array,\\n inverse_covariance_matrix: Array, expected_xi_jacobian: Array, expected_omega_jacobian: Array,\\n expected_prices: Array, expected_shares: Array, start_time: float, end_time: float, draws: int,\\n iteration_stats: Sequence[Mapping[Hashable, SolverStats]]) -> None:\\n self.problem_results = problem_results\\n self.demand_instruments = demand_instruments\\n self.supply_instruments = supply_instruments\\n self.inverse_covariance_matrix = inverse_covariance_matrix\\n self.expected_xi_by_theta_jacobian = expected_xi_jacobian\\n self.expected_omega_by_theta_jacobian = expected_omega_jacobian\\n self.expected_prices = expected_prices\\n self.expected_shares = expected_shares\\n self.computation_time = end_time - start_time\\n self.draws = draws\\n unique_market_ids = problem_results.problem.unique_market_ids\\n self.fp_converged = np.array(\\n [[m[t].converged if m else True for m in iteration_stats] for t in unique_market_ids], dtype=np.bool_\\n )\\n self.fp_iterations = np.array(\\n [[m[t].iterations if m else 0 for m in iteration_stats] for t in unique_market_ids], dtype=np.int64\\n )\\n self.contraction_evaluations = np.array(\\n [[m[t].evaluations if m else 0 for m in iteration_stats] for t in unique_market_ids], dtype=np.int64\\n )\\n\\n # construct default supply and demand shifter formulations\\n self.supply_shifter_formulation = self.demand_shifter_formulation = None\\n if self.problem_results.problem.K3 > 0:\\n assert self.problem_results.problem.product_formulations[0] is not None\\n assert self.problem_results.problem.product_formulations[2] is not None\\n\\n X1_terms = self.problem_results.problem.product_formulations[0]._terms\\n X3_terms = self.problem_results.problem.product_formulations[2]._terms\\n X1_expressions = self.problem_results.problem.product_formulations[0]._expressions\\n X3_expressions = self.problem_results.problem.product_formulations[2]._expressions\\n\\n supply_shifters = set()\\n for term, expression in zip(X3_terms, X3_expressions):\\n if all(str(s) != 'shares' for s in expression.free_symbols) and term.name() != 'Intercept':\\n supply_shifters.add(term.name())\\n\\n demand_shifters = set()\\n for term, expression in zip(X1_terms, X1_expressions):\\n if all(str(s) != 'prices' for s in expression.free_symbols) and term.name() != 'Intercept':\\n demand_shifters.add(term.name())\\n\\n if supply_shifters - demand_shifters:\\n supply_shifter_formula = ' + '.join(sorted(supply_shifters - demand_shifters))\\n self.supply_shifter_formulation = Formulation(f'{supply_shifter_formula} - 1')\\n\\n if demand_shifters - supply_shifters:\\n demand_shifter_formula = ' + '.join(sorted(demand_shifters - supply_shifters))\\n self.demand_shifter_formulation = Formulation(f'{demand_shifter_formula} - 1')\",\n \"def start_trading(self):\\n backtest_result_dir = os.path.join(self.result_dir, str(self.backtest_date))\\n os.mkdir(backtest_result_dir)\\n backtest_scores_path = os.path.join(backtest_result_dir, 'scores.csv')\\n # all_backtest_scores_exists = os.path.isfile(self.all_backtest_scores_path)\\n\\n last_backtest_scores = open(self.last_backtest_scores_path, \\\"w\\\")\\n backtest_scores = open(backtest_scores_path, \\\"w\\\")\\n\\n\\n fieldnames = [ 'Backtest Name', 'Backtest Date', 'Strategy', 'Start Date', 'End Date', 'Instrument(s)', 'Params'] + \\\\\\n ['Number of signals', 'Number of orders', 'Number of trades', 'Total USD Return', 'Total BTC Return',\\n 'Sharpe Ratio', 'BTC Sharpe Ratio', 'Max Drawdown', 'BTC Max Drawdown', 'Drawdown Duration', 'BTC Drawdown Duration',\\n 'Monthly BTC Return', 'Yearly BTC Return', 'Avg. winning trade', 'Median duration', 'Avg. losing trade', 'Median returns winning', 'Largest losing trade',\\n 'Gross loss', 'Largest winning trade', 'Avg duration', 'Avg returns losing', 'Median returns losing', 'Profit factor',\\n 'Winning round trips', 'Percent profitable', 'Total profit', 'Shortest duration', 'Median returns all round trips',\\n 'Losing round trips', 'Longest duration', 'Avg returns all round trips', 'Gross profit', 'Avg returns winning',\\n 'Total number of round trips', 'Ratio Avg. Win:Avg. Loss', 'Avg. trade net profit', 'Even round trips',\\n 'Configuration Filename', 'Leverage']\\n\\n try:\\n with last_backtest_scores as a, backtest_scores as b:\\n writer_a = csv.DictWriter(a, fieldnames=fieldnames)\\n writer_b = csv.DictWriter(b, fieldnames=fieldnames)\\n writer_a.writeheader()\\n writer_b.writeheader()\\n\\n # if not all_backtest_scores_exists:\\n # writer_c.writeheader()\\n\\n for i, (start, end) in enumerate(zip(self.start_dates, self.end_dates)):\\n for j, instruments in enumerate(self.instrument_list):\\n for k, params in enumerate(self.strategy_params):\\n num_backtest = i * (self.num_instruments * self.num_params + 1) + j * (self.num_params + 1) + k + 1\\n print(\\\"Strategy %s out of %s\\\" % (num_backtest, self.num_backtests))\\n self._generate_trading_instances(start, end, instruments, params)\\n self._run()\\n stats = self._process_results()\\n\\n general_stats = stats['general']\\n pnl_stats = stats['pnl']['All trades'].to_dict()\\n summary_stats = stats['summary']['All trades'].to_dict()\\n duration_stats = stats['duration']['All trades'].to_dict()\\n return_stats = stats['returns']['All trades'].to_dict()\\n params_value = '/'.join([ '{}:{}'.format(item[0], item[1]) for item in params.items() ])\\n\\n row = {\\n 'Backtest Name': self.backtest_name,\\n 'Backtest Date': self.backtest_date,\\n 'Strategy': self.strategy.strategy_name,\\n 'Start Date': start,\\n 'End Date': end,\\n 'Instrument(s)': format_instrument_list(instruments),\\n 'Params': params_value,\\n 'Number of signals': self.signals,\\n 'Number of orders': self.orders,\\n 'Number of trades': self.fills,\\n **general_stats,\\n **pnl_stats,\\n **summary_stats,\\n **duration_stats,\\n **return_stats,\\n 'Configuration Filename': self.configuration_filename,\\n 'Leverage': self.default_leverage\\n }\\n\\n writer_a.writerow(row)\\n writer_b.writerow(row)\\n\\n except IOError:\\n print('I/O Error')\\n\\n all_scores_csv = pd.concat([ pd.read_csv(self.all_backtest_scores_path), pd.read_csv(backtest_scores_path)])\\n all_scores_csv.to_csv(self.all_backtest_scores_path, columns=fieldnames, index=False, encoding='utf-8-sig')\\n\\n self._open_results_in_excel()\",\n \"def grid_evaluation(param_list_one, param_list_two, param_eval, n_trials=16, \\n aggr_method=np.mean, save_dir='data/', file_name='grid evaluation',\\n save_to_disk=True, save_each=1000, chunksize=1.):\\n \\n \\n if not list(param_list_two): # If `param_list_two` is empty\\n params = param_list_one\\n grid_shape = (len(param_list_one),)\\n is_really_grid = False\\n \\n else:\\n params = list(itertools.product(param_list_one, param_list_two))\\n grid_shape = (len(param_list_one), len(param_list_two))\\n is_really_grid = True\\n \\n def grid_fun(point): # Function to compute for each grid point\\n \\n trial_out = np.nan * np.ones((n_trials,))\\n \\n for i in np.arange(n_trials):\\n \\n if is_really_grid:\\n trial_out[i] = param_eval(point[0], point[1])\\n else: # If `param_list_two` is empty\\n trial_out[i] = param_eval(point)\\n \\n return aggr_method(trial_out)\\n \\n n_grid_pts = len(params)\\n \\n # Recording procedure\\n def record_experiment(grid):\\n now = datetime.datetime.now().strftime(\\\"%Y-%m-%d %H:%M:%S\\\")\\n save_path = save_dir + now + ' ' + file_name + '.pkl'\\n experiment = {\\n 'date': now,\\n 'rows': param_list_one,\\n 'cols': param_list_two,\\n 'n_trials': n_trials,\\n 'grid': np.reshape(grid, grid_shape),\\n 'path': save_path\\n }\\n if save_to_disk:\\n utils.save_obj(experiment, save_path)\\n return experiment\\n \\n # Set a pool of workers\\n nb_workers = min(mp.cpu_count(), 24)\\n print('Working with {} processes.'.format(nb_workers))\\n pool = mp.Pool(nb_workers)\\n \\n # Iterate `grid_fun` across workers\\n it = pool.imap(grid_fun, params, chunksize=chunksize)\\n grid = np.nan * np.ones((n_grid_pts,))\\n\\n for idx, val in enumerate(tqdm(it, total=n_grid_pts)):\\n grid[idx] = val\\n \\n # Make sure that we save after each couple of iterations\\n if (idx >= save_each) and (idx % save_each == 0): \\n experiment = record_experiment(grid)\\n \\n # Close pool\\n pool.close()\\n pool.join()\\n \\n experiment = record_experiment(grid)\\n \\n return experiment\",\n \"def __init__(self):\\n if cons.is_boa == True:\\n algorithm = \\\"XCS/BOA\\\"\\n else:\\n algorithm = \\\"XCS\\\"\\n print(algorithm + \\\": Running \\\" + str( cons.multiple_runs ) + \\\" times\\\")\\n #Global Parameters-------------------------------------------------------------------------------------\\n self.population = None # The rule population (the 'solution/model' evolved by XCS)\\n self.learn_track = None # Output file that will store tracking information during learning\\n #self.previousState = None # states received from environment from last time step\\n\\n for count in range( cons.multiple_runs ):\\n print(\\\"Time \\\" + str( count ) + \\\": Initializing Algorithm...\\\")\\n #-------------------------------------------------------\\n # POPULATION REBOOT - Begin XCS learning from an existing saved rule population\\n #-------------------------------------------------------\\n if cons.do_population_reboot:\\n self.populationReboot( count )\\n #-------------------------------------------------------\\n # NORMAL XCS - Run XCS from scratch on given data\\n #-------------------------------------------------------\\n else:\\n try:\\n self.learn_track = open(cons.out_file+str(count)+'_LearnTrack.txt','w')\\n except Exception as inst:\\n print(type(inst))\\n print(inst.args)\\n print(inst)\\n print('cannot open', cons.out_file+str(count)+'_LearnTrack.txt')\\n raise\\n else:\\n self.learn_track.write(\\\"Explore_Iteration\\\\tMacroPopSize\\\\tMicroPopSize\\\\tAccuracy_Estimate\\\\tAveGenerality\\\\tExpRules\\\\tTime(min)\\\\n\\\")\\n\\n # Instantiate Population---------\\n self.population = ClassifierSet()\\n self.explore_iter = 0\\n self.tracked_results = [0] * cons.tracking_frequency\\n self.exploit_iters = 0\\n\\n #Run the XCS/BOA algorithm--------------------------------------------------------------------------\\n self.boa = None\\n bcons.setCycleTime( int( cons.N/2 ) )\\n self.run_XCS_BOA( count )\",\n \"def run_simulation(**kwargs):\\n print(\\\"executing run_simulation() in file\\\", __file__)\\n print(\\\"got the dictionary kwargs =\\\", kwargs)\\n\\n # HERE is where you would usually run your simulation (e.g. DMRG).\\n # simulate some heavy calculations:\\n for i in range(30):\\n print(\\\"step \\\", i, flush=True) # (remove `flush=True` for Python 2)\\n # the flush=True makes the output appear immediately\\n time.sleep(5)\\n\\n results = {'kwargs': kwargs, 'example_data': np.random.random((2, 2))}\\n\\n output_filename = kwargs['output_filename']\\n print(\\\"save results to \\\", output_filename)\\n with open(output_filename, 'wb') as f:\\n pickle.dump(results, f)\",\n \"def run(self,step=2,\\n sizePop=100,\\n infoFields=['migrate_to','fitness'],\\n recombination_rate = 0.00375,\\n migration_rate = 0.01,\\n mutation_rate = [0.00000001],\\n subPopNames = ['x','y','z','w'],\\n alleleNames = ['A','B'],\\n s1 = 0.1,\\n burnin=50,\\n **kargs):\\n\\n self.reset()\\n pop=sim.Population(size=[sizePop]*self.numPop, loci=self.loci, lociPos=list(range(self.dist, (self.dist*self.loci)+1,self.dist)), subPopNames=subPopNames, infoFields=infoFields)\\n \\n simu = sim.Simulator(pop)\\n print(\\\"The simulation has started\\\")\\n t1 = time.time()\\n\\n\\n mutate_snps=range(0,50)+range(51,101)\\n\\n # define the initialization of each loci based the beta distribution where a and b parameters are allele frequencies from noncoding human regions\\n snps=[0.14, 0.11, 0.17, 0.11, 0.32, 0.33, 0.21, 0.11, 0.11, 0.28, 0.11, 0.12, 0.8, 0.66, 0.74, 0.68, 0.66, 0.77, 0.77, 0.76, 0.77, 0.74, 0.72, 0.11, 0.73, 0.72, 0.72, 0.72, 0.54, 0.17, 0.78, 0.64, 0.78, 0.2, 0.24, 0.25, 0.78, 0.66, 0.2, 0.14, 0.75, 0.16, 0.72, 0.18, 0.77, 0.42, 0.34, 0.7, 0.17, 0.14, 0.2, 0.46, 0.13, 0.26, 0.16, 0.13, 0.14, 0.24, 0.18, 0.36, 0.71, 0.27, 0.28, 0.25, 0.25, 0.3, 0.19, 0.14, 0.16, 0.3, 0.39, 0.16, 0.24, 0.32, 0.11, 0.18, 0.48, 0.31, 0.21, 0.15, 0.34, 0.71, 0.33, 0.18, 0.71, 0.13, 0.23, 0.2, 0.22, 0.23, 0.16, 0.23, 0.23, 0.22, 0.24, 0.82, 0.36, 0.37, 0.72, 0.16, 0.14]\\n self.initFreq=[]\\n\\n \\n for i in range(len(snps)):\\n alpha=float(4*sizePop*migration_rate*snps[i])\\n bhta=float(4*sizePop*migration_rate*(1-snps[i])) \\n p=numpy.random.beta(alpha,bhta)\\n while (p>=0.9 or p<=0.1):\\n p=numpy.random.beta(alpha,bhta)\\n \\n print \\\" SNP {snp} with alpha {alpha}, bhta {bhta} and frequency {p}\\\".format(snp=i, alpha=alpha, bhta=bhta, p=p)\\n self.initFreq.append(p)\\n\\n simu.evolve(\\n \\n initOps=[sim.InitGenotype(freq=[self.initFreq[i], 1-self.initFreq[i]], loci=i) for i in range(len(snps))],\\n \\n\\n # initialize the sex and select the 50 loci (parents)\\n preOps = [sim.InitSex(maleProp=0.5,at=[0]),\\n\\n # initialize the genotype of locus 50 at generation 0 (in the beginning of the simulation)\\n sim.PyOperator(self.genotypeBegin,at=[0]),\\n \\n # Wait 50 generations for the system to reach equilibrium\\n # Then, change the the genotype of locus 50 at generation 50 by inserting a single copy of allele 0 in one individual \\n sim.PyOperator(self.genotypeAfter,at=[50]),\\n\\n # function that carries out the selection proccess\\n sim.MaSelector(loci=50,wildtype=0,fitness=[1+s1, 1+s1/2, 1],begin=50, end=-1,subPops=1)],\\n\\n # recombination\\n matingScheme=sim.RandomMating(ops=[\\n sim.Recombinator(rates=recombination_rate)]),\\n \\n # mutation and migration of offsprings\\n postOps = [\\n\\n \\n sim.SNPMutator(u=mutation_rate,loci=mutate_snps),\\n \\n # call function to calculate Fst and check for equilibrium state\\n sim.PyOperator(self.calcFst,step=step),\\n\\n #migration\\n # Here we define an island model, but this can easily be changed.\\n # For more information about the migration models, please look in the documentation of SimuPOP here http://simupop.sourceforge.net/manual_svn/build/userGuide_ch7_sec3.html\\n sim.Migrator(sim.utils.migrIslandRates(migration_rate,self.numPop)),\\n \\n # call function to save the allele frequencies\\n sim.PyOperator(self.checkAlleles, step=step, param = subPopNames),\\n \\n \\n # check if locus 50 is lost due to genetic drift. If yes, we terminate the simulation\\n sim.Stat(alleleFreq=50,step=step,subPops=1,begin=50,end=-1),\\n sim.TerminateIf('alleleFreq[50][0] == 0',step=step,begin=50,end=-1),\\n \\n # check the progress of the simulation\\n sim.PyEval('\\\"Gen: %d\\\" % gen',step=step),\\n sim.PyOutput('\\\\n',step=step),\\n \\n ],\\n gen=self.Gen\\n \\n )\\n \\n \\n t2 = time.time()\\n print \\\"simulation took\\\", t2-t1, \\\"seconds.\\\"\",\n \"def main():\\n\\n global time\\n global MAX_ITER\\n global output_dt\\n\\n # ==========================================================================\\n # Initialize\\n # - Returns a dictionary of dictionaries, which are the parameters from the\\n # configuration file (by section) and from the command line (listed as\\n # section 'CommandLine')\\n params = init()\\n\\n # ==========================================================================\\n # Evolution loop\\n\\n prev_write = -1\\n\\n for n_iter in xrange(MAX_ITER):\\n\\n # Exceeded maximum time\\n if time >= tmax:\\n break\\n\\n # Unexpected exit? (e.g. like \\\"touch .dump_restart\\\")\\n # TODO\\n\\n # Write output\\n do_write = False\\n if output_dt > 0:\\n curr_write = int(math.floor(time / output_dt))\\n if curr_write > prev_write:\\n do_write = True\\n prev_write = curr_write\\n if do_write: # to allow for a condition based on step number\\n Grid.write_data(n_iter)\\n\\n # Boundary condition fill\\n Grid.fill_boundary_conditions()\\n\\n # Compute step size\\n dt = compute_time_step()\\n\\n # Evolve a single step\\n Hydro.one_step(dt)\\n\\n # Update time\\n time = time + dt\\n\\n # End evolution loop\\n\\n # ==========================================================================\\n # Finalize\\n\\n # Write final output\\n Grid.write_data(n_iter+1)\\n finalize()\",\n \"def test(self, num_decisions=1000):\\n all_grid_states = []\\n all_grid_rewards = []\\n\\n all_grid_states_cluster = []\\n all_surrounding_states_cluster = []\\n\\n all_system_rewards = []\\n all_system_states = []\\n all_system_states_cluster = []\\n\\n all_actions = []\\n all_dissipation = []\\n\\n os.system(\\\"mkdir \\\" + self.folder_name + \\\"Test/\\\")\\n filename = self.folder_name + \\\"Test/\\\" + \\\"TEST.h5\\\"\\n self.system.reset_context(filename)\\n tag = \\\"_test_init\\\"\\n self.system.run_decorrelation(20)\\n\\n grid_dist, surrounding_dist, _, _, _, _ = self.system.get_state_reward()\\n state = self._get_state(grid_dist, surrounding_dist)\\n\\n all_dissipation.append(self.system.get_dissipation())\\n for i in range(num_decisions):\\n action_index = self._get_action(state, episode=10000)\\n tag = \\\"_test_\\\" + str(i)\\n actions = [self.all_actions[i] for i in action_index]\\n all_actions.append(actions)\\n self.system.update_action(actions)\\n system_states, system_rewards, system_states_cluster = self.system.run_step(\\n is_detailed=True, tag=tag)\\n\\n grid_dist, surrounding_dist, grid_reward, surrounding_reward, grid_states_cluster, surrounding_states_cluster = self.system.get_state_reward()\\n\\n # The \\\"grid states\\\" and dissipation are recorded at the end of a decision\\n # Dissipation here is total entropy production (not epr)\\n # Actions are recorded at the beginning of the decision\\n\\n state = self._get_state(grid_dist, surrounding_dist)\\n reward = self._get_reward(grid_reward, surrounding_reward)\\n\\n all_grid_states.append(state)\\n all_grid_rewards.append(reward)\\n\\n all_grid_states_cluster.append(grid_states_cluster)\\n all_surrounding_states_cluster.append(surrounding_states_cluster)\\n\\n all_dissipation.append(self.system.get_dissipation())\\n\\n # The \\\"System States\\\" are recorded every 0.25 seconds. Excludes 0th second\\n all_system_states.append(system_states)\\n # Just to have a 1D array use extend\\n all_system_rewards.append(system_rewards)\\n all_system_states_cluster.append(system_states_cluster)\\n\\n if (i % 100 == 99):\\n np.save(self.folder_name + \\\"grid_rewards\\\",\\n np.array(all_grid_rewards))\\n np.save(self.folder_name + \\\"grid_states\\\",\\n np.array(all_grid_states, dtype=object))\\n np.save(self.folder_name + \\\"grid_states_cluster\\\",\\n np.array(all_grid_states_cluster, dtype=object))\\n\\n np.save(self.folder_name + \\\"surrounding_states_cluster\\\",\\n np.array(all_surrounding_states_cluster))\\n\\n np.save(self.folder_name + \\\"actions\\\", np.array(all_actions))\\n\\n np.save(self.folder_name + \\\"dissipation\\\",\\n np.array(all_dissipation))\\n np.save(self.folder_name + \\\"system_states\\\",\\n np.array(all_system_states))\\n np.save(self.folder_name + \\\"system_states_cluster\\\",\\n np.array(all_system_states_cluster))\\n np.save(self.folder_name + \\\"system_rewards\\\",\\n np.array(all_system_rewards))\",\n \"def main(args, base_dir):\\n for i in range(args.n_training):\\n # value of the next seed\\n seed = args.seed + i\\n\\n # The time when the current experiment started.\\n now = strftime(\\\"%Y-%m-%d-%H:%M:%S\\\")\\n\\n # Create a save directory folder (if it doesn't exist).\\n if args.log_dir is not None:\\n dir_name = args.log_dir\\n else:\\n dir_name = os.path.join(base_dir, '{}/{}'.format(\\n args.env_name, now))\\n ensure_dir(dir_name)\\n\\n # Get the policy class.\\n if args.alg == \\\"TD3\\\":\\n from hbaselines.multiagent.td3 import MultiFeedForwardPolicy\\n elif args.alg == \\\"SAC\\\":\\n from hbaselines.multiagent.sac import MultiFeedForwardPolicy\\n elif args.alg == \\\"PPO\\\":\\n from hbaselines.multiagent.ppo import MultiFeedForwardPolicy\\n elif args.alg == \\\"TRPO\\\":\\n from hbaselines.multiagent.trpo import MultiFeedForwardPolicy\\n else:\\n raise ValueError(\\\"Unknown algorithm: {}\\\".format(args.alg))\\n\\n # Get the hyperparameters.\\n hp = get_hyperparameters(args, MultiFeedForwardPolicy)\\n\\n # add the seed for logging purposes\\n params_with_extra = hp.copy()\\n params_with_extra['seed'] = seed\\n params_with_extra['env_name'] = args.env_name\\n params_with_extra['policy_name'] = \\\"MultiFeedForwardPolicy\\\"\\n params_with_extra['algorithm'] = args.alg\\n params_with_extra['date/time'] = now\\n\\n # Add the hyperparameters to the folder.\\n with open(os.path.join(dir_name, 'hyperparameters.json'), 'w') as f:\\n json.dump(params_with_extra, f, sort_keys=True, indent=4)\\n\\n run_exp(\\n env=args.env_name,\\n policy=MultiFeedForwardPolicy,\\n hp=hp,\\n dir_name=dir_name,\\n evaluate=args.evaluate,\\n seed=seed,\\n eval_interval=args.eval_interval,\\n log_interval=args.log_interval,\\n save_interval=args.save_interval,\\n initial_exploration_steps=args.initial_exploration_steps,\\n ckpt_path=args.ckpt_path,\\n )\",\n \"def BayesPaperStats(maxIters, numRuns):\\n\\n comm = MPI.COMM_WORLD\\n rank = comm.Get_rank()\\n\\n assert comm.Get_size() == numRuns, \\\"Please ensure there is one process running per run i.e \\\" + str(numRuns) + \\\" processes.\\\"\\n \\n problemBounds = {\\\"Bfield\\\": choco.uniform(10, 1300), \\\"T\\\": choco.uniform(50, 230), \\\"Btheta\\\": choco.uniform(0, 90), \\\"Etheta\\\": choco.uniform(0, 90)}\\n\\n # The target for each algorithm. This was determined by using the values in the literature, so there is clearly some deviation either due to the detuning or computation.\\n globalFoM = 1.033\\n\\n if rank == 0:\\n timeList = []\\n iterationList = []\\n\\n # Set up the database for the chocolate optimiser.\\n connection = choco.SQLiteConnection(\\\"sqlite:///bayes_paper_\\\" + str(rank) + \\\"_db.db\\\")\\n\\n # Define which solver will be used.\\n solver = choco.Bayes(connection, problemBounds, utility_function = \\\"ei\\\", n_bootstrap = int(np.ceil(maxIters/10)), clear_db = True)\\n\\n # Clear the database. TODO: To do this?\\n connection.clear()\\n\\n # Start timing.\\n startTime = time.time()\\n timeElapsed = None\\n iterationSuccess = None\\n\\n # Start optimisation.\\n for iteration in range(maxIters):\\n\\n # Make one suggestion.\\n try:\\n token, nextParams = solver.next()\\n except:\\n print(\\\"Error suggesting a new point. Here are the last set of parameters sampled:\\\")\\n print(str(nextParams))\\n print(\\\"Iteration number: \\\" + str(iteration))\\n continue\\n\\n # Check what FoM this gives. Go negative as this is a minimisation routine.\\n fEval = abs(FitnessPaper(**nextParams))\\n\\n # Update best FoM.\\n if fEval >= globalFoM:\\n # The algorithm has managed to surpass or equal the paper value.\\n iterationSuccess = iteration\\n timeElapsed = time.time() - startTime\\n \\n if rank == 0:\\n iterationList.append(iterationSuccess)\\n timeList.append(timeElapsed)\\n\\n break\\n \\n # Tell the optimiser about the result.\\n solver.update(token, fEval)\\n\\n # Run complete. Send results to main process. Tags are unique identifiers.\\n if rank != 0:\\n comm.send(timeElapsed, dest = 0, tag = 1)\\n comm.send(iterationSuccess, dest = 0, tag = 2)\\n\\n # Wait for all the processes to end.\\n comm.Barrier()\\n\\n if rank == 0:\\n # Aggregate the data.\\n for process in range(comm.Get_size() - 1):\\n # Get the data.\\n individualTime = None\\n individualTime = comm.recv(individualTime, source = process + 1, tag = 1)\\n\\n individualIter = None\\n individualIter = comm.recv(individualIter, source = process + 1, tag = 2)\\n\\n if individualIter is not None:\\n # Both values must therefore be non-null.\\n iterationList.append(individualIter)\\n timeList.append(individualTime)\\n\\n avgRuntime = np.average(timeList)\\n avgIters = np.average(iterationList)\\n try:\\n\\n fastestTime = np.min(timeList)\\n\\n except ValueError:\\n \\n # List is empty.\\n fastestTime = float('NaN')\\n\\n numSuccess = len(iterationList)\\n successRate = numSuccess/numRuns\\n\\n print(\\\"Bayesian optimisation paper testing complete! Here are the stats:\\\")\\n print(\\\"Number of successful runs: \\\" + str(numSuccess) + \\\" (Success rate of \\\" + str(successRate) + \\\")\\\")\\n print(\\\"Average iterations required for success: \\\" + str(avgIters))\\n print(\\\"Average time required for success: \\\" + str(avgRuntime))\\n print(\\\"Fastest convergence time: \\\" + str(fastestTime))\\n print(\\\"------------------------------------------------------------------------------------------------------------------\\\")\\n \\n return\",\n \"def eval_calc(self):\\n\\n # Define the GmfGetter\\n\\n #for args_tag in range(len(self.args)-1):\\n # Looping over all source models (Note: the last attribute in self.args is a monitor - so skipping it)\\n \\n from openquake.calculators import getters\\n from openquake.baselib import general\\n from openquake.hazardlib import const, calc, gsim\\n from openquake.commands import dbserver as cdbs\\n if self.vtag >= 12:\\n from openquake.hazardlib.const import StdDev\\n if self.vtag >= 12:\\n from openquake.commonlib import datastore\\n else:\\n from openquake.baselib import datastore\\n\\n cur_getter = getters.GmfGetter(self.args[0][0], calc.filters.SourceFilter(\\n self.dstore['sitecol'], self.dstore['oqparam'].maximum_distance), \\n self.calculator.param['oqparam'], self.calculator.param['amplifier'], \\n self.calculator.param['sec_perils'])\\n\\n # Evaluate each computer\\n print('FetchOpenQuake: Evaluting ground motion models.')\\n for computer in cur_getter.gen_computers(self.mon):\\n # Looping over rupture(s) in the current realization\\n sids = computer.sids\\n #print('eval_calc: site ID sids = ')\\n #print(sids)\\n eids_by_rlz = computer.ebrupture.get_eids_by_rlz(\\n cur_getter.rlzs_by_gsim)\\n mag = computer.ebrupture.rupture.mag\\n im_list = []\\n data = general.AccumDict(accum=[])\\n cur_T = self.event_info['IntensityMeasure'].get('Periods', None)\\n for cur_gs, rlzs in cur_getter.rlzs_by_gsim.items():\\n # Looping over GMPE(s)\\n #print('eval_calc: cur_gs = ')\\n #print(cur_gs)\\n num_events = sum(len(eids_by_rlz[rlz]) for rlz in rlzs)\\n if num_events == 0: # it may happen\\n continue\\n # NB: the trick for performance is to keep the call to\\n # .compute outside of the loop over the realizations;\\n # it is better to have few calls producing big arrays\\n tmpMean = []\\n tmpstdtot = []\\n tmpstdinter = []\\n tmpstdintra = []\\n if self.vtag >= 12:\\n mean_stds_all = computer.cmaker.get_mean_stds([computer.ctx], StdDev.EVENT)[0]\\n for imti, imt in enumerate(computer.imts): \\n # Looping over IM(s)\\n #print('eval_calc: imt = ', imt)\\n if str(imt) in ['PGA', 'PGV', 'PGD']:\\n cur_T = [0.0]\\n im_list.append(str(imt))\\n imTag = 'ln' + str(imt)\\n else:\\n if 'SA' not in im_list:\\n im_list.append('SA')\\n imTag = 'lnSA'\\n if isinstance(cur_gs, gsim.multi.MultiGMPE):\\n gs = cur_gs[str(imt)] # MultiGMPE\\n else:\\n gs = cur_gs # regular GMPE\\n try:\\n if self.vtag >= 12:\\n mean_stds = mean_stds_all[:, imti]\\n num_sids = len(computer.sids)\\n num_stds = len(mean_stds)\\n if num_stds == 1:\\n # no standard deviation is available\\n # for truncation_level = 0 there is only mean, no stds\\n if computer.correlation_model:\\n raise ValueError('truncation_level=0 requires '\\n 'no correlation model')\\n mean = mean_stds[0]\\n stddev_intra = 0\\n stddev_inter = 0\\n stddev_total = 0\\n if imti == 0:\\n tmpMean = mean\\n tmpstdinter = np.concatenate((tmpstdinter, stddev_inter), axis=1)\\n tmpstdintra = np.concatenate((tmpstdintra, stddev_intra), axis=1)\\n tmpstdtot = stddev_total\\n else:\\n tmpMean = np.concatenate((tmpMean, mean), axis=0)\\n tmpstdinter = np.concatenate((tmpstdinter, stddev_inter), axis=1)\\n tmpstdintra = np.concatenate((tmpstdintra, stddev_intra), axis=1)\\n tmpstdtot = np.concatenate((tmpstdtot, stddev_total), axis=0)\\n elif num_stds == 2:\\n # If the GSIM provides only total standard deviation, we need\\n # to compute mean and total standard deviation at the sites\\n # of interest.\\n # In this case, we also assume no correlation model is used.\\n # By default, we evaluate stddev_inter as the stddev_total\\n\\n if self.correlation_model:\\n raise CorrelationButNoInterIntraStdDevs(\\n self.correlation_model, gsim)\\n\\n mean, stddev_total = mean_stds\\n stddev_total = stddev_total.reshape(stddev_total.shape + (1, ))\\n mean = mean.reshape(mean.shape + (1, ))\\n stddev_inter = stddev_total\\n stddev_intra = 0\\n if imti == 0:\\n tmpMean = mean\\n tmpstdinter = np.concatenate((tmpstdinter, stddev_inter), axis=1)\\n tmpstdintra = np.concatenate((tmpstdintra, stddev_intra), axis=1)\\n tmpstdtot = stddev_total\\n else:\\n tmpMean = np.concatenate((tmpMean, mean), axis=0)\\n tmpstdinter = np.concatenate((tmpstdinter, stddev_inter), axis=1)\\n tmpstdintra = np.concatenate((tmpstdintra, stddev_intra), axis=1)\\n tmpstdtot = np.concatenate((tmpstdtot, stddev_total), axis=0)\\n else:\\n mean, stddev_inter, stddev_intra = mean_stds\\n stddev_intra = stddev_intra.reshape(stddev_intra.shape + (1, ))\\n stddev_inter = stddev_inter.reshape(stddev_inter.shape + (1, ))\\n mean = mean.reshape(mean.shape + (1, ))\\n if imti == 0:\\n tmpMean = mean\\n tmpstdinter = stddev_inter\\n tmpstdintra = stddev_intra\\n tmpstdtot = np.sqrt(stddev_inter * stddev_inter + stddev_intra * stddev_intra)\\n else:\\n tmpMean = np.concatenate((tmpMean, mean), axis=1)\\n tmpstdinter = np.concatenate((tmpstdinter, stddev_inter), axis=1)\\n tmpstdintra = np.concatenate((tmpstdintra, stddev_intra), axis=1)\\n tmpstdtot = np.concatenate((tmpstdtot,np.sqrt(stddev_inter * stddev_inter + stddev_intra * stddev_intra)), axis=1)\\n\\n elif self.vtag == 11:\\n # v11\\n dctx = computer.dctx.roundup(\\n cur_gs.minimum_distance)\\n if computer.distribution is None:\\n if computer.correlation_model:\\n raise ValueError('truncation_level=0 requires '\\n 'no correlation model')\\n mean, _stddevs = cur_gs.get_mean_and_stddevs(\\n computer.sctx, computer.rctx, dctx, imt, stddev_types=[])\\n num_sids = len(computer.sids)\\n if cur_gs.DEFINED_FOR_STANDARD_DEVIATION_TYPES == {const.StdDev.TOTAL}:\\n # If the GSIM provides only total standard deviation, we need\\n # to compute mean and total standard deviation at the sites\\n # of interest.\\n # In this case, we also assume no correlation model is used.\\n if computer.correlation_model:\\n raise CorrelationButNoInterIntraStdDevs(\\n computer.correlation_model, cur_gs)\\n\\n mean, [stddev_total] = cur_gs.get_mean_and_stddevs(\\n computer.sctx, computer.rctx, dctx, imt, [const.StdDev.TOTAL])\\n stddev_total = stddev_total.reshape(\\n stddev_total.shape + (1, ))\\n mean = mean.reshape(mean.shape + (1, ))\\n if imti == 0:\\n tmpMean = mean\\n tmpstdtot = stddev_total\\n else:\\n tmpMean = np.concatenate((tmpMean, mean), axis=0)\\n tmpstdtot = np.concatenate((tmpstdtot, stddev_total), axis=0)\\n else:\\n mean, [stddev_inter, stddev_intra] = cur_gs.get_mean_and_stddevs(\\n computer.sctx, computer.rctx, dctx, imt,\\n [const.StdDev.INTER_EVENT, const.StdDev.INTRA_EVENT])\\n stddev_intra = stddev_intra.reshape(\\n stddev_intra.shape + (1, ))\\n stddev_inter = stddev_inter.reshape(\\n stddev_inter.shape + (1, ))\\n mean = mean.reshape(mean.shape + (1, ))\\n\\n if imti == 0:\\n tmpMean = mean\\n tmpstdinter = stddev_inter\\n tmpstdintra = stddev_intra\\n tmpstdtot = np.sqrt(stddev_inter * stddev_inter + stddev_intra * stddev_intra)\\n else:\\n tmpMean = np.concatenate((tmpMean, mean), axis=1)\\n tmpstdinter = np.concatenate((tmpstdinter, stddev_inter), axis=1)\\n tmpstdintra = np.concatenate((tmpstdintra, stddev_intra), axis=1)\\n tmpstdtot = np.concatenate((tmpstdtot,np.sqrt(stddev_inter * stddev_inter + stddev_intra * stddev_intra)), axis=1)\\n\\n else:\\n # v10\\n dctx = computer.dctx.roundup(\\n cur_gs.minimum_distance)\\n if computer.truncation_level == 0:\\n if computer.correlation_model:\\n raise ValueError('truncation_level=0 requires '\\n 'no correlation model')\\n mean, _stddevs = cur_gs.get_mean_and_stddevs(\\n computer.sctx, computer.rctx, dctx, imt, stddev_types=[])\\n num_sids = len(computer.sids)\\n if cur_gs.DEFINED_FOR_STANDARD_DEVIATION_TYPES == {const.StdDev.TOTAL}:\\n # If the GSIM provides only total standard deviation, we need\\n # to compute mean and total standard deviation at the sites\\n # of interest.\\n # In this case, we also assume no correlation model is used.\\n if computer.correlation_model:\\n raise CorrelationButNoInterIntraStdDevs(\\n computer.correlation_model, cur_gs)\\n\\n mean, [stddev_total] = cur_gs.get_mean_and_stddevs(\\n computer.sctx, computer.rctx, dctx, imt, [const.StdDev.TOTAL])\\n stddev_total = stddev_total.reshape(\\n stddev_total.shape + (1, ))\\n mean = mean.reshape(mean.shape + (1, ))\\n if imti == 0:\\n tmpMean = mean\\n tmpstdtot = stddev_total\\n else:\\n tmpMean = np.concatenate((tmpMean, mean), axis=0)\\n tmpstdtot = np.concatenate((tmpstdtot, stddev_total), axis=0)\\n else:\\n mean, [stddev_inter, stddev_intra] = cur_gs.get_mean_and_stddevs(\\n computer.sctx, computer.rctx, dctx, imt,\\n [const.StdDev.INTER_EVENT, const.StdDev.INTRA_EVENT])\\n stddev_intra = stddev_intra.reshape(\\n stddev_intra.shape + (1, ))\\n stddev_inter = stddev_inter.reshape(\\n stddev_inter.shape + (1, ))\\n mean = mean.reshape(mean.shape + (1, ))\\n\\n if imti == 0:\\n tmpMean = mean\\n tmpstdinter = stddev_inter\\n tmpstdintra = stddev_intra\\n tmpstdtot = np.sqrt(stddev_inter * stddev_inter + stddev_intra * stddev_intra)\\n else:\\n tmpMean = np.concatenate((tmpMean, mean), axis=1)\\n tmpstdinter = np.concatenate((tmpstdinter, stddev_inter), axis=1)\\n tmpstdintra = np.concatenate((tmpstdintra, stddev_intra), axis=1)\\n tmpstdtot = np.concatenate((tmpstdtot,np.sqrt(stddev_inter * stddev_inter + stddev_intra * stddev_intra)), axis=1)\\n\\n except Exception as exc:\\n raise RuntimeError(\\n '(%s, %s, source_id=%r) %s: %s' %\\n (gs, imt, computer.source_id.decode('utf8'),\\n exc.__class__.__name__, exc)\\n ).with_traceback(exc.__traceback__)\\n\\n # initialize\\n # NOTE: needs to be extended for gmpe logic tree\\n gm_collector = []\\n # collect data\\n for k in range(tmpMean.shape[0]):\\n imResult = {}\\n if len(tmpMean):\\n imResult.update({'Mean': [float(x) for x in tmpMean[k].tolist()]})\\n if len(tmpstdtot):\\n imResult.update({'TotalStdDev': [float(x) for x in tmpstdtot[k].tolist()]})\\n if len(tmpstdinter):\\n imResult.update({'InterEvStdDev': [float(x) for x in tmpstdinter[k].tolist()]})\\n if len(tmpstdintra):\\n imResult.update({'IntraEvStdDev': [float(x) for x in tmpstdintra[k].tolist()]})\\n gm_collector.append({imTag: imResult})\\n #print(gm_collector)\\n \\n # close datastore instance\\n self.calculator.datastore.close()\\n \\n # stop dbserver\\n if self.vtag >= 11:\\n cdbs.main('stop')\\n else:\\n cdbs.dbserver('stop')\\n \\n # terminate the subprocess\\n if self.prc:\\n self.prc.kill()\\n\\n # copy calc hdf file\\n if self.vtag >= 11:\\n calc_id = datastore.get_last_calc_id()\\n path = os.path.join(datastore.get_datadir(), 'calc_%d.hdf5' % calc_id)\\n else:\\n path = os.path.join(datastore.get_datadir(), 'calc_%d.hdf5' % self.calc_id)\\n\\n if self.dir_info:\\n dir_output = self.dir_info['Output']\\n try:\\n shutil.copy2(path, dir_output)\\n print('FetchOpenQuake: calc hdf file saved.')\\n except:\\n print('FetchOpenQuake: failed to copy calc hdf file.')\\n\\n # Final results\\n res = {'Magnitude': mag,\\n 'Periods': cur_T,\\n 'IM': im_list,\\n 'GroundMotions': gm_collector}\\n \\n # return\\n return res\",\n \"def _optimization_loop(self, iteration=0):\\n self.logger.print_optimization_header()\\n\\n while iteration < self.iterations:\\n try:\\n self._execute_experiment()\\n except RepeatedExperimentError:\\n # G.debug_(F'Skipping repeated Experiment: {_ex!s}\\\\n')\\n if len(self.similar_experiments) + len(self.tested_keys) >= self.search_space_size:\\n G.log_(f\\\"Hyperparameter search space has been exhausted\\\")\\n break\\n self.skipped_iterations += 1\\n continue\\n except StopIteration:\\n if len(self.similar_experiments) + len(self.tested_keys) >= self.search_space_size:\\n G.log_(f\\\"Hyperparameter search space has been exhausted\\\")\\n break\\n # G.debug_(f'Re-initializing hyperparameter grid after testing {len(self.tested_keys)} keys')\\n self._set_hyperparameter_space()\\n continue\\n\\n self.logger.print_result(\\n self.current_hyperparameters_list,\\n self.current_score,\\n experiment_id=self.current_experiment.experiment_id,\\n )\\n\\n if (\\n (self.best_experiment is None) # First evaluation\\n or (self.do_maximize and (self.best_score < self.current_score)) # New best max\\n or (not self.do_maximize and (self.best_score > self.current_score)) # New best min\\n ):\\n self.best_experiment = self.current_experiment.experiment_id\\n self.best_score = self.current_score\\n\\n iteration += 1\",\n \"def main():\\n\\n convergence_rates_e1 = pd.DataFrame(index=['Mean', 'St.D'])\\n convergence_rates_e4 = pd.DataFrame(index=['Mean', 'St.D'])\\n success_rates = pd.DataFrame(index=['1e-1', '1e-2', '1e-3', '1e-4', '1e-5'])\\n peak_ratios = pd.DataFrame(index=['1e-1', '1e-2', '1e-3', '1e-4', '1e-5'])\\n\\n for function in range(1, 21):\\n results = pickle.load(open('results/benchmarking_result_{}.pkl'.format(function), 'rb'))\\n\\n col_name = 'F{}'.format(function)\\n index = function-1\\n\\n convergence_rates_e1.insert(index, col_name, results.ConvergenceRates[0])\\n convergence_rates_e4.insert(index, col_name, results.ConvergenceRates[3])\\n\\n success_rates.insert(index, col_name, results.SuccessRate)\\n peak_ratios.insert(index, col_name, results.PeakRatio)\\n\\n for i in results.SimulationSwarms:\\n\\n x_axis = [k for k, _ in i.items()]\\n y_axis = [v for _, v in i.items()]\\n\\n plt.subplot(4, 5, function) # subplot indexes from 1\\n plt.plot(x_axis, y_axis, 'k-')\\n\\n plt.savefig('results/nmmso_benchmark.png')\\n plt.show()\\n\\n pd.set_option('display.max_columns', None) # make sure all columns are printed below\\n print(convergence_rates_e1)\\n print(convergence_rates_e4)\\n print(success_rates)\\n print(peak_ratios)\\n\\n # Table V from Fieldsend et al.\\n table4 = pd.Series([\\n peak_ratios.stack().median(),\\n peak_ratios.stack().mean(),\\n peak_ratios.stack().std()\\n ])\\n print(table4)\\n\\n # do we want to reproduce Fig. 3\",\n \"def performFEA(params, aquifer, size, timestep, t1endtime):\\n\\n # Initialize parameters\\n Hpdf = params[0]\\n φpdf = params[1]\\n Kpdf = params[2]\\n ctinvpdf = 1/params[3]\\n Qpdf = params[4]\\n cspdf = params[5]\\n\\n # Calculate total number of time steps\\n t1 = round(t1endtime / timestep)\\n timeperiod = timestep * np.linspace(0, 2*t1, 2*t1+1)\\n\\n # Initialize boundary conditions\\n rw = aquifer.rw #0.1\\n rmax = aquifer.rmax #1000\\n mu = aquifer.mu #0.31e-3\\n elems = 25\\n\\n # Construct empty containers\\n pmatrixwell = np.zeros([size, 2*t1+1])\\n Tmatrixwell = np.zeros([size, 2*t1+1])\\n\\n # Run forward model with finite element method\\n for index in range(size):\\n pmatrixwell[index, :], Tmatrixwell[index, :] = main(aquifer=aquifer, degree=2, btype=\\\"spline\\\", elems=elems, rw=rw, rmax=rmax, H=Hpdf[index], mu=mu,\\n φ=φpdf[index], ctinv=ctinvpdf[index], k_int=Kpdf[index], Q=Qpdf[index], timestep=timestep,\\n t1endtime=t1endtime)\\n\\n # save array after each timestep for each run, export matrix from main()\\n # save seperate runs in csv file, use mean from each timestep, plot 95% CI with seaborn\\n # with open('pmatrix.npy', 'wb') as f:\\n # np.save(f, pmatrixwell)\\n\\n # np.savetxt('data.csv', (col1_array, col2_array, col3_array), delimiter=',')\\n\\n return pmatrixwell, Tmatrixwell\",\n \"def run_experiments(size, approach):\\n\\n # Create folder (if needed) where to store query evaluation logs and raw results\\n if not os.path.isdir(f\\\"{path_reports}/eval-query-{current_run}\\\"):\\n shell(f\\\"mkdir -p {path_reports}/eval-query-{current_run}\\\")\\n\\n # Create folder (if needed) where to store store evaluation logs and raw results\\n if not os.path.isdir(f\\\"{path_reports}/eval-store-{current_run}\\\"):\\n shell(f\\\"mkdir -p {path_reports}/eval-store-{current_run}\\\")\\n\\n # Helper function computing the path of a file/folder for the given query evaluation iteration \\n def query_path(iteration, filename=None):\\n folder = size[\\\"id\\\"] + \\\"_\\\" + str(iteration)\\n return path_reports + \\\"/eval-query-\\\" + current_run + \\\"/\\\" + folder + (\\\"/\\\" + filename if filename != None else \\\"\\\") \\n\\n # Helper function computing the path of a file/folder for the given store evaluation test / cpu setting\\n def store_path(test, cpu, filename=None):\\n folder = size[\\\"id\\\"] + \\\"_\\\" + approach[\\\"id\\\"] + \\\"_\\\" + test + (\\\"_\\\" + cpu[\\\"id\\\"] if cpu != None else \\\"\\\")\\n return path_reports + \\\"/eval-store-\\\" + current_run + \\\"/\\\" + folder + (\\\"/\\\" + filename if filename != None else \\\"\\\")\\n \\n # Determine whether partial traces and named graphs are supported\\n partial = approach[\\\"supports_partial\\\"]\\n graphs = approach[\\\"supports_graphs\\\"]\\n \\n # Skip setting if all data is available (check for presence of log files - delete them to repeat test)\\n may_skip = (not sp_enable or not partial or not graphs or os.path.isfile(store_path(\\\"sp\\\", None, \\\"eval.log\\\")))\\n may_skip = may_skip and (not sf_enable or os.path.isfile(store_path(\\\"sf\\\", None, \\\"eval.log\\\")))\\n if query_enable and approach[\\\"id\\\"] == query_approach_id:\\n for i in range(0, query_num_iterations):\\n may_skip = may_skip and os.path.isfile(query_path(i, \\\"eval.log\\\"))\\n for cpu in cpus:\\n may_skip = may_skip and (not pp_enable or not partial or not graphs or os.path.isfile(store_path(\\\"pp\\\", cpu, \\\"eval.log\\\")))\\n may_skip = may_skip and (not pf_enable or os.path.isfile(store_path(\\\"pf\\\", cpu, \\\"eval.log\\\")))\\n if may_skip:\\n return\\n\\n # Delete (if needed) and extract again the repository from its .tar.xz file, so to work on a clean repository (at the end of this test, the repository is no more clean)\\n path_dump = path_data + \\\"/\\\" + size[\\\"id\\\"] + \\\"_\\\" + approach[\\\"id\\\"] + \\\".tar.lz\\\"\\n path_repo = path_repos + \\\"/\\\" + size[\\\"id\\\"] + \\\"_\\\" + approach[\\\"id\\\"]\\n if not os.path.isfile(path_dump):\\n log(f\\\"Missing required file {path_dump}\\\")\\n sys.exit()\\n if os.path.isdir(path_repo):\\n shell(f\\\"rm -Rf {path_repo}\\\")\\n shell(f\\\"{cmd_plzip} -kdc {path_dump} | tar xf - -C {path_repos}\\\")\\n\\n # Locate the repository URL\\n repo_url = f\\\"http://localhost:{server_port}/repositories/promo\\\"\\n \\n # Query test (if enabled)\\n if query_enable and approach[\\\"id\\\"] == query_approach_id:\\n for i in range(0, query_num_iterations):\\n if not os.path.isfile(query_path(i, \\\"eval.log\\\")):\\n shell(f\\\"mkdir -p {query_path(i)}\\\")\\n shell(f\\\"taskset -a {query_taskset} {cmd_graphdb} start {path_repo}\\\")\\n shell(f\\\"taskset -a {query_taskset} {cmd_mokip} eval-query -w -u {repo_url} -q {path_queries} -r {query_path(i)} \\\" +\\n f\\\"| tee {query_path(i, 'eval.log')}\\\")\\n shell(f\\\"taskset -a {query_taskset} {cmd_graphdb} stop {path_repo}\\\")\\n\\n # Sequential Partial test (to assess store times per trace and their components)\\n if sp_enable and partial and graphs and not os.path.isfile(store_path(\\\"sp\\\", None, \\\"eval.log\\\")):\\n shell(f\\\"mkdir -p {store_path('sp', None)}\\\")\\n shell(f\\\"taskset -a {sp_taskset} {cmd_graphdb} start {path_repo}\\\")\\n shell(f\\\"taskset -a {sp_taskset} {cmd_mokip} eval-store -d {path_data}/traces_pp.jsonl.gz \\\" + \\n f\\\"-u {repo_url} -i {approach['inference']} -U REPLACE_GRAPH_PROTOCOL \\\" + \\n f\\\"-o {path_ontology} --trace-namespace '{namespace_trace}' --graph-namespace '{namespace_graph}' \\\" +\\n f\\\"-T {timeout} -r {store_path('sp', None)} -t 1 -w 50 -p -D \\\" + \\n f\\\"| tee {store_path('sp', None, 'eval.log')}\\\")\\n shell(f\\\"taskset -a {sp_taskset} {cmd_graphdb} stop {path_repo}\\\")\\n\\n # Parallel Partial (to assess throughput, varying # of CPU cores)\\n for cpu in cpus:\\n if pp_enable and partial and graphs and not os.path.isfile(store_path(\\\"pp\\\", cpu, \\\"eval.log\\\")):\\n shell(f\\\"mkdir -p {store_path('pp', cpu)}\\\")\\n shell(f\\\"taskset -a {cpu['taskset']} {cmd_graphdb} start {path_repo}\\\")\\n shell(f\\\"taskset -a {cpu['taskset']} {cmd_mokip} eval-store -d {path_data}/traces_pp.jsonl.gz \\\" +\\n f\\\"-u {repo_url} -i {approach['inference']} -U REPLACE_GRAPH_PROTOCOL \\\" + \\n f\\\"-o {path_ontology} --trace-namespace '{namespace_trace}' --graph-namespace '{namespace_graph}' \\\" + \\n f\\\"-T {timeout} -r {store_path('pp', cpu)} -t {max(2, cpu['num_threads'])} -w 50 -p -D \\\" + \\n f\\\"| tee {store_path('pp', cpu, 'eval.log')}\\\")\\n shell(f\\\"taskset -a {cpu['taskset']} {cmd_graphdb} stop {path_repo}\\\")\\n\\n # Sequential Full test (to assess store times per trace and their components)\\n if sf_enable and not os.path.isfile(store_path(\\\"sf\\\", None, \\\"eval.log\\\")):\\n shell(f\\\"mkdir -p {store_path('sf', None)}\\\")\\n shell(f\\\"taskset -a {sf_taskset} {cmd_graphdb} start {path_repo}\\\")\\n shell(f\\\"taskset -a {sf_taskset} {cmd_mokip} eval-store -d {path_data}/traces_sf.jsonl.gz \\\" + \\n f\\\"-u {repo_url} -i {approach['inference']} -U {'APPEND' if graphs else 'APPEND_DEFAULT_GRAPH'} \\\" +\\n f\\\"-o {path_ontology} --trace-namespace '{namespace_trace}' --graph-namespace '{namespace_graph}' \\\" +\\n f\\\"-T {timeout} -r {store_path('sf', None)} -t 1 -w 50 \\\" + \\n f\\\"| tee {store_path('sf', None, 'eval.log')}\\\")\\n shell(f\\\"taskset -a {sf_taskset} {cmd_graphdb} stop {path_repo}\\\")\\n\\n # Parallel Full (to assess throughput where data is also deleted, varying # of CPU cores)\\n for cpu in cpus:\\n if pf_enable and not os.path.isfile(store_path(\\\"pf\\\", cpu, \\\"eval.log\\\")):\\n update = \\\"APPEND\\\" if graphs else \\\"APPEND_DEFAULT_GRAPH\\\"\\n shell(f\\\"mkdir -p {store_path('pf', cpu)}\\\")\\n shell(f\\\"taskset -a {cpu['taskset']} {cmd_graphdb} start {path_repo}\\\")\\n shell(f\\\"taskset -a {cpu['taskset']} {cmd_mokip} eval-store -d {path_data}/traces_pf_{cpu['id']}.jsonl.gz \\\" +\\n f\\\"-u {repo_url} -i {approach['inference']} -U {'APPEND' if graphs else 'APPEND_DEFAULT_GRAPH'} \\\" + \\n f\\\"-o {path_ontology} --trace-namespace '{namespace_trace}' --graph-namespace '{namespace_graph}' \\\" +\\n f\\\"-T {timeout} -r {store_path('pf', cpu)} -t {max(2, cpu['num_threads'])} -w 50 \\\" + \\n f\\\"| tee {store_path('pf', cpu, 'eval.log')}\\\")\\n shell(f\\\"taskset -a {cpu['taskset']} {cmd_graphdb} stop {path_repo}\\\")\\n\\n # Drop the repository (both to save space and since it is not clean anymore)\\n shell(f\\\"rm -Rf {path_repo}\\\")\",\n \"def one_experiment():\\n\\n # set the name of the experiment\\n now = datetime.datetime.now()\\n experiment_id = str(now.day) + \\\"_\\\" + str(now.month) + \\\"_\\\" + str(now.hour) + \\\".\\\" + str(now.minute)\\n experiment_name = 'overfit_' + str(experiment_id)\\n\\n # define if you want to use preprocessed data from file\\n use_prep_data = False\\n if use_prep_data:\\n set_params(preproc_data_id='16_5_10.16.47')\\n\\n # define the changing parameter and its value\\n changing_param_name = 'class_weights'\\n changing_param_value = [{0: 15, 1: 85}]\\n # {0:15, 1:85}]#, {0:4, 1:100}, {0:3, 1:100}, {0:2, 1:100}, {0:1, 1:100}] #[{0:1, 1:1}, {0:15, 1:85}]#\\n\\n features_to_use = ['user', 'countries', 'session', 'format', 'token']\\n # set constant parameters\\n set_params(use_word_emb=1)\\n set_params(epochs=40)\\n set_params(features_to_use=features_to_use)\\n\\n # save constant parameters to a new \\\"experiment_..\\\" filgithx+P@2ub\\n save_constant_parameters(experiment_name, changing_param_name)\\n\\n # run experiment for every parameter value\\n for value in changing_param_value:\\n process = psutil.Process(os.getpid())\\n print(\\\"-----MEMORY before starting experiment ------\\\", int(process.memory_info().rss/(8*10**3)), \\\"KB\\\")\\n\\n # update the parameter value\\n set_params(class_weights_1=value)\\n\\n # update the model_id for this new model\\n now = datetime.datetime.now()\\n new_model_id = str(now.day) + \\\"_\\\" + str(now.month) + \\\"_\\\" + str(now.hour) + \\\".\\\" + str(now.minute) + \\\".\\\" + str(now.second)\\n\\n set_params(model_id=new_model_id)\\n\\n # evaluate the new model and save the results in the experiment file\\n oneExperiment = Process(target=run_experiment, args=(experiment_name,\\n new_model_id, changing_param_name, value,))\\n oneExperiment.start()\\n oneExperiment.join()\",\n \"def test_sales_forecasting_multiprocessing(self):\\n with TemporaryDirectory() as temp_dir:\\n self.run_command(\\n (\\n \\\"python3 main.py --use-synthetic-data --epochs 1\\\"\\n f\\\" --mov-mean-window 0 --log-dir {temp_dir}\\\"\\n \\\" --multiprocessing\\\"\\n ),\\n working_path,\\n [\\n \\\"Begin training loop\\\", \\\"Training:\\\", r\\\"epoch:\\\\s+1\\\",\\n \\\"Validation:\\\", \\\"Best RMSPE|no valid RMSPE results\\\"\\n ]\\n )\",\n \"def run():\\n \\n start_time = time.time()\\n \\n args = parse_args_plotting()\\n config = ConfigParser()\\n config.read(args.config_file)\\n \\n # initialize the OP class object\\n OPs = initialize_plot_options(config)\\n \\n # which plot\\n plot_settings = {}\\n burnin = config.getint('plotting', 'burnin', fallback=0)\\n plot_astr = config.getboolean('plotting', 'Astrometry_orbits_plot', fallback=False)\\n plot_astr_pred = config.getboolean('plotting', 'Astrometric_prediction_plot', fallback=False)\\n plot_rv_full = config.getboolean('plotting', 'RV_orbits_plot', fallback=False)\\n plot_rv = config.getboolean('plotting', 'RV_plot', fallback=False)\\n plot_rel_sep = config.getboolean('plotting', 'Relative_separation_plot', fallback=False)\\n plot_position_angle = config.getboolean('plotting', 'Position_angle_plot', fallback=False)\\n plot_proper_motions = config.getboolean('plotting', 'Proper_motion_plot', fallback=False)\\n plot_corner = config.getboolean('plotting', 'Corner_plot', fallback=False)\\n save_params = config.getboolean('save_results', 'save_params', fallback=True)\\n checkconv = config.getboolean('plotting', 'check_convergence', fallback=False)\\n \\n if checkconv:\\n OPs.plot_chains()\\n if plot_astr:\\n OPs.astrometry()\\n if plot_astr_pred:\\n OPs.astrometric_prediction_plot()\\n if plot_rv_full:\\n OPs.RV_fullorbit()\\n if plot_rv:\\n OPs.RV()\\n if plot_rel_sep:\\n OPs.relsep()\\n if plot_position_angle:\\n OPs.PA()\\n if plot_proper_motions:\\n OPs.proper_motions()\\n if plot_corner:\\n OPs.plot_corner()\\n if save_params:\\n OPs.save_data()\",\n \"def run_and_store(self):\\n # Initialization assumptions\\n z = self.draw_normal_initial()\\n gradient = self.cv_gradient_initial(z)\\n gradient[np.isnan(gradient)] = 0\\n variance = np.power(gradient,2) \\n final_parameters = self.current_parameters()\\n final_samples = 1\\n\\n # Create optimizer\\n if self.optimizer == 'ADAM':\\n self.optim = ADAM(final_parameters, variance, self.learning_rate, 0.9, 0.999)\\n elif self.optimizer == 'RMSProp':\\n self.optim = RMSProp(final_parameters, variance, self.learning_rate, 0.99)\\n\\n # Stored updates\\n stored_means = np.zeros((self.iterations,len(final_parameters)/2))\\n stored_predictive_likelihood = np.zeros(self.iterations)\\n\\n # Record elbo\\n if self.record_elbo is True:\\n elbo_records = np.zeros(self.iterations)\\n else:\\n elbo_records = None\\n\\n for i in range(self.iterations):\\n gradient = self.cv_gradient(self.draw_normal())\\n gradient[np.isnan(gradient)] = 0\\n new_parameters = self.optim.update(gradient)\\n self.change_parameters(new_parameters)\\n\\n stored_means[i] = self.optim.parameters[::2]\\n stored_predictive_likelihood[i] = self.neg_posterior(stored_means[i])\\n\\n if self.printer is True:\\n self.print_progress(i,self.optim.parameters[::2])\\n\\n # Construct final parameters using final 10% of samples\\n if i > self.iterations-round(self.iterations/10):\\n final_samples += 1\\n final_parameters = final_parameters+self.optim.parameters\\n\\n if self.record_elbo is True:\\n elbo_records[i] = self.get_elbo(self.optim.parameters[::2])\\n\\n final_parameters = final_parameters/float(final_samples)\\n self.change_parameters(final_parameters)\\n final_means = np.array([final_parameters[el] for el in range(len(final_parameters)) if el%2==0])\\n final_ses = np.array([final_parameters[el] for el in range(len(final_parameters)) if el%2!=0])\\n\\n if not self.quiet_progress:\\n print(\\\"\\\")\\n print(\\\"Final model ELBO is \\\" + str(-self.neg_posterior(final_means)-self.create_normal_logq(final_means)))\\n return self.q, final_means, final_ses, stored_means, stored_predictive_likelihood, elbo_records\",\n \"def run_and_store(self):\\n # Initialization assumptions\\n z = self.draw_normal_initial()\\n gradient = self.cv_gradient_initial(z)\\n gradient[np.isnan(gradient)] = 0\\n variance = np.power(gradient,2) \\n final_parameters = self.current_parameters()\\n final_samples = 1\\n\\n # Create optimizer\\n if self.optimizer == 'ADAM':\\n self.optim = ADAM(final_parameters, variance, self.learning_rate, 0.9, 0.999)\\n elif self.optimizer == 'RMSProp':\\n self.optim = RMSProp(final_parameters, variance, self.learning_rate, 0.99)\\n\\n # Stored updates\\n stored_means = np.zeros((self.iterations,len(final_parameters)/2))\\n stored_predictive_likelihood = np.zeros(self.iterations)\\n\\n # Record elbo\\n if self.record_elbo is True:\\n elbo_records = np.zeros(self.iterations)\\n else:\\n elbo_records = None\\n\\n for i in range(self.iterations):\\n gradient = self.cv_gradient(self.draw_normal())\\n gradient[np.isnan(gradient)] = 0\\n new_parameters = self.optim.update(gradient)\\n self.change_parameters(new_parameters)\\n\\n stored_means[i] = self.optim.parameters[::2]\\n stored_predictive_likelihood[i] = self.neg_posterior(stored_means[i])\\n\\n if self.printer is True:\\n self.print_progress(i,self.optim.parameters[::2])\\n\\n # Construct final parameters using final 10% of samples\\n if i > self.iterations-round(self.iterations/10):\\n final_samples += 1\\n final_parameters = final_parameters+self.optim.parameters\\n\\n if self.record_elbo is True:\\n elbo_records[i] = self.get_elbo(self.optim.parameters[::2])\\n\\n final_parameters = final_parameters/float(final_samples)\\n self.change_parameters(final_parameters)\\n final_means = np.array([final_parameters[el] for el in range(len(final_parameters)) if el%2==0])\\n final_ses = np.array([final_parameters[el] for el in range(len(final_parameters)) if el%2!=0])\\n\\n if not self.quiet_progress:\\n print(\\\"\\\")\\n print(\\\"Final model ELBO is \\\" + str(-self.full_neg_posterior(final_means)-self.create_normal_logq(final_means)))\\n return self.q, final_means, final_ses, stored_means, stored_predictive_likelihood, elbo_records\",\n \"def main():\\n # Load data and pre-process it\\n path = \\\"data2.csv\\\"\\n features, labels, df = load_data(path)\\n\\n # Learning rates - including our own\\n learning_rates = [0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1, 5, 10, 0.75]\\n\\n # Num iterations\\n num_iterations = [100 for i in range(9)] + [1000]\\n\\n # Keep track of all final weights for different learning rates\\n lines = []\\n\\n # Compute weights for each learning rate\\n for rate, num_iters in zip(learning_rates, num_iterations):\\n\\n # Get weights from gradient descent and add to weights list\\n weights = gradient_descent(features, labels, rate, num_iters)\\n lines.append([rate, num_iters] + weights)\\n\\n # Now write 'lines' to file\\n with open('results2.csv', \\\"w\\\") as out_file:\\n for line in lines:\\n out_file.write(\\\"{}, {}, {}, {}, {} \\\\n\\\".format(line[0], line[1],\\n line[2], line[3], line[4]))\\n out_file.close()\\n\\n # Select which weights to use for plotting\\n index = -1\\n\\n\\n plot_db.visualize_3d(df, lin_reg_weights=lines[index][2:],\\n feat1='norm_x1', feat2='norm_x2', labels='label',\\n xlim=(-1, 1), ylim=(-1, 1), zlim=(0, 3),\\n alpha=learning_rates[index], xlabel='age',\\n ylabel='weight', zlabel='height',\\n title='')\",\n \"def generate_trajectories():\\n\\n setup_timestamp_logging()\\n\\n logger = logging.getLogger()\\n\\n substance = Substance.from_components('C(C(C(C(C(F)(F)Br)(F)F)(F)F)(F)F)(C(C(C(F)(F)F)(F)F)(F)F)(F)F')\\n\\n logger.info('Building system.')\\n\\n build_system = BuildSmirnoffSystem('build_system')\\n build_system.coordinate_file_path = 'coords.pdb'\\n build_system.substance = substance\\n build_system.force_field_path = 'smirnoff99Frosst-1.1.0.offxml'\\n build_system.execute('', None)\\n\\n logger.info('System built.')\\n\\n production_simulation = RunOpenMMSimulation(f'production_simulation')\\n production_simulation.steps_per_iteration = 500\\n production_simulation.output_frequency = 1\\n production_simulation.timestep = 2.0 * unit.femtosecond\\n production_simulation.thermodynamic_state = ThermodynamicState(temperature=298.15*unit.kelvin,\\n pressure=1.0*unit.atmosphere)\\n production_simulation.input_coordinate_file = 'coords.pdb'\\n production_simulation.system_path = 'system.xml'\\n\\n compute_resources = ComputeResources(number_of_threads=4)\\n\\n logger.info(f'Simulation started.')\\n production_simulation_schema = production_simulation.schema\\n production_simulation.execute('', compute_resources)\\n production_simulation.schema = production_simulation_schema\\n logger.info(f'Simulation finished.')\",\n \"def run(params, payoff):\\n\\n # Canvas for drawing.\\n pylab.figure(1, figsize = (12, 6))\\n pylab.ion()\\n pylab.draw()\\n\\n trait_matrix = []\\n\\n # Replay of an experiment.\\n if os.path.isfile(\\\"results.pkl\\\"):\\n fh = open(\\\"results.pkl\\\", \\\"r\\\")\\n pickle.load(fh) \\n population = pickle.load(fh)\\n fh.close()\\n for time_step in range(0, params[\\\"generations\\\"] / \\\\\\n params[\\\"report_freq\\\"] + 1):\\n print(\\\"Generation %d of %d\\\" %(time_step * params[\\\"report_freq\\\"], \\n params[\\\"generations\\\"]))\\n traits = []\\n for p in population:\\n traits.append(p.get_trait_list()[time_step])\\n plot_trait_distribution(params, trait_matrix, traits)\\n plot_trait_histogram(params, traits)\\n \\n # New experiment.\\n else:\\n start = 0\\n \\n # Create a population.\\n population = [Player(i) for i in range(0, params[\\\"population\\\"])]\\n\\n # Create a network.\\n net = network.build_network(population, params[\\\"network_topology\\\"], \\n params[\\\"network_params\\\"])\\n \\n # Seed the random number generator for the experiment.\\n random.seed(params[\\\"seed\\\"])\\n \\n # Assign a trait for each individual in the population. \\n for p in population:\\n p.inherit_trait(params[\\\"init_trait\\\"])\\n p.commit_inheritance()\\n\\n # Create a dynamics module based on network (complete or other) \\n # type and update rule selected. \\n dynamics = dynamics_module(params[\\\"network_topology\\\"], \\n params[\\\"update_rule\\\"])(net, params, payoff)\\n\\n # The dynamics.\\n for time_step in range(0, params[\\\"generations\\\"]):\\n\\n # Pre interaction.\\n dynamics.pre_interaction()\\n\\n # Plot results at report_freq.\\n if time_step % params[\\\"report_freq\\\"] == 0:\\n print(\\\"Generation %d of %d\\\" %(time_step, params[\\\"generations\\\"]))\\n traits = []\\n for p in population:\\n traits.append(p.get_trait())\\n plot_trait_distribution(params, trait_matrix, traits)\\n plot_trait_histogram(params, traits)\\n\\n # Interact.\\n for count in range(0, params[\\\"population\\\"]):\\n dynamics.interact()\\n\\n # Post interaction.\\n dynamics.post_interaction()\\n \\n # Update.\\n for count in range(0, params[\\\"population\\\"]):\\n dynamics.update()\\n\\n # Post update; commit trait inheritance.\\n dynamics.post_update()\\n\\n # Keep the final plot window around until the user shuts it.\\n pylab.show()\\n pylab.close(1)\",\n \"def backtest(self, strategy): \\n \\n # load data\\n data = self.import_data(currency_ids = np.unique(self.signals.currency_id),\\n start_date = self.start_date,\\n end_date = self.end_date)\\n \\n # add cash and columns for currencies\\n data[\\\"capital\\\"] = 0\\n data[\\\"actions\\\"] = \\\"\\\" # should become a dictW \\n data[\\\"description\\\"] = \\\"\\\"\\n data[\\\"capital\\\"].iloc[0] = self.initial_capital\\n data[\\\"worth_usd\\\"] = 0\\n \\n for currency_id in np.unique(self.signals.currency_id):\\n data[currency_id + '_position'] = 0\\n \\n # a sell or buy can influence subsequent positions, so calculate iteratively\\n for observation in range(1, len(data.index)):\\n \\n date = data.index[observation]\\n \\n # investment this period is zero\\n investment_capital_period = 0\\n \\n # amount of currency_ids initially same as last period\\n for currency_id in np.unique(self.signals.currency_id):\\n data[currency_id + '_position'].iloc[observation] = data[currency_id + '_position'].iloc[observation-1] \\n \\n # at each point, compute size of each position (cash and currencies), and record actions\\n if(data.index[observation] in self.signals.index):\\n \\n action_df = pd.DataFrame(columns=list([\\\"Currency\\\",\\\"NominalAmount\\\", \\\"CapitalAmount\\\"]))\\n \\n # could be multiple actions\\n for index, action in self.signals.loc[date].iterrows(): \\n currency_id = action['currency_id']\\n signal = action['signal']\\n \\n # Buy\\n if signal == 1:\\n \\n # buy for 10% currency_id\\n investment_capital = data[\\\"capital\\\"].iloc[observation-1] * 0.10 \\n\\n # estimate how many coins\\n investment_nominal = round(investment_capital / data[currency_id].iloc[observation])\\n \\n # calculate exact capital needed\\n investment_capital_exact = investment_nominal * data[currency_id].iloc[observation]\\n investment_capital_period = investment_capital_period + investment_capital_exact \\n \\n # change the amount of currency hold\\n data[currency_id + '_position'].iloc[observation] = data[currency_id + '_position'].iloc[observation-1] + investment_nominal\\n \\n # report action by appending a Series to the (empty) dataframe\\n action_df = action_df.append(pd.Series({\\\"Currency\\\": currency_id, \\n \\\"NominalAmount\\\": investment_nominal, \\n \\\"CapitalAmount\\\": investment_capital_exact}),ignore_index=True)\\n \\n # report description\\n data[\\\"description\\\"].iloc[observation] = (data[\\\"actions\\\"].iloc[observation] + \\\"\\\\n Buy \\\" + \\n str(investment_nominal) + \\\" \\\" + str(currency_id) + \\n \\\" for \\\" + str(investment_capital_exact))\\n \\n # Sell\\n if signal == -1:\\n \\n # sell currency_id for 10% of total capital\\n investment_capital = data[\\\"capital\\\"].iloc[observation-1] * 0.10 \\n \\n # estimate how many coins\\n investment_nominal = round(investment_capital / data[currency_id].iloc[observation])\\n \\n # calculate exact capital needed\\n investment_capital_exact = investment_nominal * data[currency_id].iloc[observation]\\n investment_capital_period = investment_capital_period - investment_capital_exact\\n \\n # change the amount of currency hold\\n data[currency_id + '_position'].iloc[observation] = data[currency_id + '_position'].iloc[observation-1] - investment_nominal\\n \\n # report action\\n action_df = action_df.append(pd.Series({\\\"Currency\\\": currency_id, \\n \\\"NominalAmount\\\": investment_nominal, \\n \\\"CapitalAmount\\\": investment_capital_exact}),ignore_index=True)\\n \\n # report description\\n data[\\\"description\\\"].iloc[observation] = data[\\\"actions\\\"].iloc[observation] + \\\"Sell \\\" + str(investment_nominal) + \\\" \\\" + str(currency_id) + \\\" for \\\" + str(investment_capital_exact)\\n \\n # report actions\\n data[\\\"actions\\\"].iloc[observation] = action_df.to_json()\\n \\n # calculate resulting cash capital\\n data[\\\"capital\\\"].iloc[observation] = data[\\\"capital\\\"].iloc[observation-1] - investment_capital_period\\n \\n # calculate worth by capital (usd) and each currency * price\\n data[\\\"worth_usd\\\"].iloc[observation] = data[\\\"capital\\\"].iloc[observation] \\n return data\",\n \"def main(**kwargs):\\n flowsheet = Flowsheet(name='MB_Model') \\n \\n # Fix variables\\n setInputs(flowsheet) \\n\\n ts = time.time() \\n\\n mb = flowsheet.MB_fuel\\n \\n # Initialize fuel reactor\\n flowsheet.MB_fuel._initialize(outlvl=1,\\n optarg={\\\"tol\\\" : 1e-8,\\n \\\"max_cpu_time\\\" : 600,\\n \\\"print_level\\\" : 5,\\n \\\"halt_on_ampl_error\\\": 'yes'}) \\n \\n # Create a solver\\n opt = SolverFactory('ipopt')\\n opt.options = {'tol': 1e-8,\\n 'linear_solver' : 'ma27',\\n 'bound_push': 1e-8,\\n 'max_cpu_time': 600,\\n 'print_level': 5}\\n \\n results = opt.solve(flowsheet,tee=True,symbolic_solver_labels=False,\\n keepfiles=False)\\n\\n #flowsheet.MB_fuel.Solid_In_M.fix(691.4)\\n #flowsheet.MB_fuel.Gas_In_y['CO2'].fix(0.03999)\\n #flowsheet.MB_fuel.Gas_In_y['H2O'].fix(0.00001)\\n #flowsheet.MB_fuel.Gas_In_y['CH4'].fix(0.96)\\n\\n\\n\\n #results = opt.solve(flowsheet,tee=True,symbolic_solver_labels=False,\\n # keepfiles=False)\\n \\n \\n print(\\\"\\\\n\\\")\\n print(\\\"----------------------------------------------------------\\\")\\n print('Total simulation time: ', value(time.time() - ts), \\\" s\\\")\\n print(\\\"----------------------------------------------------------\\\")\\n\\n \\n # Print some variables \\n #print_summary_fuel_reactor(flowsheet) \\n\\n # Plot some variables \\n #results_plot_fuel_reactor(flowsheet) \\n\\n m = flowsheet.MB_fuel\\n if 'Solid_M' in kwargs:\\n m.Solid_In_M.fix(kwargs['Solid_M'])\\n if 'Solid_T' in kwargs:\\n m.Solid_In_Ts[t].fix(kwargs['Solid_T'])\\n if 'Solid_x' in kwargs:\\n m.Solid_In_x['Fe2O3'].fix(kwargs['Solid_x']['Fe2O3'])\\n m.Solid_In_x['Fe3O4'].fix(kwargs['Solid_x']['Fe3O4'])\\n m.Solid_In_x['Al2O3'].fix(kwargs['Solid_x']['Al2O3'])\\n if 'Gas_F' in kwargs:\\n m.Gas_In_F.fix(kwargs['Gas_F'])\\n if 'Gas_P' in kwargs:\\n m.Gas_In_P.fix(kwargs['Gas_P'])\\n if 'Gas_T' in kwargs:\\n m.Gas_In_T.fix(kwargs['Gas_T'])\\n if 'Gas_y' in kwargs:\\n m.Gas_In_y['CO2'].fix(kwargs['Gas_y']['CO2'])\\n m.Gas_In_y['H2O'].fix(kwargs['Gas_y']['H2O'])\\n m.Gas_In_y['CH4'].fix(kwargs['Gas_y']['CH4'])\\n\\n results = opt.solve(flowsheet, tee=True)\\n\\n with open('ss_fs.txt','w') as f:\\n flowsheet.display(ostream=f)\\n\\n dt_Gflux_CO2 = []\\n dt_Gflux_H2O = []\\n dt_Gflux_CH4 = []\\n dt_Sflux_Fe2O3 = []\\n dt_Sflux_Fe3O4 = []\\n dt_Sflux_Al2O3 = []\\n dt_Ctrans_CO2 = []\\n dt_Ctrans_H2O = []\\n dt_Ctrans_CH4 = []\\n dt_qtrans_Fe2O3 = []\\n dt_qtrans_Fe3O4 = []\\n dt_qtrans_Al2O3 = []\\n dt_Ghflux = []\\n dt_Ts = []\\n dt_TgGS = []\\n dt_TsGS = []\\n dt_vg = []\\n dt_vs = []\\n\\n# for z in mb.z.get_finite_elements():\\n# if z != mb.z.first() and z != mb.z.last():\\n#\\n# dt_Gflux_CO2.append( (mb.Cg[z,'CO2'].value-mb.Cg[prev,'CO2'].value)/\\\\\\n# (mb.G_flux[z,'CO2'].value-mb.G_flux[prev,'CO2'].value) \\\\\\n# *(z-prev)*mb.eps.value*mb.L.value /(z-prev))\\n#\\n# dt_Gflux_H2O.append( (mb.Cg[z,'H2O'].value-mb.Cg[prev,'H2O'].value)/\\\\\\n# (mb.G_flux[z,'H2O'].value-mb.G_flux[prev,'H2O'].value) \\\\\\n# *(z-prev)*mb.eps.value*mb.L.value /(z-prev))\\n#\\n# dt_Gflux_CH4.append( (mb.Cg[z,'CH4'].value-mb.Cg[prev,'CH4'].value)/\\\\\\n# (mb.G_flux[z,'CH4'].value-mb.G_flux[prev,'CH4'].value) \\\\\\n# *(z-prev)*mb.eps.value*mb.L.value /(z-prev))\\n#\\n# dt_Ctrans_CO2.append( (mb.Cg[z,'CO2'].value-mb.Cg[prev,'CO2'].value)/\\\\\\n# (mb.Ctrans[z,'CO2'].value)* \\\\\\n# #-mv.Ctrans[prev,'CO2'].value)*\\\\\\n# mb.eps.value/(1-mb.eps.value) /(z-prev))\\n#\\n# dt_Ctrans_H2O.append( (mb.Cg[z,'H2O'].value-mb.Cg[prev,'H2O'].value)/\\\\\\n# (mb.Ctrans[z,'H2O'].value)* \\\\\\n# #-mv.Ctrans[prev,'H2O'].value)*\\\\\\n# mb.eps.value/(1-mb.eps.value) /(z-prev))\\n#\\n# dt_Ctrans_CH4.append( (mb.Cg[z,'CH4'].value-mb.Cg[prev,'CH4'].value)/\\\\\\n# (mb.Ctrans[z,'CH4'].value)* \\\\\\n# #-mv.Ctrans[prev,'CH4'].value)*\\\\\\n# mb.eps.value/(1-mb.eps.value) /(z-prev))\\n#\\n# dt_Sflux_Fe2O3.append( (mb.q[z,'Fe2O3'].value-mb.q[prev,'Fe2O3'].value)/\\\\\\n# (mb.S_flux[z,'Fe2O3'].value-mb.S_flux[prev,'Fe2O3'].value)*\\\\\\n# (z-prev)/(1-mb.eps.value)*mb.L.value /(z-prev))\\n#\\n# dt_Sflux_Fe3O4.append( (mb.q[z,'Fe3O4'].value-mb.q[prev,'Fe3O4'].value)/\\\\\\n# (mb.S_flux[z,'Fe3O4'].value-mb.S_flux[prev,'Fe3O4'].value)*\\\\\\n# (z-prev)/(1-mb.eps.value)*mb.L.value /(z-prev))\\n#\\n# dt_Sflux_Al2O3.append( (mb.q[z,'Al2O3'].value-mb.q[prev,'Al2O3'].value)/\\\\\\n# (mb.S_flux[z,'Al2O3'].value-mb.S_flux[prev,'Al2O3'].value)*\\\\\\n# (z-prev)/(1-mb.eps.value)*mb.L.value /(z-prev))\\n#\\n# dt_qtrans_Fe2O3.append( (mb.q[z,'Fe2O3'].value-mb.q[prev,'Fe2O3'].value)/\\\\\\n# (mb.qtrans[z,'Fe2O3'].value )/(z-prev)) \\n# #-mb.qtrans[prev,'Fe2O3'].value) )\\n#\\n# dt_qtrans_Fe3O4.append( (mb.q[z,'Fe3O4'].value-mb.q[prev,'Fe3O4'].value)/\\\\\\n# (mb.qtrans[z,'Fe3O4'].value )/(z-prev)) \\n# #-mb.qtrans[prev,'Fe3O4'].value) )\\n#\\n# dt_qtrans_Al2O3.append( (mb.q[z,'Fe3O4'].value-mb.q[prev,'Fe3O4'].value)/\\\\\\n# (mb.qtrans[z,'Fe3O4'].value )/(z-prev)) \\n# #-mb.qtrans[prev,'Fe3O4'].value) )\\n#\\n# dt_Ghflux.append( (mb.Tg[z].value-mb.Tg[prev].value)/\\\\\\n# (mb.Gh_flux[z].value-mb.Gh_flux[prev].value)* (z-prev)* mb.eps.value*\\\\\\n# mb.L.value* mb.rho_vap[z].value* mb.cp_gas[z].value /(z-prev)) \\n#\\n# dt_Ts.append( (z-prev)*(1-mb.eps.value)*mb.L.value/mb.vs.value /(z-prev))\\n#\\n# dt_TgGS.append( (mb.Tg[z].value - mb.Tg[prev].value)/\\\\\\n# mb.Tg_GS[z].value* mb.eps.value* mb.rho_vap[z].value* mb.cp_gas[z].value \\n# /(z-prev))\\n# \\n# dt_TsGS.append( (mb.Ts[z].value - mb.Ts[prev].value)/\\\\\\n# mb.Tg_GS[z].value* (1-mb.eps.value)* mb.rho_sol.value* mb.cp_sol[z].value*1e-3 \\n# /(z-prev))\\n# \\n# dt_vg.append( mb.L.value*(z-prev)/mb.vg[z].value /(z-prev))\\n# \\n# dt_vs.append( mb.L.value*(z-prev)/mb.vs.value /(z-prev))\\n#\\n# prev = z\\n#\\n# with open('dt.txt','w') as f:\\n# f.write('dt_Gflux_CO2\\\\t')\\n# for t in dt_Gflux_CO2:\\n# f.write('%1.3f'%t +'\\\\t')\\n#\\n# f.write('\\\\ndt_Gflux_H2O\\\\t')\\n# for t in dt_Gflux_H2O:\\n# f.write('%1.3f'%t +'\\\\t')\\n#\\n# f.write('\\\\ndt_Gflux_CH4\\\\t') \\n# for t in dt_Gflux_CH4:\\n# f.write('%1.3f'%t +'\\\\t')\\n#\\n# f.write('\\\\ndt_Sflux_Fe2O3\\\\t') \\n# for t in dt_Sflux_Fe2O3:\\n# f.write('%1.3f'%t +'\\\\t')\\n#\\n# f.write('\\\\ndt_Sflux_Fe3O4\\\\t') \\n# for t in dt_Sflux_Fe3O4:\\n# f.write('%1.3f'%t +'\\\\t')\\n#\\n# f.write('\\\\ndt_Sflux_Al2O3\\\\t') \\n# for t in dt_Sflux_Al2O3:\\n# f.write('%1.3f'%t +'\\\\t')\\n#\\n# f.write('\\\\ndt_Ctrans_CO2\\\\t') \\n# for t in dt_Ctrans_CO2:\\n# f.write('%1.3f'%t +'\\\\t')\\n# \\n# f.write('\\\\ndt_Ctrans_H2O\\\\t') \\n# for t in dt_Ctrans_H2O:\\n# f.write('%1.3f'%t +'\\\\t')\\n#\\n# f.write('\\\\ndt_Ctrans_CH4\\\\t') \\n# for t in dt_Ctrans_CH4:\\n# f.write('%1.3f'%t +'\\\\t')\\n#\\n# f.write('\\\\ndt_qtrans_Fe2O3\\\\t') \\n# for t in dt_qtrans_Fe2O3:\\n# f.write('%1.3f'%t +'\\\\t')\\n#\\n# f.write('\\\\ndt_qtrans_Fe3O4\\\\t') \\n# for t in dt_qtrans_Fe3O4:\\n# f.write('%1.3f'%t +'\\\\t')\\n#\\n# f.write('\\\\ndt_qtrans_Al2O3\\\\t') \\n# for t in dt_qtrans_Al2O3:\\n# f.write('%1.3f'%t +'\\\\t')\\n#\\n# f.write('\\\\ndt_Ghflux\\\\t') \\n# for t in dt_Ghflux:\\n# f.write('%1.3f'%t +'\\\\t')\\n#\\n# f.write('\\\\ndt_Ts\\\\t\\\\t') \\n# for t in dt_Ts:\\n# f.write('%1.3f'%t +'\\\\t')\\n#\\n# f.write('\\\\ndt_TgGS\\\\t\\\\t') \\n# for t in dt_TgGS:\\n# f.write('%1.3f'%t +'\\\\t')\\n#\\n# f.write('\\\\ndt_TsGS\\\\t\\\\t') \\n# for t in dt_TsGS:\\n# f.write('%1.3f'%t +'\\\\t')\\n#\\n# f.write('\\\\ndt_vg\\\\t\\\\t') \\n# for t in dt_vg:\\n# f.write('%1.3f'%t +'\\\\t')\\n#\\n# f.write('\\\\ndt_vs\\\\t\\\\t') \\n# for t in dt_vs:\\n# f.write('%1.3f'%t +'\\\\t')\\n\\n\\n # Store the flowsheet \\n return flowsheet\",\n \"def main():\\n parser = argparse.ArgumentParser(\\n description=\\\"Generate bar plots comparing Matlab solvers to RLSO\\\"\\n )\\n parser.add_argument(\\n \\\"--path\\\", type=str, default=None, help=\\\"the path to saved testing data for RLSO\\\"\\n )\\n parser.add_argument(\\n \\\"--trials\\\",\\n type=int,\\n default=1000,\\n help=\\\"Number of test problems to use defaults to 1000\\\",\\n )\\n parser.add_argument(\\n \\\"--savestring\\\",\\n type=str,\\n default=None,\\n help=\\\"Name to save our plot as. Defaults to random int .png\\\",\\n )\\n parser.add_argument(\\n \\\"--titlestring\\\",\\n type=str,\\n default=None,\\n help=\\\"String to append to our plot title. Defaults to None\\\",\\n )\\n parser.add_argument(\\n \\\"--excludeinter\\\",\\n type=bool,\\n default=True,\\n help=\\\"Exclude interior point method form testing. Reasons Convex only, and cannot set starting point. Default True\\\",\\n )\\n\\n args = parser.parse_args()\\n\\n if args.path is None:\\n print(\\\"Path to pickle data needed!\\\")\\n return\\n\\n pickle_savepath = args.path\\n with open(pickle_savepath, \\\"rb\\\") as pickle_file:\\n pickle_tuple = pickle.load(pickle_file)\\n\\n complex_dic = pickle_tuple[4]\\n lastbest = unravel_c_dic_lastbest(complex_dic)\\n Abx0 = generate_Abx0(complex_dic, lastbest)\\n\\n if args.trials is None:\\n trials = 1000\\n else:\\n trials = args.trials\\n if args.savestring is None:\\n save_string = str(np.random.randint(low=1e15, high=9e15)) + \\\".png\\\"\\n else:\\n save_string = args.savestring\\n\\n start = time.time()\\n print(f\\\"start time {start}\\\")\\n\\n solutions = []\\n for tuples in Abx0[:trials]:\\n solutions.append(quad_solve(tuples, printout=False))\\n end = time.time()\\n print(f\\\"Matlab run time: {end - start}\\\")\\n\\n rlso = []\\n active = []\\n inter = []\\n trust = []\\n for [x, y, z, w] in solutions:\\n rlso.append(x)\\n active.append(y)\\n inter.append(z)\\n trust.append(w)\\n title_string = args.titlestring\\n plot_box(\\n rlso,\\n active,\\n inter,\\n trust,\\n title_string,\\n save_string,\\n exclude_inter=args.excludeinter,\\n )\\n\\n return\",\n \"def run_all_models(self):\\n #self.process_nitrate()\\n try:\\n sur_df = self.store.get('/said/{}/iv'.format(self.site['id']))\\n con_df = self.store.get('/said/{}/qwdata'.format(self.site['id']))\\n\\n except KeyError:\\n print('site {} not found'.format(site['name']))\\n\\n\\n #determine start and end for plots\\n start_date, end_date = get_time_limit(sur_df, con_df)\\n\\n #update start and end according to user\\n user_start = self.site.get('start')\\n user_end = self.site.get('end')\\n\\n if user_start:\\n start_date = pd.to_datetime(user_start)\\n\\n if user_end:\\n end_date = pd.to_datetime(user_end)\\n\\n\\n #plot_ssc(ssc_model, filename='plots/{}_ssc.png'.format(site['name']),\\n # start_date=start_date, end_date=end_date)\\n\\n #append the model results to summary\\n #summary_table= summary_table.append(model_row_summary(ssc_model))\\n\\n for directory in ['model_data','report']:\\n try:\\n os.stat(directory)\\n except:\\n os.mkdir(directory)\\n\\n #pp_model_list = import pdb; pdb.set_trace()[\\n # ['log(PP)',['log(Turb_HACH)']],\\n # ['log(PP)',['log(Turb_YSI)']]\\n #]\\n\\n #self.run_model(pp_model_list, 'PP')\\n\\n no3_model_list = [\\n ['Nitrate',['NitrateSurr']],\\n ]\\n self.run_model(no3_model_list, 'Nitrate')\\n\\n ssc_model_list = [\\n ['log(SSC)',['log(Turb_HACH)']],\\n ['log(SSC)',['log(Turb_YSI)']]\\n ]\\n self.run_model(ssc_model_list, 'SSC')\\n\\n tp_model_list = [\\n ['log(TP)',['log(OrthoP)','log(Turb_HACH)']],\\n ['log(TP)',['log(OrthoP)','log(Turb_YSI)']],\\n ['log(TP)',['log(Turb_HACH)']],\\n ['log(TP)',['log(Turb_YSI)']]\\n ]\\n self.run_model(tp_model_list, 'TP')\\n\\n #write ssc model report\\n #reportfile = 'report/{}_ssc_report.txt'.format(site['name'])\\n #with open(reportfile, 'w') as f:\\n # f.write(ssc_model.get_model_report().as_text())\\n #summary_table= summary_table.append(model_row_summary(p_model1))\\n #summary_table= summary_table.append(model_row_summary(p_model2))\\n #plot_model(ssc_model, filename='plots/{}_ssc_model.png'.format(site['name']))\\n #plot_phos(p_model1, p_model2, filename='plots/{}_tp.png'.format(site['name']),\\n # start_date=start_date, end_date=end_date)\\n #plot_model(p_model1, filename='plots/{}_orthoP_model.png'.format(site['name']))\\n #\\n ## try to plot phosphate\\n #try:\\n # phos_plot(con_data, sur_data, filename='plots/{}_p.png'.format(site['name']), title=site['name'],\\n # return_model=True)\\n #except:\\n # print('phospate plot didnt work')\\n #\\n self.summary_table.to_csv('report/{}_model_summary.csv'.format(self.site['name']),\\n index=False)\",\n \"def main(rand,mu,lamb,cxpb,mutpb,ngen,param):\\n \\n random.seed(rand)\\n NGEN = ngen\\n MU = mu\\n LAMBDA = lamb\\n CXPB = cxpb\\n MUTPB = mutpb\\n \\n # Used for printing the results. It is the parameter that is changed one run from another\\n if param==\\\"rand\\\" or param==\\\"optimal\\\":\\n list_results=[rand]\\n elif param==\\\"mu\\\":\\n list_results=[mu]\\n elif param==\\\"lamb\\\":\\n list_results=[lamb]\\n elif param==\\\"cross\\\":\\n list_results=[cxpb]\\n elif param==\\\"mutate\\\":\\n list_results=[mutpb]\\n elif param==\\\"ngen\\\":\\n list_results=[ngen]\\n elif param==\\\"original\\\":\\n list_results=[0]\\n \\n # Initialization of the objects for the GA\\n pop = toolbox.population(n=MU)\\n hof = tools.ParetoFront()\\n stats = tools.Statistics(lambda ind: ind.fitness.values)\\n stats.register(\\\"avg\\\", np.mean, axis=0)\\n stats.register(\\\"std\\\", np.std, axis=0)\\n stats.register(\\\"min\\\", np.min, axis=0)\\n stats.register(\\\"max\\\", np.max, axis=0)\\n\\n # Run of the GA\\n p,logbook=algorithms.eaMuPlusLambda(pop, toolbox, MU, LAMBDA, CXPB, MUTPB, NGEN, stats,\\n halloffame=hof,verbose=0)\\n with open(results_path+param+'_logbook.csv', 'a',newline='') as f:\\n w = csv.DictWriter(f, logbook[0].keys())\\n w.writeheader()\\n for el in logbook:\\n w.writerow(el)\\n w.writerow({})\\n \\n # Takes the max fitness of the population from all of the runs\\n max_fit=0\\n max_gen=0\\n for elt in logbook:\\n if elt['max'][0]>max_fit:\\n max_fit=elt['max'][0]\\n max_gen=elt['gen']\\n list_results.append(max_fit)\\n list_results.append(max_gen)\\n \\n #TODO\\n# for ind in hof:\\n# dist = numpy.linalg.norm(a-b)\\n\\n print (\\\"{0} {1} {2} {3}\\\".format(round(list_results[1],3),round(list_results[2],3),round(list_results[0],3),hof[0]))\\n current_out_writer.writerow([list_results[0],list_results[1],list_results[2],hof[0]])\\n \\n return pop, stats, hof\",\n \"def calc_performance(self):\\n for symbol in self.portfolio.assets.keys():\\n\\n # Total the Performance of all the trades\\n start = self.portfolio.trades[symbol].index[0]\\n end = self.portfolio.trades[symbol].index[-1]\\n trades = len(self.record[symbol])\\n profit = self.record[symbol]['profit'].sum()\\n loss = self.record[symbol]['loss'].sum()\\n # Total or average the trade info for all the trades\\n try:\\n wins = len(self.record[symbol].groupby('win/loose').groups['w'])\\n except (ValueError, KeyError):\\n wins = 0\\n try:\\n losses = len(self.record[symbol].groupby('win/loose').groups['l'])\\n except (ValueError, KeyError):\\n losses = 0\\n try:\\n washes = len(self.record[symbol].groupby('win/loose').groups['-'])\\n except (ValueError, KeyError):\\n washes = 0\\n max_drawdown = self.record[symbol]['drawdown'].max()\\n average_drawdown = self.record[symbol]['drawdown'].mean()\\n max_drawdown_time = self.record[symbol]['drawdown days'].max()\\n average_drawdown_time = self.record[symbol]['drawdown days'].mean()\\n # Average the risk and market comparisons for all trades\\n vol_risk = self.record[symbol]['volatility'].mean()\\n beta = self.record[symbol]['beta'].mean()\\n lpm_risk = self.record[symbol]['lpm'].mean()\\n e_r = self.record[symbol]['expected_return'].mean()\\n # Calculate Risk measures\\n treynor_ratio = (e_r - self.risk_free_return) / beta\\n sharpe_ratio = (e_r - self.risk_free_return) / vol_risk\\n # Package up the data for each symbol\\n self.performance[symbol] = {\\n 'start': start,\\n 'end': end,\\n 'trades': trades,\\n 'wins': wins,\\n 'losses': losses,\\n 'washes': washes,\\n 'profit': profit,\\n 'loss': loss,\\n 'net_profit': profit - loss,\\n 'profit_factor': profit / loss if loss != 0 else 1.0,\\n 'percent_profitable': wins / trades if trades != 0 else 0.0,\\n 'average_trade_net_profit' : (profit - loss) / trades if trades != 0 else 0.0,\\n 'max_drawdown' : max_drawdown,\\n 'average_drawdown' : average_drawdown,\\n 'max_drawdown_days' : max_drawdown_time,\\n 'average_drawdown_days' : average_drawdown_time,\\n 'volatility_risk' : vol_risk,\\n 'beta' : beta,\\n 'lower_partial_moment_risk' : lpm_risk,\\n 't_r' : treynor_ratio,\\n 's_r' : sharpe_ratio\\n }\\n\\n return self\",\n \"def run_simulation(conf):\\r\\n\\r\\n\\r\\n # Validate cofiguration\\r\\n conf = validate_conf(conf)\\r\\n\\r\\n # Define function to run and storage path\\r\\n newpath = create_path(conf)\\r\\n\\r\\n # Define trajectory and time per state\\r\\n nr_states = 4\\r\\n traj = [list(range(nr_states))]\\r\\n T_list = [[conf.time_per_state for _ in range(nr_states)]]\\r\\n\\r\\n # Set parameters\\r\\n params, gamma, eta, lambda_var = parameters_linear_track_value(nr_states,\\r\\n conf.time_per_state,\\r\\n conf.alpha,\\r\\n conf.A_pre,\\r\\n )\\r\\n\\r\\n params['trajectories'] = traj\\r\\n params = update_params(conf.mode, params, T_list, gamma, eta)\\r\\n\\r\\n # Define the initial weights from CA3 to CA1\\r\\n T_mat = np.array([\\r\\n [0,1,0,0,0],\\r\\n [0,0,1,0,0],\\r\\n [0,0,0,1,0],\\r\\n [0,0,0,0,1],\\r\\n [0,0,0,0,1],\\r\\n ])\\r\\n M_theor = np.linalg.inv(np.eye(nr_states)-gamma*T_mat[:-1, :-1])\\r\\n wini = np.tile(np.expand_dims(np.expand_dims(M_theor,2),3),[1,1,params['N_pre_tot'],params['N_post']])\\r\\n params['wini'] = wini\\r\\n\\r\\n # Define the reward vector\\r\\n reward_vec = np.array([0,0,0,1])\\r\\n params['reward_vec'] = reward_vec\\r\\n\\r\\n # calculate theoretical value\\r\\n v_theor = np.matmul(M_theor, reward_vec)\\r\\n print(f'Theoretical Values: {list(v_theor)}')\\r\\n\\r\\n # Run spiking network\\r\\n params['step'] = params['step']*10\\r\\n reward_neuron_activity = run_value_function(**params)\\r\\n\\r\\n\\r\\n #############################\\r\\n # STORING AND PLOTTING\\r\\n #############################\\r\\n\\r\\n if conf.store_data:\\r\\n pickle.dump(reward_neuron_activity, open(newpath + '/rebuttal_value_function.pkl','wb'))\\r\\n\\r\\n rates = np.mean(np.sum(reward_neuron_activity, axis=2)/conf.time_per_state*1000, axis=1)\\r\\n\\r\\n # fs = 16\\r\\n\\r\\n fig = plt.figure()\\r\\n plt.bar(list(range(1,nr_states+1)), rates)\\r\\n if conf.store_data:\\r\\n fig.savefig(newpath+'/fig_value_function.eps',bbox_inches='tight',format='eps')\\r\\n\\r\\n plt.figure()\\r\\n for i in range(4):\\r\\n x = reward_neuron_activity[i]\\r\\n plt.plot(np.max(x,axis=0)*(i+1), '.', markersize=15)\"\n]","isConversational":false},"negative_scores":{"kind":"list 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\"0.6824583\",\n \"0.65352273\",\n \"0.6505157\",\n \"0.63680285\",\n \"0.6147844\",\n \"0.6038669\",\n \"0.59978664\",\n \"0.59736603\",\n \"0.58891684\",\n \"0.58687943\",\n \"0.58315384\",\n \"0.58308274\",\n \"0.5772297\",\n \"0.5758246\",\n \"0.57433945\",\n \"0.57423234\",\n \"0.5734684\",\n \"0.5721028\",\n \"0.5683199\",\n \"0.5653934\",\n \"0.56521845\",\n \"0.5636789\",\n \"0.5634142\",\n \"0.56034064\",\n \"0.5599968\",\n \"0.55728346\",\n \"0.5571222\",\n \"0.55682874\",\n \"0.55668837\",\n \"0.5553678\",\n \"0.5553275\",\n \"0.55479294\",\n \"0.55475795\",\n \"0.55315703\",\n \"0.5524415\",\n \"0.55127937\",\n \"0.5504244\",\n \"0.5497529\",\n \"0.5492752\",\n \"0.5492098\",\n \"0.5486534\",\n \"0.5478117\",\n \"0.5476068\",\n \"0.54747236\",\n \"0.54706615\",\n \"0.5463475\",\n \"0.5461984\",\n \"0.5455669\",\n \"0.54528004\",\n \"0.5449655\",\n \"0.5449118\",\n \"0.5437157\",\n \"0.54358184\",\n \"0.54335326\",\n \"0.5426754\",\n \"0.5424049\",\n \"0.5420383\",\n \"0.54123443\",\n \"0.54069453\",\n \"0.54027987\",\n \"0.540205\",\n \"0.5398557\",\n \"0.5385989\",\n \"0.53837895\",\n \"0.53837234\",\n \"0.53776866\",\n \"0.53726435\",\n \"0.5368143\",\n \"0.5366066\",\n \"0.53613365\",\n \"0.5343532\",\n \"0.5343143\",\n \"0.53404045\",\n \"0.5338259\",\n \"0.5320671\",\n \"0.5315713\",\n \"0.531247\",\n \"0.53099775\",\n \"0.5302098\",\n \"0.530126\",\n \"0.53011745\",\n \"0.52978784\",\n \"0.5296097\",\n \"0.528767\",\n \"0.5285451\",\n \"0.5285104\",\n \"0.5280817\",\n \"0.52796733\",\n \"0.5278884\",\n \"0.5278884\",\n \"0.5277867\",\n \"0.52738225\",\n \"0.5273309\",\n \"0.5272044\",\n \"0.52702475\",\n \"0.52607316\",\n \"0.5260716\",\n \"0.52601373\",\n \"0.5259046\",\n \"0.52526665\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6787927"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":14371,"cells":{"query":{"kind":"string","value":"initialize your data structure here."},"document":{"kind":"string","value":"def __init__(self):\n self.heap = []\n self.stack = []"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _init_empty(self):\n self._data = []","def __init__(self):\n self.data = []\n self.record = {}","def __init__(self):\n self.structure = {}","def __init__(self):\n self._data = []","def __init__(self):\n self._data = []","def __init__(self):\n self._data = []","def __init__(self):\n self._data = []","def __init__(self):\n self._data = []","def __init__(self):\n self._data = []","def initialize(self):\n\t\tpass","def initialize(self):\n pass","def initialize(self):\n pass","def initialize(self):\n pass","def initialize(self):\n pass","def initialize(self):\n pass","def initialize(self):\r\n pass","def initialize(self):\r\n pass","def __init__(self):\n self.data = []\n self.idx = {}","def initialize(self):\n self.data = None\n self.errors = []","def init(self):\n pass","def init(self):\n pass","def init(self):\n pass","def init(self):\n pass","def init(self):\n pass","def init(self):\n pass","def init(self):\n pass","def init(self):\n pass","def __init__(self):\n self.d = {}\n self.l = []","def __init__(self):\n self.l = {}\n self.s = {}","def __init__(self):\n self.data = []","def __init__(self):\n self.data = []","def __init__(self):\n self._distance_data = []\n self._location_data = []\n self._package_data = []","def init(self) -> None:","def initialize(self):\n self.muondEdx = []\n self.muondNdx = []\n self.muonmomentum = []\n self.piondEdx = []\n self.piondNdx = []\n self.pionmomentum = []\n self.kaondEdx = []\n self.kaondNdx = []\n self.kaonmomentum = []\n self.protdEdx = []\n self.protdNdx = []\n self.protmomentum = []\n self.elecdEdx = []\n self.elecdNdx = []\n self.elecmomentum = []","def __init__(self):\n self._data = [] # non-public underlying Python list as storage","def __init__(self):\n self._dict = {}\n self._array = []","def initialise(self):","def __init__(self):\n\n # initialise the empty mappings dictionary\n self.data = {\n 'loan_id': None,\n 'product': None,\n 'origination_date': None,\n 'reversion_date': None,\n 'rate_term': None,\n 'loan_amount': None,\n 'initial_rate': None,\n 'reversion_rate': None,\n 'term': None,\n 'interest_only_amount': None,\n 'upfront_fees': None,\n 'upfront_costs': None,\n 'entity_eir': None\n }","def initialize(self):","def initialize(self):","def initialize(self):","def initialize(self):","def __init__(self):\n self.d = {}\n self.h = []","def initialize(self):\n\n db = dict()\n\n db['meta'] = Meta(None)\n db['race'] = Race(None, None, None, None, None)\n db['track'] = Track(None, None)\n db['classes'] = set([])\n db['teams'] = set([])\n db['drivers'] = set([])\n\n self.db = db","def __init__(self):\n self._data=[]","def __init__(self):\n self.relation = ''\n self.attributes = []\n self.attribute_types = dict()\n self.attribute_data = dict()\n self.comment = []\n self.data = []\n pass","def __init__(self):\n self.dic={}\n self.data=[]","def initialize(self):\n return","def __init__(self):\n self.keys = []\n self.values = []","def __init__(self, initial_data=[]):\n hdict.__init__(self)\n\n for elt in initial_data:\n self.add(elt)","def _init(self):\n pass","def memb_init(self):\n self.initialize()","def __init__(self):\n self.d = {}","def __init__(self):\n self.d = {}","def __init__(self):\n self.d = {}","def __init__(self):\n self.d = {}","def initialize(self):\n pass # pragma: no cover","def __init__(self):\n\t\tsuper().__init__()\n\t\t\n\t\t# Typically a list of data here\n\t\t# Typically a dict of header keys and values here","def _initialize(self):\n pass","def _initialize(self):\n pass","def _initialize(self):\n pass","def initialize(self) -> None:\n pass","def __init__(self):\n self.table = {}\n self.ls = []","def initialize(self):\n raise NotImplementedError","def initialize(self):\n raise NotImplementedError","def initialize(self):\n raise NotImplementedError","def init(self) -> None:\n ...","def __init__(self):\n self.metadata = {}\n self.geometry = {'array': None, \n 'geom': None, \n 'wkt': None}","def __init__(self):\n self.root = [None, dict(), False] # val, sons, end-able","def init(self):","def init(self):","def __init__(self):\n dict.__init__(self)\n self.datatype = None","def _init_data(self) -> None:\n self.dtype = dict()\n self.shape = dict()\n self.size = dict()\n self.attrs = dict()\n self.data_ptr = dict()\n\n if self.mode == 'r':\n for k in self.fp.keys():\n self.dtype[k] = self.fp[k].dtype\n self.shape[k] = self.fp[k].shape\n self.size[k] = self.fp[k].shape[0]\n self.data_ptr[k] = 0","def __init__(self):\r\n self.indices = {}\r\n self.data = []\r\n self.len = 0","def init(self):\n raise NotImplementedError","def init(self):\n raise NotImplementedError","def __init__(self):\n self.data = {}\n self.refresh()","def __init__(self):\n self._data = PositionalList() # list of _Item instances","def initialize(self): \r\n pass","def __init__(self):\n self.key_dict = {}\n self.value_dict = {}\n self.head, self.last = None, None","def initialize(self):\n self.keys = [None] * BUCKET_SIZE\n self.values = [None] * BUCKET_SIZE","def initialize(self):\r\n self.bucket_array.initialize()","def __init__(self):\n # Dict of minecraft object in form of \"dict[id] = name\"\n self.data_values = dict()\n self.parser = self.setup_parser()","def __init__(self):\n self.table = {}","def initialize(self):\n self.voteskips = []\n self.response = {}\n self.route = {}\n self.userlist = []\n self.poll = []\n self.media = []\n self.init = False\n self.question = None\n self.jumble = None\n self.imgur = None","def __init__(self):\n self._data = set()","def __init__(self, data={}):\n self._update_(data)","def _init(self):\n raise NotImplementedError","def __init__(self):\n self.x = {}\n self.len = 0\n self.annotations = {}","def _init_data(self, data):\n assert type(data) is dict, \"dict expected: %r\" % type(data)\n assert len(data) is 1, \"size of dict should be 1: %r\" % len(data)\n self._name = data.keys()[0]\n self._data = np.asarray(data[self._name])\n self._set = True","def initialise(self):\r\n return","def initialise(self):\r\n return","def __init__(self):\n self._list = []\n self._dict = {}","def __init__(self):\n self.elements = {}","def _initialize_data(self):\n self.reset_count = 0\n self._idn_no_firmware = \"KEPCO,BOP 50-20,E1234,\"\n self._firmware = 2.6\n self._init_data()","def __init__(self):\n self.map = {}\n self.array = []","def __init__(self):\n self._data = []","def __init__(self):\n self._data = []","def __init__(self):\n\n self.nodes = {}","def __init__(self):\n self._data = PositionalList() # list of Item instances","def initialize(self):\n self.initialize_edges()\n self.initialize_prob()\n self.initialize_total_input_dict()\n\n self.initialize_fpmusigv_dict()"],"string":"[\n \"def _init_empty(self):\\n self._data = []\",\n \"def __init__(self):\\n self.data = []\\n self.record = {}\",\n \"def __init__(self):\\n self.structure = {}\",\n \"def __init__(self):\\n self._data = []\",\n \"def __init__(self):\\n self._data = []\",\n \"def __init__(self):\\n self._data = []\",\n \"def __init__(self):\\n self._data = []\",\n \"def __init__(self):\\n self._data = []\",\n \"def __init__(self):\\n self._data = []\",\n \"def initialize(self):\\n\\t\\tpass\",\n \"def initialize(self):\\n pass\",\n \"def initialize(self):\\n pass\",\n \"def initialize(self):\\n pass\",\n \"def initialize(self):\\n pass\",\n \"def initialize(self):\\n pass\",\n \"def initialize(self):\\r\\n pass\",\n \"def initialize(self):\\r\\n pass\",\n \"def __init__(self):\\n self.data = []\\n self.idx = {}\",\n \"def initialize(self):\\n self.data = None\\n self.errors = []\",\n \"def init(self):\\n pass\",\n \"def init(self):\\n pass\",\n \"def init(self):\\n pass\",\n \"def init(self):\\n pass\",\n \"def init(self):\\n pass\",\n \"def init(self):\\n pass\",\n \"def init(self):\\n pass\",\n \"def init(self):\\n pass\",\n \"def __init__(self):\\n self.d = {}\\n self.l = []\",\n \"def __init__(self):\\n self.l = {}\\n self.s = {}\",\n \"def __init__(self):\\n self.data = []\",\n \"def __init__(self):\\n self.data = []\",\n \"def __init__(self):\\n self._distance_data = []\\n self._location_data = []\\n self._package_data = []\",\n \"def init(self) -> None:\",\n \"def initialize(self):\\n self.muondEdx = []\\n self.muondNdx = []\\n self.muonmomentum = []\\n self.piondEdx = []\\n self.piondNdx = []\\n self.pionmomentum = []\\n self.kaondEdx = []\\n self.kaondNdx = []\\n self.kaonmomentum = []\\n self.protdEdx = []\\n self.protdNdx = []\\n self.protmomentum = []\\n self.elecdEdx = []\\n self.elecdNdx = []\\n self.elecmomentum = []\",\n \"def __init__(self):\\n self._data = [] # non-public underlying Python list as storage\",\n \"def __init__(self):\\n self._dict = {}\\n self._array = []\",\n \"def initialise(self):\",\n \"def __init__(self):\\n\\n # initialise the empty mappings dictionary\\n self.data = {\\n 'loan_id': None,\\n 'product': None,\\n 'origination_date': None,\\n 'reversion_date': None,\\n 'rate_term': None,\\n 'loan_amount': None,\\n 'initial_rate': None,\\n 'reversion_rate': None,\\n 'term': None,\\n 'interest_only_amount': None,\\n 'upfront_fees': None,\\n 'upfront_costs': None,\\n 'entity_eir': None\\n }\",\n \"def initialize(self):\",\n \"def initialize(self):\",\n \"def initialize(self):\",\n \"def initialize(self):\",\n \"def __init__(self):\\n self.d = {}\\n self.h = []\",\n \"def initialize(self):\\n\\n db = dict()\\n\\n db['meta'] = Meta(None)\\n db['race'] = Race(None, None, None, None, None)\\n db['track'] = Track(None, None)\\n db['classes'] = set([])\\n db['teams'] = set([])\\n db['drivers'] = set([])\\n\\n self.db = db\",\n \"def __init__(self):\\n self._data=[]\",\n \"def __init__(self):\\n self.relation = ''\\n self.attributes = []\\n self.attribute_types = dict()\\n self.attribute_data = dict()\\n self.comment = []\\n self.data = []\\n pass\",\n \"def __init__(self):\\n self.dic={}\\n self.data=[]\",\n \"def initialize(self):\\n return\",\n \"def __init__(self):\\n self.keys = []\\n self.values = []\",\n \"def __init__(self, initial_data=[]):\\n hdict.__init__(self)\\n\\n for elt in initial_data:\\n self.add(elt)\",\n \"def _init(self):\\n pass\",\n \"def memb_init(self):\\n self.initialize()\",\n \"def __init__(self):\\n self.d = {}\",\n \"def __init__(self):\\n self.d = {}\",\n \"def __init__(self):\\n self.d = {}\",\n \"def __init__(self):\\n self.d = {}\",\n \"def initialize(self):\\n pass # pragma: no cover\",\n \"def __init__(self):\\n\\t\\tsuper().__init__()\\n\\t\\t\\n\\t\\t# Typically a list of data here\\n\\t\\t# Typically a dict of header keys and values here\",\n \"def _initialize(self):\\n pass\",\n \"def _initialize(self):\\n pass\",\n \"def _initialize(self):\\n pass\",\n \"def initialize(self) -> None:\\n pass\",\n \"def __init__(self):\\n self.table = {}\\n self.ls = []\",\n \"def initialize(self):\\n raise NotImplementedError\",\n \"def initialize(self):\\n raise NotImplementedError\",\n \"def initialize(self):\\n raise NotImplementedError\",\n \"def init(self) -> None:\\n ...\",\n \"def __init__(self):\\n self.metadata = {}\\n self.geometry = {'array': None, \\n 'geom': None, \\n 'wkt': None}\",\n \"def __init__(self):\\n self.root = [None, dict(), False] # val, sons, end-able\",\n \"def init(self):\",\n \"def init(self):\",\n \"def __init__(self):\\n dict.__init__(self)\\n self.datatype = None\",\n \"def _init_data(self) -> None:\\n self.dtype = dict()\\n self.shape = dict()\\n self.size = dict()\\n self.attrs = dict()\\n self.data_ptr = dict()\\n\\n if self.mode == 'r':\\n for k in self.fp.keys():\\n self.dtype[k] = self.fp[k].dtype\\n self.shape[k] = self.fp[k].shape\\n self.size[k] = self.fp[k].shape[0]\\n self.data_ptr[k] = 0\",\n \"def __init__(self):\\r\\n self.indices = {}\\r\\n self.data = []\\r\\n self.len = 0\",\n \"def init(self):\\n raise NotImplementedError\",\n \"def init(self):\\n raise NotImplementedError\",\n \"def __init__(self):\\n self.data = {}\\n self.refresh()\",\n \"def __init__(self):\\n self._data = PositionalList() # list of _Item instances\",\n \"def initialize(self): \\r\\n pass\",\n \"def __init__(self):\\n self.key_dict = {}\\n self.value_dict = {}\\n self.head, self.last = None, None\",\n \"def initialize(self):\\n self.keys = [None] * BUCKET_SIZE\\n self.values = [None] * BUCKET_SIZE\",\n \"def initialize(self):\\r\\n self.bucket_array.initialize()\",\n \"def __init__(self):\\n # Dict of minecraft object in form of \\\"dict[id] = name\\\"\\n self.data_values = dict()\\n self.parser = self.setup_parser()\",\n \"def __init__(self):\\n self.table = {}\",\n \"def initialize(self):\\n self.voteskips = []\\n self.response = {}\\n self.route = {}\\n self.userlist = []\\n self.poll = []\\n self.media = []\\n self.init = False\\n self.question = None\\n self.jumble = None\\n self.imgur = None\",\n \"def __init__(self):\\n self._data = set()\",\n \"def __init__(self, data={}):\\n self._update_(data)\",\n \"def _init(self):\\n raise NotImplementedError\",\n \"def __init__(self):\\n self.x = {}\\n self.len = 0\\n self.annotations = {}\",\n \"def _init_data(self, data):\\n assert type(data) is dict, \\\"dict expected: %r\\\" % type(data)\\n assert len(data) is 1, \\\"size of dict should be 1: %r\\\" % len(data)\\n self._name = data.keys()[0]\\n self._data = np.asarray(data[self._name])\\n self._set = True\",\n \"def initialise(self):\\r\\n return\",\n \"def initialise(self):\\r\\n return\",\n \"def __init__(self):\\n self._list = []\\n self._dict = {}\",\n \"def __init__(self):\\n self.elements = {}\",\n \"def _initialize_data(self):\\n self.reset_count = 0\\n self._idn_no_firmware = \\\"KEPCO,BOP 50-20,E1234,\\\"\\n self._firmware = 2.6\\n self._init_data()\",\n \"def __init__(self):\\n self.map = {}\\n self.array = []\",\n \"def __init__(self):\\n self._data = []\",\n \"def __init__(self):\\n self._data = []\",\n \"def __init__(self):\\n\\n self.nodes = {}\",\n \"def __init__(self):\\n self._data = PositionalList() # list of Item instances\",\n \"def initialize(self):\\n self.initialize_edges()\\n self.initialize_prob()\\n self.initialize_total_input_dict()\\n\\n self.initialize_fpmusigv_dict()\"\n]","isConversational":false},"negative_scores":{"kind":"list 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\"0.7761043\",\n \"0.76102185\",\n \"0.7555967\",\n \"0.7549892\",\n \"0.7549892\",\n \"0.7549892\",\n \"0.7549892\",\n \"0.7549892\",\n \"0.7549892\",\n \"0.752797\",\n \"0.7446006\",\n \"0.7446006\",\n \"0.7446006\",\n \"0.7446006\",\n \"0.7446006\",\n \"0.743338\",\n \"0.743338\",\n \"0.7408609\",\n \"0.7385719\",\n \"0.737986\",\n \"0.737986\",\n \"0.737986\",\n \"0.737986\",\n \"0.737986\",\n \"0.737986\",\n \"0.737986\",\n \"0.737986\",\n \"0.736901\",\n \"0.7362467\",\n \"0.7338006\",\n \"0.7338006\",\n \"0.73374635\",\n \"0.7331393\",\n \"0.73284763\",\n \"0.7322671\",\n \"0.73200834\",\n \"0.73099154\",\n \"0.72804767\",\n \"0.72795427\",\n \"0.72795427\",\n \"0.72795427\",\n \"0.72795427\",\n \"0.7271782\",\n \"0.727051\",\n \"0.72517675\",\n \"0.723993\",\n \"0.7239426\",\n \"0.72144645\",\n \"0.72107\",\n \"0.7197864\",\n \"0.71817285\",\n \"0.7179268\",\n \"0.71732044\",\n \"0.71732044\",\n \"0.71732044\",\n \"0.71732044\",\n \"0.71713084\",\n \"0.7168557\",\n \"0.7164963\",\n \"0.7164963\",\n \"0.7164963\",\n \"0.7150857\",\n \"0.7138392\",\n \"0.7127341\",\n \"0.7127341\",\n \"0.7127341\",\n \"0.71187997\",\n \"0.7115647\",\n \"0.71066\",\n \"0.71065164\",\n \"0.71065164\",\n \"0.7088567\",\n \"0.70847344\",\n \"0.70709914\",\n \"0.7046784\",\n \"0.7046784\",\n \"0.7046169\",\n \"0.70175964\",\n \"0.7016285\",\n \"0.70112973\",\n \"0.69934696\",\n \"0.6990926\",\n \"0.6983394\",\n \"0.6975254\",\n \"0.69616264\",\n \"0.69537103\",\n \"0.6949236\",\n \"0.69455796\",\n \"0.6944689\",\n \"0.6937002\",\n \"0.69317675\",\n \"0.69317675\",\n \"0.69307524\",\n \"0.69156027\",\n \"0.6909471\",\n \"0.6908198\",\n \"0.6902257\",\n \"0.6902257\",\n \"0.69008106\",\n \"0.6895019\",\n \"0.687953\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":14372,"cells":{"query":{"kind":"string","value":"Function to allow the new name of the file to contain the number at an arbitrary position or the old name of the file at an arbitrary position in the string of the new file name."},"document":{"kind":"string","value":"def stitch(toName, oldName, num, zfill = -1, mult=1):\n if zfill != -1:\n tempNum = str(num).zfill(zfill)\n if mult > 1:\n for i in range(num+1, num + mult):\n tempNum += ' & ' + str(i).zfill(zfill)\n num = tempNum\n\n if len(toName) > 0:\n\n # We have 5 potential parts in the final string that revolve around two points.\n # The points are are the former string and the position of the number.\n # It is not know which of the two points will come first, or either exist.\n # If neither point exists we simply add the number to the end of the string.\n # Else we divide the string into the following parts:\n #\n # part[0] That which comes before the first point.\n # part[1] The first point.\n # part[2] That which is between the first and second point.\n # part[3] The second point.\n # part[4] That which comes after the second point.\n #\n # posNum and posName are the points.\n # We look for them in toName which is the user's input.\n parts = []\n posNum = toName.find('*')\n posName = toName.find('<')\n\n # Check is the name or the num come first.\n # (Check in which order the points are.)\n if posNum < posName:\n first, second = posNum, posName\n find = 0\n else:\n first, second = posName, posNum\n find = 1\n\n # Zero the unfound so we don't cause issues with the string copying.\n if first == -1:\n first = 0\n if second == -1:\n second = 0\n\n # Only copy what is before the first point if there is something.\n if first != 0:\n parts.append(toName[:first])\n else:\n parts.append('')\n parts.append(toName[first])\n parts.append(toName[first+1:second])\n parts.append(toName[second])\n parts.append(toName[second+1:])\n\n # Add the former string if there is a place for it.\n if posName != -1:\n if find == 0:\n parts[3] = oldName\n else:\n parts[1] = oldName\n\n # Add the number in its place. Otherwise it will be added at the end later.\n if posNum != -1:\n if find == 0:\n parts[1] = num\n else:\n parts[3] = num\n\n string = ''\n\n # Congeal the parts into a usable string.\n for part in parts:\n #if part != ' ':\n string += part\n\n # This will only be true if first and second are 0 (there is no * and <)\n # or one is the first character and the other does not exist.\n # Either way it causes an errant first character (either the first character or repeated symbol) \n if first == second:\n string = string[1:]\n\n # If no number place was given, and we aren't using the old name,\n # then we auto append the number to the end of the name. \n # if posNum == -1 and posName:\n # string += num\n else:\n # If the user didn't enter a name we use the number as the new name.\n string = num\n\n return string"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def fileRename(current_file,num,digits):\n # Key, value pairs of what to replace.\n dictobj = {\n '<num>': get_numbering_format(digits, num),\n '<datetaken>': date_to_string(get_date_taken(current_file),'%Y%m%d__%H_%M'),\n '<dname>': dirname\n }\n # Rename\n new_filename = multi_replace(filename_pattern, dictobj)\n shutil.move(current_file, new_filename)","def create_new_name(self, file_name):\n new_name = file_name\n index = 0\n while new_name in self.used_names:\n new_name = file_name + \"-\" + str(index)\n index += 1\n return new_name","def get_new_filename(old_filename, index, directory):\n snippet = os.path.splitext(os.path.basename(old_filename))[0]\n new_name = '{}_{:03d}.shp'.format(snippet, index)\n new_dir = os.path.join(directory, snippet)\n ensure_directories(new_dir)\n return os.path.join(new_dir, new_name)","def increment_index_in_filename(filename):\n # check for an index i.e. dash then number then dot extension\n regex = re.compile(r\"(.+?)\\-(\\d+)(\\..+)\")\n match = regex.match(filename)\n if match:\n basename = match.groups()[0]\n index = int(match.groups()[1]) + 1\n ext = match.groups()[2]\n else:\n index = 1\n # split filename from ext\n basename, ext = os.path.splitext(filename)\n new_filename = f\"{basename}-{index}{ext}\"\n\n return new_filename","def increment_filename(self, filename, path=\"\", insert=\"\"):\n path = path.strip(\"/\")\n basename, ext = os.path.splitext(filename)\n for i in itertools.count():\n if i:\n insert_i = \"{}{}\".format(insert, i)\n else:\n insert_i = \"\"\n name = \"{basename}{insert}{ext}\".format(\n basename=basename, insert=insert_i, ext=ext\n )\n if not self.exists(\"{}/{}\".format(path, name)):\n break\n return name","def update_destination_file_name (file_name):\n\tglobal COUNTER \n\tCOUNTER += 1\n\tsplitted = file_name.split('/')\n\treturn file_name[:len(file_name)-len(splitted[-1])] + 'Image%05d' % COUNTER +'_'+splitted[-1]","def updateName(g):\n try:\n n = int(g.group(2))\n except TypeError:\n n = 0\n\n return \"%s-%d\" % (g.group(1), n + 1)","def _name_increment_revision(name):\n revre = r\"^(.*?)([0-9]+)$\"\n m = re.search(revre, name)\n if m:\n name = m.group(1) + str(int(m.group(2)) + 1)\n else:\n name = name + \" (copy)\"\n return name","def rename(old, new):","def rename(old, new):","def new_filename(fname=None,ndigits=3):\n if fname is None:\n ext = (\"%%.%ii\" % ndigits) % 1\n fname = \"%s.%s\" % (random_string(6), ext)\n \n if os.path.exists(fname): \n fname = increment_filename(fname,ndigits=ndigits)\n\n return fname","def change_file_name(self, n):\n if type(n) != str or n is None:\n raise TypeError(\"Wrong type! Please pass 'n' as a string!\")\n self.name = n","def _MaybeNewName(self, name):\n if not name:\n return name\n if name == self._old[:-1]:\n return self._module_name\n before, match, after = name.partition(self._old)\n if match and not before and \".\" not in after:\n return self._new + after\n else:\n return name","def new_shp_name(file_path):\n while os.path.exists(file_path):\n try:\n int(file_path[-5])\n except ValueError:\n file_path = file_path[0:-4] + \"1.shp\"\n else:\n number = str(int(file_path[-5]) + 1)\n file_path = file_path[0:-5] + number + \".shp\"\n return file_path","def increment_filename(filename, marker='-'):\n # First we split the filename into three parts:\n #\n # 1) a \"base\" - the part before the counter\n # 2) a \"counter\" - the integer which is incremented\n # 3) an \"extension\" - the file extension\n basename, fileext = os.path.splitext(filename)\n\n # Check if there's a counter in the filename already - if not, start a new\n # counter at 0.\n if marker not in basename:\n base = basename\n value = 0\n\n # If it looks like there might be a counter, then try to coerce it to an\n # integer to get its value. Otherwise, start with a new counter at 0.\n else:\n base, counter = basename.rsplit(marker, 1)\n\n try:\n value = int(counter)\n except ValueError:\n base = basename\n value = 0\n\n # The counter is just an integer, so we can increment it indefinitely.\n while True:\n if value == 0:\n value += 1\n return '%s%s%d%s' % (base, marker, value, fileext)\n value += 1\n break\n return '%s%s%d%s' % (base, marker, value, fileext)","def rename(oldname, newname):","def RenameFile(self, oldname: str, newname: str) -> None:\n ...","def increment_name(name: str) -> str:\n\n match = _number_suffix.search(name)\n if match:\n number_str = match.group(1)\n next_number = int(number_str) + 1\n return f'{name[:-len(number_str)]}{next_number}'\n else:\n return f'{name}-1'","def fname( file_, base=None, new_base=None, new_ext=None ):\n if base and new_base:\n file_ = file_.replace(base, new_base, 1)\n if new_ext:\n file_ = os.path.splitext(file_)[0] + new_ext\n return file_","def rename_file(file_path, equipt_nr):\n work_tuples = parse_columns()\n # Regex used to get differents parts of the file path\n path_regex = re.compile(r'(?P<path>[\\w\\\\:]*)\\\\(?P<filename>[\\w]*).(?P<extension>[\\w].)')\n # Match object containing the different parts of the file path\n match = path_regex.search(file_path)\n\n # Getting the right file to rename\n associated_nr = 0\n for ii in work_tuples:\n if match.group('filename') == ii[0]:\n associated_nr = ii[equipt_nr+1]\n\n # Renaming the file\n os.rename(file_path, match.group('path')+'\\\\'+associated_nr+'.'+match.group('extension'))","def increment_name(base, existing):\r\n if not base in existing:\r\n return base\r\n n = 1\r\n make_name = lambda: base + str(n)\r\n while make_name() in existing:\r\n n += 1\r\n return make_name()","def __return_new_file_name(self, file_name: str, file_path: str):\n\n fastq_runid = re.split('[_.]', file_name) # split on `_` or `.`\n barcode_number = file_path.split(\"/\")[-1] # get the barcode number\n fastq_or_fasta = fastq_runid[-1] # get the .fastq/.fasta file extension\n\n # create the new file name\n new_file_name = \"_\".join(fastq_runid[:3]) # join first three elements\n new_file_name += \"_%s.%s\" % (barcode_number, fastq_or_fasta) # append the barcode number and file extension\n\n return new_file_name","def replace_suffix (name, new_suffix):\n assert isinstance(name, basestring)\n assert isinstance(new_suffix, basestring)\n split = os.path.splitext (name)\n return split [0] + new_suffix","def file_name(self, new_file_name):\n self._file_name = os.path.abspath(new_file_name).replace(\"\\\\\", \"/\")","def re_name(name,new_name):\n\n try:\n os.rename(config_tools.full_dest+name,config_tools.full_dest+new_name)\n except OSError:\n print(f\"Не удалось переименовать {name}\")\n else:\n print(f\"{name} успешно переименновавано в {new_name}\")","def RenameFile(path, org, new):\n\n cont = zip(org, new)\n if not path.endswith((\"/\", \"\\\\\")):\n path = path + \"\\\\\"\n for file in cont:\n if os.path.isfile(path + file[0]):\n os.rename(path + file[0], path + file[1])\n\n return len(org) - len(new)","def playlist_rename_idx(_id, name):\n _id = int(_id) - 1\n playlist_rename(sorted(g.userpl)[_id] + \" \" + name)","def generate_name(file_name, index):\n name = ''\n with open(file_name) as names:\n for i, line in enumerate(names):\n if i == index:\n name += line.split(None, 1)[0]\n return name","def increment_file_number(dir_path, filename, count=1):\n if os.path.exists(os.path.join(dir_path, filename)):\n log.debug('{} already exists in {}'.format(filename, dir_path))\n name, ext = os.path.splitext(filename)\n name += '-{}'.format(count)\n new_filename = name + ext\n log.debug('Generated new name, \"{}\". Checking availability...'.format(new_filename))\n if os.path.exists(os.path.join(dir_path, new_filename)):\n return increment_file_number(dir_path, filename, count + 1)\n log.info('{} identified.'.format(new_filename))\n return new_filename\n log.info('{} identified.'.format(filename))\n return filename","def adjust_event_name(event_name):\n pos=find_first_digit(event_name)\n return event_name[pos:]","def rename_file(f: pathlib.Path) -> str:\n m = mutagen.File(f)\n if m is None: return\n new_name_parts = []\n if \"tracknumber\" in m:\n if \"discnumber\" in m:\n new_name_parts.append(pad_num_str(m[\"discnumber\"][0]) + \".\")\n new_name_parts.append(pad_num_str(m[\"tracknumber\"][0]) + \" - \")\n new_name_parts.append(m[\"title\"][0].replace(\"/\", \"_\"))\n if \"version\" in m:\n new_name_parts.append(\" - \" + \" - \".join(m[\"version\"]).replace(\"/\", \"_\"))\n return \"\".join(new_name_parts)","def naming_convention(file_dir, file_name):\n long_hash = sha1sum(os.path.join(file_dir, file_name))\n file_prefix, file_sufix = file_name.split('.')\n new_name = '{file_prefix}-{short_hash}.{file_sufix}'.format(\n file_prefix=file_prefix,\n short_hash=long_hash[:8],\n file_sufix=file_sufix)\n return new_name, long_hash","def change_filepath_name(filepath: str, new_name: str, new_ext: Optional[str] = None) -> str:\n ext = new_ext or filepath_ext(filepath)\n return str(pathlib.Path(filepath).with_name(new_name).with_suffix(ext))","def rename_file(source, oldname, newname):\n #source = client_variables.output_folder\n renamefiles = os.listdir(source)\n ext = (\".xlsx\", \".csv\", \".pdf\", \".png\")\n for renamefile in renamefiles:\n if renamefile.endswith(ext):\n renamefile = source + \"/\" + renamefile\n print \"renaming:\", renamefile\n newname = source + \"/\" + newname\n print \"newname:\", newname\n os.rename(renamefile, newname)\n elif renamefile.startswith(oldname):\n renamefile = source + \"/\" + renamefile\n print \"renaming:\", renamefile\n newname = source + \"/\" + newname\n print \"newname:\", newname\n os.rename(renamefile, newname)","def increment_filename(inpfile,ndigits=3, delim='.'):\n\n dirname, filename = os.path.split(inpfile)\n base, ext = os.path.splitext(filename)\n if ext == '':\n ext = '.000'\n\n if ext.startswith('.'):\n ext = ext[1:]\n if ndigits < 3:\n ndigits = 3\n form = \"%%.%ii\" % (ndigits)\n\n def _incr(base, ext):\n try: # first, try incrementing the file extension\n ext = form % (int(ext)+1)\n except ValueError:\n try: # try incrementing the part of the base after the last '_'\n bparts = base.split('_')\n bparts[-1] = form % (int(bparts[-1])+1)\n base = '_'.join(bparts)\n except: # last, add a '_001' appendix\n base = \"%s_001\" % base\n return (base,ext)\n\n # increment once\n\n base,ext = _incr(base, ext)\n fout = pathOf(dirname, base, ext, delim=delim)\n\n # then gaurantee that file does not exist,\n # continuing to increment if necessary\n while(os.path.exists(fout)):\n base,ext = _incr(base, ext)\n fout = pathOf(dirname, base, ext, delim=delim)\n return fout","def get_fixed_filename(filename):\n initial_changed_name = filename.replace(\" \", \"_\").replace(\".TXT\", \".txt\")\n previous_character = \"\"\n new_name = \"\"\n for i, character in enumerate(initial_changed_name):\n current_character = character\n if previous_character == \"_\" and current_character.islower():\n new_name += current_character.upper()\n elif previous_character == \"(\" and current_character.islower():\n new_name += current_character.upper()\n elif previous_character.islower() and current_character.isupper():\n new_name += \"_{}\".format(current_character)\n else:\n new_name += current_character\n previous_character = character\n return new_name","def drename(filepath, namelength = 15):\n\tfilepath = os.path.abspath(filepath)\n\tif not os.path.isfile(filepath):\n\t\traise ValueError(\"The path is not a valid file path: \"+str(filepath))\n\t\n\tpath, name = os.path.split(filepath)\n\tname, ext = os.path.splitext(name)\n\tos.rename(filepath, os.path.join(path, dname(namelength)+ext)) #We replace name by the random name and reconstruct the path","def get_oldname(fname):\n assert isinstance(fname, str), f\"fname is not a string, aborting. fname: {fname}\"\n dirname = path.dirname(fname)\n oldbasename = path.basename(fname)\n if \"GA_\" in oldbasename:\n return fname\n elif (\"_Y_\" in oldbasename) or (\"_I_\" in oldbasename):\n split = oldbasename.split(\"_\")\n split[2] = split[2].split(\".\")[0]\n newbasename = \"_\".join(split)\n return path.join(dirname, newbasename)\n elif \"_S_\" in fname:\n return fname\n else:\n raise NotImplementedError(f\"filetype not supported. fname: {fname}\")","def change_file_format(filename, old_format_extension, new_format_extension, append = ''):\r\n filename = unmake_file_format(filename, old_format_extension)\r\n filename += append + new_format_extension\r\n \r\n return(filename)","def get_name(self, old_name):\n if old_name not in self.record:\n self.record[old_name] = [self.PLACEHOLDER]\n suffix = \"\"\n else:\n self.record[old_name].append(self.PLACEHOLDER)\n suffix = f\"{len(self.record[old_name]) - 1}\"\n\n new_name = f\"{old_name}{suffix}\"\n self.topo_order.append(new_name)\n\n return new_name","def base_rename(self, new_name):\n\n new_path = join(dirname(self.fspath), new_name)\n\n return self.rename(new_path)","def generate_file_name(old_file_name: str) -> str:\r\n return old_file_name.split(\".\")[0] + '_features' + '.npy'","def renamefile(filename):\n new_data_list = []\n with open(filename, 'r') as f:\n data_list = f.read().split('\\n')\n\n print('Generating new data list..')\n for data in tqdm(data_list):\n if len(data) == 0:\n continue\n data_info = data.split(' ')\n\n #data_info[0] = data_info[0].replace('jpg', 'png')\n #data_info[1] = data_info[1].replace('jpg', 'png')\n for it, name in enumerate(data_info):\n data_info[it] = '/'.join(name.split('/')[1:])\n if data_info[2].find('extras') == -1:\n new_data_list.append(' '.join(data_info))\n\n with open(filename, 'w') as f:\n print('writing new data names..')\n\n for it, data in tqdm(enumerate(new_data_list)):\n if len(data) == 0:\n continue\n\n if it == len(new_data_list)-1:\n f.write(data)\n else:\n f.write(data+'\\n')\n\n print('Done.')","def ChangeName(self, newName):\n if newName != \"\":\n newPath = self.format + os.sep + \"playlists\" + os.sep + newName + \".txt\"\n os.replace(self.path, newPath)\n self.path = newPath","def change_file_name():\n path = \"/etc/atuned/webserver/\"\n file_list = os.listdir(path)\n file_list.sort(key=lambda fn: os.path.getmtime(path + fn))\n if len(file_list) > 0 and re.match(r'\\S*-\\d{17}\\S*', file_list[-1]) is None:\n old_name = file_list[-1].split(\".\")[0]\n curr_time = time.strftime(\"%Y%m%d%H%M%S\", time.localtime())\n new_name = old_name + \"-\" + str(curr_time) + str(random.randint(100, 999))\n os.rename(path + old_name + \".txt\", path + new_name + \".txt\")","def pad_filename(filenumber, n_of_zeros=5):\n padding = \"0\"\n str_file_n = str(filenumber)\n n = n_of_zeros - len(str_file_n)\n file_idx = n * padding + str_file_n\n return file_idx","def make_frame_name(frame_num):\n return str(frame_num).zfill(3) + '.jpg'","def get_padded_filename(num_pad, idx):\n file_len = len(\"%d\" % (idx))\n filename = \"0\" * (num_pad - file_len) + \"%d\" % (idx)\n return filename","def __rename_file(filename, suffix):\n filename = PDFWorkshop.__clean_filename(filename)\n return PDFWorkshop.__add_filename_suffix(filename, suffix)","def _create_file_name(file_path):\r\n file_base, file_ext = os.path.splitext(file_path)\r\n if os.path.isfile(file_path):\r\n nfile = 1\r\n check = True\r\n while check:\r\n name_add = '0000' + str(nfile)\r\n file_path = file_base + \"_\" + name_add[-4:] + file_ext\r\n if os.path.isfile(file_path):\r\n nfile = nfile + 1\r\n else:\r\n check = False\r\n return file_path","def get_fixed_filename(filename):\n new_name = \"\"\n for i, char in enumerate(filename):\n if i + 1 != len(filename):\n previous_character = filename[i - 1]\n next_character = filename[i + 1]\n if char.islower() and next_character.isupper():\n new_name += char + \"_\"\n elif previous_character == \".\":\n new_name += char\n elif char.islower() and not previous_character.isalpha():\n new_name += char.upper()\n else:\n new_name += char\n else:\n new_name += char\n new_name = new_name.replace(\" \", \"_\").replace(\".TXT\", \".txt\")\n return new_name","def update_name(old_name, new_name, phonebook):\n\n phonebook_data = read_phonebook(phonebook)\n\n if not phonebook_data.get(old_name):\n raise NoEntryError(\"This entry does not exist! \"\n \"(Names are case-sensitive.)\")\n\n else:\n print \"Previous entry:\", old_name, phonebook_data[old_name]\n number = phonebook_data[old_name]\n del phonebook_data[old_name]\n phonebook_data[new_name] = number\n print \"New entry:\", new_name, phonebook_data[new_name]\n save(phonebook_data, phonebook)","def rrename(filepath, namelength = 15):\n\tfilepath = os.path.abspath(filepath)\n\tif not os.path.isfile(filepath):\n\t\traise ValueError(\"The path is not a valid file path: \"+str(filepath))\n\t\n\tpath, name = os.path.split(filepath)\n\tname, ext = os.path.splitext(name)\n\tos.rename(filepath, os.path.join(path, rname(namelength)+ext)) #We replace name by the random name and reconstruct the path","def rename_file(fname):\n x,y = load_file(fname)\n date=y[0].split(\".\")\n if len(y[2])<20:\n title=y[2]\n else:\n title=y[2][0:20]\n title=title.replace(\" \",\"_\")\n \n new_name=\"{}{}{}{}.csv\".format(date[2],date[1],date[0],title)\n new_appendix=rename_appendix(y[10],new_name)\n os.rename(fname,new_name)\n replace_line(new_name,10,'Anhang;\"{}\"'.format(new_appendix))\n return new_name","def backup_int_in_fname(num: int, file_name: str) -> None:\n with open(file_name, 'w') as fname:\n fname.write(str(num))","def file_rename(old_path, image_class):\n\tdirectory, filename, extension = filename_split(old_path)\n\tnew_filename = filename_generate(image_class)\n\tnew_path = os.path.join(directory, new_filename + extension)\n\tos.rename(old_path, new_path)\n\treturn new_path","def on_path(self, new):\n self.name = basename(new)","def change_nm(src,dst):\n\timport os\n\ttry:\n\t\tos.rename(src,dst)\n\texcept:\n\t\tprint \"this is a mistake\"\n\t\treturn -1\n\n\treturn 0","def fileRenameandReplace(filename,newfilename):\n try:\n os.rename(filename,newfilename)\n logging.info(\"Json file renamed in PD path\")\n except Exception as er:\n print (\"Not able to rename the json file \")\n return False","def name(self, new_name):\n self.rename(new_name)","def rename_name_gene(listOfFile, PATH_FASTA_RENAME) :\n\n\tprint \"\\n#################\"\n\tprint \"# Rename protein\"\n\tprint \"#################\\n\"\n\n\tcreate_folder(PATH_FASTA_RENAME)\n\n\tnew_listOfFile=[]\n\n\tfor my_file in listOfFile :\n\t\tif os.stat(my_file).st_size != 0 :\n\t\t\tnew_listOfFile.append(my_file)\n\n\tseq_to_rename = find_rename_fasta(new_listOfFile)\n\tdict_count = dict([(sequence[1:].rstrip(\" \"), 0) for sequence in seq_to_rename])\n\tprogression=1\n\tnumber_of_file = len(new_listOfFile)\n\n\tfor my_file in new_listOfFile :\n\n\t\tfile_name = os.path.basename(my_file)\n\n\t\tsys.stdout.write(\"{:.2f}% : {}/{} files renamed\\r\".format(progression/float(number_of_file)*100, progression,number_of_file))\n\t\tsys.stdout.flush()\n\t\tprogression += 1\n\n\t\thandle = open(os.path.join(PATH_FASTA_RENAME, file_name), 'w')\n\t\tfasta_reading = SeqIO.parse(my_file, \"fasta\")\n\n\t\tfor seq in fasta_reading :\n\t\t\tif seq.id in dict_count :\n\t\t\t\tif dict_count[seq.id] == 0 :\n\t\t\t\t\tdict_count[seq.id] += 1\n\t\t\t\telse :\n\t\t\t\t\tdict_count[seq.id] += 1\n\t\t\t\t\tif \"NC_\" in seq.id :\n\t\t\t\t\t\t# NOTE New name : NC_XXXXXX[_numero de systeme si deux systemes trouvés][_Num(et le nombre de fois nom trouvé)]_nomSysteme_D_nomProteine\n\t\t\t\t\t\tseq.id = \"_\".join(seq.id.split(\"_\")[:2])+\"_Num\"+str(dict_count[seq.id])+\"_\"+\"_\".join(seq.id.split(\"_\")[2:])\n\n\t\t\t\t\telse :\n\t\t\t\t\t\t# NOTE New name : NNNN[_numero de systeme si deux systemes trouvés][_Num(et le nombre de fois nom trouvé)]_nomSysteme_V_nomProteine\n\t\t\t\t\t\tseq.id = seq.id.split(\"_\")[0]+\"_Num\"+str(dict_count[seq.id])+\"_\"+\"_\".join(seq.id.split(\"_\")[1:])\n\t\t\t\t\tseq.name = seq.id\n\t\t\t\t\tseq.description = \"\"\n\n\t\t\tSeqIO.write(seq, handle, \"fasta\")\n\n\t\thandle.close()\n\n\tprint\n\tprint \"Done!\"\n\treturn","def calculting_name():\n\n list_of_files = glob.glob('./muestras/*') # * means all if need specific format then *.csv\n latest_file = max(list_of_files, key=os.path.getctime)\n _, name_file = os.path.split(latest_file)\n name, _ = os.path.splitext(name_file)\n name_number = str(name)\n\n return name_number","def rename(img):\n ext = splitext(img)[1].lower()\n name = get_date(open(img))\n if name is not None:\n name = name + ext\n return copy(img, name)","def update_edge_node_name(node_name, node_number):\n return node_name.replace('block_' + str(node_number), 'block_' + str(node_number+1))","def auto_rename(file_path, new_name):\n \n # Return if no file given\n if not file_path:\n return ''\n else:\n file_path = file_path\n \n # Get the new name\n new_path = change_basename(file_path, new_name)\n \n \n # Changed?\n if new_path != file_path:\n # Try to rename\n try:\n shutil.move(os.path.join(settings.MEDIA_ROOT, file_path), os.path.join(settings.MEDIA_ROOT, new_path))\n except IOError:\n # Error? Restore original name\n new_path = file_path\n \n # Return the new path replacing backslashes (for Windows)\n return new_path","def new_filename(original_filename, new_locale):\r\n f = path(original_filename)\r\n new_file = f.parent.parent.parent / new_locale / f.parent.name / f.name\r\n return new_file.abspath()","def inject_filename_counter(fname, counterval, maxextlen=6):\n cvstr = '%05d' % counterval\n parts = fname.split('.')\n def isext(part):\n return (len(part) <= maxextlen) or (part.startswith('container-'))\n if (len(parts) == 1) or (not isext(parts[-1])):\n return '%s-%s' % (fname, cvstr)\n else:\n return '%s-%s.%s' % ('.'.join(parts[:-1]), cvstr, parts[-1])","def next_version(file_):\n split_file = file_.rsplit(\"_\", 1)\n name_file = split_file[0]\n version = split_file[-1]\n padding = len(version)\n\n if version.isdigit():\n next_version = int(version) + 1\n next_version = str(next_version).zfill(padding)\n\n return concat(name_file, next_version, separator=\"_\")\n else:\n e = concat(file_, \" is incorrect.\")\n raise ValueError(e)","def rotate_file(filename, format=\"%(basename)s.%(number)03i%(extension)s\"):\n\tif os.path.exists(filename):\n\t\tpathlocation, basename, extension = filename_split(filename)\n\t\tfn = lambda n: os.path.join(pathlocation,format%{'basename':basename, 'extension':extension, 'number':n})\n\t\tn = 1\n\t\twhile os.path.exists(fn(n)):\n\t\t\tn += 1\n\t\twhile n > 1:\n\t\t\tos.rename(fn(n-1),fn(n))\n\t\t\tn -= 1\n\t\tos.rename(filename,fn(1))\n\telse:\n\t\traise FileNotFoundError(\"File %s does not exist\"%filename)","def refactor(path: str, files: List):\n skipped = []\n for filename in files:\n try:\n number = get_number_from_name(filename)\n except AttributeError:\n skipped.append(os.path.join(path, filename))\n continue\n new_number = update_number(number)\n\n file_path = os.path.join(path, filename)\n new_file_path = file_path.replace(number, new_number)\n\n with open(file_path, 'r') as file:\n data = file.read()\n data = data.replace(number, new_number)\n with open(file_path, 'w') as file:\n file.write(data)\n\n os.rename(file_path, new_file_path)\n return skipped","def rename(file, format_spec, dir=DIR()):\n\tfile = pathlib.Path(file)\n\t\n\tprint(\"Parsing {name}...\".format(name=file.name))\n\t\n\tarticle = Article(file.read_bytes())\n\t\n\tnew_file = format_spec.format(\n\t\tarticle = article,\n\t\ttitle = dir.getTitle(file) or article.getTitle(),\n\t\tauthor = article.getAuthor() or dir.getAuthor(file),\n\t\tboard = article.getBoard(),\n\t\ttime = article.getTime() or dir.getTime(file) or format_dummy\n\t)\n\tnew_file = safe_file_name(new_file)\n\tnew_file = file.with_name(new_file)\n\t\n\tif file == new_file:\n\t\tprint(\"Same file name!\\n\")\n\t\treturn\n\t\n\tif new_file.exists():\n\t\tnum = 2\n\t\t\n\t\twhile True:\n\t\t\ttemp_file = \"{name} ({num}){ext}\".format(\n\t\t\t\tnum = num,\n\t\t\t\tname = new_file.stem,\n\t\t\t\text = new_file.suffix\n\t\t\t)\n\t\t\ttemp_file = new_file.with_name(temp_file)\n\t\t\t\n\t\t\tif file == temp_file:\n\t\t\t\tprint(\"Same file name!\\n\")\n\t\t\t\treturn\n\t\t\t\t\n\t\t\tif not temp_file.exists():\n\t\t\t\tnew_file = temp_file\n\t\t\t\tbreak\n\t\t\t\t\n\t\t\tnum += 1\n\t\n\tprint(\"Rename to {name}...\\n\".format(name=new_file.name))\n\t\n\tfile.rename(new_file)","def rename_file(self, path, new_name):\n try:\n self.rename_narrative(self._parse_path(path), self.get_userid(), new_name)\n except WorkspaceError as err:\n raise HTTPError(err.http_code, err.message)\n except Exception as err:\n raise HTTPError(\n 500, \"An error occurred while renaming your Narrative: {}\".format(err)\n )","def update_node_name(node_name, bb):\n node_number = int(re.search(r'\\d+', re.search(r'block_\\d+', node_name).group()).group())\n if bb < node_number:\n return node_name.replace('block_' + str(node_number), 'block_' + str(node_number+1))\n return node_name","def filename_formatter(source, destination, file, order_format):\n\n # Attempt loading taglib.\n try:\n song = taglib.File(file)\n except:\n raise CoreError('taglib.File() failed.')\n\n # Begin formatting the filename.\n new_name = destination\n for i in range(0, len(order_format)):\n # If next item is a tag, this block will execute.\n try:\n sub_name = song.tags[order_format[i]][0]\n # Clear filename of any illegal characters.\n for j in range(0, len(MuzikArkive.illegal_name_characters)):\n if MuzikArkive.illegal_name_characters[j] in sub_name:\n sub_name = sub_name.replace(\n MuzikArkive.illegal_name_characters[j], '_'\n )\n new_name += sub_name\n # If next item is a char or string, this block will execute.\n except:\n new_name += order_format[i]\n\n # Add the files extenstion to the new name formatting.\n new_name += '.' + file.rsplit('.', 1)[1]\n\n song.close()\n return new_name","def new_dirname(dirname=None, ndigits=3):\n if dirname is None:\n ext = (\"%%_%ii\" % ndigits) % 1\n dirname = \"%s_%s\" % (random_string(6), ext)\n \n dirname = dirname.replace('.', '_')\n if os.path.exists(dirname):\n dirname = increment_filename(dirname, ndigits=ndigits, delim='_')\n \n\n return dirname","def projectFileRenamed(self, oldfn, newfn):\n editor = self.getOpenEditor(oldfn)\n if editor:\n editor.fileRenamed(newfn)","def getNumberFromName(self, idx):\n\t\tfile = self.all_file_names[idx]\n\t\tnumber = file[4]\n\t\tif file[5].isdigit(): \n\t\t\tnumber += file[5]\n\t\treturn int(number)","def filename(i):\n rand_name = os.path.join(os.getcwd(), \"input-%d.txt\" % i)\n ref_name = os.path.join(os.getcwd(), \"input-%d.ref\" % i)\n return rand_name, ref_name","def rename_files():\n folder_dir = r\"C:\\Users\\keithmoore1.AD\\Desktop\\HAFB\\prankOrig\"\n files = os.listdir(folder_dir)\n save_path = os.getcwd() # current working directory\n for file in files:\n #remove digits from name\n new_file = file.lstrip(\"0123456789\")\n print(file, \" - \", new_file)\n # rename filename\n os.chdir(folder_dir)\n os.rename(file,new_file)\n # get back home\n os.chdir(save_path)","def rename_appendix(appendix,fname):\n listnewname=[]\n string=\"\"\n if appendix!=\"\":\n appendix=appendix.split(\",\")\n fname=fname.split(\".\")[0]\n for i in range(0,len(appendix)):\n new_name=fname+\"_Anhang\"+str(i+1)+\".pdf\"\n os.rename(appendix[i],new_name)\n listnewname.append(new_name)\n for i in range(0,len(listnewname)):\n if i!=0:\n string=string+\",\"+listnewname[i]\n else:\n string=listnewname[i]\n return string","def append_random_number_to_filename(self, local_img_file):\n date = datetime.datetime.now()\n date_string = date.strftime(\"%m-%d-%Y\")\n return \"%s-glitched.%s\" % (local_img_file.split(\".\")[0], local_img_file.split(\".\")[1])","def newname(self, newname) :\n\t\ttry :\n\t\t\tself._newname = newname\n\t\texcept Exception as e:\n\t\t\traise e","def file_num_increment(full_fpath):\r\n while os.path.isfile(full_fpath) == True:\r\n \r\n fpath, fext = os.path.splitext(full_fpath) #['C:\\Users\\Desktop\\file(1)', '.ext']\r\n\r\n if re.findall(\"[(]\\d+[)]\", fpath) != []: #Check if there is (x) in the path.\r\n for counter in range(1000): #Loop 1000 times\r\n if fpath.endswith(f\"({counter})\"): \r\n fpath = replace_last(fpath, f\"({counter})\", f\"({counter+1})\") #Replace the last occurence of (counter) in the string.\r\n full_fpath = fpath + fext\r\n break\r\n else: #here we pass for cases where (counter) is in the file/folder name itself. We skip them.\r\n continue\r\n else: #If there is no (counter), we create (1)\r\n counter = 1\r\n full_fpath = fpath + '(' + str(counter) + ')' + fext\r\n\r\n return full_fpath","def _get_newfilename(oldname):\n # example filename: 20160929_M1_05057144.015_I_CrabNebula-W0.40+215_mergecols.csv\n # removes subrun (bc files are merged to runlevel) and prepends eventfiltered\n dirname = path.dirname(oldname)\n newdirname = path.join(dirname, FILTERDIR)\n oldbasename = path.basename(oldname)\n if \"GA_\" in oldbasename:\n if (\"_Y_\" in oldbasename) or (\"_I_\" in oldbasename):\n newbasename = FILTERPREFIX + oldbasename\n elif (\"_Y_\" in oldbasename) or (\"_I_\" in oldbasename):\n splitname = oldbasename.split(\"_\")\n splitname[2] = splitname[2].split(\".\")[0]\n newbasename = FILTERPREFIX + \"_\".join(splitname)\n newfilename = path.join(newdirname, newbasename)\n return newfilename","def _set_filename(self, filename):\n tmp_file = '_'.join(filename.split())\n# new_file = new_file.replace(\"'\",\n# '_').replace('-',\n# '_').replace(' ',\n# '_').replace('(', '_').replace(')', '_')\n new_file = ''\n pathsep = os.path.sep \n if sys.platform == 'win32':\n pathsep = '/'\n for char in tmp_file:\n if char.isalnum() or char in ['.', '_', ':', pathsep, '-']:\n new_file += char\n try:\n shutil.copy(filename, new_file)\n except shutil.Error, err:\n msg = \"`%s` and `%s` are the same file\" % (filename, new_file)\n if str(err) == msg:\n pass\n else:\n raise err\n utils.ensure_file_exists(new_file)\n self._filename = new_file\n self._basename, self._ext = os.path.splitext(self._filename)","def suffix_replace(original, old, new):\n ...","def setFinalFileName(self, offset=0):\n while True:\n tempFinalFileName = input(\n offset * \" \" + \"Specify a final file name [%s]: \" % self.finalFileName) or self.finalFileName\n # Check 'tempFinalFileName' for bad formatting\n if len(re.findall(r'[^A-Za-z0-9_-]', tempFinalFileName)) == 0:\n # If no illegal characters then change finalFileName\n self.finalFileName = tempFinalFileName\n break\n else:\n print(\"Invalid file name!\")","def newname(self) :\n\t\ttry :\n\t\t\treturn self._newname\n\t\texcept Exception as e:\n\t\t\traise e","def fix_filename(s):\n t = str(s).translate(TRANS_FILE)\n if t.count('.') > 1:\n for i in range(t.count('.') - 1):\n idot = t.find('.')\n t = \"%s_%s\" % (t[:idot], t[idot+1:])\n return t","def rename_sz(sz):\n sz = str(sz)\n sz_set = sz[0]\n num = sz[1]\n return f\"S{sz_set}_{num}\"","def __newFileName(self):\n now = datetime.now()\n dateTimeAppend = now.strftime('%y%m%d_%H%M%S')\n self.__fileName = '{}/{}_{}.wav'.format(RECORDING,\n FILE_NAME_PREFIX, \n dateTimeAppend)","def handle_filename_collision(filename, filenames):\n if filename.lower() in filenames:\n try:\n basename, ext=filename.rsplit('.', 1)\n ext='.'+ext\n except ValueError:\n basename, ext=filename, '' \n\n i=1\n while True:\n filename='%s-%02d%s' % (basename, i, ext)\n if filename.lower() not in filenames:\n break\n i+=1\n \n return filename","def test_6a_change_file_name(self):\n if (not GST.logged_in) or (not GST.data_testing_swift_mounted):\n raise unittest.SkipTest(\"Skipped for failed login or failed mounting container.\")\n elif not GST.rename_file_test_ready:\n raise unittest.SkipTest(\"Skipped for failed to prepare renaming test.\")\n self.dismiss_dialogs()\n function = js_func[\"rename\"] % (GST.gs_file_paths[\"file_to_rename_path\"], GST.gs_file_paths[\"after_rename_path\"])\n try:\n self.send_request(function, \"rename()\")\n except Exception as e:\n raise RenameException(\"Failed to rename the file: \" + e.__str__())\n try:\n response = self.get_response()\n assert \"Success\" in response\n self.refresh_page()\n except AssertionError:\n raise RenameException(\"Failed to rename the file: \" + response)","def renewFile(filename):\n\n\tfileRepo = repertoire + filename + extension # Position du fichier\n\n\t# Ouvre en ecriture et l'ecrase\n\t# La methode with ferme le fichier automatiquement\n\twith open(fileRepo, \"w\") as robFile:\n\t\trobFile.write(filename + \"\\n\") # Ecrit le nom du fichier au debut","def name(self, new_name: str) -> None:\n raise NotImplementedError()","def renameSample(old_name, new_name):\n def switch(val):\n return new_name.join(val.split(old_name))\n\n with Repo.loadRepo() as repo:\n sample = repo.db.sampleTable.get(old_name)\n print(f'{old_name} -> {new_name}', file=sys.stderr)\n sample.rename(new_name)\n old_file_paths = []\n for result in sample.results():\n new_result_name = switch(result.name)\n result.rename(new_result_name)\n for _, filerec in result.files():\n new_file_name = switch(filerec.name)\n filerec.rename(new_file_name)\n old_file_paths.append(filerec.filepath())\n new_file_path = switch(filerec.filepath())\n filerec.copy(new_file_path)\n filerec.save(modify=True)\n result.save(modify=True)\n sample.save(modify=True)\n\n for old_path in old_file_paths:\n print(old_path)","def find_new_name(self, std, name):\n all_names = [case.read_name() for case in std.get_all(aster_s.Case)]\n new_name = name\n for idx in xrange(100):\n if new_name not in all_names:\n return new_name\n new_name = name + str(idx)\n else:\n mod.launch(ERROR, \"Too many wizards '%s' in use\" % name)","def rename_file (self):\n\t\tassert self.__filename, \"Renaming could not complete because the new filename could not be determined, one or more needed arguments is empty!\"\n\t\tos.rename( self._file.path, self.__filename )\n\t\t\n\t\tif self.verbose and self.log :\tself.log.info( 'File renamed from %s to %s' % (self._file.path, self.__filename))","def rotate_filenames_and_rename(filename, num_files):\n filename_max = '%s.%d' % (filename, num_files)\n if os.path.exists(filename_max):\n os.remove(filename_max)\n for i in range(num_files, 0, -1):\n old_filename = '%s.%d' % (filename, i)\n new_filename = '%s.%d' % (filename, i+1)\n if os.path.exists(old_filename):\n os.rename(old_filename, new_filename)\n os.rename(filename, '%s.1' % (filename))","def get_fixed_filename(filename):\n new_name = filename.replace(\" \", \"_\").replace(\".TXT\", \".txt\")\n new_filename = \"\"\n check_symbol = [\"(\", \"_\"] # A list of symbols where uppercase must follow\n\n for count, character in enumerate(new_name):\n current_character = character\n substitute_character = current_character\n if count > 0:\n if previous_character in check_symbol:\n substitute_character = current_character.upper()\n elif current_character.isupper() and previous_character.islower(): # for example: sW\n substitute_character = \"_\" + current_character\n previous_character = current_character\n new_filename = new_filename + substitute_character\n return new_filename","def supplement_file_name(file: Union[str, pathlib.Path], sup: str) -> pathlib.Path:\n\n file = pathlib.Path(file)\n\n # the `suffix` is incorporated into the file name\n return file.with_name(file.stem + f'_{sup}' + file.suffix)"],"string":"[\n \"def fileRename(current_file,num,digits):\\n # Key, value pairs of what to replace.\\n dictobj = {\\n '<num>': get_numbering_format(digits, num),\\n '<datetaken>': date_to_string(get_date_taken(current_file),'%Y%m%d__%H_%M'),\\n '<dname>': dirname\\n }\\n # Rename\\n new_filename = multi_replace(filename_pattern, dictobj)\\n shutil.move(current_file, new_filename)\",\n \"def create_new_name(self, file_name):\\n new_name = file_name\\n index = 0\\n while new_name in self.used_names:\\n new_name = file_name + \\\"-\\\" + str(index)\\n index += 1\\n return new_name\",\n \"def get_new_filename(old_filename, index, directory):\\n snippet = os.path.splitext(os.path.basename(old_filename))[0]\\n new_name = '{}_{:03d}.shp'.format(snippet, index)\\n new_dir = os.path.join(directory, snippet)\\n ensure_directories(new_dir)\\n return os.path.join(new_dir, new_name)\",\n \"def increment_index_in_filename(filename):\\n # check for an index i.e. dash then number then dot extension\\n regex = re.compile(r\\\"(.+?)\\\\-(\\\\d+)(\\\\..+)\\\")\\n match = regex.match(filename)\\n if match:\\n basename = match.groups()[0]\\n index = int(match.groups()[1]) + 1\\n ext = match.groups()[2]\\n else:\\n index = 1\\n # split filename from ext\\n basename, ext = os.path.splitext(filename)\\n new_filename = f\\\"{basename}-{index}{ext}\\\"\\n\\n return new_filename\",\n \"def increment_filename(self, filename, path=\\\"\\\", insert=\\\"\\\"):\\n path = path.strip(\\\"/\\\")\\n basename, ext = os.path.splitext(filename)\\n for i in itertools.count():\\n if i:\\n insert_i = \\\"{}{}\\\".format(insert, i)\\n else:\\n insert_i = \\\"\\\"\\n name = \\\"{basename}{insert}{ext}\\\".format(\\n basename=basename, insert=insert_i, ext=ext\\n )\\n if not self.exists(\\\"{}/{}\\\".format(path, name)):\\n break\\n return name\",\n \"def update_destination_file_name (file_name):\\n\\tglobal COUNTER \\n\\tCOUNTER += 1\\n\\tsplitted = file_name.split('/')\\n\\treturn file_name[:len(file_name)-len(splitted[-1])] + 'Image%05d' % COUNTER +'_'+splitted[-1]\",\n \"def updateName(g):\\n try:\\n n = int(g.group(2))\\n except TypeError:\\n n = 0\\n\\n return \\\"%s-%d\\\" % (g.group(1), n + 1)\",\n \"def _name_increment_revision(name):\\n revre = r\\\"^(.*?)([0-9]+)$\\\"\\n m = re.search(revre, name)\\n if m:\\n name = m.group(1) + str(int(m.group(2)) + 1)\\n else:\\n name = name + \\\" (copy)\\\"\\n return name\",\n \"def rename(old, new):\",\n \"def rename(old, new):\",\n \"def new_filename(fname=None,ndigits=3):\\n if fname is None:\\n ext = (\\\"%%.%ii\\\" % ndigits) % 1\\n fname = \\\"%s.%s\\\" % (random_string(6), ext)\\n \\n if os.path.exists(fname): \\n fname = increment_filename(fname,ndigits=ndigits)\\n\\n return fname\",\n \"def change_file_name(self, n):\\n if type(n) != str or n is None:\\n raise TypeError(\\\"Wrong type! Please pass 'n' as a string!\\\")\\n self.name = n\",\n \"def _MaybeNewName(self, name):\\n if not name:\\n return name\\n if name == self._old[:-1]:\\n return self._module_name\\n before, match, after = name.partition(self._old)\\n if match and not before and \\\".\\\" not in after:\\n return self._new + after\\n else:\\n return name\",\n \"def new_shp_name(file_path):\\n while os.path.exists(file_path):\\n try:\\n int(file_path[-5])\\n except ValueError:\\n file_path = file_path[0:-4] + \\\"1.shp\\\"\\n else:\\n number = str(int(file_path[-5]) + 1)\\n file_path = file_path[0:-5] + number + \\\".shp\\\"\\n return file_path\",\n \"def increment_filename(filename, marker='-'):\\n # First we split the filename into three parts:\\n #\\n # 1) a \\\"base\\\" - the part before the counter\\n # 2) a \\\"counter\\\" - the integer which is incremented\\n # 3) an \\\"extension\\\" - the file extension\\n basename, fileext = os.path.splitext(filename)\\n\\n # Check if there's a counter in the filename already - if not, start a new\\n # counter at 0.\\n if marker not in basename:\\n base = basename\\n value = 0\\n\\n # If it looks like there might be a counter, then try to coerce it to an\\n # integer to get its value. Otherwise, start with a new counter at 0.\\n else:\\n base, counter = basename.rsplit(marker, 1)\\n\\n try:\\n value = int(counter)\\n except ValueError:\\n base = basename\\n value = 0\\n\\n # The counter is just an integer, so we can increment it indefinitely.\\n while True:\\n if value == 0:\\n value += 1\\n return '%s%s%d%s' % (base, marker, value, fileext)\\n value += 1\\n break\\n return '%s%s%d%s' % (base, marker, value, fileext)\",\n \"def rename(oldname, newname):\",\n \"def RenameFile(self, oldname: str, newname: str) -> None:\\n ...\",\n \"def increment_name(name: str) -> str:\\n\\n match = _number_suffix.search(name)\\n if match:\\n number_str = match.group(1)\\n next_number = int(number_str) + 1\\n return f'{name[:-len(number_str)]}{next_number}'\\n else:\\n return f'{name}-1'\",\n \"def fname( file_, base=None, new_base=None, new_ext=None ):\\n if base and new_base:\\n file_ = file_.replace(base, new_base, 1)\\n if new_ext:\\n file_ = os.path.splitext(file_)[0] + new_ext\\n return file_\",\n \"def rename_file(file_path, equipt_nr):\\n work_tuples = parse_columns()\\n # Regex used to get differents parts of the file path\\n path_regex = re.compile(r'(?P<path>[\\\\w\\\\\\\\:]*)\\\\\\\\(?P<filename>[\\\\w]*).(?P<extension>[\\\\w].)')\\n # Match object containing the different parts of the file path\\n match = path_regex.search(file_path)\\n\\n # Getting the right file to rename\\n associated_nr = 0\\n for ii in work_tuples:\\n if match.group('filename') == ii[0]:\\n associated_nr = ii[equipt_nr+1]\\n\\n # Renaming the file\\n os.rename(file_path, match.group('path')+'\\\\\\\\'+associated_nr+'.'+match.group('extension'))\",\n \"def increment_name(base, existing):\\r\\n if not base in existing:\\r\\n return base\\r\\n n = 1\\r\\n make_name = lambda: base + str(n)\\r\\n while make_name() in existing:\\r\\n n += 1\\r\\n return make_name()\",\n \"def __return_new_file_name(self, file_name: str, file_path: str):\\n\\n fastq_runid = re.split('[_.]', file_name) # split on `_` or `.`\\n barcode_number = file_path.split(\\\"/\\\")[-1] # get the barcode number\\n fastq_or_fasta = fastq_runid[-1] # get the .fastq/.fasta file extension\\n\\n # create the new file name\\n new_file_name = \\\"_\\\".join(fastq_runid[:3]) # join first three elements\\n new_file_name += \\\"_%s.%s\\\" % (barcode_number, fastq_or_fasta) # append the barcode number and file extension\\n\\n return new_file_name\",\n \"def replace_suffix (name, new_suffix):\\n assert isinstance(name, basestring)\\n assert isinstance(new_suffix, basestring)\\n split = os.path.splitext (name)\\n return split [0] + new_suffix\",\n \"def file_name(self, new_file_name):\\n self._file_name = os.path.abspath(new_file_name).replace(\\\"\\\\\\\\\\\", \\\"/\\\")\",\n \"def re_name(name,new_name):\\n\\n try:\\n os.rename(config_tools.full_dest+name,config_tools.full_dest+new_name)\\n except OSError:\\n print(f\\\"Не удалось переименовать {name}\\\")\\n else:\\n print(f\\\"{name} успешно переименновавано в {new_name}\\\")\",\n \"def RenameFile(path, org, new):\\n\\n cont = zip(org, new)\\n if not path.endswith((\\\"/\\\", \\\"\\\\\\\\\\\")):\\n path = path + \\\"\\\\\\\\\\\"\\n for file in cont:\\n if os.path.isfile(path + file[0]):\\n os.rename(path + file[0], path + file[1])\\n\\n return len(org) - len(new)\",\n \"def playlist_rename_idx(_id, name):\\n _id = int(_id) - 1\\n playlist_rename(sorted(g.userpl)[_id] + \\\" \\\" + name)\",\n \"def generate_name(file_name, index):\\n name = ''\\n with open(file_name) as names:\\n for i, line in enumerate(names):\\n if i == index:\\n name += line.split(None, 1)[0]\\n return name\",\n \"def increment_file_number(dir_path, filename, count=1):\\n if os.path.exists(os.path.join(dir_path, filename)):\\n log.debug('{} already exists in {}'.format(filename, dir_path))\\n name, ext = os.path.splitext(filename)\\n name += '-{}'.format(count)\\n new_filename = name + ext\\n log.debug('Generated new name, \\\"{}\\\". Checking availability...'.format(new_filename))\\n if os.path.exists(os.path.join(dir_path, new_filename)):\\n return increment_file_number(dir_path, filename, count + 1)\\n log.info('{} identified.'.format(new_filename))\\n return new_filename\\n log.info('{} identified.'.format(filename))\\n return filename\",\n \"def adjust_event_name(event_name):\\n pos=find_first_digit(event_name)\\n return event_name[pos:]\",\n \"def rename_file(f: pathlib.Path) -> str:\\n m = mutagen.File(f)\\n if m is None: return\\n new_name_parts = []\\n if \\\"tracknumber\\\" in m:\\n if \\\"discnumber\\\" in m:\\n new_name_parts.append(pad_num_str(m[\\\"discnumber\\\"][0]) + \\\".\\\")\\n new_name_parts.append(pad_num_str(m[\\\"tracknumber\\\"][0]) + \\\" - \\\")\\n new_name_parts.append(m[\\\"title\\\"][0].replace(\\\"/\\\", \\\"_\\\"))\\n if \\\"version\\\" in m:\\n new_name_parts.append(\\\" - \\\" + \\\" - \\\".join(m[\\\"version\\\"]).replace(\\\"/\\\", \\\"_\\\"))\\n return \\\"\\\".join(new_name_parts)\",\n \"def naming_convention(file_dir, file_name):\\n long_hash = sha1sum(os.path.join(file_dir, file_name))\\n file_prefix, file_sufix = file_name.split('.')\\n new_name = '{file_prefix}-{short_hash}.{file_sufix}'.format(\\n file_prefix=file_prefix,\\n short_hash=long_hash[:8],\\n file_sufix=file_sufix)\\n return new_name, long_hash\",\n \"def change_filepath_name(filepath: str, new_name: str, new_ext: Optional[str] = None) -> str:\\n ext = new_ext or filepath_ext(filepath)\\n return str(pathlib.Path(filepath).with_name(new_name).with_suffix(ext))\",\n \"def rename_file(source, oldname, newname):\\n #source = client_variables.output_folder\\n renamefiles = os.listdir(source)\\n ext = (\\\".xlsx\\\", \\\".csv\\\", \\\".pdf\\\", \\\".png\\\")\\n for renamefile in renamefiles:\\n if renamefile.endswith(ext):\\n renamefile = source + \\\"/\\\" + renamefile\\n print \\\"renaming:\\\", renamefile\\n newname = source + \\\"/\\\" + newname\\n print \\\"newname:\\\", newname\\n os.rename(renamefile, newname)\\n elif renamefile.startswith(oldname):\\n renamefile = source + \\\"/\\\" + renamefile\\n print \\\"renaming:\\\", renamefile\\n newname = source + \\\"/\\\" + newname\\n print \\\"newname:\\\", newname\\n os.rename(renamefile, newname)\",\n \"def increment_filename(inpfile,ndigits=3, delim='.'):\\n\\n dirname, filename = os.path.split(inpfile)\\n base, ext = os.path.splitext(filename)\\n if ext == '':\\n ext = '.000'\\n\\n if ext.startswith('.'):\\n ext = ext[1:]\\n if ndigits < 3:\\n ndigits = 3\\n form = \\\"%%.%ii\\\" % (ndigits)\\n\\n def _incr(base, ext):\\n try: # first, try incrementing the file extension\\n ext = form % (int(ext)+1)\\n except ValueError:\\n try: # try incrementing the part of the base after the last '_'\\n bparts = base.split('_')\\n bparts[-1] = form % (int(bparts[-1])+1)\\n base = '_'.join(bparts)\\n except: # last, add a '_001' appendix\\n base = \\\"%s_001\\\" % base\\n return (base,ext)\\n\\n # increment once\\n\\n base,ext = _incr(base, ext)\\n fout = pathOf(dirname, base, ext, delim=delim)\\n\\n # then gaurantee that file does not exist,\\n # continuing to increment if necessary\\n while(os.path.exists(fout)):\\n base,ext = _incr(base, ext)\\n fout = pathOf(dirname, base, ext, delim=delim)\\n return fout\",\n \"def get_fixed_filename(filename):\\n initial_changed_name = filename.replace(\\\" \\\", \\\"_\\\").replace(\\\".TXT\\\", \\\".txt\\\")\\n previous_character = \\\"\\\"\\n new_name = \\\"\\\"\\n for i, character in enumerate(initial_changed_name):\\n current_character = character\\n if previous_character == \\\"_\\\" and current_character.islower():\\n new_name += current_character.upper()\\n elif previous_character == \\\"(\\\" and current_character.islower():\\n new_name += current_character.upper()\\n elif previous_character.islower() and current_character.isupper():\\n new_name += \\\"_{}\\\".format(current_character)\\n else:\\n new_name += current_character\\n previous_character = character\\n return new_name\",\n \"def drename(filepath, namelength = 15):\\n\\tfilepath = os.path.abspath(filepath)\\n\\tif not os.path.isfile(filepath):\\n\\t\\traise ValueError(\\\"The path is not a valid file path: \\\"+str(filepath))\\n\\t\\n\\tpath, name = os.path.split(filepath)\\n\\tname, ext = os.path.splitext(name)\\n\\tos.rename(filepath, os.path.join(path, dname(namelength)+ext)) #We replace name by the random name and reconstruct the path\",\n \"def get_oldname(fname):\\n assert isinstance(fname, str), f\\\"fname is not a string, aborting. fname: {fname}\\\"\\n dirname = path.dirname(fname)\\n oldbasename = path.basename(fname)\\n if \\\"GA_\\\" in oldbasename:\\n return fname\\n elif (\\\"_Y_\\\" in oldbasename) or (\\\"_I_\\\" in oldbasename):\\n split = oldbasename.split(\\\"_\\\")\\n split[2] = split[2].split(\\\".\\\")[0]\\n newbasename = \\\"_\\\".join(split)\\n return path.join(dirname, newbasename)\\n elif \\\"_S_\\\" in fname:\\n return fname\\n else:\\n raise NotImplementedError(f\\\"filetype not supported. fname: {fname}\\\")\",\n \"def change_file_format(filename, old_format_extension, new_format_extension, append = ''):\\r\\n filename = unmake_file_format(filename, old_format_extension)\\r\\n filename += append + new_format_extension\\r\\n \\r\\n return(filename)\",\n \"def get_name(self, old_name):\\n if old_name not in self.record:\\n self.record[old_name] = [self.PLACEHOLDER]\\n suffix = \\\"\\\"\\n else:\\n self.record[old_name].append(self.PLACEHOLDER)\\n suffix = f\\\"{len(self.record[old_name]) - 1}\\\"\\n\\n new_name = f\\\"{old_name}{suffix}\\\"\\n self.topo_order.append(new_name)\\n\\n return new_name\",\n \"def base_rename(self, new_name):\\n\\n new_path = join(dirname(self.fspath), new_name)\\n\\n return self.rename(new_path)\",\n \"def generate_file_name(old_file_name: str) -> str:\\r\\n return old_file_name.split(\\\".\\\")[0] + '_features' + '.npy'\",\n \"def renamefile(filename):\\n new_data_list = []\\n with open(filename, 'r') as f:\\n data_list = f.read().split('\\\\n')\\n\\n print('Generating new data list..')\\n for data in tqdm(data_list):\\n if len(data) == 0:\\n continue\\n data_info = data.split(' ')\\n\\n #data_info[0] = data_info[0].replace('jpg', 'png')\\n #data_info[1] = data_info[1].replace('jpg', 'png')\\n for it, name in enumerate(data_info):\\n data_info[it] = '/'.join(name.split('/')[1:])\\n if data_info[2].find('extras') == -1:\\n new_data_list.append(' '.join(data_info))\\n\\n with open(filename, 'w') as f:\\n print('writing new data names..')\\n\\n for it, data in tqdm(enumerate(new_data_list)):\\n if len(data) == 0:\\n continue\\n\\n if it == len(new_data_list)-1:\\n f.write(data)\\n else:\\n f.write(data+'\\\\n')\\n\\n print('Done.')\",\n \"def ChangeName(self, newName):\\n if newName != \\\"\\\":\\n newPath = self.format + os.sep + \\\"playlists\\\" + os.sep + newName + \\\".txt\\\"\\n os.replace(self.path, newPath)\\n self.path = newPath\",\n \"def change_file_name():\\n path = \\\"/etc/atuned/webserver/\\\"\\n file_list = os.listdir(path)\\n file_list.sort(key=lambda fn: os.path.getmtime(path + fn))\\n if len(file_list) > 0 and re.match(r'\\\\S*-\\\\d{17}\\\\S*', file_list[-1]) is None:\\n old_name = file_list[-1].split(\\\".\\\")[0]\\n curr_time = time.strftime(\\\"%Y%m%d%H%M%S\\\", time.localtime())\\n new_name = old_name + \\\"-\\\" + str(curr_time) + str(random.randint(100, 999))\\n os.rename(path + old_name + \\\".txt\\\", path + new_name + \\\".txt\\\")\",\n \"def pad_filename(filenumber, n_of_zeros=5):\\n padding = \\\"0\\\"\\n str_file_n = str(filenumber)\\n n = n_of_zeros - len(str_file_n)\\n file_idx = n * padding + str_file_n\\n return file_idx\",\n \"def make_frame_name(frame_num):\\n return str(frame_num).zfill(3) + '.jpg'\",\n \"def get_padded_filename(num_pad, idx):\\n file_len = len(\\\"%d\\\" % (idx))\\n filename = \\\"0\\\" * (num_pad - file_len) + \\\"%d\\\" % (idx)\\n return filename\",\n \"def __rename_file(filename, suffix):\\n filename = PDFWorkshop.__clean_filename(filename)\\n return PDFWorkshop.__add_filename_suffix(filename, suffix)\",\n \"def _create_file_name(file_path):\\r\\n file_base, file_ext = os.path.splitext(file_path)\\r\\n if os.path.isfile(file_path):\\r\\n nfile = 1\\r\\n check = True\\r\\n while check:\\r\\n name_add = '0000' + str(nfile)\\r\\n file_path = file_base + \\\"_\\\" + name_add[-4:] + file_ext\\r\\n if os.path.isfile(file_path):\\r\\n nfile = nfile + 1\\r\\n else:\\r\\n check = False\\r\\n return file_path\",\n \"def get_fixed_filename(filename):\\n new_name = \\\"\\\"\\n for i, char in enumerate(filename):\\n if i + 1 != len(filename):\\n previous_character = filename[i - 1]\\n next_character = filename[i + 1]\\n if char.islower() and next_character.isupper():\\n new_name += char + \\\"_\\\"\\n elif previous_character == \\\".\\\":\\n new_name += char\\n elif char.islower() and not previous_character.isalpha():\\n new_name += char.upper()\\n else:\\n new_name += char\\n else:\\n new_name += char\\n new_name = new_name.replace(\\\" \\\", \\\"_\\\").replace(\\\".TXT\\\", \\\".txt\\\")\\n return new_name\",\n \"def update_name(old_name, new_name, phonebook):\\n\\n phonebook_data = read_phonebook(phonebook)\\n\\n if not phonebook_data.get(old_name):\\n raise NoEntryError(\\\"This entry does not exist! \\\"\\n \\\"(Names are case-sensitive.)\\\")\\n\\n else:\\n print \\\"Previous entry:\\\", old_name, phonebook_data[old_name]\\n number = phonebook_data[old_name]\\n del phonebook_data[old_name]\\n phonebook_data[new_name] = number\\n print \\\"New entry:\\\", new_name, phonebook_data[new_name]\\n save(phonebook_data, phonebook)\",\n \"def rrename(filepath, namelength = 15):\\n\\tfilepath = os.path.abspath(filepath)\\n\\tif not os.path.isfile(filepath):\\n\\t\\traise ValueError(\\\"The path is not a valid file path: \\\"+str(filepath))\\n\\t\\n\\tpath, name = os.path.split(filepath)\\n\\tname, ext = os.path.splitext(name)\\n\\tos.rename(filepath, os.path.join(path, rname(namelength)+ext)) #We replace name by the random name and reconstruct the path\",\n \"def rename_file(fname):\\n x,y = load_file(fname)\\n date=y[0].split(\\\".\\\")\\n if len(y[2])<20:\\n title=y[2]\\n else:\\n title=y[2][0:20]\\n title=title.replace(\\\" \\\",\\\"_\\\")\\n \\n new_name=\\\"{}{}{}{}.csv\\\".format(date[2],date[1],date[0],title)\\n new_appendix=rename_appendix(y[10],new_name)\\n os.rename(fname,new_name)\\n replace_line(new_name,10,'Anhang;\\\"{}\\\"'.format(new_appendix))\\n return new_name\",\n \"def backup_int_in_fname(num: int, file_name: str) -> None:\\n with open(file_name, 'w') as fname:\\n fname.write(str(num))\",\n \"def file_rename(old_path, image_class):\\n\\tdirectory, filename, extension = filename_split(old_path)\\n\\tnew_filename = filename_generate(image_class)\\n\\tnew_path = os.path.join(directory, new_filename + extension)\\n\\tos.rename(old_path, new_path)\\n\\treturn new_path\",\n \"def on_path(self, new):\\n self.name = basename(new)\",\n \"def change_nm(src,dst):\\n\\timport os\\n\\ttry:\\n\\t\\tos.rename(src,dst)\\n\\texcept:\\n\\t\\tprint \\\"this is a mistake\\\"\\n\\t\\treturn -1\\n\\n\\treturn 0\",\n \"def fileRenameandReplace(filename,newfilename):\\n try:\\n os.rename(filename,newfilename)\\n logging.info(\\\"Json file renamed in PD path\\\")\\n except Exception as er:\\n print (\\\"Not able to rename the json file \\\")\\n return False\",\n \"def name(self, new_name):\\n self.rename(new_name)\",\n \"def rename_name_gene(listOfFile, PATH_FASTA_RENAME) :\\n\\n\\tprint \\\"\\\\n#################\\\"\\n\\tprint \\\"# Rename protein\\\"\\n\\tprint \\\"#################\\\\n\\\"\\n\\n\\tcreate_folder(PATH_FASTA_RENAME)\\n\\n\\tnew_listOfFile=[]\\n\\n\\tfor my_file in listOfFile :\\n\\t\\tif os.stat(my_file).st_size != 0 :\\n\\t\\t\\tnew_listOfFile.append(my_file)\\n\\n\\tseq_to_rename = find_rename_fasta(new_listOfFile)\\n\\tdict_count = dict([(sequence[1:].rstrip(\\\" \\\"), 0) for sequence in seq_to_rename])\\n\\tprogression=1\\n\\tnumber_of_file = len(new_listOfFile)\\n\\n\\tfor my_file in new_listOfFile :\\n\\n\\t\\tfile_name = os.path.basename(my_file)\\n\\n\\t\\tsys.stdout.write(\\\"{:.2f}% : {}/{} files renamed\\\\r\\\".format(progression/float(number_of_file)*100, progression,number_of_file))\\n\\t\\tsys.stdout.flush()\\n\\t\\tprogression += 1\\n\\n\\t\\thandle = open(os.path.join(PATH_FASTA_RENAME, file_name), 'w')\\n\\t\\tfasta_reading = SeqIO.parse(my_file, \\\"fasta\\\")\\n\\n\\t\\tfor seq in fasta_reading :\\n\\t\\t\\tif seq.id in dict_count :\\n\\t\\t\\t\\tif dict_count[seq.id] == 0 :\\n\\t\\t\\t\\t\\tdict_count[seq.id] += 1\\n\\t\\t\\t\\telse :\\n\\t\\t\\t\\t\\tdict_count[seq.id] += 1\\n\\t\\t\\t\\t\\tif \\\"NC_\\\" in seq.id :\\n\\t\\t\\t\\t\\t\\t# NOTE New name : NC_XXXXXX[_numero de systeme si deux systemes trouvés][_Num(et le nombre de fois nom trouvé)]_nomSysteme_D_nomProteine\\n\\t\\t\\t\\t\\t\\tseq.id = \\\"_\\\".join(seq.id.split(\\\"_\\\")[:2])+\\\"_Num\\\"+str(dict_count[seq.id])+\\\"_\\\"+\\\"_\\\".join(seq.id.split(\\\"_\\\")[2:])\\n\\n\\t\\t\\t\\t\\telse :\\n\\t\\t\\t\\t\\t\\t# NOTE New name : NNNN[_numero de systeme si deux systemes trouvés][_Num(et le nombre de fois nom trouvé)]_nomSysteme_V_nomProteine\\n\\t\\t\\t\\t\\t\\tseq.id = seq.id.split(\\\"_\\\")[0]+\\\"_Num\\\"+str(dict_count[seq.id])+\\\"_\\\"+\\\"_\\\".join(seq.id.split(\\\"_\\\")[1:])\\n\\t\\t\\t\\t\\tseq.name = seq.id\\n\\t\\t\\t\\t\\tseq.description = \\\"\\\"\\n\\n\\t\\t\\tSeqIO.write(seq, handle, \\\"fasta\\\")\\n\\n\\t\\thandle.close()\\n\\n\\tprint\\n\\tprint \\\"Done!\\\"\\n\\treturn\",\n \"def calculting_name():\\n\\n list_of_files = glob.glob('./muestras/*') # * means all if need specific format then *.csv\\n latest_file = max(list_of_files, key=os.path.getctime)\\n _, name_file = os.path.split(latest_file)\\n name, _ = os.path.splitext(name_file)\\n name_number = str(name)\\n\\n return name_number\",\n \"def rename(img):\\n ext = splitext(img)[1].lower()\\n name = get_date(open(img))\\n if name is not None:\\n name = name + ext\\n return copy(img, name)\",\n \"def update_edge_node_name(node_name, node_number):\\n return node_name.replace('block_' + str(node_number), 'block_' + str(node_number+1))\",\n \"def auto_rename(file_path, new_name):\\n \\n # Return if no file given\\n if not file_path:\\n return ''\\n else:\\n file_path = file_path\\n \\n # Get the new name\\n new_path = change_basename(file_path, new_name)\\n \\n \\n # Changed?\\n if new_path != file_path:\\n # Try to rename\\n try:\\n shutil.move(os.path.join(settings.MEDIA_ROOT, file_path), os.path.join(settings.MEDIA_ROOT, new_path))\\n except IOError:\\n # Error? Restore original name\\n new_path = file_path\\n \\n # Return the new path replacing backslashes (for Windows)\\n return new_path\",\n \"def new_filename(original_filename, new_locale):\\r\\n f = path(original_filename)\\r\\n new_file = f.parent.parent.parent / new_locale / f.parent.name / f.name\\r\\n return new_file.abspath()\",\n \"def inject_filename_counter(fname, counterval, maxextlen=6):\\n cvstr = '%05d' % counterval\\n parts = fname.split('.')\\n def isext(part):\\n return (len(part) <= maxextlen) or (part.startswith('container-'))\\n if (len(parts) == 1) or (not isext(parts[-1])):\\n return '%s-%s' % (fname, cvstr)\\n else:\\n return '%s-%s.%s' % ('.'.join(parts[:-1]), cvstr, parts[-1])\",\n \"def next_version(file_):\\n split_file = file_.rsplit(\\\"_\\\", 1)\\n name_file = split_file[0]\\n version = split_file[-1]\\n padding = len(version)\\n\\n if version.isdigit():\\n next_version = int(version) + 1\\n next_version = str(next_version).zfill(padding)\\n\\n return concat(name_file, next_version, separator=\\\"_\\\")\\n else:\\n e = concat(file_, \\\" is incorrect.\\\")\\n raise ValueError(e)\",\n \"def rotate_file(filename, format=\\\"%(basename)s.%(number)03i%(extension)s\\\"):\\n\\tif os.path.exists(filename):\\n\\t\\tpathlocation, basename, extension = filename_split(filename)\\n\\t\\tfn = lambda n: os.path.join(pathlocation,format%{'basename':basename, 'extension':extension, 'number':n})\\n\\t\\tn = 1\\n\\t\\twhile os.path.exists(fn(n)):\\n\\t\\t\\tn += 1\\n\\t\\twhile n > 1:\\n\\t\\t\\tos.rename(fn(n-1),fn(n))\\n\\t\\t\\tn -= 1\\n\\t\\tos.rename(filename,fn(1))\\n\\telse:\\n\\t\\traise FileNotFoundError(\\\"File %s does not exist\\\"%filename)\",\n \"def refactor(path: str, files: List):\\n skipped = []\\n for filename in files:\\n try:\\n number = get_number_from_name(filename)\\n except AttributeError:\\n skipped.append(os.path.join(path, filename))\\n continue\\n new_number = update_number(number)\\n\\n file_path = os.path.join(path, filename)\\n new_file_path = file_path.replace(number, new_number)\\n\\n with open(file_path, 'r') as file:\\n data = file.read()\\n data = data.replace(number, new_number)\\n with open(file_path, 'w') as file:\\n file.write(data)\\n\\n os.rename(file_path, new_file_path)\\n return skipped\",\n \"def rename(file, format_spec, dir=DIR()):\\n\\tfile = pathlib.Path(file)\\n\\t\\n\\tprint(\\\"Parsing {name}...\\\".format(name=file.name))\\n\\t\\n\\tarticle = Article(file.read_bytes())\\n\\t\\n\\tnew_file = format_spec.format(\\n\\t\\tarticle = article,\\n\\t\\ttitle = dir.getTitle(file) or article.getTitle(),\\n\\t\\tauthor = article.getAuthor() or dir.getAuthor(file),\\n\\t\\tboard = article.getBoard(),\\n\\t\\ttime = article.getTime() or dir.getTime(file) or format_dummy\\n\\t)\\n\\tnew_file = safe_file_name(new_file)\\n\\tnew_file = file.with_name(new_file)\\n\\t\\n\\tif file == new_file:\\n\\t\\tprint(\\\"Same file name!\\\\n\\\")\\n\\t\\treturn\\n\\t\\n\\tif new_file.exists():\\n\\t\\tnum = 2\\n\\t\\t\\n\\t\\twhile True:\\n\\t\\t\\ttemp_file = \\\"{name} ({num}){ext}\\\".format(\\n\\t\\t\\t\\tnum = num,\\n\\t\\t\\t\\tname = new_file.stem,\\n\\t\\t\\t\\text = new_file.suffix\\n\\t\\t\\t)\\n\\t\\t\\ttemp_file = new_file.with_name(temp_file)\\n\\t\\t\\t\\n\\t\\t\\tif file == temp_file:\\n\\t\\t\\t\\tprint(\\\"Same file name!\\\\n\\\")\\n\\t\\t\\t\\treturn\\n\\t\\t\\t\\t\\n\\t\\t\\tif not temp_file.exists():\\n\\t\\t\\t\\tnew_file = temp_file\\n\\t\\t\\t\\tbreak\\n\\t\\t\\t\\t\\n\\t\\t\\tnum += 1\\n\\t\\n\\tprint(\\\"Rename to {name}...\\\\n\\\".format(name=new_file.name))\\n\\t\\n\\tfile.rename(new_file)\",\n \"def rename_file(self, path, new_name):\\n try:\\n self.rename_narrative(self._parse_path(path), self.get_userid(), new_name)\\n except WorkspaceError as err:\\n raise HTTPError(err.http_code, err.message)\\n except Exception as err:\\n raise HTTPError(\\n 500, \\\"An error occurred while renaming your Narrative: {}\\\".format(err)\\n )\",\n \"def update_node_name(node_name, bb):\\n node_number = int(re.search(r'\\\\d+', re.search(r'block_\\\\d+', node_name).group()).group())\\n if bb < node_number:\\n return node_name.replace('block_' + str(node_number), 'block_' + str(node_number+1))\\n return node_name\",\n \"def filename_formatter(source, destination, file, order_format):\\n\\n # Attempt loading taglib.\\n try:\\n song = taglib.File(file)\\n except:\\n raise CoreError('taglib.File() failed.')\\n\\n # Begin formatting the filename.\\n new_name = destination\\n for i in range(0, len(order_format)):\\n # If next item is a tag, this block will execute.\\n try:\\n sub_name = song.tags[order_format[i]][0]\\n # Clear filename of any illegal characters.\\n for j in range(0, len(MuzikArkive.illegal_name_characters)):\\n if MuzikArkive.illegal_name_characters[j] in sub_name:\\n sub_name = sub_name.replace(\\n MuzikArkive.illegal_name_characters[j], '_'\\n )\\n new_name += sub_name\\n # If next item is a char or string, this block will execute.\\n except:\\n new_name += order_format[i]\\n\\n # Add the files extenstion to the new name formatting.\\n new_name += '.' + file.rsplit('.', 1)[1]\\n\\n song.close()\\n return new_name\",\n \"def new_dirname(dirname=None, ndigits=3):\\n if dirname is None:\\n ext = (\\\"%%_%ii\\\" % ndigits) % 1\\n dirname = \\\"%s_%s\\\" % (random_string(6), ext)\\n \\n dirname = dirname.replace('.', '_')\\n if os.path.exists(dirname):\\n dirname = increment_filename(dirname, ndigits=ndigits, delim='_')\\n \\n\\n return dirname\",\n \"def projectFileRenamed(self, oldfn, newfn):\\n editor = self.getOpenEditor(oldfn)\\n if editor:\\n editor.fileRenamed(newfn)\",\n \"def getNumberFromName(self, idx):\\n\\t\\tfile = self.all_file_names[idx]\\n\\t\\tnumber = file[4]\\n\\t\\tif file[5].isdigit(): \\n\\t\\t\\tnumber += file[5]\\n\\t\\treturn int(number)\",\n \"def filename(i):\\n rand_name = os.path.join(os.getcwd(), \\\"input-%d.txt\\\" % i)\\n ref_name = os.path.join(os.getcwd(), \\\"input-%d.ref\\\" % i)\\n return rand_name, ref_name\",\n \"def rename_files():\\n folder_dir = r\\\"C:\\\\Users\\\\keithmoore1.AD\\\\Desktop\\\\HAFB\\\\prankOrig\\\"\\n files = os.listdir(folder_dir)\\n save_path = os.getcwd() # current working directory\\n for file in files:\\n #remove digits from name\\n new_file = file.lstrip(\\\"0123456789\\\")\\n print(file, \\\" - \\\", new_file)\\n # rename filename\\n os.chdir(folder_dir)\\n os.rename(file,new_file)\\n # get back home\\n os.chdir(save_path)\",\n \"def rename_appendix(appendix,fname):\\n listnewname=[]\\n string=\\\"\\\"\\n if appendix!=\\\"\\\":\\n appendix=appendix.split(\\\",\\\")\\n fname=fname.split(\\\".\\\")[0]\\n for i in range(0,len(appendix)):\\n new_name=fname+\\\"_Anhang\\\"+str(i+1)+\\\".pdf\\\"\\n os.rename(appendix[i],new_name)\\n listnewname.append(new_name)\\n for i in range(0,len(listnewname)):\\n if i!=0:\\n string=string+\\\",\\\"+listnewname[i]\\n else:\\n string=listnewname[i]\\n return string\",\n \"def append_random_number_to_filename(self, local_img_file):\\n date = datetime.datetime.now()\\n date_string = date.strftime(\\\"%m-%d-%Y\\\")\\n return \\\"%s-glitched.%s\\\" % (local_img_file.split(\\\".\\\")[0], local_img_file.split(\\\".\\\")[1])\",\n \"def newname(self, newname) :\\n\\t\\ttry :\\n\\t\\t\\tself._newname = newname\\n\\t\\texcept Exception as e:\\n\\t\\t\\traise e\",\n \"def file_num_increment(full_fpath):\\r\\n while os.path.isfile(full_fpath) == True:\\r\\n \\r\\n fpath, fext = os.path.splitext(full_fpath) #['C:\\\\Users\\\\Desktop\\\\file(1)', '.ext']\\r\\n\\r\\n if re.findall(\\\"[(]\\\\d+[)]\\\", fpath) != []: #Check if there is (x) in the path.\\r\\n for counter in range(1000): #Loop 1000 times\\r\\n if fpath.endswith(f\\\"({counter})\\\"): \\r\\n fpath = replace_last(fpath, f\\\"({counter})\\\", f\\\"({counter+1})\\\") #Replace the last occurence of (counter) in the string.\\r\\n full_fpath = fpath + fext\\r\\n break\\r\\n else: #here we pass for cases where (counter) is in the file/folder name itself. We skip them.\\r\\n continue\\r\\n else: #If there is no (counter), we create (1)\\r\\n counter = 1\\r\\n full_fpath = fpath + '(' + str(counter) + ')' + fext\\r\\n\\r\\n return full_fpath\",\n \"def _get_newfilename(oldname):\\n # example filename: 20160929_M1_05057144.015_I_CrabNebula-W0.40+215_mergecols.csv\\n # removes subrun (bc files are merged to runlevel) and prepends eventfiltered\\n dirname = path.dirname(oldname)\\n newdirname = path.join(dirname, FILTERDIR)\\n oldbasename = path.basename(oldname)\\n if \\\"GA_\\\" in oldbasename:\\n if (\\\"_Y_\\\" in oldbasename) or (\\\"_I_\\\" in oldbasename):\\n newbasename = FILTERPREFIX + oldbasename\\n elif (\\\"_Y_\\\" in oldbasename) or (\\\"_I_\\\" in oldbasename):\\n splitname = oldbasename.split(\\\"_\\\")\\n splitname[2] = splitname[2].split(\\\".\\\")[0]\\n newbasename = FILTERPREFIX + \\\"_\\\".join(splitname)\\n newfilename = path.join(newdirname, newbasename)\\n return newfilename\",\n \"def _set_filename(self, filename):\\n tmp_file = '_'.join(filename.split())\\n# new_file = new_file.replace(\\\"'\\\",\\n# '_').replace('-',\\n# '_').replace(' ',\\n# '_').replace('(', '_').replace(')', '_')\\n new_file = ''\\n pathsep = os.path.sep \\n if sys.platform == 'win32':\\n pathsep = '/'\\n for char in tmp_file:\\n if char.isalnum() or char in ['.', '_', ':', pathsep, '-']:\\n new_file += char\\n try:\\n shutil.copy(filename, new_file)\\n except shutil.Error, err:\\n msg = \\\"`%s` and `%s` are the same file\\\" % (filename, new_file)\\n if str(err) == msg:\\n pass\\n else:\\n raise err\\n utils.ensure_file_exists(new_file)\\n self._filename = new_file\\n self._basename, self._ext = os.path.splitext(self._filename)\",\n \"def suffix_replace(original, old, new):\\n ...\",\n \"def setFinalFileName(self, offset=0):\\n while True:\\n tempFinalFileName = input(\\n offset * \\\" \\\" + \\\"Specify a final file name [%s]: \\\" % self.finalFileName) or self.finalFileName\\n # Check 'tempFinalFileName' for bad formatting\\n if len(re.findall(r'[^A-Za-z0-9_-]', tempFinalFileName)) == 0:\\n # If no illegal characters then change finalFileName\\n self.finalFileName = tempFinalFileName\\n break\\n else:\\n print(\\\"Invalid file name!\\\")\",\n \"def newname(self) :\\n\\t\\ttry :\\n\\t\\t\\treturn self._newname\\n\\t\\texcept Exception as e:\\n\\t\\t\\traise e\",\n \"def fix_filename(s):\\n t = str(s).translate(TRANS_FILE)\\n if t.count('.') > 1:\\n for i in range(t.count('.') - 1):\\n idot = t.find('.')\\n t = \\\"%s_%s\\\" % (t[:idot], t[idot+1:])\\n return t\",\n \"def rename_sz(sz):\\n sz = str(sz)\\n sz_set = sz[0]\\n num = sz[1]\\n return f\\\"S{sz_set}_{num}\\\"\",\n \"def __newFileName(self):\\n now = datetime.now()\\n dateTimeAppend = now.strftime('%y%m%d_%H%M%S')\\n self.__fileName = '{}/{}_{}.wav'.format(RECORDING,\\n FILE_NAME_PREFIX, \\n dateTimeAppend)\",\n \"def handle_filename_collision(filename, filenames):\\n if filename.lower() in filenames:\\n try:\\n basename, ext=filename.rsplit('.', 1)\\n ext='.'+ext\\n except ValueError:\\n basename, ext=filename, '' \\n\\n i=1\\n while True:\\n filename='%s-%02d%s' % (basename, i, ext)\\n if filename.lower() not in filenames:\\n break\\n i+=1\\n \\n return filename\",\n \"def test_6a_change_file_name(self):\\n if (not GST.logged_in) or (not GST.data_testing_swift_mounted):\\n raise unittest.SkipTest(\\\"Skipped for failed login or failed mounting container.\\\")\\n elif not GST.rename_file_test_ready:\\n raise unittest.SkipTest(\\\"Skipped for failed to prepare renaming test.\\\")\\n self.dismiss_dialogs()\\n function = js_func[\\\"rename\\\"] % (GST.gs_file_paths[\\\"file_to_rename_path\\\"], GST.gs_file_paths[\\\"after_rename_path\\\"])\\n try:\\n self.send_request(function, \\\"rename()\\\")\\n except Exception as e:\\n raise RenameException(\\\"Failed to rename the file: \\\" + e.__str__())\\n try:\\n response = self.get_response()\\n assert \\\"Success\\\" in response\\n self.refresh_page()\\n except AssertionError:\\n raise RenameException(\\\"Failed to rename the file: \\\" + response)\",\n \"def renewFile(filename):\\n\\n\\tfileRepo = repertoire + filename + extension # Position du fichier\\n\\n\\t# Ouvre en ecriture et l'ecrase\\n\\t# La methode with ferme le fichier automatiquement\\n\\twith open(fileRepo, \\\"w\\\") as robFile:\\n\\t\\trobFile.write(filename + \\\"\\\\n\\\") # Ecrit le nom du fichier au debut\",\n \"def name(self, new_name: str) -> None:\\n raise NotImplementedError()\",\n \"def renameSample(old_name, new_name):\\n def switch(val):\\n return new_name.join(val.split(old_name))\\n\\n with Repo.loadRepo() as repo:\\n sample = repo.db.sampleTable.get(old_name)\\n print(f'{old_name} -> {new_name}', file=sys.stderr)\\n sample.rename(new_name)\\n old_file_paths = []\\n for result in sample.results():\\n new_result_name = switch(result.name)\\n result.rename(new_result_name)\\n for _, filerec in result.files():\\n new_file_name = switch(filerec.name)\\n filerec.rename(new_file_name)\\n old_file_paths.append(filerec.filepath())\\n new_file_path = switch(filerec.filepath())\\n filerec.copy(new_file_path)\\n filerec.save(modify=True)\\n result.save(modify=True)\\n sample.save(modify=True)\\n\\n for old_path in old_file_paths:\\n print(old_path)\",\n \"def find_new_name(self, std, name):\\n all_names = [case.read_name() for case in std.get_all(aster_s.Case)]\\n new_name = name\\n for idx in xrange(100):\\n if new_name not in all_names:\\n return new_name\\n new_name = name + str(idx)\\n else:\\n mod.launch(ERROR, \\\"Too many wizards '%s' in use\\\" % name)\",\n \"def rename_file (self):\\n\\t\\tassert self.__filename, \\\"Renaming could not complete because the new filename could not be determined, one or more needed arguments is empty!\\\"\\n\\t\\tos.rename( self._file.path, self.__filename )\\n\\t\\t\\n\\t\\tif self.verbose and self.log :\\tself.log.info( 'File renamed from %s to %s' % (self._file.path, self.__filename))\",\n \"def rotate_filenames_and_rename(filename, num_files):\\n filename_max = '%s.%d' % (filename, num_files)\\n if os.path.exists(filename_max):\\n os.remove(filename_max)\\n for i in range(num_files, 0, -1):\\n old_filename = '%s.%d' % (filename, i)\\n new_filename = '%s.%d' % (filename, i+1)\\n if os.path.exists(old_filename):\\n os.rename(old_filename, new_filename)\\n os.rename(filename, '%s.1' % (filename))\",\n \"def get_fixed_filename(filename):\\n new_name = filename.replace(\\\" \\\", \\\"_\\\").replace(\\\".TXT\\\", \\\".txt\\\")\\n new_filename = \\\"\\\"\\n check_symbol = [\\\"(\\\", \\\"_\\\"] # A list of symbols where uppercase must follow\\n\\n for count, character in enumerate(new_name):\\n current_character = character\\n substitute_character = current_character\\n if count > 0:\\n if previous_character in check_symbol:\\n substitute_character = current_character.upper()\\n elif current_character.isupper() and previous_character.islower(): # for example: sW\\n substitute_character = \\\"_\\\" + current_character\\n previous_character = current_character\\n new_filename = new_filename + substitute_character\\n return new_filename\",\n \"def supplement_file_name(file: Union[str, pathlib.Path], sup: str) -> pathlib.Path:\\n\\n file = pathlib.Path(file)\\n\\n # the `suffix` is incorporated into the file name\\n return file.with_name(file.stem + f'_{sup}' + file.suffix)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7255178","0.71344984","0.7102937","0.6988627","0.6971764","0.6734604","0.67219466","0.671219","0.6661564","0.6661564","0.6653499","0.663018","0.66124356","0.6611557","0.6605207","0.655132","0.6525605","0.64201343","0.63903785","0.6386055","0.636861","0.63640803","0.63537276","0.6308822","0.62571514","0.62445515","0.6243406","0.6195981","0.61519116","0.61367863","0.6134469","0.6113221","0.6105702","0.60694355","0.6067424","0.6042784","0.6024481","0.60053015","0.5975505","0.5975333","0.59727937","0.5966539","0.59639364","0.5960776","0.5960329","0.595214","0.59343994","0.59342015","0.59038556","0.5901134","0.5893842","0.5893698","0.58611655","0.5860748","0.5845585","0.5835228","0.58344567","0.58270824","0.58248216","0.5811889","0.5810717","0.5803851","0.5801868","0.5784485","0.57640976","0.5762084","0.57456607","0.5744668","0.5739014","0.5737173","0.573243","0.5729698","0.5724808","0.5715291","0.57088584","0.5702285","0.56891876","0.56890416","0.56873065","0.5682931","0.56819284","0.56817895","0.5681393","0.5681214","0.5669932","0.56621015","0.56600434","0.5656483","0.5642081","0.5625081","0.5623113","0.56185496","0.56180847","0.56076086","0.55965394","0.5594965","0.55871254","0.557627","0.5567002","0.5565124","0.55593336"],"string":"[\n \"0.7255178\",\n \"0.71344984\",\n \"0.7102937\",\n \"0.6988627\",\n \"0.6971764\",\n \"0.6734604\",\n \"0.67219466\",\n \"0.671219\",\n \"0.6661564\",\n \"0.6661564\",\n \"0.6653499\",\n \"0.663018\",\n \"0.66124356\",\n \"0.6611557\",\n \"0.6605207\",\n \"0.655132\",\n \"0.6525605\",\n \"0.64201343\",\n \"0.63903785\",\n \"0.6386055\",\n \"0.636861\",\n \"0.63640803\",\n \"0.63537276\",\n \"0.6308822\",\n \"0.62571514\",\n \"0.62445515\",\n \"0.6243406\",\n \"0.6195981\",\n \"0.61519116\",\n \"0.61367863\",\n \"0.6134469\",\n \"0.6113221\",\n \"0.6105702\",\n \"0.60694355\",\n \"0.6067424\",\n \"0.6042784\",\n \"0.6024481\",\n \"0.60053015\",\n \"0.5975505\",\n \"0.5975333\",\n \"0.59727937\",\n \"0.5966539\",\n \"0.59639364\",\n \"0.5960776\",\n \"0.5960329\",\n \"0.595214\",\n \"0.59343994\",\n \"0.59342015\",\n \"0.59038556\",\n \"0.5901134\",\n \"0.5893842\",\n \"0.5893698\",\n \"0.58611655\",\n \"0.5860748\",\n \"0.5845585\",\n \"0.5835228\",\n \"0.58344567\",\n \"0.58270824\",\n \"0.58248216\",\n \"0.5811889\",\n \"0.5810717\",\n \"0.5803851\",\n \"0.5801868\",\n \"0.5784485\",\n \"0.57640976\",\n \"0.5762084\",\n \"0.57456607\",\n \"0.5744668\",\n \"0.5739014\",\n \"0.5737173\",\n \"0.573243\",\n \"0.5729698\",\n \"0.5724808\",\n \"0.5715291\",\n \"0.57088584\",\n \"0.5702285\",\n \"0.56891876\",\n \"0.56890416\",\n \"0.56873065\",\n \"0.5682931\",\n \"0.56819284\",\n \"0.56817895\",\n \"0.5681393\",\n \"0.5681214\",\n \"0.5669932\",\n \"0.56621015\",\n \"0.56600434\",\n \"0.5656483\",\n \"0.5642081\",\n \"0.5625081\",\n \"0.5623113\",\n \"0.56185496\",\n \"0.56180847\",\n \"0.56076086\",\n \"0.55965394\",\n \"0.5594965\",\n \"0.55871254\",\n \"0.557627\",\n \"0.5567002\",\n \"0.5565124\",\n \"0.55593336\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":14373,"cells":{"query":{"kind":"string","value":"Anonymous users can make `whoami` requests. They receive a 401 response confirming they are not logged in."},"document":{"kind":"string","value":"def test_whoami_by_anonymous_user(self):\n response = self.client.get(\"/api/users/whoami/\")\n self.assertEqual(response.status_code, 401)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def whoami():\n g.data['authenticated_user'] = g.current_user","def whoami():\n try:\n\n token = request.headers['token']\n username, uid, wid = read_auth_token(token)\n return dict(username=username, uid=uid, wid=wid)\n\n except SignatureExpired as e:\n return dict(error=str(e)), 401\n except BadSignature as e:\n return dict(error=str(e)), 401\n except Exception as e:\n return dict(error=str(e)), 500","def whoami(self):\n if self.auth:\n return self.auth[0]\n else:\n return \"Anonymous\"","def whoami(ctx):\n ctx.setup_logger(format='')\n AuthCmd(ctx).whoami()","def test_home_as_anonymous(self):\n response = self.client.get(\"/images/contents/\")\n self.assertEqual(response.status_code, 401)","def test_home_as_anonymous(self):\n response = self.client.get(\"/images/contents/\")\n self.assertEqual(response.status_code, 401)","def get_anonymous_user():\n return User.objects.get(username = \"AnonymousUser\")","def test_anonymous(self):\n self.logout_user()\n\n response = self.client.get(self.api_link)\n self.assertContains(response, \"sign in to use private threads\", status_code=403)","def login_anonymously(self):\n username = b\"anonymous\"\n password = b\"anonymous\"\n self.send_cmd(b\"USER \" + username + B_CRLF)\n self.send_cmd(b\"PASS \" + password + B_CRLF)","def is_anonymous():\n return False","def test_status_code_for_anonymous_user(self):\n response = self.client.get(self.get_url())\n self.assertEqual(response.status_code, self.status_anonymous)","def before_request() -> None:\n if current_user.is_anonymous() or not current_user.is_allowed():\n abort(401)","def whoami(self):","def login(self):\n self._client.clear_credentials()\n self._client.get('/v1/whoami')","def test_get_all_tokens_anonymous_user(self):\r\n\r\n # Anonymoues users should be unauthorized, no matter which kind of token are requesting\r\n res = self.app.get('/api/token')\r\n err = json.loads(res.data)\r\n\r\n assert res.status_code == 401, err\r\n assert err['status'] == 'failed', err\r\n assert err['status_code'] == 401, err\r\n assert err['exception_cls'] == 'Unauthorized', err\r\n assert err['target'] == 'token', err","def whoami():\n return current_user._get_current_object()","def _is_anonymous_user(auth_info):\n auth_info = auth_info or _get_auth_info_for_id_or_from_request()\n return str(auth_info.get(\"user_id\")) == ANONYMOUS_USER_ID","def test_anonymous_user_read(self):\r\n with self.flask_app.test_request_context('/'):\r\n for token in self.auth_providers:\r\n assert_raises(Unauthorized,\r\n getattr(require, 'token').read,\r\n token)","def test_get_specific_token_anonymous_user(self):\r\n\r\n res = self.app.get('/api/token/twitter')\r\n err = json.loads(res.data)\r\n\r\n assert res.status_code == 401, err\r\n assert err['status'] == 'failed', err\r\n assert err['status_code'] == 401, err\r\n assert err['exception_cls'] == 'Unauthorized', err\r\n assert err['target'] == 'token', err","def get_authenticated_denied(self):","def whoami(self):\n response = requests.get(self.ENDPOINT + '/user-resource/user', headers={'apikey':self.apikey})\n\n return response.json()","def user_logged_in():\n if not session.get('user_id'):\n return \"nope\", 401\n else:\n return \"yep\", 200","def test_not_authenticated(self):\n pass # lint-amnesty, pylint: disable=unnecessary-pass","def test_not_authenticated(self):\n pass # lint-amnesty, pylint: disable=unnecessary-pass","def hidden_basic_auth(user=\"user\", passwd=\"passwd\"):\n\n if not check_basic_auth(user, passwd):\n return status_code(404)\n return jsonify(authenticated=True, user=user)","def test_authenticated_inherits_anonymous_permission(self):\n resource = Resource('milestone', 'milestone1')\n self.assertTrue(self.check_permission('MILESTONE_VIEW',\n 'anonymous', resource))\n self.assertTrue(self.check_permission('MILESTONE_VIEW',\n 'authenticated', resource))\n self.assertIn('MILESTONE_VIEW', self.get_perm('anonymous',\n resource))\n self.assertIn('MILESTONE_VIEW', self.get_perm('authenticated',\n resource))","def run_whoami(self, expanded, unexpanded) :\n\t\tif expanded :\n\t\t\treturn self.errormessage(\"Doesn't need any argument\")\n\t\t(username, roles) = self.WhoAmI()\n\t\tself.htmlmessage('Username: %s Roles: %s' % (username, string.join(roles, ', ')), safe=1)\n\t\tself.printf(\"%s\\n\" % username)","def is_anonymous(self):\n return False","def whoami(self):\n print(\"-----\",self._whoami)","def is_anonymous(self):\n return False","def is_anonymous(self):\n return False","def is_anonymous(self):\n return False","def is_anonymous(self):\n return False","def is_anonymous(self):\n return False","def is_anonymous(self):\n return False","def is_anonymous(self):\n return False","def is_anonymous(self):\n return False","def is_anonymous(self):\n return False","def is_anonymous(self):\n return False","def is_anonymous(self):\n return False","def is_anonymous(self):\n return False","def is_anonymous(self):\n return False","def is_anonymous(self):\n return False","def is_anonymous(self):\n return False","def is_anonymous(self):\n return False","def is_anonymous(self):\n return False","def is_anonymous(self):\n return False","def is_anonymous(self):\n return False","def is_anonymous(self):\n return False","def test_get_user_by_id_unauthenticated(client: FlaskClient) -> None:\n username = create_random_username()\n # Unauthenticated users are not allowed to make the request\n response = get_user(client, username)\n assert_error_response(response, HTTPStatus.UNAUTHORIZED)","def test_help_for_anonymous_user(mock_user_moira_lists):\n request = RequestFactory()\n request.method = \"GET\"\n request.user = AnonymousUser()\n response = HelpPageView.as_view()(request)\n assert response.status_code == status.HTTP_200_OK","def is_anonymous_access_allowed(self):\n return self._is_anonymous_access_allowed","def test_read_attendances_by_anonymous_user(self):\n response = self.client.get(\n self._get_url(self.live),\n content_type=\"application/json\",\n )\n self.assertEqual(response.status_code, 401)","def _respond_unauthorized(self, request, message=\"Unauthorized\"):\n resp = Response()\n resp.status = 401\n resp.headers = self.forget(request.environ, {})\n resp.content_type = \"text/plain\"\n resp.body = message\n request.environ[\"repoze.who.application\"] = resp\n return None","def is_anonymous(self):\r\n return False","def test_api_livesession_read_anonymous(self):\n livesession = AnonymousLiveSessionFactory()\n response = self.client.get(self._get_url(livesession.video, livesession))\n self.assertEqual(response.status_code, 401)\n self.assertEqual(\n response.json(), {\"detail\": \"Authentication credentials were not provided.\"}\n )","def denied_response(self, req):\n if req.remote_user:\n return HTTPForbidden(request=req)\n else:\n return HTTPUnauthorized(request=req)","def unauthorized():\n return HttpError(401)","def grant_access(user, endpoint):\n return not user.is_anonymous()","def test_retrive_user_unauthenticated(self):\n res = self.client.get(ME_URL)\n\n self.assertEqual(res.status_code, status.HTTP_401_UNAUTHORIZED)","def test_profile_api_anon(self):\n self.client.logout()\n response = self.client.get(self.url)\n self.assertEqual(response.status_code, 403)","def test_anonymous_cannot_get_userprofileview(dclient):\n resp = dclient.get(\"/api/record/profile/\", follow=True)\n assert resp.status_code == 403","def test_get_user_template_as_anonymous_raises_access_control_error(self):\n mock_request = create_mock_request(user=self.anonymous_user)\n with self.assertRaises(AccessControlError):\n template_api.get_by_id(\n self.fixture.user1_template.id, request=mock_request\n )","def test_anonymous_user_doesnt_have_classroom_state(self):\n self.client.force_authenticate(None)\n resp = self.client.get(self.api_classroom_list_url)\n self.assertEqual(resp.status_code, 401)","def authcheck():\n user = get_user()\n return jsonify({'current_identity': user.username})","def ping_missing_user(request):\r\n return _api_response(request, {\r\n 'success': False,\r\n 'message': 'Missing username in your api url.'\r\n })","def test_closed_api_not_authenticated(self):\n anonymous = Group.objects.get(name='Anonymous')\n anonymous.permissions.remove(\n Permission.objects.get(codename='api_read_initiative')\n )\n\n response = self.client.get(self.initiatives_url)\n self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)","def test_get_global_template_as_anonymous_raises_access_control_error(\n self,\n ):\n mock_request = create_mock_request(user=self.anonymous_user)\n with self.assertRaises(AccessControlError):\n template_api.get_by_id(\n self.fixture.global_template.id, request=mock_request\n )","def test_an_admin_view_anonymous(client):\n response = client.get('/admin/')\n assert status(response) == 'found'\n assert response.url.startswith('/admin/login/')","def test_anonymous_user_create(self):\r\n with self.flask_app.test_request_context('/'):\r\n for token in self.auth_providers:\r\n assert_raises(Unauthorized,\r\n getattr(require, 'token').create,\r\n token)","def test_admin_api_organization_accesses_request_anonymous(self):\n organization = factories.OrganizationFactory()\n response = self.client.get(\n f\"/api/v1.0/admin/organizations/{organization.id}/accesses/\"\n )\n\n self.assertEqual(response.status_code, 401)\n content = response.json()\n self.assertEqual(\n content[\"detail\"], \"Authentication credentials were not provided.\"\n )","def protected():\n return jsonify(message=f'protected endpoint (allowed user {flask_praetorian.current_user().username})')","def check_user():\n token = request.headers['Authorization'].replace('Bearer ', '')\n return jsonify({\"access_token\": token}), 200","async def test_whoami_query_raises(raising_session) -> None:\n info = await location_util._get_whoami(raising_session)\n assert info is None","def handle_whoami(self, client_socket):\n user = self.clients[client_socket]['data'].decode('utf-8')\n print(f'User {user} queried their identity')\n msg = f'You are currently user {user}'\n self.log_and_send(client_socket, msg)","def who_am_i():\n request = req('GET', USER_API + 'session/whoami')\n r = request.json()\n user = {\n 'Email': demisto.get(r, 'data.email'),\n 'Login': demisto.get(r, 'data.login'),\n 'Name': demisto.get(r, 'data.name'),\n 'Organization': demisto.get(r, 'data.organization_id'),\n 'Role': demisto.get(r, 'data.role')\n }\n demisto.results({\n 'Type': entryTypes['note'],\n 'EntryContext': {'ThreatGrid.User': user},\n 'HumanReadable': tableToMarkdown('ThreatGrid - Current Session User', [user], [\n 'Email', 'Login', 'Name', 'Organization', 'Role'\n ]),\n 'ContentsFormat': formats['json'],\n 'Contents': user\n })","def identity_authentication(realm, blacklist=[\"nobody\"]):\n def checkpassword(realm, username, password):\n return username and password and username == password and username not in blacklist\n cherrypy.lib.auth_basic.basic_auth(realm, checkpassword)\n cherrypy.request.security = { \"user\" : cherrypy.request.login, \"name\" : cherrypy.request.login, \"roles\": [] }","def test_anonymous_user_update(self):\r\n with self.flask_app.test_request_context('/'):\r\n for token in self.auth_providers:\r\n assert_raises(Unauthorized,\r\n getattr(require, 'token').update,\r\n token)","def whoami(self):\n response = self._client.get('/v1/whoami')\n # XXX Facundo 2020-06-30: Every time we consume data from the Store (after a succesful\n # call) we need to wrap it with a context manager that will raise UnknownError (after\n # logging in debug the received response). This would catch API changes, for example,\n # without making charmcraft to badly crash. Related: issue #73.\n result = User(\n name=response['display-name'],\n username=response['username'],\n userid=response['id'],\n )\n return result","def remote_loginAnonymous(self, mind):\n d = self.portal.login(Anonymous(), mind, IPerspective)\n d.addCallback(self._cb_login)\n d.addErrback(self._login_error)\n return d","def rest_test_no_auth():\n # Permission check is not applicable here\n return jsonify({\"answer\": 42})","def test_anonymous_public(self):\n rv = self.client.get('/')\n self.assertEqual(b'public', rv.data)","def authenticate():\n return Response('Not Authorized', 401, {'WWW-Authenticate': 'Basic realm=\"api\"'})","def test_unauthenticated_request(self):\n url = self.get_url(self.active_user.id)\n response = self.client.get(url)\n\n expected_status_code = 401\n self.assertEqual(response.status_code, expected_status_code)","def test_retrieve_unauthorized_user(self):\n\n response = self.client.get(URL_ME)\n self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)","def authenticate():\n return Response(render_template('index.html', auth=False), 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","def test_xblockcompletion_get_user_is_anonymous(self):\n client = Client()\n response = self.client.get(reverse('xblockcompletion-data:data'))\n request = response.request\n self.assertEqual(response.status_code, 404)","def test_post_forbid_anonymous(self):\n self.check_post_forbid_anonymous('FORBID_ANONYMOUS')","def add_anonymous(self, homedir, **kwargs):\n DummyAuthorizer.add_user(self, 'anonymous', '', homedir, **kwargs)","def before_request():\n if g.current_user.is_anonymous:\n return forbidden('Not signed in')\n\n if not g.current_user.confirmed:\n return forbidden('Unconfirmed account')","def test_access_control_is_superuser_as_anonymous_raises_access_control_error(\n self,\n ):\n # Arrange\n mock_request = create_mock_request(user=self.anonymous_user)\n\n # Act # Assert\n with self.assertRaises(AccessControlError):\n access_control_api.is_superuser(\n mock_function, request=mock_request\n )","def user_must_authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","def authenticate():\n return flask.Response('Login required.', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","def test_get_all_accessible_by_hash_as_anonymous_does_not_return_user_template(\n self,\n ):\n mock_request = create_mock_request(user=self.anonymous_user)\n templates = template_api.get_all_accessible_by_hash(\n self.fixture.user1_template.hash, request=mock_request\n )\n self.assertTrue(templates.count() == 0)","def test_00_anonymous(self):\r\n res = self.app.get(self.url, follow_redirects=True)\r\n dom = BeautifulSoup(res.data)\r\n err_msg = \"The CKAN exporter should not be available for anon users\"\r\n assert dom.find(id=\"ckan\") is None, err_msg","def test_get_users_unauthenticated(client: FlaskClient) -> None:\n # Unauthenticated users are not allowed to make the request\n response = get_users(client)\n assert_error_response(response, HTTPStatus.UNAUTHORIZED)","def test_04_admin_featured_apps_as_anonymous(self):\r\n res = self.app.get('/admin/featured', follow_redirects=True)\r\n assert \"Please sign in to access this page\" in res.data, res.data","def anonymous_required(func):\n\n async def wrapped(self, *args, **kwargs):\n if self.request.user is not None:\n print(\"Login please.\")\n # redirect(self.request, 'index')\n\n return await func(self, *args, **kwargs)\n\n return wrapped","def user():\n\treturn request.authorization.username if zk.get_http_login() else zk.get_username()","def test_unauthenticated_user_denial(self):\n\n self.response = self.client.get(\"/api/users/users_list/\")\n self.assertEqual(self.response.status_code, status.HTTP_403_FORBIDDEN)\n self.assertEqual(\n 'Authentication credentials were not provided.', self.response.data['detail'])"],"string":"[\n \"def whoami():\\n g.data['authenticated_user'] = g.current_user\",\n \"def whoami():\\n try:\\n\\n token = request.headers['token']\\n username, uid, wid = read_auth_token(token)\\n return dict(username=username, uid=uid, wid=wid)\\n\\n except SignatureExpired as e:\\n return dict(error=str(e)), 401\\n except BadSignature as e:\\n return dict(error=str(e)), 401\\n except Exception as e:\\n return dict(error=str(e)), 500\",\n \"def whoami(self):\\n if self.auth:\\n return self.auth[0]\\n else:\\n return \\\"Anonymous\\\"\",\n \"def whoami(ctx):\\n ctx.setup_logger(format='')\\n AuthCmd(ctx).whoami()\",\n \"def test_home_as_anonymous(self):\\n response = self.client.get(\\\"/images/contents/\\\")\\n self.assertEqual(response.status_code, 401)\",\n \"def test_home_as_anonymous(self):\\n response = self.client.get(\\\"/images/contents/\\\")\\n self.assertEqual(response.status_code, 401)\",\n \"def get_anonymous_user():\\n return User.objects.get(username = \\\"AnonymousUser\\\")\",\n \"def test_anonymous(self):\\n self.logout_user()\\n\\n response = self.client.get(self.api_link)\\n self.assertContains(response, \\\"sign in to use private threads\\\", status_code=403)\",\n \"def login_anonymously(self):\\n username = b\\\"anonymous\\\"\\n password = b\\\"anonymous\\\"\\n self.send_cmd(b\\\"USER \\\" + username + B_CRLF)\\n self.send_cmd(b\\\"PASS \\\" + password + B_CRLF)\",\n \"def is_anonymous():\\n return False\",\n \"def test_status_code_for_anonymous_user(self):\\n response = self.client.get(self.get_url())\\n self.assertEqual(response.status_code, self.status_anonymous)\",\n \"def before_request() -> None:\\n if current_user.is_anonymous() or not current_user.is_allowed():\\n abort(401)\",\n \"def whoami(self):\",\n \"def login(self):\\n self._client.clear_credentials()\\n self._client.get('/v1/whoami')\",\n \"def test_get_all_tokens_anonymous_user(self):\\r\\n\\r\\n # Anonymoues users should be unauthorized, no matter which kind of token are requesting\\r\\n res = self.app.get('/api/token')\\r\\n err = json.loads(res.data)\\r\\n\\r\\n assert res.status_code == 401, err\\r\\n assert err['status'] == 'failed', err\\r\\n assert err['status_code'] == 401, err\\r\\n assert err['exception_cls'] == 'Unauthorized', err\\r\\n assert err['target'] == 'token', err\",\n \"def whoami():\\n return current_user._get_current_object()\",\n \"def _is_anonymous_user(auth_info):\\n auth_info = auth_info or _get_auth_info_for_id_or_from_request()\\n return str(auth_info.get(\\\"user_id\\\")) == ANONYMOUS_USER_ID\",\n \"def test_anonymous_user_read(self):\\r\\n with self.flask_app.test_request_context('/'):\\r\\n for token in self.auth_providers:\\r\\n assert_raises(Unauthorized,\\r\\n getattr(require, 'token').read,\\r\\n token)\",\n \"def test_get_specific_token_anonymous_user(self):\\r\\n\\r\\n res = self.app.get('/api/token/twitter')\\r\\n err = json.loads(res.data)\\r\\n\\r\\n assert res.status_code == 401, err\\r\\n assert err['status'] == 'failed', err\\r\\n assert err['status_code'] == 401, err\\r\\n assert err['exception_cls'] == 'Unauthorized', err\\r\\n assert err['target'] == 'token', err\",\n \"def get_authenticated_denied(self):\",\n \"def whoami(self):\\n response = requests.get(self.ENDPOINT + '/user-resource/user', headers={'apikey':self.apikey})\\n\\n return response.json()\",\n \"def user_logged_in():\\n if not session.get('user_id'):\\n return \\\"nope\\\", 401\\n else:\\n return \\\"yep\\\", 200\",\n \"def test_not_authenticated(self):\\n pass # lint-amnesty, pylint: disable=unnecessary-pass\",\n \"def test_not_authenticated(self):\\n pass # lint-amnesty, pylint: disable=unnecessary-pass\",\n \"def hidden_basic_auth(user=\\\"user\\\", passwd=\\\"passwd\\\"):\\n\\n if not check_basic_auth(user, passwd):\\n return status_code(404)\\n return jsonify(authenticated=True, user=user)\",\n \"def test_authenticated_inherits_anonymous_permission(self):\\n resource = Resource('milestone', 'milestone1')\\n self.assertTrue(self.check_permission('MILESTONE_VIEW',\\n 'anonymous', resource))\\n self.assertTrue(self.check_permission('MILESTONE_VIEW',\\n 'authenticated', resource))\\n self.assertIn('MILESTONE_VIEW', self.get_perm('anonymous',\\n resource))\\n self.assertIn('MILESTONE_VIEW', self.get_perm('authenticated',\\n resource))\",\n \"def run_whoami(self, expanded, unexpanded) :\\n\\t\\tif expanded :\\n\\t\\t\\treturn self.errormessage(\\\"Doesn't need any argument\\\")\\n\\t\\t(username, roles) = self.WhoAmI()\\n\\t\\tself.htmlmessage('Username: %s Roles: %s' % (username, string.join(roles, ', ')), safe=1)\\n\\t\\tself.printf(\\\"%s\\\\n\\\" % username)\",\n \"def is_anonymous(self):\\n return False\",\n \"def whoami(self):\\n print(\\\"-----\\\",self._whoami)\",\n \"def is_anonymous(self):\\n return False\",\n \"def is_anonymous(self):\\n return False\",\n \"def is_anonymous(self):\\n return False\",\n \"def is_anonymous(self):\\n return False\",\n \"def is_anonymous(self):\\n return False\",\n \"def is_anonymous(self):\\n return False\",\n \"def is_anonymous(self):\\n return False\",\n \"def is_anonymous(self):\\n return False\",\n \"def is_anonymous(self):\\n return False\",\n \"def is_anonymous(self):\\n return False\",\n \"def is_anonymous(self):\\n return False\",\n \"def is_anonymous(self):\\n return False\",\n \"def is_anonymous(self):\\n return False\",\n \"def is_anonymous(self):\\n return False\",\n \"def is_anonymous(self):\\n return False\",\n \"def is_anonymous(self):\\n return False\",\n \"def is_anonymous(self):\\n return False\",\n \"def is_anonymous(self):\\n return False\",\n \"def is_anonymous(self):\\n return False\",\n \"def is_anonymous(self):\\n return False\",\n \"def test_get_user_by_id_unauthenticated(client: FlaskClient) -> None:\\n username = create_random_username()\\n # Unauthenticated users are not allowed to make the request\\n response = get_user(client, username)\\n assert_error_response(response, HTTPStatus.UNAUTHORIZED)\",\n \"def test_help_for_anonymous_user(mock_user_moira_lists):\\n request = RequestFactory()\\n request.method = \\\"GET\\\"\\n request.user = AnonymousUser()\\n response = HelpPageView.as_view()(request)\\n assert response.status_code == status.HTTP_200_OK\",\n \"def is_anonymous_access_allowed(self):\\n return self._is_anonymous_access_allowed\",\n \"def test_read_attendances_by_anonymous_user(self):\\n response = self.client.get(\\n self._get_url(self.live),\\n content_type=\\\"application/json\\\",\\n )\\n self.assertEqual(response.status_code, 401)\",\n \"def _respond_unauthorized(self, request, message=\\\"Unauthorized\\\"):\\n resp = Response()\\n resp.status = 401\\n resp.headers = self.forget(request.environ, {})\\n resp.content_type = \\\"text/plain\\\"\\n resp.body = message\\n request.environ[\\\"repoze.who.application\\\"] = resp\\n return None\",\n \"def is_anonymous(self):\\r\\n return False\",\n \"def test_api_livesession_read_anonymous(self):\\n livesession = AnonymousLiveSessionFactory()\\n response = self.client.get(self._get_url(livesession.video, livesession))\\n self.assertEqual(response.status_code, 401)\\n self.assertEqual(\\n response.json(), {\\\"detail\\\": \\\"Authentication credentials were not provided.\\\"}\\n )\",\n \"def denied_response(self, req):\\n if req.remote_user:\\n return HTTPForbidden(request=req)\\n else:\\n return HTTPUnauthorized(request=req)\",\n \"def unauthorized():\\n return HttpError(401)\",\n \"def grant_access(user, endpoint):\\n return not user.is_anonymous()\",\n \"def test_retrive_user_unauthenticated(self):\\n res = self.client.get(ME_URL)\\n\\n self.assertEqual(res.status_code, status.HTTP_401_UNAUTHORIZED)\",\n \"def test_profile_api_anon(self):\\n self.client.logout()\\n response = self.client.get(self.url)\\n self.assertEqual(response.status_code, 403)\",\n \"def test_anonymous_cannot_get_userprofileview(dclient):\\n resp = dclient.get(\\\"/api/record/profile/\\\", follow=True)\\n assert resp.status_code == 403\",\n \"def test_get_user_template_as_anonymous_raises_access_control_error(self):\\n mock_request = create_mock_request(user=self.anonymous_user)\\n with self.assertRaises(AccessControlError):\\n template_api.get_by_id(\\n self.fixture.user1_template.id, request=mock_request\\n )\",\n \"def test_anonymous_user_doesnt_have_classroom_state(self):\\n self.client.force_authenticate(None)\\n resp = self.client.get(self.api_classroom_list_url)\\n self.assertEqual(resp.status_code, 401)\",\n \"def authcheck():\\n user = get_user()\\n return jsonify({'current_identity': user.username})\",\n \"def ping_missing_user(request):\\r\\n return _api_response(request, {\\r\\n 'success': False,\\r\\n 'message': 'Missing username in your api url.'\\r\\n })\",\n \"def test_closed_api_not_authenticated(self):\\n anonymous = Group.objects.get(name='Anonymous')\\n anonymous.permissions.remove(\\n Permission.objects.get(codename='api_read_initiative')\\n )\\n\\n response = self.client.get(self.initiatives_url)\\n self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)\",\n \"def test_get_global_template_as_anonymous_raises_access_control_error(\\n self,\\n ):\\n mock_request = create_mock_request(user=self.anonymous_user)\\n with self.assertRaises(AccessControlError):\\n template_api.get_by_id(\\n self.fixture.global_template.id, request=mock_request\\n )\",\n \"def test_an_admin_view_anonymous(client):\\n response = client.get('/admin/')\\n assert status(response) == 'found'\\n assert response.url.startswith('/admin/login/')\",\n \"def test_anonymous_user_create(self):\\r\\n with self.flask_app.test_request_context('/'):\\r\\n for token in self.auth_providers:\\r\\n assert_raises(Unauthorized,\\r\\n getattr(require, 'token').create,\\r\\n token)\",\n \"def test_admin_api_organization_accesses_request_anonymous(self):\\n organization = factories.OrganizationFactory()\\n response = self.client.get(\\n f\\\"/api/v1.0/admin/organizations/{organization.id}/accesses/\\\"\\n )\\n\\n self.assertEqual(response.status_code, 401)\\n content = response.json()\\n self.assertEqual(\\n content[\\\"detail\\\"], \\\"Authentication credentials were not provided.\\\"\\n )\",\n \"def protected():\\n return jsonify(message=f'protected endpoint (allowed user {flask_praetorian.current_user().username})')\",\n \"def check_user():\\n token = request.headers['Authorization'].replace('Bearer ', '')\\n return jsonify({\\\"access_token\\\": token}), 200\",\n \"async def test_whoami_query_raises(raising_session) -> None:\\n info = await location_util._get_whoami(raising_session)\\n assert info is None\",\n \"def handle_whoami(self, client_socket):\\n user = self.clients[client_socket]['data'].decode('utf-8')\\n print(f'User {user} queried their identity')\\n msg = f'You are currently user {user}'\\n self.log_and_send(client_socket, msg)\",\n \"def who_am_i():\\n request = req('GET', USER_API + 'session/whoami')\\n r = request.json()\\n user = {\\n 'Email': demisto.get(r, 'data.email'),\\n 'Login': demisto.get(r, 'data.login'),\\n 'Name': demisto.get(r, 'data.name'),\\n 'Organization': demisto.get(r, 'data.organization_id'),\\n 'Role': demisto.get(r, 'data.role')\\n }\\n demisto.results({\\n 'Type': entryTypes['note'],\\n 'EntryContext': {'ThreatGrid.User': user},\\n 'HumanReadable': tableToMarkdown('ThreatGrid - Current Session User', [user], [\\n 'Email', 'Login', 'Name', 'Organization', 'Role'\\n ]),\\n 'ContentsFormat': formats['json'],\\n 'Contents': user\\n })\",\n \"def identity_authentication(realm, blacklist=[\\\"nobody\\\"]):\\n def checkpassword(realm, username, password):\\n return username and password and username == password and username not in blacklist\\n cherrypy.lib.auth_basic.basic_auth(realm, checkpassword)\\n cherrypy.request.security = { \\\"user\\\" : cherrypy.request.login, \\\"name\\\" : cherrypy.request.login, \\\"roles\\\": [] }\",\n \"def test_anonymous_user_update(self):\\r\\n with self.flask_app.test_request_context('/'):\\r\\n for token in self.auth_providers:\\r\\n assert_raises(Unauthorized,\\r\\n getattr(require, 'token').update,\\r\\n token)\",\n \"def whoami(self):\\n response = self._client.get('/v1/whoami')\\n # XXX Facundo 2020-06-30: Every time we consume data from the Store (after a succesful\\n # call) we need to wrap it with a context manager that will raise UnknownError (after\\n # logging in debug the received response). This would catch API changes, for example,\\n # without making charmcraft to badly crash. Related: issue #73.\\n result = User(\\n name=response['display-name'],\\n username=response['username'],\\n userid=response['id'],\\n )\\n return result\",\n \"def remote_loginAnonymous(self, mind):\\n d = self.portal.login(Anonymous(), mind, IPerspective)\\n d.addCallback(self._cb_login)\\n d.addErrback(self._login_error)\\n return d\",\n \"def rest_test_no_auth():\\n # Permission check is not applicable here\\n return jsonify({\\\"answer\\\": 42})\",\n \"def test_anonymous_public(self):\\n rv = self.client.get('/')\\n self.assertEqual(b'public', rv.data)\",\n \"def authenticate():\\n return Response('Not Authorized', 401, {'WWW-Authenticate': 'Basic realm=\\\"api\\\"'})\",\n \"def test_unauthenticated_request(self):\\n url = self.get_url(self.active_user.id)\\n response = self.client.get(url)\\n\\n expected_status_code = 401\\n self.assertEqual(response.status_code, expected_status_code)\",\n \"def test_retrieve_unauthorized_user(self):\\n\\n response = self.client.get(URL_ME)\\n self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)\",\n \"def authenticate():\\n return Response(render_template('index.html', auth=False), 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"def test_xblockcompletion_get_user_is_anonymous(self):\\n client = Client()\\n response = self.client.get(reverse('xblockcompletion-data:data'))\\n request = response.request\\n self.assertEqual(response.status_code, 404)\",\n \"def test_post_forbid_anonymous(self):\\n self.check_post_forbid_anonymous('FORBID_ANONYMOUS')\",\n \"def add_anonymous(self, homedir, **kwargs):\\n DummyAuthorizer.add_user(self, 'anonymous', '', homedir, **kwargs)\",\n \"def before_request():\\n if g.current_user.is_anonymous:\\n return forbidden('Not signed in')\\n\\n if not g.current_user.confirmed:\\n return forbidden('Unconfirmed account')\",\n \"def test_access_control_is_superuser_as_anonymous_raises_access_control_error(\\n self,\\n ):\\n # Arrange\\n mock_request = create_mock_request(user=self.anonymous_user)\\n\\n # Act # Assert\\n with self.assertRaises(AccessControlError):\\n access_control_api.is_superuser(\\n mock_function, request=mock_request\\n )\",\n \"def user_must_authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"def authenticate():\\n return flask.Response('Login required.', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"def test_get_all_accessible_by_hash_as_anonymous_does_not_return_user_template(\\n self,\\n ):\\n mock_request = create_mock_request(user=self.anonymous_user)\\n templates = template_api.get_all_accessible_by_hash(\\n self.fixture.user1_template.hash, request=mock_request\\n )\\n self.assertTrue(templates.count() == 0)\",\n \"def test_00_anonymous(self):\\r\\n res = self.app.get(self.url, follow_redirects=True)\\r\\n dom = BeautifulSoup(res.data)\\r\\n err_msg = \\\"The CKAN exporter should not be available for anon users\\\"\\r\\n assert dom.find(id=\\\"ckan\\\") is None, err_msg\",\n \"def test_get_users_unauthenticated(client: FlaskClient) -> None:\\n # Unauthenticated users are not allowed to make the request\\n response = get_users(client)\\n assert_error_response(response, HTTPStatus.UNAUTHORIZED)\",\n \"def test_04_admin_featured_apps_as_anonymous(self):\\r\\n res = self.app.get('/admin/featured', follow_redirects=True)\\r\\n assert \\\"Please sign in to access this page\\\" in res.data, res.data\",\n \"def anonymous_required(func):\\n\\n async def wrapped(self, *args, **kwargs):\\n if self.request.user is not None:\\n print(\\\"Login please.\\\")\\n # redirect(self.request, 'index')\\n\\n return await func(self, *args, **kwargs)\\n\\n return wrapped\",\n \"def user():\\n\\treturn request.authorization.username if zk.get_http_login() else zk.get_username()\",\n \"def test_unauthenticated_user_denial(self):\\n\\n self.response = self.client.get(\\\"/api/users/users_list/\\\")\\n self.assertEqual(self.response.status_code, status.HTTP_403_FORBIDDEN)\\n self.assertEqual(\\n 'Authentication credentials were not provided.', self.response.data['detail'])\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.71340424","0.7014765","0.6927591","0.6695747","0.6655952","0.6655952","0.6635073","0.65991694","0.65672773","0.6493297","0.64832705","0.64729846","0.6437622","0.6405885","0.64006865","0.6381664","0.6346517","0.6344521","0.6308586","0.6299756","0.62978876","0.62631","0.62279135","0.62279135","0.6215758","0.6158293","0.6147617","0.61027783","0.60672724","0.60541975","0.60541975","0.60541975","0.60541975","0.60541975","0.60541975","0.60541975","0.60541975","0.60541975","0.60541975","0.60541975","0.60541975","0.60541975","0.60541975","0.60541975","0.60541975","0.60541975","0.60541975","0.60541975","0.60541975","0.60184395","0.6007753","0.60068005","0.6001562","0.59996194","0.5983602","0.59827274","0.59475166","0.59086275","0.58834803","0.58625716","0.5856419","0.5839549","0.5798452","0.5795942","0.57888985","0.57847065","0.5781492","0.5776897","0.5772621","0.5771921","0.57677615","0.5752866","0.5742152","0.573858","0.5738463","0.5735308","0.5730293","0.57209677","0.57165664","0.57118404","0.57113296","0.5696324","0.56933266","0.5680631","0.56477094","0.5643644","0.56303465","0.56278294","0.56215864","0.5617581","0.56163275","0.5608191","0.56055236","0.55976295","0.55964756","0.5586999","0.5584946","0.55731004","0.5569453","0.5567659"],"string":"[\n \"0.71340424\",\n \"0.7014765\",\n \"0.6927591\",\n \"0.6695747\",\n \"0.6655952\",\n \"0.6655952\",\n \"0.6635073\",\n \"0.65991694\",\n \"0.65672773\",\n \"0.6493297\",\n \"0.64832705\",\n \"0.64729846\",\n \"0.6437622\",\n \"0.6405885\",\n \"0.64006865\",\n \"0.6381664\",\n \"0.6346517\",\n \"0.6344521\",\n \"0.6308586\",\n \"0.6299756\",\n \"0.62978876\",\n \"0.62631\",\n \"0.62279135\",\n \"0.62279135\",\n \"0.6215758\",\n \"0.6158293\",\n \"0.6147617\",\n \"0.61027783\",\n \"0.60672724\",\n \"0.60541975\",\n \"0.60541975\",\n \"0.60541975\",\n \"0.60541975\",\n \"0.60541975\",\n \"0.60541975\",\n \"0.60541975\",\n \"0.60541975\",\n \"0.60541975\",\n \"0.60541975\",\n \"0.60541975\",\n \"0.60541975\",\n \"0.60541975\",\n \"0.60541975\",\n \"0.60541975\",\n \"0.60541975\",\n \"0.60541975\",\n \"0.60541975\",\n \"0.60541975\",\n \"0.60541975\",\n \"0.60184395\",\n \"0.6007753\",\n \"0.60068005\",\n \"0.6001562\",\n \"0.59996194\",\n \"0.5983602\",\n \"0.59827274\",\n \"0.59475166\",\n \"0.59086275\",\n \"0.58834803\",\n \"0.58625716\",\n \"0.5856419\",\n \"0.5839549\",\n \"0.5798452\",\n \"0.5795942\",\n \"0.57888985\",\n \"0.57847065\",\n \"0.5781492\",\n \"0.5776897\",\n \"0.5772621\",\n \"0.5771921\",\n \"0.57677615\",\n \"0.5752866\",\n \"0.5742152\",\n \"0.573858\",\n \"0.5738463\",\n \"0.5735308\",\n \"0.5730293\",\n \"0.57209677\",\n \"0.57165664\",\n \"0.57118404\",\n \"0.57113296\",\n \"0.5696324\",\n \"0.56933266\",\n \"0.5680631\",\n \"0.56477094\",\n \"0.5643644\",\n \"0.56303465\",\n \"0.56278294\",\n \"0.56215864\",\n \"0.5617581\",\n \"0.56163275\",\n \"0.5608191\",\n \"0.56055236\",\n \"0.55976295\",\n \"0.55964756\",\n \"0.5586999\",\n \"0.5584946\",\n \"0.55731004\",\n \"0.5569453\",\n \"0.5567659\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8336867"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":14374,"cells":{"query":{"kind":"string","value":"Loggedin users can make `whoami` requests. They receive their own user object."},"document":{"kind":"string","value":"def test_whoami_by_logged_in_user(self):\n user = factories.UserFactory(\n first_name=\"Jane\", last_name=\"Doe\", email=\"jane.doe@example.com\"\n )\n org_1 = factories.OrganizationFactory()\n org_access_1 = factories.OrganizationAccessFactory(\n user=user, organization=org_1\n )\n org_2 = factories.OrganizationFactory()\n org_access_2 = factories.OrganizationAccessFactory(\n user=user, organization=org_2\n )\n\n jwt_token = AccessToken()\n jwt_token.payload[\"resource_id\"] = str(user.id)\n jwt_token.payload[\"user\"] = {\n \"id\": str(user.id),\n \"username\": str(user.username),\n }\n print(jwt_token.payload[\"user\"])\n\n with self.assertNumQueries(3):\n response = self.client.get(\n \"/api/users/whoami/\",\n HTTP_AUTHORIZATION=f\"Bearer {jwt_token}\",\n )\n\n self.assertEqual(response.status_code, 200)\n self.assertEqual(\n response.json()[\"date_joined\"],\n user.date_joined.isoformat()[:-6] + \"Z\", # NB: DRF literally does this\n )\n self.assertEqual(response.json()[\"email\"], \"jane.doe@example.com\")\n self.assertEqual(response.json()[\"first_name\"], \"Jane\")\n self.assertEqual(response.json()[\"id\"], str(user.id))\n self.assertEqual(response.json()[\"is_staff\"], False)\n self.assertEqual(response.json()[\"is_superuser\"], False)\n self.assertEqual(response.json()[\"last_name\"], \"Doe\")\n\n resp_accesses = response.json()[\"organization_accesses\"]\n resp_org_access_1 = (\n resp_accesses.pop(0)\n if resp_accesses[0][\"organization\"] == str(org_1.id)\n else resp_accesses.pop(1)\n )\n self.assertEqual(\n resp_org_access_1,\n {\n \"organization\": str(org_1.id),\n \"organization_name\": org_1.name,\n \"role\": org_access_1.role,\n \"user\": str(user.id),\n },\n )\n resp_org_access_2 = resp_accesses.pop(0)\n self.assertEqual(\n resp_org_access_2,\n {\n \"organization\": str(org_2.id),\n \"organization_name\": org_2.name,\n \"role\": org_access_2.role,\n \"user\": str(user.id),\n },\n )"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def whoami():\n g.data['authenticated_user'] = g.current_user","def whoami():\n return current_user._get_current_object()","def whoami(self):","def whoami(self):\n response = requests.get(self.ENDPOINT + '/user-resource/user', headers={'apikey':self.apikey})\n\n return response.json()","def whoami(ctx):\n ctx.setup_logger(format='')\n AuthCmd(ctx).whoami()","def handle_whoami(self, client_socket):\n user = self.clients[client_socket]['data'].decode('utf-8')\n print(f'User {user} queried their identity')\n msg = f'You are currently user {user}'\n self.log_and_send(client_socket, msg)","def whoami(self):\n print(\"-----\",self._whoami)","def whoami(bot, event, *args):\n\n if bot.memory.exists(['user_data', event.user_id.chat_id, \"nickname\"]):\n try:\n fullname = '{0} ({1})'.format(event.user.full_name.split(' ', 1)[0]\n , bot.get_memory_suboption(event.user_id.chat_id, 'nickname'))\n except TypeError:\n fullname = event.user.full_name\n else:\n fullname = event.user.full_name\n\n bot.send_message_parsed(event.conv, _(\"<b>{}</b>, chat_id = <i>{}</i>\").format(fullname, event.user.id_.chat_id))","def whoami(bot, event, *args):\n\n if bot.memory.exists(['user_data', event.user_id.chat_id, \"nickname\"]):\n try:\n fullname = '{0} ({1})'.format(event.user.full_name.split(' ', 1)[0]\n , bot.get_memory_suboption(event.user_id.chat_id, 'nickname'))\n except TypeError:\n fullname = event.user.full_name\n else:\n fullname = event.user.full_name\n\n yield from bot.coro_send_message(event.conv, _(\"<b><pre>{}</pre></b>, chat_id = <i>{}</i>\").format(fullname, event.user.id_.chat_id))","def run_whoami(self, expanded, unexpanded) :\n\t\tif expanded :\n\t\t\treturn self.errormessage(\"Doesn't need any argument\")\n\t\t(username, roles) = self.WhoAmI()\n\t\tself.htmlmessage('Username: %s Roles: %s' % (username, string.join(roles, ', ')), safe=1)\n\t\tself.printf(\"%s\\n\" % username)","def login(self):\n self._client.clear_credentials()\n self._client.get('/v1/whoami')","def whoami(self):\n response = self._client.get('/v1/whoami')\n # XXX Facundo 2020-06-30: Every time we consume data from the Store (after a succesful\n # call) we need to wrap it with a context manager that will raise UnknownError (after\n # logging in debug the received response). This would catch API changes, for example,\n # without making charmcraft to badly crash. Related: issue #73.\n result = User(\n name=response['display-name'],\n username=response['username'],\n userid=response['id'],\n )\n return result","def whoami(self):\n if self.auth:\n return self.auth[0]\n else:\n return \"Anonymous\"","def whoami():\n try:\n\n token = request.headers['token']\n username, uid, wid = read_auth_token(token)\n return dict(username=username, uid=uid, wid=wid)\n\n except SignatureExpired as e:\n return dict(error=str(e)), 401\n except BadSignature as e:\n return dict(error=str(e)), 401\n except Exception as e:\n return dict(error=str(e)), 500","def who_am_i():\n request = req('GET', USER_API + 'session/whoami')\n r = request.json()\n user = {\n 'Email': demisto.get(r, 'data.email'),\n 'Login': demisto.get(r, 'data.login'),\n 'Name': demisto.get(r, 'data.name'),\n 'Organization': demisto.get(r, 'data.organization_id'),\n 'Role': demisto.get(r, 'data.role')\n }\n demisto.results({\n 'Type': entryTypes['note'],\n 'EntryContext': {'ThreatGrid.User': user},\n 'HumanReadable': tableToMarkdown('ThreatGrid - Current Session User', [user], [\n 'Email', 'Login', 'Name', 'Organization', 'Role'\n ]),\n 'ContentsFormat': formats['json'],\n 'Contents': user\n })","def test_whoami_by_anonymous_user(self):\n response = self.client.get(\"/api/users/whoami/\")\n self.assertEqual(response.status_code, 401)","def mine(request):\n request.user_to_show = request.user\n return _show_user(request)","def mine(request):\n request.user_to_show = request.user\n return _show_user(request)","def user_for_login(request):\n log = get_log(\"user_for_login\")\n\n identifier = None\n\n # standard repoze related identity:\n if 'repoze.who.identity' in request.environ:\n identity = request.environ['repoze.who.identity']\n\n if 'username' in identity:\n identifier = identity['username']\n\n elif 'repoze.who.userid' in identity:\n identifier = identity['repoze.who.userid']\n\n # token based identity:\n elif 'pp.api_access.identity' in request.environ:\n identifier = request.environ['pp.api_access.identity']\n\n else:\n log.debug(\"No identifier recovered from environment!\")\n\n if not identifier:\n raise HTTPForbidden()\n\n if _USERS.get(identifier):\n found = _USERS.get(identifier)\n\n if _NAME_TO_ID.get(identifier):\n found = _USERS.get(_NAME_TO_ID.get(identifier))\n\n return found","def user_logged_in(self, sender, request, user, **kwargs):","def user():","def user_info(self):\n return self.auth.get_user_by_session()","def get_self_account_details(self):\n return self.mrr_obj.get('/whoami')","def __call__(self, req):\n return req.remote_user","def current_user(self, **kwargs):\n return self.me(**kwargs)","def make_logged_in_owner(self):\n response = self.client.post('', self.credentials, follow=True)","def _get_current_user(self):\n\n out, err, exitcode = self.execute('whoami')\n\n if exitcode == 0:\n return User(out[0])\n return None","def get_logged_info():\n user = current_identity\n return make_response(dumps({\"status\": True, \"user\": user}), 200)","def user():\n\treturn request.authorization.username if zk.get_http_login() else zk.get_username()","def user(self):\n pass","def me():\n return current_user.get()","def user(request, user_id):\n raise NotImplementedError","def get_current_user(self):\n return None","def log_user_logged_in(sender, request, user, **kwargs):\n\n ip = get_client_ip(request)\n date = datetime.now()\n log.info('Login user: %s , IP: %s , Date: %s', user, ip, str(date))","def user():\n pass","def user_login():\n \n data = user_obj.user_login()\n return data","def user_logged_in_callback(sender, request, user, **kwargs):\n logger.info(f'User {user.username} logged in from {get_client_ip(request)}')","def current_user(request):\n \n serializer = UserSerializer(request.user)\n return Response(serializer.data)","def current_user(request):\n \n serializer = UserSerializer(request.user)\n return Response(serializer.data)","def current_user(request):\n \n serializer = UserSerializer(request.user)\n return Response(serializer.data)","def get_user_me(request):\r\n auth = (DaemonServer._user['_email'], DaemonServer._user['_token'])\r\n res = requests.get(DaemonServer._base_url + '/user/me.json', auth=auth)\r\n return res","def get_user(request: Request):\n return request.user","def login_user():\n pass","def current_user_info():\n\n return current_user","def get_object(self):\n requested_user = self.kwargs.get('username')\n loggedin_user = self.request.user.username\n if str(requested_user) == str(loggedin_user) or requested_user == 'me':\n requested_user = loggedin_user\n return get_object_or_404(User, username__iexact=requested_user, is_active=True)\n else:\n raise PermissionDenied","def get_one_user():","def log_user_logged_in(sender, request, user, **kwargs):\n ip_address = get_client_ip(request)\n logger.info('User \"%s\" logged in with IP address \"%s\".' % (user, ip_address))","def do_login(self, backend, user):","def do_login(user):\n session[CURRENT_USER_KEY] = user.id","def get_object(self):\n return User.objects.get(username=self.request.user.username)","def get(self):\r\n return get_user(request)","def show_user(request):\n return _show_user(request)","def show_user(request):\n return _show_user(request)","def simple_basic_auth_callback(request, user, *args, **kwargs):\r\n login(request, user)\r\n request.user = user","def get_user():\n return getpass.getuser()","def get_user():\n return getpass.getuser()","def user(self):","def user(application):\n yield application.user","def auth(self, user):","def get_current_user(request): \n user_id = request.session.get('user_id', False)\n \n return Member.objects.get(id=user_id)","def get_user(id):\n pass","def user(self):\n if not flask_login.current_user.is_anonymous():\n return flask_login.current_user._get_current_object()\n else:\n return None","def _current_login_user(self):\n return self.env.uid","def whoami(self,\n\t note=None,\n\t shutit_pexpect_child=None,\n\t loglevel=logging.DEBUG):\n\t\tshutit_global.shutit_global_object.yield_to_draw()\n\t\tshutit_pexpect_child = shutit_pexpect_child or self.get_current_shutit_pexpect_session().pexpect_child\n\t\tshutit_pexpect_session = self.get_shutit_pexpect_session_from_child(shutit_pexpect_child)\n\t\treturn shutit_pexpect_session.whoami(note=note,loglevel=loglevel)","def logged_in(request):\n return request.current_user is not None","def get_current_user():\n handler = FakeRequest()\n return handler.get_current_user()","def find_user(self, *args, **kwargs):\n raise NotImplementedError","def mock_get_real_user(_anon_id):\r\n return self.user","def current_user(request):\n\n serializer = UserSerializer(request.user)\n return Response(serializer.data)","def impersonate_user(self, username, password):","def current_user(request):\n serializer = UserSerializer(request.user)\n return Response(serializer.data)","def get_logged_user():\n return get_from_session(KEY_USER)","def get_user(self):\n raise NotImplementedError","def auth_user(request):\n print('user', request.COOKIES, request.user)\n if request.user.is_authenticated:\n response = json.dumps({\"user\":True, \"username\":request.user.username})\n else:\n response = json.dumps({\"user\":False})\n return HttpResponse(response, content_type='application/json')","def get_user_name(self):\n whoami = subprocess.Popen('whoami', stdin=None, stdout=subprocess.PIPE,\n shell=True, close_fds=True)\n whoami = whoami.communicate()[0]\n if '\\n' in whoami:\n newline_index = whoami.find('\\n')\n whoami = whoami[:newline_index]\n return whoami","def check_user_and_login(self) -> Response:\n pass","def current_user(self):\n user_dict = self.auth.get_user_by_session()\n return self.auth.store.user_model.get_by_id(user_dict['user_id'])","def get_object(self):\n return self.request.user","def get_object(self):\n return self.request.user","def get_object(self):\n return self.request.user","def get_object(self):\n return self.request.user","def get_object(self):\n return self.request.user","def user_logged_in():\n if not session.get('user_id'):\n return \"nope\", 401\n else:\n return \"yep\", 200","def get_object(self, queryset=None):\n\t\tobj = self.request.user\n\t\treturn obj","def test_serve_user(self):\n pass","def get_remote_user(request):\n\n if settings.DEBUG:\n logger.debug(\"Getting Remote User\")\n me = {}\n\n me['url'] = settings.OAUTH_TEST_INFO['BASE']\n\n me['ask'] = \"/api/v1/me\" + \"?_format=json\"\n\n\n me = fhir_request(request, me)\n logger.debug(\"me...\", me)\n if 'errors' and 'code' in me:\n msg = build_message(request,me['errors'])\n return kickout(msg, me['code'])\n\n return me","async def team_whoami(self, ctx: commands.Context):\n team_id = await self.config.user(ctx.author).team_id()\n\n if team_id == -1:\n await ctx.send('You are not affiliated with any team.')\n return\n if team_id not in self.teams:\n await ctx.send('You are affiliated with a team whose ID I do not'\n f'recognize. ({team_id})')\n return\n await ctx.send('You are affiliated with Team **%s**.' % (\n self.teams[team_id].display_name),)","def logged_in_user():\n try:\n return session['user']\n except KeyError:\n raise NoLoggedInPlayerException()","def get_object(self, queryset=None):\n return self.request.user","def post(self, request):\n try:\n user = User.objects.all()\n print(user)\n if user.filter(username=request.GET.get(\"username\")).filter(password=request.GET.get(\"password\")):\n return Response({\"message\": \"USER LOGGED IN\"}, status=200)\n print(request.user)\n return Response({\"message\": \"USER LOGGED IN\"}, status=200)\n except ObjectDoesNotExist as e:\n return Response(e)","def get_user(self):\n return None","def get_user(self):\n return self.get('users/self')","def home(request):\n\n user = request.authenticated_userid\n return {'user': user}","def _setup_current_user():\n admin_user = set_admin_current_user(server.app)\n login_manager = server.app.extensions['security'].login_manager\n login_manager.anonymous_user = MagicMock(return_value=admin_user)","def getUser(request, returnAnonymous=False):\n if SESSION_KEY in request.session:\n user = ezidapp.models.getUserById(request.session[SESSION_KEY])\n if user != None and user.loginEnabled:\n return user\n else:\n return ezidapp.models.AnonymousUser if returnAnonymous else None\n else:\n return ezidapp.models.AnonymousUser if returnAnonymous else None","def before_request ():\n try:\n g.user = current_user\n except NameError:\n pass","def get_user():\n\treturn '1', 200","def inbound(request):\n session = {}\n if 'session' in request.cookie:\n token = request.cookie['session'].value\n session = load_session(token)\n request.user = User(session)","def WhoAmI(self) :\n\t\tuser = AccessControl.getSecurityManager().getUser()\n\t\treturn (user.getUserName(), user.getRoles())","def get_user():\n global USER\n return USER","def _get_current_user(self):\r\n real_user = self.runtime.get_real_user(self.runtime.anonymous_student_id)\r\n return real_user"],"string":"[\n \"def whoami():\\n g.data['authenticated_user'] = g.current_user\",\n \"def whoami():\\n return current_user._get_current_object()\",\n \"def whoami(self):\",\n \"def whoami(self):\\n response = requests.get(self.ENDPOINT + '/user-resource/user', headers={'apikey':self.apikey})\\n\\n return response.json()\",\n \"def whoami(ctx):\\n ctx.setup_logger(format='')\\n AuthCmd(ctx).whoami()\",\n \"def handle_whoami(self, client_socket):\\n user = self.clients[client_socket]['data'].decode('utf-8')\\n print(f'User {user} queried their identity')\\n msg = f'You are currently user {user}'\\n self.log_and_send(client_socket, msg)\",\n \"def whoami(self):\\n print(\\\"-----\\\",self._whoami)\",\n \"def whoami(bot, event, *args):\\n\\n if bot.memory.exists(['user_data', event.user_id.chat_id, \\\"nickname\\\"]):\\n try:\\n fullname = '{0} ({1})'.format(event.user.full_name.split(' ', 1)[0]\\n , bot.get_memory_suboption(event.user_id.chat_id, 'nickname'))\\n except TypeError:\\n fullname = event.user.full_name\\n else:\\n fullname = event.user.full_name\\n\\n bot.send_message_parsed(event.conv, _(\\\"<b>{}</b>, chat_id = <i>{}</i>\\\").format(fullname, event.user.id_.chat_id))\",\n \"def whoami(bot, event, *args):\\n\\n if bot.memory.exists(['user_data', event.user_id.chat_id, \\\"nickname\\\"]):\\n try:\\n fullname = '{0} ({1})'.format(event.user.full_name.split(' ', 1)[0]\\n , bot.get_memory_suboption(event.user_id.chat_id, 'nickname'))\\n except TypeError:\\n fullname = event.user.full_name\\n else:\\n fullname = event.user.full_name\\n\\n yield from bot.coro_send_message(event.conv, _(\\\"<b><pre>{}</pre></b>, chat_id = <i>{}</i>\\\").format(fullname, event.user.id_.chat_id))\",\n \"def run_whoami(self, expanded, unexpanded) :\\n\\t\\tif expanded :\\n\\t\\t\\treturn self.errormessage(\\\"Doesn't need any argument\\\")\\n\\t\\t(username, roles) = self.WhoAmI()\\n\\t\\tself.htmlmessage('Username: %s Roles: %s' % (username, string.join(roles, ', ')), safe=1)\\n\\t\\tself.printf(\\\"%s\\\\n\\\" % username)\",\n \"def login(self):\\n self._client.clear_credentials()\\n self._client.get('/v1/whoami')\",\n \"def whoami(self):\\n response = self._client.get('/v1/whoami')\\n # XXX Facundo 2020-06-30: Every time we consume data from the Store (after a succesful\\n # call) we need to wrap it with a context manager that will raise UnknownError (after\\n # logging in debug the received response). This would catch API changes, for example,\\n # without making charmcraft to badly crash. Related: issue #73.\\n result = User(\\n name=response['display-name'],\\n username=response['username'],\\n userid=response['id'],\\n )\\n return result\",\n \"def whoami(self):\\n if self.auth:\\n return self.auth[0]\\n else:\\n return \\\"Anonymous\\\"\",\n \"def whoami():\\n try:\\n\\n token = request.headers['token']\\n username, uid, wid = read_auth_token(token)\\n return dict(username=username, uid=uid, wid=wid)\\n\\n except SignatureExpired as e:\\n return dict(error=str(e)), 401\\n except BadSignature as e:\\n return dict(error=str(e)), 401\\n except Exception as e:\\n return dict(error=str(e)), 500\",\n \"def who_am_i():\\n request = req('GET', USER_API + 'session/whoami')\\n r = request.json()\\n user = {\\n 'Email': demisto.get(r, 'data.email'),\\n 'Login': demisto.get(r, 'data.login'),\\n 'Name': demisto.get(r, 'data.name'),\\n 'Organization': demisto.get(r, 'data.organization_id'),\\n 'Role': demisto.get(r, 'data.role')\\n }\\n demisto.results({\\n 'Type': entryTypes['note'],\\n 'EntryContext': {'ThreatGrid.User': user},\\n 'HumanReadable': tableToMarkdown('ThreatGrid - Current Session User', [user], [\\n 'Email', 'Login', 'Name', 'Organization', 'Role'\\n ]),\\n 'ContentsFormat': formats['json'],\\n 'Contents': user\\n })\",\n \"def test_whoami_by_anonymous_user(self):\\n response = self.client.get(\\\"/api/users/whoami/\\\")\\n self.assertEqual(response.status_code, 401)\",\n \"def mine(request):\\n request.user_to_show = request.user\\n return _show_user(request)\",\n \"def mine(request):\\n request.user_to_show = request.user\\n return _show_user(request)\",\n \"def user_for_login(request):\\n log = get_log(\\\"user_for_login\\\")\\n\\n identifier = None\\n\\n # standard repoze related identity:\\n if 'repoze.who.identity' in request.environ:\\n identity = request.environ['repoze.who.identity']\\n\\n if 'username' in identity:\\n identifier = identity['username']\\n\\n elif 'repoze.who.userid' in identity:\\n identifier = identity['repoze.who.userid']\\n\\n # token based identity:\\n elif 'pp.api_access.identity' in request.environ:\\n identifier = request.environ['pp.api_access.identity']\\n\\n else:\\n log.debug(\\\"No identifier recovered from environment!\\\")\\n\\n if not identifier:\\n raise HTTPForbidden()\\n\\n if _USERS.get(identifier):\\n found = _USERS.get(identifier)\\n\\n if _NAME_TO_ID.get(identifier):\\n found = _USERS.get(_NAME_TO_ID.get(identifier))\\n\\n return found\",\n \"def user_logged_in(self, sender, request, user, **kwargs):\",\n \"def user():\",\n \"def user_info(self):\\n return self.auth.get_user_by_session()\",\n \"def get_self_account_details(self):\\n return self.mrr_obj.get('/whoami')\",\n \"def __call__(self, req):\\n return req.remote_user\",\n \"def current_user(self, **kwargs):\\n return self.me(**kwargs)\",\n \"def make_logged_in_owner(self):\\n response = self.client.post('', self.credentials, follow=True)\",\n \"def _get_current_user(self):\\n\\n out, err, exitcode = self.execute('whoami')\\n\\n if exitcode == 0:\\n return User(out[0])\\n return None\",\n \"def get_logged_info():\\n user = current_identity\\n return make_response(dumps({\\\"status\\\": True, \\\"user\\\": user}), 200)\",\n \"def user():\\n\\treturn request.authorization.username if zk.get_http_login() else zk.get_username()\",\n \"def user(self):\\n pass\",\n \"def me():\\n return current_user.get()\",\n \"def user(request, user_id):\\n raise NotImplementedError\",\n \"def get_current_user(self):\\n return None\",\n \"def log_user_logged_in(sender, request, user, **kwargs):\\n\\n ip = get_client_ip(request)\\n date = datetime.now()\\n log.info('Login user: %s , IP: %s , Date: %s', user, ip, str(date))\",\n \"def user():\\n pass\",\n \"def user_login():\\n \\n data = user_obj.user_login()\\n return data\",\n \"def user_logged_in_callback(sender, request, user, **kwargs):\\n logger.info(f'User {user.username} logged in from {get_client_ip(request)}')\",\n \"def current_user(request):\\n \\n serializer = UserSerializer(request.user)\\n return Response(serializer.data)\",\n \"def current_user(request):\\n \\n serializer = UserSerializer(request.user)\\n return Response(serializer.data)\",\n \"def current_user(request):\\n \\n serializer = UserSerializer(request.user)\\n return Response(serializer.data)\",\n \"def get_user_me(request):\\r\\n auth = (DaemonServer._user['_email'], DaemonServer._user['_token'])\\r\\n res = requests.get(DaemonServer._base_url + '/user/me.json', auth=auth)\\r\\n return res\",\n \"def get_user(request: Request):\\n return request.user\",\n \"def login_user():\\n pass\",\n \"def current_user_info():\\n\\n return current_user\",\n \"def get_object(self):\\n requested_user = self.kwargs.get('username')\\n loggedin_user = self.request.user.username\\n if str(requested_user) == str(loggedin_user) or requested_user == 'me':\\n requested_user = loggedin_user\\n return get_object_or_404(User, username__iexact=requested_user, is_active=True)\\n else:\\n raise PermissionDenied\",\n \"def get_one_user():\",\n \"def log_user_logged_in(sender, request, user, **kwargs):\\n ip_address = get_client_ip(request)\\n logger.info('User \\\"%s\\\" logged in with IP address \\\"%s\\\".' % (user, ip_address))\",\n \"def do_login(self, backend, user):\",\n \"def do_login(user):\\n session[CURRENT_USER_KEY] = user.id\",\n \"def get_object(self):\\n return User.objects.get(username=self.request.user.username)\",\n \"def get(self):\\r\\n return get_user(request)\",\n \"def show_user(request):\\n return _show_user(request)\",\n \"def show_user(request):\\n return _show_user(request)\",\n \"def simple_basic_auth_callback(request, user, *args, **kwargs):\\r\\n login(request, user)\\r\\n request.user = user\",\n \"def get_user():\\n return getpass.getuser()\",\n \"def get_user():\\n return getpass.getuser()\",\n \"def user(self):\",\n \"def user(application):\\n yield application.user\",\n \"def auth(self, user):\",\n \"def get_current_user(request): \\n user_id = request.session.get('user_id', False)\\n \\n return Member.objects.get(id=user_id)\",\n \"def get_user(id):\\n pass\",\n \"def user(self):\\n if not flask_login.current_user.is_anonymous():\\n return flask_login.current_user._get_current_object()\\n else:\\n return None\",\n \"def _current_login_user(self):\\n return self.env.uid\",\n \"def whoami(self,\\n\\t note=None,\\n\\t shutit_pexpect_child=None,\\n\\t loglevel=logging.DEBUG):\\n\\t\\tshutit_global.shutit_global_object.yield_to_draw()\\n\\t\\tshutit_pexpect_child = shutit_pexpect_child or self.get_current_shutit_pexpect_session().pexpect_child\\n\\t\\tshutit_pexpect_session = self.get_shutit_pexpect_session_from_child(shutit_pexpect_child)\\n\\t\\treturn shutit_pexpect_session.whoami(note=note,loglevel=loglevel)\",\n \"def logged_in(request):\\n return request.current_user is not None\",\n \"def get_current_user():\\n handler = FakeRequest()\\n return handler.get_current_user()\",\n \"def find_user(self, *args, **kwargs):\\n raise NotImplementedError\",\n \"def mock_get_real_user(_anon_id):\\r\\n return self.user\",\n \"def current_user(request):\\n\\n serializer = UserSerializer(request.user)\\n return Response(serializer.data)\",\n \"def impersonate_user(self, username, password):\",\n \"def current_user(request):\\n serializer = UserSerializer(request.user)\\n return Response(serializer.data)\",\n \"def get_logged_user():\\n return get_from_session(KEY_USER)\",\n \"def get_user(self):\\n raise NotImplementedError\",\n \"def auth_user(request):\\n print('user', request.COOKIES, request.user)\\n if request.user.is_authenticated:\\n response = json.dumps({\\\"user\\\":True, \\\"username\\\":request.user.username})\\n else:\\n response = json.dumps({\\\"user\\\":False})\\n return HttpResponse(response, content_type='application/json')\",\n \"def get_user_name(self):\\n whoami = subprocess.Popen('whoami', stdin=None, stdout=subprocess.PIPE,\\n shell=True, close_fds=True)\\n whoami = whoami.communicate()[0]\\n if '\\\\n' in whoami:\\n newline_index = whoami.find('\\\\n')\\n whoami = whoami[:newline_index]\\n return whoami\",\n \"def check_user_and_login(self) -> Response:\\n pass\",\n \"def current_user(self):\\n user_dict = self.auth.get_user_by_session()\\n return self.auth.store.user_model.get_by_id(user_dict['user_id'])\",\n \"def get_object(self):\\n return self.request.user\",\n \"def get_object(self):\\n return self.request.user\",\n \"def get_object(self):\\n return self.request.user\",\n \"def get_object(self):\\n return self.request.user\",\n \"def get_object(self):\\n return self.request.user\",\n \"def user_logged_in():\\n if not session.get('user_id'):\\n return \\\"nope\\\", 401\\n else:\\n return \\\"yep\\\", 200\",\n \"def get_object(self, queryset=None):\\n\\t\\tobj = self.request.user\\n\\t\\treturn obj\",\n \"def test_serve_user(self):\\n pass\",\n \"def get_remote_user(request):\\n\\n if settings.DEBUG:\\n logger.debug(\\\"Getting Remote User\\\")\\n me = {}\\n\\n me['url'] = settings.OAUTH_TEST_INFO['BASE']\\n\\n me['ask'] = \\\"/api/v1/me\\\" + \\\"?_format=json\\\"\\n\\n\\n me = fhir_request(request, me)\\n logger.debug(\\\"me...\\\", me)\\n if 'errors' and 'code' in me:\\n msg = build_message(request,me['errors'])\\n return kickout(msg, me['code'])\\n\\n return me\",\n \"async def team_whoami(self, ctx: commands.Context):\\n team_id = await self.config.user(ctx.author).team_id()\\n\\n if team_id == -1:\\n await ctx.send('You are not affiliated with any team.')\\n return\\n if team_id not in self.teams:\\n await ctx.send('You are affiliated with a team whose ID I do not'\\n f'recognize. ({team_id})')\\n return\\n await ctx.send('You are affiliated with Team **%s**.' % (\\n self.teams[team_id].display_name),)\",\n \"def logged_in_user():\\n try:\\n return session['user']\\n except KeyError:\\n raise NoLoggedInPlayerException()\",\n \"def get_object(self, queryset=None):\\n return self.request.user\",\n \"def post(self, request):\\n try:\\n user = User.objects.all()\\n print(user)\\n if user.filter(username=request.GET.get(\\\"username\\\")).filter(password=request.GET.get(\\\"password\\\")):\\n return Response({\\\"message\\\": \\\"USER LOGGED IN\\\"}, status=200)\\n print(request.user)\\n return Response({\\\"message\\\": \\\"USER LOGGED IN\\\"}, status=200)\\n except ObjectDoesNotExist as e:\\n return Response(e)\",\n \"def get_user(self):\\n return None\",\n \"def get_user(self):\\n return self.get('users/self')\",\n \"def home(request):\\n\\n user = request.authenticated_userid\\n return {'user': user}\",\n \"def _setup_current_user():\\n admin_user = set_admin_current_user(server.app)\\n login_manager = server.app.extensions['security'].login_manager\\n login_manager.anonymous_user = MagicMock(return_value=admin_user)\",\n \"def getUser(request, returnAnonymous=False):\\n if SESSION_KEY in request.session:\\n user = ezidapp.models.getUserById(request.session[SESSION_KEY])\\n if user != None and user.loginEnabled:\\n return user\\n else:\\n return ezidapp.models.AnonymousUser if returnAnonymous else None\\n else:\\n return ezidapp.models.AnonymousUser if returnAnonymous else None\",\n \"def before_request ():\\n try:\\n g.user = current_user\\n except NameError:\\n pass\",\n \"def get_user():\\n\\treturn '1', 200\",\n \"def inbound(request):\\n session = {}\\n if 'session' in request.cookie:\\n token = request.cookie['session'].value\\n session = load_session(token)\\n request.user = User(session)\",\n \"def WhoAmI(self) :\\n\\t\\tuser = AccessControl.getSecurityManager().getUser()\\n\\t\\treturn (user.getUserName(), user.getRoles())\",\n \"def get_user():\\n global USER\\n return USER\",\n \"def _get_current_user(self):\\r\\n real_user = self.runtime.get_real_user(self.runtime.anonymous_student_id)\\r\\n return real_user\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7910836","0.74055797","0.730647","0.72178155","0.71626765","0.7057506","0.6882682","0.6858402","0.6839091","0.6702892","0.6689809","0.6612816","0.6563662","0.65627337","0.6543319","0.65190023","0.6462778","0.6462778","0.63894653","0.62800586","0.6200437","0.6183747","0.6147335","0.61253774","0.61132926","0.608535","0.6066319","0.6008634","0.6008138","0.5991308","0.5969985","0.5958006","0.59108996","0.58952975","0.58850163","0.584948","0.58364874","0.58287704","0.58287704","0.58287704","0.58249587","0.5810526","0.5807767","0.57941395","0.578861","0.57821333","0.57729876","0.5753681","0.5745055","0.57436526","0.57333905","0.57241136","0.57241136","0.5716649","0.57100654","0.57100654","0.5709224","0.57038325","0.5703523","0.57020384","0.5701417","0.56943816","0.56816983","0.5678035","0.567495","0.56739855","0.5673495","0.5667407","0.5652864","0.565129","0.56457883","0.5645221","0.5639826","0.561938","0.561308","0.5606052","0.56038934","0.560238","0.560238","0.560238","0.560238","0.560238","0.5601562","0.56011","0.5599888","0.5595583","0.55903393","0.5579615","0.55713487","0.5553737","0.55439585","0.55395526","0.55350715","0.5528435","0.5523051","0.5507991","0.55056876","0.54976225","0.5495208","0.5492897","0.5483774"],"string":"[\n \"0.7910836\",\n \"0.74055797\",\n \"0.730647\",\n \"0.72178155\",\n \"0.71626765\",\n \"0.7057506\",\n \"0.6882682\",\n \"0.6858402\",\n \"0.6839091\",\n \"0.6702892\",\n \"0.6689809\",\n \"0.6612816\",\n \"0.6563662\",\n \"0.65627337\",\n \"0.6543319\",\n \"0.65190023\",\n \"0.6462778\",\n \"0.6462778\",\n \"0.63894653\",\n \"0.62800586\",\n \"0.6200437\",\n \"0.6183747\",\n \"0.6147335\",\n \"0.61253774\",\n \"0.61132926\",\n \"0.608535\",\n \"0.6066319\",\n \"0.6008634\",\n \"0.6008138\",\n \"0.5991308\",\n \"0.5969985\",\n \"0.5958006\",\n \"0.59108996\",\n \"0.58952975\",\n \"0.58850163\",\n \"0.584948\",\n \"0.58364874\",\n \"0.58287704\",\n \"0.58287704\",\n \"0.58287704\",\n \"0.58249587\",\n \"0.5810526\",\n \"0.5807767\",\n \"0.57941395\",\n \"0.578861\",\n \"0.57821333\",\n \"0.57729876\",\n \"0.5753681\",\n \"0.5745055\",\n \"0.57436526\",\n \"0.57333905\",\n \"0.57241136\",\n \"0.57241136\",\n \"0.5716649\",\n \"0.57100654\",\n \"0.57100654\",\n \"0.5709224\",\n \"0.57038325\",\n \"0.5703523\",\n \"0.57020384\",\n \"0.5701417\",\n \"0.56943816\",\n \"0.56816983\",\n \"0.5678035\",\n \"0.567495\",\n \"0.56739855\",\n \"0.5673495\",\n \"0.5667407\",\n \"0.5652864\",\n \"0.565129\",\n \"0.56457883\",\n \"0.5645221\",\n \"0.5639826\",\n \"0.561938\",\n \"0.561308\",\n \"0.5606052\",\n \"0.56038934\",\n \"0.560238\",\n \"0.560238\",\n \"0.560238\",\n \"0.560238\",\n \"0.560238\",\n \"0.5601562\",\n \"0.56011\",\n \"0.5599888\",\n \"0.5595583\",\n \"0.55903393\",\n \"0.5579615\",\n \"0.55713487\",\n \"0.5553737\",\n \"0.55439585\",\n \"0.55395526\",\n \"0.55350715\",\n \"0.5528435\",\n \"0.5523051\",\n \"0.5507991\",\n \"0.55056876\",\n \"0.54976225\",\n \"0.5495208\",\n \"0.5492897\",\n \"0.5483774\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":14375,"cells":{"query":{"kind":"string","value":"Fetch node data using k8s API"},"document":{"kind":"string","value":"def get_node_data(cluster_id):\n try:\n # fetching the token from secret of the namespace 'dashboard'\n _TOKEN = [base64.b64decode(secret_item.data['token']).decode('UTF-8') for secret_item in client.CoreV1Api(\n ).list_namespaced_secret('dashboard').items if base64.b64decode(secret_item.data['namespace']).decode(\n 'UTF-8') == 'dashboard'][0]\n\n # generating User Agent\n version_detail = client.VersionApi().get_code()\n _USER_AGENT = 'kubectl/' + version_detail.git_version + ' (' + version_detail.platform + ') ' + 'kubernetes/' + \\\n version_detail.git_commit[0:7]\n _HEADERS = {'User-Agent': _USER_AGENT, 'Accept': 'application/json',\n 'Authorization': f\"Bearer {_TOKEN}\"}\n\n # generating url for node listing\n _URL = client.Configuration().host + '/api/v1/nodes'\n requests.packages.urllib3.disable_warnings(category=InsecureRequestWarning)\n return requests.get(url=_URL, headers=_HEADERS, verify=False).json()['items']\n except Exception as e:\n logger.error(e)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get(node_instance_id, logger, client, tenant_name):\n if tenant_name:\n logger.info('Explicitly using tenant `{0}`'.format(tenant_name))\n logger.info('Retrieving node instance {0}'.format(node_instance_id))\n try:\n node_instance = client.node_instances.get(node_instance_id)\n except CloudifyClientError as e:\n if e.status_code != 404:\n raise\n raise CloudifyCliError('Node instance {0} not found'.format(\n node_instance_id))\n\n print_data(NODE_INSTANCE_COLUMNS, node_instance, 'Node-instance:', 50)\n\n # print node instance runtime properties\n logger.info('Instance runtime properties:')\n for prop_name, prop_value in utils.decode_dict(\n node_instance.runtime_properties).iteritems():\n logger.info('\\t{0}: {1}'.format(prop_name, prop_value))\n logger.info('')","def cli():\n while True:\n try:\n # Get the whole information on each edge.\n l_edge = list()\n s_rsc = '{}/edge'.format(etcdc.prefix)\n \n try:\n r = etcdc.read(s_rsc, recursive=True)\n except etcd.EtcdKeyNotFound as e:\n log.error(e)\n else:\n for child in r.children:\n l_app = list()\n d = ast.literal_eval(child.value)\n # get hosts\n print(PROJECT_ROOT + '/' + d['endpoint'])\n l_hosts = kube_list_node(PROJECT_ROOT + '/' + d['endpoint'])\n d['hosts'] = len(l_hosts)\n d_nodes = dict() # {'name': 'ip', ...}\n for item in l_hosts:\n d_nodes[item.metadata.name] = item.status.addresses[0].address\n # log.debug(d_nodes)\n # get # of tenants and apps\n l_tenants = get_tenant(d['name'])\n d['tenants'] = len(l_tenants)\n d['apps'] = 0\n for e in l_tenants:\n if 'app' in e:\n d['apps'] += len(e['app'])\n \n d['cpu'] = 0\n d['memory'] = 0\n i_total_cores = 0\n i_total_memory = 0\n i_total_storage = 0\n for h in l_hosts:\n i_total_cores += int(h.status.capacity['cpu'])\n i_total_memory += int(h.status.capacity['memory'].\n replace('Ki', ''))\n d['tot_cpu'] = i_total_cores\n d['tot_mem'] = int(i_total_memory / (1024*1024))\n \n # Get loadavg and free mem\n if d['name'] == 'edge1':\n ssh_server = 'harden.iorchard.co.kr'\n elif d['name'] == 'edge2':\n ssh_server = 'durant.iorchard.co.kr'\n RSC = 'ssh -p42544 {} get_rsc.sh'.format(ssh_server)\n (b_res, s_out) = cmd(RSC, 3, False)\n l = s_out.split(\"\\n\")\n d['used_cpu'] = (float(l[0]) + float(l[1]) + float(l[2]))\n avail_mem = (int(l[3]) + int(l[4]) + int(l[5])) / (1024*1024)\n d['used_mem'] = d['tot_mem'] - avail_mem\n d['cpu'] = int(d['used_cpu'] / d['tot_cpu'] * 100)\n d['memory'] = int(d['used_mem'] / d['tot_mem'] * 100)\n # ceph storage\n CEPH = \"kubectl --kubeconfig \" + PROJECT_ROOT + '/' \\\n + d['endpoint'] + \" -n rook-ceph exec -it \" \\\n + \"$(kubectl --kubeconfig \" + PROJECT_ROOT + '/' \\\n + d['endpoint'] + \" -n rook-ceph get po \" \\\n + \"-l app=rook-ceph-tools \" \\\n + \"-o jsonpath='{.items[0].metadata.name}') -- \" \\\n + \"ceph df --format json\"\n (b_res, s_out) = cmd(CEPH, 3, False)\n print(s_out)\n d['status'] = 'Healthy' if b_res else 'Unhealthy'\n d_stor = ast.literal_eval(s_out)\n d['tot_stor'] = int(d_stor['stats']['total_bytes'] / pow(1024, 3))\n d['used_stor'] = int(d_stor['stats']['total_used_bytes'] / pow(1024, 3))\n d['storage'] = int(d['used_stor'] / d['tot_stor'] * 100)\n # Update etcd status\n try:\n s = '{}/edge/{}'.format(etcdc.prefix,\n d['name'])\n # log.debug(d)\n etcdc.write(s, d, prevExist=True)\n except etcd.EtcdKeyNotFound as e:\n log.error(e)\n \n # Update app status\n s_app = '{}/app'.format(etcdc.prefix)\n try:\n r_app = etcdc.read(s_app, recursive=True)\n except etcd.EtcdKeyNotFound as e:\n log.error(e)\n else:\n for app_child in r_app.children:\n if app_child.value is not None:\n d_app = dict()\n app = ast.literal_eval(app_child.value)\n if app['edge'] == d['name']:\n d_app['name'] = app['name']\n d_app['username'] = GUAC_USER\n d_app['password'] = GUAC_PASS\n # Get catalog info.\n s_cat = '{}/catalog/{}'.format(etcdc.prefix,\n app['catalog'])\n try:\n r_cat = etcdc.read(s_cat)\n except etcd.EtcdKeyNotFound as e:\n log.error(e)\n else:\n cat = ast.literal_eval(r_cat.value)\n app['cat_type'] = cat['type']\n app['cat_name'] = cat['name']\n app['cat_logo'] = cat['logo']\n # Get app status\n if app['cat_type'] == 'vm':\n # first, look at DataVolume status of app.\n CMD = \"kubectl --kubeconfig \" + PROJECT_ROOT + '/' \\\n + d['endpoint'] + ' get dv ' \\\n + app['name'] \\\n + \" -o jsonpath='{range .status}{.phase},{.progress}{end}'\"\n (b_res, s_out) = cmd(CMD, 5, False)\n l_out = s_out.split(',')\n if l_out[0] == 'Succeeded':\n # Get vm status of app\n CMD = \"kubectl --kubeconfig \" + PROJECT_ROOT \\\n + '/' \\\n + d['endpoint'] + ' get vm ' \\\n + app['name'] \\\n + \" -o jsonpath='{.status.ready}'\"\n (b_res, s_out) = cmd(CMD, 5, False)\n if b_res and s_out == 'true':\n # update app status 'running'.\n app.update({'status': 'running'})\n \n if app['edge'] == d['name']:\n # Get where app is running.\n CMD = \"kubectl --kubeconfig \" \\\n + PROJECT_ROOT + '/' \\\n + d['endpoint'] + ' get vmi ' \\\n + app['name'] \\\n + \" -o jsonpath='{.status.nodeName}'\"\n (b_res, s_out) = cmd(CMD, 5, False)\n if b_res:\n d_app['hostname'] = d_nodes[s_out]\n # Get nodeport for app.\n CMD = \"kubectl --kubeconfig \" \\\n + PROJECT_ROOT + '/' \\\n + d['endpoint'] + ' get svc ' \\\n + app['name'] \\\n + \" -o jsonpath='{.spec.ports[0].nodePort}'\"\n (b_res, s_out) = cmd(CMD, 5, False)\n if b_res:\n d_app['port'] = s_out\n else:\n # update app status 'stopped'\n app.update({'status': 'stopped'})\n elif l_out[0] == 'ImportInProgress':\n # update app status 'building' and \n app.update({'status': 'building ({})'.format(l_out[1])})\n elif app['cat_type'] == 'container':\n app.update({'status': 'running'})\n \n try:\n s = '{}/app/{}'.format(etcdc.prefix,\n app['name'])\n # log.debug(app)\n etcdc.write(s, app, prevExist=True)\n except etcd.EtcdKeyNotFound as e:\n log.error(e)\n \n if 'port' in d_app:\n l_app.append(d_app)\n # render guac-config.j2 and copy it to guac broker server\n log.debug(l_app)\n template = env.get_template('broker.j2')\n s_out = template.render(l_app=l_app)\n s_tmp = '/tmp/{}.broker'.format(d['name'])\n try:\n with open(s_tmp, 'w') as f:\n f.write(s_out)\n except Exception as e:\n log.error(e)\n else:\n CMD = \"scp \" \\\n + \"-P42544 {} {}\".format(s_tmp, d['broker_ip']) \\\n + \":/etc/guacamole/noauth-config.xml\"\n log.debug(CMD)\n (b_res, s_out) = cmd(CMD, 5, False)\n if b_res:\n d_app['port'] = s_out\n \n l_edge.append(d)\n \n # log.debug(l_edge)\n log.debug(l_app)\n \n time.sleep(1)\n except:\n log.error('unknown error')","async def GET(node_id, path):\n r = session().get(f\"{HOST}:{BASE_PORT + node_id}{path}\")\n if r.status_code != 200:\n raise ValueError(f\"Got bad response {r.status_code} from \" +\n f\"node {node_id} on {path}.\")\n return {\"status_code\": r.status_code, \"data\": r.json()}","def get_nodes(self):\n return requests.get(self.__url + 'nodes').json()","async def retrieve_node(request: web.Request) -> web.Response:\n path_params = parse_request_path_parameters_as(_NodePathParams, request)\n\n try:\n data = await request.json()\n port_keys = data.get(\"port_keys\", [])\n except json.JSONDecodeError as exc:\n raise web.HTTPBadRequest(reason=f\"Invalid request body: {exc}\") from exc\n\n return web.json_response(\n await director_v2_api.retrieve(\n request.app, f\"{path_params.node_id}\", port_keys\n ),\n dumps=json_dumps,\n )","def get(self, key):\n \n print(\"Getting from node {}\".format(self.url))\n\n (headers, content) = self.http_client.request(self.url + \"/\" + key + \"?r=1\", method=\"GET\", redirections=0)\n return (headers, content)","def _get_nodes(prefix, cloud, configs):\n # Get nodes with prefix\n nodes = Node.nodes(prefix, cloud, **configs)\n\n # Check for nodes available with prefix\n if not nodes:\n log.info(f\"No resources available with prefix '{prefix}'. Exiting ....\")\n sys.exit(1)\n\n log.info(f\"Nodes with prefix '{prefix}' are {', '.join([n for n, _ in nodes])}\")\n return nodes","def get_nodes(self):\n self.get_status()\n old_api = self.version[0] <= '3'\n if old_api:\n certs_path = \"%s/certificate_statuses/*\" % (self.environment)\n nodeinfo_path_tpl = \"{env}/node/{node}\"\n else:\n certs_path = \"puppet-ca/v1/certificate_statuses/no_key?environment=%s\" % (self.environment)\n nodeinfo_path_tpl = \"puppet/v3/node/{node}?environment={env}\"\n\n csts = self._send('GET', certs_path)\n nodes_names = []\n for cst in csts:\n nodes_names.append(cst['name'])\n\n all_nodes = []\n for nname in nodes_names:\n path = nodeinfo_path_tpl.format(node=nname, env=self.environment)\n nodeinfo = self._send('GET', path)\n if old_api:\n nodeinfo = self._from_pson(nodeinfo['data'])\n else:\n nodeinfo = self._from_pson(nodeinfo)\n if 'parameters' in nodeinfo:\n node = nodeinfo['parameters']\n if self.onlynodes:\n if not (node.get('hostname') in self.onlynodes or\n node.get('ipaddress') in self.onlynodes or\n node.get('fqdn') in self.onlynodes or\n node.get('uuid') in self.onlynodes):\n continue\n all_nodes.append(node)\n\n return all_nodes","def get_nodes():\n\n host = str(request.args['host'])\n days = float(request.args['days'])\n\n to_time = int(time.time())\n to_day = int(time.strftime('%Y%m%d', time.gmtime(float(to_time))))\n from_time = to_time-int(days*24*60*60)\n from_day = int(time.strftime('%Y%m%d', time.gmtime(float(from_time))))\n day_in=''\n for x in range(from_day, to_day+1):\n day_in = day_in + ',' + str(x)\n day_in=re.sub(r\"^,\", \"\", day_in)\n day_in=re.sub(r\",$\", \"\", day_in)\n query = \"SELECT * FROM metrics WHERE host='\" + str(host) + \"' and date IN (\"\n query = query + str(day_in) + \") and time>=\" + str(int(int(from_time)*1000)) + \" and time<=\"\n query = query + str(int(int(to_time)*1000)) + \" ALLOW FILTERING\"\n rows = session.execute(query);\n reply={}\n last_value={}\n for r in rows:\n if str(r.host) not in reply:\n reply[r.host]={}\n last_value[r.host]={}\n if str(r.metric) not in reply[r.host]:\n reply[r.host][r.metric]=[]\n last_value[r.host][r.metric]=int(r.value)\n continue\n real_value = (r.value-last_value[r.host][r.metric])/60\n\tlast_value[r.host][r.metric]=int(r.value)\n reply[str(r.host)][r.metric].append({ 'value': int(real_value),\n 'time': str(r.time) })\n return json.dumps(reply)","def get_kong_node_usage_metrics(opts):\n\n url = \"{0}/status\".format(opts['base_url'])\n\n r = requests.get(url)\n try:\n r.raise_for_status()\n except requests.exceptions.RequestException as e:\n logging.debug(\"http response body - %s\", r.text)\n logging.error(\"An exception occurred: (%s)\", e)\n sys.exit(2)\n\n print r.text\n\n return True","def get_host_info(self):\n\n if len(self.index) == 0:\n # Need to load index from cache\n self.load_index_from_cache()\n\n if not self.args.host in self.index:\n # try updating the cache\n self.do_api_calls_update_cache()\n if not self.args.host in self.index:\n # host might not exist anymore\n return self.json_format_dict({}, True)\n\n node_id = self.index[self.args.host]\n print \"NODE ID %s\" % node_id\n print \"INDEX: %s\" % self.index\n\n node = self.get_node(node_id)\n node_vars = {}\n for direct_attr in [\n \"api_id\",\n \"datacenter_id\",\n \"label\",\n \"display_group\",\n \"create_dt\",\n \"total_hd\",\n \"total_xfer\",\n \"total_ram\",\n \"status\",\n \"alert_cpu_enabled\",\n \"alert_cpu_threshold\",\n \"alert_diskio_enabled\",\n \"alert_diskio_threshold\",\n \"alert_bwin_enabled\",\n \"alert_bwin_threshold\",\n \"alert_bwout_enabled\",\n \"alert_bwout_threshold\",\n \"alert_bwquota_enabled\",\n \"alert_bwquota_threshold\",\n \"backup_weekly_daily\",\n \"backup_window\",\n \"watchdog\"\n ]:\n node_vars[direct_attr] = getattr(node, direct_attr)\n\n node_vars[\"datacenter_city\"] = self.get_datacenter_city(node)\n node_vars[\"public_ip\"] = [addr.address for addr in node.ipaddresses if addr.is_public][0]\n\n return self.json_format_dict(node_vars, True)","def cluster_znode_data(cluster_name, znode, headers=None):\n\n zdata_resp = cluster_znode(cluster_name,\n znode,\n headers=headers)\n zdata = json.loads(zdata_resp.get_data())\n resp = Response(zdata[\"data\"],\n status=200,\n mimetype=\"text/plain\")\n return resp","def get(resource_name: str,\n id: pulumi.Input[str],\n opts: Optional[pulumi.ResourceOptions] = None,\n auto_renew: Optional[pulumi.Input[bool]] = None,\n auto_renew_period: Optional[pulumi.Input[int]] = None,\n cluster_name: Optional[pulumi.Input[str]] = None,\n data_center_name: Optional[pulumi.Input[str]] = None,\n disk_size: Optional[pulumi.Input[int]] = None,\n disk_type: Optional[pulumi.Input[str]] = None,\n enable_public: Optional[pulumi.Input[bool]] = None,\n instance_type: Optional[pulumi.Input[str]] = None,\n ip_white: Optional[pulumi.Input[str]] = None,\n maintain_end_time: Optional[pulumi.Input[str]] = None,\n maintain_start_time: Optional[pulumi.Input[str]] = None,\n major_version: Optional[pulumi.Input[str]] = None,\n node_count: Optional[pulumi.Input[int]] = None,\n password: Optional[pulumi.Input[str]] = None,\n pay_type: Optional[pulumi.Input[str]] = None,\n period: Optional[pulumi.Input[int]] = None,\n period_unit: Optional[pulumi.Input[str]] = None,\n public_points: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,\n security_groups: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,\n status: Optional[pulumi.Input[str]] = None,\n tags: Optional[pulumi.Input[Mapping[str, Any]]] = None,\n vswitch_id: Optional[pulumi.Input[str]] = None,\n zone_id: Optional[pulumi.Input[str]] = None) -> 'Cluster':\n opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))\n\n __props__ = _ClusterState.__new__(_ClusterState)\n\n __props__.__dict__[\"auto_renew\"] = auto_renew\n __props__.__dict__[\"auto_renew_period\"] = auto_renew_period\n __props__.__dict__[\"cluster_name\"] = cluster_name\n __props__.__dict__[\"data_center_name\"] = data_center_name\n __props__.__dict__[\"disk_size\"] = disk_size\n __props__.__dict__[\"disk_type\"] = disk_type\n __props__.__dict__[\"enable_public\"] = enable_public\n __props__.__dict__[\"instance_type\"] = instance_type\n __props__.__dict__[\"ip_white\"] = ip_white\n __props__.__dict__[\"maintain_end_time\"] = maintain_end_time\n __props__.__dict__[\"maintain_start_time\"] = maintain_start_time\n __props__.__dict__[\"major_version\"] = major_version\n __props__.__dict__[\"node_count\"] = node_count\n __props__.__dict__[\"password\"] = password\n __props__.__dict__[\"pay_type\"] = pay_type\n __props__.__dict__[\"period\"] = period\n __props__.__dict__[\"period_unit\"] = period_unit\n __props__.__dict__[\"public_points\"] = public_points\n __props__.__dict__[\"security_groups\"] = security_groups\n __props__.__dict__[\"status\"] = status\n __props__.__dict__[\"tags\"] = tags\n __props__.__dict__[\"vswitch_id\"] = vswitch_id\n __props__.__dict__[\"zone_id\"] = zone_id\n return Cluster(resource_name, opts=opts, __props__=__props__)","def get_node(conn, name):\n datacenter_id = get_datacenter_id()\n\n for item in conn.list_servers(datacenter_id)[\"items\"]:\n if item[\"properties\"][\"name\"] == name:\n node = {\"id\": item[\"id\"]}\n node.update(item[\"properties\"])\n return node","def cluster_list():\n request_debug(r, logger)\n json_body = r.get_json(force=True, silent=True) or {}\n result = cluster_handler.list(filter_data=json_body)\n response_ok[\"data\"] = result\n return jsonify(response_ok), CODE_OK","def send_simple_get_request(hostname, node, key, causal_payload=''):\n get_str = \"http://\" + hostname + \":\" + node.access_port + \"/kvs/\" + key\n data = {'causal_payload':causal_payload}\n if PRINT_HTTP_REQUESTS:\n print \"Get request: \" + get_str + ' data field:' + str(data)\n r = req.get(get_str, data=data)\n if PRINT_HTTP_RESPONSES:\n print r.text, r.status_code\n return r","def get(self, controller, data, *args, **kwargs): \n task_manager = controller.get_task_manager()\n res = task_manager.get_all_tasks(details=True)\n resp = {\n u'task-instances':res,\n u'count':len(res)\n } \n return resp","def describe_cluster_response():\n return {\n \"cluster\": {\n \"status\": \"ACTIVE\",\n \"endpoint\": \"https://endpoint.amazonaws.com\",\n \"name\": EXAMPLE_NAME,\n \"certificateAuthority\": {\n \"data\": \"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\"\n },\n \"roleArn\": \"arn:aws:iam::111222333444/eksRole\",\n \"resourcesVpcConfig\": {\n \"subnetIds\": [\n \"subnet-00000000000000000\",\n \"subnet-00000000000000001\",\n \"subnet-00000000000000002\"\n ],\n \"vpcId\": \"vpc-00000000000000000\",\n \"securityGroupIds\": [\n \"sg-00000000000000000\"\n ]\n },\n \"version\": \"1.10\",\n \"arn\": \"arn:aws:eks:region:111222333444:cluster/\" + EXAMPLE_NAME,\n \"createdAt\": 1500000000.000\n }\n }","def list_nodes(hosts):\n if hosts=='hosts':\n query = \"SELECT DISTINCT host FROM metrics_index\";\n reply=[]\n elif hosts=='all':\n query = \"SELECT * FROM metrics_index\";\n reply={}\n\n rows = session.execute(query);\n for r in rows:\n if hosts=='hosts':\n if str(r.host) not in reply:\n reply.append(str(r.host))\n elif hosts=='all':\n if str(r.host) not in reply:\n reply[str(r.host)]=[]\n if str(r.metric) not in reply[str(r.host)]:\n reply[str(r.host)].append(str(r.metric))\n return json.dumps(reply)","def memcache_nodes(self) -> pulumi.Output[Sequence['outputs.NodeResponse']]:\n return pulumi.get(self, \"memcache_nodes\")","def getContainers(self,node):\n data = self.connect('get','nodes/%s/lxc' % node,None)\n return data","def cluster_node_get(self, node_name, desired_attributes=None):\n return self.request( \"cluster-node-get\", {\n 'node_name': [ node_name, 'node-name', [ basestring, 'node-name' ], False ],\n 'desired_attributes': [ desired_attributes, 'desired-attributes', [ ClusterNodeInfo, 'None' ], False ],\n }, {\n 'attributes': [ ClusterNodeInfo, False ],\n } )","def test_get_entity(self):\n\n storage = StringIO.StringIO()\n c = pycurl.Curl()\n c.setopt(c.URL,\"http://127.0.0.1:8090/compute/9930\")\n c.setopt(c.HTTPHEADER, ['Accept:application/occi+json'])\n c.setopt(c.VERBOSE, True)\n c.setopt(c.CUSTOMREQUEST, 'GET')\n c.setopt(c.WRITEFUNCTION, storage.write)\n c.perform()\n content = storage.getvalue()\n print \" ===== Body content =====\\n \" + content + \" ==========\\n\"","def get(self):\n\n self.response.headers['Access-Control-Allow-Origin'] = '*' # Required until JSONP is supported\n self.response.headers['Content-Type'] = 'application/json' # All responses are json, so JSONP to come\n\n if(self.request.get(\"cid\")):\n cid = self.request.get(\"cid\")\n node = memcache.get(cid)\n\n if node is not None:\n # if it is a valid cid then check to see if there are any commands\n # in the nodes command queue\n # and update some fields on the node\n\n command = node.getNextCommand()\n if command is not None:\n self.write(JSONResponse.execCommand(command))\n if command.autoComplete is True:\n logging.info(\"auto completing\")\n node.completeCommand(command.id)\n else:\n self.write(json.dumps({\"type\":\"noCommand\"}))\n else:\n # if it turned out to be a non valid cid, just make a new node\n node = createNode(self.request)\n self.write(JSONResponse.assignment(node.cid))\n \n\n else: # if the request had no cid attached\n node = createNode(self.request)\n self.write(JSONResponse.assignment(node.cid))","def get(self, request, nnid, wfver, desc):\n try:\n return_data = NNCommonManager().get_nn_node_info(nnid, wfver, desc)\n return Response(json.dumps(return_data))\n except Exception as e:\n return_data = {\"status\": \"404\", \"result\": str(e)}\n return Response(json.dumps(return_data))","def handle_status(self, request):\n \"\"\"\n @api {get} /status Get node status\n @apiName GetNodeStatus\n @apiGroup Node\n @apiVersion 1.1.0\n\n @apiSuccess {Boolean} execution_enabled Task execution is enabled on the node.\n @apiSuccess {Boolean} leader Node is the leader.\n @apiSuccess {String} name Node name.\n @apiSuccess {Boolean} scheduler_running The scheduler is running on the node.\n @apiSuccess {String} address Node IP address.\n @apiSuccess {String[]} pools Pools in which the node is registered.\n @apiSuccess {Object} running_processes Processes running on the host.\n @apiSuccess {Object} running_processes.process Process.\n @apiSuccess {String} running_processes.process.start_time Time the process started, ISO 8601 formatted.\n @apiSuccess {String} running_processes.process.task ID of the task.\n @apiSuccess {Boolean} cluster_joined Node has joined the cluster.\n @apiSuccess {Boolean} contending_for_lead Node is contending for lead.\n @apiSuccess {Boolean} pools_joined Node has joined its pools.\n\n @apiSuccessExample {json} Example response:\n {\n \"execution_enabled\": true,\n \"leader\": false,\n \"name\": \"node2\",\n \"scheduler_running\": false,\n \"address\": \"127.0.0.1:32002\",\n \"pools\": [\"pool1\", \"pool2\"],\n \"running_processes\": {\n \"b26e5cc2ef3f11e4817b0026b951c045\": {\n \"start_time\": \"2015-04-30T13:49:18.351494+00:00\",\n \"task\": \"508b4b72e44611e49e76c81f66cd0cca\"\n }\n },\n \"cluster_joined\": true,\n \"contending_for_lead\": true,\n \"pools_joined\": true\n }\n \"\"\"\n\n headers = {\n 'Content-Type': 'application/javascript',\n 'Access-Control-Allow-Origin': '*'\n }\n\n status = {\n 'name': self.cluster.nodename,\n 'address': self.cluster.addr,\n 'pools': self.cluster.mypools,\n 'leader': self.cluster.is_leader,\n 'cluster_joined': self.cluster.cluster_joined,\n 'pools_joined': self.cluster.pools_joined,\n 'contending_for_lead': self.cluster.contending_for_lead,\n\n 'execution_enabled': self.manager.enabled,\n 'running_processes': dict([ (execid, { 'task': details['task'], 'start_time': details['start_time'].isoformat() }) for (execid, details) in self.manager.running_processes.items() ]),\n\n 'scheduler_running': self.cluster.scheduler.running\n }\n\n return HTTPReply(body = json.dumps(status), headers = headers)","def getNodes(self):\n data = self.connect('get','nodes',None)\n return data","def compute_node_get_by_host(context, host):\n session = get_session()\n with session.begin():\n service = session.query(models.Service).\\\n filter_by(host=host, binary=\"monitor-bmc\").first()\n node = session.query(models.ComputeNode).\\\n options(joinedload('service')).\\\n filter_by(deleted=False,service_id=service.id)\n return node.first()","def _load_cluster(self):","def node(*,bucket, endpoint, key, crn, auth=IBM_CLOUD_OAUTH_URL):\n # Get bearer token to access COS S3 API\n # payload to generate auth token\n token_req_data = {\n 'grant_type' :'urn:ibm:params:oauth:grant-type:apikey',\n 'response_type':'cloud_iam',\n 'apikey' :key\n }\n response = requests.post(auth, data=token_req_data, headers={'Content-type': 'application/x-www-form-urlencoded'})\n if response.status_code != 200:\n raise Exception(\"error\")\n bearer_token_info = response.json()\n logging.debug(bearer_token_info)\n \n # Get Aspera connection information for the bucket\n header_auth = {\n 'ibm-service-instance-id':crn,\n 'Authorization':bearer_token_info['token_type'] + \" \" + bearer_token_info['access_token'],\n 'Accept':'application/xml'\n }\n response = requests.get(endpoint + \"/\" + bucket, headers=header_auth, params={'faspConnectionInfo':True})\n if response.status_code != 200:\n raise Exception(\"error accessing endpoint\")\n logging.debug(response.content)\n ats_info_root = xml.dom.minidom.parseString(response.content.decode('utf-8'));\n ats_ak = ats_info_root.getElementsByTagName('AccessKey')[0]\n ats_url = ats_info_root.getElementsByTagName('ATSEndpoint')[0].firstChild.nodeValue\n ats_ak_id = ats_ak.getElementsByTagName('Id')[0].firstChild.nodeValue\n ats_ak_secret = ats_ak.getElementsByTagName('Secret')[0].firstChild.nodeValue\n \n # Get delegated token to access the node api\n token_req_data['response_type'] = 'delegated_refresh_token'\n token_req_data['receiver_client_ids'] = 'aspera_ats'\n response = requests.post(auth, data=token_req_data, headers={'Content-type': 'application/x-www-form-urlencoded'})\n if response.status_code != 200:\n raise Exception(\"error when generating token\")\n delegated_token_info = response.json()\n aspera_storage_credentials = {\n 'type': 'token',\n 'token': delegated_token_info\n }\n logging.debug(aspera_storage_credentials)\n return {\n 'url': ats_url,\n 'auth': requests.auth.HTTPBasicAuth(ats_ak_id, ats_ak_secret),\n 'headers': {\n 'X-Aspera-Storage-Credentials':json.dumps(aspera_storage_credentials),\n },\n 'tspec': {'tags':{'aspera':{'node':{'storage_credentials':aspera_storage_credentials}}}}\n }","def connectnode():\n content = request.get_json()\n \n #Check request method\n if content[\"method\"] == \"GET\":\n res = hl.nodeGet(content[\"url\"])\n elif content[\"method\"] == \"GET\":\n res = hl.nodePost(content[\"url\"], content[\"data\"])\n\n #Error check\n if res:\n if ('result' in res and res['result']) or 'result' not in res:\n try:\n res.pop(\"result\")\n except:\n pass\n \n result = {\"result\" : True, \"data\" : res} \n else:\n result = {\"result\" : False} \n else:\n result = {\"result\" : False} \n \n #Return response\n return(jsonify(result))","def get_cluster(t2_url, t2_token, id):\n response = requests.get(f\"{t2_url}/api/clusters/{id}\", headers={ \"t2-token\": t2_token })\n if(response.status_code != 200):\n log(f\"API call to get cluster returned error code {response.status_code}\")\n return None\n return response.json()","def describe_cluster_no_status_response():\n return {\n \"cluster\": {\n \"endpoint\": \"https://endpoint.amazonaws.com\",\n \"name\": EXAMPLE_NAME,\n \"certificateAuthority\": {\n \"data\": \"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\"\n },\n \"roleArn\": \"arn:aws:iam::111222333444/eksRole\",\n \"resourcesVpcConfig\": {\n \"subnetIds\": [\n \"subnet-00000000000000000\",\n \"subnet-00000000000000001\",\n \"subnet-00000000000000002\"\n ],\n \"vpcId\": \"vpc-00000000000000000\",\n \"securityGroupIds\": [\n \"sg-00000000000000000\"\n ]\n },\n \"version\": \"1.10\",\n \"arn\": \"arn:aws:eks:region:111222333444:cluster/\" + EXAMPLE_NAME,\n \"createdAt\": 1500000000.000\n }\n }","def GetNodeInfo(self, request, context):\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')","def cluster_nodes(self) -> ResponseT:\n return self.execute_command(\"CLUSTER NODES\")","def list(args, config):\n\n api = config['API']\n headers = {}\n if args.stack_name:\n headers = {'stack-name': args.stack_name} # put stack name in headers\n r = requests.get(api['list'], headers=headers) # send the GET request\n print('\\nThe following clusters exist:\\n{}\\n'.format(r.json()))\n return","def list_cluster_response():\n return {\n \"clusters\": [\n EXAMPLE_NAME\n ]\n }","def get_node(self, key: str) -> Node:","def kitero():\n return dict(hostname=hostname)","def getNodeDNS(self,node):\n data = self.connect('get','nodes/%s/dns' % (node),None)\n return data","def list():\n index = 0\n while True:\n node = Node.from_index(index)\n if os.path.exists(node.path()):\n click.echo(f'{index}: node_{index}')\n click.echo(run_lncli(node, 'getinfo | jq .identity_pubkey'))\n else:\n break\n index += 1","def GetNodeInfo(self, hvparams=None):\n return self.GetLinuxNodeInfo()","def get_node(self, _id):\n return self.make_request(\"GET\", \"nodes/\"+_id, {})","def get_pods(namespace, k8s_cli, selector=\"\"):\n if selector:\n selector = '--selector=\"{}\"'.format(selector)\n cmd = '{} get pod -n {} {} -o json '.format(k8s_cli, namespace, selector)\n return_code, out = run_shell_command(cmd)\n if return_code:\n logger.warning(\"Failed to get pods: %s\", out)\n return None\n return json.loads(out)['items']","def main():\n pods = openshift_object.get_running_pods()\n me = openshift_object.get_self()\n routes = openshift_object.get_routes()\n nodes = openshift_object.get_nodes()\n pvc = openshift_object.get_pvcs()\n pv = openshift_object.get_pv()\n project = openshift_object.get_projects()\n return jsonify({\n \"pods\": pods,\n \"me\": me,\n \"routes\": routes, \n \"nodes\":nodes,\n \"pvcs\":pvc,\n \"pv\":pv,\n \"projects\":project})","def test_openshift_node_with_all_cluster_and_node_access_view(self):\n with schema_context(self.schema_name):\n expected = (\n OCPCostSummaryByNodeP.objects.annotate(**{\"value\": F(\"node\")})\n .values(\"value\")\n .distinct()\n .filter(node__in=[RBAC_NODE])\n .count()\n )\n self.assertTrue(expected)\n url = reverse(\"openshift-nodes\")\n response = self.client.get(url, **self.headers)\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n json_result = response.json()\n self.assertIsNotNone(json_result.get(\"data\"))\n self.assertIsInstance(json_result.get(\"data\"), list)\n self.assertEqual(len(json_result.get(\"data\")), expected)","def get_nodes(self):\n _url = f\"{self.connector.base_url}/projects/{self.project_id}/nodes\"\n\n _response = self.connector.http_call(\"get\", _url)\n\n # Create the Nodes array but cleanup cache if there is one\n if self.nodes:\n self.nodes = []\n for _node in _response.json():\n _n = Node(connector=self.connector, **_node)\n _n.project_id = self.project_id\n self.nodes.append(_n)","def list_nodes(self):\n\n return list(\n dict(\n self._from_json(self.manage.run(override=\"list-nodes\"))\n ).keys()\n )","async def node(self, node, *, dc=None, watch=None, consistency=None):\n node_id = extract_attr(node, keys=[\"Node\", \"ID\"])\n response = await self._api.get(\"/v1/session/node\", node_id, params={\n \"dc\": dc}, watch=watch, consistency=consistency)\n return consul(response)","def test_get_node_status(self):\n pass","def test_openshift_node_with_cluster_and_all_node_access_view(self):\n with schema_context(self.schema_name):\n expected = (\n OCPCostSummaryByNodeP.objects.annotate(**{\"value\": F(\"node\")})\n .values(\"value\")\n .distinct()\n .filter(cluster_id__in=[\"OCP-on-AWS\"])\n .count()\n )\n self.assertTrue(expected)\n url = reverse(\"openshift-nodes\")\n response = self.client.get(url, **self.headers)\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n json_result = response.json()\n self.assertIsNotNone(json_result.get(\"data\"))\n self.assertIsInstance(json_result.get(\"data\"), list)\n self.assertEqual(len(json_result.get(\"data\")), expected)","def check_nodes_available(label, label_name):\r\n api_instance = client.CoreV1Api()\r\n label_selector = \"\"\r\n for label_val in label:\r\n label_selector += str(label_val[\"key\"])+\"=\"+str(label_val[\"value\"])+\",\"\r\n label_selector = label_selector[0:-1]\r\n try:\r\n api_response_2 = api_instance.list_node(\r\n pretty=True, label_selector=label_selector)\r\n if len(api_response_2.items) == 0:\r\n LOGGER.error(f\"0 nodes matches with {label_name}\")\r\n LOGGER.error(\"please check labels\")\r\n assert False\r\n except ApiException as e:\r\n LOGGER.error(f\"Exception when calling CoreV1Api->list_node: {e}\")\r\n assert False","def local(node_id, blueprint_id, logger):\n env = load_env(blueprint_id)\n node_instances = env.storage.get_node_instances()\n if node_id:\n node_instances = [instance for instance in node_instances\n if instance.node_id == node_id]\n if not node_instances:\n raise CloudifyCliError(\n 'Could not find node {0}'.format(node_id))\n logger.info(json.dumps(node_instances, sort_keys=True, indent=2))","def test_get_hyperflex_cluster_list(self):\n pass","def list_nodes(conn=None, call=None):\n if call == \"action\":\n raise SaltCloudSystemExit(\n \"The list_nodes function must be called with -f or --function.\"\n )\n\n if not conn:\n conn = get_conn()\n\n ret = {}\n datacenter_id = get_datacenter_id()\n\n try:\n nodes = conn.list_servers(datacenter_id=datacenter_id)\n except PBNotFoundError:\n log.error(\"Failed to get nodes list from datacenter: %s\", datacenter_id)\n raise\n\n for item in nodes[\"items\"]:\n node = {\"id\": item[\"id\"]}\n node.update(item[\"properties\"])\n node[\"state\"] = node.pop(\"vmState\")\n ret[node[\"name\"]] = node\n\n return ret","async def get(id):\n cluster = clusters.get_by_id(id)\n\n if cluster is None:\n raise HTTPException(status_code=404, detail=\"Cluster not found for ID: {0}\".format(id))\n\n return cluster.export()","def test_get_node_partitions(self):\n pass","async def list_nodes(self, *, option: ListApiOptions) -> ListApiResponse:\n try:\n reply = await self._client.get_all_node_info(timeout=option.timeout)\n except DataSourceUnavailable:\n raise DataSourceUnavailable(GCS_QUERY_FAILURE_WARNING)\n\n result = []\n for message in reply.node_info_list:\n data = protobuf_message_to_dict(\n message=message, fields_to_decode=[\"node_id\"]\n )\n data[\"node_ip\"] = data[\"node_manager_address\"]\n data[\"start_time_ms\"] = int(data[\"start_time_ms\"])\n data[\"end_time_ms\"] = int(data[\"end_time_ms\"])\n\n result.append(data)\n\n total_nodes = len(result)\n # No reason to truncate node because they are usually small.\n num_after_truncation = len(result)\n\n result = self._filter(result, option.filters, NodeState, option.detail)\n num_filtered = len(result)\n\n # Sort to make the output deterministic.\n result.sort(key=lambda entry: entry[\"node_id\"])\n result = list(islice(result, option.limit))\n return ListApiResponse(\n result=result,\n total=total_nodes,\n num_after_truncation=num_after_truncation,\n num_filtered=num_filtered,\n )","def hello_pubsub(event, context):\n pubsub_message = base64.b64decode(event['data']).decode('utf-8')\n print(pubsub_message)\n x = json.loads(pubsub_message)\n vm_name = x[\"jsonPayload\"][\"resource\"][\"name\"]\n vm_zone = x[\"jsonPayload\"][\"resource\"][\"zone\"]\n project_id = x[\"resource\"][\"labels\"][\"project_id\"]\n print(\"vm_name=\"+vm_name)\n print(\"vm_zone=\"+vm_zone)\n print(\"project_id=\"+project_id)\n \n compute = discovery.build('compute', 'v1')\n \n print(\"getting metadata fingerprint\")\n request = compute.instances().get(project= project_id, zone= vm_zone, instance= vm_name)\n response = request.execute()\n pprint(response)\n metadata_fingerprint= response['metadata']['fingerprint']\n print(\"existing metadata fingerprint = \" + metadata_fingerprint)\n\n vm_status=response['status']\n print(\"vm_status = \" + vm_status)\n while vm_status != 'RUNNING' :\n print(\"going to sleep for 1 second...\")\n time.sleep(1)\n request = compute.instances().get(project= project_id, zone= vm_zone, instance= vm_name)\n response = request.execute()\n vm_status=response['status']\n print(\"vm_status = \" + vm_status)\n\n print(\"Setting VM metadata\")\n metadata_body = {\n \"fingerprint\": metadata_fingerprint,\n \"items\": [\n {\n \"key\": \"startup-script-url\",\n \"value\": \"gs://mybucket/my_script.sh\"\n }\n ]\n }\n\n request = compute.instances().setMetadata(project=project_id, zone=vm_zone, instance=vm_name, body=metadata_body)\n response = request.execute()\n pprint(response)\n\n print(\"Restarting VM\")\n request = compute.instances().reset(project=project_id, zone=vm_zone, instance=vm_name)\n response = request.execute()\n pprint(response)","def get_lbnodes_by_pcc(conn: dict, id: str) -> dict:\n return get(conn, f\"{S3PCCS}/{id}/nodes/cephlb\")","def test_read_cluster_network(self):\n pass","def compute_api(self):\n path = self.compute.path = '/'\n res = self.compute.call('', 'GET', data='', \n token=self.manager.identity.token)\n self.logger.debug('Get openstack compute services: %s' % truncate(res))\n self.compute.path = path\n return res[0]","def get(ctx, key):\n config = buildConfig(ctx.obj[\"HOST\"], ctx.obj[\"PORT\"])\n client = getClient(config)\n click.echo(client.get(key))","def getNodeStatus(self,node):\n data = self.connect('get','nodes/%s/status' % (node),None)\n return data","def test_get_cloud_resources(self):\n pass","def apicall():\r\n# try:\r\n print request.get_json()\r\n test_json = request.get_json()\r\n logger.info(\"input json object loaded\")\r\n logger.info(test_json)\r\n k=MetaData(test_json)\r\n int_res=k.getData()\r\n print '------------------------------'\r\n print int_res\r\n return jsonify(int_res)","def fusion_api_get_ha_nodes(self, uri=None, param='', api=None, headers=None):\n return self.ha_nodes.get(uri=uri, api=api, headers=headers, param=param)","def get_kubeconfig(t2_url, t2_token, id):\n response = requests.get(f\"{t2_url}/api/clusters/{id}/kubeconfig\", headers={ \"t2-token\": t2_token })\n if(response.status_code != 200):\n log(f\"API call to get Stackable kubeconfig returned error code {response.status_code}\")\n return None\n return response.text","def get_list_node(showOnly=False, full=False):\n # start Requests session\n sc = requests.Session()\n\n # import cookies from Firefox\n sc.cookies.update(get_cookies('imhsc.imhadmin.net'))\n\n # send request\n vpx = sc.post('https://imhsc.imhadmin.net/index.php?v=VPNodes')\n\n # check if login failed\n check_sc_login(vpx.text)\n\n # parse with BS4\n bs = BeautifulSoup(vpx.text, \"xml\")\n\n # server=0\n slist = []\n for trr in bs.tbody.find_all('tr'):\n try:\n if full:\n tsrv = {\n 'name': trr.find_all('td')[0].text.strip(),\n 'ip': trr.find_all('td')[2].text.strip(),\n 'lan': trr.find_all('td')[3].text.strip(),\n 'cpus': trr.find_all('td')[4].text.strip(),\n 'memory': trr.find_all('td')[5].text.strip(),\n 'os': trr.find_all('td')[6].text.strip(),\n 'vz': trr.find_all('td')[7].text.strip(),\n 'license': trr.find_all('td')[8].text.strip(),\n 'cap': int(trr.find_all('td')[9].text.strip()),\n 'on': int(trr.find_all('td')[10].text.strip()),\n 'off': int(trr.find_all('td')[11].text.strip()),\n 'disk_used': trr.find_all('td')[12].text.strip(),\n 'disk_free': trr.find_all('td')[13].text.strip(),\n 'psc1': trr.find_all('td')[14].text.strip(),\n 'psc2': trr.find_all('td')[15].text.strip(),\n 'loc': trr.find_all('td')[16].text.strip(),\n 'ra': trr.find_all('td')[17].text.strip(),\n 'ba': trr.find_all('td')[18].text.strip(),\n 'model': trr.find_all('td')[19].text.strip()\n }\n else:\n tsrv = trr.find_all('td')[0].text.strip()\n except:\n continue\n slist.append(tsrv)\n if not showOnly:\n if full:\n json.dumps(tsrv)\n else:\n print(tsrv)\n\n return slist","def GET(self):\n user_data = web.input(cluster_id=None)\n nodes = db().query(Node).options(\n joinedload('cluster'),\n joinedload('interfaces'),\n joinedload('interfaces.assigned_networks'),\n joinedload('role_list'),\n joinedload('pending_role_list'))\n if user_data.cluster_id == '':\n nodes = nodes.filter_by(\n cluster_id=None).all()\n elif user_data.cluster_id:\n nodes = nodes.filter_by(\n cluster_id=user_data.cluster_id).all()\n else:\n nodes = nodes.all()\n return self.render(nodes)","def list_nodes_full(conn=None, call=None):\n if call == \"action\":\n raise SaltCloudSystemExit(\n \"The list_nodes_full function must be called with -f or --function.\"\n )\n\n if not conn:\n conn = get_conn() # pylint: disable=E0602\n\n ret = {}\n datacenter_id = get_datacenter_id()\n nodes = conn.list_servers(datacenter_id=datacenter_id, depth=3)\n\n for item in nodes[\"items\"]:\n node = {\"id\": item[\"id\"]}\n node.update(item[\"properties\"])\n node[\"state\"] = node.pop(\"vmState\")\n node[\"public_ips\"] = []\n node[\"private_ips\"] = []\n if item[\"entities\"][\"nics\"][\"items\"] > 0:\n for nic in item[\"entities\"][\"nics\"][\"items\"]:\n if nic[\"properties\"][\"ips\"]:\n pass\n ip_address = nic[\"properties\"][\"ips\"][0]\n if salt.utils.cloud.is_public_ip(ip_address):\n node[\"public_ips\"].append(ip_address)\n else:\n node[\"private_ips\"].append(ip_address)\n\n ret[node[\"name\"]] = node\n\n __utils__[\"cloud.cache_node_list\"](\n ret, _get_active_provider_name().split(\":\")[0], __opts__\n )\n\n return ret","def get_bootstrap_node(self):\n self.clear_screen()\n default = 'bootstrap'\n bootstrap_name = input('enter the bootstrap node name\\n'\n 'default [bootstrap]: ')\n bootstrap_name = set_values(bootstrap_name, default)\n bootstrap_ip = get_ip(node_name=bootstrap_name, ip_type='os')\n bootstrap_ip = validate_ip(bootstrap_ip)\n bootstrap_mac = get_network_device_mac(node_name=bootstrap_name, ip_type='idrac')\n logging.info('adding bootstrap_node values as name: {} ip: {} mac: {}'.format(bootstrap_name, bootstrap_ip,\n bootstrap_mac)) \n self.inventory_dict['csah']['vars']['bootstrap_node'] = [{'name': '{}'.format(bootstrap_name),\n 'ip': '{}'.format(bootstrap_ip),\n 'mac': '{}'.format(bootstrap_mac)}]","def get(self, get_links=True):\n _url = (\n f\"{self.connector.base_url}/projects/{self.project_id}/nodes/{self.node_id}\"\n )\n _response = self.connector.http_call(\"get\", _url)\n\n # Update object\n self._update(_response.json())\n\n if get_links:\n self.get_links()","def test_get_hyperflex_node_list(self):\n pass","def getNodeServiceList(self,node):\n data = self.connect('get','nodes/%s/services' % (node),None)\n return data","def callapi(uri: str, data=None):\n if data is not None:\n data = json.dumps(data).encode('utf8')\n req = ur.Request(node_url + uri, data=data, headers=headers)\n res = ur.urlopen(req)\n jsonstr = res.read().decode('utf8')\n return json.loads(jsonstr)","def handle_cluster_status(self, request):\n \"\"\"\n @api {get} /cluster/status Get cluster status\n @apiName GetClusterStatus\n @apiGroup Cluster\n @apiVersion 1.0.0\n\n @apiSuccess {Object} nodes Nodes in the cluster.\n @apiSuccess {Object} nodes.node Node.\n @apiSuccess {String[]} nodes.node.pools Pools in which the node is registered.\n @apiSuccess {String} nodes.node.address IP address of the node.\n @apiSuccess {String} leader Leader node.\n\n @apiSuccessExample {json} Example response:\n {\n \"nodes\": {\n \"node1\": {\n \"pools\": [\"pool1\", \"pool2\"],\n \"address\": \"127.0.0.1:32001\"\n },\n \"node2\": {\n \"pools\": [\"pool1\"],\n \"address\": \"127.0.0.1:32002\"\n },\n \"node3\": {\n \"pools\": [\"pool2\"],\n \"address\": \"127.0.0.1:32003\"\n },\n },\n \"leader\": \"node1\"\n }\n \"\"\"\n\n headers = {\n 'Content-Type': 'application/javascript',\n 'Access-Control-Allow-Origin': '*'\n }\n\n status = {\n 'nodes': self.cluster.nodes,\n 'leader': self.cluster.leader\n }\n\n return HTTPReply(body = json.dumps(status), headers = headers)","def getNodeContainerIndex(self,node):\n data = self.connect('get','nodes/%s/lxc' % (node),None)\n return data","def fetch_data(self):","def get(self, id, timeout=None):\n req = NodeGetRequest()\n\n req.id = (id)\n tries = 0\n plumbing_response = None\n while True:\n try:\n plumbing_response = self.stub.Get(\n req,\n metadata=self.parent.get_metadata('Nodes.Get', req),\n timeout=timeout)\n except Exception as e:\n if self.parent.shouldRetry(tries, e):\n tries += 1\n self.parent.jitterSleep(tries)\n continue\n raise plumbing.convert_error_to_porcelain(e) from e\n break\n\n resp = models.NodeGetResponse()\n resp.meta = plumbing.convert_get_response_metadata_to_porcelain(\n plumbing_response.meta)\n resp.node = plumbing.convert_node_to_porcelain(plumbing_response.node)\n resp.rate_limit = plumbing.convert_rate_limit_metadata_to_porcelain(\n plumbing_response.rate_limit)\n return resp","def _nodeinfo_endpoint(host):\n zkclient = context.GLOBAL.zk.conn\n nodeinfo_zk_path = '{}/{}'.format(z.ENDPOINTS, 'root')\n for node in zkclient.get_children(nodeinfo_zk_path):\n if 'nodeinfo' in node and host in node:\n data, _metadata = zkclient.get(\n '{}/{}'.format(nodeinfo_zk_path, node)\n )\n return data.decode().split(':')","def get_data(self, **kwargs):\n\n self.data = {}\n #node_data = ''\n #link_data = ''\n templates_data = self.request_from_server('templates')\n self.templates = templates_data\n project_data = self.request_from_server('projects')\n for project in project_data:\n project_name = project['name']\n if 'project_name' in kwargs:\n if project_name != kwargs['project_name']:\n continue\n\n self.data[project_name] = {}\n self.data[project_name]['project_id'] = project['project_id']\n self.data[project_name]['nodes'] = {}\n node_data = self.request_from_server('projects/{}/nodes'.format(project['project_id']))\n link_data = self.request_from_server('projects/{}/links'.format(project['project_id']))\n for node in node_data:\n node_name = node['name']\n self.data[project_name]['nodes'][node_name] = {}\n self.data[project_name]['nodes'][node_name]['node_id'] = node['node_id']\n self.data[project_name]['nodes'][node_name]['template_id'] = node['template_id']\n self.data[project_name]['nodes'][node_name]['node_type'] = node['node_type']\n self.data[project_name]['nodes'][node_name]['console_port'] = node['console']\n self.data[project_name]['nodes'][node_name]['console_session'] = None\n self.data[project_name]['nodes'][node_name]['x'] = node['x']\n self.data[project_name]['nodes'][node_name]['y'] = node['y']\n self.data[project_name]['nodes'][node_name]['ports'] = {}\n if project['status'] != 'closed':\n self.data[project_name]['nodes'][node_name]['status'] = node['status']\n for port in node['ports']:\n port_name = port['short_name']\n self.data[project_name]['nodes'][node_name]['ports'][port_name] = {}\n self.data[project_name]['nodes'][node_name]['ports'][port_name]['adapter_number'] = port['adapter_number']\n self.data[project_name]['nodes'][node_name]['ports'][port_name]['port_number'] = port['port_number']\n self.data[project_name]['nodes'][node_name]['ports'][port_name]['link_type'] = port['link_type']\n self.data[project_name]['nodes'][node_name]['ports'][port_name]['link_id'] = None\n self.data[project_name]['nodes'][node_name]['ports'][port_name]['in_use'] = False\n self.data[project_name]['nodes'][node_name]['ports'][port_name]['connected_to'] = None\n for link in link_data:\n for link_node in link['nodes']:\n if node['node_id'] == link_node['node_id']:\n if link_node['label']['text'] == port_name:\n self.data[project_name]['nodes'][node_name]['ports'][port_name]['link_id'] = link['link_id']\n self.data[project_name]['nodes'][node_name]['ports'][port_name]['in_use'] = True\n if link['nodes'].index(link_node) == 0:\n self.data[project_name]['nodes'][node_name]['ports'][port_name]['connected_to_id'] = link['nodes'][1]['node_id']\n self.data[project_name]['nodes'][node_name]['ports'][port_name]['connected_to'] = self.get_node_name_from_id(project_name,link['nodes'][1]['node_id'])\n else:\n self.data[project_name]['nodes'][node_name]['ports'][port_name]['connected_to_id'] = link['nodes'][0]['node_id']\n self.data[project_name]['nodes'][node_name]['ports'][port_name]['connected_to'] = self.get_node_name_from_id(project_name,link['nodes'][0]['node_id'])","async def fetch_data(self) -> T:","def command_node(parser_options, request, parser):\n if parser_options.experiment_id is not None:\n experiment_id = parser_options.experiment_id\n else:\n queryset = \"state=Running&limit=0&offset=0\"\n experiments_json = json.loads(request.get_experiments(queryset))\n experiment_id = helpers.check_experiments_running(\n experiments_json, parser)\n nodes = []\n if parser_options.nodes_list is not None:\n for nodes_list in parser_options.nodes_list:\n param_list = helpers.check_command_list(nodes_list, parser)\n sites_json = json.loads(request.get_sites())\n site = helpers.check_site(param_list[0], sites_json, parser)\n archi = helpers.check_archi(param_list[1], parser)\n nodes += helpers.check_nodes_list(site,\n archi,\n param_list[2],\n parser)\n nodes_json = json.dumps(\n nodes, cls=rest.Encoder, sort_keys=True, indent=4)\n elif parser_options.exclude_nodes_list is not None:\n exclude_nodes = []\n for exclude_list in parser_options.exclude_nodes_list:\n param_list = helpers.check_command_list(exclude_list, parser)\n sites_json = json.loads(request.get_sites())\n site = helpers.check_site(param_list[0], sites_json, parser)\n archi = helpers.check_archi(param_list[1], parser)\n exclude_nodes += helpers.check_nodes_list(site,\n archi,\n param_list[2],\n parser)\n experiment_resources_json = \\\n json.loads(request.get_experiment_resources(experiment_id))\n experiment_nodes = []\n for res in experiment_resources_json[\"items\"]:\n experiment_nodes.append(res[\"network_address\"])\n nodes = [node for node in experiment_nodes if node not in exclude_nodes] \n nodes_json = json.dumps(\n nodes, cls=rest.Encoder, sort_keys=True, indent=4)\n else:\n # all the nodes\n nodes_json = '[]'\n if parser_options.start:\n json_start = request.start_command(experiment_id, nodes_json)\n print json.dumps(json.loads(json_start), indent=4, sort_keys=True)\n elif parser_options.stop:\n json_stop = request.stop_command(experiment_id, nodes_json)\n print json.dumps(json.loads(json_stop), indent=4, sort_keys=True)\n elif parser_options.reset:\n json_reset = request.reset_command(experiment_id, nodes_json)\n print json.dumps(json.loads(json_reset), indent=4, sort_keys=True)\n elif parser_options.path_file is not None:\n command_files = {}\n firmware_name, firmware_data = helpers.open_file(\n parser_options.path_file, parser)\n command_files[firmware_name] = firmware_data\n command_filehandle = StringIO(nodes_json)\n command_files['nodes.json'] = command_filehandle.read()\n command_filehandle.close()\n json_update = request.update_command(experiment_id, command_files)\n print json.dumps(json.loads(json_update), indent=4, sort_keys=True)","def fetch_data(swarming, start, end, state, tags):\n # Split the work in days. That's a lot of requests to do.\n queue = Queue.Queue()\n threads = []\n def run(start, cmd):\n data = json.loads(subprocess.check_output(cmd))\n queue.put((start, int(data['count'])))\n\n day = start\n while day != end:\n data = [\n ('start', int((day - _EPOCH).total_seconds())),\n ('end', int((day + datetime.timedelta(days=1)-_EPOCH).total_seconds())),\n ('state', state),\n ]\n for tag in tags:\n data.append(('tags', tag))\n cmd = [\n sys.executable, os.path.join(CLIENT_DIR, 'swarming.py'),\n 'query', '-S', swarming, 'tasks/count?' + urllib.urlencode(data),\n ]\n thread = threading.Thread(target=run, args=(day.strftime('%Y-%m-%d'), cmd))\n thread.daemon = True\n thread.start()\n threads.append(thread)\n while len(threads) > 100:\n # Throttle a bit.\n for i, thread in enumerate(threads):\n if not thread.is_alive():\n thread.join()\n threads.pop(i)\n sys.stdout.write('.')\n sys.stdout.flush()\n break\n day = day + datetime.timedelta(days=1)\n\n while threads:\n # Throttle a bit.\n for i, thread in enumerate(threads):\n if not thread.is_alive():\n thread.join()\n threads.pop(i)\n sys.stdout.write('.')\n sys.stdout.flush()\n break\n print('')\n data = []\n while True:\n try:\n data.append(queue.get_nowait())\n except Queue.Empty:\n break\n return dict(data)","def fetch_from_redis(self):\n cache= get_redis()\n egdes_list = json.loads(cache.execute_command('JSON.GET', 'edges'))\n self.graph= Graph([(one[0],one[1]) for one in egdes_list])\n self.graph.vs[\"name\"]=json.loads(cache.execute_command('JSON.GET', 'vertices_name'))\n self.graph.vs[\"parent\"]=json.loads( cache.execute_command('JSON.GET', 'vertices_parent'))\n self.graph.vs[\"size\"]=json.loads( cache.execute_command('JSON.GET', 'vertices_size'))\n self.graph.vs[\"last_modified\"]=json.loads( cache.execute_command('JSON.GET', 'vertices_last_modified'))\n self.graph.vs[\"last_accessed\"]=json.loads( cache.execute_command('JSON.GET', 'vertices_last_accessed'))","def test_get_node_templates(self):\n if self.service_is_up:\n file_id = self.upload_tosca_template('application_example_provisioned.yaml')\n node_templates = self.api.get_node_templates(file_id)\n self.assertIsNotNone(node_templates)\n nodes_to_deploy = self.api.get_node_templates(file_id,type_name = 'tosca.nodes.QC.Application')","def getNodeRRDData(self,node):\n data = self.connect('get','nodes/%s/rrddata' % (node),None)\n return data","def get_node(self, project_id, node_id):\n _url = f\"{self.base_url}/projects/{project_id}/nodes/{node_id}\"\n return self.http_call(\"get\", _url).json()","def GetResourceMetadata(self):\n result = super().GetResourceMetadata()\n if self.created:\n result['container_cluster_version'] = self.k8s_version\n return result","def list_all_keys(riak_host,riak_port,bucket):\n url='http://%s:%s/buckets/%s/keys?keys=true' % (riak_host,riak_port,bucket)\n #print url\n r=requests.get(url)\n print json.dumps(r.json(), sort_keys=True, indent=4)","def getClusterData(clusterName,data):\n clusters = rhevGet(\"/api/clusters\")\n doc = libxml2.parseDoc(clusters)\n ctxt = doc.xpathNewContext()\n res = ctxt.xpathEval(\"/clusters/cluster[name [position()=1]= '\"+ clusterName + \"']\")\n return res[0].prop(data)","def describe_cluster_deleting_response():\n return {\n \"cluster\": {\n \"status\": \"DELETING\",\n \"endpoint\": \"https://endpoint.amazonaws.com\",\n \"name\": EXAMPLE_NAME,\n \"certificateAuthority\": {\n \"data\": \"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\"\n },\n \"roleArn\": \"arn:aws:iam::111222333444/eksRole\",\n \"resourcesVpcConfig\": {\n \"subnetIds\": [\n \"subnet-00000000000000000\",\n \"subnet-00000000000000001\",\n \"subnet-00000000000000002\"\n ],\n \"vpcId\": \"vpc-00000000000000000\",\n \"securityGroupIds\": [\n \"sg-00000000000000000\"\n ]\n },\n \"version\": \"1.10\",\n \"arn\": \"arn:aws:eks:region:111222333444:cluster/\" + EXAMPLE_NAME,\n \"createdAt\": 1500000000.000\n }\n }","def data():\n return volumes_fetchers.get_json_data()","def fetch_server(client, server):\n server_obj = {\n \"name\": server['name'],\n \"url\": server['url'],\n \"client\": client\n }\n if (server['name'] in SERVERZ) \\\n and ('cluster_members' in SERVERZ[server['name']]):\n clustered = SERVERZ[server['name']]['cluster_members']\n server_obj['cluster_members'] = clustered\n\n if 'parent' in server:\n server_obj['parent'] = server['parent']\n\n try:\n init = client.is_initialized()\n except hvac.exceptions.InternalServerError:\n return server_obj\n except requests.ConnectionError:\n return server_obj\n except requests.ReadTimeout:\n return server_obj\n\n server_obj['init'] = init\n if init:\n try:\n status = client.seal_status\n except (hvac.exceptions.VaultDown, requests.exceptions.ReadTimeout):\n return server_obj\n\n server_obj['version'] = status['version']\n seal = server_obj['sealed'] = status['sealed']\n if seal:\n server_obj['unseal_required'] = status['t']\n server_obj['unseal_progress'] = status['progress']\n else:\n server_obj['cluster_name'] = status['cluster_name']\n server_obj['cluster_id'] = status['cluster_id']\n\n server_obj['rekey'] = False\n try:\n rekey_obj = client.rekey_status\n rekey = server_obj['rekey'] = rekey_obj['started']\n if rekey:\n server_obj['rekey_backup'] = rekey_obj['backup']\n server_obj['rekey_progress'] = rekey_obj['progress']\n server_obj['rekey_required'] = rekey_obj['required']\n except (hvac.exceptions.VaultDown, requests.exceptions.ReadTimeout):\n pass\n except hvac.exceptions.InternalServerError as vault_exception:\n if vault_exception.message == 'node not active but active '\\\n 'node not found':\n pass\n\n server_obj['ha'] = False\n server_obj['leader'] = False\n try:\n leader_obj = client.read('sys/leader')\n server_obj['ha'] = leader_obj['ha_enabled']\n if leader_obj['ha_enabled']:\n server_obj['leader'] = leader_obj['is_self']\n except (hvac.exceptions.VaultDown, requests.exceptions.ReadTimeout):\n pass\n except hvac.exceptions.InternalServerError as e:\n if e.message == 'node not active but active node not found':\n pass\n\n if not server_obj['ha'] or \\\n (server_obj['ha'] and server_obj['leader']):\n try:\n regen_obj = client.read('sys/generate-root/attempt')\n server_obj['regenerating'] = regen_obj.get('started', False)\n if server_obj['regenerating']:\n server_obj['regen_progress'] = regen_obj['progress']\n server_obj['regen_required'] = regen_obj['required']\n except (hvac.exceptions.VaultDown, requests.exceptions.ReadTimeout):\n pass\n except hvac.exceptions.InternalServerError as e:\n if e.message == 'node not active but ' \\\n 'active node not found':\n pass\n\n client = root_client(server_obj)\n server_obj['is_root'] = False\n if client:\n try:\n key_obj = client.key_status\n server_obj['key_term'] = key_obj['term']\n server_obj['is_root'] = True\n except (hvac.exceptions.VaultDown, requests.exceptions.ReadTimeout):\n pass\n except hvac.exceptions.InternalServerError as e:\n if e.message == 'node not active but active '\\\n 'node not found':\n pass\n\n return server_obj","def get_nodes(self, type, query_args={}):\n endpoint = '/v3/educator/%ss' % (Node.TYPE_MAP[type])\n result = self.request(endpoint, query_args)\n\n nodes = []\n for data in result.response:\n node = Node.instance(type, data)\n nodes.append(node)\n\n return nodes","def nodes(env):\n envs = environments()\n status_arg = request.args.get('status', '')\n check_env(env, envs)\n\n query = AndOperator()\n\n if env != '*':\n query.add(EqualsOperator(\"catalog_environment\", env))\n query.add(EqualsOperator(\"facts_environment\", env))\n\n if status_arg in ['failed', 'changed', 'unchanged']:\n query.add(EqualsOperator('latest_report_status', status_arg))\n elif status_arg == 'unreported':\n unreported = datetime.datetime.utcnow()\n unreported = (unreported -\n timedelta(hours=app.config['UNRESPONSIVE_HOURS']))\n unreported = unreported.replace(microsecond=0).isoformat()\n\n unrep_query = OrOperator()\n unrep_query.add(NullOperator('report_timestamp', True))\n unrep_query.add(LessEqualOperator('report_timestamp', unreported))\n\n query.add(unrep_query)\n\n if len(query.operations) == 0:\n query = None\n\n nodelist = puppetdb.nodes(\n query=query,\n unreported=app.config['UNRESPONSIVE_HOURS'],\n with_status=True)\n nodes = []\n for node in yield_or_stop(nodelist):\n if status_arg:\n if node.status == status_arg:\n nodes.append(node)\n else:\n nodes.append(node)\n return Response(stream_with_context(\n stream_template('nodes.html',\n nodes=nodes,\n envs=envs,\n current_env=env)))","def describe_cluster_creating_response():\n return {\n \"cluster\": {\n \"status\": \"CREATING\",\n \"name\": EXAMPLE_NAME,\n \"certificateAuthority\": {},\n \"roleArn\": \"arn:aws:iam::111222333444/eksRole\",\n \"resourcesVpcConfig\": {\n \"subnetIds\": [\n \"subnet-00000000000000000\",\n \"subnet-00000000000000001\",\n \"subnet-00000000000000002\"\n ],\n \"vpcId\": \"vpc-00000000000000000\",\n \"securityGroupIds\": [\n \"sg-00000000000000000\"\n ]\n },\n \"version\": \"1.10\",\n \"arn\": \"arn:aws:eks:region:111222333444:cluster/\" + EXAMPLE_NAME,\n \"createdAt\": 1500000000.000\n }\n }","def get_kubernetes_cluster(name: Optional[str] = None,\n tags: Optional[Sequence[str]] = None,\n opts: Optional[pulumi.InvokeOptions] = None) -> AwaitableGetKubernetesClusterResult:\n __args__ = dict()\n __args__['name'] = name\n __args__['tags'] = tags\n opts = pulumi.InvokeOptions.merge(_utilities.get_invoke_opts_defaults(), opts)\n __ret__ = pulumi.runtime.invoke('digitalocean:index/getKubernetesCluster:getKubernetesCluster', __args__, opts=opts, typ=GetKubernetesClusterResult).value\n\n return AwaitableGetKubernetesClusterResult(\n auto_upgrade=pulumi.get(__ret__, 'auto_upgrade'),\n cluster_subnet=pulumi.get(__ret__, 'cluster_subnet'),\n created_at=pulumi.get(__ret__, 'created_at'),\n endpoint=pulumi.get(__ret__, 'endpoint'),\n ha=pulumi.get(__ret__, 'ha'),\n id=pulumi.get(__ret__, 'id'),\n ipv4_address=pulumi.get(__ret__, 'ipv4_address'),\n kube_configs=pulumi.get(__ret__, 'kube_configs'),\n maintenance_policies=pulumi.get(__ret__, 'maintenance_policies'),\n name=pulumi.get(__ret__, 'name'),\n node_pools=pulumi.get(__ret__, 'node_pools'),\n region=pulumi.get(__ret__, 'region'),\n service_subnet=pulumi.get(__ret__, 'service_subnet'),\n status=pulumi.get(__ret__, 'status'),\n surge_upgrade=pulumi.get(__ret__, 'surge_upgrade'),\n tags=pulumi.get(__ret__, 'tags'),\n updated_at=pulumi.get(__ret__, 'updated_at'),\n urn=pulumi.get(__ret__, 'urn'),\n version=pulumi.get(__ret__, 'version'),\n vpc_uuid=pulumi.get(__ret__, 'vpc_uuid'))","def get(resource_name: str,\n id: pulumi.Input[str],\n opts: Optional[pulumi.ResourceOptions] = None,\n acl_name: Optional[pulumi.Input[str]] = None,\n arn: Optional[pulumi.Input[str]] = None,\n auto_minor_version_upgrade: Optional[pulumi.Input[bool]] = None,\n cluster_endpoints: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ClusterClusterEndpointArgs']]]]] = None,\n data_tiering: Optional[pulumi.Input[bool]] = None,\n description: Optional[pulumi.Input[str]] = None,\n engine_patch_version: Optional[pulumi.Input[str]] = None,\n engine_version: Optional[pulumi.Input[str]] = None,\n final_snapshot_name: Optional[pulumi.Input[str]] = None,\n kms_key_arn: Optional[pulumi.Input[str]] = None,\n maintenance_window: Optional[pulumi.Input[str]] = None,\n name: Optional[pulumi.Input[str]] = None,\n name_prefix: Optional[pulumi.Input[str]] = None,\n node_type: Optional[pulumi.Input[str]] = None,\n num_replicas_per_shard: Optional[pulumi.Input[int]] = None,\n num_shards: Optional[pulumi.Input[int]] = None,\n parameter_group_name: Optional[pulumi.Input[str]] = None,\n port: Optional[pulumi.Input[int]] = None,\n security_group_ids: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,\n shards: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ClusterShardArgs']]]]] = None,\n snapshot_arns: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,\n snapshot_name: Optional[pulumi.Input[str]] = None,\n snapshot_retention_limit: Optional[pulumi.Input[int]] = None,\n snapshot_window: Optional[pulumi.Input[str]] = None,\n sns_topic_arn: Optional[pulumi.Input[str]] = None,\n subnet_group_name: Optional[pulumi.Input[str]] = None,\n tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\n tags_all: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\n tls_enabled: Optional[pulumi.Input[bool]] = None) -> 'Cluster':\n opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))\n\n __props__ = _ClusterState.__new__(_ClusterState)\n\n __props__.__dict__[\"acl_name\"] = acl_name\n __props__.__dict__[\"arn\"] = arn\n __props__.__dict__[\"auto_minor_version_upgrade\"] = auto_minor_version_upgrade\n __props__.__dict__[\"cluster_endpoints\"] = cluster_endpoints\n __props__.__dict__[\"data_tiering\"] = data_tiering\n __props__.__dict__[\"description\"] = description\n __props__.__dict__[\"engine_patch_version\"] = engine_patch_version\n __props__.__dict__[\"engine_version\"] = engine_version\n __props__.__dict__[\"final_snapshot_name\"] = final_snapshot_name\n __props__.__dict__[\"kms_key_arn\"] = kms_key_arn\n __props__.__dict__[\"maintenance_window\"] = maintenance_window\n __props__.__dict__[\"name\"] = name\n __props__.__dict__[\"name_prefix\"] = name_prefix\n __props__.__dict__[\"node_type\"] = node_type\n __props__.__dict__[\"num_replicas_per_shard\"] = num_replicas_per_shard\n __props__.__dict__[\"num_shards\"] = num_shards\n __props__.__dict__[\"parameter_group_name\"] = parameter_group_name\n __props__.__dict__[\"port\"] = port\n __props__.__dict__[\"security_group_ids\"] = security_group_ids\n __props__.__dict__[\"shards\"] = shards\n __props__.__dict__[\"snapshot_arns\"] = snapshot_arns\n __props__.__dict__[\"snapshot_name\"] = snapshot_name\n __props__.__dict__[\"snapshot_retention_limit\"] = snapshot_retention_limit\n __props__.__dict__[\"snapshot_window\"] = snapshot_window\n __props__.__dict__[\"sns_topic_arn\"] = sns_topic_arn\n __props__.__dict__[\"subnet_group_name\"] = subnet_group_name\n __props__.__dict__[\"tags\"] = tags\n __props__.__dict__[\"tags_all\"] = tags_all\n __props__.__dict__[\"tls_enabled\"] = tls_enabled\n return Cluster(resource_name, opts=opts, __props__=__props__)"],"string":"[\n \"def get(node_instance_id, logger, client, tenant_name):\\n if tenant_name:\\n logger.info('Explicitly using tenant `{0}`'.format(tenant_name))\\n logger.info('Retrieving node instance {0}'.format(node_instance_id))\\n try:\\n node_instance = client.node_instances.get(node_instance_id)\\n except CloudifyClientError as e:\\n if e.status_code != 404:\\n raise\\n raise CloudifyCliError('Node instance {0} not found'.format(\\n node_instance_id))\\n\\n print_data(NODE_INSTANCE_COLUMNS, node_instance, 'Node-instance:', 50)\\n\\n # print node instance runtime properties\\n logger.info('Instance runtime properties:')\\n for prop_name, prop_value in utils.decode_dict(\\n node_instance.runtime_properties).iteritems():\\n logger.info('\\\\t{0}: {1}'.format(prop_name, prop_value))\\n logger.info('')\",\n \"def cli():\\n while True:\\n try:\\n # Get the whole information on each edge.\\n l_edge = list()\\n s_rsc = '{}/edge'.format(etcdc.prefix)\\n \\n try:\\n r = etcdc.read(s_rsc, recursive=True)\\n except etcd.EtcdKeyNotFound as e:\\n log.error(e)\\n else:\\n for child in r.children:\\n l_app = list()\\n d = ast.literal_eval(child.value)\\n # get hosts\\n print(PROJECT_ROOT + '/' + d['endpoint'])\\n l_hosts = kube_list_node(PROJECT_ROOT + '/' + d['endpoint'])\\n d['hosts'] = len(l_hosts)\\n d_nodes = dict() # {'name': 'ip', ...}\\n for item in l_hosts:\\n d_nodes[item.metadata.name] = item.status.addresses[0].address\\n # log.debug(d_nodes)\\n # get # of tenants and apps\\n l_tenants = get_tenant(d['name'])\\n d['tenants'] = len(l_tenants)\\n d['apps'] = 0\\n for e in l_tenants:\\n if 'app' in e:\\n d['apps'] += len(e['app'])\\n \\n d['cpu'] = 0\\n d['memory'] = 0\\n i_total_cores = 0\\n i_total_memory = 0\\n i_total_storage = 0\\n for h in l_hosts:\\n i_total_cores += int(h.status.capacity['cpu'])\\n i_total_memory += int(h.status.capacity['memory'].\\n replace('Ki', ''))\\n d['tot_cpu'] = i_total_cores\\n d['tot_mem'] = int(i_total_memory / (1024*1024))\\n \\n # Get loadavg and free mem\\n if d['name'] == 'edge1':\\n ssh_server = 'harden.iorchard.co.kr'\\n elif d['name'] == 'edge2':\\n ssh_server = 'durant.iorchard.co.kr'\\n RSC = 'ssh -p42544 {} get_rsc.sh'.format(ssh_server)\\n (b_res, s_out) = cmd(RSC, 3, False)\\n l = s_out.split(\\\"\\\\n\\\")\\n d['used_cpu'] = (float(l[0]) + float(l[1]) + float(l[2]))\\n avail_mem = (int(l[3]) + int(l[4]) + int(l[5])) / (1024*1024)\\n d['used_mem'] = d['tot_mem'] - avail_mem\\n d['cpu'] = int(d['used_cpu'] / d['tot_cpu'] * 100)\\n d['memory'] = int(d['used_mem'] / d['tot_mem'] * 100)\\n # ceph storage\\n CEPH = \\\"kubectl --kubeconfig \\\" + PROJECT_ROOT + '/' \\\\\\n + d['endpoint'] + \\\" -n rook-ceph exec -it \\\" \\\\\\n + \\\"$(kubectl --kubeconfig \\\" + PROJECT_ROOT + '/' \\\\\\n + d['endpoint'] + \\\" -n rook-ceph get po \\\" \\\\\\n + \\\"-l app=rook-ceph-tools \\\" \\\\\\n + \\\"-o jsonpath='{.items[0].metadata.name}') -- \\\" \\\\\\n + \\\"ceph df --format json\\\"\\n (b_res, s_out) = cmd(CEPH, 3, False)\\n print(s_out)\\n d['status'] = 'Healthy' if b_res else 'Unhealthy'\\n d_stor = ast.literal_eval(s_out)\\n d['tot_stor'] = int(d_stor['stats']['total_bytes'] / pow(1024, 3))\\n d['used_stor'] = int(d_stor['stats']['total_used_bytes'] / pow(1024, 3))\\n d['storage'] = int(d['used_stor'] / d['tot_stor'] * 100)\\n # Update etcd status\\n try:\\n s = '{}/edge/{}'.format(etcdc.prefix,\\n d['name'])\\n # log.debug(d)\\n etcdc.write(s, d, prevExist=True)\\n except etcd.EtcdKeyNotFound as e:\\n log.error(e)\\n \\n # Update app status\\n s_app = '{}/app'.format(etcdc.prefix)\\n try:\\n r_app = etcdc.read(s_app, recursive=True)\\n except etcd.EtcdKeyNotFound as e:\\n log.error(e)\\n else:\\n for app_child in r_app.children:\\n if app_child.value is not None:\\n d_app = dict()\\n app = ast.literal_eval(app_child.value)\\n if app['edge'] == d['name']:\\n d_app['name'] = app['name']\\n d_app['username'] = GUAC_USER\\n d_app['password'] = GUAC_PASS\\n # Get catalog info.\\n s_cat = '{}/catalog/{}'.format(etcdc.prefix,\\n app['catalog'])\\n try:\\n r_cat = etcdc.read(s_cat)\\n except etcd.EtcdKeyNotFound as e:\\n log.error(e)\\n else:\\n cat = ast.literal_eval(r_cat.value)\\n app['cat_type'] = cat['type']\\n app['cat_name'] = cat['name']\\n app['cat_logo'] = cat['logo']\\n # Get app status\\n if app['cat_type'] == 'vm':\\n # first, look at DataVolume status of app.\\n CMD = \\\"kubectl --kubeconfig \\\" + PROJECT_ROOT + '/' \\\\\\n + d['endpoint'] + ' get dv ' \\\\\\n + app['name'] \\\\\\n + \\\" -o jsonpath='{range .status}{.phase},{.progress}{end}'\\\"\\n (b_res, s_out) = cmd(CMD, 5, False)\\n l_out = s_out.split(',')\\n if l_out[0] == 'Succeeded':\\n # Get vm status of app\\n CMD = \\\"kubectl --kubeconfig \\\" + PROJECT_ROOT \\\\\\n + '/' \\\\\\n + d['endpoint'] + ' get vm ' \\\\\\n + app['name'] \\\\\\n + \\\" -o jsonpath='{.status.ready}'\\\"\\n (b_res, s_out) = cmd(CMD, 5, False)\\n if b_res and s_out == 'true':\\n # update app status 'running'.\\n app.update({'status': 'running'})\\n \\n if app['edge'] == d['name']:\\n # Get where app is running.\\n CMD = \\\"kubectl --kubeconfig \\\" \\\\\\n + PROJECT_ROOT + '/' \\\\\\n + d['endpoint'] + ' get vmi ' \\\\\\n + app['name'] \\\\\\n + \\\" -o jsonpath='{.status.nodeName}'\\\"\\n (b_res, s_out) = cmd(CMD, 5, False)\\n if b_res:\\n d_app['hostname'] = d_nodes[s_out]\\n # Get nodeport for app.\\n CMD = \\\"kubectl --kubeconfig \\\" \\\\\\n + PROJECT_ROOT + '/' \\\\\\n + d['endpoint'] + ' get svc ' \\\\\\n + app['name'] \\\\\\n + \\\" -o jsonpath='{.spec.ports[0].nodePort}'\\\"\\n (b_res, s_out) = cmd(CMD, 5, False)\\n if b_res:\\n d_app['port'] = s_out\\n else:\\n # update app status 'stopped'\\n app.update({'status': 'stopped'})\\n elif l_out[0] == 'ImportInProgress':\\n # update app status 'building' and \\n app.update({'status': 'building ({})'.format(l_out[1])})\\n elif app['cat_type'] == 'container':\\n app.update({'status': 'running'})\\n \\n try:\\n s = '{}/app/{}'.format(etcdc.prefix,\\n app['name'])\\n # log.debug(app)\\n etcdc.write(s, app, prevExist=True)\\n except etcd.EtcdKeyNotFound as e:\\n log.error(e)\\n \\n if 'port' in d_app:\\n l_app.append(d_app)\\n # render guac-config.j2 and copy it to guac broker server\\n log.debug(l_app)\\n template = env.get_template('broker.j2')\\n s_out = template.render(l_app=l_app)\\n s_tmp = '/tmp/{}.broker'.format(d['name'])\\n try:\\n with open(s_tmp, 'w') as f:\\n f.write(s_out)\\n except Exception as e:\\n log.error(e)\\n else:\\n CMD = \\\"scp \\\" \\\\\\n + \\\"-P42544 {} {}\\\".format(s_tmp, d['broker_ip']) \\\\\\n + \\\":/etc/guacamole/noauth-config.xml\\\"\\n log.debug(CMD)\\n (b_res, s_out) = cmd(CMD, 5, False)\\n if b_res:\\n d_app['port'] = s_out\\n \\n l_edge.append(d)\\n \\n # log.debug(l_edge)\\n log.debug(l_app)\\n \\n time.sleep(1)\\n except:\\n log.error('unknown error')\",\n \"async def GET(node_id, path):\\n r = session().get(f\\\"{HOST}:{BASE_PORT + node_id}{path}\\\")\\n if r.status_code != 200:\\n raise ValueError(f\\\"Got bad response {r.status_code} from \\\" +\\n f\\\"node {node_id} on {path}.\\\")\\n return {\\\"status_code\\\": r.status_code, \\\"data\\\": r.json()}\",\n \"def get_nodes(self):\\n return requests.get(self.__url + 'nodes').json()\",\n \"async def retrieve_node(request: web.Request) -> web.Response:\\n path_params = parse_request_path_parameters_as(_NodePathParams, request)\\n\\n try:\\n data = await request.json()\\n port_keys = data.get(\\\"port_keys\\\", [])\\n except json.JSONDecodeError as exc:\\n raise web.HTTPBadRequest(reason=f\\\"Invalid request body: {exc}\\\") from exc\\n\\n return web.json_response(\\n await director_v2_api.retrieve(\\n request.app, f\\\"{path_params.node_id}\\\", port_keys\\n ),\\n dumps=json_dumps,\\n )\",\n \"def get(self, key):\\n \\n print(\\\"Getting from node {}\\\".format(self.url))\\n\\n (headers, content) = self.http_client.request(self.url + \\\"/\\\" + key + \\\"?r=1\\\", method=\\\"GET\\\", redirections=0)\\n return (headers, content)\",\n \"def _get_nodes(prefix, cloud, configs):\\n # Get nodes with prefix\\n nodes = Node.nodes(prefix, cloud, **configs)\\n\\n # Check for nodes available with prefix\\n if not nodes:\\n log.info(f\\\"No resources available with prefix '{prefix}'. Exiting ....\\\")\\n sys.exit(1)\\n\\n log.info(f\\\"Nodes with prefix '{prefix}' are {', '.join([n for n, _ in nodes])}\\\")\\n return nodes\",\n \"def get_nodes(self):\\n self.get_status()\\n old_api = self.version[0] <= '3'\\n if old_api:\\n certs_path = \\\"%s/certificate_statuses/*\\\" % (self.environment)\\n nodeinfo_path_tpl = \\\"{env}/node/{node}\\\"\\n else:\\n certs_path = \\\"puppet-ca/v1/certificate_statuses/no_key?environment=%s\\\" % (self.environment)\\n nodeinfo_path_tpl = \\\"puppet/v3/node/{node}?environment={env}\\\"\\n\\n csts = self._send('GET', certs_path)\\n nodes_names = []\\n for cst in csts:\\n nodes_names.append(cst['name'])\\n\\n all_nodes = []\\n for nname in nodes_names:\\n path = nodeinfo_path_tpl.format(node=nname, env=self.environment)\\n nodeinfo = self._send('GET', path)\\n if old_api:\\n nodeinfo = self._from_pson(nodeinfo['data'])\\n else:\\n nodeinfo = self._from_pson(nodeinfo)\\n if 'parameters' in nodeinfo:\\n node = nodeinfo['parameters']\\n if self.onlynodes:\\n if not (node.get('hostname') in self.onlynodes or\\n node.get('ipaddress') in self.onlynodes or\\n node.get('fqdn') in self.onlynodes or\\n node.get('uuid') in self.onlynodes):\\n continue\\n all_nodes.append(node)\\n\\n return all_nodes\",\n \"def get_nodes():\\n\\n host = str(request.args['host'])\\n days = float(request.args['days'])\\n\\n to_time = int(time.time())\\n to_day = int(time.strftime('%Y%m%d', time.gmtime(float(to_time))))\\n from_time = to_time-int(days*24*60*60)\\n from_day = int(time.strftime('%Y%m%d', time.gmtime(float(from_time))))\\n day_in=''\\n for x in range(from_day, to_day+1):\\n day_in = day_in + ',' + str(x)\\n day_in=re.sub(r\\\"^,\\\", \\\"\\\", day_in)\\n day_in=re.sub(r\\\",$\\\", \\\"\\\", day_in)\\n query = \\\"SELECT * FROM metrics WHERE host='\\\" + str(host) + \\\"' and date IN (\\\"\\n query = query + str(day_in) + \\\") and time>=\\\" + str(int(int(from_time)*1000)) + \\\" and time<=\\\"\\n query = query + str(int(int(to_time)*1000)) + \\\" ALLOW FILTERING\\\"\\n rows = session.execute(query);\\n reply={}\\n last_value={}\\n for r in rows:\\n if str(r.host) not in reply:\\n reply[r.host]={}\\n last_value[r.host]={}\\n if str(r.metric) not in reply[r.host]:\\n reply[r.host][r.metric]=[]\\n last_value[r.host][r.metric]=int(r.value)\\n continue\\n real_value = (r.value-last_value[r.host][r.metric])/60\\n\\tlast_value[r.host][r.metric]=int(r.value)\\n reply[str(r.host)][r.metric].append({ 'value': int(real_value),\\n 'time': str(r.time) })\\n return json.dumps(reply)\",\n \"def get_kong_node_usage_metrics(opts):\\n\\n url = \\\"{0}/status\\\".format(opts['base_url'])\\n\\n r = requests.get(url)\\n try:\\n r.raise_for_status()\\n except requests.exceptions.RequestException as e:\\n logging.debug(\\\"http response body - %s\\\", r.text)\\n logging.error(\\\"An exception occurred: (%s)\\\", e)\\n sys.exit(2)\\n\\n print r.text\\n\\n return True\",\n \"def get_host_info(self):\\n\\n if len(self.index) == 0:\\n # Need to load index from cache\\n self.load_index_from_cache()\\n\\n if not self.args.host in self.index:\\n # try updating the cache\\n self.do_api_calls_update_cache()\\n if not self.args.host in self.index:\\n # host might not exist anymore\\n return self.json_format_dict({}, True)\\n\\n node_id = self.index[self.args.host]\\n print \\\"NODE ID %s\\\" % node_id\\n print \\\"INDEX: %s\\\" % self.index\\n\\n node = self.get_node(node_id)\\n node_vars = {}\\n for direct_attr in [\\n \\\"api_id\\\",\\n \\\"datacenter_id\\\",\\n \\\"label\\\",\\n \\\"display_group\\\",\\n \\\"create_dt\\\",\\n \\\"total_hd\\\",\\n \\\"total_xfer\\\",\\n \\\"total_ram\\\",\\n \\\"status\\\",\\n \\\"alert_cpu_enabled\\\",\\n \\\"alert_cpu_threshold\\\",\\n \\\"alert_diskio_enabled\\\",\\n \\\"alert_diskio_threshold\\\",\\n \\\"alert_bwin_enabled\\\",\\n \\\"alert_bwin_threshold\\\",\\n \\\"alert_bwout_enabled\\\",\\n \\\"alert_bwout_threshold\\\",\\n \\\"alert_bwquota_enabled\\\",\\n \\\"alert_bwquota_threshold\\\",\\n \\\"backup_weekly_daily\\\",\\n \\\"backup_window\\\",\\n \\\"watchdog\\\"\\n ]:\\n node_vars[direct_attr] = getattr(node, direct_attr)\\n\\n node_vars[\\\"datacenter_city\\\"] = self.get_datacenter_city(node)\\n node_vars[\\\"public_ip\\\"] = [addr.address for addr in node.ipaddresses if addr.is_public][0]\\n\\n return self.json_format_dict(node_vars, True)\",\n \"def cluster_znode_data(cluster_name, znode, headers=None):\\n\\n zdata_resp = cluster_znode(cluster_name,\\n znode,\\n headers=headers)\\n zdata = json.loads(zdata_resp.get_data())\\n resp = Response(zdata[\\\"data\\\"],\\n status=200,\\n mimetype=\\\"text/plain\\\")\\n return resp\",\n \"def get(resource_name: str,\\n id: pulumi.Input[str],\\n opts: Optional[pulumi.ResourceOptions] = None,\\n auto_renew: Optional[pulumi.Input[bool]] = None,\\n auto_renew_period: Optional[pulumi.Input[int]] = None,\\n cluster_name: Optional[pulumi.Input[str]] = None,\\n data_center_name: Optional[pulumi.Input[str]] = None,\\n disk_size: Optional[pulumi.Input[int]] = None,\\n disk_type: Optional[pulumi.Input[str]] = None,\\n enable_public: Optional[pulumi.Input[bool]] = None,\\n instance_type: Optional[pulumi.Input[str]] = None,\\n ip_white: Optional[pulumi.Input[str]] = None,\\n maintain_end_time: Optional[pulumi.Input[str]] = None,\\n maintain_start_time: Optional[pulumi.Input[str]] = None,\\n major_version: Optional[pulumi.Input[str]] = None,\\n node_count: Optional[pulumi.Input[int]] = None,\\n password: Optional[pulumi.Input[str]] = None,\\n pay_type: Optional[pulumi.Input[str]] = None,\\n period: Optional[pulumi.Input[int]] = None,\\n period_unit: Optional[pulumi.Input[str]] = None,\\n public_points: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,\\n security_groups: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,\\n status: Optional[pulumi.Input[str]] = None,\\n tags: Optional[pulumi.Input[Mapping[str, Any]]] = None,\\n vswitch_id: Optional[pulumi.Input[str]] = None,\\n zone_id: Optional[pulumi.Input[str]] = None) -> 'Cluster':\\n opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))\\n\\n __props__ = _ClusterState.__new__(_ClusterState)\\n\\n __props__.__dict__[\\\"auto_renew\\\"] = auto_renew\\n __props__.__dict__[\\\"auto_renew_period\\\"] = auto_renew_period\\n __props__.__dict__[\\\"cluster_name\\\"] = cluster_name\\n __props__.__dict__[\\\"data_center_name\\\"] = data_center_name\\n __props__.__dict__[\\\"disk_size\\\"] = disk_size\\n __props__.__dict__[\\\"disk_type\\\"] = disk_type\\n __props__.__dict__[\\\"enable_public\\\"] = enable_public\\n __props__.__dict__[\\\"instance_type\\\"] = instance_type\\n __props__.__dict__[\\\"ip_white\\\"] = ip_white\\n __props__.__dict__[\\\"maintain_end_time\\\"] = maintain_end_time\\n __props__.__dict__[\\\"maintain_start_time\\\"] = maintain_start_time\\n __props__.__dict__[\\\"major_version\\\"] = major_version\\n __props__.__dict__[\\\"node_count\\\"] = node_count\\n __props__.__dict__[\\\"password\\\"] = password\\n __props__.__dict__[\\\"pay_type\\\"] = pay_type\\n __props__.__dict__[\\\"period\\\"] = period\\n __props__.__dict__[\\\"period_unit\\\"] = period_unit\\n __props__.__dict__[\\\"public_points\\\"] = public_points\\n __props__.__dict__[\\\"security_groups\\\"] = security_groups\\n __props__.__dict__[\\\"status\\\"] = status\\n __props__.__dict__[\\\"tags\\\"] = tags\\n __props__.__dict__[\\\"vswitch_id\\\"] = vswitch_id\\n __props__.__dict__[\\\"zone_id\\\"] = zone_id\\n return Cluster(resource_name, opts=opts, __props__=__props__)\",\n \"def get_node(conn, name):\\n datacenter_id = get_datacenter_id()\\n\\n for item in conn.list_servers(datacenter_id)[\\\"items\\\"]:\\n if item[\\\"properties\\\"][\\\"name\\\"] == name:\\n node = {\\\"id\\\": item[\\\"id\\\"]}\\n node.update(item[\\\"properties\\\"])\\n return node\",\n \"def cluster_list():\\n request_debug(r, logger)\\n json_body = r.get_json(force=True, silent=True) or {}\\n result = cluster_handler.list(filter_data=json_body)\\n response_ok[\\\"data\\\"] = result\\n return jsonify(response_ok), CODE_OK\",\n \"def send_simple_get_request(hostname, node, key, causal_payload=''):\\n get_str = \\\"http://\\\" + hostname + \\\":\\\" + node.access_port + \\\"/kvs/\\\" + key\\n data = {'causal_payload':causal_payload}\\n if PRINT_HTTP_REQUESTS:\\n print \\\"Get request: \\\" + get_str + ' data field:' + str(data)\\n r = req.get(get_str, data=data)\\n if PRINT_HTTP_RESPONSES:\\n print r.text, r.status_code\\n return r\",\n \"def get(self, controller, data, *args, **kwargs): \\n task_manager = controller.get_task_manager()\\n res = task_manager.get_all_tasks(details=True)\\n resp = {\\n u'task-instances':res,\\n u'count':len(res)\\n } \\n return resp\",\n \"def describe_cluster_response():\\n return {\\n \\\"cluster\\\": {\\n \\\"status\\\": \\\"ACTIVE\\\",\\n \\\"endpoint\\\": \\\"https://endpoint.amazonaws.com\\\",\\n \\\"name\\\": EXAMPLE_NAME,\\n \\\"certificateAuthority\\\": {\\n \\\"data\\\": \\\"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\\\"\\n },\\n \\\"roleArn\\\": \\\"arn:aws:iam::111222333444/eksRole\\\",\\n \\\"resourcesVpcConfig\\\": {\\n \\\"subnetIds\\\": [\\n \\\"subnet-00000000000000000\\\",\\n \\\"subnet-00000000000000001\\\",\\n \\\"subnet-00000000000000002\\\"\\n ],\\n \\\"vpcId\\\": \\\"vpc-00000000000000000\\\",\\n \\\"securityGroupIds\\\": [\\n \\\"sg-00000000000000000\\\"\\n ]\\n },\\n \\\"version\\\": \\\"1.10\\\",\\n \\\"arn\\\": \\\"arn:aws:eks:region:111222333444:cluster/\\\" + EXAMPLE_NAME,\\n \\\"createdAt\\\": 1500000000.000\\n }\\n }\",\n \"def list_nodes(hosts):\\n if hosts=='hosts':\\n query = \\\"SELECT DISTINCT host FROM metrics_index\\\";\\n reply=[]\\n elif hosts=='all':\\n query = \\\"SELECT * FROM metrics_index\\\";\\n reply={}\\n\\n rows = session.execute(query);\\n for r in rows:\\n if hosts=='hosts':\\n if str(r.host) not in reply:\\n reply.append(str(r.host))\\n elif hosts=='all':\\n if str(r.host) not in reply:\\n reply[str(r.host)]=[]\\n if str(r.metric) not in reply[str(r.host)]:\\n reply[str(r.host)].append(str(r.metric))\\n return json.dumps(reply)\",\n \"def memcache_nodes(self) -> pulumi.Output[Sequence['outputs.NodeResponse']]:\\n return pulumi.get(self, \\\"memcache_nodes\\\")\",\n \"def getContainers(self,node):\\n data = self.connect('get','nodes/%s/lxc' % node,None)\\n return data\",\n \"def cluster_node_get(self, node_name, desired_attributes=None):\\n return self.request( \\\"cluster-node-get\\\", {\\n 'node_name': [ node_name, 'node-name', [ basestring, 'node-name' ], False ],\\n 'desired_attributes': [ desired_attributes, 'desired-attributes', [ ClusterNodeInfo, 'None' ], False ],\\n }, {\\n 'attributes': [ ClusterNodeInfo, False ],\\n } )\",\n \"def test_get_entity(self):\\n\\n storage = StringIO.StringIO()\\n c = pycurl.Curl()\\n c.setopt(c.URL,\\\"http://127.0.0.1:8090/compute/9930\\\")\\n c.setopt(c.HTTPHEADER, ['Accept:application/occi+json'])\\n c.setopt(c.VERBOSE, True)\\n c.setopt(c.CUSTOMREQUEST, 'GET')\\n c.setopt(c.WRITEFUNCTION, storage.write)\\n c.perform()\\n content = storage.getvalue()\\n print \\\" ===== Body content =====\\\\n \\\" + content + \\\" ==========\\\\n\\\"\",\n \"def get(self):\\n\\n self.response.headers['Access-Control-Allow-Origin'] = '*' # Required until JSONP is supported\\n self.response.headers['Content-Type'] = 'application/json' # All responses are json, so JSONP to come\\n\\n if(self.request.get(\\\"cid\\\")):\\n cid = self.request.get(\\\"cid\\\")\\n node = memcache.get(cid)\\n\\n if node is not None:\\n # if it is a valid cid then check to see if there are any commands\\n # in the nodes command queue\\n # and update some fields on the node\\n\\n command = node.getNextCommand()\\n if command is not None:\\n self.write(JSONResponse.execCommand(command))\\n if command.autoComplete is True:\\n logging.info(\\\"auto completing\\\")\\n node.completeCommand(command.id)\\n else:\\n self.write(json.dumps({\\\"type\\\":\\\"noCommand\\\"}))\\n else:\\n # if it turned out to be a non valid cid, just make a new node\\n node = createNode(self.request)\\n self.write(JSONResponse.assignment(node.cid))\\n \\n\\n else: # if the request had no cid attached\\n node = createNode(self.request)\\n self.write(JSONResponse.assignment(node.cid))\",\n \"def get(self, request, nnid, wfver, desc):\\n try:\\n return_data = NNCommonManager().get_nn_node_info(nnid, wfver, desc)\\n return Response(json.dumps(return_data))\\n except Exception as e:\\n return_data = {\\\"status\\\": \\\"404\\\", \\\"result\\\": str(e)}\\n return Response(json.dumps(return_data))\",\n \"def handle_status(self, request):\\n \\\"\\\"\\\"\\n @api {get} /status Get node status\\n @apiName GetNodeStatus\\n @apiGroup Node\\n @apiVersion 1.1.0\\n\\n @apiSuccess {Boolean} execution_enabled Task execution is enabled on the node.\\n @apiSuccess {Boolean} leader Node is the leader.\\n @apiSuccess {String} name Node name.\\n @apiSuccess {Boolean} scheduler_running The scheduler is running on the node.\\n @apiSuccess {String} address Node IP address.\\n @apiSuccess {String[]} pools Pools in which the node is registered.\\n @apiSuccess {Object} running_processes Processes running on the host.\\n @apiSuccess {Object} running_processes.process Process.\\n @apiSuccess {String} running_processes.process.start_time Time the process started, ISO 8601 formatted.\\n @apiSuccess {String} running_processes.process.task ID of the task.\\n @apiSuccess {Boolean} cluster_joined Node has joined the cluster.\\n @apiSuccess {Boolean} contending_for_lead Node is contending for lead.\\n @apiSuccess {Boolean} pools_joined Node has joined its pools.\\n\\n @apiSuccessExample {json} Example response:\\n {\\n \\\"execution_enabled\\\": true,\\n \\\"leader\\\": false,\\n \\\"name\\\": \\\"node2\\\",\\n \\\"scheduler_running\\\": false,\\n \\\"address\\\": \\\"127.0.0.1:32002\\\",\\n \\\"pools\\\": [\\\"pool1\\\", \\\"pool2\\\"],\\n \\\"running_processes\\\": {\\n \\\"b26e5cc2ef3f11e4817b0026b951c045\\\": {\\n \\\"start_time\\\": \\\"2015-04-30T13:49:18.351494+00:00\\\",\\n \\\"task\\\": \\\"508b4b72e44611e49e76c81f66cd0cca\\\"\\n }\\n },\\n \\\"cluster_joined\\\": true,\\n \\\"contending_for_lead\\\": true,\\n \\\"pools_joined\\\": true\\n }\\n \\\"\\\"\\\"\\n\\n headers = {\\n 'Content-Type': 'application/javascript',\\n 'Access-Control-Allow-Origin': '*'\\n }\\n\\n status = {\\n 'name': self.cluster.nodename,\\n 'address': self.cluster.addr,\\n 'pools': self.cluster.mypools,\\n 'leader': self.cluster.is_leader,\\n 'cluster_joined': self.cluster.cluster_joined,\\n 'pools_joined': self.cluster.pools_joined,\\n 'contending_for_lead': self.cluster.contending_for_lead,\\n\\n 'execution_enabled': self.manager.enabled,\\n 'running_processes': dict([ (execid, { 'task': details['task'], 'start_time': details['start_time'].isoformat() }) for (execid, details) in self.manager.running_processes.items() ]),\\n\\n 'scheduler_running': self.cluster.scheduler.running\\n }\\n\\n return HTTPReply(body = json.dumps(status), headers = headers)\",\n \"def getNodes(self):\\n data = self.connect('get','nodes',None)\\n return data\",\n \"def compute_node_get_by_host(context, host):\\n session = get_session()\\n with session.begin():\\n service = session.query(models.Service).\\\\\\n filter_by(host=host, binary=\\\"monitor-bmc\\\").first()\\n node = session.query(models.ComputeNode).\\\\\\n options(joinedload('service')).\\\\\\n filter_by(deleted=False,service_id=service.id)\\n return node.first()\",\n \"def _load_cluster(self):\",\n \"def node(*,bucket, endpoint, key, crn, auth=IBM_CLOUD_OAUTH_URL):\\n # Get bearer token to access COS S3 API\\n # payload to generate auth token\\n token_req_data = {\\n 'grant_type' :'urn:ibm:params:oauth:grant-type:apikey',\\n 'response_type':'cloud_iam',\\n 'apikey' :key\\n }\\n response = requests.post(auth, data=token_req_data, headers={'Content-type': 'application/x-www-form-urlencoded'})\\n if response.status_code != 200:\\n raise Exception(\\\"error\\\")\\n bearer_token_info = response.json()\\n logging.debug(bearer_token_info)\\n \\n # Get Aspera connection information for the bucket\\n header_auth = {\\n 'ibm-service-instance-id':crn,\\n 'Authorization':bearer_token_info['token_type'] + \\\" \\\" + bearer_token_info['access_token'],\\n 'Accept':'application/xml'\\n }\\n response = requests.get(endpoint + \\\"/\\\" + bucket, headers=header_auth, params={'faspConnectionInfo':True})\\n if response.status_code != 200:\\n raise Exception(\\\"error accessing endpoint\\\")\\n logging.debug(response.content)\\n ats_info_root = xml.dom.minidom.parseString(response.content.decode('utf-8'));\\n ats_ak = ats_info_root.getElementsByTagName('AccessKey')[0]\\n ats_url = ats_info_root.getElementsByTagName('ATSEndpoint')[0].firstChild.nodeValue\\n ats_ak_id = ats_ak.getElementsByTagName('Id')[0].firstChild.nodeValue\\n ats_ak_secret = ats_ak.getElementsByTagName('Secret')[0].firstChild.nodeValue\\n \\n # Get delegated token to access the node api\\n token_req_data['response_type'] = 'delegated_refresh_token'\\n token_req_data['receiver_client_ids'] = 'aspera_ats'\\n response = requests.post(auth, data=token_req_data, headers={'Content-type': 'application/x-www-form-urlencoded'})\\n if response.status_code != 200:\\n raise Exception(\\\"error when generating token\\\")\\n delegated_token_info = response.json()\\n aspera_storage_credentials = {\\n 'type': 'token',\\n 'token': delegated_token_info\\n }\\n logging.debug(aspera_storage_credentials)\\n return {\\n 'url': ats_url,\\n 'auth': requests.auth.HTTPBasicAuth(ats_ak_id, ats_ak_secret),\\n 'headers': {\\n 'X-Aspera-Storage-Credentials':json.dumps(aspera_storage_credentials),\\n },\\n 'tspec': {'tags':{'aspera':{'node':{'storage_credentials':aspera_storage_credentials}}}}\\n }\",\n \"def connectnode():\\n content = request.get_json()\\n \\n #Check request method\\n if content[\\\"method\\\"] == \\\"GET\\\":\\n res = hl.nodeGet(content[\\\"url\\\"])\\n elif content[\\\"method\\\"] == \\\"GET\\\":\\n res = hl.nodePost(content[\\\"url\\\"], content[\\\"data\\\"])\\n\\n #Error check\\n if res:\\n if ('result' in res and res['result']) or 'result' not in res:\\n try:\\n res.pop(\\\"result\\\")\\n except:\\n pass\\n \\n result = {\\\"result\\\" : True, \\\"data\\\" : res} \\n else:\\n result = {\\\"result\\\" : False} \\n else:\\n result = {\\\"result\\\" : False} \\n \\n #Return response\\n return(jsonify(result))\",\n \"def get_cluster(t2_url, t2_token, id):\\n response = requests.get(f\\\"{t2_url}/api/clusters/{id}\\\", headers={ \\\"t2-token\\\": t2_token })\\n if(response.status_code != 200):\\n log(f\\\"API call to get cluster returned error code {response.status_code}\\\")\\n return None\\n return response.json()\",\n \"def describe_cluster_no_status_response():\\n return {\\n \\\"cluster\\\": {\\n \\\"endpoint\\\": \\\"https://endpoint.amazonaws.com\\\",\\n \\\"name\\\": EXAMPLE_NAME,\\n \\\"certificateAuthority\\\": {\\n \\\"data\\\": \\\"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\\\"\\n },\\n \\\"roleArn\\\": \\\"arn:aws:iam::111222333444/eksRole\\\",\\n \\\"resourcesVpcConfig\\\": {\\n \\\"subnetIds\\\": [\\n \\\"subnet-00000000000000000\\\",\\n \\\"subnet-00000000000000001\\\",\\n \\\"subnet-00000000000000002\\\"\\n ],\\n \\\"vpcId\\\": \\\"vpc-00000000000000000\\\",\\n \\\"securityGroupIds\\\": [\\n \\\"sg-00000000000000000\\\"\\n ]\\n },\\n \\\"version\\\": \\\"1.10\\\",\\n \\\"arn\\\": \\\"arn:aws:eks:region:111222333444:cluster/\\\" + EXAMPLE_NAME,\\n \\\"createdAt\\\": 1500000000.000\\n }\\n }\",\n \"def GetNodeInfo(self, request, context):\\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\\n context.set_details('Method not implemented!')\\n raise NotImplementedError('Method not implemented!')\",\n \"def cluster_nodes(self) -> ResponseT:\\n return self.execute_command(\\\"CLUSTER NODES\\\")\",\n \"def list(args, config):\\n\\n api = config['API']\\n headers = {}\\n if args.stack_name:\\n headers = {'stack-name': args.stack_name} # put stack name in headers\\n r = requests.get(api['list'], headers=headers) # send the GET request\\n print('\\\\nThe following clusters exist:\\\\n{}\\\\n'.format(r.json()))\\n return\",\n \"def list_cluster_response():\\n return {\\n \\\"clusters\\\": [\\n EXAMPLE_NAME\\n ]\\n }\",\n \"def get_node(self, key: str) -> Node:\",\n \"def kitero():\\n return dict(hostname=hostname)\",\n \"def getNodeDNS(self,node):\\n data = self.connect('get','nodes/%s/dns' % (node),None)\\n return data\",\n \"def list():\\n index = 0\\n while True:\\n node = Node.from_index(index)\\n if os.path.exists(node.path()):\\n click.echo(f'{index}: node_{index}')\\n click.echo(run_lncli(node, 'getinfo | jq .identity_pubkey'))\\n else:\\n break\\n index += 1\",\n \"def GetNodeInfo(self, hvparams=None):\\n return self.GetLinuxNodeInfo()\",\n \"def get_node(self, _id):\\n return self.make_request(\\\"GET\\\", \\\"nodes/\\\"+_id, {})\",\n \"def get_pods(namespace, k8s_cli, selector=\\\"\\\"):\\n if selector:\\n selector = '--selector=\\\"{}\\\"'.format(selector)\\n cmd = '{} get pod -n {} {} -o json '.format(k8s_cli, namespace, selector)\\n return_code, out = run_shell_command(cmd)\\n if return_code:\\n logger.warning(\\\"Failed to get pods: %s\\\", out)\\n return None\\n return json.loads(out)['items']\",\n \"def main():\\n pods = openshift_object.get_running_pods()\\n me = openshift_object.get_self()\\n routes = openshift_object.get_routes()\\n nodes = openshift_object.get_nodes()\\n pvc = openshift_object.get_pvcs()\\n pv = openshift_object.get_pv()\\n project = openshift_object.get_projects()\\n return jsonify({\\n \\\"pods\\\": pods,\\n \\\"me\\\": me,\\n \\\"routes\\\": routes, \\n \\\"nodes\\\":nodes,\\n \\\"pvcs\\\":pvc,\\n \\\"pv\\\":pv,\\n \\\"projects\\\":project})\",\n \"def test_openshift_node_with_all_cluster_and_node_access_view(self):\\n with schema_context(self.schema_name):\\n expected = (\\n OCPCostSummaryByNodeP.objects.annotate(**{\\\"value\\\": F(\\\"node\\\")})\\n .values(\\\"value\\\")\\n .distinct()\\n .filter(node__in=[RBAC_NODE])\\n .count()\\n )\\n self.assertTrue(expected)\\n url = reverse(\\\"openshift-nodes\\\")\\n response = self.client.get(url, **self.headers)\\n self.assertEqual(response.status_code, status.HTTP_200_OK)\\n json_result = response.json()\\n self.assertIsNotNone(json_result.get(\\\"data\\\"))\\n self.assertIsInstance(json_result.get(\\\"data\\\"), list)\\n self.assertEqual(len(json_result.get(\\\"data\\\")), expected)\",\n \"def get_nodes(self):\\n _url = f\\\"{self.connector.base_url}/projects/{self.project_id}/nodes\\\"\\n\\n _response = self.connector.http_call(\\\"get\\\", _url)\\n\\n # Create the Nodes array but cleanup cache if there is one\\n if self.nodes:\\n self.nodes = []\\n for _node in _response.json():\\n _n = Node(connector=self.connector, **_node)\\n _n.project_id = self.project_id\\n self.nodes.append(_n)\",\n \"def list_nodes(self):\\n\\n return list(\\n dict(\\n self._from_json(self.manage.run(override=\\\"list-nodes\\\"))\\n ).keys()\\n )\",\n \"async def node(self, node, *, dc=None, watch=None, consistency=None):\\n node_id = extract_attr(node, keys=[\\\"Node\\\", \\\"ID\\\"])\\n response = await self._api.get(\\\"/v1/session/node\\\", node_id, params={\\n \\\"dc\\\": dc}, watch=watch, consistency=consistency)\\n return consul(response)\",\n \"def test_get_node_status(self):\\n pass\",\n \"def test_openshift_node_with_cluster_and_all_node_access_view(self):\\n with schema_context(self.schema_name):\\n expected = (\\n OCPCostSummaryByNodeP.objects.annotate(**{\\\"value\\\": F(\\\"node\\\")})\\n .values(\\\"value\\\")\\n .distinct()\\n .filter(cluster_id__in=[\\\"OCP-on-AWS\\\"])\\n .count()\\n )\\n self.assertTrue(expected)\\n url = reverse(\\\"openshift-nodes\\\")\\n response = self.client.get(url, **self.headers)\\n self.assertEqual(response.status_code, status.HTTP_200_OK)\\n json_result = response.json()\\n self.assertIsNotNone(json_result.get(\\\"data\\\"))\\n self.assertIsInstance(json_result.get(\\\"data\\\"), list)\\n self.assertEqual(len(json_result.get(\\\"data\\\")), expected)\",\n \"def check_nodes_available(label, label_name):\\r\\n api_instance = client.CoreV1Api()\\r\\n label_selector = \\\"\\\"\\r\\n for label_val in label:\\r\\n label_selector += str(label_val[\\\"key\\\"])+\\\"=\\\"+str(label_val[\\\"value\\\"])+\\\",\\\"\\r\\n label_selector = label_selector[0:-1]\\r\\n try:\\r\\n api_response_2 = api_instance.list_node(\\r\\n pretty=True, label_selector=label_selector)\\r\\n if len(api_response_2.items) == 0:\\r\\n LOGGER.error(f\\\"0 nodes matches with {label_name}\\\")\\r\\n LOGGER.error(\\\"please check labels\\\")\\r\\n assert False\\r\\n except ApiException as e:\\r\\n LOGGER.error(f\\\"Exception when calling CoreV1Api->list_node: {e}\\\")\\r\\n assert False\",\n \"def local(node_id, blueprint_id, logger):\\n env = load_env(blueprint_id)\\n node_instances = env.storage.get_node_instances()\\n if node_id:\\n node_instances = [instance for instance in node_instances\\n if instance.node_id == node_id]\\n if not node_instances:\\n raise CloudifyCliError(\\n 'Could not find node {0}'.format(node_id))\\n logger.info(json.dumps(node_instances, sort_keys=True, indent=2))\",\n \"def test_get_hyperflex_cluster_list(self):\\n pass\",\n \"def list_nodes(conn=None, call=None):\\n if call == \\\"action\\\":\\n raise SaltCloudSystemExit(\\n \\\"The list_nodes function must be called with -f or --function.\\\"\\n )\\n\\n if not conn:\\n conn = get_conn()\\n\\n ret = {}\\n datacenter_id = get_datacenter_id()\\n\\n try:\\n nodes = conn.list_servers(datacenter_id=datacenter_id)\\n except PBNotFoundError:\\n log.error(\\\"Failed to get nodes list from datacenter: %s\\\", datacenter_id)\\n raise\\n\\n for item in nodes[\\\"items\\\"]:\\n node = {\\\"id\\\": item[\\\"id\\\"]}\\n node.update(item[\\\"properties\\\"])\\n node[\\\"state\\\"] = node.pop(\\\"vmState\\\")\\n ret[node[\\\"name\\\"]] = node\\n\\n return ret\",\n \"async def get(id):\\n cluster = clusters.get_by_id(id)\\n\\n if cluster is None:\\n raise HTTPException(status_code=404, detail=\\\"Cluster not found for ID: {0}\\\".format(id))\\n\\n return cluster.export()\",\n \"def test_get_node_partitions(self):\\n pass\",\n \"async def list_nodes(self, *, option: ListApiOptions) -> ListApiResponse:\\n try:\\n reply = await self._client.get_all_node_info(timeout=option.timeout)\\n except DataSourceUnavailable:\\n raise DataSourceUnavailable(GCS_QUERY_FAILURE_WARNING)\\n\\n result = []\\n for message in reply.node_info_list:\\n data = protobuf_message_to_dict(\\n message=message, fields_to_decode=[\\\"node_id\\\"]\\n )\\n data[\\\"node_ip\\\"] = data[\\\"node_manager_address\\\"]\\n data[\\\"start_time_ms\\\"] = int(data[\\\"start_time_ms\\\"])\\n data[\\\"end_time_ms\\\"] = int(data[\\\"end_time_ms\\\"])\\n\\n result.append(data)\\n\\n total_nodes = len(result)\\n # No reason to truncate node because they are usually small.\\n num_after_truncation = len(result)\\n\\n result = self._filter(result, option.filters, NodeState, option.detail)\\n num_filtered = len(result)\\n\\n # Sort to make the output deterministic.\\n result.sort(key=lambda entry: entry[\\\"node_id\\\"])\\n result = list(islice(result, option.limit))\\n return ListApiResponse(\\n result=result,\\n total=total_nodes,\\n num_after_truncation=num_after_truncation,\\n num_filtered=num_filtered,\\n )\",\n \"def hello_pubsub(event, context):\\n pubsub_message = base64.b64decode(event['data']).decode('utf-8')\\n print(pubsub_message)\\n x = json.loads(pubsub_message)\\n vm_name = x[\\\"jsonPayload\\\"][\\\"resource\\\"][\\\"name\\\"]\\n vm_zone = x[\\\"jsonPayload\\\"][\\\"resource\\\"][\\\"zone\\\"]\\n project_id = x[\\\"resource\\\"][\\\"labels\\\"][\\\"project_id\\\"]\\n print(\\\"vm_name=\\\"+vm_name)\\n print(\\\"vm_zone=\\\"+vm_zone)\\n print(\\\"project_id=\\\"+project_id)\\n \\n compute = discovery.build('compute', 'v1')\\n \\n print(\\\"getting metadata fingerprint\\\")\\n request = compute.instances().get(project= project_id, zone= vm_zone, instance= vm_name)\\n response = request.execute()\\n pprint(response)\\n metadata_fingerprint= response['metadata']['fingerprint']\\n print(\\\"existing metadata fingerprint = \\\" + metadata_fingerprint)\\n\\n vm_status=response['status']\\n print(\\\"vm_status = \\\" + vm_status)\\n while vm_status != 'RUNNING' :\\n print(\\\"going to sleep for 1 second...\\\")\\n time.sleep(1)\\n request = compute.instances().get(project= project_id, zone= vm_zone, instance= vm_name)\\n response = request.execute()\\n vm_status=response['status']\\n print(\\\"vm_status = \\\" + vm_status)\\n\\n print(\\\"Setting VM metadata\\\")\\n metadata_body = {\\n \\\"fingerprint\\\": metadata_fingerprint,\\n \\\"items\\\": [\\n {\\n \\\"key\\\": \\\"startup-script-url\\\",\\n \\\"value\\\": \\\"gs://mybucket/my_script.sh\\\"\\n }\\n ]\\n }\\n\\n request = compute.instances().setMetadata(project=project_id, zone=vm_zone, instance=vm_name, body=metadata_body)\\n response = request.execute()\\n pprint(response)\\n\\n print(\\\"Restarting VM\\\")\\n request = compute.instances().reset(project=project_id, zone=vm_zone, instance=vm_name)\\n response = request.execute()\\n pprint(response)\",\n \"def get_lbnodes_by_pcc(conn: dict, id: str) -> dict:\\n return get(conn, f\\\"{S3PCCS}/{id}/nodes/cephlb\\\")\",\n \"def test_read_cluster_network(self):\\n pass\",\n \"def compute_api(self):\\n path = self.compute.path = '/'\\n res = self.compute.call('', 'GET', data='', \\n token=self.manager.identity.token)\\n self.logger.debug('Get openstack compute services: %s' % truncate(res))\\n self.compute.path = path\\n return res[0]\",\n \"def get(ctx, key):\\n config = buildConfig(ctx.obj[\\\"HOST\\\"], ctx.obj[\\\"PORT\\\"])\\n client = getClient(config)\\n click.echo(client.get(key))\",\n \"def getNodeStatus(self,node):\\n data = self.connect('get','nodes/%s/status' % (node),None)\\n return data\",\n \"def test_get_cloud_resources(self):\\n pass\",\n \"def apicall():\\r\\n# try:\\r\\n print request.get_json()\\r\\n test_json = request.get_json()\\r\\n logger.info(\\\"input json object loaded\\\")\\r\\n logger.info(test_json)\\r\\n k=MetaData(test_json)\\r\\n int_res=k.getData()\\r\\n print '------------------------------'\\r\\n print int_res\\r\\n return jsonify(int_res)\",\n \"def fusion_api_get_ha_nodes(self, uri=None, param='', api=None, headers=None):\\n return self.ha_nodes.get(uri=uri, api=api, headers=headers, param=param)\",\n \"def get_kubeconfig(t2_url, t2_token, id):\\n response = requests.get(f\\\"{t2_url}/api/clusters/{id}/kubeconfig\\\", headers={ \\\"t2-token\\\": t2_token })\\n if(response.status_code != 200):\\n log(f\\\"API call to get Stackable kubeconfig returned error code {response.status_code}\\\")\\n return None\\n return response.text\",\n \"def get_list_node(showOnly=False, full=False):\\n # start Requests session\\n sc = requests.Session()\\n\\n # import cookies from Firefox\\n sc.cookies.update(get_cookies('imhsc.imhadmin.net'))\\n\\n # send request\\n vpx = sc.post('https://imhsc.imhadmin.net/index.php?v=VPNodes')\\n\\n # check if login failed\\n check_sc_login(vpx.text)\\n\\n # parse with BS4\\n bs = BeautifulSoup(vpx.text, \\\"xml\\\")\\n\\n # server=0\\n slist = []\\n for trr in bs.tbody.find_all('tr'):\\n try:\\n if full:\\n tsrv = {\\n 'name': trr.find_all('td')[0].text.strip(),\\n 'ip': trr.find_all('td')[2].text.strip(),\\n 'lan': trr.find_all('td')[3].text.strip(),\\n 'cpus': trr.find_all('td')[4].text.strip(),\\n 'memory': trr.find_all('td')[5].text.strip(),\\n 'os': trr.find_all('td')[6].text.strip(),\\n 'vz': trr.find_all('td')[7].text.strip(),\\n 'license': trr.find_all('td')[8].text.strip(),\\n 'cap': int(trr.find_all('td')[9].text.strip()),\\n 'on': int(trr.find_all('td')[10].text.strip()),\\n 'off': int(trr.find_all('td')[11].text.strip()),\\n 'disk_used': trr.find_all('td')[12].text.strip(),\\n 'disk_free': trr.find_all('td')[13].text.strip(),\\n 'psc1': trr.find_all('td')[14].text.strip(),\\n 'psc2': trr.find_all('td')[15].text.strip(),\\n 'loc': trr.find_all('td')[16].text.strip(),\\n 'ra': trr.find_all('td')[17].text.strip(),\\n 'ba': trr.find_all('td')[18].text.strip(),\\n 'model': trr.find_all('td')[19].text.strip()\\n }\\n else:\\n tsrv = trr.find_all('td')[0].text.strip()\\n except:\\n continue\\n slist.append(tsrv)\\n if not showOnly:\\n if full:\\n json.dumps(tsrv)\\n else:\\n print(tsrv)\\n\\n return slist\",\n \"def GET(self):\\n user_data = web.input(cluster_id=None)\\n nodes = db().query(Node).options(\\n joinedload('cluster'),\\n joinedload('interfaces'),\\n joinedload('interfaces.assigned_networks'),\\n joinedload('role_list'),\\n joinedload('pending_role_list'))\\n if user_data.cluster_id == '':\\n nodes = nodes.filter_by(\\n cluster_id=None).all()\\n elif user_data.cluster_id:\\n nodes = nodes.filter_by(\\n cluster_id=user_data.cluster_id).all()\\n else:\\n nodes = nodes.all()\\n return self.render(nodes)\",\n \"def list_nodes_full(conn=None, call=None):\\n if call == \\\"action\\\":\\n raise SaltCloudSystemExit(\\n \\\"The list_nodes_full function must be called with -f or --function.\\\"\\n )\\n\\n if not conn:\\n conn = get_conn() # pylint: disable=E0602\\n\\n ret = {}\\n datacenter_id = get_datacenter_id()\\n nodes = conn.list_servers(datacenter_id=datacenter_id, depth=3)\\n\\n for item in nodes[\\\"items\\\"]:\\n node = {\\\"id\\\": item[\\\"id\\\"]}\\n node.update(item[\\\"properties\\\"])\\n node[\\\"state\\\"] = node.pop(\\\"vmState\\\")\\n node[\\\"public_ips\\\"] = []\\n node[\\\"private_ips\\\"] = []\\n if item[\\\"entities\\\"][\\\"nics\\\"][\\\"items\\\"] > 0:\\n for nic in item[\\\"entities\\\"][\\\"nics\\\"][\\\"items\\\"]:\\n if nic[\\\"properties\\\"][\\\"ips\\\"]:\\n pass\\n ip_address = nic[\\\"properties\\\"][\\\"ips\\\"][0]\\n if salt.utils.cloud.is_public_ip(ip_address):\\n node[\\\"public_ips\\\"].append(ip_address)\\n else:\\n node[\\\"private_ips\\\"].append(ip_address)\\n\\n ret[node[\\\"name\\\"]] = node\\n\\n __utils__[\\\"cloud.cache_node_list\\\"](\\n ret, _get_active_provider_name().split(\\\":\\\")[0], __opts__\\n )\\n\\n return ret\",\n \"def get_bootstrap_node(self):\\n self.clear_screen()\\n default = 'bootstrap'\\n bootstrap_name = input('enter the bootstrap node name\\\\n'\\n 'default [bootstrap]: ')\\n bootstrap_name = set_values(bootstrap_name, default)\\n bootstrap_ip = get_ip(node_name=bootstrap_name, ip_type='os')\\n bootstrap_ip = validate_ip(bootstrap_ip)\\n bootstrap_mac = get_network_device_mac(node_name=bootstrap_name, ip_type='idrac')\\n logging.info('adding bootstrap_node values as name: {} ip: {} mac: {}'.format(bootstrap_name, bootstrap_ip,\\n bootstrap_mac)) \\n self.inventory_dict['csah']['vars']['bootstrap_node'] = [{'name': '{}'.format(bootstrap_name),\\n 'ip': '{}'.format(bootstrap_ip),\\n 'mac': '{}'.format(bootstrap_mac)}]\",\n \"def get(self, get_links=True):\\n _url = (\\n f\\\"{self.connector.base_url}/projects/{self.project_id}/nodes/{self.node_id}\\\"\\n )\\n _response = self.connector.http_call(\\\"get\\\", _url)\\n\\n # Update object\\n self._update(_response.json())\\n\\n if get_links:\\n self.get_links()\",\n \"def test_get_hyperflex_node_list(self):\\n pass\",\n \"def getNodeServiceList(self,node):\\n data = self.connect('get','nodes/%s/services' % (node),None)\\n return data\",\n \"def callapi(uri: str, data=None):\\n if data is not None:\\n data = json.dumps(data).encode('utf8')\\n req = ur.Request(node_url + uri, data=data, headers=headers)\\n res = ur.urlopen(req)\\n jsonstr = res.read().decode('utf8')\\n return json.loads(jsonstr)\",\n \"def handle_cluster_status(self, request):\\n \\\"\\\"\\\"\\n @api {get} /cluster/status Get cluster status\\n @apiName GetClusterStatus\\n @apiGroup Cluster\\n @apiVersion 1.0.0\\n\\n @apiSuccess {Object} nodes Nodes in the cluster.\\n @apiSuccess {Object} nodes.node Node.\\n @apiSuccess {String[]} nodes.node.pools Pools in which the node is registered.\\n @apiSuccess {String} nodes.node.address IP address of the node.\\n @apiSuccess {String} leader Leader node.\\n\\n @apiSuccessExample {json} Example response:\\n {\\n \\\"nodes\\\": {\\n \\\"node1\\\": {\\n \\\"pools\\\": [\\\"pool1\\\", \\\"pool2\\\"],\\n \\\"address\\\": \\\"127.0.0.1:32001\\\"\\n },\\n \\\"node2\\\": {\\n \\\"pools\\\": [\\\"pool1\\\"],\\n \\\"address\\\": \\\"127.0.0.1:32002\\\"\\n },\\n \\\"node3\\\": {\\n \\\"pools\\\": [\\\"pool2\\\"],\\n \\\"address\\\": \\\"127.0.0.1:32003\\\"\\n },\\n },\\n \\\"leader\\\": \\\"node1\\\"\\n }\\n \\\"\\\"\\\"\\n\\n headers = {\\n 'Content-Type': 'application/javascript',\\n 'Access-Control-Allow-Origin': '*'\\n }\\n\\n status = {\\n 'nodes': self.cluster.nodes,\\n 'leader': self.cluster.leader\\n }\\n\\n return HTTPReply(body = json.dumps(status), headers = headers)\",\n \"def getNodeContainerIndex(self,node):\\n data = self.connect('get','nodes/%s/lxc' % (node),None)\\n return data\",\n \"def fetch_data(self):\",\n \"def get(self, id, timeout=None):\\n req = NodeGetRequest()\\n\\n req.id = (id)\\n tries = 0\\n plumbing_response = None\\n while True:\\n try:\\n plumbing_response = self.stub.Get(\\n req,\\n metadata=self.parent.get_metadata('Nodes.Get', req),\\n timeout=timeout)\\n except Exception as e:\\n if self.parent.shouldRetry(tries, e):\\n tries += 1\\n self.parent.jitterSleep(tries)\\n continue\\n raise plumbing.convert_error_to_porcelain(e) from e\\n break\\n\\n resp = models.NodeGetResponse()\\n resp.meta = plumbing.convert_get_response_metadata_to_porcelain(\\n plumbing_response.meta)\\n resp.node = plumbing.convert_node_to_porcelain(plumbing_response.node)\\n resp.rate_limit = plumbing.convert_rate_limit_metadata_to_porcelain(\\n plumbing_response.rate_limit)\\n return resp\",\n \"def _nodeinfo_endpoint(host):\\n zkclient = context.GLOBAL.zk.conn\\n nodeinfo_zk_path = '{}/{}'.format(z.ENDPOINTS, 'root')\\n for node in zkclient.get_children(nodeinfo_zk_path):\\n if 'nodeinfo' in node and host in node:\\n data, _metadata = zkclient.get(\\n '{}/{}'.format(nodeinfo_zk_path, node)\\n )\\n return data.decode().split(':')\",\n \"def get_data(self, **kwargs):\\n\\n self.data = {}\\n #node_data = ''\\n #link_data = ''\\n templates_data = self.request_from_server('templates')\\n self.templates = templates_data\\n project_data = self.request_from_server('projects')\\n for project in project_data:\\n project_name = project['name']\\n if 'project_name' in kwargs:\\n if project_name != kwargs['project_name']:\\n continue\\n\\n self.data[project_name] = {}\\n self.data[project_name]['project_id'] = project['project_id']\\n self.data[project_name]['nodes'] = {}\\n node_data = self.request_from_server('projects/{}/nodes'.format(project['project_id']))\\n link_data = self.request_from_server('projects/{}/links'.format(project['project_id']))\\n for node in node_data:\\n node_name = node['name']\\n self.data[project_name]['nodes'][node_name] = {}\\n self.data[project_name]['nodes'][node_name]['node_id'] = node['node_id']\\n self.data[project_name]['nodes'][node_name]['template_id'] = node['template_id']\\n self.data[project_name]['nodes'][node_name]['node_type'] = node['node_type']\\n self.data[project_name]['nodes'][node_name]['console_port'] = node['console']\\n self.data[project_name]['nodes'][node_name]['console_session'] = None\\n self.data[project_name]['nodes'][node_name]['x'] = node['x']\\n self.data[project_name]['nodes'][node_name]['y'] = node['y']\\n self.data[project_name]['nodes'][node_name]['ports'] = {}\\n if project['status'] != 'closed':\\n self.data[project_name]['nodes'][node_name]['status'] = node['status']\\n for port in node['ports']:\\n port_name = port['short_name']\\n self.data[project_name]['nodes'][node_name]['ports'][port_name] = {}\\n self.data[project_name]['nodes'][node_name]['ports'][port_name]['adapter_number'] = port['adapter_number']\\n self.data[project_name]['nodes'][node_name]['ports'][port_name]['port_number'] = port['port_number']\\n self.data[project_name]['nodes'][node_name]['ports'][port_name]['link_type'] = port['link_type']\\n self.data[project_name]['nodes'][node_name]['ports'][port_name]['link_id'] = None\\n self.data[project_name]['nodes'][node_name]['ports'][port_name]['in_use'] = False\\n self.data[project_name]['nodes'][node_name]['ports'][port_name]['connected_to'] = None\\n for link in link_data:\\n for link_node in link['nodes']:\\n if node['node_id'] == link_node['node_id']:\\n if link_node['label']['text'] == port_name:\\n self.data[project_name]['nodes'][node_name]['ports'][port_name]['link_id'] = link['link_id']\\n self.data[project_name]['nodes'][node_name]['ports'][port_name]['in_use'] = True\\n if link['nodes'].index(link_node) == 0:\\n self.data[project_name]['nodes'][node_name]['ports'][port_name]['connected_to_id'] = link['nodes'][1]['node_id']\\n self.data[project_name]['nodes'][node_name]['ports'][port_name]['connected_to'] = self.get_node_name_from_id(project_name,link['nodes'][1]['node_id'])\\n else:\\n self.data[project_name]['nodes'][node_name]['ports'][port_name]['connected_to_id'] = link['nodes'][0]['node_id']\\n self.data[project_name]['nodes'][node_name]['ports'][port_name]['connected_to'] = self.get_node_name_from_id(project_name,link['nodes'][0]['node_id'])\",\n \"async def fetch_data(self) -> T:\",\n \"def command_node(parser_options, request, parser):\\n if parser_options.experiment_id is not None:\\n experiment_id = parser_options.experiment_id\\n else:\\n queryset = \\\"state=Running&limit=0&offset=0\\\"\\n experiments_json = json.loads(request.get_experiments(queryset))\\n experiment_id = helpers.check_experiments_running(\\n experiments_json, parser)\\n nodes = []\\n if parser_options.nodes_list is not None:\\n for nodes_list in parser_options.nodes_list:\\n param_list = helpers.check_command_list(nodes_list, parser)\\n sites_json = json.loads(request.get_sites())\\n site = helpers.check_site(param_list[0], sites_json, parser)\\n archi = helpers.check_archi(param_list[1], parser)\\n nodes += helpers.check_nodes_list(site,\\n archi,\\n param_list[2],\\n parser)\\n nodes_json = json.dumps(\\n nodes, cls=rest.Encoder, sort_keys=True, indent=4)\\n elif parser_options.exclude_nodes_list is not None:\\n exclude_nodes = []\\n for exclude_list in parser_options.exclude_nodes_list:\\n param_list = helpers.check_command_list(exclude_list, parser)\\n sites_json = json.loads(request.get_sites())\\n site = helpers.check_site(param_list[0], sites_json, parser)\\n archi = helpers.check_archi(param_list[1], parser)\\n exclude_nodes += helpers.check_nodes_list(site,\\n archi,\\n param_list[2],\\n parser)\\n experiment_resources_json = \\\\\\n json.loads(request.get_experiment_resources(experiment_id))\\n experiment_nodes = []\\n for res in experiment_resources_json[\\\"items\\\"]:\\n experiment_nodes.append(res[\\\"network_address\\\"])\\n nodes = [node for node in experiment_nodes if node not in exclude_nodes] \\n nodes_json = json.dumps(\\n nodes, cls=rest.Encoder, sort_keys=True, indent=4)\\n else:\\n # all the nodes\\n nodes_json = '[]'\\n if parser_options.start:\\n json_start = request.start_command(experiment_id, nodes_json)\\n print json.dumps(json.loads(json_start), indent=4, sort_keys=True)\\n elif parser_options.stop:\\n json_stop = request.stop_command(experiment_id, nodes_json)\\n print json.dumps(json.loads(json_stop), indent=4, sort_keys=True)\\n elif parser_options.reset:\\n json_reset = request.reset_command(experiment_id, nodes_json)\\n print json.dumps(json.loads(json_reset), indent=4, sort_keys=True)\\n elif parser_options.path_file is not None:\\n command_files = {}\\n firmware_name, firmware_data = helpers.open_file(\\n parser_options.path_file, parser)\\n command_files[firmware_name] = firmware_data\\n command_filehandle = StringIO(nodes_json)\\n command_files['nodes.json'] = command_filehandle.read()\\n command_filehandle.close()\\n json_update = request.update_command(experiment_id, command_files)\\n print json.dumps(json.loads(json_update), indent=4, sort_keys=True)\",\n \"def fetch_data(swarming, start, end, state, tags):\\n # Split the work in days. That's a lot of requests to do.\\n queue = Queue.Queue()\\n threads = []\\n def run(start, cmd):\\n data = json.loads(subprocess.check_output(cmd))\\n queue.put((start, int(data['count'])))\\n\\n day = start\\n while day != end:\\n data = [\\n ('start', int((day - _EPOCH).total_seconds())),\\n ('end', int((day + datetime.timedelta(days=1)-_EPOCH).total_seconds())),\\n ('state', state),\\n ]\\n for tag in tags:\\n data.append(('tags', tag))\\n cmd = [\\n sys.executable, os.path.join(CLIENT_DIR, 'swarming.py'),\\n 'query', '-S', swarming, 'tasks/count?' + urllib.urlencode(data),\\n ]\\n thread = threading.Thread(target=run, args=(day.strftime('%Y-%m-%d'), cmd))\\n thread.daemon = True\\n thread.start()\\n threads.append(thread)\\n while len(threads) > 100:\\n # Throttle a bit.\\n for i, thread in enumerate(threads):\\n if not thread.is_alive():\\n thread.join()\\n threads.pop(i)\\n sys.stdout.write('.')\\n sys.stdout.flush()\\n break\\n day = day + datetime.timedelta(days=1)\\n\\n while threads:\\n # Throttle a bit.\\n for i, thread in enumerate(threads):\\n if not thread.is_alive():\\n thread.join()\\n threads.pop(i)\\n sys.stdout.write('.')\\n sys.stdout.flush()\\n break\\n print('')\\n data = []\\n while True:\\n try:\\n data.append(queue.get_nowait())\\n except Queue.Empty:\\n break\\n return dict(data)\",\n \"def fetch_from_redis(self):\\n cache= get_redis()\\n egdes_list = json.loads(cache.execute_command('JSON.GET', 'edges'))\\n self.graph= Graph([(one[0],one[1]) for one in egdes_list])\\n self.graph.vs[\\\"name\\\"]=json.loads(cache.execute_command('JSON.GET', 'vertices_name'))\\n self.graph.vs[\\\"parent\\\"]=json.loads( cache.execute_command('JSON.GET', 'vertices_parent'))\\n self.graph.vs[\\\"size\\\"]=json.loads( cache.execute_command('JSON.GET', 'vertices_size'))\\n self.graph.vs[\\\"last_modified\\\"]=json.loads( cache.execute_command('JSON.GET', 'vertices_last_modified'))\\n self.graph.vs[\\\"last_accessed\\\"]=json.loads( cache.execute_command('JSON.GET', 'vertices_last_accessed'))\",\n \"def test_get_node_templates(self):\\n if self.service_is_up:\\n file_id = self.upload_tosca_template('application_example_provisioned.yaml')\\n node_templates = self.api.get_node_templates(file_id)\\n self.assertIsNotNone(node_templates)\\n nodes_to_deploy = self.api.get_node_templates(file_id,type_name = 'tosca.nodes.QC.Application')\",\n \"def getNodeRRDData(self,node):\\n data = self.connect('get','nodes/%s/rrddata' % (node),None)\\n return data\",\n \"def get_node(self, project_id, node_id):\\n _url = f\\\"{self.base_url}/projects/{project_id}/nodes/{node_id}\\\"\\n return self.http_call(\\\"get\\\", _url).json()\",\n \"def GetResourceMetadata(self):\\n result = super().GetResourceMetadata()\\n if self.created:\\n result['container_cluster_version'] = self.k8s_version\\n return result\",\n \"def list_all_keys(riak_host,riak_port,bucket):\\n url='http://%s:%s/buckets/%s/keys?keys=true' % (riak_host,riak_port,bucket)\\n #print url\\n r=requests.get(url)\\n print json.dumps(r.json(), sort_keys=True, indent=4)\",\n \"def getClusterData(clusterName,data):\\n clusters = rhevGet(\\\"/api/clusters\\\")\\n doc = libxml2.parseDoc(clusters)\\n ctxt = doc.xpathNewContext()\\n res = ctxt.xpathEval(\\\"/clusters/cluster[name [position()=1]= '\\\"+ clusterName + \\\"']\\\")\\n return res[0].prop(data)\",\n \"def describe_cluster_deleting_response():\\n return {\\n \\\"cluster\\\": {\\n \\\"status\\\": \\\"DELETING\\\",\\n \\\"endpoint\\\": \\\"https://endpoint.amazonaws.com\\\",\\n \\\"name\\\": EXAMPLE_NAME,\\n \\\"certificateAuthority\\\": {\\n \\\"data\\\": \\\"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\\\"\\n },\\n \\\"roleArn\\\": \\\"arn:aws:iam::111222333444/eksRole\\\",\\n \\\"resourcesVpcConfig\\\": {\\n \\\"subnetIds\\\": [\\n \\\"subnet-00000000000000000\\\",\\n \\\"subnet-00000000000000001\\\",\\n \\\"subnet-00000000000000002\\\"\\n ],\\n \\\"vpcId\\\": \\\"vpc-00000000000000000\\\",\\n \\\"securityGroupIds\\\": [\\n \\\"sg-00000000000000000\\\"\\n ]\\n },\\n \\\"version\\\": \\\"1.10\\\",\\n \\\"arn\\\": \\\"arn:aws:eks:region:111222333444:cluster/\\\" + EXAMPLE_NAME,\\n \\\"createdAt\\\": 1500000000.000\\n }\\n }\",\n \"def data():\\n return volumes_fetchers.get_json_data()\",\n \"def fetch_server(client, server):\\n server_obj = {\\n \\\"name\\\": server['name'],\\n \\\"url\\\": server['url'],\\n \\\"client\\\": client\\n }\\n if (server['name'] in SERVERZ) \\\\\\n and ('cluster_members' in SERVERZ[server['name']]):\\n clustered = SERVERZ[server['name']]['cluster_members']\\n server_obj['cluster_members'] = clustered\\n\\n if 'parent' in server:\\n server_obj['parent'] = server['parent']\\n\\n try:\\n init = client.is_initialized()\\n except hvac.exceptions.InternalServerError:\\n return server_obj\\n except requests.ConnectionError:\\n return server_obj\\n except requests.ReadTimeout:\\n return server_obj\\n\\n server_obj['init'] = init\\n if init:\\n try:\\n status = client.seal_status\\n except (hvac.exceptions.VaultDown, requests.exceptions.ReadTimeout):\\n return server_obj\\n\\n server_obj['version'] = status['version']\\n seal = server_obj['sealed'] = status['sealed']\\n if seal:\\n server_obj['unseal_required'] = status['t']\\n server_obj['unseal_progress'] = status['progress']\\n else:\\n server_obj['cluster_name'] = status['cluster_name']\\n server_obj['cluster_id'] = status['cluster_id']\\n\\n server_obj['rekey'] = False\\n try:\\n rekey_obj = client.rekey_status\\n rekey = server_obj['rekey'] = rekey_obj['started']\\n if rekey:\\n server_obj['rekey_backup'] = rekey_obj['backup']\\n server_obj['rekey_progress'] = rekey_obj['progress']\\n server_obj['rekey_required'] = rekey_obj['required']\\n except (hvac.exceptions.VaultDown, requests.exceptions.ReadTimeout):\\n pass\\n except hvac.exceptions.InternalServerError as vault_exception:\\n if vault_exception.message == 'node not active but active '\\\\\\n 'node not found':\\n pass\\n\\n server_obj['ha'] = False\\n server_obj['leader'] = False\\n try:\\n leader_obj = client.read('sys/leader')\\n server_obj['ha'] = leader_obj['ha_enabled']\\n if leader_obj['ha_enabled']:\\n server_obj['leader'] = leader_obj['is_self']\\n except (hvac.exceptions.VaultDown, requests.exceptions.ReadTimeout):\\n pass\\n except hvac.exceptions.InternalServerError as e:\\n if e.message == 'node not active but active node not found':\\n pass\\n\\n if not server_obj['ha'] or \\\\\\n (server_obj['ha'] and server_obj['leader']):\\n try:\\n regen_obj = client.read('sys/generate-root/attempt')\\n server_obj['regenerating'] = regen_obj.get('started', False)\\n if server_obj['regenerating']:\\n server_obj['regen_progress'] = regen_obj['progress']\\n server_obj['regen_required'] = regen_obj['required']\\n except (hvac.exceptions.VaultDown, requests.exceptions.ReadTimeout):\\n pass\\n except hvac.exceptions.InternalServerError as e:\\n if e.message == 'node not active but ' \\\\\\n 'active node not found':\\n pass\\n\\n client = root_client(server_obj)\\n server_obj['is_root'] = False\\n if client:\\n try:\\n key_obj = client.key_status\\n server_obj['key_term'] = key_obj['term']\\n server_obj['is_root'] = True\\n except (hvac.exceptions.VaultDown, requests.exceptions.ReadTimeout):\\n pass\\n except hvac.exceptions.InternalServerError as e:\\n if e.message == 'node not active but active '\\\\\\n 'node not found':\\n pass\\n\\n return server_obj\",\n \"def get_nodes(self, type, query_args={}):\\n endpoint = '/v3/educator/%ss' % (Node.TYPE_MAP[type])\\n result = self.request(endpoint, query_args)\\n\\n nodes = []\\n for data in result.response:\\n node = Node.instance(type, data)\\n nodes.append(node)\\n\\n return nodes\",\n \"def nodes(env):\\n envs = environments()\\n status_arg = request.args.get('status', '')\\n check_env(env, envs)\\n\\n query = AndOperator()\\n\\n if env != '*':\\n query.add(EqualsOperator(\\\"catalog_environment\\\", env))\\n query.add(EqualsOperator(\\\"facts_environment\\\", env))\\n\\n if status_arg in ['failed', 'changed', 'unchanged']:\\n query.add(EqualsOperator('latest_report_status', status_arg))\\n elif status_arg == 'unreported':\\n unreported = datetime.datetime.utcnow()\\n unreported = (unreported -\\n timedelta(hours=app.config['UNRESPONSIVE_HOURS']))\\n unreported = unreported.replace(microsecond=0).isoformat()\\n\\n unrep_query = OrOperator()\\n unrep_query.add(NullOperator('report_timestamp', True))\\n unrep_query.add(LessEqualOperator('report_timestamp', unreported))\\n\\n query.add(unrep_query)\\n\\n if len(query.operations) == 0:\\n query = None\\n\\n nodelist = puppetdb.nodes(\\n query=query,\\n unreported=app.config['UNRESPONSIVE_HOURS'],\\n with_status=True)\\n nodes = []\\n for node in yield_or_stop(nodelist):\\n if status_arg:\\n if node.status == status_arg:\\n nodes.append(node)\\n else:\\n nodes.append(node)\\n return Response(stream_with_context(\\n stream_template('nodes.html',\\n nodes=nodes,\\n envs=envs,\\n current_env=env)))\",\n \"def describe_cluster_creating_response():\\n return {\\n \\\"cluster\\\": {\\n \\\"status\\\": \\\"CREATING\\\",\\n \\\"name\\\": EXAMPLE_NAME,\\n \\\"certificateAuthority\\\": {},\\n \\\"roleArn\\\": \\\"arn:aws:iam::111222333444/eksRole\\\",\\n \\\"resourcesVpcConfig\\\": {\\n \\\"subnetIds\\\": [\\n \\\"subnet-00000000000000000\\\",\\n \\\"subnet-00000000000000001\\\",\\n \\\"subnet-00000000000000002\\\"\\n ],\\n \\\"vpcId\\\": \\\"vpc-00000000000000000\\\",\\n \\\"securityGroupIds\\\": [\\n \\\"sg-00000000000000000\\\"\\n ]\\n },\\n \\\"version\\\": \\\"1.10\\\",\\n \\\"arn\\\": \\\"arn:aws:eks:region:111222333444:cluster/\\\" + EXAMPLE_NAME,\\n \\\"createdAt\\\": 1500000000.000\\n }\\n }\",\n \"def get_kubernetes_cluster(name: Optional[str] = None,\\n tags: Optional[Sequence[str]] = None,\\n opts: Optional[pulumi.InvokeOptions] = None) -> AwaitableGetKubernetesClusterResult:\\n __args__ = dict()\\n __args__['name'] = name\\n __args__['tags'] = tags\\n opts = pulumi.InvokeOptions.merge(_utilities.get_invoke_opts_defaults(), opts)\\n __ret__ = pulumi.runtime.invoke('digitalocean:index/getKubernetesCluster:getKubernetesCluster', __args__, opts=opts, typ=GetKubernetesClusterResult).value\\n\\n return AwaitableGetKubernetesClusterResult(\\n auto_upgrade=pulumi.get(__ret__, 'auto_upgrade'),\\n cluster_subnet=pulumi.get(__ret__, 'cluster_subnet'),\\n created_at=pulumi.get(__ret__, 'created_at'),\\n endpoint=pulumi.get(__ret__, 'endpoint'),\\n ha=pulumi.get(__ret__, 'ha'),\\n id=pulumi.get(__ret__, 'id'),\\n ipv4_address=pulumi.get(__ret__, 'ipv4_address'),\\n kube_configs=pulumi.get(__ret__, 'kube_configs'),\\n maintenance_policies=pulumi.get(__ret__, 'maintenance_policies'),\\n name=pulumi.get(__ret__, 'name'),\\n node_pools=pulumi.get(__ret__, 'node_pools'),\\n region=pulumi.get(__ret__, 'region'),\\n service_subnet=pulumi.get(__ret__, 'service_subnet'),\\n status=pulumi.get(__ret__, 'status'),\\n surge_upgrade=pulumi.get(__ret__, 'surge_upgrade'),\\n tags=pulumi.get(__ret__, 'tags'),\\n updated_at=pulumi.get(__ret__, 'updated_at'),\\n urn=pulumi.get(__ret__, 'urn'),\\n version=pulumi.get(__ret__, 'version'),\\n vpc_uuid=pulumi.get(__ret__, 'vpc_uuid'))\",\n \"def get(resource_name: str,\\n id: pulumi.Input[str],\\n opts: Optional[pulumi.ResourceOptions] = None,\\n acl_name: Optional[pulumi.Input[str]] = None,\\n arn: Optional[pulumi.Input[str]] = None,\\n auto_minor_version_upgrade: Optional[pulumi.Input[bool]] = None,\\n cluster_endpoints: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ClusterClusterEndpointArgs']]]]] = None,\\n data_tiering: Optional[pulumi.Input[bool]] = None,\\n description: Optional[pulumi.Input[str]] = None,\\n engine_patch_version: Optional[pulumi.Input[str]] = None,\\n engine_version: Optional[pulumi.Input[str]] = None,\\n final_snapshot_name: Optional[pulumi.Input[str]] = None,\\n kms_key_arn: Optional[pulumi.Input[str]] = None,\\n maintenance_window: Optional[pulumi.Input[str]] = None,\\n name: Optional[pulumi.Input[str]] = None,\\n name_prefix: Optional[pulumi.Input[str]] = None,\\n node_type: Optional[pulumi.Input[str]] = None,\\n num_replicas_per_shard: Optional[pulumi.Input[int]] = None,\\n num_shards: Optional[pulumi.Input[int]] = None,\\n parameter_group_name: Optional[pulumi.Input[str]] = None,\\n port: Optional[pulumi.Input[int]] = None,\\n security_group_ids: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,\\n shards: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ClusterShardArgs']]]]] = None,\\n snapshot_arns: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,\\n snapshot_name: Optional[pulumi.Input[str]] = None,\\n snapshot_retention_limit: Optional[pulumi.Input[int]] = None,\\n snapshot_window: Optional[pulumi.Input[str]] = None,\\n sns_topic_arn: Optional[pulumi.Input[str]] = None,\\n subnet_group_name: Optional[pulumi.Input[str]] = None,\\n tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\\n tags_all: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\\n tls_enabled: Optional[pulumi.Input[bool]] = None) -> 'Cluster':\\n opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))\\n\\n __props__ = _ClusterState.__new__(_ClusterState)\\n\\n __props__.__dict__[\\\"acl_name\\\"] = acl_name\\n __props__.__dict__[\\\"arn\\\"] = arn\\n __props__.__dict__[\\\"auto_minor_version_upgrade\\\"] = auto_minor_version_upgrade\\n __props__.__dict__[\\\"cluster_endpoints\\\"] = cluster_endpoints\\n __props__.__dict__[\\\"data_tiering\\\"] = data_tiering\\n __props__.__dict__[\\\"description\\\"] = description\\n __props__.__dict__[\\\"engine_patch_version\\\"] = engine_patch_version\\n __props__.__dict__[\\\"engine_version\\\"] = engine_version\\n __props__.__dict__[\\\"final_snapshot_name\\\"] = final_snapshot_name\\n __props__.__dict__[\\\"kms_key_arn\\\"] = kms_key_arn\\n __props__.__dict__[\\\"maintenance_window\\\"] = maintenance_window\\n __props__.__dict__[\\\"name\\\"] = name\\n __props__.__dict__[\\\"name_prefix\\\"] = name_prefix\\n __props__.__dict__[\\\"node_type\\\"] = node_type\\n __props__.__dict__[\\\"num_replicas_per_shard\\\"] = num_replicas_per_shard\\n __props__.__dict__[\\\"num_shards\\\"] = num_shards\\n __props__.__dict__[\\\"parameter_group_name\\\"] = parameter_group_name\\n __props__.__dict__[\\\"port\\\"] = port\\n __props__.__dict__[\\\"security_group_ids\\\"] = security_group_ids\\n __props__.__dict__[\\\"shards\\\"] = shards\\n __props__.__dict__[\\\"snapshot_arns\\\"] = snapshot_arns\\n __props__.__dict__[\\\"snapshot_name\\\"] = snapshot_name\\n __props__.__dict__[\\\"snapshot_retention_limit\\\"] = snapshot_retention_limit\\n __props__.__dict__[\\\"snapshot_window\\\"] = snapshot_window\\n __props__.__dict__[\\\"sns_topic_arn\\\"] = sns_topic_arn\\n __props__.__dict__[\\\"subnet_group_name\\\"] = subnet_group_name\\n __props__.__dict__[\\\"tags\\\"] = tags\\n __props__.__dict__[\\\"tags_all\\\"] = tags_all\\n __props__.__dict__[\\\"tls_enabled\\\"] = tls_enabled\\n return Cluster(resource_name, opts=opts, __props__=__props__)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.60346806","0.59235954","0.578973","0.57561266","0.5736296","0.5608303","0.5593132","0.5583009","0.556707","0.5539366","0.5538481","0.55283237","0.5513567","0.54998785","0.5490597","0.5488156","0.5446512","0.5438874","0.5410196","0.53977907","0.5386005","0.5373807","0.53584456","0.53544533","0.53529906","0.53329325","0.5312624","0.5312026","0.5297073","0.5284067","0.5269918","0.52643746","0.52359474","0.52033716","0.51997155","0.5197944","0.51895356","0.5168151","0.51677376","0.5162609","0.5154223","0.5148443","0.5146906","0.51454705","0.51414853","0.5138897","0.513586","0.51347613","0.5132505","0.51210654","0.51185066","0.51154333","0.5112542","0.5106796","0.51002604","0.5095504","0.509338","0.5091327","0.5086289","0.50806683","0.50764185","0.5072632","0.5069872","0.5069085","0.5068561","0.5045443","0.50414103","0.50340647","0.50324","0.5028808","0.5026299","0.5023339","0.50197047","0.50083995","0.50071687","0.49952188","0.49874297","0.49825794","0.4974886","0.49748093","0.4973238","0.4967001","0.49627987","0.49606094","0.4959258","0.49534214","0.49498358","0.4949462","0.49449638","0.49432692","0.49387333","0.49386752","0.492273","0.49193552","0.4919001","0.49149904","0.49135846","0.49114367","0.49091977","0.49048486"],"string":"[\n \"0.60346806\",\n \"0.59235954\",\n \"0.578973\",\n \"0.57561266\",\n \"0.5736296\",\n \"0.5608303\",\n \"0.5593132\",\n \"0.5583009\",\n \"0.556707\",\n \"0.5539366\",\n \"0.5538481\",\n \"0.55283237\",\n \"0.5513567\",\n \"0.54998785\",\n \"0.5490597\",\n \"0.5488156\",\n \"0.5446512\",\n \"0.5438874\",\n \"0.5410196\",\n \"0.53977907\",\n \"0.5386005\",\n \"0.5373807\",\n \"0.53584456\",\n \"0.53544533\",\n \"0.53529906\",\n \"0.53329325\",\n \"0.5312624\",\n \"0.5312026\",\n \"0.5297073\",\n \"0.5284067\",\n \"0.5269918\",\n \"0.52643746\",\n \"0.52359474\",\n \"0.52033716\",\n \"0.51997155\",\n \"0.5197944\",\n \"0.51895356\",\n \"0.5168151\",\n \"0.51677376\",\n \"0.5162609\",\n \"0.5154223\",\n \"0.5148443\",\n \"0.5146906\",\n \"0.51454705\",\n \"0.51414853\",\n \"0.5138897\",\n \"0.513586\",\n \"0.51347613\",\n \"0.5132505\",\n \"0.51210654\",\n \"0.51185066\",\n \"0.51154333\",\n \"0.5112542\",\n \"0.5106796\",\n \"0.51002604\",\n \"0.5095504\",\n \"0.509338\",\n \"0.5091327\",\n \"0.5086289\",\n \"0.50806683\",\n \"0.50764185\",\n \"0.5072632\",\n \"0.5069872\",\n \"0.5069085\",\n \"0.5068561\",\n \"0.5045443\",\n \"0.50414103\",\n \"0.50340647\",\n \"0.50324\",\n \"0.5028808\",\n \"0.5026299\",\n \"0.5023339\",\n \"0.50197047\",\n \"0.50083995\",\n \"0.50071687\",\n \"0.49952188\",\n \"0.49874297\",\n \"0.49825794\",\n \"0.4974886\",\n \"0.49748093\",\n \"0.4973238\",\n \"0.4967001\",\n \"0.49627987\",\n \"0.49606094\",\n \"0.4959258\",\n \"0.49534214\",\n \"0.49498358\",\n \"0.4949462\",\n \"0.49449638\",\n \"0.49432692\",\n \"0.49387333\",\n \"0.49386752\",\n \"0.492273\",\n \"0.49193552\",\n \"0.4919001\",\n \"0.49149904\",\n \"0.49135846\",\n \"0.49114367\",\n \"0.49091977\",\n \"0.49048486\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6105052"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":14376,"cells":{"query":{"kind":"string","value":"Fetch namespace listing and detail"},"document":{"kind":"string","value":"def get_namespace_data(cluster_id, namespace_id=None):\n # namespace detail\n if namespace_id:\n resource_count = get_resource_count(cluster_id, namespace_id)\n resource_info = get_resource_info(cluster_id, 'pods', namespace_id)\n return {'resources': resource_count, 'resource_info': resource_info}\n # namespace listing\n else:\n namespace_data = client.CoreV1Api().list_namespace().items\n if namespace_is_hidden == 'true':\n namespace_list = [{'value': namespace_item.metadata.name, 'label': namespace_item.metadata.name}\n for namespace_item in namespace_data if\n namespace_item.metadata.name != 'dashboard']\n else:\n namespace_list = [{'value': namespace_item.metadata.name, 'label': namespace_item.metadata.name}\n for namespace_item in namespace_data]\n return namespace_list"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def namespace(self, namespace):\n return self.client.call('GET',\n self.name, params={'namespace': namespace})","def _fetch_all_namespaces():\n response = _fetch_herd_session() \\\n .get('{}://{}/{}/{}'.format(HERD_REST_PROTOCOL, HERD_BASE_URL,\n HERD_REST_BASE_PATH, 'namespaces')) \\\n .json()\n\n namespaces = []\n for namespaceKey in response['namespaceKeys']:\n namespaces.append(namespaceKey['namespaceCode'])\n\n _print_info('Retrieved {} namespaces.'.format(len(namespaces)))\n return namespaces","def test_list_net_namespace(self):\n pass","def _load_namespaces(self, result):\n self._namespaces = {}\n\n for namespace in result[\"query\"][\"namespaces\"].values():\n ns_id = namespace[\"id\"]\n name = namespace[\"*\"]\n try:\n canonical = namespace[\"canonical\"]\n except KeyError:\n self._namespaces[ns_id] = [name]\n else:\n if name != canonical:\n self._namespaces[ns_id] = [name, canonical]\n else:\n self._namespaces[ns_id] = [name]\n\n for namespace in result[\"query\"][\"namespacealiases\"]:\n ns_id = namespace[\"id\"]\n alias = namespace[\"*\"]\n self._namespaces[ns_id].append(alias)","async def list_namespaces(self) -> list:\n return await self.AD.state.list_namespaces()","def test_get_namespaces_names(self):\n pass","def get_namespaces():\r\n\r\n print 'Getting namespaces'\r\n tree = etree.parse('http://lesswrong.wikia.com/wiki/Special:AllPages', parser)\r\n options = tree.xpath('//select[@id=\"namespace\"]/option')\r\n namespaces = [option.get('value') for option in options]\r\n pprint(namespaces)\r\n return namespaces","def _get_cloud_function_namespaces_metadata(offset=0):\n\n res = requests.get(\n f\"{self.cf_namespaces_url}?limit=200&offset={offset}\",\n headers=self.get_headers(),\n )\n return json.loads(res.text)","def test_get_namespaces_from_account(self):\n pass","def get_edge_namespaces():\n hint = request.form['namespaces']\n\n result = {'status': FAIL, 'message': '', 'data': {}}\n try:\n result['status'] = SUCCESS\n result['data']['autocomplete_field'] = []\n result['data']['select_field'] = []\n if hint != '':\n edge_session = edge(edge_create_internal_ns_configuration.edge_url,\n edge_create_internal_ns_configuration.client_id,\n edge_create_internal_ns_configuration.clientSecret)\n\n namespaces = edge_session.get_namespaces()\n count = 0\n for namespace in namespaces:\n if namespace['name'].startswith(hint):\n\n result['data']['autocomplete_field'].append({\n 'input': namespace['id'],\n 'value': '%s (%s)' % (namespace['name'], namespace['id'])\n })\n result['data']['select_field'].append({\n 'id': namespace['id'],\n 'txt': namespace['name']\n })\n if count == 10:\n break\n count += 1\n except Exception as e:\n result['status'] = FAIL\n result['message'] = 'Error while searching for Namespaces: %s and hint: %s!' % (util.safe_str(e), hint)\n return result","def get_all_namespaces():\n cmds.namespace(setNamespace=':')\n return cmds.namespaceInfo(listOnlyNamespaces=True, recurse=True)","def sns(self,**kwargs):\n\n\t\tcursor = kwargs.get('cursor',self.cursor)\n\t\treturn self.toc[cursor].keys()","def get_ing_from_ns(namespace):\n ing_list = get_all_ingress_from(namespace)\n ing_list_json = get_json_list(ing_list)\n return Response(response=ing_list_json, status=200, mimetype='application/json')","def list(self, dict_output=False, field_selector=\"\"):\n namespaces_list = self.client_core.list_namespace().items\n logger.info(\"Got namespaces\")\n\n if field_selector:\n namespaces_list = field_filter(obj_list=namespaces_list,\n field_selector=field_selector)\n # convert the list to list of dicts if required\n if dict_output:\n namespaces_list = [convert_obj_to_dict(namespace) for namespace in\n namespaces_list]\n else:\n for namespace in namespaces_list:\n namespace.metadata.resource_version = ''\n return namespaces_list","def test_list_build_for_all_namespaces(self):\n pass","def list_namespaced_net_namespace(self, **kwargs):\n\n all_params = ['pretty', 'label_selector', 'field_selector', 'watch', 'resource_version', 'timeout_seconds']\n all_params.append('callback')\n\n params = locals()\n for key, val in iteritems(params['kwargs']):\n if key not in all_params:\n raise TypeError(\n \"Got an unexpected keyword argument '%s'\"\n \" to method list_namespaced_net_namespace\" % key\n )\n params[key] = val\n del params['kwargs']\n\n\n resource_path = '/oapi/v1/netnamespaces'.replace('{format}', 'json')\n path_params = {}\n\n query_params = {}\n if 'pretty' in params:\n query_params['pretty'] = params['pretty']\n if 'label_selector' in params:\n query_params['labelSelector'] = params['label_selector']\n if 'field_selector' in params:\n query_params['fieldSelector'] = params['field_selector']\n if 'watch' in params:\n query_params['watch'] = params['watch']\n if 'resource_version' in params:\n query_params['resourceVersion'] = params['resource_version']\n if 'timeout_seconds' in params:\n query_params['timeoutSeconds'] = params['timeout_seconds']\n\n header_params = {}\n\n form_params = []\n local_var_files = {}\n\n body_params = None\n\n # HTTP header `Accept`\n header_params['Accept'] = self.api_client.\\\n select_header_accept(['application/json', 'application/yaml'])\n if not header_params['Accept']:\n del header_params['Accept']\n\n # HTTP header `Content-Type`\n header_params['Content-Type'] = self.api_client.\\\n select_header_content_type(['*/*'])\n\n # Authentication setting\n auth_settings = []\n\n response = self.api_client.call_api(resource_path, 'GET',\n path_params,\n query_params,\n header_params,\n body=body_params,\n post_params=form_params,\n files=local_var_files,\n response_type='V1NetNamespaceList',\n auth_settings=auth_settings,\n callback=params.get('callback'))\n return response","def create_or_fetch_namespace(self):\n\n def _create_new_namespace():\n logger.info(\n f\"Creating a new namespace: {self.namespace_name} in {self.namespace_region}\"\n )\n\n data = {\n \"name\": self.namespace_name,\n \"resource_group_id\": self.resource_group_id,\n \"resource_plan_id\": \"functions-base-plan\",\n }\n\n res = requests.post(\n self.cf_namespaces_url, headers=self.get_headers(), json=data\n ).json()\n if res.status_code != 200:\n logger.error(res.text)\n namespace_id = res[\"id\"]\n logger.info(f\"Created new namespace with id: {namespace_id}\")\n return namespace_id\n\n def _get_cloud_function_namespaces_metadata(offset=0):\n \"\"\"returns meta data on namespaces of ibm cloud functions within a specified region\n :param offset - offset from the beginning of the list of results attained from the GET request,\n which may contain up to 200 namespaces per http response\"\"\"\n\n res = requests.get(\n f\"{self.cf_namespaces_url}?limit=200&offset={offset}\",\n headers=self.get_headers(),\n )\n return json.loads(res.text)\n\n def _get_cloud_function_namespaces():\n \"\"\"returns relevant metadata on existing namespaces within a given region.\"\"\"\n logger.info(\n f\"Obtaining Cloud Function namespaces in {self.namespace_region}\"\n )\n\n namespaces = []\n\n collecting_namespaces = True\n max_limit = 200\n offset = 0\n\n # request for namespaces is limited to 200 at a time, thus the request is fulfilled in increments of 200s.\n while collecting_namespaces:\n namespace_metadata = _get_cloud_function_namespaces_metadata(offset)\n if namespace_metadata[\"total_count\"] == max_limit:\n offset += max_limit\n else:\n collecting_namespaces = False\n\n for name_space in namespace_metadata[\"namespaces\"]:\n if \"name\" in name_space: # API based namespace\n namespaces.append(\n {\n \"name\": name_space[\"name\"],\n \"type\": \"API_based\",\n \"id\": name_space[\"id\"],\n \"region\": name_space[\"location\"],\n }\n )\n\n else: # cloud foundry based namespace\n namespaces.append(\n {\n \"name\": name_space[\"id\"],\n \"type\": \"CF_based\",\n \"region\": name_space[\"location\"],\n }\n )\n\n return namespaces\n\n namespaces_in_region = _get_cloud_function_namespaces()\n target_namespace_id = None\n if namespaces_in_region:\n target_namespace_id = next(\n (\n namespace[\"id\"]\n for namespace in namespaces_in_region\n if namespace[\"name\"] == self.namespace_name\n ),\n None,\n )\n if not target_namespace_id:\n target_namespace_id = _create_new_namespace()\n else:\n logger.info(f\"Reusing namespace: {target_namespace_id}\")\n return target_namespace_id","def test_list_template_for_all_namespaces(self):\n pass","def test_list_route_for_all_namespaces(self):\n pass","def test_get_namespaces_from_accounts(self):\n pass","def namespaces(self, psuedo=True):\n if self._namespaces == None:\n result = self.call({'action': 'query',\n 'meta': 'siteinfo',\n 'siprop': 'namespaces'})\n self._namespaces = {}\n self._psuedo_namespaces = {}\n for nsid in result['query']['namespaces']:\n if int(nsid) >= 0:\n self._namespaces[int(nsid)] = \\\n result['query']['namespaces'][nsid]['*']\n else:\n self._psuedo_namespaces[int(nsid)] = \\\n result['query']['namespaces'][nsid]['*']\n if psuedo:\n retval = {}\n retval.update(self._namespaces)\n retval.update(self._psuedo_namespaces)\n return retval\n else:\n return self._namespaces","def get_all_by_namespace(context, namespace_name, session):\n\n # namespace get raises an exception if not visible\n namespace = namespace_api.get(\n context, namespace_name, session)\n\n db_recs = (\n session.query(models.MetadefResourceType)\n .join(models.MetadefResourceType.associations)\n .filter_by(namespace_id=namespace['id'])\n .values(models.MetadefResourceType.name,\n models.MetadefNamespaceResourceType.properties_target,\n models.MetadefNamespaceResourceType.prefix,\n models.MetadefNamespaceResourceType.created_at,\n models.MetadefNamespaceResourceType.updated_at))\n\n model_dict_list = []\n for name, properties_target, prefix, created_at, updated_at in db_recs:\n model_dict_list.append(\n _set_model_dict\n (name, properties_target, prefix, created_at, updated_at)\n )\n\n return model_dict_list","def get_ing_from_all_ns():\n ing_list = get_all_ingress()\n ing_list_json = get_json_list(ing_list)\n return Response(response=ing_list_json, status=200, mimetype='application/json')","def get(self, *args):\n return _libsbml.ListWrapperSBMLNamespaces_get(self, *args)","def test_get_namespace(self):\n pass","def get_namespaces(self, label_selector=None):\n return self.core_client.list_namespace(label_selector=label_selector)","def all_in_namespace(cls, ns):\n return filter_by_prefix(cls.all(), ns + ':')","def getnamespaceinfo (self, namespaceguid, jobguid = \"\", executionparams = {}):\n params =dict()\n params['namespaceguid'] = namespaceguid\n executionparams['rootobjecttype'] = 'dssnamespace'\n\n \n return q.workflowengine.actionmanager.startRootobjectAction('dssnamespace', 'getnamespaceinfo', params, jobguid=jobguid, executionparams=executionparams)","def collection_names(self, callback):\n callback = partial(self._collection_names_result, callback)\n self[\"system.namespaces\"].find(_must_use_master=True, callback=callback)","def get_ns_list(logger,body,v1=None):\n if v1 is None:\n v1 = client.CoreV1Api()\n logger.debug('new client - fn get_ns_list')\n \n try:\n matchNamespace = body.get('matchNamespace')\n except KeyError:\n matchNamespace = '*'\n logger.debug(\"matching all namespaces.\")\n logger.debug(f'Matching namespaces: {matchNamespace}')\n \n try:\n avoidNamespaces = body.get('avoidNamespaces')\n except KeyError:\n avoidNamespaces = ''\n logger.debug(\"not avoiding namespaces\")\n\n nss = v1.list_namespace().items\n matchedns = []\n avoidedns = []\n\n for matchns in matchNamespace:\n for ns in nss:\n if re.match(matchns, ns.metadata.name):\n matchedns.append(ns.metadata.name)\n logger.debug(f'Matched namespaces: {ns.metadata.name} matchpathern: {matchns}')\n if avoidNamespaces:\n for avoidns in avoidNamespaces:\n for ns in nss:\n if re.match(avoidns, ns.metadata.name):\n avoidedns.append(ns.metadata.name)\n logger.debug(f'Skipping namespaces: {ns.metadata.name} avoidpatrn: {avoidns}') \n # purge\n for ns in matchedns.copy():\n if ns in avoidedns:\n matchedns.remove(ns)\n\n return matchedns","def namespaces(self):\n return ()","def _getnamespaces(cls):\n return \" \".join(Kmlable._namespaces)","def fetch_list(self):\n\t\treturn self.fetch(self.list_url % ART_SERVER_HOST)","def test_read_net_namespace(self):\n pass","def get_all_pages_for_namespace(ns):\r\n\r\n print 'Getting pages in namespace %s' % ns\r\n url = 'http://lesswrong.wikia.com/index.php?title=Special:AllPages&from=&to=&namespace=%s' % ns\r\n tree = etree.parse(url, parser)\r\n pages = tree.xpath('//table[2]//a[@title]')\r\n page_names = [page.get('title') for page in pages]\r\n pprint(page_names)\r\n return page_names","def getNamespaces(self, *args):\n return _libsbml.SBMLNamespaces_getNamespaces(self, *args)","def _load_namespaces(self):\n nsdocs = self._docset.get_namespaces()\n for nsdoc in nsdocs:\n nsobj = Namespace(nsdoc)\n self._docmap[nsdoc] = nsobj\n self._namespaces.add(nsobj)","def namespaces(self):\n return [self._namespace_prefix]","def getNamespaces(self):\n return _libsbml.SBase_getNamespaces(self)","def getCatalogs():","def ls():\n return dynamodb.ls(OrganizationModel)","def explore_all_nf_data():\n request = app.current_request\n resource_type = request.query_params[\"resource_type\"]\n offset = int(request.query_params[\"offset\"])\n limit = int(request.query_params[\"limit\"])\n explorer = UnogsExplorer(resource_type)\n success = explorer.explore(limit, offset)\n return {\"success\": success}","def test_list_namespaced_route(self):\n pass","def get_services_in_namespace(self, namespace):\n ret = self.v1_service_list.get(namespace=namespace)\n return [each.metadata.name for each in ret.items]","def test_list_policy_for_all_namespaces(self):\n pass","def namespaces(self):\n return list(self._namespace_schemas.keys())","def list(self, urn):\n uri = '/'.join([self.urn2uri(urn), self.IDENTIFIER])\n return self.rest_client.get(uri)","def update_namespaces_info(self):\n namespaces = BlockDev.nvdimm_list_namespaces(idle=True)\n\n self._namespaces = dict((namespace.dev, namespace) for namespace in namespaces)","def getNamespaces(self):\n return _libsbml.SBMLDocument_getNamespaces(self)","def get_namespaces():\n return list(StaticAsset._load_namespaces().keys())","def get_streams(namespace_url):\n streams = requests.get(namespace_url + '/Streams', headers=headers)\n return streams.json()","def xmlrpc_namespace():","def pyreq_ns_search(httpreq_handler: HTTPRequestHandler, namespace: str,\n json_resp: bool = False, need_global_lookup_result: bool = False,\n glblsrch_totmp: bool = False, ) -> Optional[Union[Dict, str, Tuple[Dict, Dict]]]:\n result: List[Dict] = pyreq_list_affiliated_ns(httpreq_handler)\n for ns_meta in result:\n if ns_meta['name'] == namespace:\n httpreq_handler.debug(f'Found member namespace that matches {namespace}: ' + json.dumps(ns_meta))\n if json_resp:\n return ns_meta\n else:\n return ns_meta['id']\n httpreq_handler.debug(f'Target namespace {namespace} not found in affiliated results, need global search.')\n httpreq_handler.debug(f'Look up on overall nuance.com namespace. We redirect network payloads to a file')\n # this request would return a huge payload containing exhaustive info for all application configurations\n # for all users and namespaces. Therefore we rather ask curl to save the output to a file instead of\n # trying to receive that from stdout piping\n endpoint = '/bolt/applications'\n tmp_outfile = None\n if glblsrch_totmp:\n timestamp = datetime.datetime.now().strftime('%Y%m%dT%H%M%S')\n tmp_outfile = os.path.join(os.getcwd(),\n f'tmp_app_config_lookup_{timestamp}.json')\n httpreq_handler.debug(f'Temp file for redirected CURL output: {tmp_outfile}')\n resp: Dict = httpreq_handler.request(url=endpoint, method=GET_METHOD, default_headers=True,\n stream=True, outfile=tmp_outfile, json_resp=True)\n for app_conf_grp in resp['data']:\n if app_conf_grp['namespace_name'] != namespace:\n continue\n ns_search_result = dict()\n ns_search_result['name'] = namespace\n ns_search_result['id'] = app_conf_grp['namespace_id']\n ns_search_result['is_member'] = False\n if need_global_lookup_result:\n httpreq_handler.debug(f'Retruning global lookup resp and result: {json.dumps(ns_search_result)}')\n return ns_search_result, resp\n else:\n httpreq_handler.debug(f'Retruning result: {json.dumps(ns_search_result)}')\n return ns_search_result\n raise ValueError(__ERR_MSG_NS_NOTFOUND.format(ns_name=namespace))","def namespaces(self):\n if not self._namespaces:\n self.update_namespaces_info()\n\n return self._namespaces","def test_list_namespaced_build(self):\n pass","def namespaces(self):\n return self.namespaced_fields().namespaces()","def test_create_local_resource_access_review_for_all_namespaces(self):\n pass","def test_list_build_config_for_all_namespaces(self):\n pass","def _get_cloud_function_namespaces():\n logger.info(\n f\"Obtaining Cloud Function namespaces in {self.namespace_region}\"\n )\n\n namespaces = []\n\n collecting_namespaces = True\n max_limit = 200\n offset = 0\n\n # request for namespaces is limited to 200 at a time, thus the request is fulfilled in increments of 200s.\n while collecting_namespaces:\n namespace_metadata = _get_cloud_function_namespaces_metadata(offset)\n if namespace_metadata[\"total_count\"] == max_limit:\n offset += max_limit\n else:\n collecting_namespaces = False\n\n for name_space in namespace_metadata[\"namespaces\"]:\n if \"name\" in name_space: # API based namespace\n namespaces.append(\n {\n \"name\": name_space[\"name\"],\n \"type\": \"API_based\",\n \"id\": name_space[\"id\"],\n \"region\": name_space[\"location\"],\n }\n )\n\n else: # cloud foundry based namespace\n namespaces.append(\n {\n \"name\": name_space[\"id\"],\n \"type\": \"CF_based\",\n \"region\": name_space[\"location\"],\n }\n )\n\n return namespaces","def test_list_role_for_all_namespaces(self):\n pass","def GetNamespaces(self):\n return list(self.type_namespaces_map.values())","def test_list_image_stream_for_all_namespaces(self):\n pass","def get(self, name, dict_output=False):\n namespace = self.client_core.read_namespace(name=name)\n logger.info(f\"Got namespace {name}\")\n\n # convert the obj to dict if required\n if dict_output:\n namespace = convert_obj_to_dict(namespace)\n else:\n namespace.metadata.resource_version = ''\n\n return namespace","def show_tablespaces(self):\n sql = \"SELECT TABLESPACE_NAME FROM DBA_TABLESPACES WHERE CONTENTS <> 'TEMPORARY' ORDER BY 1\"\n self.cur.execute(sql)\n res = self.cur.fetchall()\n key = ['{#TABLESPACE}']\n lst = []\n for i in res:\n d = dict(zip(key, i))\n lst.append(d)\n print(json.dumps({'data': lst}))","def test_list_namespaced_template(self):\n pass","def watch_namespaced_net_namespace_list(self, **kwargs):\n\n all_params = ['pretty', 'label_selector', 'field_selector', 'watch', 'resource_version', 'timeout_seconds']\n all_params.append('callback')\n\n params = locals()\n for key, val in iteritems(params['kwargs']):\n if key not in all_params:\n raise TypeError(\n \"Got an unexpected keyword argument '%s'\"\n \" to method watch_namespaced_net_namespace_list\" % key\n )\n params[key] = val\n del params['kwargs']\n\n\n resource_path = '/oapi/v1/watch/netnamespaces'.replace('{format}', 'json')\n path_params = {}\n\n query_params = {}\n if 'pretty' in params:\n query_params['pretty'] = params['pretty']\n if 'label_selector' in params:\n query_params['labelSelector'] = params['label_selector']\n if 'field_selector' in params:\n query_params['fieldSelector'] = params['field_selector']\n if 'watch' in params:\n query_params['watch'] = params['watch']\n if 'resource_version' in params:\n query_params['resourceVersion'] = params['resource_version']\n if 'timeout_seconds' in params:\n query_params['timeoutSeconds'] = params['timeout_seconds']\n\n header_params = {}\n\n form_params = []\n local_var_files = {}\n\n body_params = None\n\n # HTTP header `Accept`\n header_params['Accept'] = self.api_client.\\\n select_header_accept(['application/json'])\n if not header_params['Accept']:\n del header_params['Accept']\n\n # HTTP header `Content-Type`\n header_params['Content-Type'] = self.api_client.\\\n select_header_content_type(['*/*'])\n\n # Authentication setting\n auth_settings = []\n\n response = self.api_client.call_api(resource_path, 'GET',\n path_params,\n query_params,\n header_params,\n body=body_params,\n post_params=form_params,\n files=local_var_files,\n response_type='JsonWatchEvent',\n auth_settings=auth_settings,\n callback=params.get('callback'))\n return response","def get_namespaces(self):\n if self.namespaces is None:\n namespaces = unpack(self.api.get_namespaces())\n self.namespaces = {\n namespace['name']: DevopsSecurityNamespace(namespace)\n for namespace in namespaces\n }\n return self.namespaces","def test_create_resource_access_review_for_all_namespaces(self):\n pass","def list_apiscout(self):\n return self.__make_api_call('list/apiscout')","def _get_namespace(self, data):\n ns_name = data['filename'].namespace\n try:\n return models.Namespace.objects.get(name=ns_name)\n except models.Namespace.DoesNotExist:\n raise exceptions.ValidationError(\n f'Namespace \"{ns_name}\" does not exist.'\n )","def list(self):\n return self.request(\"GET\")","def namespaceInfo(*args, absoluteName: bool=True, baseName: bool=True, currentNamespace:\n bool=True, dagPath: bool=True, fullName: bool=True, internal: bool=True,\n isRootNamespace: bool=True, listNamespace: bool=True,\n listOnlyDependencyNodes: bool=True, listOnlyNamespaces: bool=True, parent:\n bool=True, recurse: bool=True, shortName: bool=True, **kwargs)->AnyStr:\n pass","def get_namespace(self, namespace, lowercase=True, trim_namespace=True):\n\t\treturn self.get_namespace_view(namespace, lowercase, trim_namespace).copy()","def namespace_selection(base_url, namespaces):\n top_menu = ConsoleMenu(\"Select Namespace\", \"\")\n for ns in namespaces:\n namespace_url = ns['Self']\n namespace_menu = FunctionItem(ns['Id'], stream_selection, [namespace_url, get_streams(namespace_url)])\n top_menu.append_item(namespace_menu)\n top_menu.show()","def _getSubNsList(self):\n return LinkedList(InternalRack(self, 2))","def getNamespace(self):\n pass;","def registered_dde_schemas(verbose=False):\n url = DDE_SCHEMA_BASE_URL + \"?field=_id&size=20\"\n if verbose:\n print(f'Loading registered DDE schema list from \"{url}\"')\n data = load_json_or_yaml(url)\n return [s[\"namespace\"] for s in data[\"hits\"]]","def get_catalog():\n return jsonify(getCatalog())","def test_get_ns_descriptors(get_ns_descriptors_keys):\r\n sonata_nsd = SONATAClient.Nsd(HOST_URL)\r\n sonata_auth = SONATAClient.Auth(HOST_URL)\r\n _token = json.loads(sonata_auth.auth(username=USERNAME, password=PASSWORD))\r\n _token = json.loads(_token[\"data\"])\r\n\r\n response = json.loads(sonata_nsd.get_ns_descriptors(\r\n token=_token[\"token\"][\"access_token\"], limit=1000))\r\n response = json.loads(response[\"data\"])\r\n\r\n assert isinstance(response, list)\r\n if len(response) > 0:\r\n assert set(get_ns_descriptors_keys).issubset(\r\n response[0].keys()), \"All keys should be in the response\"","def update():\n for namespace in metadata.get_namespaces():\n logging.info('Switching namespace: \\'%s\\'', namespace)\n namespace_manager.set_namespace(namespace)\n update_per_namespace()\n\n namespace_manager.set_namespace('')\n return ('', 204)","def namespaces(self) -> NamespacesType:\n return self.schema.namespaces","def get_ns_info(self, is_sol=False):\n if self.ns_filter and \"ns_inst_id\" in self.ns_filter:\n ns_inst_id = self.ns_filter[\"ns_inst_id\"]\n ns_insts = NSInstModel.objects.filter(id=ns_inst_id)\n else:\n ns_insts = NSInstModel.objects.all()\n result = []\n for ns_inst in ns_insts:\n if ns_inst.status != 'null':\n result.append(self.get_single_ns_info(ns_inst, is_sol))\n return result","def list_namespaced_identity(self, **kwargs):\n\n all_params = ['pretty', 'label_selector', 'field_selector', 'watch', 'resource_version', 'timeout_seconds']\n all_params.append('callback')\n\n params = locals()\n for key, val in iteritems(params['kwargs']):\n if key not in all_params:\n raise TypeError(\n \"Got an unexpected keyword argument '%s'\"\n \" to method list_namespaced_identity\" % key\n )\n params[key] = val\n del params['kwargs']\n\n\n resource_path = '/oapi/v1/identities'.replace('{format}', 'json')\n path_params = {}\n\n query_params = {}\n if 'pretty' in params:\n query_params['pretty'] = params['pretty']\n if 'label_selector' in params:\n query_params['labelSelector'] = params['label_selector']\n if 'field_selector' in params:\n query_params['fieldSelector'] = params['field_selector']\n if 'watch' in params:\n query_params['watch'] = params['watch']\n if 'resource_version' in params:\n query_params['resourceVersion'] = params['resource_version']\n if 'timeout_seconds' in params:\n query_params['timeoutSeconds'] = params['timeout_seconds']\n\n header_params = {}\n\n form_params = []\n local_var_files = {}\n\n body_params = None\n\n # HTTP header `Accept`\n header_params['Accept'] = self.api_client.\\\n select_header_accept(['application/json', 'application/yaml'])\n if not header_params['Accept']:\n del header_params['Accept']\n\n # HTTP header `Content-Type`\n header_params['Content-Type'] = self.api_client.\\\n select_header_content_type(['*/*'])\n\n # Authentication setting\n auth_settings = []\n\n response = self.api_client.call_api(resource_path, 'GET',\n path_params,\n query_params,\n header_params,\n body=body_params,\n post_params=form_params,\n files=local_var_files,\n response_type='V1IdentityList',\n auth_settings=auth_settings,\n callback=params.get('callback'))\n return response","def list(self):\n return self.connection.get(self.service)","def cmd_ns_search(mixcli: MixCli, **kwargs: Union[str, bool]):\n \"\"\"\n Example payload Json\n when the user, for whom the auth token represents, is a member of the inquired namespace:\n {\n \"id\": 322, \n \"name\": \"zhuoyan.li@nuance.com\", \n \"path\": \"zhuoyan.li@nuance.com\", \n \"url\": \"\", \n \"is_personal\": true, \n \"accurate_model_enabled\": true, \n \"is_member\": true\n }\n when the user is not a member of the inquired namespace:\n {\n \"name\": \"not.for.me@nuance.com\", \n \"id\": 111,\n \"is_member\": false\n }\n \"\"\"\n ns_name = kwargs['name']\n saveto_tempfile = kwargs['tempfile']\n json_srchrslt = ns_search(mixcli, ns_name, json_resp=True, glblsrch_totmp=saveto_tempfile)\n out_file = kwargs['out_file']\n if out_file:\n write_result_outfile(content=json_srchrslt, out_file=out_file, is_json=True, logger=mixcli)\n else:\n mixcli.info('Namespace search result: '+json.dumps(json_srchrslt))\n return True","def list(self) -> List[Organisation]:\n ...","def test_list_namespaced_build_config(self):\n pass","def namespace(self):\n return VarLookupDict(self._namespaces)","def test_list_deployment_config_for_all_namespaces(self):\n pass","def grouplist(self, namespace=None):\n source = self._source(namespace)\n return self._list(source, 'list')","def index(self, req):\n return self._get_models(req, is_detail=False)","def ns_list(self):\n return sorted(self.get_ns_name(ns) for ns in self.profile.authoritative_servers)","def get_ns_descriptors(self, token, _filter=None, host=None, port=None): \n if host is None:\n base_path = self._base_path.format(self._host, self._port)\n else:\n base_path = self._base_path.format(host, port)\n\n query_path = ''\n if _filter:\n query_path = '?_admin.type=' + _filter\n\n _endpoint = \"{0}/nsd/v1/ns_descriptors_content{1}\".format(base_path, query_path)\n result = {'error': True, 'data': ''}\n headers = {\"Content-Type\": \"application/yaml\", \"accept\": \"application/json\",\n 'Authorization': 'Bearer {}'.format(token)}\n\n try:\n r = requests.get(_endpoint, params=None, verify=False, stream=True, headers=headers)\n except Exception as e:\n result['data'] = str(e)\n return result\n\n if r.status_code == requests.codes.ok:\n result['error'] = False\n\n result['data'] = r.text\n return json.dumps(result)","def test_namespaces(self):\n collection = lookup.TemplateLookup()\n\n collection.put_string(\n \"base\",\n \"\"\"\n <%def name=\"a()\">base_a</%def>\n <%def name=\"b()\">base_b</%def>\n This is the base.\n ${next.body()}\n\"\"\",\n )\n\n collection.put_string(\n \"layout\",\n \"\"\"\n <%inherit file=\"base\"/>\n <%def name=\"a()\">layout_a</%def>\n This is the layout..\n ${next.body()}\n\"\"\",\n )\n\n collection.put_string(\n \"index\",\n \"\"\"\n <%inherit file=\"base\"/>\n <%namespace name=\"sc\" file=\"secondary\"/>\n this is index.\n a is: ${self.a()}\n sc.a is: ${sc.a()}\n sc.b is: ${sc.b()}\n sc.c is: ${sc.c()}\n sc.body is: ${sc.body()}\n\"\"\",\n )\n\n collection.put_string(\n \"secondary\",\n \"\"\"\n <%inherit file=\"layout\"/>\n <%def name=\"c()\">secondary_c. a is ${self.a()} b is ${self.b()} \"\"\"\n \"\"\"d is ${self.d()}</%def>\n <%def name=\"d()\">secondary_d.</%def>\n this is secondary.\n a is: ${self.a()}\n c is: ${self.c()}\n\"\"\",\n )\n\n assert result_lines(collection.get_template(\"index\").render()) == [\n \"This is the base.\",\n \"this is index.\",\n \"a is: base_a\",\n \"sc.a is: layout_a\",\n \"sc.b is: base_b\",\n \"sc.c is: secondary_c. a is layout_a b is base_b d is \"\n \"secondary_d.\",\n \"sc.body is:\",\n \"this is secondary.\",\n \"a is: layout_a\",\n \"c is: secondary_c. a is layout_a b is base_b d is secondary_d.\",\n ]","def registered_dde_schemas(self):\n url = DDE_SCHEMA_BASE_URL + \"?field=_id&size=20\"\n if self.verbose:\n print(f'Loading registered DDE schema list from \"{url}\"')\n data = load_json_or_yaml(url)\n return [s[\"namespace\"] for s in data[\"hits\"]]","def identify_cluster_namespaces():\n # Execute 'kubectl get namespaces' command, returned as CompletedProcess\n namespaces_completed_process = run([\"kubectl\", \"get\", \"namespaces\"], stdout=PIPE, stderr=PIPE)\n\n status = namespaces_completed_process.returncode\n # If return code is not 0\n if status:\n return {'status': status, 'value': namespaces_completed_process.stderr.decode('utf-8').strip()}\n else:\n return {'status': status, 'value': namespaces_completed_process.stdout.decode('utf-8').strip()}","def get_clientsby_namespace(\n namespace: Optional[str] = None,\n x_additional_headers: Optional[Dict[str, str]] = None,\n **kwargs\n):\n if namespace is None:\n namespace, error = get_services_namespace()\n if error:\n return None, error\n request = GetClientsbyNamespace.create(\n namespace=namespace,\n )\n return run_request(request, additional_headers=x_additional_headers, **kwargs)","def collect_resources_list(namespace, output_dir, k8s_cli, mode):\n selector = \"\"\n if mode == MODE_RESTRICTED:\n selector = '--selector=\"{}\"'.format(OPERATOR_LABEL)\n collect_helper(output_dir,\n cmd=\"{} get all -o wide -n {} {}\".format(k8s_cli, namespace, selector),\n file_name=\"resources_list\",\n resource_name=\"resources list\",\n namespace=namespace)","def get_edge_namespaces_endpoint(workflow_name, element_id, permissions, result_decorator=None):\n endpoint = 'get_edge_namespaces'\n function_endpoint = '%sget_edge_namespaces' % workflow_name\n view_function = app.view_functions.get(function_endpoint)\n if view_function is not None:\n return endpoint\n\n if not result_decorator:\n result_decorator = empty_decorator\n\n @route(app, '/%s/%s' % (workflow_name, endpoint), methods=['POST'])\n @rest_workflow_permission_required(permissions)\n @rest_exception_catcher\n # pylint: disable=unused-variable\n def get_edge_namespaces():\n \"\"\"\n Exposed endpoint for retrieving networks by hint.\n Used for populating autocomplete fields, does not return all information.\n \"\"\"\n hint = request.form['namespaces']\n\n result = {'status': FAIL, 'message': '', 'data': {}}\n try:\n result['status'] = SUCCESS\n result['data']['autocomplete_field'] = []\n result['data']['select_field'] = []\n if hint != '':\n edge_session = edge(edge_create_internal_ns_configuration.edge_url,\n edge_create_internal_ns_configuration.client_id,\n edge_create_internal_ns_configuration.clientSecret)\n\n namespaces = edge_session.get_namespaces()\n count = 0\n for namespace in namespaces:\n if namespace['name'].startswith(hint):\n\n result['data']['autocomplete_field'].append({\n 'input': namespace['id'],\n 'value': '%s (%s)' % (namespace['name'], namespace['id'])\n })\n result['data']['select_field'].append({\n 'id': namespace['id'],\n 'txt': namespace['name']\n })\n if count == 10:\n break\n count += 1\n except Exception as e:\n result['status'] = FAIL\n result['message'] = 'Error while searching for Namespaces: %s and hint: %s!' % (util.safe_str(e), hint)\n return result\n\n return endpoint","def namespaces(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:\n return pulumi.get(self, \"namespaces\")"],"string":"[\n \"def namespace(self, namespace):\\n return self.client.call('GET',\\n self.name, params={'namespace': namespace})\",\n \"def _fetch_all_namespaces():\\n response = _fetch_herd_session() \\\\\\n .get('{}://{}/{}/{}'.format(HERD_REST_PROTOCOL, HERD_BASE_URL,\\n HERD_REST_BASE_PATH, 'namespaces')) \\\\\\n .json()\\n\\n namespaces = []\\n for namespaceKey in response['namespaceKeys']:\\n namespaces.append(namespaceKey['namespaceCode'])\\n\\n _print_info('Retrieved {} namespaces.'.format(len(namespaces)))\\n return namespaces\",\n \"def test_list_net_namespace(self):\\n pass\",\n \"def _load_namespaces(self, result):\\n self._namespaces = {}\\n\\n for namespace in result[\\\"query\\\"][\\\"namespaces\\\"].values():\\n ns_id = namespace[\\\"id\\\"]\\n name = namespace[\\\"*\\\"]\\n try:\\n canonical = namespace[\\\"canonical\\\"]\\n except KeyError:\\n self._namespaces[ns_id] = [name]\\n else:\\n if name != canonical:\\n self._namespaces[ns_id] = [name, canonical]\\n else:\\n self._namespaces[ns_id] = [name]\\n\\n for namespace in result[\\\"query\\\"][\\\"namespacealiases\\\"]:\\n ns_id = namespace[\\\"id\\\"]\\n alias = namespace[\\\"*\\\"]\\n self._namespaces[ns_id].append(alias)\",\n \"async def list_namespaces(self) -> list:\\n return await self.AD.state.list_namespaces()\",\n \"def test_get_namespaces_names(self):\\n pass\",\n \"def get_namespaces():\\r\\n\\r\\n print 'Getting namespaces'\\r\\n tree = etree.parse('http://lesswrong.wikia.com/wiki/Special:AllPages', parser)\\r\\n options = tree.xpath('//select[@id=\\\"namespace\\\"]/option')\\r\\n namespaces = [option.get('value') for option in options]\\r\\n pprint(namespaces)\\r\\n return namespaces\",\n \"def _get_cloud_function_namespaces_metadata(offset=0):\\n\\n res = requests.get(\\n f\\\"{self.cf_namespaces_url}?limit=200&offset={offset}\\\",\\n headers=self.get_headers(),\\n )\\n return json.loads(res.text)\",\n \"def test_get_namespaces_from_account(self):\\n pass\",\n \"def get_edge_namespaces():\\n hint = request.form['namespaces']\\n\\n result = {'status': FAIL, 'message': '', 'data': {}}\\n try:\\n result['status'] = SUCCESS\\n result['data']['autocomplete_field'] = []\\n result['data']['select_field'] = []\\n if hint != '':\\n edge_session = edge(edge_create_internal_ns_configuration.edge_url,\\n edge_create_internal_ns_configuration.client_id,\\n edge_create_internal_ns_configuration.clientSecret)\\n\\n namespaces = edge_session.get_namespaces()\\n count = 0\\n for namespace in namespaces:\\n if namespace['name'].startswith(hint):\\n\\n result['data']['autocomplete_field'].append({\\n 'input': namespace['id'],\\n 'value': '%s (%s)' % (namespace['name'], namespace['id'])\\n })\\n result['data']['select_field'].append({\\n 'id': namespace['id'],\\n 'txt': namespace['name']\\n })\\n if count == 10:\\n break\\n count += 1\\n except Exception as e:\\n result['status'] = FAIL\\n result['message'] = 'Error while searching for Namespaces: %s and hint: %s!' % (util.safe_str(e), hint)\\n return result\",\n \"def get_all_namespaces():\\n cmds.namespace(setNamespace=':')\\n return cmds.namespaceInfo(listOnlyNamespaces=True, recurse=True)\",\n \"def sns(self,**kwargs):\\n\\n\\t\\tcursor = kwargs.get('cursor',self.cursor)\\n\\t\\treturn self.toc[cursor].keys()\",\n \"def get_ing_from_ns(namespace):\\n ing_list = get_all_ingress_from(namespace)\\n ing_list_json = get_json_list(ing_list)\\n return Response(response=ing_list_json, status=200, mimetype='application/json')\",\n \"def list(self, dict_output=False, field_selector=\\\"\\\"):\\n namespaces_list = self.client_core.list_namespace().items\\n logger.info(\\\"Got namespaces\\\")\\n\\n if field_selector:\\n namespaces_list = field_filter(obj_list=namespaces_list,\\n field_selector=field_selector)\\n # convert the list to list of dicts if required\\n if dict_output:\\n namespaces_list = [convert_obj_to_dict(namespace) for namespace in\\n namespaces_list]\\n else:\\n for namespace in namespaces_list:\\n namespace.metadata.resource_version = ''\\n return namespaces_list\",\n \"def test_list_build_for_all_namespaces(self):\\n pass\",\n \"def list_namespaced_net_namespace(self, **kwargs):\\n\\n all_params = ['pretty', 'label_selector', 'field_selector', 'watch', 'resource_version', 'timeout_seconds']\\n all_params.append('callback')\\n\\n params = locals()\\n for key, val in iteritems(params['kwargs']):\\n if key not in all_params:\\n raise TypeError(\\n \\\"Got an unexpected keyword argument '%s'\\\"\\n \\\" to method list_namespaced_net_namespace\\\" % key\\n )\\n params[key] = val\\n del params['kwargs']\\n\\n\\n resource_path = '/oapi/v1/netnamespaces'.replace('{format}', 'json')\\n path_params = {}\\n\\n query_params = {}\\n if 'pretty' in params:\\n query_params['pretty'] = params['pretty']\\n if 'label_selector' in params:\\n query_params['labelSelector'] = params['label_selector']\\n if 'field_selector' in params:\\n query_params['fieldSelector'] = params['field_selector']\\n if 'watch' in params:\\n query_params['watch'] = params['watch']\\n if 'resource_version' in params:\\n query_params['resourceVersion'] = params['resource_version']\\n if 'timeout_seconds' in params:\\n query_params['timeoutSeconds'] = params['timeout_seconds']\\n\\n header_params = {}\\n\\n form_params = []\\n local_var_files = {}\\n\\n body_params = None\\n\\n # HTTP header `Accept`\\n header_params['Accept'] = self.api_client.\\\\\\n select_header_accept(['application/json', 'application/yaml'])\\n if not header_params['Accept']:\\n del header_params['Accept']\\n\\n # HTTP header `Content-Type`\\n header_params['Content-Type'] = self.api_client.\\\\\\n select_header_content_type(['*/*'])\\n\\n # Authentication setting\\n auth_settings = []\\n\\n response = self.api_client.call_api(resource_path, 'GET',\\n path_params,\\n query_params,\\n header_params,\\n body=body_params,\\n post_params=form_params,\\n files=local_var_files,\\n response_type='V1NetNamespaceList',\\n auth_settings=auth_settings,\\n callback=params.get('callback'))\\n return response\",\n \"def create_or_fetch_namespace(self):\\n\\n def _create_new_namespace():\\n logger.info(\\n f\\\"Creating a new namespace: {self.namespace_name} in {self.namespace_region}\\\"\\n )\\n\\n data = {\\n \\\"name\\\": self.namespace_name,\\n \\\"resource_group_id\\\": self.resource_group_id,\\n \\\"resource_plan_id\\\": \\\"functions-base-plan\\\",\\n }\\n\\n res = requests.post(\\n self.cf_namespaces_url, headers=self.get_headers(), json=data\\n ).json()\\n if res.status_code != 200:\\n logger.error(res.text)\\n namespace_id = res[\\\"id\\\"]\\n logger.info(f\\\"Created new namespace with id: {namespace_id}\\\")\\n return namespace_id\\n\\n def _get_cloud_function_namespaces_metadata(offset=0):\\n \\\"\\\"\\\"returns meta data on namespaces of ibm cloud functions within a specified region\\n :param offset - offset from the beginning of the list of results attained from the GET request,\\n which may contain up to 200 namespaces per http response\\\"\\\"\\\"\\n\\n res = requests.get(\\n f\\\"{self.cf_namespaces_url}?limit=200&offset={offset}\\\",\\n headers=self.get_headers(),\\n )\\n return json.loads(res.text)\\n\\n def _get_cloud_function_namespaces():\\n \\\"\\\"\\\"returns relevant metadata on existing namespaces within a given region.\\\"\\\"\\\"\\n logger.info(\\n f\\\"Obtaining Cloud Function namespaces in {self.namespace_region}\\\"\\n )\\n\\n namespaces = []\\n\\n collecting_namespaces = True\\n max_limit = 200\\n offset = 0\\n\\n # request for namespaces is limited to 200 at a time, thus the request is fulfilled in increments of 200s.\\n while collecting_namespaces:\\n namespace_metadata = _get_cloud_function_namespaces_metadata(offset)\\n if namespace_metadata[\\\"total_count\\\"] == max_limit:\\n offset += max_limit\\n else:\\n collecting_namespaces = False\\n\\n for name_space in namespace_metadata[\\\"namespaces\\\"]:\\n if \\\"name\\\" in name_space: # API based namespace\\n namespaces.append(\\n {\\n \\\"name\\\": name_space[\\\"name\\\"],\\n \\\"type\\\": \\\"API_based\\\",\\n \\\"id\\\": name_space[\\\"id\\\"],\\n \\\"region\\\": name_space[\\\"location\\\"],\\n }\\n )\\n\\n else: # cloud foundry based namespace\\n namespaces.append(\\n {\\n \\\"name\\\": name_space[\\\"id\\\"],\\n \\\"type\\\": \\\"CF_based\\\",\\n \\\"region\\\": name_space[\\\"location\\\"],\\n }\\n )\\n\\n return namespaces\\n\\n namespaces_in_region = _get_cloud_function_namespaces()\\n target_namespace_id = None\\n if namespaces_in_region:\\n target_namespace_id = next(\\n (\\n namespace[\\\"id\\\"]\\n for namespace in namespaces_in_region\\n if namespace[\\\"name\\\"] == self.namespace_name\\n ),\\n None,\\n )\\n if not target_namespace_id:\\n target_namespace_id = _create_new_namespace()\\n else:\\n logger.info(f\\\"Reusing namespace: {target_namespace_id}\\\")\\n return target_namespace_id\",\n \"def test_list_template_for_all_namespaces(self):\\n pass\",\n \"def test_list_route_for_all_namespaces(self):\\n pass\",\n \"def test_get_namespaces_from_accounts(self):\\n pass\",\n \"def namespaces(self, psuedo=True):\\n if self._namespaces == None:\\n result = self.call({'action': 'query',\\n 'meta': 'siteinfo',\\n 'siprop': 'namespaces'})\\n self._namespaces = {}\\n self._psuedo_namespaces = {}\\n for nsid in result['query']['namespaces']:\\n if int(nsid) >= 0:\\n self._namespaces[int(nsid)] = \\\\\\n result['query']['namespaces'][nsid]['*']\\n else:\\n self._psuedo_namespaces[int(nsid)] = \\\\\\n result['query']['namespaces'][nsid]['*']\\n if psuedo:\\n retval = {}\\n retval.update(self._namespaces)\\n retval.update(self._psuedo_namespaces)\\n return retval\\n else:\\n return self._namespaces\",\n \"def get_all_by_namespace(context, namespace_name, session):\\n\\n # namespace get raises an exception if not visible\\n namespace = namespace_api.get(\\n context, namespace_name, session)\\n\\n db_recs = (\\n session.query(models.MetadefResourceType)\\n .join(models.MetadefResourceType.associations)\\n .filter_by(namespace_id=namespace['id'])\\n .values(models.MetadefResourceType.name,\\n models.MetadefNamespaceResourceType.properties_target,\\n models.MetadefNamespaceResourceType.prefix,\\n models.MetadefNamespaceResourceType.created_at,\\n models.MetadefNamespaceResourceType.updated_at))\\n\\n model_dict_list = []\\n for name, properties_target, prefix, created_at, updated_at in db_recs:\\n model_dict_list.append(\\n _set_model_dict\\n (name, properties_target, prefix, created_at, updated_at)\\n )\\n\\n return model_dict_list\",\n \"def get_ing_from_all_ns():\\n ing_list = get_all_ingress()\\n ing_list_json = get_json_list(ing_list)\\n return Response(response=ing_list_json, status=200, mimetype='application/json')\",\n \"def get(self, *args):\\n return _libsbml.ListWrapperSBMLNamespaces_get(self, *args)\",\n \"def test_get_namespace(self):\\n pass\",\n \"def get_namespaces(self, label_selector=None):\\n return self.core_client.list_namespace(label_selector=label_selector)\",\n \"def all_in_namespace(cls, ns):\\n return filter_by_prefix(cls.all(), ns + ':')\",\n \"def getnamespaceinfo (self, namespaceguid, jobguid = \\\"\\\", executionparams = {}):\\n params =dict()\\n params['namespaceguid'] = namespaceguid\\n executionparams['rootobjecttype'] = 'dssnamespace'\\n\\n \\n return q.workflowengine.actionmanager.startRootobjectAction('dssnamespace', 'getnamespaceinfo', params, jobguid=jobguid, executionparams=executionparams)\",\n \"def collection_names(self, callback):\\n callback = partial(self._collection_names_result, callback)\\n self[\\\"system.namespaces\\\"].find(_must_use_master=True, callback=callback)\",\n \"def get_ns_list(logger,body,v1=None):\\n if v1 is None:\\n v1 = client.CoreV1Api()\\n logger.debug('new client - fn get_ns_list')\\n \\n try:\\n matchNamespace = body.get('matchNamespace')\\n except KeyError:\\n matchNamespace = '*'\\n logger.debug(\\\"matching all namespaces.\\\")\\n logger.debug(f'Matching namespaces: {matchNamespace}')\\n \\n try:\\n avoidNamespaces = body.get('avoidNamespaces')\\n except KeyError:\\n avoidNamespaces = ''\\n logger.debug(\\\"not avoiding namespaces\\\")\\n\\n nss = v1.list_namespace().items\\n matchedns = []\\n avoidedns = []\\n\\n for matchns in matchNamespace:\\n for ns in nss:\\n if re.match(matchns, ns.metadata.name):\\n matchedns.append(ns.metadata.name)\\n logger.debug(f'Matched namespaces: {ns.metadata.name} matchpathern: {matchns}')\\n if avoidNamespaces:\\n for avoidns in avoidNamespaces:\\n for ns in nss:\\n if re.match(avoidns, ns.metadata.name):\\n avoidedns.append(ns.metadata.name)\\n logger.debug(f'Skipping namespaces: {ns.metadata.name} avoidpatrn: {avoidns}') \\n # purge\\n for ns in matchedns.copy():\\n if ns in avoidedns:\\n matchedns.remove(ns)\\n\\n return matchedns\",\n \"def namespaces(self):\\n return ()\",\n \"def _getnamespaces(cls):\\n return \\\" \\\".join(Kmlable._namespaces)\",\n \"def fetch_list(self):\\n\\t\\treturn self.fetch(self.list_url % ART_SERVER_HOST)\",\n \"def test_read_net_namespace(self):\\n pass\",\n \"def get_all_pages_for_namespace(ns):\\r\\n\\r\\n print 'Getting pages in namespace %s' % ns\\r\\n url = 'http://lesswrong.wikia.com/index.php?title=Special:AllPages&from=&to=&namespace=%s' % ns\\r\\n tree = etree.parse(url, parser)\\r\\n pages = tree.xpath('//table[2]//a[@title]')\\r\\n page_names = [page.get('title') for page in pages]\\r\\n pprint(page_names)\\r\\n return page_names\",\n \"def getNamespaces(self, *args):\\n return _libsbml.SBMLNamespaces_getNamespaces(self, *args)\",\n \"def _load_namespaces(self):\\n nsdocs = self._docset.get_namespaces()\\n for nsdoc in nsdocs:\\n nsobj = Namespace(nsdoc)\\n self._docmap[nsdoc] = nsobj\\n self._namespaces.add(nsobj)\",\n \"def namespaces(self):\\n return [self._namespace_prefix]\",\n \"def getNamespaces(self):\\n return _libsbml.SBase_getNamespaces(self)\",\n \"def getCatalogs():\",\n \"def ls():\\n return dynamodb.ls(OrganizationModel)\",\n \"def explore_all_nf_data():\\n request = app.current_request\\n resource_type = request.query_params[\\\"resource_type\\\"]\\n offset = int(request.query_params[\\\"offset\\\"])\\n limit = int(request.query_params[\\\"limit\\\"])\\n explorer = UnogsExplorer(resource_type)\\n success = explorer.explore(limit, offset)\\n return {\\\"success\\\": success}\",\n \"def test_list_namespaced_route(self):\\n pass\",\n \"def get_services_in_namespace(self, namespace):\\n ret = self.v1_service_list.get(namespace=namespace)\\n return [each.metadata.name for each in ret.items]\",\n \"def test_list_policy_for_all_namespaces(self):\\n pass\",\n \"def namespaces(self):\\n return list(self._namespace_schemas.keys())\",\n \"def list(self, urn):\\n uri = '/'.join([self.urn2uri(urn), self.IDENTIFIER])\\n return self.rest_client.get(uri)\",\n \"def update_namespaces_info(self):\\n namespaces = BlockDev.nvdimm_list_namespaces(idle=True)\\n\\n self._namespaces = dict((namespace.dev, namespace) for namespace in namespaces)\",\n \"def getNamespaces(self):\\n return _libsbml.SBMLDocument_getNamespaces(self)\",\n \"def get_namespaces():\\n return list(StaticAsset._load_namespaces().keys())\",\n \"def get_streams(namespace_url):\\n streams = requests.get(namespace_url + '/Streams', headers=headers)\\n return streams.json()\",\n \"def xmlrpc_namespace():\",\n \"def pyreq_ns_search(httpreq_handler: HTTPRequestHandler, namespace: str,\\n json_resp: bool = False, need_global_lookup_result: bool = False,\\n glblsrch_totmp: bool = False, ) -> Optional[Union[Dict, str, Tuple[Dict, Dict]]]:\\n result: List[Dict] = pyreq_list_affiliated_ns(httpreq_handler)\\n for ns_meta in result:\\n if ns_meta['name'] == namespace:\\n httpreq_handler.debug(f'Found member namespace that matches {namespace}: ' + json.dumps(ns_meta))\\n if json_resp:\\n return ns_meta\\n else:\\n return ns_meta['id']\\n httpreq_handler.debug(f'Target namespace {namespace} not found in affiliated results, need global search.')\\n httpreq_handler.debug(f'Look up on overall nuance.com namespace. We redirect network payloads to a file')\\n # this request would return a huge payload containing exhaustive info for all application configurations\\n # for all users and namespaces. Therefore we rather ask curl to save the output to a file instead of\\n # trying to receive that from stdout piping\\n endpoint = '/bolt/applications'\\n tmp_outfile = None\\n if glblsrch_totmp:\\n timestamp = datetime.datetime.now().strftime('%Y%m%dT%H%M%S')\\n tmp_outfile = os.path.join(os.getcwd(),\\n f'tmp_app_config_lookup_{timestamp}.json')\\n httpreq_handler.debug(f'Temp file for redirected CURL output: {tmp_outfile}')\\n resp: Dict = httpreq_handler.request(url=endpoint, method=GET_METHOD, default_headers=True,\\n stream=True, outfile=tmp_outfile, json_resp=True)\\n for app_conf_grp in resp['data']:\\n if app_conf_grp['namespace_name'] != namespace:\\n continue\\n ns_search_result = dict()\\n ns_search_result['name'] = namespace\\n ns_search_result['id'] = app_conf_grp['namespace_id']\\n ns_search_result['is_member'] = False\\n if need_global_lookup_result:\\n httpreq_handler.debug(f'Retruning global lookup resp and result: {json.dumps(ns_search_result)}')\\n return ns_search_result, resp\\n else:\\n httpreq_handler.debug(f'Retruning result: {json.dumps(ns_search_result)}')\\n return ns_search_result\\n raise ValueError(__ERR_MSG_NS_NOTFOUND.format(ns_name=namespace))\",\n \"def namespaces(self):\\n if not self._namespaces:\\n self.update_namespaces_info()\\n\\n return self._namespaces\",\n \"def test_list_namespaced_build(self):\\n pass\",\n \"def namespaces(self):\\n return self.namespaced_fields().namespaces()\",\n \"def test_create_local_resource_access_review_for_all_namespaces(self):\\n pass\",\n \"def test_list_build_config_for_all_namespaces(self):\\n pass\",\n \"def _get_cloud_function_namespaces():\\n logger.info(\\n f\\\"Obtaining Cloud Function namespaces in {self.namespace_region}\\\"\\n )\\n\\n namespaces = []\\n\\n collecting_namespaces = True\\n max_limit = 200\\n offset = 0\\n\\n # request for namespaces is limited to 200 at a time, thus the request is fulfilled in increments of 200s.\\n while collecting_namespaces:\\n namespace_metadata = _get_cloud_function_namespaces_metadata(offset)\\n if namespace_metadata[\\\"total_count\\\"] == max_limit:\\n offset += max_limit\\n else:\\n collecting_namespaces = False\\n\\n for name_space in namespace_metadata[\\\"namespaces\\\"]:\\n if \\\"name\\\" in name_space: # API based namespace\\n namespaces.append(\\n {\\n \\\"name\\\": name_space[\\\"name\\\"],\\n \\\"type\\\": \\\"API_based\\\",\\n \\\"id\\\": name_space[\\\"id\\\"],\\n \\\"region\\\": name_space[\\\"location\\\"],\\n }\\n )\\n\\n else: # cloud foundry based namespace\\n namespaces.append(\\n {\\n \\\"name\\\": name_space[\\\"id\\\"],\\n \\\"type\\\": \\\"CF_based\\\",\\n \\\"region\\\": name_space[\\\"location\\\"],\\n }\\n )\\n\\n return namespaces\",\n \"def test_list_role_for_all_namespaces(self):\\n pass\",\n \"def GetNamespaces(self):\\n return list(self.type_namespaces_map.values())\",\n \"def test_list_image_stream_for_all_namespaces(self):\\n pass\",\n \"def get(self, name, dict_output=False):\\n namespace = self.client_core.read_namespace(name=name)\\n logger.info(f\\\"Got namespace {name}\\\")\\n\\n # convert the obj to dict if required\\n if dict_output:\\n namespace = convert_obj_to_dict(namespace)\\n else:\\n namespace.metadata.resource_version = ''\\n\\n return namespace\",\n \"def show_tablespaces(self):\\n sql = \\\"SELECT TABLESPACE_NAME FROM DBA_TABLESPACES WHERE CONTENTS <> 'TEMPORARY' ORDER BY 1\\\"\\n self.cur.execute(sql)\\n res = self.cur.fetchall()\\n key = ['{#TABLESPACE}']\\n lst = []\\n for i in res:\\n d = dict(zip(key, i))\\n lst.append(d)\\n print(json.dumps({'data': lst}))\",\n \"def test_list_namespaced_template(self):\\n pass\",\n \"def watch_namespaced_net_namespace_list(self, **kwargs):\\n\\n all_params = ['pretty', 'label_selector', 'field_selector', 'watch', 'resource_version', 'timeout_seconds']\\n all_params.append('callback')\\n\\n params = locals()\\n for key, val in iteritems(params['kwargs']):\\n if key not in all_params:\\n raise TypeError(\\n \\\"Got an unexpected keyword argument '%s'\\\"\\n \\\" to method watch_namespaced_net_namespace_list\\\" % key\\n )\\n params[key] = val\\n del params['kwargs']\\n\\n\\n resource_path = '/oapi/v1/watch/netnamespaces'.replace('{format}', 'json')\\n path_params = {}\\n\\n query_params = {}\\n if 'pretty' in params:\\n query_params['pretty'] = params['pretty']\\n if 'label_selector' in params:\\n query_params['labelSelector'] = params['label_selector']\\n if 'field_selector' in params:\\n query_params['fieldSelector'] = params['field_selector']\\n if 'watch' in params:\\n query_params['watch'] = params['watch']\\n if 'resource_version' in params:\\n query_params['resourceVersion'] = params['resource_version']\\n if 'timeout_seconds' in params:\\n query_params['timeoutSeconds'] = params['timeout_seconds']\\n\\n header_params = {}\\n\\n form_params = []\\n local_var_files = {}\\n\\n body_params = None\\n\\n # HTTP header `Accept`\\n header_params['Accept'] = self.api_client.\\\\\\n select_header_accept(['application/json'])\\n if not header_params['Accept']:\\n del header_params['Accept']\\n\\n # HTTP header `Content-Type`\\n header_params['Content-Type'] = self.api_client.\\\\\\n select_header_content_type(['*/*'])\\n\\n # Authentication setting\\n auth_settings = []\\n\\n response = self.api_client.call_api(resource_path, 'GET',\\n path_params,\\n query_params,\\n header_params,\\n body=body_params,\\n post_params=form_params,\\n files=local_var_files,\\n response_type='JsonWatchEvent',\\n auth_settings=auth_settings,\\n callback=params.get('callback'))\\n return response\",\n \"def get_namespaces(self):\\n if self.namespaces is None:\\n namespaces = unpack(self.api.get_namespaces())\\n self.namespaces = {\\n namespace['name']: DevopsSecurityNamespace(namespace)\\n for namespace in namespaces\\n }\\n return self.namespaces\",\n \"def test_create_resource_access_review_for_all_namespaces(self):\\n pass\",\n \"def list_apiscout(self):\\n return self.__make_api_call('list/apiscout')\",\n \"def _get_namespace(self, data):\\n ns_name = data['filename'].namespace\\n try:\\n return models.Namespace.objects.get(name=ns_name)\\n except models.Namespace.DoesNotExist:\\n raise exceptions.ValidationError(\\n f'Namespace \\\"{ns_name}\\\" does not exist.'\\n )\",\n \"def list(self):\\n return self.request(\\\"GET\\\")\",\n \"def namespaceInfo(*args, absoluteName: bool=True, baseName: bool=True, currentNamespace:\\n bool=True, dagPath: bool=True, fullName: bool=True, internal: bool=True,\\n isRootNamespace: bool=True, listNamespace: bool=True,\\n listOnlyDependencyNodes: bool=True, listOnlyNamespaces: bool=True, parent:\\n bool=True, recurse: bool=True, shortName: bool=True, **kwargs)->AnyStr:\\n pass\",\n \"def get_namespace(self, namespace, lowercase=True, trim_namespace=True):\\n\\t\\treturn self.get_namespace_view(namespace, lowercase, trim_namespace).copy()\",\n \"def namespace_selection(base_url, namespaces):\\n top_menu = ConsoleMenu(\\\"Select Namespace\\\", \\\"\\\")\\n for ns in namespaces:\\n namespace_url = ns['Self']\\n namespace_menu = FunctionItem(ns['Id'], stream_selection, [namespace_url, get_streams(namespace_url)])\\n top_menu.append_item(namespace_menu)\\n top_menu.show()\",\n \"def _getSubNsList(self):\\n return LinkedList(InternalRack(self, 2))\",\n \"def getNamespace(self):\\n pass;\",\n \"def registered_dde_schemas(verbose=False):\\n url = DDE_SCHEMA_BASE_URL + \\\"?field=_id&size=20\\\"\\n if verbose:\\n print(f'Loading registered DDE schema list from \\\"{url}\\\"')\\n data = load_json_or_yaml(url)\\n return [s[\\\"namespace\\\"] for s in data[\\\"hits\\\"]]\",\n \"def get_catalog():\\n return jsonify(getCatalog())\",\n \"def test_get_ns_descriptors(get_ns_descriptors_keys):\\r\\n sonata_nsd = SONATAClient.Nsd(HOST_URL)\\r\\n sonata_auth = SONATAClient.Auth(HOST_URL)\\r\\n _token = json.loads(sonata_auth.auth(username=USERNAME, password=PASSWORD))\\r\\n _token = json.loads(_token[\\\"data\\\"])\\r\\n\\r\\n response = json.loads(sonata_nsd.get_ns_descriptors(\\r\\n token=_token[\\\"token\\\"][\\\"access_token\\\"], limit=1000))\\r\\n response = json.loads(response[\\\"data\\\"])\\r\\n\\r\\n assert isinstance(response, list)\\r\\n if len(response) > 0:\\r\\n assert set(get_ns_descriptors_keys).issubset(\\r\\n response[0].keys()), \\\"All keys should be in the response\\\"\",\n \"def update():\\n for namespace in metadata.get_namespaces():\\n logging.info('Switching namespace: \\\\'%s\\\\'', namespace)\\n namespace_manager.set_namespace(namespace)\\n update_per_namespace()\\n\\n namespace_manager.set_namespace('')\\n return ('', 204)\",\n \"def namespaces(self) -> NamespacesType:\\n return self.schema.namespaces\",\n \"def get_ns_info(self, is_sol=False):\\n if self.ns_filter and \\\"ns_inst_id\\\" in self.ns_filter:\\n ns_inst_id = self.ns_filter[\\\"ns_inst_id\\\"]\\n ns_insts = NSInstModel.objects.filter(id=ns_inst_id)\\n else:\\n ns_insts = NSInstModel.objects.all()\\n result = []\\n for ns_inst in ns_insts:\\n if ns_inst.status != 'null':\\n result.append(self.get_single_ns_info(ns_inst, is_sol))\\n return result\",\n \"def list_namespaced_identity(self, **kwargs):\\n\\n all_params = ['pretty', 'label_selector', 'field_selector', 'watch', 'resource_version', 'timeout_seconds']\\n all_params.append('callback')\\n\\n params = locals()\\n for key, val in iteritems(params['kwargs']):\\n if key not in all_params:\\n raise TypeError(\\n \\\"Got an unexpected keyword argument '%s'\\\"\\n \\\" to method list_namespaced_identity\\\" % key\\n )\\n params[key] = val\\n del params['kwargs']\\n\\n\\n resource_path = '/oapi/v1/identities'.replace('{format}', 'json')\\n path_params = {}\\n\\n query_params = {}\\n if 'pretty' in params:\\n query_params['pretty'] = params['pretty']\\n if 'label_selector' in params:\\n query_params['labelSelector'] = params['label_selector']\\n if 'field_selector' in params:\\n query_params['fieldSelector'] = params['field_selector']\\n if 'watch' in params:\\n query_params['watch'] = params['watch']\\n if 'resource_version' in params:\\n query_params['resourceVersion'] = params['resource_version']\\n if 'timeout_seconds' in params:\\n query_params['timeoutSeconds'] = params['timeout_seconds']\\n\\n header_params = {}\\n\\n form_params = []\\n local_var_files = {}\\n\\n body_params = None\\n\\n # HTTP header `Accept`\\n header_params['Accept'] = self.api_client.\\\\\\n select_header_accept(['application/json', 'application/yaml'])\\n if not header_params['Accept']:\\n del header_params['Accept']\\n\\n # HTTP header `Content-Type`\\n header_params['Content-Type'] = self.api_client.\\\\\\n select_header_content_type(['*/*'])\\n\\n # Authentication setting\\n auth_settings = []\\n\\n response = self.api_client.call_api(resource_path, 'GET',\\n path_params,\\n query_params,\\n header_params,\\n body=body_params,\\n post_params=form_params,\\n files=local_var_files,\\n response_type='V1IdentityList',\\n auth_settings=auth_settings,\\n callback=params.get('callback'))\\n return response\",\n \"def list(self):\\n return self.connection.get(self.service)\",\n \"def cmd_ns_search(mixcli: MixCli, **kwargs: Union[str, bool]):\\n \\\"\\\"\\\"\\n Example payload Json\\n when the user, for whom the auth token represents, is a member of the inquired namespace:\\n {\\n \\\"id\\\": 322, \\n \\\"name\\\": \\\"zhuoyan.li@nuance.com\\\", \\n \\\"path\\\": \\\"zhuoyan.li@nuance.com\\\", \\n \\\"url\\\": \\\"\\\", \\n \\\"is_personal\\\": true, \\n \\\"accurate_model_enabled\\\": true, \\n \\\"is_member\\\": true\\n }\\n when the user is not a member of the inquired namespace:\\n {\\n \\\"name\\\": \\\"not.for.me@nuance.com\\\", \\n \\\"id\\\": 111,\\n \\\"is_member\\\": false\\n }\\n \\\"\\\"\\\"\\n ns_name = kwargs['name']\\n saveto_tempfile = kwargs['tempfile']\\n json_srchrslt = ns_search(mixcli, ns_name, json_resp=True, glblsrch_totmp=saveto_tempfile)\\n out_file = kwargs['out_file']\\n if out_file:\\n write_result_outfile(content=json_srchrslt, out_file=out_file, is_json=True, logger=mixcli)\\n else:\\n mixcli.info('Namespace search result: '+json.dumps(json_srchrslt))\\n return True\",\n \"def list(self) -> List[Organisation]:\\n ...\",\n \"def test_list_namespaced_build_config(self):\\n pass\",\n \"def namespace(self):\\n return VarLookupDict(self._namespaces)\",\n \"def test_list_deployment_config_for_all_namespaces(self):\\n pass\",\n \"def grouplist(self, namespace=None):\\n source = self._source(namespace)\\n return self._list(source, 'list')\",\n \"def index(self, req):\\n return self._get_models(req, is_detail=False)\",\n \"def ns_list(self):\\n return sorted(self.get_ns_name(ns) for ns in self.profile.authoritative_servers)\",\n \"def get_ns_descriptors(self, token, _filter=None, host=None, port=None): \\n if host is None:\\n base_path = self._base_path.format(self._host, self._port)\\n else:\\n base_path = self._base_path.format(host, port)\\n\\n query_path = ''\\n if _filter:\\n query_path = '?_admin.type=' + _filter\\n\\n _endpoint = \\\"{0}/nsd/v1/ns_descriptors_content{1}\\\".format(base_path, query_path)\\n result = {'error': True, 'data': ''}\\n headers = {\\\"Content-Type\\\": \\\"application/yaml\\\", \\\"accept\\\": \\\"application/json\\\",\\n 'Authorization': 'Bearer {}'.format(token)}\\n\\n try:\\n r = requests.get(_endpoint, params=None, verify=False, stream=True, headers=headers)\\n except Exception as e:\\n result['data'] = str(e)\\n return result\\n\\n if r.status_code == requests.codes.ok:\\n result['error'] = False\\n\\n result['data'] = r.text\\n return json.dumps(result)\",\n \"def test_namespaces(self):\\n collection = lookup.TemplateLookup()\\n\\n collection.put_string(\\n \\\"base\\\",\\n \\\"\\\"\\\"\\n <%def name=\\\"a()\\\">base_a</%def>\\n <%def name=\\\"b()\\\">base_b</%def>\\n This is the base.\\n ${next.body()}\\n\\\"\\\"\\\",\\n )\\n\\n collection.put_string(\\n \\\"layout\\\",\\n \\\"\\\"\\\"\\n <%inherit file=\\\"base\\\"/>\\n <%def name=\\\"a()\\\">layout_a</%def>\\n This is the layout..\\n ${next.body()}\\n\\\"\\\"\\\",\\n )\\n\\n collection.put_string(\\n \\\"index\\\",\\n \\\"\\\"\\\"\\n <%inherit file=\\\"base\\\"/>\\n <%namespace name=\\\"sc\\\" file=\\\"secondary\\\"/>\\n this is index.\\n a is: ${self.a()}\\n sc.a is: ${sc.a()}\\n sc.b is: ${sc.b()}\\n sc.c is: ${sc.c()}\\n sc.body is: ${sc.body()}\\n\\\"\\\"\\\",\\n )\\n\\n collection.put_string(\\n \\\"secondary\\\",\\n \\\"\\\"\\\"\\n <%inherit file=\\\"layout\\\"/>\\n <%def name=\\\"c()\\\">secondary_c. a is ${self.a()} b is ${self.b()} \\\"\\\"\\\"\\n \\\"\\\"\\\"d is ${self.d()}</%def>\\n <%def name=\\\"d()\\\">secondary_d.</%def>\\n this is secondary.\\n a is: ${self.a()}\\n c is: ${self.c()}\\n\\\"\\\"\\\",\\n )\\n\\n assert result_lines(collection.get_template(\\\"index\\\").render()) == [\\n \\\"This is the base.\\\",\\n \\\"this is index.\\\",\\n \\\"a is: base_a\\\",\\n \\\"sc.a is: layout_a\\\",\\n \\\"sc.b is: base_b\\\",\\n \\\"sc.c is: secondary_c. a is layout_a b is base_b d is \\\"\\n \\\"secondary_d.\\\",\\n \\\"sc.body is:\\\",\\n \\\"this is secondary.\\\",\\n \\\"a is: layout_a\\\",\\n \\\"c is: secondary_c. a is layout_a b is base_b d is secondary_d.\\\",\\n ]\",\n \"def registered_dde_schemas(self):\\n url = DDE_SCHEMA_BASE_URL + \\\"?field=_id&size=20\\\"\\n if self.verbose:\\n print(f'Loading registered DDE schema list from \\\"{url}\\\"')\\n data = load_json_or_yaml(url)\\n return [s[\\\"namespace\\\"] for s in data[\\\"hits\\\"]]\",\n \"def identify_cluster_namespaces():\\n # Execute 'kubectl get namespaces' command, returned as CompletedProcess\\n namespaces_completed_process = run([\\\"kubectl\\\", \\\"get\\\", \\\"namespaces\\\"], stdout=PIPE, stderr=PIPE)\\n\\n status = namespaces_completed_process.returncode\\n # If return code is not 0\\n if status:\\n return {'status': status, 'value': namespaces_completed_process.stderr.decode('utf-8').strip()}\\n else:\\n return {'status': status, 'value': namespaces_completed_process.stdout.decode('utf-8').strip()}\",\n \"def get_clientsby_namespace(\\n namespace: Optional[str] = None,\\n x_additional_headers: Optional[Dict[str, str]] = None,\\n **kwargs\\n):\\n if namespace is None:\\n namespace, error = get_services_namespace()\\n if error:\\n return None, error\\n request = GetClientsbyNamespace.create(\\n namespace=namespace,\\n )\\n return run_request(request, additional_headers=x_additional_headers, **kwargs)\",\n \"def collect_resources_list(namespace, output_dir, k8s_cli, mode):\\n selector = \\\"\\\"\\n if mode == MODE_RESTRICTED:\\n selector = '--selector=\\\"{}\\\"'.format(OPERATOR_LABEL)\\n collect_helper(output_dir,\\n cmd=\\\"{} get all -o wide -n {} {}\\\".format(k8s_cli, namespace, selector),\\n file_name=\\\"resources_list\\\",\\n resource_name=\\\"resources list\\\",\\n namespace=namespace)\",\n \"def get_edge_namespaces_endpoint(workflow_name, element_id, permissions, result_decorator=None):\\n endpoint = 'get_edge_namespaces'\\n function_endpoint = '%sget_edge_namespaces' % workflow_name\\n view_function = app.view_functions.get(function_endpoint)\\n if view_function is not None:\\n return endpoint\\n\\n if not result_decorator:\\n result_decorator = empty_decorator\\n\\n @route(app, '/%s/%s' % (workflow_name, endpoint), methods=['POST'])\\n @rest_workflow_permission_required(permissions)\\n @rest_exception_catcher\\n # pylint: disable=unused-variable\\n def get_edge_namespaces():\\n \\\"\\\"\\\"\\n Exposed endpoint for retrieving networks by hint.\\n Used for populating autocomplete fields, does not return all information.\\n \\\"\\\"\\\"\\n hint = request.form['namespaces']\\n\\n result = {'status': FAIL, 'message': '', 'data': {}}\\n try:\\n result['status'] = SUCCESS\\n result['data']['autocomplete_field'] = []\\n result['data']['select_field'] = []\\n if hint != '':\\n edge_session = edge(edge_create_internal_ns_configuration.edge_url,\\n edge_create_internal_ns_configuration.client_id,\\n edge_create_internal_ns_configuration.clientSecret)\\n\\n namespaces = edge_session.get_namespaces()\\n count = 0\\n for namespace in namespaces:\\n if namespace['name'].startswith(hint):\\n\\n result['data']['autocomplete_field'].append({\\n 'input': namespace['id'],\\n 'value': '%s (%s)' % (namespace['name'], namespace['id'])\\n })\\n result['data']['select_field'].append({\\n 'id': namespace['id'],\\n 'txt': namespace['name']\\n })\\n if count == 10:\\n break\\n count += 1\\n except Exception as e:\\n result['status'] = FAIL\\n result['message'] = 'Error while searching for Namespaces: %s and hint: %s!' % (util.safe_str(e), hint)\\n return result\\n\\n return endpoint\",\n \"def namespaces(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:\\n return pulumi.get(self, \\\"namespaces\\\")\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.66033626","0.65595657","0.6453324","0.632987","0.63076866","0.62413985","0.61975986","0.61743927","0.6086349","0.604603","0.60328054","0.6023155","0.6000177","0.59959286","0.59717184","0.59505755","0.5948675","0.5923967","0.5918093","0.5893192","0.5852171","0.5851812","0.5849848","0.57639766","0.57189506","0.5714099","0.57124686","0.5711624","0.5691262","0.56867975","0.56779796","0.5664741","0.5644136","0.563603","0.5635638","0.5625232","0.5594758","0.5589974","0.55517924","0.55449986","0.5513485","0.5512695","0.5484204","0.5454294","0.54488933","0.5434983","0.5396366","0.53772646","0.53768826","0.5370249","0.5351303","0.53474385","0.5334766","0.5319315","0.53173804","0.5313578","0.53082186","0.5307137","0.53067815","0.53019774","0.530182","0.5298568","0.52892643","0.52881914","0.5254543","0.5243453","0.52433103","0.52327394","0.5232076","0.5222607","0.5220072","0.5217499","0.5215827","0.5213426","0.5183504","0.51783735","0.5159816","0.51534927","0.5150231","0.5134058","0.5128859","0.5125734","0.5118521","0.51132256","0.51081973","0.5097057","0.50960135","0.509347","0.508436","0.5079284","0.5076587","0.507618","0.50708485","0.5064603","0.50645304","0.5064142","0.50616604","0.5056981","0.5056399","0.5053023"],"string":"[\n \"0.66033626\",\n \"0.65595657\",\n \"0.6453324\",\n \"0.632987\",\n \"0.63076866\",\n \"0.62413985\",\n \"0.61975986\",\n \"0.61743927\",\n \"0.6086349\",\n \"0.604603\",\n \"0.60328054\",\n \"0.6023155\",\n \"0.6000177\",\n \"0.59959286\",\n \"0.59717184\",\n \"0.59505755\",\n \"0.5948675\",\n \"0.5923967\",\n \"0.5918093\",\n \"0.5893192\",\n \"0.5852171\",\n \"0.5851812\",\n \"0.5849848\",\n \"0.57639766\",\n \"0.57189506\",\n \"0.5714099\",\n \"0.57124686\",\n \"0.5711624\",\n \"0.5691262\",\n \"0.56867975\",\n \"0.56779796\",\n \"0.5664741\",\n \"0.5644136\",\n \"0.563603\",\n \"0.5635638\",\n \"0.5625232\",\n \"0.5594758\",\n \"0.5589974\",\n \"0.55517924\",\n \"0.55449986\",\n \"0.5513485\",\n \"0.5512695\",\n \"0.5484204\",\n \"0.5454294\",\n \"0.54488933\",\n \"0.5434983\",\n \"0.5396366\",\n \"0.53772646\",\n \"0.53768826\",\n \"0.5370249\",\n \"0.5351303\",\n \"0.53474385\",\n \"0.5334766\",\n \"0.5319315\",\n \"0.53173804\",\n \"0.5313578\",\n \"0.53082186\",\n \"0.5307137\",\n \"0.53067815\",\n \"0.53019774\",\n \"0.530182\",\n \"0.5298568\",\n \"0.52892643\",\n \"0.52881914\",\n \"0.5254543\",\n \"0.5243453\",\n \"0.52433103\",\n \"0.52327394\",\n \"0.5232076\",\n \"0.5222607\",\n \"0.5220072\",\n \"0.5217499\",\n \"0.5215827\",\n \"0.5213426\",\n \"0.5183504\",\n \"0.51783735\",\n \"0.5159816\",\n \"0.51534927\",\n \"0.5150231\",\n \"0.5134058\",\n \"0.5128859\",\n \"0.5125734\",\n \"0.5118521\",\n \"0.51132256\",\n \"0.51081973\",\n \"0.5097057\",\n \"0.50960135\",\n \"0.509347\",\n \"0.508436\",\n \"0.5079284\",\n \"0.5076587\",\n \"0.507618\",\n \"0.50708485\",\n \"0.5064603\",\n \"0.50645304\",\n \"0.5064142\",\n \"0.50616604\",\n \"0.5056981\",\n \"0.5056399\",\n \"0.5053023\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.57417464"},"document_rank":{"kind":"string","value":"24"}}},{"rowIdx":14377,"cells":{"query":{"kind":"string","value":"Fetch deployment listing and detail"},"document":{"kind":"string","value":"def get_deployment_data(cluster_id, namespace_id=None, deployment_id=None):\n # deployment detail\n if deployment_id and namespace_id is not None:\n # creating cell-pod mapping for getting cell details\n cell_pod_map = get_cell_pod_map(cluster_id)\n # getting pod count\n pods_data = [pod for pod in get_pod_data(cluster_id, namespace_id, deployment_id)]\n core_api = client.CoreV1Api()\n apps_api = client.AppsV1Api()\n deployment_cell_list = list()\n deployment_pod_list = list()\n for pod_name in pods_data:\n if pod_name['name'] in cell_pod_map:\n namespaced_pod_info = core_api.read_namespaced_pod(pod_name['name'], namespace_id).metadata.owner_references[0]\n if namespaced_pod_info.kind == 'ReplicaSet':\n replica_set = apps_api.read_namespaced_replica_set(core_api.read_namespaced_pod(\n pod_name['name'], namespace_id).metadata.owner_references[0].name, namespace_id)\n if replica_set.metadata.owner_references[0].name == deployment_id and pod_name['name'] in cell_pod_map \\\n and pod_name['status']=='Running':\n # fetching pods based on deployment\n deployment_pod_list.append(pod_name['name'])\n # fetching cells based on pods and deployment\n deployment_cell_list.append(cell_pod_map[pod_name['name']]['cell_name'])\n else:\n continue\n else:\n continue\n # if there are no pods for the passed deployment\n if len(deployment_pod_list) == 0:\n pods_for_resource_calculation = 'no_pod_resource'\n else:\n pods_for_resource_calculation = deployment_pod_list\n\n deployments_info = {\n 'resource_count': {\n 'cells': len(deployment_cell_list),\n 'pods': len(deployment_pod_list)\n },\n 'resource_info': get_resource_info(cluster_id, 'pods', namespace_id, pods_for_resource_calculation)\n }\n\n # deployment listing\n else:\n if namespace_id:\n deployments_info = [{'value': deployment_item.metadata.name, 'label': deployment_item.metadata.name}\n for deployment_item in\n client.AppsV1beta2Api().list_namespaced_deployment(namespace_id).items]\n else:\n deployments_info = [{'value': deployment_item.metadata.name, 'label': deployment_item.metadata.name}\n for deployment_item in\n client.AppsV1beta2Api().list_deployment_for_all_namespaces().items]\n return deployments_info"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def list_deployments() -> JSONResponse:\n\n deploy_manager = DeployManager()\n deployments = deploy_manager.list()\n return JSONResponse(deployments)","def test_get_deployments(self):\n pass","def test_get_deployments(self):\n pass","def detail(request, deployment_id):\n if request.method == \"GET\": \n try:\n dep = Deployment.objects.get(pk=deployment_id)\n return JsonResponse(serializers.serialize(\"json\", [dep]), safe=False)\n except ObjectDoesNotExist as e:\n return JsonResponse({'message': 'Error retriving deployment.'})\n else:\n return JsonResponse({'message':'not found'}, status=404)","def view_deployment(request, deployment, **_kwargs):\n pass","def test_get_deployment(self):\n pass","def test_get_deployment(self):\n pass","def test_get_deployment_resources(self):\n pass","def deployments(self) -> List[Dict]:\n return [\n {\n 'name': self.name,\n 'head_host': self.head_host,\n 'head_port': self.head_port,\n }\n ]","def test_get_deployments_expanded(self):\n pass","def handle_get_deployment(project_id, deployment_id):\n return jsonify(get_deployment(uuid=deployment_id, project_id=project_id))","def handle_list_deployments(project_id):\n return jsonify(list_deployments(project_id=project_id))","def list_deployment_config(self, **kwargs):\n\n all_params = ['pretty', 'label_selector', 'field_selector', 'watch', 'resource_version', 'timeout_seconds']\n all_params.append('callback')\n\n params = locals()\n for key, val in iteritems(params['kwargs']):\n if key not in all_params:\n raise TypeError(\n \"Got an unexpected keyword argument '%s'\"\n \" to method list_deployment_config\" % key\n )\n params[key] = val\n del params['kwargs']\n\n\n resource_path = '/oapi/v1/deploymentconfigs'.replace('{format}', 'json')\n path_params = {}\n\n query_params = {}\n if 'pretty' in params:\n query_params['pretty'] = params['pretty']\n if 'label_selector' in params:\n query_params['labelSelector'] = params['label_selector']\n if 'field_selector' in params:\n query_params['fieldSelector'] = params['field_selector']\n if 'watch' in params:\n query_params['watch'] = params['watch']\n if 'resource_version' in params:\n query_params['resourceVersion'] = params['resource_version']\n if 'timeout_seconds' in params:\n query_params['timeoutSeconds'] = params['timeout_seconds']\n\n header_params = {}\n\n form_params = []\n local_var_files = {}\n\n body_params = None\n\n # HTTP header `Accept`\n header_params['Accept'] = self.api_client.\\\n select_header_accept(['application/json', 'application/yaml'])\n if not header_params['Accept']:\n del header_params['Accept']\n\n # HTTP header `Content-Type`\n header_params['Content-Type'] = self.api_client.\\\n select_header_content_type(['*/*'])\n\n # Authentication setting\n auth_settings = []\n\n response = self.api_client.call_api(resource_path, 'GET',\n path_params,\n query_params,\n header_params,\n body=body_params,\n post_params=form_params,\n files=local_var_files,\n response_type='V1DeploymentConfigList',\n auth_settings=auth_settings,\n callback=params.get('callback'))\n return response","def test_get_deployment_runs(self):\n pass","def test_get_deployment_runs1(self):\n pass","def get(self):\n deploys = get_heroku_deployments('SERVER GOES HERE!')\n write_msg(deploys)\n return deploys","def ls(_):\n client = utils.s3_client()\n\n projects = []\n\n config = utils.get_config()\n bucket = config[\"release\"][\"s3_bucket\"]\n deploys = config[\"deploy\"]\n\n resp = client.list_objects_v2(Bucket=bucket)\n for data in resp.get(\"Contents\", []):\n name = data[\"Key\"]\n\n projects.append(name)\n\n projects = sorted(projects)\n\n _projects = []\n\n for name in projects:\n try:\n release = get_release(client, bucket, name)\n except InvalidRelease:\n continue\n\n data = {\n \"Name\": name,\n \"Latest Release\": f\"v{release.version} {release.timestamp} ({release.commit})\",\n }\n\n for env_name, cfg in deploys.items():\n env_version, env_commit, env_timestamp = get_deployed_version(\n client, cfg[\"s3_bucket\"], name\n )\n\n data[env_name.title()] = f\"v{env_version} {env_timestamp} ({env_commit})\"\n\n _projects.append(data)\n\n projects = _projects\n\n utils.printfmt(projects)","def test_get_deployment_resource(self):\n pass","def test_get_deployment_run(self):\n pass","def test_get_deployment_resource_data(self):\n pass","def list_server_deployment(self, feed_id=None):\n resources = self.list_resource(feed_id=feed_id, resource_type_id='Deployment')\n resources.extend(self.list_resource(\n feed_id=feed_id,\n resource_type_id='SubDeployment'))\n deployments = []\n if resources:\n for resource in resources:\n deployments.append(Deployment(resource.id, resource.name, resource.path))\n return deployments","def list(deployment_id, all_workflows, logger, client, tenant_name):\n utils.explicit_tenant_name_message(tenant_name, logger)\n logger.info('Listing workflows for deployment %s...', deployment_id)\n deployment = client.deployments.get(deployment_id)\n\n workflows = sorted(deployment.workflows, key=lambda w: w.name)\n columns = WORKFLOW_COLUMNS\n hidden_count = 0\n if not all_workflows:\n total_count = len(workflows)\n workflows = [wf for wf in workflows if wf.is_available]\n hidden_count = total_count - len(workflows)\n else:\n columns = columns + ['is_available']\n\n defaults = {\n 'blueprint_id': deployment.blueprint_id,\n 'deployment_id': deployment.id\n }\n if not get_global_json_output():\n workflows = [_format_workflow(wf) for wf in workflows]\n print_data(columns, workflows, 'Workflows:', defaults=defaults)\n if hidden_count:\n logger.info('%d unavailable workflows hidden (use --all to show)',\n hidden_count)","def list_server_deployment(self, feed_id=None):\n deployments = self.list_resource(feed_id=feed_id,\n resource_type_id='Deployment',\n cls=Deployment,\n list_children=True)\n deployments.extend(self.list_resource(\n feed_id=feed_id,\n resource_type_id='SubDeployment',\n cls=Deployment,\n list_children=True))\n return deployments","def describe_deployments(StackId=None, AppId=None, DeploymentIds=None):\n pass","def test_list_namespaced_deployment_config(self):\n pass","def resolve_deployment(self, args, context, info):\n params = {\n 'id': self.deployment_id,\n }\n return DeploymentLoader.get().load(params)[0]","def get_environment_list(name, filter_by, limit, offset, quiet, out, project_name):\n\n client = get_api_client()\n\n params = {\"length\": limit, \"offset\": offset}\n filter_query = \"\"\n if name:\n filter_query = get_name_query([name])\n if filter_by:\n filter_query = filter_query + \";(\" + filter_by + \")\"\n if project_name:\n project_data = get_project(project_name)\n project_id = project_data[\"metadata\"][\"uuid\"]\n filter_query = filter_query + \";project_reference=={}\".format(project_id)\n if filter_query.startswith(\";\"):\n filter_query = filter_query[1:]\n\n if filter_query:\n params[\"filter\"] = filter_query\n\n res, err = client.environment.list(params=params)\n\n if err:\n context = get_context()\n server_config = context.get_server_config()\n pc_ip = server_config[\"pc_ip\"]\n\n LOG.warning(\"Cannot fetch environments from {}\".format(pc_ip))\n return\n\n if out == \"json\":\n click.echo(json.dumps(res.json(), indent=4, separators=(\",\", \": \")))\n return\n\n json_rows = res.json()[\"entities\"]\n if not json_rows:\n click.echo(highlight_text(\"No environment found !!!\\n\"))\n return\n\n if quiet:\n for _row in json_rows:\n row = _row[\"status\"]\n click.echo(highlight_text(row[\"name\"]))\n return\n\n table = PrettyTable()\n table.field_names = [\n \"NAME\",\n \"PROJECT\",\n \"STATE\",\n \"CREATED ON\",\n \"LAST UPDATED\",\n \"UUID\",\n ]\n for _row in json_rows:\n row = _row[\"status\"]\n metadata = _row[\"metadata\"]\n\n project = (\n metadata[\"project_reference\"][\"name\"]\n if \"project_reference\" in metadata\n else None\n )\n\n creation_time = int(metadata[\"creation_time\"]) // 1000000\n last_update_time = int(metadata[\"last_update_time\"]) // 1000000\n\n table.add_row(\n [\n highlight_text(row[\"name\"]),\n highlight_text(project),\n highlight_text(row[\"state\"]),\n highlight_text(time.ctime(creation_time)),\n \"{}\".format(arrow.get(last_update_time).humanize()),\n highlight_text(row.get(\"uuid\", \"\")),\n ]\n )\n click.echo(table)","def _service_list(res, ctx):\n\n if _has_error_code(res):\n return print_errors(res, ctx)\n\n max_len = {'name': 10,\n 'flags': 5,\n 'deployment': 20,\n 'config': 10}\n\n deploys = res['deploys'].values()\n if len(deploys) == 0:\n return \"\"\n\n for deploy in deploys:\n for key in ('name', 'deployment', 'config'):\n max_len[key] = max(len(deploy.get(key, '')), max_len.get(key, 0))\n\n lines = []\n header = \" \".join((\"ID\".ljust(max_len['name'], ' '),\n \"FLAGS\",\n 'DEPLOY'.ljust(max_len['deployment'], ' '),\n 'CONFIG'.ljust(max_len['config'], ' ')))\n lines.append(header)\n\n for deploy in res['deploys'].values():\n line = \" \".join((deploy['name'].ljust(max_len['name'], ' '),\n _build_flags(deploy).ljust(max_len['flags'], ' '),\n deploy['deployment'].ljust(max_len['deployment'], ' '),\n deploy['config'].ljust(max_len['config'], ' ')))\n lines.append(line)\n\n return \"\\n\".join(lines)","def test_list_deployment_config_for_all_namespaces(self):\n pass","def list_bundles():\n response = houston.get(\"/zipline/bundles\")\n\n houston.raise_for_status_with_json(response)\n return response.json()","def list():\n index = config.index\n output_format = \"%-7s %-20s %s\"\n click.secho(output_format % (\"ID\", \"CREATED\", \"BACKENDS\"), fg=\"cyan\")\n for archive in sorted(index.archives(), key=lambda x: x[\"id\"]):\n # Print it out\n click.echo(\n output_format\n % (\n archive[\"id\"],\n datetime.datetime.fromtimestamp(archive[\"created\"]).strftime(\n \"%Y-%m-%d %H:%M:%S\"\n ),\n \", \".join(sorted(archive[\"backend_names\"])),\n )\n )","def archive_list():\n data = list_archived_services()\n\n table = present(lambda: data,\n renderer='table',\n headers=['Id', 'Service Name', 'Memory(Mi)', 'Created At'],\n columns=['id', 'name', 'memory', 'created_at'])\n if table:\n click.echo(table)\n else:\n\n click.echo('\\nYou have no archived services right now, why don\\'t you try deploying one? \\n'\n 'have fun and follow the link below:\\n')\n click.echo('https://docs.fandogh.cloud/docs/services.html\\n')","def list_cube_deployment(\n self, req: typing.Optional[dict] = None, **kwargs\n ) -> dict:\n # build request\n d = {\n \"ProjectId\": self.config.project_id,\n \"Region\": self.config.region,\n }\n req and d.update(req)\n d = apis.ListCubeDeploymentRequestSchema().dumps(d)\n\n # build options\n kwargs[\"max_retries\"] = 0 # ignore retry when api is not idempotent\n\n resp = self.invoke(\"ListCubeDeployment\", d, **kwargs)\n return apis.ListCubeDeploymentResponseSchema().loads(resp)","def test_get_deployment_run_reports(self):\n pass","async def read_deployment_by_name(\n self,\n name: str,\n ) -> DeploymentResponse:\n try:\n response = await self._client.get(f\"/deployments/name/{name}\")\n except httpx.HTTPStatusError as e:\n if e.response.status_code == status.HTTP_404_NOT_FOUND:\n raise prefect.exceptions.ObjectNotFound(http_exc=e) from e\n else:\n raise\n\n return DeploymentResponse.parse_obj(response.json())","def do_list(cs, args):\n data = []\n _, repositories = cs.repositories.list(args.project_id)\n for repo in repositories:\n _, tags = cs.repositories.list_tags(repo)\n for tag in tags:\n _, manifests = cs.repositories.get_manifests(repo, tag)\n manifests['Name'] = repo\n manifests['Tag'] = tag\n manifests['Project'] = args.project_id\n manifests['Id'] = manifests['Id'][0:12]\n data.append(manifests)\n fields = [\n \"Id\", \"Name\", \"Tag\", \"Author\", 'Project', \"Created\", \"Docker Version\",\n \"Architecture\", \"OS\"\n ]\n utils.print_list(data, fields, sortby=args.sortby)","async def read_deployment(\n self,\n deployment_id: UUID,\n ) -> DeploymentResponse:\n try:\n response = await self._client.get(f\"/deployments/{deployment_id}\")\n except httpx.HTTPStatusError as e:\n if e.response.status_code == status.HTTP_404_NOT_FOUND:\n raise prefect.exceptions.ObjectNotFound(http_exc=e) from e\n else:\n raise\n return DeploymentResponse.parse_obj(response.json())","def getEnvironments(request):\n environments = Environment.objects.all()\n serializer = environmentSerializer(environments, many=True)\n result = {'data':serializer.data, 'code':HTTP_200_OK, 'message':OK}\n return result","def get_deployment_output(account_name: Optional[pulumi.Input[str]] = None,\n deployment_name: Optional[pulumi.Input[str]] = None,\n resource_group_name: Optional[pulumi.Input[str]] = None,\n opts: Optional[pulumi.InvokeOptions] = None) -> pulumi.Output[GetDeploymentResult]:\n ...","def deployment_fetch_status(self) -> Optional[pulumi.Input[Union[str, 'DeploymentFetchStatus']]]:\n return pulumi.get(self, \"deployment_fetch_status\")","def test_get_deployment_runs_in_virtualization_realm(self):\n pass","def deploy():","def watch_deployment_config_list(self, **kwargs):\n\n all_params = ['pretty', 'label_selector', 'field_selector', 'watch', 'resource_version', 'timeout_seconds']\n all_params.append('callback')\n\n params = locals()\n for key, val in iteritems(params['kwargs']):\n if key not in all_params:\n raise TypeError(\n \"Got an unexpected keyword argument '%s'\"\n \" to method watch_deployment_config_list\" % key\n )\n params[key] = val\n del params['kwargs']\n\n\n resource_path = '/oapi/v1/watch/deploymentconfigs'.replace('{format}', 'json')\n path_params = {}\n\n query_params = {}\n if 'pretty' in params:\n query_params['pretty'] = params['pretty']\n if 'label_selector' in params:\n query_params['labelSelector'] = params['label_selector']\n if 'field_selector' in params:\n query_params['fieldSelector'] = params['field_selector']\n if 'watch' in params:\n query_params['watch'] = params['watch']\n if 'resource_version' in params:\n query_params['resourceVersion'] = params['resource_version']\n if 'timeout_seconds' in params:\n query_params['timeoutSeconds'] = params['timeout_seconds']\n\n header_params = {}\n\n form_params = []\n local_var_files = {}\n\n body_params = None\n\n # HTTP header `Accept`\n header_params['Accept'] = self.api_client.\\\n select_header_accept(['application/json'])\n if not header_params['Accept']:\n del header_params['Accept']\n\n # HTTP header `Content-Type`\n header_params['Content-Type'] = self.api_client.\\\n select_header_content_type(['*/*'])\n\n # Authentication setting\n auth_settings = []\n\n response = self.api_client.call_api(resource_path, 'GET',\n path_params,\n query_params,\n header_params,\n body=body_params,\n post_params=form_params,\n files=local_var_files,\n response_type='JsonWatchEvent',\n auth_settings=auth_settings,\n callback=params.get('callback'))\n return response","def deployment_properties(self) -> pulumi.Output[Optional['outputs.EnvironmentDeploymentPropertiesResponse']]:\n return pulumi.get(self, \"deployment_properties\")","def get_deployments(self, ApiId: str, MaxResults: str = None, NextToken: str = None) -> Dict:\n pass","def deployment_configs(self) -> pulumi.Output['outputs.PagesProjectDeploymentConfigs']:\n return pulumi.get(self, \"deployment_configs\")","def deploy(request):\n data = json.loads(request.GET.get(\"data\"))\n host = data[\"host\"]\n\n if host[\"deployment_environment\"] == \"docker\":\n tc_file = \"TC_microservices_host_docker_operations.xml\"\n elif host[\"deployment_environment\"] == \"kubernetes\":\n tc_file = \"TC_microservices_host_kubernetes_operations.xml\"\n if not data[\"host\"][\"scripts\"]:\n data[\"host\"][\"scripts\"] = \";\"\n\n generate_host_system_data(data[\"host\"])\n\n image = data[\"registry\"][\"image_name\"]\n if data[\"registry\"][\"image_tag\"]:\n image = \"{}:{}\".format(image, data[\"registry\"][\"image_tag\"])\n data[\"registry\"][\"image\"] = image\n if data[\"registry\"][\"port\"]:\n data[\"registry\"][\"address\"] = \"{}:{}\".format(data[\"registry\"][\"address\"],\n data[\"registry\"][\"port\"])\n generate_registry_operations(data)\n\n return StreamingHttpResponse(stream(tc_file))","def test_get_deployments_count(self):\n pass","def history_list(name):\n service_histories = request_service_history(name)\n table = present(lambda: service_histories,\n renderer='table',\n headers=['History Version', 'Service Name', 'Date Created', 'Manifest'],\n columns=['id', 'name', 'created_at', 'manifest'])\n if table:\n click.echo(table)\n else:\n click.echo('There is no record of your service deployments available.')\n # click.echo('https://docs.fandogh.cloud/docs/services.html\\n')","def deploy_environments(self):\n for key in self.deploy:\n yield key","def GET(self, env, start_response):\n qs = env.get('QUERY_STRING', '')\n args = urlparse.parse_qs(qs, 1)\n\n key_args = set(['cors','lifecycle', 'policy', 'logging', 'notification',\n 'tagging', 'requestPayment', 'versioning', 'versions',\n 'website', 'location'])\n\n if not key_args & set(args):\n # GET bucket to list objects\n max_keys = self.MAX_BUCKET_LISTING\n if 'max-keys' in args:\n if args.get('max-keys')[0].isdigit() is False:\n return self.get_err_response('InvalidArgument')\n max_keys = min(int(args.get('max-keys')[0]), self.MAX_BUCKET_LISTING)\n\n\n if 'acl' not in args:\n #acl request sent with format=json etc confuses swift\n env['QUERY_STRING'] = 'format=json&limit=%s' % (max_keys + 1)\n if 'marker' in args:\n env['QUERY_STRING'] += '&marker=%s' % quote(args['marker'])\n if 'prefix' in args:\n env['QUERY_STRING'] += '&prefix=%s' % quote(args['prefix'])\n if 'delimiter' in args:\n env['QUERY_STRING'] += '&delimiter=%s' % quote(args['delimiter'])\n body_iter = self._app_call(env)\n if env['REQUEST_METHOD'] == 'HEAD':\n body_iter = ''\n status = self._get_status_int()\n headers = dict(self._response_headers)\n\n if is_success(status) and 'acl' in args:\n return self.get_acl(self.account_name, headers)\n\n if 'versioning' in args:\n # Just report there is no versioning configured here.\n body = ('<VersioningConfiguration '\n 'xmlns=\"http://s3.amazonaws.com/doc/2006-03-01/\"/>')\n return Response(body=body, content_type=\"text/plain\")\n\n if status != HTTP_OK:\n if status in (HTTP_UNAUTHORIZED, HTTP_FORBIDDEN):\n return self.get_err_response('AccessDenied')\n elif status == HTTP_NOT_FOUND:\n return self.get_err_response('NoSuchBucket')\n else:\n return self.get_err_response('InvalidURI')\n\n if 'location' in args:\n body = ('<?xml version=\"1.0\" encoding=\"UTF-8\"?>'\n '<LocationConstraint '\n 'xmlns=\"http://s3.amazonaws.com/doc/2006-03-01/\"')\n if self.location == 'US':\n body += '/>'\n else:\n body += ('>%s</LocationConstraint>' % self.location)\n return Response(body=body, content_type='application/xml')\n\n if 'logging' in args:\n # logging disabled\n body = ('<?xml version=\"1.0\" encoding=\"UTF-8\"?>'\n '<BucketLoggingStatus '\n 'xmlns=\"http://doc.s3.amazonaws.com/2006-03-01\" />')\n return Response(body=body, content_type='application/xml')\n\n objects = loads(''.join(list(body_iter)))\n body = ('<?xml version=\"1.0\" encoding=\"UTF-8\"?>'\n '<ListBucketResult '\n 'xmlns=\"http://s3.amazonaws.com/doc/2006-03-01\">'\n '<Prefix>%s</Prefix>'\n '<Marker>%s</Marker>'\n '<Delimiter>%s</Delimiter>'\n '<IsTruncated>%s</IsTruncated>'\n '<MaxKeys>%s</MaxKeys>'\n '<Name>%s</Name>'\n '%s'\n '%s'\n '</ListBucketResult>' %\n (\n xml_escape(args.get('prefix', '')),\n xml_escape(args.get('marker', '')),\n xml_escape(args.get('delimiter', '')),\n 'true' if max_keys > 0 and len(objects) == (max_keys + 1) else\n 'false',\n max_keys,\n xml_escape(self.container_name),\n \"\".join(['<Contents><Key>%s</Key><LastModified>%sZ</LastModif'\n 'ied><ETag>%s</ETag><Size>%s</Size><StorageClass>STA'\n 'NDARD</StorageClass><Owner><ID>%s</ID><DisplayName>'\n '%s</DisplayName></Owner></Contents>' %\n (xml_escape(unquote(i['name'])), i['last_modified'],\n i['hash'],\n i['bytes'], self.account_name, self.account_name)\n for i in objects[:max_keys] if 'subdir' not in i]),\n \"\".join(['<CommonPrefixes><Prefix>%s</Prefix></CommonPrefixes>'\n % xml_escape(i['subdir'])\n for i in objects[:max_keys] if 'subdir' in i])))\n return Response(body=body, content_type='application/xml')\n else:\n # GET specified data\n #env['REQUEST_METHOD'] = 'HEAD'\n body_iter = self._app_call(env)\n status = self._get_status_int()\n headers = dict(self._response_headers)\n\n action = args.keys().pop()\n if action == 'acl':\n # get acl\n # get policy\n acl = headers.get('X-Container-Meta-Policy') or ''\n\n if is_success(status):\n if acl:\n return Response(status=HTTP_OK, content_type='application/xml', body=unquote(acl))\n else:\n return self.get_err_response('NotSuchPolicy')\n\n elif status in (HTTP_UNAUTHORIZED, HTTP_FORBIDDEN):\n return self.get_err_response('AccessDenied')\n else:\n return self.get_err_response('InvalidURI')\n elif action == 'cors':\n # get cors\n _headers = set(['X-Container-Meta-Access-Control-Expose-Headers',\n 'X-Container-Meta-Access-Control-Allow-Origin',\n 'X-Container-Meta-Access-Control-Max-Age',\n 'X-Container-Meta-Access-Control-Allow-Method'])\n bodye = etree.Element('CORSConfiguration')\n if _headers & set(headers):\n rule = etree.Element('CORSRule')\n if 'X-Container-Meta-Access-Control-Expose-Headers' in headers:\n valuel = headers['X-Container-Meta-Access-Control-Expose-Headers'].split(',')\n for i in valuel:\n eh = self.create_elem('ExposeHeader', i)\n rule.append(eh)\n if 'X-Container-Meta-Access-Control-Allow-Origin' in headers:\n valuel = headers['X-Container-Meta-Access-Control-Allow-Origin'].split(',')\n for i in valuel:\n ao = self.create_elem('AllowedOrigin', i)\n rule.append(ao)\n if 'X-Container-Meta-Access-Control-Max-Age' in headers:\n valuel = headers['X-Container-Meta-Access-Control-Max-Age'].split(',')\n for i in valuel:\n ma = self.create_elem('MaxAgeSeconds', i)\n rule.append(ma)\n if 'X-Container-Meta-Access-Control-Allow-Method' in headers:\n valuel = headers['X-Container-Meta-Access-Control-Allow-Method'].split(',')\n for i in valuel:\n al = self.create_elem('AllowedMethod', i)\n rule.append(al)\n rule.append(self.create_elem('ID', 'unique_rule'))\n bodye.append(rule)\n else:\n bodye.text = ''\n\n if is_success(status):\n return Response(status=HTTP_OK, content_type='application/xml', body=self.elem2xmlbody(bodye))\n elif status in (HTTP_UNAUTHORIZED, HTTP_FORBIDDEN):\n return self.get_err_response('AccessDenied')\n else:\n return self.get_err_response('InvalidURI')\n\n elif action == 'lifecycle':\n # get lifecycle\n bodye = etree.Element('LifecycleConfiguration')\n if 'X-Container-Meta-Expiration-Status' in headers:\n rule = etree.Element('Rule')\n rule.append(self.create_elem('Status', headers['X-Container-Meta-Expiration-Status']))\n rule.append(self.create_elem('ID', 'unique_rule'))\n if 'X-Container-Meta-Expiration-Prefix' in headers:\n rule.append(self.create_elem('Prefix', headers['X-Container-Meta-Expiration-Prefix']))\n if 'X-Container-Meta-Expiration-At' in headers or \\\n 'X-Container-Meta-Expiration-After' in headers:\n expir = etree.Element('Expiration')\n if 'X-Container-Meta-Expiration-At' in headers:\n expir.append(self.create_elem('Date', headers['X-Container-Meta-Expiration-At']))\n if 'X-Container-Meta-Expiration-After' in headers:\n expir.append(self.create_elem('Days', headers['X-Container-Meta-Expiration-After']))\n rule.append(expir)\n if 'X-Container-Meta-Trans-Class' in headers:\n trans = etree.Element('Transition')\n cls = self.create_elem('StorageClass', headers['X-Container-Meta-Trans-Class'])\n trans.append(cls)\n if 'X-Container-Meta-Trans-At' in headers:\n trans.append(self.create_elem('Date', headers['X-Container-Meta-Trans-At']))\n if 'X-Container-Meta-Trans-After' in headers:\n trans.append(self.create_elem('Days', headers['X-Container-Meta-Trans-After']))\n rule.append(trans)\n bodye.append(rule)\n else:\n bodye.text = ''\n\n if is_success(status):\n return Response(status=HTTP_OK, content_type='application/xml', body=self.elem2xmlbody(bodye))\n elif status in (HTTP_UNAUTHORIZED, HTTP_FORBIDDEN):\n return self.get_err_response('AccessDenied')\n else:\n return self.get_err_response('InvalidURI')\n\n elif action == 'policy':\n # get policy\n json = headers.get('X-Container-Meta-Policy') or ''\n\n if is_success(status):\n if json:\n return Response(status=HTTP_OK, content_type='application/json', body=unquote(json))\n else:\n return self.get_err_response('NotSuchPolicy')\n\n elif status in (HTTP_UNAUTHORIZED, HTTP_FORBIDDEN):\n return self.get_err_response('AccessDenied')\n else:\n return self.get_err_response('InvalidURI')\n elif action == 'logging':\n # get logging\n target = headers.get('X-Container-Meta-Logging-Target') or ''\n prefix = headers.get('X-Container-Meta-Logging-Prefix') or ''\n statuse = etree.Element('BucketLoggingStatus')\n if target:\n enabled = etree.Element('LoggingEnabled')\n target_bucket = self.create_elem('TargetBucket', target)\n if prefix:\n target_prefix = self.create_elem('TargetPrefix', prefix)\n enabled.append(target_bucket)\n enabled.append(target_prefix)\n statuse.append(enabled)\n else:\n pass # set text None\n\n if is_success(status):\n return Response(status=HTTP_OK, content_type='application/xml', body=self.elem2xmlbody(statuse))\n elif status in (HTTP_UNAUTHORIZED, HTTP_FORBIDDEN):\n return self.get_err_response('AccessDenied')\n else:\n return self.get_err_response('InvalidURI')\n elif action == 'notification':\n # get it\n topic = headers.get('X-Container-Meta-Noti-Topic')\n event = headers.get('X-Container-Meta-Noti-Event')\n if is_success(status):\n if topic:\n body = ('<WebsiteConfiguration xmlns=\"http://s3.amazonaws.com/doc/2006-03-01/\">'\n '<NotificationConfiguration> '\n '<TopicConfiguration>'\n '<Topic>%s</Topic>'\n '<Event>%s</Event>'\n '</TopicConfiguration>'\n '</NotificationConfiguration>',\n topic, event)\n return Response(status=HTTP_OK, content_type='application/xml', body=body)\n else:\n return self.get_err_response('NotSuchWebsite')\n\n elif status in (HTTP_UNAUTHORIZED, HTTP_FORBIDDEN):\n return self.get_err_response('AccessDenied')\n else:\n return self.get_err_response('InvalidURI')\n elif action == 'tagging':\n # get tagging\n Tagging = etree.Element('Tagging')\n TagSet = etree.Element('TagSet')\n meta_keys = [header[21:] for header in headers if header.startswith('X-Container-Meta-Tag-')]\n for key in meta_keys:\n Tag = etree.Element('Tag')\n keyvalues = headers['X-Container-Meta-Tag-' + key]\n _key = keyvalues[:len(key)]\n _value = keyvalues[len(key):]\n Tag.append(self.create_elem('Key', _key))\n Tag.append(self.create_elem('Value', _value))\n TagSet.append(Tag)\n Tagging.append(TagSet)\n if is_success(status):\n return Response(status=HTTP_OK, content_type='application/xml', body=self.elem2xmlbody(Tagging))\n elif status in (HTTP_UNAUTHORIZED, HTTP_FORBIDDEN):\n return self.get_err_response('AccessDenied')\n else:\n return self.get_err_response('InvalidURI')\n\n elif action == 'requestPayment':\n # get it\n # default value is BucketOwner\n pay = headers.get('X-Container-Meta-Payment', 'BucketOwner')\n if is_success(status):\n if pay:\n return Response(status=HTTP_OK, content_type='application/xml', body=unquote(pay))\n else:\n return self.get_err_response('NotSuchWebsite')\n\n elif status in (HTTP_UNAUTHORIZED, HTTP_FORBIDDEN):\n return self.get_err_response('AccessDenied')\n else:\n return self.get_err_response('InvalidURI')\n\n elif action == 'versioning':\n versioning = 'Enabled' if 'X-Versions-Location' in headers else 'Suspended'\n bodye = etree.Element('VersioningConfiguration')\n stat = self.create_elem('Status', versioning)\n bodye.append(stat)\n if is_success(status):\n return Response(status=HTTP_OK, content_type='application/xml', body=self.elem2xmlbody(bodye))\n elif status in (HTTP_UNAUTHORIZED, HTTP_FORBIDDEN):\n return self.get_err_response('AccessDenied')\n else:\n return self.get_err_response('InvalidURI')\n elif action == 'website':\n # get website\n website = headers.get('X-Container-Meta-Website')\n fake = ('<WebsiteConfiguration xmlns=\"http://s3.amazonaws.com/doc/2006-03-01/\">'\n '<IndexDocument>'\n '<Suffix>index.html</Suffix>'\n '</IndexDocument>'\n '<ErrorDocument>'\n '<Key>SomeErrorDocument.html</Key>'\n '</ErrorDocument>'\n '</WebsiteConfiguration>')\n\n if is_success(status):\n if website:\n # return fake data\n return Response(status=HTTP_OK, content_type='application/xml', body=fake)\n else:\n return self.get_err_response('NotSuchWebsite')\n\n elif status in (HTTP_UNAUTHORIZED, HTTP_FORBIDDEN):\n return self.get_err_response('AccessDenied')\n else:\n return self.get_err_response('InvalidURI')\n elif action == 'location':\n bodye = self.create_elem('LocationConstraint', 'CN')\n return Response(status=HTTP_OK, content_type='application/xml', body=self.elem2xmlbody(bodye))\n elif action == 'versions':\n # get versions container\n path = '/v1/AUTH_%s/%s' % (self.account_name, self.container_name)\n env = copyenv(env, method='GET', path=path, query_string='')\n body_iter = self._app_call(env)\n status = self._get_status_int()\n\n # get origin container\n path = '/v1/AUTH_%s/%s' % (quote(self.account_name), quote(self.version_name(self.container_name)))\n env2 = copyenv(env, method='GET', path=path, query_string='')\n body_iter2 = self._app_call(env2)\n status2 = self._get_status_int()\n\n last = list(body_iter)\n history = list(body_iter2)\n res = etree.Element('ListVersionsResult')\n bucket = self.create_elem('Name', self.container_name)\n res.append(bucket)\n if last:\n last = [i for i in last[0].split('\\n') if i]\n for i in last:\n ver = etree.Element('Version')\n ver.append(self.create_elem('Key', i))\n ver.append(self.create_elem('VersionId', 'lastest'))\n ver.append(self.create_elem('IsLastest', 'true'))\n res.append(ver)\n\n if history:\n history = [i for i in history[0].split('\\n') if i]\n for i in history:\n ver = etree.Element('Version')\n ver.append(self.create_elem('Key', i.split('/')[0][3:]))\n ver.append(self.create_elem('VersionId', i.split('/')[1]))\n ver.append(self.create_elem('IsLastest', 'false'))\n res.append(ver)\n\n if is_success(status) and is_success(status2):\n return Response(status=HTTP_OK, content_type='application/xml', body=self.elem2xmlbody(res))\n elif status in (HTTP_UNAUTHORIZED, HTTP_FORBIDDEN):\n return self.get_err_response('AccessDenied')\n else:\n return self.get_err_response('InvalidURI')\n else:\n return self.get_err_response('InvalidURI')","def list(deployment_id,\n node_name,\n sort_by,\n descending,\n all_tenants,\n logger,\n client,\n tenant_name):\n if tenant_name:\n logger.info('Explicitly using tenant `{0}`'.format(tenant_name))\n try:\n if deployment_id:\n logger.info('Listing instances for deployment {0}...'.format(\n deployment_id))\n else:\n logger.info('Listing all instances...')\n node_instances = client.node_instances.list(\n deployment_id=deployment_id,\n node_name=node_name,\n sort=sort_by,\n is_descending=descending,\n _all_tenants=all_tenants)\n except CloudifyClientError as e:\n if e.status_code != 404:\n raise\n raise CloudifyCliError('Deployment {0} does not exist'.format(\n deployment_id))\n\n print_data(NODE_INSTANCE_COLUMNS, node_instances, 'Node-instances:')","def _get_deployment_friendly_name(auth_header, async_kvstore_client, request_context):\n\n response = yield async_kvstore_client.async_kvstore_get_request(\n constants.META_COLLECTION_NAME, auth_header=auth_header, owner=constants.NOBODY)\n\n if response.code == http.OK:\n response_json = yield response.json()\n defer.returnValue(response_json[0][constants.DEPLOYMENT_FRIENDLY_NAME])\n\n LOGGER.error(\"Unable to fetch deployment friendly name for instance, code={}\".format(response.code))\n defer.returnValue(\"\")","def _get_deployment_logs(namespace, name, tail_lines=TAIL_LINES_DEFAULT):\n pods = []\n try:\n api_response = k8s_client.list_namespaced_pod(namespace, label_selector='release={}'.format(name))\n for api_items in api_response.items:\n pods.append(api_items.metadata.name)\n except ApiException as e:\n print(\"Exception when calling CoreV1Api->list_namespaced_pod: %s\\n\" % e)\n\n # Iterate over list of pods and concatenate logs\n logs = \"\"\n try:\n for pod in pods:\n logs += pod + \"\\n\"\n logs += k8s_client.read_namespaced_pod_log(pod, namespace, tail_lines=tail_lines)\n except ApiException as e:\n logging.error(\"Exception when calling CoreV1Api->read_namespaced_pod_log: %s\\n\" % e)\n return logs","def service_list():\n data = list_services()\n table = present(lambda: data,\n renderer='table',\n headers=['Service Name', 'URLS', 'Service Type', \"Memory Usages\", 'Replicas', 'Started at',\n 'Updated at',\n 'State', 'Restarts'],\n columns=['name', 'urls', 'service_type', 'memory', 'replicas', 'start_date', 'last_update',\n 'state',\n 'service_restarts'])\n if table:\n click.echo(table)\n else:\n click.echo('\\nYou have no running services right now, why don\\'t you try deploying one? \\n'\n 'have fun and follow the link below:\\n')\n click.echo('https://docs.fandogh.cloud/docs/services.html\\n')","async def info(\n self,\n owner: AccAddress = None,\n dseq: int = None,\n ) -> dict:\n if owner is not None and dseq is not None:\n params = {\n \"id.owner\": owner,\n \"id.dseq\": dseq,\n }\n res = await self._c._get(\n f\"/akash/deployment/v1beta1/deployments/info\",\n params,\n raw=True\n )\n return res\n else:\n raise TypeError(\"arguments delegator and validator cannot both be None\")","def list(obj):\n # lists pf9-express config files\n pf9_exp_conf_dir = obj['pf9_exp_conf_dir']\n\n if os.path.exists(pf9_exp_conf_dir):\n count = 1\n result = PrettyTable()\n result.field_names = [\"#\",\"Active\", \"Conf\", \"Management Plane\", \"Region\"]\n files = [f for f in os.listdir(pf9_exp_conf_dir) if os.path.isfile(os.path.join(pf9_exp_conf_dir, f))]\n\n for f in files:\n active = False\n if f == 'express.conf':\n active = True\n with open(pf9_exp_conf_dir + f, 'r') as config_file:\n config = Utils().config_to_dict(config_file)\n if active:\n result.add_row([count,'*', config[\"name\"], config[\"du_url\"], config[\"os_region\"]])\n else:\n result.add_row([count,' ', config[\"name\"], config[\"du_url\"], config[\"os_region\"]])\n count = count + 1\n\n click.echo(result)\n\n else:\n click.echo('No Platform9 management plane configs exist')","def waitForDeployment(aNs: str, aDeploy: str):\n count = 1\n while count < MAX_WAIT:\n _ = showPods(aDeploy)\n specStr = \"{.spec.replicas}\"\n tt = runCmd(f\"kubectl -n {ns} get deployment {aDeploy} --output=jsonpath={specStr}\".split())\n if not tt.returncode == 0:\n print(tt.stderr.decode())\n sys.exit(1)\n else:\n replicas = tt.stdout.decode()\n print(f\"Replicas: {replicas}\")\n \n specStr = \"{.status.availableReplicas}\"\n tt = runCmd(f\"kubectl -n {ns} get deployment {aDeploy} --output=jsonpath={specStr}\".split())\n if not tt.returncode == 0:\n print(tt.stderr.decode())\n sys.exit(1)\n else:\n availableReplicas = tt.stdout.decode()\n print(f\"Available: {availableReplicas}\")\n\n if replicas == availableReplicas:\n break\n\n print()\n print(f\"{count}) sleeping for 1 ...\")\n time.sleep(1)\n count += 1\n\n else:\n print(f\"Error: The {aDeploy} deployment never returned to fully up\")\n sys.exit(1)","def index(request):\n if not request.user.is_authenticated:\n return JsonResponse({'message': 'You are not logged in.'}, status=403)\n # Now check if this is a POST or GET request\n if request.method == 'GET':\n # We kow that they are logged in now\n # So send them all of the deployment models\n return JsonResponse(\n serializers.serialize(\"json\", Deployment.objects.all()),\n safe=False)\n elif request.method == 'POST':\n #try:\n params = json.loads(request.body.decode('utf-8'))\n name = params['name']\n git = params['git']\n dirt = params['dir']\n run = params['run']\n cport = params['cport']\n lport = params['lport']\n create_new_key = params['gen_new_key']\n compose = params['compose']\n\n \n\n git_name = parse_git_url(git)\n \n # Make sure we have a valid git name\n if git_name == '':\n return JsonResponse({'message': 'invalid git url', 'reason': 'bad_git_url'}, status=400)\n \n # Replace copied ssh with proper ssh\n #if \"git@github.com\" in git:\n # git = \"ssh://\" + git\n \n # append our repo name to our dir name\n if dirt.endswith('/'):\n dirt = dirt + git_name\n else:\n dirt = dirt + '/' + git_name\n\n # awesome, now lets create our custom url\n webhook = hashlib.md5((params['name'] + SUPER_SECRET_KEY).encode())\n\n # First see if we need to generate an ssh key\n # If we do we cannot pull automatically\n if create_new_key:\n generate_ssh_key(dirt)\n else:\n # Let's try to clone our repo\n try:\n clone_repository(git, dirt)\n except GitError:\n return JsonResponse({'message': 'invalid git url', 'reason': 'bad_git_url'}, status=400)\n except ValueError:\n return JsonResponse({'message': 'invalid directory, perhaps the destination folder exists?', 'reason': 'bad_dir'}, status=400)\n\n d = Deployment.objects.create(\n name_text=name,\n git_url_text=git,\n dir_text=dirt,\n is_running=run,\n webhook_text=webhook.hexdigest(),\n container_port=cport,\n local_port=lport,\n is_compose=compose)\n\n return JsonResponse({'message': 'success', 'id': d.id})\n\n '''except Exception as e:\n print(e)\n print(e.__class__.__name__)\n return JsonResponse({'message': 'Invalid params'}, status=400)'''\n\n else:\n return JsonResponse({'message': 'Unknown method'}, status=404)","def execution_list(request, version, project, format=None):\n if request.method == 'GET':\n executions = Execution.objects.filter(versionName=version, tckey__startswith=project).order_by('execId')\n serializer = ExecutionSerializer(executions, many=True)\n return Response(serializer.data)","def list_versions(self):\n if not USE_GCLOUD:\n return self.run_appcfg(['list_versions'])\n data = self.run_gcloud(['app', 'versions', 'list'])\n per_module = collections.defaultdict(list)\n for deployment in data:\n service = deployment['service'].encode('utf-8')\n version_id = deployment['id'].encode('utf-8')\n per_module[service].append(version_id)\n return dict(per_module)","def api_asset_list():\n return jsonify(app.bank.to_list()), 200","def fetch_list(self):\n\t\treturn self.fetch(self.list_url % ART_SERVER_HOST)","def index():\r\n\r\n module_name = deployment_settings.modules[module].name_nice\r\n response.title = module_name\r\n return dict(module_name=module_name)","def get_inventory(self, context):\n logger = get_logger_with_thread_id(context)\n logger.info(\"Autoload\")\n\n with ErrorHandlingContext(logger):\n cs_api = get_api(context)\n\n vblade_resource = TeraVMTrafficGeneratorVBladeResource.from_context(context)\n\n # get VM uuid of the Deployed App\n deployed_vm_resource = cs_api.GetResourceDetails(vblade_resource.fullname)\n vmuid = deployed_vm_resource.VmDetails.UID\n logger.info(\"Deployed TVM Module App uuid: {}\".format(vmuid))\n\n # get vCenter name\n app_request_data = json.loads(context.resource.app_context.app_request_json)\n vcenter_name = app_request_data[\"deploymentService\"][\"cloudProviderName\"]\n logger.info(\"vCenter shell resource name: {}\".format(vcenter_name))\n\n vsphere = pyVmomiService(SmartConnect, Disconnect, task_waiter=None)\n\n # get vCenter credentials\n vcenter_resource = cs_api.GetResourceDetails(resourceFullPath=vcenter_name)\n user = self._get_resource_attribute_value(resource=vcenter_resource,\n attribute_name=VCENTER_RESOURCE_USER_ATTR)\n\n encrypted_password = self._get_resource_attribute_value(resource=vcenter_resource,\n attribute_name=VCENTER_RESOURCE_PASSWORD_ATTR)\n\n password = cs_api.DecryptPassword(encrypted_password).Value\n\n logger.info(\"Connecting to the vCenter: {}\".format(vcenter_name))\n si = vsphere.connect(address=vcenter_resource.Address, user=user, password=password)\n\n # find Deployed App VM on the vCenter\n vm = vsphere.get_vm_by_uuid(si, vmuid)\n\n phys_interfaces = []\n comms_mac_addr = None\n\n for device in vm.config.hardware.device:\n if isinstance(device, vim.vm.device.VirtualEthernetCard):\n if device.deviceInfo.summary.lower() == vblade_resource.tvm_comms_network.lower():\n comms_mac_addr = device.macAddress\n else:\n phys_interfaces.append(device)\n\n if comms_mac_addr is None:\n raise Exception(\"Unable to find TVM Comms network with name '{}' on the device\"\n .format(vblade_resource.tvm_comms_network))\n\n logger.info(\"Found interfaces on the device: {}\".format(phys_interfaces))\n module_res = models.TeraVMModule(shell_name=\"\",\n name=\"Module {}\".format(comms_mac_addr.replace(\":\", \"-\")),\n unique_id=hash(comms_mac_addr))\n\n logger.info(\"Updating resource address for the module to {}\".format(comms_mac_addr))\n cs_api.UpdateResourceAddress(context.resource.fullname, comms_mac_addr)\n\n for port_number, phys_interface in enumerate(phys_interfaces, start=1):\n network_adapter_number = phys_interface.deviceInfo.label.lower().strip(\"network adapter \")\n unique_id = hash(phys_interface.macAddress)\n port_res = models.TeraVMPort(shell_name=\"\",\n name=\"Port {}\".format(port_number),\n unique_id=unique_id)\n\n port_res.mac_address = phys_interface.macAddress\n port_res.requested_vnic_name = network_adapter_number\n module_res.add_sub_resource(unique_id, port_res)\n\n return AutoloadDetailsBuilder(module_res).autoload_details()","def deployment_summary(self):\n if \"deploymentSummary\" in self._prop_dict:\n if isinstance(self._prop_dict[\"deploymentSummary\"], OneDriveObjectBase):\n return self._prop_dict[\"deploymentSummary\"]\n else :\n self._prop_dict[\"deploymentSummary\"] = ManagedAppPolicyDeploymentSummary(self._prop_dict[\"deploymentSummary\"])\n return self._prop_dict[\"deploymentSummary\"]\n\n return None","async def read_deployments(\n self,\n *,\n flow_filter: FlowFilter = None,\n flow_run_filter: FlowRunFilter = None,\n task_run_filter: TaskRunFilter = None,\n deployment_filter: DeploymentFilter = None,\n work_pool_filter: WorkPoolFilter = None,\n work_queue_filter: WorkQueueFilter = None,\n limit: int = None,\n sort: DeploymentSort = None,\n offset: int = 0,\n ) -> List[DeploymentResponse]:\n body = {\n \"flows\": flow_filter.dict(json_compatible=True) if flow_filter else None,\n \"flow_runs\": (\n flow_run_filter.dict(json_compatible=True, exclude_unset=True)\n if flow_run_filter\n else None\n ),\n \"task_runs\": (\n task_run_filter.dict(json_compatible=True) if task_run_filter else None\n ),\n \"deployments\": (\n deployment_filter.dict(json_compatible=True)\n if deployment_filter\n else None\n ),\n \"work_pools\": (\n work_pool_filter.dict(json_compatible=True)\n if work_pool_filter\n else None\n ),\n \"work_pool_queues\": (\n work_queue_filter.dict(json_compatible=True)\n if work_queue_filter\n else None\n ),\n \"limit\": limit,\n \"offset\": offset,\n \"sort\": sort,\n }\n\n response = await self._client.post(\"/deployments/filter\", json=body)\n return pydantic.parse_obj_as(List[DeploymentResponse], response.json())","def getCatalogs():","def test_list_monitorings_deployment_not_found(self):\n project_id = util.MOCK_UUID_1\n deployment_id = \"unk\"\n\n rv = TEST_CLIENT.get(\n f\"/projects/{project_id}/deployments/{deployment_id}/monitorings\"\n )\n result = rv.json()\n expected = {\n \"message\": \"The specified deployment does not exist\",\n \"code\": \"DeploymentNotFound\",\n }\n self.assertDictEqual(expected, result)\n self.assertEqual(rv.status_code, 404)","def get_deployment_batch(self):\r\n pkg_path = self.package.path\r\n pkg_name = self.package.name\r\n\r\n self.batch.clear()\r\n # add unpacking script to batch\r\n LOGGER.info('Adding batch to unpack {0} from {1}'.format(\r\n pkg_name, pkg_path))\r\n self.get_package_batch()\r\n\r\n # add deployment script to batch\r\n self.batch.add('TARGET', self.cli_composer.get_target_cmd())\r\n self.batch.add('LOGIN', self.cli_composer.get_login_cmd())\r\n self.batch.add(\r\n 'REMOVED', self.cli_composer.get_delete_cmd(pkg_name), True)\r\n self.batch.add('LIST', self.cli_composer.get_list_cmd())\r\n self.batch.add('DEPLOYED', self.cli_composer.get_push_cmd(\r\n pkg_name, '{0}'.format(pkg_name)))\r\n self.batch.add('NEWLIST', self.cli_composer.get_list_cmd())\r\n self.batch.add('DIR', ['ls', '-al'])","def get_tasks(self):\n assert self.cluster\n query = objects.TransactionCollection.filter_by(\n None,\n cluster_id=self.cluster.id, name=consts.TASK_NAMES.deployment\n )\n query = objects.TransactionCollection.filter_by_not(\n query, deployment_info=None\n )\n return objects.TransactionCollection.order_by(query, 'id')","def fusion_api_get_deployment_manager(self, uri=None, param='', api=None, headers=None):\n return self.dep_mgr.get(uri=uri, api=api, headers=headers, param=param)","def _load_deployments(self):\n deployments = []\n with open(DEPLOYMENTS_YML, \"rb\") as dep_yaml:\n deployments = yaml.load(dep_yaml.read(), Loader=yaml.Loader)\n for dep in deployments:\n if len(BASE_DIR) > 0 and not BASE_DIR.endswith(os.sep):\n dep.path = os.sep.join((BASE_DIR, dep.path))\n comps = []\n for comp in dep.components:\n comps.append(comp.get_instance())\n dep.components = comps\n return deployments","def populate_deployment_statuses():\n _set_latest_execution()\n _set_installation_status()\n _set_deployment_status()","def test_get_hyperflex_app_catalog_list(self):\n pass","def bamboo_deployments(ctx, from_date, to_date, use_cache):\r\n\r\n if from_date is None:\r\n from_date, to_date = previous_month_range()\r\n\r\n log.info('Getting Bamboo deployments between {} and {}'.format(from_date, to_date))\r\n report = BambooDeploymentsReport(\r\n ctx.obj,\r\n from_date=from_date,\r\n to_date=to_date\r\n )\r\n report.run_report(use_cache=use_cache)","def list(number=0):\n buckets = get_buckets(number)\n data = [(\"Name\", \"Versioned\", \"LifeCycle\")]\n col_width = [0, 0, 0]\n rows = []\n for name, bucket in buckets.items():\n v = bucket[\"versioning\"]\n l = bucket[\"lifecycle\"]\n v = v if v else \"Disabled\"\n if l:\n l = json.dumps(l, indent=1)\n else:\n l = \"None\"\n data.append((name, v, l))\n for row in data:\n for i, info in enumerate(row):\n col_width[i] = min(max(len(info) + 2, col_width[i]), 48)\n dashes = tuple((\"-\" * (width - 1) for width in col_width))\n data.insert(1, dashes)\n click.echo(f\"The status of the buckets:\")\n for row in data:\n output = \"\"\n for i in range(3):\n output += row[i].ljust(col_width[i])\n if not VERBOSE:\n click.echo(output)\n logger.info(output)","def list(args, config):\n\n api = config['API']\n headers = {}\n if args.stack_name:\n headers = {'stack-name': args.stack_name} # put stack name in headers\n r = requests.get(api['list'], headers=headers) # send the GET request\n print('\\nThe following clusters exist:\\n{}\\n'.format(r.json()))\n return","def get_indexes(self):\n resources = []\n\n data = self.get_resource_data(type='sources')\n data['url'] = constants.URLS['sources'] % data\n resources.append(data)\n\n for arch in self.data['arch']:\n data = self.get_resource_data(type='packages', arch=arch)\n data['url'] = constants.URLS['packages'] % data\n resources.append(data)\n return resources","def do_list(cs, args):\n opts = {}\n opts['all_projects'] = args.all_projects\n opts['marker'] = args.marker\n opts['limit'] = args.limit\n opts['sort_key'] = args.sort_key\n opts['sort_dir'] = args.sort_dir\n opts['image'] = args.image\n opts['name'] = args.name\n opts['project_id'] = args.project_id\n opts['user_id'] = args.user_id\n opts['host'] = args.host\n opts['task_state'] = args.task_state\n opts['memory'] = args.memory\n opts['auto_remove'] = args.auto_remove\n opts['status'] = args.status\n opts = zun_utils.remove_null_parms(**opts)\n containers = cs.containers.list(**opts)\n zun_utils.list_containers(containers)","def deploy():\n return do_deploy(do_pack())","def deploy():\n return do_deploy(do_pack())","def deploy():\n return do_deploy(do_pack())","def deploy_changes(self):\n logging.debug(\"In the deploy_changes() class method.\")\n\n url = \"/deployment/deploymentrequests\"\n devices = self.get_deployable_devices()\n if not devices:\n logging.info(\"No devices need deployed.\\n\\n\")\n return\n nowtime = int(1000000 * datetime.datetime.now().timestamp())\n json_data = {\n 'type': 'DeploymentRequest',\n 'forceDeploy': True,\n 'ignoreWarning': True,\n 'version': nowtime,\n 'deviceList': []\n }\n for device in devices:\n logging.info(\"Adding device {} to deployment queue.\".format(device))\n json_data['deviceList'].append(device)\n logging.info(\"Deploying changes to devices.\")\n response = self.send_to_api(method='post', url=url, json_data=json_data)\n return response['deviceList']","def test_deployment(self):\n config = {'example.com': {\n 'applications': {\n 'site-hybridcluster': {\n 'image': 'unknown',\n },\n 'mysql-hybridcluster': {\n 'image': 'unknown',\n }\n },\n 'version': 1\n }}\n expected = Deployment(nodes=frozenset([\n Node(hostname='example.com', applications=frozenset([\n Application(\n name='mysql-hybridcluster',\n image=DockerImage.from_string('unknown'),\n ports=frozenset(),\n ),\n Application(\n name='site-hybridcluster',\n image=DockerImage.from_string('unknown'),\n )]))]))\n self.assertEqual(expected,\n current_from_configuration(config))","def get(self):\n return read_heroku_apps(request.args)","def select_deployment_to_run(deployments=None, command='build'): # noqa pylint: disable=too-many-branches,too-many-statements\n if deployments is None or not deployments:\n return []\n deployments_to_run = []\n\n if len(deployments) == 1:\n selected_index = 1\n else:\n print('')\n print('Configured deployments:')\n pretty_index = 1\n for i in deployments:\n print(\"%s: %s\" % (pretty_index, json.dumps(i)))\n pretty_index += 1\n print('')\n print('')\n if command == 'destroy':\n print('(Operating in destroy mode -- \"all\" will destroy all '\n 'deployments in reverse order)')\n selected_index = input('Enter number of deployment to run '\n '(or \"all\"): ')\n\n if selected_index == 'all':\n return deployments\n if selected_index == '':\n LOGGER.error('Please select a valid number (or \"all\")')\n sys.exit(1)\n\n selected_deploy = deployments[int(selected_index) - 1]\n if selected_deploy.get('current_dir', False):\n deployments_to_run.append(selected_deploy)\n elif not selected_deploy.get('modules', []):\n LOGGER.error('No modules configured in selected deployment')\n sys.exit(1)\n elif len(selected_deploy['modules']) == 1:\n # No need to select a module in the deployment - there's only one\n if command == 'destroy':\n LOGGER.info('(only one deployment detected; all modules '\n 'automatically selected for termination)')\n if not strtobool(input('Proceed?: ')):\n sys.exit(0)\n deployments_to_run.append(selected_deploy)\n else:\n print('')\n print('Configured modules in deployment:')\n pretty_index = 1\n for i in selected_deploy['modules']:\n print(\"%s: %s\" % (pretty_index, json.dumps(i)))\n pretty_index += 1\n print('')\n print('')\n if command == 'destroy':\n print('(Operating in destroy mode -- \"all\" will destroy all '\n 'deployments in reverse order)')\n selected_index = input('Enter number of module to run '\n '(or \"all\"): ')\n if selected_index == 'all':\n deployments_to_run.append(selected_deploy)\n elif selected_index == '':\n LOGGER.error('Please select a valid number (or \"all\")')\n sys.exit(1)\n else:\n module_list = [selected_deploy['modules'][int(selected_index) - 1]] # noqa\n selected_deploy['modules'] = module_list\n deployments_to_run.append(selected_deploy)\n\n LOGGER.debug('Selected deployment is %s...', deployments_to_run)\n return deployments_to_run","def deployment_configs(self) -> Optional[pulumi.Input['PagesProjectDeploymentConfigsArgs']]:\n return pulumi.get(self, \"deployment_configs\")","def deployment_configs(self) -> Optional[pulumi.Input['PagesProjectDeploymentConfigsArgs']]:\n return pulumi.get(self, \"deployment_configs\")","def deploy():\n test()\n if not env.is_staging:\n backup()\n prepare()\n restart_api()","def test_list_operators_deployment_not_found(self):\n project_id = util.MOCK_UUID_1\n deployment_id = \"unk\"\n\n rv = TEST_CLIENT.get(\n f\"/projects/{project_id}/deployments/{deployment_id}/operators\"\n )\n result = rv.json()\n expected = {\n \"message\": \"The specified deployment does not exist\",\n \"code\": \"DeploymentNotFound\",\n }\n self.assertDictEqual(expected, result)\n self.assertEqual(rv.status_code, 404)","def get(workflow_id, deployment_id, logger, client, tenant_name):\n utils.explicit_tenant_name_message(tenant_name, logger)\n try:\n logger.info('Retrieving workflow {0} for deployment {1}'.format(\n workflow_id, deployment_id))\n deployment = client.deployments.get(deployment_id)\n workflow = next((wf for wf in deployment.workflows if\n wf.name == workflow_id), None)\n if not workflow:\n raise CloudifyCliError(\n 'Workflow {0} of deployment {1} not found'\n .format(workflow_id, deployment_id))\n except CloudifyClientError as e:\n if e.status_code != 404:\n raise\n raise CloudifyCliError('Deployment {0} not found'.format(\n deployment_id))\n\n defaults = {\n 'blueprint_id': deployment.blueprint_id,\n 'deployment_id': deployment.id\n }\n columns = WORKFLOW_COLUMNS\n\n if get_global_json_output():\n columns += ['parameters']\n print_single(columns, workflow, 'Workflows:', defaults=defaults)\n\n if not get_global_json_output():\n # print workflow parameters\n mandatory_params = dict()\n optional_params = dict()\n for param_name, param in workflow.parameters.items():\n params_group = optional_params if 'default' in param else \\\n mandatory_params\n params_group[param_name] = param\n\n logger.info('Workflow Parameters:')\n logger.info('\\tMandatory Parameters:')\n for param_name, param in mandatory_params.items():\n if 'description' in param:\n logger.info('\\t\\t{0}\\t({1})'.format(param_name,\n param['description']))\n else:\n logger.info('\\t\\t{0}'.format(param_name))\n\n logger.info('\\tOptional Parameters:')\n for param_name, param in optional_params.items():\n if 'description' in param:\n logger.info('\\t\\t{0}: \\t{1}\\t({2})'.format(\n param_name, param['default'], param['description']))\n else:\n logger.info('\\t\\t{0}: \\t{1}'.format(param_name,\n param['default']))\n logger.info('')","def get_details(self):\n status = []\n for key, container in self.containers.items():\n container.details = container.daemon.connection.inspect_container(self.config['release_name'])\n status.append(container.details)\n return status","def get_applications(status):\n return status['applications']","def index(self, request):\n versions = []\n for key, data in VERSIONS.items():\n v = BaseVersion(\n data[\"id\"],\n data[\"status\"],\n request.application_url,\n data[\"updated\"])\n versions.append(v)\n return wsgi.Result(VersionsDataView(versions))","def show(self):\n self.parser.add_argument('assembly_uuid',\n help=\"Assembly uuid or name\")\n args = self.parser.parse_args()\n response = self.client.assemblies.find(name_or_id=args.assembly_uuid)\n fields = ['uuid', 'name', 'description', 'status', 'application_uri',\n 'trigger_uri']\n data = dict([(f, getattr(response, f, ''))\n for f in fields])\n cliutils.print_dict(data, wrap=72)","def get_resources():\n return Response(f\"{Resource.get_all_resources()}\", 200, mimetype='text/plain')","def list(host, mqtt_port, rest_port):\n\n click.echo(\"Listing things from host \" + host + \".\")\n service = Service(host, mqtt_port, str(rest_port))\n things = service.get_all_things()\n for thing in things:\n click.echo(thing.__dict__)\n if len(things) == 0:\n click.secho(\"Unable to retrieve any things!\", fg=\"red\", bold=True)","def test_read_namespaced_deployment_config_status(self):\n pass","def list_pods_in_a_deployment(self, namespace, dc):\n pods_in_dc = None\n pods_in_namespace = self.list_pods_in_a_namespace(namespace=namespace, label_selector=\"deploymentconfig\")\n if pods_in_namespace:\n pod_list = pods_in_namespace.items\n pods_in_dc = [\n pod.metadata.name\n for pod in pod_list\n if pod.metadata.annotations[\"openshift.io/deployment-config.name\"] == dc\n ]\n return pods_in_dc"],"string":"[\n \"def list_deployments() -> JSONResponse:\\n\\n deploy_manager = DeployManager()\\n deployments = deploy_manager.list()\\n return JSONResponse(deployments)\",\n \"def test_get_deployments(self):\\n pass\",\n \"def test_get_deployments(self):\\n pass\",\n \"def detail(request, deployment_id):\\n if request.method == \\\"GET\\\": \\n try:\\n dep = Deployment.objects.get(pk=deployment_id)\\n return JsonResponse(serializers.serialize(\\\"json\\\", [dep]), safe=False)\\n except ObjectDoesNotExist as e:\\n return JsonResponse({'message': 'Error retriving deployment.'})\\n else:\\n return JsonResponse({'message':'not found'}, status=404)\",\n \"def view_deployment(request, deployment, **_kwargs):\\n pass\",\n \"def test_get_deployment(self):\\n pass\",\n \"def test_get_deployment(self):\\n pass\",\n \"def test_get_deployment_resources(self):\\n pass\",\n \"def deployments(self) -> List[Dict]:\\n return [\\n {\\n 'name': self.name,\\n 'head_host': self.head_host,\\n 'head_port': self.head_port,\\n }\\n ]\",\n \"def test_get_deployments_expanded(self):\\n pass\",\n \"def handle_get_deployment(project_id, deployment_id):\\n return jsonify(get_deployment(uuid=deployment_id, project_id=project_id))\",\n \"def handle_list_deployments(project_id):\\n return jsonify(list_deployments(project_id=project_id))\",\n \"def list_deployment_config(self, **kwargs):\\n\\n all_params = ['pretty', 'label_selector', 'field_selector', 'watch', 'resource_version', 'timeout_seconds']\\n all_params.append('callback')\\n\\n params = locals()\\n for key, val in iteritems(params['kwargs']):\\n if key not in all_params:\\n raise TypeError(\\n \\\"Got an unexpected keyword argument '%s'\\\"\\n \\\" to method list_deployment_config\\\" % key\\n )\\n params[key] = val\\n del params['kwargs']\\n\\n\\n resource_path = '/oapi/v1/deploymentconfigs'.replace('{format}', 'json')\\n path_params = {}\\n\\n query_params = {}\\n if 'pretty' in params:\\n query_params['pretty'] = params['pretty']\\n if 'label_selector' in params:\\n query_params['labelSelector'] = params['label_selector']\\n if 'field_selector' in params:\\n query_params['fieldSelector'] = params['field_selector']\\n if 'watch' in params:\\n query_params['watch'] = params['watch']\\n if 'resource_version' in params:\\n query_params['resourceVersion'] = params['resource_version']\\n if 'timeout_seconds' in params:\\n query_params['timeoutSeconds'] = params['timeout_seconds']\\n\\n header_params = {}\\n\\n form_params = []\\n local_var_files = {}\\n\\n body_params = None\\n\\n # HTTP header `Accept`\\n header_params['Accept'] = self.api_client.\\\\\\n select_header_accept(['application/json', 'application/yaml'])\\n if not header_params['Accept']:\\n del header_params['Accept']\\n\\n # HTTP header `Content-Type`\\n header_params['Content-Type'] = self.api_client.\\\\\\n select_header_content_type(['*/*'])\\n\\n # Authentication setting\\n auth_settings = []\\n\\n response = self.api_client.call_api(resource_path, 'GET',\\n path_params,\\n query_params,\\n header_params,\\n body=body_params,\\n post_params=form_params,\\n files=local_var_files,\\n response_type='V1DeploymentConfigList',\\n auth_settings=auth_settings,\\n callback=params.get('callback'))\\n return response\",\n \"def test_get_deployment_runs(self):\\n pass\",\n \"def test_get_deployment_runs1(self):\\n pass\",\n \"def get(self):\\n deploys = get_heroku_deployments('SERVER GOES HERE!')\\n write_msg(deploys)\\n return deploys\",\n \"def ls(_):\\n client = utils.s3_client()\\n\\n projects = []\\n\\n config = utils.get_config()\\n bucket = config[\\\"release\\\"][\\\"s3_bucket\\\"]\\n deploys = config[\\\"deploy\\\"]\\n\\n resp = client.list_objects_v2(Bucket=bucket)\\n for data in resp.get(\\\"Contents\\\", []):\\n name = data[\\\"Key\\\"]\\n\\n projects.append(name)\\n\\n projects = sorted(projects)\\n\\n _projects = []\\n\\n for name in projects:\\n try:\\n release = get_release(client, bucket, name)\\n except InvalidRelease:\\n continue\\n\\n data = {\\n \\\"Name\\\": name,\\n \\\"Latest Release\\\": f\\\"v{release.version} {release.timestamp} ({release.commit})\\\",\\n }\\n\\n for env_name, cfg in deploys.items():\\n env_version, env_commit, env_timestamp = get_deployed_version(\\n client, cfg[\\\"s3_bucket\\\"], name\\n )\\n\\n data[env_name.title()] = f\\\"v{env_version} {env_timestamp} ({env_commit})\\\"\\n\\n _projects.append(data)\\n\\n projects = _projects\\n\\n utils.printfmt(projects)\",\n \"def test_get_deployment_resource(self):\\n pass\",\n \"def test_get_deployment_run(self):\\n pass\",\n \"def test_get_deployment_resource_data(self):\\n pass\",\n \"def list_server_deployment(self, feed_id=None):\\n resources = self.list_resource(feed_id=feed_id, resource_type_id='Deployment')\\n resources.extend(self.list_resource(\\n feed_id=feed_id,\\n resource_type_id='SubDeployment'))\\n deployments = []\\n if resources:\\n for resource in resources:\\n deployments.append(Deployment(resource.id, resource.name, resource.path))\\n return deployments\",\n \"def list(deployment_id, all_workflows, logger, client, tenant_name):\\n utils.explicit_tenant_name_message(tenant_name, logger)\\n logger.info('Listing workflows for deployment %s...', deployment_id)\\n deployment = client.deployments.get(deployment_id)\\n\\n workflows = sorted(deployment.workflows, key=lambda w: w.name)\\n columns = WORKFLOW_COLUMNS\\n hidden_count = 0\\n if not all_workflows:\\n total_count = len(workflows)\\n workflows = [wf for wf in workflows if wf.is_available]\\n hidden_count = total_count - len(workflows)\\n else:\\n columns = columns + ['is_available']\\n\\n defaults = {\\n 'blueprint_id': deployment.blueprint_id,\\n 'deployment_id': deployment.id\\n }\\n if not get_global_json_output():\\n workflows = [_format_workflow(wf) for wf in workflows]\\n print_data(columns, workflows, 'Workflows:', defaults=defaults)\\n if hidden_count:\\n logger.info('%d unavailable workflows hidden (use --all to show)',\\n hidden_count)\",\n \"def list_server_deployment(self, feed_id=None):\\n deployments = self.list_resource(feed_id=feed_id,\\n resource_type_id='Deployment',\\n cls=Deployment,\\n list_children=True)\\n deployments.extend(self.list_resource(\\n feed_id=feed_id,\\n resource_type_id='SubDeployment',\\n cls=Deployment,\\n list_children=True))\\n return deployments\",\n \"def describe_deployments(StackId=None, AppId=None, DeploymentIds=None):\\n pass\",\n \"def test_list_namespaced_deployment_config(self):\\n pass\",\n \"def resolve_deployment(self, args, context, info):\\n params = {\\n 'id': self.deployment_id,\\n }\\n return DeploymentLoader.get().load(params)[0]\",\n \"def get_environment_list(name, filter_by, limit, offset, quiet, out, project_name):\\n\\n client = get_api_client()\\n\\n params = {\\\"length\\\": limit, \\\"offset\\\": offset}\\n filter_query = \\\"\\\"\\n if name:\\n filter_query = get_name_query([name])\\n if filter_by:\\n filter_query = filter_query + \\\";(\\\" + filter_by + \\\")\\\"\\n if project_name:\\n project_data = get_project(project_name)\\n project_id = project_data[\\\"metadata\\\"][\\\"uuid\\\"]\\n filter_query = filter_query + \\\";project_reference=={}\\\".format(project_id)\\n if filter_query.startswith(\\\";\\\"):\\n filter_query = filter_query[1:]\\n\\n if filter_query:\\n params[\\\"filter\\\"] = filter_query\\n\\n res, err = client.environment.list(params=params)\\n\\n if err:\\n context = get_context()\\n server_config = context.get_server_config()\\n pc_ip = server_config[\\\"pc_ip\\\"]\\n\\n LOG.warning(\\\"Cannot fetch environments from {}\\\".format(pc_ip))\\n return\\n\\n if out == \\\"json\\\":\\n click.echo(json.dumps(res.json(), indent=4, separators=(\\\",\\\", \\\": \\\")))\\n return\\n\\n json_rows = res.json()[\\\"entities\\\"]\\n if not json_rows:\\n click.echo(highlight_text(\\\"No environment found !!!\\\\n\\\"))\\n return\\n\\n if quiet:\\n for _row in json_rows:\\n row = _row[\\\"status\\\"]\\n click.echo(highlight_text(row[\\\"name\\\"]))\\n return\\n\\n table = PrettyTable()\\n table.field_names = [\\n \\\"NAME\\\",\\n \\\"PROJECT\\\",\\n \\\"STATE\\\",\\n \\\"CREATED ON\\\",\\n \\\"LAST UPDATED\\\",\\n \\\"UUID\\\",\\n ]\\n for _row in json_rows:\\n row = _row[\\\"status\\\"]\\n metadata = _row[\\\"metadata\\\"]\\n\\n project = (\\n metadata[\\\"project_reference\\\"][\\\"name\\\"]\\n if \\\"project_reference\\\" in metadata\\n else None\\n )\\n\\n creation_time = int(metadata[\\\"creation_time\\\"]) // 1000000\\n last_update_time = int(metadata[\\\"last_update_time\\\"]) // 1000000\\n\\n table.add_row(\\n [\\n highlight_text(row[\\\"name\\\"]),\\n highlight_text(project),\\n highlight_text(row[\\\"state\\\"]),\\n highlight_text(time.ctime(creation_time)),\\n \\\"{}\\\".format(arrow.get(last_update_time).humanize()),\\n highlight_text(row.get(\\\"uuid\\\", \\\"\\\")),\\n ]\\n )\\n click.echo(table)\",\n \"def _service_list(res, ctx):\\n\\n if _has_error_code(res):\\n return print_errors(res, ctx)\\n\\n max_len = {'name': 10,\\n 'flags': 5,\\n 'deployment': 20,\\n 'config': 10}\\n\\n deploys = res['deploys'].values()\\n if len(deploys) == 0:\\n return \\\"\\\"\\n\\n for deploy in deploys:\\n for key in ('name', 'deployment', 'config'):\\n max_len[key] = max(len(deploy.get(key, '')), max_len.get(key, 0))\\n\\n lines = []\\n header = \\\" \\\".join((\\\"ID\\\".ljust(max_len['name'], ' '),\\n \\\"FLAGS\\\",\\n 'DEPLOY'.ljust(max_len['deployment'], ' '),\\n 'CONFIG'.ljust(max_len['config'], ' ')))\\n lines.append(header)\\n\\n for deploy in res['deploys'].values():\\n line = \\\" \\\".join((deploy['name'].ljust(max_len['name'], ' '),\\n _build_flags(deploy).ljust(max_len['flags'], ' '),\\n deploy['deployment'].ljust(max_len['deployment'], ' '),\\n deploy['config'].ljust(max_len['config'], ' ')))\\n lines.append(line)\\n\\n return \\\"\\\\n\\\".join(lines)\",\n \"def test_list_deployment_config_for_all_namespaces(self):\\n pass\",\n \"def list_bundles():\\n response = houston.get(\\\"/zipline/bundles\\\")\\n\\n houston.raise_for_status_with_json(response)\\n return response.json()\",\n \"def list():\\n index = config.index\\n output_format = \\\"%-7s %-20s %s\\\"\\n click.secho(output_format % (\\\"ID\\\", \\\"CREATED\\\", \\\"BACKENDS\\\"), fg=\\\"cyan\\\")\\n for archive in sorted(index.archives(), key=lambda x: x[\\\"id\\\"]):\\n # Print it out\\n click.echo(\\n output_format\\n % (\\n archive[\\\"id\\\"],\\n datetime.datetime.fromtimestamp(archive[\\\"created\\\"]).strftime(\\n \\\"%Y-%m-%d %H:%M:%S\\\"\\n ),\\n \\\", \\\".join(sorted(archive[\\\"backend_names\\\"])),\\n )\\n )\",\n \"def archive_list():\\n data = list_archived_services()\\n\\n table = present(lambda: data,\\n renderer='table',\\n headers=['Id', 'Service Name', 'Memory(Mi)', 'Created At'],\\n columns=['id', 'name', 'memory', 'created_at'])\\n if table:\\n click.echo(table)\\n else:\\n\\n click.echo('\\\\nYou have no archived services right now, why don\\\\'t you try deploying one? \\\\n'\\n 'have fun and follow the link below:\\\\n')\\n click.echo('https://docs.fandogh.cloud/docs/services.html\\\\n')\",\n \"def list_cube_deployment(\\n self, req: typing.Optional[dict] = None, **kwargs\\n ) -> dict:\\n # build request\\n d = {\\n \\\"ProjectId\\\": self.config.project_id,\\n \\\"Region\\\": self.config.region,\\n }\\n req and d.update(req)\\n d = apis.ListCubeDeploymentRequestSchema().dumps(d)\\n\\n # build options\\n kwargs[\\\"max_retries\\\"] = 0 # ignore retry when api is not idempotent\\n\\n resp = self.invoke(\\\"ListCubeDeployment\\\", d, **kwargs)\\n return apis.ListCubeDeploymentResponseSchema().loads(resp)\",\n \"def test_get_deployment_run_reports(self):\\n pass\",\n \"async def read_deployment_by_name(\\n self,\\n name: str,\\n ) -> DeploymentResponse:\\n try:\\n response = await self._client.get(f\\\"/deployments/name/{name}\\\")\\n except httpx.HTTPStatusError as e:\\n if e.response.status_code == status.HTTP_404_NOT_FOUND:\\n raise prefect.exceptions.ObjectNotFound(http_exc=e) from e\\n else:\\n raise\\n\\n return DeploymentResponse.parse_obj(response.json())\",\n \"def do_list(cs, args):\\n data = []\\n _, repositories = cs.repositories.list(args.project_id)\\n for repo in repositories:\\n _, tags = cs.repositories.list_tags(repo)\\n for tag in tags:\\n _, manifests = cs.repositories.get_manifests(repo, tag)\\n manifests['Name'] = repo\\n manifests['Tag'] = tag\\n manifests['Project'] = args.project_id\\n manifests['Id'] = manifests['Id'][0:12]\\n data.append(manifests)\\n fields = [\\n \\\"Id\\\", \\\"Name\\\", \\\"Tag\\\", \\\"Author\\\", 'Project', \\\"Created\\\", \\\"Docker Version\\\",\\n \\\"Architecture\\\", \\\"OS\\\"\\n ]\\n utils.print_list(data, fields, sortby=args.sortby)\",\n \"async def read_deployment(\\n self,\\n deployment_id: UUID,\\n ) -> DeploymentResponse:\\n try:\\n response = await self._client.get(f\\\"/deployments/{deployment_id}\\\")\\n except httpx.HTTPStatusError as e:\\n if e.response.status_code == status.HTTP_404_NOT_FOUND:\\n raise prefect.exceptions.ObjectNotFound(http_exc=e) from e\\n else:\\n raise\\n return DeploymentResponse.parse_obj(response.json())\",\n \"def getEnvironments(request):\\n environments = Environment.objects.all()\\n serializer = environmentSerializer(environments, many=True)\\n result = {'data':serializer.data, 'code':HTTP_200_OK, 'message':OK}\\n return result\",\n \"def get_deployment_output(account_name: Optional[pulumi.Input[str]] = None,\\n deployment_name: Optional[pulumi.Input[str]] = None,\\n resource_group_name: Optional[pulumi.Input[str]] = None,\\n opts: Optional[pulumi.InvokeOptions] = None) -> pulumi.Output[GetDeploymentResult]:\\n ...\",\n \"def deployment_fetch_status(self) -> Optional[pulumi.Input[Union[str, 'DeploymentFetchStatus']]]:\\n return pulumi.get(self, \\\"deployment_fetch_status\\\")\",\n \"def test_get_deployment_runs_in_virtualization_realm(self):\\n pass\",\n \"def deploy():\",\n \"def watch_deployment_config_list(self, **kwargs):\\n\\n all_params = ['pretty', 'label_selector', 'field_selector', 'watch', 'resource_version', 'timeout_seconds']\\n all_params.append('callback')\\n\\n params = locals()\\n for key, val in iteritems(params['kwargs']):\\n if key not in all_params:\\n raise TypeError(\\n \\\"Got an unexpected keyword argument '%s'\\\"\\n \\\" to method watch_deployment_config_list\\\" % key\\n )\\n params[key] = val\\n del params['kwargs']\\n\\n\\n resource_path = '/oapi/v1/watch/deploymentconfigs'.replace('{format}', 'json')\\n path_params = {}\\n\\n query_params = {}\\n if 'pretty' in params:\\n query_params['pretty'] = params['pretty']\\n if 'label_selector' in params:\\n query_params['labelSelector'] = params['label_selector']\\n if 'field_selector' in params:\\n query_params['fieldSelector'] = params['field_selector']\\n if 'watch' in params:\\n query_params['watch'] = params['watch']\\n if 'resource_version' in params:\\n query_params['resourceVersion'] = params['resource_version']\\n if 'timeout_seconds' in params:\\n query_params['timeoutSeconds'] = params['timeout_seconds']\\n\\n header_params = {}\\n\\n form_params = []\\n local_var_files = {}\\n\\n body_params = None\\n\\n # HTTP header `Accept`\\n header_params['Accept'] = self.api_client.\\\\\\n select_header_accept(['application/json'])\\n if not header_params['Accept']:\\n del header_params['Accept']\\n\\n # HTTP header `Content-Type`\\n header_params['Content-Type'] = self.api_client.\\\\\\n select_header_content_type(['*/*'])\\n\\n # Authentication setting\\n auth_settings = []\\n\\n response = self.api_client.call_api(resource_path, 'GET',\\n path_params,\\n query_params,\\n header_params,\\n body=body_params,\\n post_params=form_params,\\n files=local_var_files,\\n response_type='JsonWatchEvent',\\n auth_settings=auth_settings,\\n callback=params.get('callback'))\\n return response\",\n \"def deployment_properties(self) -> pulumi.Output[Optional['outputs.EnvironmentDeploymentPropertiesResponse']]:\\n return pulumi.get(self, \\\"deployment_properties\\\")\",\n \"def get_deployments(self, ApiId: str, MaxResults: str = None, NextToken: str = None) -> Dict:\\n pass\",\n \"def deployment_configs(self) -> pulumi.Output['outputs.PagesProjectDeploymentConfigs']:\\n return pulumi.get(self, \\\"deployment_configs\\\")\",\n \"def deploy(request):\\n data = json.loads(request.GET.get(\\\"data\\\"))\\n host = data[\\\"host\\\"]\\n\\n if host[\\\"deployment_environment\\\"] == \\\"docker\\\":\\n tc_file = \\\"TC_microservices_host_docker_operations.xml\\\"\\n elif host[\\\"deployment_environment\\\"] == \\\"kubernetes\\\":\\n tc_file = \\\"TC_microservices_host_kubernetes_operations.xml\\\"\\n if not data[\\\"host\\\"][\\\"scripts\\\"]:\\n data[\\\"host\\\"][\\\"scripts\\\"] = \\\";\\\"\\n\\n generate_host_system_data(data[\\\"host\\\"])\\n\\n image = data[\\\"registry\\\"][\\\"image_name\\\"]\\n if data[\\\"registry\\\"][\\\"image_tag\\\"]:\\n image = \\\"{}:{}\\\".format(image, data[\\\"registry\\\"][\\\"image_tag\\\"])\\n data[\\\"registry\\\"][\\\"image\\\"] = image\\n if data[\\\"registry\\\"][\\\"port\\\"]:\\n data[\\\"registry\\\"][\\\"address\\\"] = \\\"{}:{}\\\".format(data[\\\"registry\\\"][\\\"address\\\"],\\n data[\\\"registry\\\"][\\\"port\\\"])\\n generate_registry_operations(data)\\n\\n return StreamingHttpResponse(stream(tc_file))\",\n \"def test_get_deployments_count(self):\\n pass\",\n \"def history_list(name):\\n service_histories = request_service_history(name)\\n table = present(lambda: service_histories,\\n renderer='table',\\n headers=['History Version', 'Service Name', 'Date Created', 'Manifest'],\\n columns=['id', 'name', 'created_at', 'manifest'])\\n if table:\\n click.echo(table)\\n else:\\n click.echo('There is no record of your service deployments available.')\\n # click.echo('https://docs.fandogh.cloud/docs/services.html\\\\n')\",\n \"def deploy_environments(self):\\n for key in self.deploy:\\n yield key\",\n \"def GET(self, env, start_response):\\n qs = env.get('QUERY_STRING', '')\\n args = urlparse.parse_qs(qs, 1)\\n\\n key_args = set(['cors','lifecycle', 'policy', 'logging', 'notification',\\n 'tagging', 'requestPayment', 'versioning', 'versions',\\n 'website', 'location'])\\n\\n if not key_args & set(args):\\n # GET bucket to list objects\\n max_keys = self.MAX_BUCKET_LISTING\\n if 'max-keys' in args:\\n if args.get('max-keys')[0].isdigit() is False:\\n return self.get_err_response('InvalidArgument')\\n max_keys = min(int(args.get('max-keys')[0]), self.MAX_BUCKET_LISTING)\\n\\n\\n if 'acl' not in args:\\n #acl request sent with format=json etc confuses swift\\n env['QUERY_STRING'] = 'format=json&limit=%s' % (max_keys + 1)\\n if 'marker' in args:\\n env['QUERY_STRING'] += '&marker=%s' % quote(args['marker'])\\n if 'prefix' in args:\\n env['QUERY_STRING'] += '&prefix=%s' % quote(args['prefix'])\\n if 'delimiter' in args:\\n env['QUERY_STRING'] += '&delimiter=%s' % quote(args['delimiter'])\\n body_iter = self._app_call(env)\\n if env['REQUEST_METHOD'] == 'HEAD':\\n body_iter = ''\\n status = self._get_status_int()\\n headers = dict(self._response_headers)\\n\\n if is_success(status) and 'acl' in args:\\n return self.get_acl(self.account_name, headers)\\n\\n if 'versioning' in args:\\n # Just report there is no versioning configured here.\\n body = ('<VersioningConfiguration '\\n 'xmlns=\\\"http://s3.amazonaws.com/doc/2006-03-01/\\\"/>')\\n return Response(body=body, content_type=\\\"text/plain\\\")\\n\\n if status != HTTP_OK:\\n if status in (HTTP_UNAUTHORIZED, HTTP_FORBIDDEN):\\n return self.get_err_response('AccessDenied')\\n elif status == HTTP_NOT_FOUND:\\n return self.get_err_response('NoSuchBucket')\\n else:\\n return self.get_err_response('InvalidURI')\\n\\n if 'location' in args:\\n body = ('<?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?>'\\n '<LocationConstraint '\\n 'xmlns=\\\"http://s3.amazonaws.com/doc/2006-03-01/\\\"')\\n if self.location == 'US':\\n body += '/>'\\n else:\\n body += ('>%s</LocationConstraint>' % self.location)\\n return Response(body=body, content_type='application/xml')\\n\\n if 'logging' in args:\\n # logging disabled\\n body = ('<?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?>'\\n '<BucketLoggingStatus '\\n 'xmlns=\\\"http://doc.s3.amazonaws.com/2006-03-01\\\" />')\\n return Response(body=body, content_type='application/xml')\\n\\n objects = loads(''.join(list(body_iter)))\\n body = ('<?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?>'\\n '<ListBucketResult '\\n 'xmlns=\\\"http://s3.amazonaws.com/doc/2006-03-01\\\">'\\n '<Prefix>%s</Prefix>'\\n '<Marker>%s</Marker>'\\n '<Delimiter>%s</Delimiter>'\\n '<IsTruncated>%s</IsTruncated>'\\n '<MaxKeys>%s</MaxKeys>'\\n '<Name>%s</Name>'\\n '%s'\\n '%s'\\n '</ListBucketResult>' %\\n (\\n xml_escape(args.get('prefix', '')),\\n xml_escape(args.get('marker', '')),\\n xml_escape(args.get('delimiter', '')),\\n 'true' if max_keys > 0 and len(objects) == (max_keys + 1) else\\n 'false',\\n max_keys,\\n xml_escape(self.container_name),\\n \\\"\\\".join(['<Contents><Key>%s</Key><LastModified>%sZ</LastModif'\\n 'ied><ETag>%s</ETag><Size>%s</Size><StorageClass>STA'\\n 'NDARD</StorageClass><Owner><ID>%s</ID><DisplayName>'\\n '%s</DisplayName></Owner></Contents>' %\\n (xml_escape(unquote(i['name'])), i['last_modified'],\\n i['hash'],\\n i['bytes'], self.account_name, self.account_name)\\n for i in objects[:max_keys] if 'subdir' not in i]),\\n \\\"\\\".join(['<CommonPrefixes><Prefix>%s</Prefix></CommonPrefixes>'\\n % xml_escape(i['subdir'])\\n for i in objects[:max_keys] if 'subdir' in i])))\\n return Response(body=body, content_type='application/xml')\\n else:\\n # GET specified data\\n #env['REQUEST_METHOD'] = 'HEAD'\\n body_iter = self._app_call(env)\\n status = self._get_status_int()\\n headers = dict(self._response_headers)\\n\\n action = args.keys().pop()\\n if action == 'acl':\\n # get acl\\n # get policy\\n acl = headers.get('X-Container-Meta-Policy') or ''\\n\\n if is_success(status):\\n if acl:\\n return Response(status=HTTP_OK, content_type='application/xml', body=unquote(acl))\\n else:\\n return self.get_err_response('NotSuchPolicy')\\n\\n elif status in (HTTP_UNAUTHORIZED, HTTP_FORBIDDEN):\\n return self.get_err_response('AccessDenied')\\n else:\\n return self.get_err_response('InvalidURI')\\n elif action == 'cors':\\n # get cors\\n _headers = set(['X-Container-Meta-Access-Control-Expose-Headers',\\n 'X-Container-Meta-Access-Control-Allow-Origin',\\n 'X-Container-Meta-Access-Control-Max-Age',\\n 'X-Container-Meta-Access-Control-Allow-Method'])\\n bodye = etree.Element('CORSConfiguration')\\n if _headers & set(headers):\\n rule = etree.Element('CORSRule')\\n if 'X-Container-Meta-Access-Control-Expose-Headers' in headers:\\n valuel = headers['X-Container-Meta-Access-Control-Expose-Headers'].split(',')\\n for i in valuel:\\n eh = self.create_elem('ExposeHeader', i)\\n rule.append(eh)\\n if 'X-Container-Meta-Access-Control-Allow-Origin' in headers:\\n valuel = headers['X-Container-Meta-Access-Control-Allow-Origin'].split(',')\\n for i in valuel:\\n ao = self.create_elem('AllowedOrigin', i)\\n rule.append(ao)\\n if 'X-Container-Meta-Access-Control-Max-Age' in headers:\\n valuel = headers['X-Container-Meta-Access-Control-Max-Age'].split(',')\\n for i in valuel:\\n ma = self.create_elem('MaxAgeSeconds', i)\\n rule.append(ma)\\n if 'X-Container-Meta-Access-Control-Allow-Method' in headers:\\n valuel = headers['X-Container-Meta-Access-Control-Allow-Method'].split(',')\\n for i in valuel:\\n al = self.create_elem('AllowedMethod', i)\\n rule.append(al)\\n rule.append(self.create_elem('ID', 'unique_rule'))\\n bodye.append(rule)\\n else:\\n bodye.text = ''\\n\\n if is_success(status):\\n return Response(status=HTTP_OK, content_type='application/xml', body=self.elem2xmlbody(bodye))\\n elif status in (HTTP_UNAUTHORIZED, HTTP_FORBIDDEN):\\n return self.get_err_response('AccessDenied')\\n else:\\n return self.get_err_response('InvalidURI')\\n\\n elif action == 'lifecycle':\\n # get lifecycle\\n bodye = etree.Element('LifecycleConfiguration')\\n if 'X-Container-Meta-Expiration-Status' in headers:\\n rule = etree.Element('Rule')\\n rule.append(self.create_elem('Status', headers['X-Container-Meta-Expiration-Status']))\\n rule.append(self.create_elem('ID', 'unique_rule'))\\n if 'X-Container-Meta-Expiration-Prefix' in headers:\\n rule.append(self.create_elem('Prefix', headers['X-Container-Meta-Expiration-Prefix']))\\n if 'X-Container-Meta-Expiration-At' in headers or \\\\\\n 'X-Container-Meta-Expiration-After' in headers:\\n expir = etree.Element('Expiration')\\n if 'X-Container-Meta-Expiration-At' in headers:\\n expir.append(self.create_elem('Date', headers['X-Container-Meta-Expiration-At']))\\n if 'X-Container-Meta-Expiration-After' in headers:\\n expir.append(self.create_elem('Days', headers['X-Container-Meta-Expiration-After']))\\n rule.append(expir)\\n if 'X-Container-Meta-Trans-Class' in headers:\\n trans = etree.Element('Transition')\\n cls = self.create_elem('StorageClass', headers['X-Container-Meta-Trans-Class'])\\n trans.append(cls)\\n if 'X-Container-Meta-Trans-At' in headers:\\n trans.append(self.create_elem('Date', headers['X-Container-Meta-Trans-At']))\\n if 'X-Container-Meta-Trans-After' in headers:\\n trans.append(self.create_elem('Days', headers['X-Container-Meta-Trans-After']))\\n rule.append(trans)\\n bodye.append(rule)\\n else:\\n bodye.text = ''\\n\\n if is_success(status):\\n return Response(status=HTTP_OK, content_type='application/xml', body=self.elem2xmlbody(bodye))\\n elif status in (HTTP_UNAUTHORIZED, HTTP_FORBIDDEN):\\n return self.get_err_response('AccessDenied')\\n else:\\n return self.get_err_response('InvalidURI')\\n\\n elif action == 'policy':\\n # get policy\\n json = headers.get('X-Container-Meta-Policy') or ''\\n\\n if is_success(status):\\n if json:\\n return Response(status=HTTP_OK, content_type='application/json', body=unquote(json))\\n else:\\n return self.get_err_response('NotSuchPolicy')\\n\\n elif status in (HTTP_UNAUTHORIZED, HTTP_FORBIDDEN):\\n return self.get_err_response('AccessDenied')\\n else:\\n return self.get_err_response('InvalidURI')\\n elif action == 'logging':\\n # get logging\\n target = headers.get('X-Container-Meta-Logging-Target') or ''\\n prefix = headers.get('X-Container-Meta-Logging-Prefix') or ''\\n statuse = etree.Element('BucketLoggingStatus')\\n if target:\\n enabled = etree.Element('LoggingEnabled')\\n target_bucket = self.create_elem('TargetBucket', target)\\n if prefix:\\n target_prefix = self.create_elem('TargetPrefix', prefix)\\n enabled.append(target_bucket)\\n enabled.append(target_prefix)\\n statuse.append(enabled)\\n else:\\n pass # set text None\\n\\n if is_success(status):\\n return Response(status=HTTP_OK, content_type='application/xml', body=self.elem2xmlbody(statuse))\\n elif status in (HTTP_UNAUTHORIZED, HTTP_FORBIDDEN):\\n return self.get_err_response('AccessDenied')\\n else:\\n return self.get_err_response('InvalidURI')\\n elif action == 'notification':\\n # get it\\n topic = headers.get('X-Container-Meta-Noti-Topic')\\n event = headers.get('X-Container-Meta-Noti-Event')\\n if is_success(status):\\n if topic:\\n body = ('<WebsiteConfiguration xmlns=\\\"http://s3.amazonaws.com/doc/2006-03-01/\\\">'\\n '<NotificationConfiguration> '\\n '<TopicConfiguration>'\\n '<Topic>%s</Topic>'\\n '<Event>%s</Event>'\\n '</TopicConfiguration>'\\n '</NotificationConfiguration>',\\n topic, event)\\n return Response(status=HTTP_OK, content_type='application/xml', body=body)\\n else:\\n return self.get_err_response('NotSuchWebsite')\\n\\n elif status in (HTTP_UNAUTHORIZED, HTTP_FORBIDDEN):\\n return self.get_err_response('AccessDenied')\\n else:\\n return self.get_err_response('InvalidURI')\\n elif action == 'tagging':\\n # get tagging\\n Tagging = etree.Element('Tagging')\\n TagSet = etree.Element('TagSet')\\n meta_keys = [header[21:] for header in headers if header.startswith('X-Container-Meta-Tag-')]\\n for key in meta_keys:\\n Tag = etree.Element('Tag')\\n keyvalues = headers['X-Container-Meta-Tag-' + key]\\n _key = keyvalues[:len(key)]\\n _value = keyvalues[len(key):]\\n Tag.append(self.create_elem('Key', _key))\\n Tag.append(self.create_elem('Value', _value))\\n TagSet.append(Tag)\\n Tagging.append(TagSet)\\n if is_success(status):\\n return Response(status=HTTP_OK, content_type='application/xml', body=self.elem2xmlbody(Tagging))\\n elif status in (HTTP_UNAUTHORIZED, HTTP_FORBIDDEN):\\n return self.get_err_response('AccessDenied')\\n else:\\n return self.get_err_response('InvalidURI')\\n\\n elif action == 'requestPayment':\\n # get it\\n # default value is BucketOwner\\n pay = headers.get('X-Container-Meta-Payment', 'BucketOwner')\\n if is_success(status):\\n if pay:\\n return Response(status=HTTP_OK, content_type='application/xml', body=unquote(pay))\\n else:\\n return self.get_err_response('NotSuchWebsite')\\n\\n elif status in (HTTP_UNAUTHORIZED, HTTP_FORBIDDEN):\\n return self.get_err_response('AccessDenied')\\n else:\\n return self.get_err_response('InvalidURI')\\n\\n elif action == 'versioning':\\n versioning = 'Enabled' if 'X-Versions-Location' in headers else 'Suspended'\\n bodye = etree.Element('VersioningConfiguration')\\n stat = self.create_elem('Status', versioning)\\n bodye.append(stat)\\n if is_success(status):\\n return Response(status=HTTP_OK, content_type='application/xml', body=self.elem2xmlbody(bodye))\\n elif status in (HTTP_UNAUTHORIZED, HTTP_FORBIDDEN):\\n return self.get_err_response('AccessDenied')\\n else:\\n return self.get_err_response('InvalidURI')\\n elif action == 'website':\\n # get website\\n website = headers.get('X-Container-Meta-Website')\\n fake = ('<WebsiteConfiguration xmlns=\\\"http://s3.amazonaws.com/doc/2006-03-01/\\\">'\\n '<IndexDocument>'\\n '<Suffix>index.html</Suffix>'\\n '</IndexDocument>'\\n '<ErrorDocument>'\\n '<Key>SomeErrorDocument.html</Key>'\\n '</ErrorDocument>'\\n '</WebsiteConfiguration>')\\n\\n if is_success(status):\\n if website:\\n # return fake data\\n return Response(status=HTTP_OK, content_type='application/xml', body=fake)\\n else:\\n return self.get_err_response('NotSuchWebsite')\\n\\n elif status in (HTTP_UNAUTHORIZED, HTTP_FORBIDDEN):\\n return self.get_err_response('AccessDenied')\\n else:\\n return self.get_err_response('InvalidURI')\\n elif action == 'location':\\n bodye = self.create_elem('LocationConstraint', 'CN')\\n return Response(status=HTTP_OK, content_type='application/xml', body=self.elem2xmlbody(bodye))\\n elif action == 'versions':\\n # get versions container\\n path = '/v1/AUTH_%s/%s' % (self.account_name, self.container_name)\\n env = copyenv(env, method='GET', path=path, query_string='')\\n body_iter = self._app_call(env)\\n status = self._get_status_int()\\n\\n # get origin container\\n path = '/v1/AUTH_%s/%s' % (quote(self.account_name), quote(self.version_name(self.container_name)))\\n env2 = copyenv(env, method='GET', path=path, query_string='')\\n body_iter2 = self._app_call(env2)\\n status2 = self._get_status_int()\\n\\n last = list(body_iter)\\n history = list(body_iter2)\\n res = etree.Element('ListVersionsResult')\\n bucket = self.create_elem('Name', self.container_name)\\n res.append(bucket)\\n if last:\\n last = [i for i in last[0].split('\\\\n') if i]\\n for i in last:\\n ver = etree.Element('Version')\\n ver.append(self.create_elem('Key', i))\\n ver.append(self.create_elem('VersionId', 'lastest'))\\n ver.append(self.create_elem('IsLastest', 'true'))\\n res.append(ver)\\n\\n if history:\\n history = [i for i in history[0].split('\\\\n') if i]\\n for i in history:\\n ver = etree.Element('Version')\\n ver.append(self.create_elem('Key', i.split('/')[0][3:]))\\n ver.append(self.create_elem('VersionId', i.split('/')[1]))\\n ver.append(self.create_elem('IsLastest', 'false'))\\n res.append(ver)\\n\\n if is_success(status) and is_success(status2):\\n return Response(status=HTTP_OK, content_type='application/xml', body=self.elem2xmlbody(res))\\n elif status in (HTTP_UNAUTHORIZED, HTTP_FORBIDDEN):\\n return self.get_err_response('AccessDenied')\\n else:\\n return self.get_err_response('InvalidURI')\\n else:\\n return self.get_err_response('InvalidURI')\",\n \"def list(deployment_id,\\n node_name,\\n sort_by,\\n descending,\\n all_tenants,\\n logger,\\n client,\\n tenant_name):\\n if tenant_name:\\n logger.info('Explicitly using tenant `{0}`'.format(tenant_name))\\n try:\\n if deployment_id:\\n logger.info('Listing instances for deployment {0}...'.format(\\n deployment_id))\\n else:\\n logger.info('Listing all instances...')\\n node_instances = client.node_instances.list(\\n deployment_id=deployment_id,\\n node_name=node_name,\\n sort=sort_by,\\n is_descending=descending,\\n _all_tenants=all_tenants)\\n except CloudifyClientError as e:\\n if e.status_code != 404:\\n raise\\n raise CloudifyCliError('Deployment {0} does not exist'.format(\\n deployment_id))\\n\\n print_data(NODE_INSTANCE_COLUMNS, node_instances, 'Node-instances:')\",\n \"def _get_deployment_friendly_name(auth_header, async_kvstore_client, request_context):\\n\\n response = yield async_kvstore_client.async_kvstore_get_request(\\n constants.META_COLLECTION_NAME, auth_header=auth_header, owner=constants.NOBODY)\\n\\n if response.code == http.OK:\\n response_json = yield response.json()\\n defer.returnValue(response_json[0][constants.DEPLOYMENT_FRIENDLY_NAME])\\n\\n LOGGER.error(\\\"Unable to fetch deployment friendly name for instance, code={}\\\".format(response.code))\\n defer.returnValue(\\\"\\\")\",\n \"def _get_deployment_logs(namespace, name, tail_lines=TAIL_LINES_DEFAULT):\\n pods = []\\n try:\\n api_response = k8s_client.list_namespaced_pod(namespace, label_selector='release={}'.format(name))\\n for api_items in api_response.items:\\n pods.append(api_items.metadata.name)\\n except ApiException as e:\\n print(\\\"Exception when calling CoreV1Api->list_namespaced_pod: %s\\\\n\\\" % e)\\n\\n # Iterate over list of pods and concatenate logs\\n logs = \\\"\\\"\\n try:\\n for pod in pods:\\n logs += pod + \\\"\\\\n\\\"\\n logs += k8s_client.read_namespaced_pod_log(pod, namespace, tail_lines=tail_lines)\\n except ApiException as e:\\n logging.error(\\\"Exception when calling CoreV1Api->read_namespaced_pod_log: %s\\\\n\\\" % e)\\n return logs\",\n \"def service_list():\\n data = list_services()\\n table = present(lambda: data,\\n renderer='table',\\n headers=['Service Name', 'URLS', 'Service Type', \\\"Memory Usages\\\", 'Replicas', 'Started at',\\n 'Updated at',\\n 'State', 'Restarts'],\\n columns=['name', 'urls', 'service_type', 'memory', 'replicas', 'start_date', 'last_update',\\n 'state',\\n 'service_restarts'])\\n if table:\\n click.echo(table)\\n else:\\n click.echo('\\\\nYou have no running services right now, why don\\\\'t you try deploying one? \\\\n'\\n 'have fun and follow the link below:\\\\n')\\n click.echo('https://docs.fandogh.cloud/docs/services.html\\\\n')\",\n \"async def info(\\n self,\\n owner: AccAddress = None,\\n dseq: int = None,\\n ) -> dict:\\n if owner is not None and dseq is not None:\\n params = {\\n \\\"id.owner\\\": owner,\\n \\\"id.dseq\\\": dseq,\\n }\\n res = await self._c._get(\\n f\\\"/akash/deployment/v1beta1/deployments/info\\\",\\n params,\\n raw=True\\n )\\n return res\\n else:\\n raise TypeError(\\\"arguments delegator and validator cannot both be None\\\")\",\n \"def list(obj):\\n # lists pf9-express config files\\n pf9_exp_conf_dir = obj['pf9_exp_conf_dir']\\n\\n if os.path.exists(pf9_exp_conf_dir):\\n count = 1\\n result = PrettyTable()\\n result.field_names = [\\\"#\\\",\\\"Active\\\", \\\"Conf\\\", \\\"Management Plane\\\", \\\"Region\\\"]\\n files = [f for f in os.listdir(pf9_exp_conf_dir) if os.path.isfile(os.path.join(pf9_exp_conf_dir, f))]\\n\\n for f in files:\\n active = False\\n if f == 'express.conf':\\n active = True\\n with open(pf9_exp_conf_dir + f, 'r') as config_file:\\n config = Utils().config_to_dict(config_file)\\n if active:\\n result.add_row([count,'*', config[\\\"name\\\"], config[\\\"du_url\\\"], config[\\\"os_region\\\"]])\\n else:\\n result.add_row([count,' ', config[\\\"name\\\"], config[\\\"du_url\\\"], config[\\\"os_region\\\"]])\\n count = count + 1\\n\\n click.echo(result)\\n\\n else:\\n click.echo('No Platform9 management plane configs exist')\",\n \"def waitForDeployment(aNs: str, aDeploy: str):\\n count = 1\\n while count < MAX_WAIT:\\n _ = showPods(aDeploy)\\n specStr = \\\"{.spec.replicas}\\\"\\n tt = runCmd(f\\\"kubectl -n {ns} get deployment {aDeploy} --output=jsonpath={specStr}\\\".split())\\n if not tt.returncode == 0:\\n print(tt.stderr.decode())\\n sys.exit(1)\\n else:\\n replicas = tt.stdout.decode()\\n print(f\\\"Replicas: {replicas}\\\")\\n \\n specStr = \\\"{.status.availableReplicas}\\\"\\n tt = runCmd(f\\\"kubectl -n {ns} get deployment {aDeploy} --output=jsonpath={specStr}\\\".split())\\n if not tt.returncode == 0:\\n print(tt.stderr.decode())\\n sys.exit(1)\\n else:\\n availableReplicas = tt.stdout.decode()\\n print(f\\\"Available: {availableReplicas}\\\")\\n\\n if replicas == availableReplicas:\\n break\\n\\n print()\\n print(f\\\"{count}) sleeping for 1 ...\\\")\\n time.sleep(1)\\n count += 1\\n\\n else:\\n print(f\\\"Error: The {aDeploy} deployment never returned to fully up\\\")\\n sys.exit(1)\",\n \"def index(request):\\n if not request.user.is_authenticated:\\n return JsonResponse({'message': 'You are not logged in.'}, status=403)\\n # Now check if this is a POST or GET request\\n if request.method == 'GET':\\n # We kow that they are logged in now\\n # So send them all of the deployment models\\n return JsonResponse(\\n serializers.serialize(\\\"json\\\", Deployment.objects.all()),\\n safe=False)\\n elif request.method == 'POST':\\n #try:\\n params = json.loads(request.body.decode('utf-8'))\\n name = params['name']\\n git = params['git']\\n dirt = params['dir']\\n run = params['run']\\n cport = params['cport']\\n lport = params['lport']\\n create_new_key = params['gen_new_key']\\n compose = params['compose']\\n\\n \\n\\n git_name = parse_git_url(git)\\n \\n # Make sure we have a valid git name\\n if git_name == '':\\n return JsonResponse({'message': 'invalid git url', 'reason': 'bad_git_url'}, status=400)\\n \\n # Replace copied ssh with proper ssh\\n #if \\\"git@github.com\\\" in git:\\n # git = \\\"ssh://\\\" + git\\n \\n # append our repo name to our dir name\\n if dirt.endswith('/'):\\n dirt = dirt + git_name\\n else:\\n dirt = dirt + '/' + git_name\\n\\n # awesome, now lets create our custom url\\n webhook = hashlib.md5((params['name'] + SUPER_SECRET_KEY).encode())\\n\\n # First see if we need to generate an ssh key\\n # If we do we cannot pull automatically\\n if create_new_key:\\n generate_ssh_key(dirt)\\n else:\\n # Let's try to clone our repo\\n try:\\n clone_repository(git, dirt)\\n except GitError:\\n return JsonResponse({'message': 'invalid git url', 'reason': 'bad_git_url'}, status=400)\\n except ValueError:\\n return JsonResponse({'message': 'invalid directory, perhaps the destination folder exists?', 'reason': 'bad_dir'}, status=400)\\n\\n d = Deployment.objects.create(\\n name_text=name,\\n git_url_text=git,\\n dir_text=dirt,\\n is_running=run,\\n webhook_text=webhook.hexdigest(),\\n container_port=cport,\\n local_port=lport,\\n is_compose=compose)\\n\\n return JsonResponse({'message': 'success', 'id': d.id})\\n\\n '''except Exception as e:\\n print(e)\\n print(e.__class__.__name__)\\n return JsonResponse({'message': 'Invalid params'}, status=400)'''\\n\\n else:\\n return JsonResponse({'message': 'Unknown method'}, status=404)\",\n \"def execution_list(request, version, project, format=None):\\n if request.method == 'GET':\\n executions = Execution.objects.filter(versionName=version, tckey__startswith=project).order_by('execId')\\n serializer = ExecutionSerializer(executions, many=True)\\n return Response(serializer.data)\",\n \"def list_versions(self):\\n if not USE_GCLOUD:\\n return self.run_appcfg(['list_versions'])\\n data = self.run_gcloud(['app', 'versions', 'list'])\\n per_module = collections.defaultdict(list)\\n for deployment in data:\\n service = deployment['service'].encode('utf-8')\\n version_id = deployment['id'].encode('utf-8')\\n per_module[service].append(version_id)\\n return dict(per_module)\",\n \"def api_asset_list():\\n return jsonify(app.bank.to_list()), 200\",\n \"def fetch_list(self):\\n\\t\\treturn self.fetch(self.list_url % ART_SERVER_HOST)\",\n \"def index():\\r\\n\\r\\n module_name = deployment_settings.modules[module].name_nice\\r\\n response.title = module_name\\r\\n return dict(module_name=module_name)\",\n \"def get_inventory(self, context):\\n logger = get_logger_with_thread_id(context)\\n logger.info(\\\"Autoload\\\")\\n\\n with ErrorHandlingContext(logger):\\n cs_api = get_api(context)\\n\\n vblade_resource = TeraVMTrafficGeneratorVBladeResource.from_context(context)\\n\\n # get VM uuid of the Deployed App\\n deployed_vm_resource = cs_api.GetResourceDetails(vblade_resource.fullname)\\n vmuid = deployed_vm_resource.VmDetails.UID\\n logger.info(\\\"Deployed TVM Module App uuid: {}\\\".format(vmuid))\\n\\n # get vCenter name\\n app_request_data = json.loads(context.resource.app_context.app_request_json)\\n vcenter_name = app_request_data[\\\"deploymentService\\\"][\\\"cloudProviderName\\\"]\\n logger.info(\\\"vCenter shell resource name: {}\\\".format(vcenter_name))\\n\\n vsphere = pyVmomiService(SmartConnect, Disconnect, task_waiter=None)\\n\\n # get vCenter credentials\\n vcenter_resource = cs_api.GetResourceDetails(resourceFullPath=vcenter_name)\\n user = self._get_resource_attribute_value(resource=vcenter_resource,\\n attribute_name=VCENTER_RESOURCE_USER_ATTR)\\n\\n encrypted_password = self._get_resource_attribute_value(resource=vcenter_resource,\\n attribute_name=VCENTER_RESOURCE_PASSWORD_ATTR)\\n\\n password = cs_api.DecryptPassword(encrypted_password).Value\\n\\n logger.info(\\\"Connecting to the vCenter: {}\\\".format(vcenter_name))\\n si = vsphere.connect(address=vcenter_resource.Address, user=user, password=password)\\n\\n # find Deployed App VM on the vCenter\\n vm = vsphere.get_vm_by_uuid(si, vmuid)\\n\\n phys_interfaces = []\\n comms_mac_addr = None\\n\\n for device in vm.config.hardware.device:\\n if isinstance(device, vim.vm.device.VirtualEthernetCard):\\n if device.deviceInfo.summary.lower() == vblade_resource.tvm_comms_network.lower():\\n comms_mac_addr = device.macAddress\\n else:\\n phys_interfaces.append(device)\\n\\n if comms_mac_addr is None:\\n raise Exception(\\\"Unable to find TVM Comms network with name '{}' on the device\\\"\\n .format(vblade_resource.tvm_comms_network))\\n\\n logger.info(\\\"Found interfaces on the device: {}\\\".format(phys_interfaces))\\n module_res = models.TeraVMModule(shell_name=\\\"\\\",\\n name=\\\"Module {}\\\".format(comms_mac_addr.replace(\\\":\\\", \\\"-\\\")),\\n unique_id=hash(comms_mac_addr))\\n\\n logger.info(\\\"Updating resource address for the module to {}\\\".format(comms_mac_addr))\\n cs_api.UpdateResourceAddress(context.resource.fullname, comms_mac_addr)\\n\\n for port_number, phys_interface in enumerate(phys_interfaces, start=1):\\n network_adapter_number = phys_interface.deviceInfo.label.lower().strip(\\\"network adapter \\\")\\n unique_id = hash(phys_interface.macAddress)\\n port_res = models.TeraVMPort(shell_name=\\\"\\\",\\n name=\\\"Port {}\\\".format(port_number),\\n unique_id=unique_id)\\n\\n port_res.mac_address = phys_interface.macAddress\\n port_res.requested_vnic_name = network_adapter_number\\n module_res.add_sub_resource(unique_id, port_res)\\n\\n return AutoloadDetailsBuilder(module_res).autoload_details()\",\n \"def deployment_summary(self):\\n if \\\"deploymentSummary\\\" in self._prop_dict:\\n if isinstance(self._prop_dict[\\\"deploymentSummary\\\"], OneDriveObjectBase):\\n return self._prop_dict[\\\"deploymentSummary\\\"]\\n else :\\n self._prop_dict[\\\"deploymentSummary\\\"] = ManagedAppPolicyDeploymentSummary(self._prop_dict[\\\"deploymentSummary\\\"])\\n return self._prop_dict[\\\"deploymentSummary\\\"]\\n\\n return None\",\n \"async def read_deployments(\\n self,\\n *,\\n flow_filter: FlowFilter = None,\\n flow_run_filter: FlowRunFilter = None,\\n task_run_filter: TaskRunFilter = None,\\n deployment_filter: DeploymentFilter = None,\\n work_pool_filter: WorkPoolFilter = None,\\n work_queue_filter: WorkQueueFilter = None,\\n limit: int = None,\\n sort: DeploymentSort = None,\\n offset: int = 0,\\n ) -> List[DeploymentResponse]:\\n body = {\\n \\\"flows\\\": flow_filter.dict(json_compatible=True) if flow_filter else None,\\n \\\"flow_runs\\\": (\\n flow_run_filter.dict(json_compatible=True, exclude_unset=True)\\n if flow_run_filter\\n else None\\n ),\\n \\\"task_runs\\\": (\\n task_run_filter.dict(json_compatible=True) if task_run_filter else None\\n ),\\n \\\"deployments\\\": (\\n deployment_filter.dict(json_compatible=True)\\n if deployment_filter\\n else None\\n ),\\n \\\"work_pools\\\": (\\n work_pool_filter.dict(json_compatible=True)\\n if work_pool_filter\\n else None\\n ),\\n \\\"work_pool_queues\\\": (\\n work_queue_filter.dict(json_compatible=True)\\n if work_queue_filter\\n else None\\n ),\\n \\\"limit\\\": limit,\\n \\\"offset\\\": offset,\\n \\\"sort\\\": sort,\\n }\\n\\n response = await self._client.post(\\\"/deployments/filter\\\", json=body)\\n return pydantic.parse_obj_as(List[DeploymentResponse], response.json())\",\n \"def getCatalogs():\",\n \"def test_list_monitorings_deployment_not_found(self):\\n project_id = util.MOCK_UUID_1\\n deployment_id = \\\"unk\\\"\\n\\n rv = TEST_CLIENT.get(\\n f\\\"/projects/{project_id}/deployments/{deployment_id}/monitorings\\\"\\n )\\n result = rv.json()\\n expected = {\\n \\\"message\\\": \\\"The specified deployment does not exist\\\",\\n \\\"code\\\": \\\"DeploymentNotFound\\\",\\n }\\n self.assertDictEqual(expected, result)\\n self.assertEqual(rv.status_code, 404)\",\n \"def get_deployment_batch(self):\\r\\n pkg_path = self.package.path\\r\\n pkg_name = self.package.name\\r\\n\\r\\n self.batch.clear()\\r\\n # add unpacking script to batch\\r\\n LOGGER.info('Adding batch to unpack {0} from {1}'.format(\\r\\n pkg_name, pkg_path))\\r\\n self.get_package_batch()\\r\\n\\r\\n # add deployment script to batch\\r\\n self.batch.add('TARGET', self.cli_composer.get_target_cmd())\\r\\n self.batch.add('LOGIN', self.cli_composer.get_login_cmd())\\r\\n self.batch.add(\\r\\n 'REMOVED', self.cli_composer.get_delete_cmd(pkg_name), True)\\r\\n self.batch.add('LIST', self.cli_composer.get_list_cmd())\\r\\n self.batch.add('DEPLOYED', self.cli_composer.get_push_cmd(\\r\\n pkg_name, '{0}'.format(pkg_name)))\\r\\n self.batch.add('NEWLIST', self.cli_composer.get_list_cmd())\\r\\n self.batch.add('DIR', ['ls', '-al'])\",\n \"def get_tasks(self):\\n assert self.cluster\\n query = objects.TransactionCollection.filter_by(\\n None,\\n cluster_id=self.cluster.id, name=consts.TASK_NAMES.deployment\\n )\\n query = objects.TransactionCollection.filter_by_not(\\n query, deployment_info=None\\n )\\n return objects.TransactionCollection.order_by(query, 'id')\",\n \"def fusion_api_get_deployment_manager(self, uri=None, param='', api=None, headers=None):\\n return self.dep_mgr.get(uri=uri, api=api, headers=headers, param=param)\",\n \"def _load_deployments(self):\\n deployments = []\\n with open(DEPLOYMENTS_YML, \\\"rb\\\") as dep_yaml:\\n deployments = yaml.load(dep_yaml.read(), Loader=yaml.Loader)\\n for dep in deployments:\\n if len(BASE_DIR) > 0 and not BASE_DIR.endswith(os.sep):\\n dep.path = os.sep.join((BASE_DIR, dep.path))\\n comps = []\\n for comp in dep.components:\\n comps.append(comp.get_instance())\\n dep.components = comps\\n return deployments\",\n \"def populate_deployment_statuses():\\n _set_latest_execution()\\n _set_installation_status()\\n _set_deployment_status()\",\n \"def test_get_hyperflex_app_catalog_list(self):\\n pass\",\n \"def bamboo_deployments(ctx, from_date, to_date, use_cache):\\r\\n\\r\\n if from_date is None:\\r\\n from_date, to_date = previous_month_range()\\r\\n\\r\\n log.info('Getting Bamboo deployments between {} and {}'.format(from_date, to_date))\\r\\n report = BambooDeploymentsReport(\\r\\n ctx.obj,\\r\\n from_date=from_date,\\r\\n to_date=to_date\\r\\n )\\r\\n report.run_report(use_cache=use_cache)\",\n \"def list(number=0):\\n buckets = get_buckets(number)\\n data = [(\\\"Name\\\", \\\"Versioned\\\", \\\"LifeCycle\\\")]\\n col_width = [0, 0, 0]\\n rows = []\\n for name, bucket in buckets.items():\\n v = bucket[\\\"versioning\\\"]\\n l = bucket[\\\"lifecycle\\\"]\\n v = v if v else \\\"Disabled\\\"\\n if l:\\n l = json.dumps(l, indent=1)\\n else:\\n l = \\\"None\\\"\\n data.append((name, v, l))\\n for row in data:\\n for i, info in enumerate(row):\\n col_width[i] = min(max(len(info) + 2, col_width[i]), 48)\\n dashes = tuple((\\\"-\\\" * (width - 1) for width in col_width))\\n data.insert(1, dashes)\\n click.echo(f\\\"The status of the buckets:\\\")\\n for row in data:\\n output = \\\"\\\"\\n for i in range(3):\\n output += row[i].ljust(col_width[i])\\n if not VERBOSE:\\n click.echo(output)\\n logger.info(output)\",\n \"def list(args, config):\\n\\n api = config['API']\\n headers = {}\\n if args.stack_name:\\n headers = {'stack-name': args.stack_name} # put stack name in headers\\n r = requests.get(api['list'], headers=headers) # send the GET request\\n print('\\\\nThe following clusters exist:\\\\n{}\\\\n'.format(r.json()))\\n return\",\n \"def get_indexes(self):\\n resources = []\\n\\n data = self.get_resource_data(type='sources')\\n data['url'] = constants.URLS['sources'] % data\\n resources.append(data)\\n\\n for arch in self.data['arch']:\\n data = self.get_resource_data(type='packages', arch=arch)\\n data['url'] = constants.URLS['packages'] % data\\n resources.append(data)\\n return resources\",\n \"def do_list(cs, args):\\n opts = {}\\n opts['all_projects'] = args.all_projects\\n opts['marker'] = args.marker\\n opts['limit'] = args.limit\\n opts['sort_key'] = args.sort_key\\n opts['sort_dir'] = args.sort_dir\\n opts['image'] = args.image\\n opts['name'] = args.name\\n opts['project_id'] = args.project_id\\n opts['user_id'] = args.user_id\\n opts['host'] = args.host\\n opts['task_state'] = args.task_state\\n opts['memory'] = args.memory\\n opts['auto_remove'] = args.auto_remove\\n opts['status'] = args.status\\n opts = zun_utils.remove_null_parms(**opts)\\n containers = cs.containers.list(**opts)\\n zun_utils.list_containers(containers)\",\n \"def deploy():\\n return do_deploy(do_pack())\",\n \"def deploy():\\n return do_deploy(do_pack())\",\n \"def deploy():\\n return do_deploy(do_pack())\",\n \"def deploy_changes(self):\\n logging.debug(\\\"In the deploy_changes() class method.\\\")\\n\\n url = \\\"/deployment/deploymentrequests\\\"\\n devices = self.get_deployable_devices()\\n if not devices:\\n logging.info(\\\"No devices need deployed.\\\\n\\\\n\\\")\\n return\\n nowtime = int(1000000 * datetime.datetime.now().timestamp())\\n json_data = {\\n 'type': 'DeploymentRequest',\\n 'forceDeploy': True,\\n 'ignoreWarning': True,\\n 'version': nowtime,\\n 'deviceList': []\\n }\\n for device in devices:\\n logging.info(\\\"Adding device {} to deployment queue.\\\".format(device))\\n json_data['deviceList'].append(device)\\n logging.info(\\\"Deploying changes to devices.\\\")\\n response = self.send_to_api(method='post', url=url, json_data=json_data)\\n return response['deviceList']\",\n \"def test_deployment(self):\\n config = {'example.com': {\\n 'applications': {\\n 'site-hybridcluster': {\\n 'image': 'unknown',\\n },\\n 'mysql-hybridcluster': {\\n 'image': 'unknown',\\n }\\n },\\n 'version': 1\\n }}\\n expected = Deployment(nodes=frozenset([\\n Node(hostname='example.com', applications=frozenset([\\n Application(\\n name='mysql-hybridcluster',\\n image=DockerImage.from_string('unknown'),\\n ports=frozenset(),\\n ),\\n Application(\\n name='site-hybridcluster',\\n image=DockerImage.from_string('unknown'),\\n )]))]))\\n self.assertEqual(expected,\\n current_from_configuration(config))\",\n \"def get(self):\\n return read_heroku_apps(request.args)\",\n \"def select_deployment_to_run(deployments=None, command='build'): # noqa pylint: disable=too-many-branches,too-many-statements\\n if deployments is None or not deployments:\\n return []\\n deployments_to_run = []\\n\\n if len(deployments) == 1:\\n selected_index = 1\\n else:\\n print('')\\n print('Configured deployments:')\\n pretty_index = 1\\n for i in deployments:\\n print(\\\"%s: %s\\\" % (pretty_index, json.dumps(i)))\\n pretty_index += 1\\n print('')\\n print('')\\n if command == 'destroy':\\n print('(Operating in destroy mode -- \\\"all\\\" will destroy all '\\n 'deployments in reverse order)')\\n selected_index = input('Enter number of deployment to run '\\n '(or \\\"all\\\"): ')\\n\\n if selected_index == 'all':\\n return deployments\\n if selected_index == '':\\n LOGGER.error('Please select a valid number (or \\\"all\\\")')\\n sys.exit(1)\\n\\n selected_deploy = deployments[int(selected_index) - 1]\\n if selected_deploy.get('current_dir', False):\\n deployments_to_run.append(selected_deploy)\\n elif not selected_deploy.get('modules', []):\\n LOGGER.error('No modules configured in selected deployment')\\n sys.exit(1)\\n elif len(selected_deploy['modules']) == 1:\\n # No need to select a module in the deployment - there's only one\\n if command == 'destroy':\\n LOGGER.info('(only one deployment detected; all modules '\\n 'automatically selected for termination)')\\n if not strtobool(input('Proceed?: ')):\\n sys.exit(0)\\n deployments_to_run.append(selected_deploy)\\n else:\\n print('')\\n print('Configured modules in deployment:')\\n pretty_index = 1\\n for i in selected_deploy['modules']:\\n print(\\\"%s: %s\\\" % (pretty_index, json.dumps(i)))\\n pretty_index += 1\\n print('')\\n print('')\\n if command == 'destroy':\\n print('(Operating in destroy mode -- \\\"all\\\" will destroy all '\\n 'deployments in reverse order)')\\n selected_index = input('Enter number of module to run '\\n '(or \\\"all\\\"): ')\\n if selected_index == 'all':\\n deployments_to_run.append(selected_deploy)\\n elif selected_index == '':\\n LOGGER.error('Please select a valid number (or \\\"all\\\")')\\n sys.exit(1)\\n else:\\n module_list = [selected_deploy['modules'][int(selected_index) - 1]] # noqa\\n selected_deploy['modules'] = module_list\\n deployments_to_run.append(selected_deploy)\\n\\n LOGGER.debug('Selected deployment is %s...', deployments_to_run)\\n return deployments_to_run\",\n \"def deployment_configs(self) -> Optional[pulumi.Input['PagesProjectDeploymentConfigsArgs']]:\\n return pulumi.get(self, \\\"deployment_configs\\\")\",\n \"def deployment_configs(self) -> Optional[pulumi.Input['PagesProjectDeploymentConfigsArgs']]:\\n return pulumi.get(self, \\\"deployment_configs\\\")\",\n \"def deploy():\\n test()\\n if not env.is_staging:\\n backup()\\n prepare()\\n restart_api()\",\n \"def test_list_operators_deployment_not_found(self):\\n project_id = util.MOCK_UUID_1\\n deployment_id = \\\"unk\\\"\\n\\n rv = TEST_CLIENT.get(\\n f\\\"/projects/{project_id}/deployments/{deployment_id}/operators\\\"\\n )\\n result = rv.json()\\n expected = {\\n \\\"message\\\": \\\"The specified deployment does not exist\\\",\\n \\\"code\\\": \\\"DeploymentNotFound\\\",\\n }\\n self.assertDictEqual(expected, result)\\n self.assertEqual(rv.status_code, 404)\",\n \"def get(workflow_id, deployment_id, logger, client, tenant_name):\\n utils.explicit_tenant_name_message(tenant_name, logger)\\n try:\\n logger.info('Retrieving workflow {0} for deployment {1}'.format(\\n workflow_id, deployment_id))\\n deployment = client.deployments.get(deployment_id)\\n workflow = next((wf for wf in deployment.workflows if\\n wf.name == workflow_id), None)\\n if not workflow:\\n raise CloudifyCliError(\\n 'Workflow {0} of deployment {1} not found'\\n .format(workflow_id, deployment_id))\\n except CloudifyClientError as e:\\n if e.status_code != 404:\\n raise\\n raise CloudifyCliError('Deployment {0} not found'.format(\\n deployment_id))\\n\\n defaults = {\\n 'blueprint_id': deployment.blueprint_id,\\n 'deployment_id': deployment.id\\n }\\n columns = WORKFLOW_COLUMNS\\n\\n if get_global_json_output():\\n columns += ['parameters']\\n print_single(columns, workflow, 'Workflows:', defaults=defaults)\\n\\n if not get_global_json_output():\\n # print workflow parameters\\n mandatory_params = dict()\\n optional_params = dict()\\n for param_name, param in workflow.parameters.items():\\n params_group = optional_params if 'default' in param else \\\\\\n mandatory_params\\n params_group[param_name] = param\\n\\n logger.info('Workflow Parameters:')\\n logger.info('\\\\tMandatory Parameters:')\\n for param_name, param in mandatory_params.items():\\n if 'description' in param:\\n logger.info('\\\\t\\\\t{0}\\\\t({1})'.format(param_name,\\n param['description']))\\n else:\\n logger.info('\\\\t\\\\t{0}'.format(param_name))\\n\\n logger.info('\\\\tOptional Parameters:')\\n for param_name, param in optional_params.items():\\n if 'description' in param:\\n logger.info('\\\\t\\\\t{0}: \\\\t{1}\\\\t({2})'.format(\\n param_name, param['default'], param['description']))\\n else:\\n logger.info('\\\\t\\\\t{0}: \\\\t{1}'.format(param_name,\\n param['default']))\\n logger.info('')\",\n \"def get_details(self):\\n status = []\\n for key, container in self.containers.items():\\n container.details = container.daemon.connection.inspect_container(self.config['release_name'])\\n status.append(container.details)\\n return status\",\n \"def get_applications(status):\\n return status['applications']\",\n \"def index(self, request):\\n versions = []\\n for key, data in VERSIONS.items():\\n v = BaseVersion(\\n data[\\\"id\\\"],\\n data[\\\"status\\\"],\\n request.application_url,\\n data[\\\"updated\\\"])\\n versions.append(v)\\n return wsgi.Result(VersionsDataView(versions))\",\n \"def show(self):\\n self.parser.add_argument('assembly_uuid',\\n help=\\\"Assembly uuid or name\\\")\\n args = self.parser.parse_args()\\n response = self.client.assemblies.find(name_or_id=args.assembly_uuid)\\n fields = ['uuid', 'name', 'description', 'status', 'application_uri',\\n 'trigger_uri']\\n data = dict([(f, getattr(response, f, ''))\\n for f in fields])\\n cliutils.print_dict(data, wrap=72)\",\n \"def get_resources():\\n return Response(f\\\"{Resource.get_all_resources()}\\\", 200, mimetype='text/plain')\",\n \"def list(host, mqtt_port, rest_port):\\n\\n click.echo(\\\"Listing things from host \\\" + host + \\\".\\\")\\n service = Service(host, mqtt_port, str(rest_port))\\n things = service.get_all_things()\\n for thing in things:\\n click.echo(thing.__dict__)\\n if len(things) == 0:\\n click.secho(\\\"Unable to retrieve any things!\\\", fg=\\\"red\\\", bold=True)\",\n \"def test_read_namespaced_deployment_config_status(self):\\n pass\",\n \"def list_pods_in_a_deployment(self, namespace, dc):\\n pods_in_dc = None\\n pods_in_namespace = self.list_pods_in_a_namespace(namespace=namespace, label_selector=\\\"deploymentconfig\\\")\\n if pods_in_namespace:\\n pod_list = pods_in_namespace.items\\n pods_in_dc = [\\n pod.metadata.name\\n for pod in pod_list\\n if pod.metadata.annotations[\\\"openshift.io/deployment-config.name\\\"] == dc\\n ]\\n return pods_in_dc\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6772889","0.66647077","0.66647077","0.6597974","0.6530962","0.6526921","0.6526921","0.6485451","0.64644516","0.63073653","0.623222","0.621316","0.61912525","0.6129055","0.6088292","0.60881466","0.6058188","0.60260034","0.6011649","0.5992773","0.5969929","0.5949145","0.58676106","0.5804405","0.57625717","0.57107437","0.5710443","0.57072955","0.57063097","0.56385994","0.5632327","0.56233186","0.55982673","0.5583589","0.5576758","0.5570646","0.5553052","0.55471975","0.5520373","0.5515145","0.5501175","0.549575","0.5493952","0.548819","0.5484794","0.5484136","0.5446613","0.5443564","0.54311454","0.5411664","0.5394615","0.536098","0.53516597","0.5349257","0.53363454","0.5285995","0.5283369","0.5279037","0.52787256","0.52779204","0.5272361","0.5271384","0.5264628","0.5247872","0.5246687","0.52433205","0.52422166","0.5234624","0.5226499","0.52240765","0.5213782","0.52101725","0.52062964","0.52027404","0.52022195","0.51889664","0.51867926","0.5174003","0.51691663","0.5159864","0.51517993","0.51517993","0.51517993","0.5147665","0.5146713","0.5144025","0.51421034","0.5135648","0.5135648","0.51331323","0.51239574","0.5122871","0.51143694","0.5104388","0.5103753","0.5092519","0.5080615","0.5078367","0.50768733","0.50656885"],"string":"[\n \"0.6772889\",\n \"0.66647077\",\n \"0.66647077\",\n \"0.6597974\",\n \"0.6530962\",\n \"0.6526921\",\n \"0.6526921\",\n \"0.6485451\",\n \"0.64644516\",\n \"0.63073653\",\n \"0.623222\",\n \"0.621316\",\n \"0.61912525\",\n \"0.6129055\",\n \"0.6088292\",\n \"0.60881466\",\n \"0.6058188\",\n \"0.60260034\",\n \"0.6011649\",\n \"0.5992773\",\n \"0.5969929\",\n \"0.5949145\",\n \"0.58676106\",\n \"0.5804405\",\n \"0.57625717\",\n \"0.57107437\",\n \"0.5710443\",\n \"0.57072955\",\n \"0.57063097\",\n \"0.56385994\",\n \"0.5632327\",\n \"0.56233186\",\n \"0.55982673\",\n \"0.5583589\",\n \"0.5576758\",\n \"0.5570646\",\n \"0.5553052\",\n \"0.55471975\",\n \"0.5520373\",\n \"0.5515145\",\n \"0.5501175\",\n \"0.549575\",\n \"0.5493952\",\n \"0.548819\",\n \"0.5484794\",\n \"0.5484136\",\n \"0.5446613\",\n \"0.5443564\",\n \"0.54311454\",\n \"0.5411664\",\n \"0.5394615\",\n \"0.536098\",\n \"0.53516597\",\n \"0.5349257\",\n \"0.53363454\",\n \"0.5285995\",\n \"0.5283369\",\n \"0.5279037\",\n \"0.52787256\",\n \"0.52779204\",\n \"0.5272361\",\n \"0.5271384\",\n \"0.5264628\",\n \"0.5247872\",\n \"0.5246687\",\n \"0.52433205\",\n \"0.52422166\",\n \"0.5234624\",\n \"0.5226499\",\n \"0.52240765\",\n \"0.5213782\",\n \"0.52101725\",\n \"0.52062964\",\n \"0.52027404\",\n \"0.52022195\",\n \"0.51889664\",\n \"0.51867926\",\n \"0.5174003\",\n \"0.51691663\",\n \"0.5159864\",\n \"0.51517993\",\n \"0.51517993\",\n \"0.51517993\",\n \"0.5147665\",\n \"0.5146713\",\n \"0.5144025\",\n \"0.51421034\",\n \"0.5135648\",\n \"0.5135648\",\n \"0.51331323\",\n \"0.51239574\",\n \"0.5122871\",\n \"0.51143694\",\n \"0.5104388\",\n \"0.5103753\",\n \"0.5092519\",\n \"0.5080615\",\n \"0.5078367\",\n \"0.50768733\",\n \"0.50656885\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6200603"},"document_rank":{"kind":"string","value":"12"}}},{"rowIdx":14378,"cells":{"query":{"kind":"string","value":"Fetch compute cell data"},"document":{"kind":"string","value":"def get_compute_cell_data(cluster_id=None, namespace_id=None):\n cells_info = client.CustomObjectsApi().list_cluster_custom_object('kiyot.elotl.co', 'v1beta1', 'cells')\n return cells_info"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _fetch_data(self):\n pass","def getdata(\n self,\n col: str,\n exp: str,\n chan: str,\n res: int,\n xs: Tuple[int, int],\n ys: Tuple[int, int],\n zs: Tuple[int, int],\n ):\n if self.hasdata(col, exp, chan, res, xs, ys, zs):\n return self.fs.retrieve(col, exp, chan, res, xs, ys, zs)\n if self.is_terminal:\n raise IndexError(\"Cannot find data at: \", (col, exp, chan, res, xs, ys, zs))\n data = self._next.getdata(col, exp, chan, res, xs, ys, zs)\n if self._cache:\n self.setdata(data, col, exp, chan, res, xs, ys, zs)\n return data","def fetch_data():\n data.fetch_data()\n data.start_updating()","def fetch_data(self):","def get_nypd_complaint_results():\n data = None\n blob = BUCKET.blob(NYPD_COMPLAINT_FNAME)\n if blob.exists():\n blob.reload(client=STORAGE_CLIENT)\n if blob.time_created.strftime(\"%Y-%m-%d\") == datetime.now().strftime(\"%Y-%m-%d\"):\n print(\"Getting cached file \" + NYPD_COMPLAINT_FNAME)\n return {\"data\": __retrieve_from_bucket(NYPD_COMPLAINT_FNAME)}\n \n df = update_nypd_complaint_results()\n data = df.to_dict(orient=\"records\")\n # print(df)\n return {\"data\": data}","def get(self, **cell):\n cellobj = Cell(**cell)\n if not cellobj.is_terrestial:\n raise Exception(\"Priogrid only has data for terrestial cells, you are trying to get data for a non-terrestial cell\")\n\n if not self.name in CACHED_VARS:\n CACHED_VARS[self.name] = dict()\n CACHED_VARS[self.name][\"data\"] = get_unknown_data(self.name, startyr=self.startyr, endyr=self.endyr, interpolated=self.interpolated)\n\n # TODO: Test that doesnt slow down...\n## if not all((yr in CACHED_VARS[self.name][\"data\"][cellobj.gid][\"data\"] for yr in range(self.startyr, self.endyr+1))):\n## newdata = get_unknown_data(self.name, startyr=self.startyr, endyr=self.endyr, interpolated=self.interpolated)\n## yrdict = CACHED_VARS[self.name][\"data\"][cellobj.gid][\"data\"]\n## yrdict.update( newdata[cellobj.gid][\"data\"] )\n \n value = CACHED_VARS[self.name][\"data\"][cellobj.gid][\"data\"]\n return value","def _read_cell_direct(cls):\n\n cell_data = {}\n cell_columns = cls._get_columns(CELL_MANIFEST)\n cell_psvs = cls._get_component_psvs(CELL_MANIFEST)\n\n for cell_psv in cell_psvs:\n for row in gzip.GzipFile(fileobj=io.BytesIO(cls._read_s3_url(cell_psv))):\n row_dict = dict(zip(cell_columns, row.strip().split(b'|')))\n cell_data[row_dict[\"cellkey\"].decode()] = {k: v.decode() for\n k, v in row_dict.items()}\n total_umis = cell_data[row_dict[\"cellkey\"].decode()][\"total_umis\"]\n cell_data[row_dict[\"cellkey\"].decode()][\"total_umis\"] = (total_umis if total_umis == \"nan\"\n else str(float(total_umis)))\n\n return cell_data","def do_fetch(self):\n pass","def get_cells(self):\n raise NotImplementedError","async def _fetch_data(self) -> T:\n raise NotImplementedError","def get_data():\n pass","async def _fetch_data(self) -> JobInfo:\n return await self.api.get_job()","async def fetch_data(self) -> T:","def compute(cls, dataset):\n return dataset","def get_data():\n return","def run(self):\n results = self.fetch()\n return results","def _get_data(self):\n raise NotImplementedError()","def getData(self, local_cache):","def fetch_data(self):\n\n data_dict = {\n 'price': self.get_current_price(),\n }\n\n return self.save_data(data_dict)","def run_calculation():\n\n print(\"Creating %d-process pool\" % mp.cpu_count())\n\n pool = mp.Pool(mp.cpu_count())\n\n f = h5py.File('/testdata/mandelbrot.hdf5', 'w')\n\n print(\"Creating output dataset with shape %s x %s\" % (NX, NY))\n\n dset = f.create_dataset('mandelbrot', (NX, NY), 'i')\n dset.attrs['XSTART'] = XSTART\n dset.attrs['YSTART'] = YSTART\n dset.attrs['XEXTENT'] = XEXTENT\n dset.attrs['YEXTENT'] = YEXTENT\n\n result = pool.imap(compute_row, (x * xincr for x in range(NX)))\n\n for idx, arr in enumerate(result):\n if idx % 25 == 0: print(\"Recording row %s\" % idx)\n dset[idx] = arr\n\n print(\"Closing HDF5 file\")\n\n f.close()\n\n print(\"Shutting down process pool\")\n\n pool.close()\n pool.join()","def get_building_complaint_results():\n data = None\n blob = BUCKET.blob(BUILDING_COMPLAINT_FNAME)\n if blob.exists():\n blob.reload(client=STORAGE_CLIENT)\n if blob.time_created.strftime(\"%Y-%m-%d\") == datetime.now().strftime(\"%Y-%m-%d\"):\n print(\"Getting cached file \" + BUILDING_COMPLAINT_FNAME)\n return {\"data\": __retrieve_from_bucket(BUILDING_COMPLAINT_FNAME)}\n \n df = update_building_complaint_results()\n data = df.to_dict(orient=\"records\")\n return {\"data\": data}","def _data(self):\n cell = self._get_cell()\n return deepcopy(cell.data)","def data_fetch(self, curs, splat_table, mcl_table, crs_no=0, output_fname=None):\n\t\tgene_no2gene_id = get_gene_no2gene_id(curs)\t#08-31-05\n\t\toutf = open(output_fname, 'w')\t#08-31-05\n\t\toutf.write(\"r:=[\")\t#08-31-05\n\t\t\n\t\tmcl_id2cluster_dstructure = {}\n\t\tno_of_total_genes = get_no_of_total_genes(curs)\n\t\tsys.stderr.write(\"Getting the basic information for all clusters...\\n\")\n\t\tcurs.execute(\"DECLARE crs%s CURSOR FOR select m.mcl_id, m.vertex_set, m.connectivity, 0,\\\n\t\t\tm.recurrence_array, s.edge_set, s.connectivity, m.cooccurrent_cluster_id from %s m, %s s where \\\n\t\t\tm.splat_id=s.splat_id\"\\\n\t\t\t%(crs_no, mcl_table, splat_table))\t#06-20-05\tconnectivity_original faked to be 0\n\t\tcurs.execute(\"fetch 5000 from crs%s\"%crs_no)\n\t\trows = curs.fetchall()\n\t\twhile rows:\n\t\t\tfor row in rows:\n\t\t\t\tunit = cluster_dstructure()\n\t\t\t\tunit.cluster_id = row[0]\n\t\t\t\tvertex_set = row[1][1:-1].split(',')\n\t\t\t\tunit.vertex_set = map(int, vertex_set)\n\t\t\t\tunit.connectivity = row[2]\n\t\t\t\tunit.connectivity_original = row[3]\n\t\t\t\trecurrence_array = row[4][1:-1].split(',')\n\t\t\t\tunit.recurrence_array = map(float, recurrence_array)\n\t\t\t\tunit.edge_set = parse_splat_table_edge_set(row[5])\n\t\t\t\tunit.splat_connectivity = row[6]\n\t\t\t\tunit.cooccurrent_cluster_id = row[7]\n\t\t\t\tunit.go_no2association_genes = self.get_go_functions_of_this_gene_set(curs, unit.vertex_set)\n\t\t\t\tunit.go_no2information = self.get_information_of_go_functions(curs, \\\n\t\t\t\t\tunit.go_no2association_genes, len(unit.vertex_set), no_of_total_genes, p_value_cut_off=0.05)\t#jasmine wants to cut some go-nos.\n\t\t\t\tunit.edge_cor_2d_list, unit.edge_sig_2d_list = self.get_cor_sig_2d_list(curs, unit.edge_set)\n\t\t\t\t\n\t\t\t\tstr_tmp = self.return_string_form_of_cluster_dstructure(unit, gene_no2gene_id)\t#08-31-05\n\t\t\t\toutf.write(\"%s,\"%str_tmp)\n\t\t\t\t#mcl_id2cluster_dstructure[unit.cluster_id] = unit\n\t\t\t\t\"\"\"\n\t\t\t\torder_1st_id, order_2nd_id = map(int, unit.cooccurrent_cluster_id.split('.'))\n\t\t\t\tif order_1st_id not in self.order_1st_id2all_clusters:\n\t\t\t\t\tself.order_1st_id2all_clusters[order_1st_id] = {}\n\t\t\t\tif order_2nd_id not in self.order_1st_id2all_clusters[order_1st_id]:\n\t\t\t\t\tself.order_1st_id2all_clusters[order_1st_id][order_2nd_id] = []\n\t\t\t\tself.order_1st_id2all_clusters[order_1st_id][order_2nd_id].append(unit)\n\t\t\t\t\"\"\"\n\t\t\tcurs.execute(\"fetch 5000 from crs%s\"%crs_no)\n\t\t\trows = curs.fetchall()\n\t\toutf.write(\"[]]:\")\t#08-31-05, 09-01-05 add the last blank []\n\t\tdel outf\n\t\tsys.stderr.write(\"Done.\\n\")\n\t\treturn mcl_id2cluster_dstructure","def read(self):\n self._load_metadata()\n return self._df.compute()","def _retrieveCachedData(self):","def get_data(self):","def cell(self):\n return self._cell","def get_data():\n pass","def get_data():\n pass","def get_data():\n pass","def get_data(self, data_to_cpu=False, *args):\n raise NotImplementedError","def fetch(self):\n # type: () -> List[List[Any]]\n if self._request.next_uri is None:\n self._finished = True\n return []\n response = self._request.get(self._request.next_uri)\n status = self._request.process(response)\n if status.columns:\n self._columns = status.columns\n self._stats.update(status.stats)\n if status.next_uri is None:\n self._finished = True\n return status.rows","def fetch(self):\n pass","def fetch(self):\n pass","def get_cell():\n q = request.query.decode()\n worksheet = q['worksheetKey']\n row = q['row']\n col = q['col']\n value = ss.read_cell(worksheet, row, col)\n response.status = '200 OK'\n response.content_type = 'application/json'\n return {'value': value,\n 'row': row,\n 'col': col,\n 'worksheetKey': worksheet}","def computation_download_data(self, uid: str, data_file: str) -> int:\n return self.context.download(\"/ckks/computation/data/%s\" % uid, data_file, None, None,\n \"CKKS:: failed downloading computed data for uid: %s\" % uid\n )","def __get(self, sock=None):\n\n # -------------------------------------------\n # Build command string\n\n cmd = dict()\n\n cmd[\"name_\"] = self.thisptr[\"df_name_\"]\n cmd[\"type_\"] = \"Column.get\"\n\n cmd[\"col_\"] = self.thisptr\n\n # -------------------------------------------\n # Establish communication with getml engine\n\n if sock is None:\n s = comm.send_and_receive_socket(cmd)\n else:\n s = sock\n comm.send_string(s, json.dumps(cmd))\n\n msg = comm.recv_string(s)\n\n # -------------------------------------------\n # Make sure everything went well, receive data\n # and close connection\n\n if msg != \"Found!\":\n s.close()\n raise Exception(msg)\n\n mat = comm.recv_matrix(s)\n\n # -------------------------------------------\n # Close connection.\n\n if sock is None:\n s.close()\n\n # -------------------------------------------\n # If this is a time stamp, then transform to\n # pd.Timestamp.\n\n if self.thisptr[\"type_\"] == \"Column\":\n if self.thisptr[\"role_\"] == \"time_stamp\" or \"time stamp\" in self.thisptr[\"unit_\"]:\n shape = mat.shape\n mat = [pd.Timestamp(ts_input=ts, unit=\"D\") for ts in mat.ravel()]\n mat = np.asarray(mat)\n mat.reshape(shape[0], shape[1])\n\n # -------------------------------------------\n\n return mat.ravel()\n\n # -------------------------------------------","def initialize(self) -> None:\n all_queries: List[Union[DataQueryInfo, MeasureQueryInfo]] = []\n entity_cells: List[DataCell] = []\n current_row_group = None\n\n # Loop over rows, columns\n for row_index, row in enumerate(self.rows):\n if isinstance(row, RowSeparator):\n current_row_group = row.name\n continue\n entity: Entity = row.entity\n if isinstance(entity, Entity):\n self.entity_map[entity.get_marquee_id()] = entity\n else:\n self.entity_map[''] = entity\n cells: List[DataCell] = []\n row_overrides = row.overrides\n\n for column_index, column in enumerate(self.columns):\n column_name = column.name\n column_processor = column.processor\n\n # Get all the data coordinate overrides and apply the processor override if it exists\n data_overrides, value_override, processor_override = _get_overrides(row_overrides, column_name)\n\n # Create the cell\n cell: DataCell = DataCell(column_name,\n column_processor,\n entity,\n data_overrides,\n column_index,\n row_index,\n current_row_group)\n\n if processor_override:\n # Check if there is a processor override and apply if so\n cell.processor = processor_override\n if value_override:\n cell.value = ProcessorResult(True, value_override.value)\n cell.updated_time = get_utc_now()\n elif isinstance(column_processor, EntityProcessor):\n # store these cells to fetch entity data during poll\n entity_cells.append(cell)\n elif isinstance(column_processor, CoordinateProcessor):\n # store these cells to fetch entity data during poll\n if len(data_overrides):\n # Get the last in the list if more than 1 override is given\n cell.processor.children['a'].set_dimensions(data_overrides[-1].dimensions)\n\n self._coord_processor_cells.append(cell)\n elif column_processor.measure_processor:\n all_queries.append(MeasureQueryInfo(attr='', entity=entity, processor=column_processor))\n else:\n # append the required queries to the map\n cell.build_cell_graph(all_queries, self.rdate_entity_map)\n\n cells.append(cell)\n\n self._cells.extend(cells)\n self.results.append(cells)\n\n self._data_queries = all_queries\n self._entity_cells = entity_cells\n self.is_initialized = True","def get_data(self):\n pass","def get_data(self):\n pass","def get_data(self, calc_throughput=False):\n n_timestamp = datetime.datetime.now()\n delta = n_timestamp - self.timestamp\n\n try:\n ret = ds_client().call('bridge\\n')\n except socket.error:\n return self.data\n\n self.timestamp = n_timestamp\n\n for brdata in ret:\n name = brdata[u'name']\n req = 'bridge ' + name.encode() + ' stats\\n'\n res = ds_client().call(req)[0]\n\n if calc_throughput == True:\n try:\n lps = calc_pps(res[u'flow-lookup-count'],\n self.data[brdata[u'name']][u'flow-lookup-count'],\n delta)\n mps = calc_pps(res[u'flow-matched-count'],\n self.data[brdata[u'name']][u'flow-matched-count'],\n delta)\n res.update({u'lookup_per_sec':lps, u'match_per_sec':mps})\n except KeyError:\n res.update({u'lookup_per_sec':0, u'match_per_sec':0})\n try:\n c_hit = (res[u'flowcache-hit']\n - self.data[brdata[u'name']][u'flowcache-hit'])\n lookup = (res[u'flow-lookup-count']\n - self.data[brdata[u'name']][u'flow-lookup-count'])\n c_rate = c_hit / lookup\n res.update({u'cache_hitrate':str(c_rate)})\n except (KeyError, ZeroDivisionError):\n res.update({u'cache_hitrate':'-'})\n self.data[res[u'name']] = res\n return self.data","def data(self):\n\t\tself.dworker()\n\t\treturn self.d","def get_data(self):\n return self._result","def get_data_in_cell_area(self, cell_parameters, ttl):\n\n # query_guid = payload[\"query_guid\"]\n this_lat = cell_parameters[\"latitude\"]\n this_lon = cell_parameters[\"longitude\"]\n # utility = cell_parameters[\"company\"]\n groom_time = cell_parameters[\"outageTime\"]\n # circuit_id = cell_parameters[\"circuitID\"]\n # asset_id = cell_parameters[\"assetID\"]\n # votes = cell_parameters[\"votes\"]\n # spatial = cell_parameters[\"spatial\"]\n # temporal = cell_parameters[\"temporal\"]\n # reputation_ena = cell_parameters[\"reputationEnabled\"]\n # zoom_t = cell_parameters[\"zoomT\"]\n # zoom_r = cell_parameters[\"zoomR\"]\n this_radius = cell_parameters[\"radius\"]\n # units = cell_parameters[\"units\"]\n\n query_type = \"ALL\"\n\n ont_serial_number_set = set()\n ont_items = {}\n\n asset_serial_number_set = set()\n asset_items = {}\n\n circuit_serial_number_set = set()\n circuit_items = {}\n # The six neighbor cells are initially set to be empty\n # This a string quid and an angle (in degrees)\n neighbor_array = [[\"\", 0], [\"\", 60], [\"\", 120], [\"\", 180], [\"\", 240], [\"\", 300]]\n this_cell = {'neighbors': neighbor_array,\n 'assets': {},\n 'onts': {},\n 'circuits': {},\n 'state': 'creating',\n 'lat_lon': [this_lat, this_lon],\n 'radius': this_radius,\n 'groom_time': groom_time,\n 'ttl': 0\n }\n page_number = 0\n page_size = 20\n query_parameter = json.dumps({\"itemType\": query_type,\n \"circle\": {\"longitude\": this_lon,\n \"latitude\": this_lat,\n \"radius\": this_radius, \"unit\": g_config.RADIUS_UNITS},\n \"pageParameter\": {\"page\": page_number, \"size\": page_size}})\n self.my_local_logger.debug(\"Formed query parameter: %s\" % query_parameter)\n dd = self.eon_api_bridge.query_post_eon_data_30(query_parameter=query_parameter)\n more_pages = True\n # Loop here until no more utility components of the first collection are found\n while more_pages and dd is not None:\n # This is the ONTs loop through them and find all the ONTs in the area\n for this_ont in dd['eligibility']['dataItems']:\n ont_dictionary_keyword = this_ont['ontSerialNumber']\n ont_serial_number_set.add(ont_dictionary_keyword)\n if ont_dictionary_keyword == \"[PENDING INSTALL]\":\n self.my_local_logger.debug(\"skipping this ont in eligibility list\")\n continue\n ont_items[ont_dictionary_keyword] = {'lat_lon': [this_ont['latitude'], this_ont['longitude']]}\n alarm_set_time = set()\n alarm_clear_time = set()\n ont_items[ont_dictionary_keyword]['events'] = [alarm_set_time, alarm_clear_time]\n ont_items[ont_dictionary_keyword]['assets'] = set()\n\n for this_alarm in dd['alarm']['dataItems']:\n alarm_dictionary_keyword = this_alarm['ontSerialNumber']\n if alarm_dictionary_keyword not in ont_serial_number_set:\n if alarm_dictionary_keyword == \"[PENDING INSTALL]\":\n self.my_local_logger.debug(\"skipping this ONT in the alarm list\")\n continue\n ont_serial_number_set.add(alarm_dictionary_keyword)\n ont_items[alarm_dictionary_keyword] = {'lat_lon': [this_alarm['latitude'], this_alarm['longitude']]}\n alarm_set_time = set()\n alarm_clear_time = set()\n ont_items[alarm_dictionary_keyword]['events'] = [alarm_set_time, alarm_clear_time]\n ont_items[alarm_dictionary_keyword]['assets'] = set()\n\n if this_alarm['alarmReceiveTime']:\n alarm_set = float(this_alarm['alarmReceiveTime']) # * 1e-3) / 60\n ont_items[alarm_dictionary_keyword]['events'][0].add(alarm_set)\n\n if this_alarm['alarmClearTime']:\n alarm_clear = float(this_alarm['alarmClearTime']) # * 1e-3) / 60\n ont_items[alarm_dictionary_keyword]['events'][1].add(alarm_clear)\n\n # Now go through the assets and associate the assets to the ONTs and the ONTs to the assets\n for this_item in dd['utility']['dataItems']:\n asset_dictionary_keyword = this_item['transformerID']\n if asset_dictionary_keyword not in asset_serial_number_set:\n asset_serial_number_set.add(asset_dictionary_keyword)\n asset_items[asset_dictionary_keyword] = {'lat_lon': [this_item['latitude'], this_item['longitude']]}\n asset_items[asset_dictionary_keyword]['events'] = [set(), set()]\n asset_items[asset_dictionary_keyword]['onts'] = set()\n asset_items[asset_dictionary_keyword]['guid'] = this_item['guid']\n asset_items[asset_dictionary_keyword]['serviceAddress'] = this_item['serviceAddress']\n for this_ont in this_item['eligibilityList']:\n ont_dictionary_keyword = this_ont['ontSerialNumber']\n if ont_dictionary_keyword not in ont_serial_number_set:\n ont_serial_number_set.add(ont_dictionary_keyword)\n ont_items[ont_dictionary_keyword] = {\n 'lat_lon': [this_ont['latitude'], this_ont['longitude']]}\n alarm_set_time = set()\n alarm_clear_time = set()\n ont_items[ont_dictionary_keyword]['events'] = [alarm_set_time, alarm_clear_time]\n ont_items[ont_dictionary_keyword]['assets'] = set()\n # Skip the ONTs that don't have an installation.\n if ont_dictionary_keyword == \"[PENDING INSTALL]\":\n self.my_local_logger.debug(\"skipping the ONT listed on eligibility list in asset_id=%s\" %\n asset_dictionary_keyword)\n self.my_local_logger.info(\"Skipping %s because it's status is PENDING INSTALL\" %\n asset_dictionary_keyword)\n continue\n # Stitch up the assets in the onts\n ont_items[ont_dictionary_keyword]['assets'].add(asset_dictionary_keyword)\n # Stitch up the onts in the assets\n asset_items[asset_dictionary_keyword]['onts'].add(ont_dictionary_keyword)\n\n circuit_dictionary_keyword = this_item['circuitID']\n if circuit_dictionary_keyword not in circuit_serial_number_set:\n # add the circuit item to the circuit_serial_number_set is needed\n circuit_serial_number_set.add(circuit_dictionary_keyword)\n # and create an empty set\n circuit_items[circuit_dictionary_keyword] = {'connected_items': set()}\n # Now add the data structure to the set\n circuit_items[circuit_dictionary_keyword]['connected_items'].add(asset_dictionary_keyword)\n\n ###########################\n # Look for the next page #\n ###########################\n if (dd['utility']['pageTotalItems'] == page_size) or \\\n (dd['alarm']['pageTotalItems'] == page_size) or \\\n (dd['eligibility']['pageTotalItems'] == page_size):\n self.my_local_logger.debug(\"Collecting next page for this message\")\n page_number += 1\n more_pages = True\n query_parameter = json.dumps({\"itemType\": query_type,\n \"circle\": {\"longitude\": this_lon,\n \"latitude\": this_lat,\n \"radius\": this_radius,\n \"unit\": g_config.RADIUS_UNITS},\n \"pageParameter\": {\"page\": page_number, \"size\": page_size}})\n dd = self.eon_api_bridge.query_post_eon_data_30(query_parameter=query_parameter)\n else:\n more_pages = False\n this_cell['assets'] = asset_items\n # Go over the ONT set and see it there are any that don't have alarms. This might happen if there were no alarms\n # posted to this ONT because the main alarm injestion loop failed for some reason. There will still be alarms\n # that are posted on the ONTs and those can be recovered here.\n for this_ont in ont_items:\n if len(ont_items[this_ont]['events'][0]) == 0 or len(ont_items[this_ont]['events'][1]) == 0:\n # To find any ONTs that don't seem to have alarms make this call:\n # where ONT_SERIAL_NUMBER is 00ABB96 in this example.\n # http://10.123.0.27:8080/eon360/api/alarms?sortBy=alarmReceiveTime&ontSerialNumber=000ABB96&p=0&s=20\n dd = self.eon_api_bridge.alarm_get_pons_nms_00(ont_serial_number=this_ont)\n if dd:\n if 'alarms' in dd.keys():\n for this_alarm in dd['alarms']:\n if this_alarm['alarmReceiveTime']:\n alarm_set = float(this_alarm['alarmReceiveTime']) # * 1e-3) / 60\n ont_items[this_ont]['events'][0].add(alarm_set)\n self.my_local_logger.info(\"Adding an AlarmReceiveTime to the data\")\n if this_alarm['alarmClearTime']:\n alarm_clear = float(this_alarm['alarmClearTime']) # * 1e-3) / 60\n ont_items[this_ont]['events'][1].add(alarm_clear)\n else:\n self.my_local_logger.warning(\"No alarms found in call to alarm_get_pons_nms_00(ont_serial_number=%s)\" % this_ont )\n else:\n self.my_local_logger.warning(\"Nothing returned from the API call\")\n this_cell['onts'] = ont_items\n this_cell['circuits'] = circuit_items\n this_cell['state'] = 'populated'\n this_cell['ttl'] = ttl\n self.my_local_logger.info(\"This CELL (radius= %3.3f %s @ lat=%f, lon=%f) has %d circuits, %d assets and %d onts.\" %\n (this_radius, g_config.RADIUS_UNITS, this_lat, this_lon,\n len(circuit_items), len(asset_items), len(ont_items))\n )\n\n\n\n # Note convert the time units into minutes by dividing by 60000\n return this_cell","def EvaluateCellDataField(self, *float, **kwargs):\n ...","def _compute_value(self, row_struct, par_struct):\n if _computephotpars is None:\n raise ImportError('_computephotpars C-extension not built')\n return _computephotpars.compute_value(row_struct, par_struct)","def get_data(self):\n raise NotImplementedError(\"Not implemented!\")","def fetch(self) -> None:\n pass","def get_data(self):\r\n pass","def fetchColumnData():\n if request.method ==\"GET\": \n return {'data':db.get_columns() }\n return{}","def read(self, epsg=None):\n if self.data is None:\n self.process.compute()\n self.data = self.process.output.data\n out_data = self.data\n if epsg and self.get_epsg() != epsg:\n out_data = reproject(self.data, epsg)\n return out_data","def test_compute_node_get_all_uuid_marker(self):\n # create the compute nodes in the non-cell0 cells\n count = 0\n for cell in self.cell_mappings[1:]:\n for x in range(3):\n compute_node_uuid = getattr(uuids, 'node_%s' % count)\n with context.target_cell(self.ctxt, cell) as cctxt:\n node = objects.ComputeNode(\n cctxt, uuid=compute_node_uuid, host=compute_node_uuid,\n vcpus=2, memory_mb=2048, local_gb=128, vcpus_used=0,\n memory_mb_used=0, local_gb_used=0, cpu_info='{}',\n hypervisor_type='fake', hypervisor_version=10)\n node.create()\n count += 1\n\n # create a host mapping for the compute to link it to the cell\n host_mapping = objects.HostMapping(\n self.ctxt, host=compute_node_uuid, cell_mapping=cell)\n host_mapping.create()\n\n # now start paging with a limit of two per page; the first page starts\n # with no marker\n compute_nodes = self.host_api.compute_node_get_all(self.ctxt, limit=2)\n # assert that we got two compute nodes from cell1\n self.assertEqual(2, len(compute_nodes))\n for compute_node in compute_nodes:\n host_mapping = objects.HostMapping.get_by_host(\n self.ctxt, compute_node.host)\n self.assertEqual(uuids.cell1, host_mapping.cell_mapping.uuid)\n\n # now our marker is the last item in the first page\n marker = compute_nodes[-1].uuid\n compute_nodes = self.host_api.compute_node_get_all(\n self.ctxt, limit=2, marker=marker)\n # assert that we got the last compute node from cell1 and the first\n # compute node from cell2\n self.assertEqual(2, len(compute_nodes))\n host_mapping = objects.HostMapping.get_by_host(\n self.ctxt, compute_nodes[0].host)\n self.assertEqual(uuids.cell1, host_mapping.cell_mapping.uuid)\n host_mapping = objects.HostMapping.get_by_host(\n self.ctxt, compute_nodes[1].host)\n self.assertEqual(uuids.cell2, host_mapping.cell_mapping.uuid)\n\n # now our marker is the last item in the second page; make the limit=3\n # so we make sure we've exhausted the pages\n marker = compute_nodes[-1].uuid\n compute_nodes = self.host_api.compute_node_get_all(\n self.ctxt, limit=3, marker=marker)\n # assert that we got two compute nodes from cell2\n self.assertEqual(2, len(compute_nodes))\n for compute_node in compute_nodes:\n host_mapping = objects.HostMapping.get_by_host(\n self.ctxt, compute_node.host)\n self.assertEqual(uuids.cell2, host_mapping.cell_mapping.uuid)","def get_compute_op_list(job_content):\n op_list = job_content[\"op_list\"]\n op_compute_list = []\n for op in op_list:\n if op[\"type\"] != \"Data\":\n op_compute_list.append(op)\n return op_compute_list","def get(self):\n\n # -------------------------------------------\n # Build command string\n\n cmd = dict()\n\n cmd[\"name_\"] = \"\"\n cmd[\"type_\"] = \"Column.aggregate\"\n\n cmd[\"aggregation_\"] = self.thisptr\n cmd[\"df_name_\"] = self.thisptr[\"col_\"][\"df_name_\"]\n\n # -------------------------------------------\n # Create connection and send the command\n\n s = send_and_receive_socket(cmd)\n\n msg = comm.recv_string(s)\n\n # -------------------------------------------\n # Make sure everything went well, receive data\n # and close connection\n\n if msg != \"Success!\":\n s.close()\n raise Exception(msg)\n\n mat = comm.recv_matrix(s)\n\n # -------------------------------------------\n # Close connection.\n\n s.close()\n\n # -------------------------------------------\n\n return mat.ravel()[0]","def data(self):\n cell = self._get_cell()\n return deepcopy(cell.data)","def fetch_gridcell(x, y, z, vtk_volume):\n s = (vtk_volume.GetScalarComponentAsFloat(x, y, z, 0),\n vtk_volume.GetScalarComponentAsFloat(x+1, y, z, 0),\n vtk_volume.GetScalarComponentAsFloat(x, y+1, z, 0),\n vtk_volume.GetScalarComponentAsFloat(x+1, y+1, z, 0),\n vtk_volume.GetScalarComponentAsFloat(x, y, z+1, 0),\n vtk_volume.GetScalarComponentAsFloat(x+1, y, z+1, 0),\n vtk_volume.GetScalarComponentAsFloat(x, y+1, z+1, 0),\n vtk_volume.GetScalarComponentAsFloat(x+1, y+1, z+1, 0))\n return s","def get_cell(self, device: bytes, row: int, column: int) -> c_void_p:\n self.allocated_resources.append(self.lib.get_cell(device, row, column))\n return self.allocated_resources[-1]","def compute(self) -> Any:\n self._check_for_increment(\"compute\")\n return self[-1].compute()","def __getitem__(self, index):\n return self.cellData[index]","def cache(self, end_user, input_data, output_data0, c_EC, Name_offloaded_data):\n\n print(\"Caching at EC server is done at %d%% \" % (random.randint(50, 99)))\n c_km = c_EC/(1+((end_user-1) * (input_data + output_data0)))\n cache_capacity_allocation_EC.append(c_km)\n cached_content.insert(Name_offloaded_data, output_data0)\n return cached_content, cache_capacity_allocation_EC","def get_cell(self, key):\n\n val = 0\n\n # -------------------------------------------------------------------------\n # YOUR CODE GOES HERE\n #\n \n #h = key[0]\n #e = key[1]\n\n val = self._table[(key[0], key[1])]\n\n \n #\n # END OF YOUR CODE\n # ------------------------------------------------------------------------- \n\n return val","def gen_compute_data(self):\n\n print \"\\t* Generating combined nova and neutron data\"\n self.init_compute_clients()\n self.compute_data[\"heat_template_version\"] = \"2013-05-23\"\n self.compute_data[\"description\"] = \"Generated Template %s on Project %s\" % \\\n (str(datetime.datetime.now().strftime(\"%A, %d. %B %Y %I:%M%p\")), str(self.tenant_name))\n self.compute_data[\"parameters\"] = {}\n self.compute_data[\"resources\"] = {}\n self.gen_parameters()\n self.gen_resources()\n self.compute_template = self.compute_data","def getCurentData(self):\n if not self.labExperiment:\n super().getCurentData()\n else:\n return np.array(self.connection.query('get_actuator_data'))","def _receive_lymphocytes(self):\n getter_thread = GetterThread(self.nodes_manager.get_next_node_address(),\n self._set_lymphocytes_to_return)\n getter_thread.start()","def onGetCell(self):\n\t\tDEBUG_MSG('Avatar::onGetCell: %i %s' % (self.id, self.cell))","def compute(\n self,\n ) -> Dict[str, torch.Tensor]:\n if self.task == \"demographic_parity\":\n return _compute_binary_demographic_parity(self.tp, self.fp, self.tn, self.fn)\n\n if self.task == \"equal_opportunity\":\n return _compute_binary_equal_opportunity(self.tp, self.fp, self.tn, self.fn)\n\n if self.task == \"all\":\n return {\n **_compute_binary_demographic_parity(self.tp, self.fp, self.tn, self.fn),\n **_compute_binary_equal_opportunity(self.tp, self.fp, self.tn, self.fn),\n }\n return None","def computeDataEnergy(self):\n _cgco.gcoComputeDataEnergy(self.handle, self.energyTempArray)\n return self._convertEnergyBack(self.energyTempArray[0])","def master(client, data, column_name):\n\n # Info messages can help you when an algorithm crashes. These info\n # messages are stored in a log file which is send to the server when\n # either a task finished or crashes.\n info('Collecting participating organizations')\n\n # Collect all organization that participate in this collaboration.\n # These organizations will receive the task to compute the partial.\n organizations = client.get_organizations_in_my_collaboration()\n ids = [organization.get(\"id\") for organization in organizations]\n\n # Request all participating parties to compute their partial. This\n # will create a new task at the central server for them to pick up.\n # We've used a kwarg but is is also possible to use `args`. Although\n # we prefer kwargs as it is clearer.\n info('Requesting partial computation')\n task = client.create_new_task(\n input_={\n 'method': 'average_partial',\n 'kwargs': {\n 'column_name': column_name\n }\n },\n organization_ids=ids\n )\n\n # Now we need to wait untill all organizations(/nodes) finished\n # their partial. We do this by polling the server for results. It is\n # also possible to subscribe to a websocket channel to get status\n # updates.\n info(\"Waiting for resuls\")\n task_id = task.get(\"id\")\n task = client.get_task(task_id)\n while not task.get(\"complete\"):\n task = client.get_task(task_id)\n info(\"Waiting for results\")\n time.sleep(1)\n\n # Once we now the partials are complete, we can collect them.\n info(\"Obtaining results\")\n results = client.get_results(task_id=task.get(\"id\"))\n\n # Now we can combine the partials to a global average.\n global_sum = 0\n global_count = 0\n for output in results:\n global_sum += output[\"sum\"]\n global_count += output[\"count\"]\n\n return {\"average\": global_sum / global_count}","def fetch(update, context):\n message = f'fetching data, this should take few seconds... {EMOJI_SLEEP*3}'\n update.message.reply_text(message)\n try:\n start = timer()\n url = execute()\n message = f'data fetched in {round(timer() - start, 1)}s. here is url to spreadsheet: {url}'\n update.message.reply_text(message)\n except Exception as e:\n message = f'there was some error {EMOJI_SCREAM*3}\\nerror message: {e}'\n update.message.reply_text(message)","def get_data():\r\n spatial_expmat = np.load('/home/anniegao/spatial_magan/data/spatial_pca_with_coords.npz')['arr_0']\r\n spatial_expmat[:,100:] *= 5\r\n rna_expmat = np.load('/home/anniegao/spatial_magan/data/rna_pca_sampled.npz')['arr_0']\r\n spatial_pca_components = np.load('/home/anniegao/spatial_magan/data/spatial_pca_100components.npz')['arr_0']\r\n rna_pca_components = np.load('/home/anniegao/spatial_magan/data/rna_pca_100components.npz')['arr_0']\r\n spatial_cluster_labels = np.load('/home/anniegao/spatial_magan/data/spatial_cluster_3_labels_phate.npz')['arr_0']\r\n rna_cluster_labels = np.load('/home/anniegao/spatial_magan/data/rna_cluster_5_labels_sampled.npz')['arr_0']\r\n return spatial_expmat, rna_expmat, spatial_pca_components, rna_pca_components, spatial_cluster_labels, rna_cluster_labels","def get_data(self): # TODO: add smooth possibility\n return self.data","def fetch(self):\n raise NotImplementedError()","def get_eval_data() -> GraphDataset:\n _load_data_if_needed()\n return eval_data","def _get_data(self):\n project_name, experiment_id = self.parent._get_parent_identifiers()\n\n self._data = self.repository.get_dataframe_data(\n project_name, self.id, experiment_id=experiment_id\n )","def test_get_cell_info(self):\n expected = { 'Experiment': \"20220101_EGS1_12345AA\",\n 'Cell': '12345AA0018/20220101_1234_1-A1-A1_AAA66666_deadbeef',\n 'Pool': '12345AA0018',\n 'Date': '20220101',\n 'Number': '1234',\n 'Slot': '1-A1-A1',\n 'CellID': 'AAA66666',\n 'Checksum': 'deadbeef',\n 'Project': '12345',\n 'Base': '12345AA0018/20220101_1234_1-A1-A1_AAA66666_deadbeef/'\n '20220101_EGS1_12345AA_12345AA0018_AAA66666_deadbeef',\n 'Files in pass': 'unknown',\n 'Files in fail': 1,\n 'Files in fast5 fail': 1,\n '_counts': [\n {'_barcode': '.', '_label': 'All passed reads', '_part': 'pass', 'total_reads': 200},\n {'_barcode': '.', '_label': 'Passed and lambda-filtered reads', '_part': 'nolambda'},\n {'_barcode': '.', '_label': 'All failed reads', '_part': 'fail'} ],\n '_blobs': ['../../__blob__'],\n '_duplex' : [ ['Duplex pairs', 1],\n ['from total passing reads', 200],\n ['% of passing reads', '1.00%'] ],\n '_filter_type': 'none',\n '_final_summary': {'is_rna': False},\n '_nanoplot': '../../__nanoplot__',\n }\n\n\n got = get_cell_info( experiment = \"20220101_EGS1_12345AA\",\n cell = \"12345AA0018/20220101_1234_1-A1-A1_AAA66666_deadbeef\",\n cell_content = { '.': dict( fast5_pass = ['x.fast5'],\n fastq_fail = ['y.fastq'],\n fast5_fail = ['y.fast5'] ) },\n counts = { ('.','pass'): dict(total_reads = 200),\n ('.','fail'): dict(),\n ('.','nolambda'): dict() },\n fin_summary = dict(is_rna = False),\n blobs = ['__blob__'],\n nanoplot = '__nanoplot__',\n duplex = 1,\n fast5_meta = dict() )\n\n if VERBOSE:\n pprint(got)\n\n self.assertEqual( type(got), OrderedDict )\n self.assertEqual( dict(got), expected )","def get_cost_data(monitor, num_batches, num_epochs):\n log = monitor.main_loop.log\n\n cost = np.zeros((2, num_epochs))\n\n for i in xrange(num_epochs):\n cost[0, i] = log[(i + 1) * num_batches]['train_cost']\n cost[1, i] = log[(i + 1) * num_batches]['test_cost']\n\n return cost","def GetGeData(self, *args, **kwargs):\n pass","def compute(self) -> Dict[str, np.ndarray]:\n return {name: self.metrics[name].compute() for name in self.metrics}","def get(self):\n\n # -------------------------------------------\n # Build command string\n\n cmd = dict()\n\n cmd[\"name_\"] = self.thisptr[\"df_name_\"]\n cmd[\"type_\"] = \"BooleanColumn.get\"\n\n cmd[\"col_\"] = self.thisptr\n\n # -------------------------------------------\n # Send command to engine\n\n s = comm.send_and_receive_socket(cmd)\n\n msg = comm.recv_string(s)\n\n # -------------------------------------------\n # Make sure everything went well, receive data\n # and close connection\n\n if msg != \"Found!\":\n s.close()\n raise Exception(msg)\n\n mat = comm.recv_boolean_matrix(s)\n\n # -------------------------------------------\n # Close connection, if necessary.\n\n s.close()\n\n # -------------------------------------------\n\n return mat.ravel()","def run(self):\n\t\t(conn, curs) = db_connect(self.hostname, self.dbname, self.schema)\n\t\t\n\t\t\n\t\t#e_splat_table = self.table+'e'\n\t\t#e_mcl_table = self.mcl_table+'e'\n\t\t#CoexprFromCooccu_instance = CoexprFromCooccu()\n\t\t#pre_2nd_cc_hierarchy = CoexprFromCooccu_instance.data_fetch(curs, self.mcl_table, e_mcl_table)\n\t\t#mcl_id2cluster_dstructure = self.data_fetch(curs, self.table, self.mcl_table, crs_no=1)\n\t\t#mcl_id_2nd_order2cluster_dstructure = self.data_fetch(curs, e_splat_table, e_mcl_table, crs_no=2)\n\t\t#self.cluster_dstructure_output_with_both_hierarchy(curs, self.output_fname, pre_2nd_cc_hierarchy,\\\n\t\t#\tmcl_id2cluster_dstructure, mcl_id_2nd_order2cluster_dstructure)\n\t\t#self.cluster_dstructure_output(curs, self.output_fname, self.order_1st_id2all_clusters)\n\t\tself.data_fetch(curs, self.table, self.mcl_table, crs_no=1, output_fname=self.output_fname)","def get_host_data(self):\n\n raise NotImplementedError","def data_fetcher(self, mp_ids: List[str] = None, mp_props: List[str] = None,\n elasticity: bool = False) -> pd.DataFrame:\n\n print('Will fetch %s inorganic compounds from Materials Project' % len(mp_ids))\n\n def grouper(iterable, n, fillvalue=None):\n \"\"\"\"\n Split requests into fixed number groups\n eg: grouper('ABCDEFG', 3, 'x') --> ABC DEF Gxx\n Collect data_cluster into fixed-length chunks or blocks\"\"\"\n args = [iter(iterable)] * n\n return zip_longest(fillvalue=fillvalue, *args)\n\n if mp_ids is None:\n mp_ids = self.ids\n # the following props will be fetched\n if mp_props is None:\n mp_props = [\n 'band_gap',\n 'density',\n \"icsd_ids\"\n 'volume',\n 'material_id',\n 'pretty_formula',\n 'elements',\n \"energy\",\n 'efermi',\n 'e_above_hull',\n 'formation_energy_per_atom',\n 'final_energy_per_atom',\n 'unit_cell_formula',\n 'spacegroup',\n 'nelements'\n \"nsites\",\n \"final_structure\",\n \"cif\",\n \"piezo\",\n \"diel\"\n ]\n\n if elasticity:\n mp_props.append(\"elasticity\")\n\n entries = []\n mpid_groups = [g for g in grouper(mp_ids, 40)]\n\n with self.m as mpr:\n for group in tqdm(mpid_groups):\n mpid_list = [ids for ids in filter(None, group)]\n chunk = mpr.query({\"material_id\": {\"$in\": mpid_list}}, mp_props)\n entries.extend(chunk)\n\n if 'material_id' in entries[0]:\n df = pd.DataFrame(entries, index=[e['material_id'] for e in entries])\n else:\n df = pd.DataFrame(entries)\n if 'unit_cell_formula' in df.columns:\n df = df.rename(columns={'unit_cell_formula': 'composition'})\n\n df = df.T\n\n self.dff = df\n\n return df","def get_data(self):\n logging.info(\n \"[IEXIntervalDataSource] started: fetching every %d seconds\",\n self._interval,\n )\n\n def _get(self):\n data = self._iex_source(**self._iex_source_kwargs)\n if data:\n should_update = True\n if self._should_hash:\n hashed = str_to_float(json.dumps(data, sort_keys=True))\n should_update = hashed != self._last_hash\n if should_update:\n self._last_hash = hashed\n if should_update:\n if self._data_cleaner:\n data = self._data_cleaner(data)\n self.table.update(data)\n \n callback = tornado.ioloop.PeriodicCallback(callback=partial(_get, self), callback_time=self._interval)\n callback.start()","def calc(self):\n self.proc_blocks = [cluster.cells for cluster in self.clusters]\n self.cell_loads = [sum([len(cell) for cell in self.proc_blocks])]\n self.particle_loads = [cluster.np for cluster in self.clusters]\n self.imbalance = LoadBalancer.get_load_imbalance(self.particle_loads)","def fetch_data(data_url):\n return requests.get(data_url).content","def compute_api(self):\n path = self.compute.path = '/'\n res = self.compute.call('', 'GET', data='', \n token=self.manager.identity.token)\n self.logger.debug('Get openstack compute services: %s' % truncate(res))\n self.compute.path = path\n return res[0]","def test_get_entity(self):\n\n storage = StringIO.StringIO()\n c = pycurl.Curl()\n c.setopt(c.URL,\"http://127.0.0.1:8090/compute/9930\")\n c.setopt(c.HTTPHEADER, ['Accept:application/occi+json'])\n c.setopt(c.VERBOSE, True)\n c.setopt(c.CUSTOMREQUEST, 'GET')\n c.setopt(c.WRITEFUNCTION, storage.write)\n c.perform()\n content = storage.getvalue()\n print \" ===== Body content =====\\n \" + content + \" ==========\\n\"","def get(self, fetch_number: int):\n return self.results[fetch_number]","def _get_io_data(self, cell_dim_only=None):\n conns, mat_ids = [], []\n if cell_dim_only is not None:\n if not is_sequence(cell_dim_only):\n cell_dim_only = [cell_dim_only]\n descs = [ii for ii in self.descs if int(ii[0]) in cell_dim_only]\n else:\n descs = self.descs\n for desc in descs:\n cmesh = self.get_cmesh(desc)\n conn, cells = self.get_conn(desc, ret_cells=True, tdim=cmesh.tdim)\n conns.append(conn)\n mat_ids.append(cmesh.cell_groups[cells])\n\n return cmesh.coors, cmesh.vertex_groups, conns, mat_ids, descs","def getDataContaining(self, address: ghidra.program.model.address.Address) -> ghidra.program.model.listing.Data:\n ...","def getDataAt(self, address: ghidra.program.model.address.Address) -> ghidra.program.model.listing.Data:\n ...","def compute(self, end_user, input_data, computation_deadline, p_ec, c_ec, RC_EC_capacity,\n computation_requirement_user, transm_delay_user_ru, Name_offloaded_data):\n global execution_latency_dc_vector # Global need to be declared in the beginning of the function\n global EC_m_cache_allocation_variable\n global cache_capacity_allocation_EC\n global DC_caching_decision_variable\n global DC_computation_decision_variable\n global output_data\n\n pkm = p_ec * (computation_requirement_user / ((end_user - 1) * computation_requirement_user))\n p_EC_n = max(computation_capacity_EC)\n pkn = p_EC_n * (computation_requirement_user / ((end_user - 1) * computation_requirement_user))\n execution_latency_ec_n = (input_data * computation_requirement_user) / ((end_user - 1) * p_ec)\n execution_latency_ec_m = (input_data * computation_requirement_user) / ((end_user - 1) * p_ec)\n if computation_requirement_user <= pkm and execution_latency_ec_m <= computation_deadline:\n execution_latency_mc = (input_data * computation_requirement_user) / ((end_user - 1) * p_ec)\n total_executing_ec_m = execution_latency_mc\n total_executing_ec_m_array.append(total_executing_ec_m)\n total_executing_ec_n_array.append(0)\n EC_m_EC_n_offloading_variable.append(0)\n EC_n_cache_allocation_variable.append(0)\n print(\" Computation at EC is done at %d%%\" % (random.randint(50, 99))) # We just print to see where the\n # task is execute\n EC_m_computation_allocation_variable.append(1)\n EC_n_computation_allocation_variable.append(0)\n output_data0 = self.input_data * (70 / 100) # We assume that after computation, the input can be reduced\n # 30%\n EC_m_cache_allocation_variable.append(1)\n cached_content, cache_capacity_allocation_EC = \\\n EC.cache(self, end_user, input_data, output_data0, c_ec, Name_offloaded_data)\n computation_capacity_allocation_EC.append(pkm)\n execution_latency_dc_vector.append(0)\n cache_capacity_allocation_dc = []\n computation_capacity_allocation_dc = []\n DC_computation_decision_variable.append(0)\n computation_capacity_allocation_dc.append(0)\n DC_caching_decision_variable.append(0)\n cache_capacity_allocation_dc.append(0)\n output_data.append(output_data0)\n transm_delay_BS_m= input_data / Fiber_Fronthaul_RU_EC[end_user]\n transm_delay_between_ru.append(transm_delay_BS_m)\n return transm_delay_between_ru, cached_content, EC_m_cloud_offloading_variable, \\\n EC_m_computation_allocation_variable, EC_n_computation_allocation_variable, \\\n total_executing_ec_m_array, total_executing_ec_n_array, \\\n computation_capacity_allocation_EC, cache_capacity_allocation_EC, \\\n EC_m_cache_allocation_variable, EC_n_cache_allocation_variable, \\\n EC_m_EC_n_offloading_variable, output_data, execution_latency_dc_vector, \\\n DC_computation_decision_variable, computation_capacity_allocation_dc, DC_caching_decision_variable, \\\n cache_capacity_allocation_dc\n elif computation_requirement_user <= pkn and execution_latency_ec_n <= computation_deadline:\n EC_n_cache_allocation_variable.append(1)\n EC_m_EC_n_offloading_variable.append(1)\n EC_m_cache_allocation_variable.append(0)\n EC_n_computation_allocation_variable.append(1)\n EC_m_computation_allocation_variable.append(0)\n # Offloading delay between EC m and EC n\n transm_delay_ECm_ECn = input_data / Bandwidth_EC_m_EC_n\n total_executing_ec_n = execution_latency_ec_n\n print(\" Computation at EC n is done at %d%%\" % (random.randint(50, 99)))\n output_data0 = input_data * (40 / 1000)\n cached_content, cache_capacity_allocation_EC = EC.cache(\n self, end_user, input_data, output_data0, c_ec, Name_offloaded_data)\n computation_capacity_allocation_EC.append(0)\n execution_latency_dc_vector.append(0)\n cache_capacity_allocation_dc = []\n output_data.append(output_data0)\n computation_capacity_allocation_dc = []\n DC_computation_decision_variable = []\n DC_computation_decision_variable.append(0)\n computation_capacity_allocation_dc.append(0)\n DC_caching_decision_variable.append(0)\n cache_capacity_allocation_dc.append(0)\n EC_m_cloud_offloading_variable.append(0)\n total_executing_ec_n_array.append(total_executing_ec_n)\n total_executing_ec_m_array.append(0)\n transm_delay_between_ru.append(transm_delay_ECm_ECn)\n return transm_delay_between_ru, cached_content, EC_m_cloud_offloading_variable, \\\n EC_m_computation_allocation_variable, EC_n_computation_allocation_variable, \\\n total_executing_ec_m_array, total_executing_ec_n_array, \\\n computation_capacity_allocation_EC, cache_capacity_allocation_EC, \\\n EC_m_cache_allocation_variable, EC_n_cache_allocation_variable, \\\n EC_m_EC_n_offloading_variable, output_data, execution_latency_dc_vector, \\\n DC_computation_decision_variable, computation_capacity_allocation_dc, DC_caching_decision_variable, \\\n cache_capacity_allocation_dc\n else:\n print(\"No available resources at EC server, task is offloaded to Data center\")\n EC_n_computation_allocation_variable.append(0)\n EC_m_computation_allocation_variable.append(0)\n EC_m_cloud_offloading_variable.append(1)\n EC_m_cache_allocation_variable.append(0)\n cache_capacity_allocation_EC.append(0)\n EC_n_cache_allocation_variable.append(0)\n EC_m_EC_n_offloading_variable.append(0)\n transm_delay_EC_dc = input_data / RC_EC_capacity\n # Request Resource at Data center\n datacenter = DataCenter(total_number_regional_cloud, computation_deadline, input_data, computation_deadline,\n computation_requirement_user, p_d, c_d)\n output_data, execution_latency_dc_vector, DC_computation_decision_variable, \\\n computation_capacity_allocation_dc, cached_content, DC_caching_decision_variable, \\\n cache_capacity_allocation_dc = datacenter.compute_dc(end_user, input_data, computation_requirement_user,\n transm_delay_EC_dc, Name_offloaded_data)\n\n computation_capacity_allocation_EC.append(0)\n total_executing_ec_m_array.append(0)\n total_executing_ec_n_array.append(0)\n transm_delay_bsm_dc = input_data / RC_EC_capacity\n transm_delay_between_ru.append(transm_delay_bsm_dc)\n\n return transm_delay_between_ru, cached_content, EC_m_cloud_offloading_variable, \\\n EC_m_computation_allocation_variable, EC_n_computation_allocation_variable, \\\n total_executing_ec_m_array, total_executing_ec_n_array, \\\n computation_capacity_allocation_EC, cache_capacity_allocation_EC, \\\n EC_m_cache_allocation_variable, EC_n_cache_allocation_variable, \\\n EC_m_EC_n_offloading_variable, output_data, execution_latency_dc_vector, \\\n DC_computation_decision_variable, computation_capacity_allocation_dc, DC_caching_decision_variable, \\\n cache_capacity_allocation_dc","def query(self):\n self._measurements[self.KEY_FMIX].df = self.fetch_data_fmix()\n self._measurements[self.KEY_EMIS].df = self.co2_calc(self._measurements[self.KEY_FMIX].df)","def value(self):\n '''\n if self.hash_pattern:\n msg = \"\"\"It is too costly to construct the full value of a deep cell\n Use cell.data instead.\n \n If you are really sure that the value of the deep cell fits in memory,\n you can assign this cell to a normal cell, e.g:\n \n ctx.othercell = Cell()\n ctx.othercell = ctx.thiscell\n \"\"\"\n raise AttributeError(msg)\n '''\n self._parent()\n hcell = self._get_hcell()\n if hcell.get(\"UNTRANSLATED\"):\n if self._path[0] == \"HELP\":\n return hcell.get(\"TEMP\")\n return None\n try:\n cell = self._get_cell()\n except Exception:\n import traceback\n\n traceback.print_exc()\n raise\n if cell is None:\n raise ValueError\n value = cell.value\n return value","def data_from_ucr_query(self):\n raise NotImplementedError","def calculate():\n print 'AJAX getJSON request to get current data and begin compute on new client'\n global isBegin, CurrentIndex, isFinished, ComputationTime, N, Row, RowIndex\n if isBegin:\n generate_random_texts(N)\n ComputationTime = time.time()\n RowIndex = (RowN - 1) * 256\n Row = FirstText[RowIndex:RowIndex + 255]\n part = SecondText[CurrentIndex:CurrentIndex+1024]\n isBegin = False\n else:\n Row = FirstText[RowIndex:RowIndex + 255]\n part = SecondText[CurrentIndex:CurrentIndex+1024]\n if isFinished:\n Row = ''\n part = ''\n return jsonify(current_row=Row, current_part=part)","def _compute_(self):\n dic = \"data/sim/{dn}/{rad}/\".format(dn=self.event.strftime(\"%Y.%m.%d.%H.%M\"), rad=self.rad)\n fbgc = \"data/sim/{dn}/{rad}/exp.bgc.bm({bm}).elv(<elv>).csv\".format(dn=self.event.strftime(\"%Y.%m.%d.%H.%M\"), \n rad=self.rad, bm=self.bmnum)\n fflare = \"data/sim/{dn}/{rad}/exp.flare.bm({bm}).elv(<elv>).csv\".format(dn=self.event.strftime(\"%Y.%m.%d.%H.%M\"),\n rad=self.rad, bm=self.bmnum)\n cmd = \"export DIR_MODELS_REF_DAT=/home/shibaji/Collaboration_NCAR/code_rt_sd/pharlap/pharlap_4.1.3/dat;\\\n cd pharlap;\\\n matlab -nodisplay -nodesktop -nosplash -nojvm -r \\\"UT=[{ut}];rad='{rad}';dic='{dic}';fbgc='{fbgc}';bm={bm};\\\n fflare='{fflare}';rt_1D_sim;exit;\\\"\".format(ut=self.event.strftime(\"%Y %m %d %H %S\"), rad=self.rad,\n dic=dic, bm=self.bmnum, fbgc=fbgc, fflare=fflare)\n os.system(cmd)\n return","def get_data(self):\n return self._results","def get_distribution(self):\n self.calc()\n proc_blocks = self.proc_blocks\n proc_num_particles = self.particle_loads\n cell_proc = LoadBalancer.get_block_proc(proc_blocks=proc_blocks)\n return cell_proc, proc_num_particles","def peridym_get_neighbor_data(mesh, horizon, omega_fun, struct_grd=False):\n\n if(struct_grd):\n cell_cent = structured_cell_centroids(mesh)\n cell_vol = structured_cell_volumes(mesh)\n else:\n cell_cent = get_cell_centroids(mesh)\n cell_vol = get_cell_volumes(mesh)\n\n tree = QuadTree()\n extents = get_domain_bounding_box(mesh)\n tree.put(extents, horizon)\n linear_tree = tree.get_linear_tree()\n nbr_lst, nbr_beta_lst = tree_nbr_search(tree.get_linear_tree(), cell_cent, horizon)\n\n start = tm.default_timer()\n print(\"computing the remaining peridynamic neighbor data for the mesh with horizon: %4.2f\" %horizon)\n\n mw = peridym_compute_weighted_volume(mesh, nbr_lst, nbr_beta_lst, horizon, omega_fun, struct_grd)\n\n nbr_bnd_vector_lst = []\n nbr_bnd_len_lst = []\n nbr_infl_fld_lst = []\n\n for i in range(len(cell_cent)):\n curr_node_coord = cell_cent[i]\n \n #declare empty lists for current node neighbor\n #attributes like neighbor bond vector, bond len,\n #and influence field \n #refer ch5 algo1 of handbook of peridynamic modelling\n #by silling etal \n\n curr_nbr_lst = nbr_lst[i] \n curr_nbr_bnd_vct = cell_cent[curr_nbr_lst] - curr_node_coord\n curr_nbr_bnd_len = la.norm(curr_nbr_bnd_vct, 2, axis=1)\n\n nbr_bnd_vector_lst.append(curr_nbr_bnd_vct)\n nbr_bnd_len_lst.append(curr_nbr_bnd_len)\n\n print(\"time taken for computation of remaining neighbor data for the given mesh is %4.3f seconds\"%(tm.default_timer()-start))\n \n return nbr_lst, nbr_beta_lst, nbr_bnd_vector_lst, nbr_bnd_len_lst, mw"],"string":"[\n \"def _fetch_data(self):\\n pass\",\n \"def getdata(\\n self,\\n col: str,\\n exp: str,\\n chan: str,\\n res: int,\\n xs: Tuple[int, int],\\n ys: Tuple[int, int],\\n zs: Tuple[int, int],\\n ):\\n if self.hasdata(col, exp, chan, res, xs, ys, zs):\\n return self.fs.retrieve(col, exp, chan, res, xs, ys, zs)\\n if self.is_terminal:\\n raise IndexError(\\\"Cannot find data at: \\\", (col, exp, chan, res, xs, ys, zs))\\n data = self._next.getdata(col, exp, chan, res, xs, ys, zs)\\n if self._cache:\\n self.setdata(data, col, exp, chan, res, xs, ys, zs)\\n return data\",\n \"def fetch_data():\\n data.fetch_data()\\n data.start_updating()\",\n \"def fetch_data(self):\",\n \"def get_nypd_complaint_results():\\n data = None\\n blob = BUCKET.blob(NYPD_COMPLAINT_FNAME)\\n if blob.exists():\\n blob.reload(client=STORAGE_CLIENT)\\n if blob.time_created.strftime(\\\"%Y-%m-%d\\\") == datetime.now().strftime(\\\"%Y-%m-%d\\\"):\\n print(\\\"Getting cached file \\\" + NYPD_COMPLAINT_FNAME)\\n return {\\\"data\\\": __retrieve_from_bucket(NYPD_COMPLAINT_FNAME)}\\n \\n df = update_nypd_complaint_results()\\n data = df.to_dict(orient=\\\"records\\\")\\n # print(df)\\n return {\\\"data\\\": data}\",\n \"def get(self, **cell):\\n cellobj = Cell(**cell)\\n if not cellobj.is_terrestial:\\n raise Exception(\\\"Priogrid only has data for terrestial cells, you are trying to get data for a non-terrestial cell\\\")\\n\\n if not self.name in CACHED_VARS:\\n CACHED_VARS[self.name] = dict()\\n CACHED_VARS[self.name][\\\"data\\\"] = get_unknown_data(self.name, startyr=self.startyr, endyr=self.endyr, interpolated=self.interpolated)\\n\\n # TODO: Test that doesnt slow down...\\n## if not all((yr in CACHED_VARS[self.name][\\\"data\\\"][cellobj.gid][\\\"data\\\"] for yr in range(self.startyr, self.endyr+1))):\\n## newdata = get_unknown_data(self.name, startyr=self.startyr, endyr=self.endyr, interpolated=self.interpolated)\\n## yrdict = CACHED_VARS[self.name][\\\"data\\\"][cellobj.gid][\\\"data\\\"]\\n## yrdict.update( newdata[cellobj.gid][\\\"data\\\"] )\\n \\n value = CACHED_VARS[self.name][\\\"data\\\"][cellobj.gid][\\\"data\\\"]\\n return value\",\n \"def _read_cell_direct(cls):\\n\\n cell_data = {}\\n cell_columns = cls._get_columns(CELL_MANIFEST)\\n cell_psvs = cls._get_component_psvs(CELL_MANIFEST)\\n\\n for cell_psv in cell_psvs:\\n for row in gzip.GzipFile(fileobj=io.BytesIO(cls._read_s3_url(cell_psv))):\\n row_dict = dict(zip(cell_columns, row.strip().split(b'|')))\\n cell_data[row_dict[\\\"cellkey\\\"].decode()] = {k: v.decode() for\\n k, v in row_dict.items()}\\n total_umis = cell_data[row_dict[\\\"cellkey\\\"].decode()][\\\"total_umis\\\"]\\n cell_data[row_dict[\\\"cellkey\\\"].decode()][\\\"total_umis\\\"] = (total_umis if total_umis == \\\"nan\\\"\\n else str(float(total_umis)))\\n\\n return cell_data\",\n \"def do_fetch(self):\\n pass\",\n \"def get_cells(self):\\n raise NotImplementedError\",\n \"async def _fetch_data(self) -> T:\\n raise NotImplementedError\",\n \"def get_data():\\n pass\",\n \"async def _fetch_data(self) -> JobInfo:\\n return await self.api.get_job()\",\n \"async def fetch_data(self) -> T:\",\n \"def compute(cls, dataset):\\n return dataset\",\n \"def get_data():\\n return\",\n \"def run(self):\\n results = self.fetch()\\n return results\",\n \"def _get_data(self):\\n raise NotImplementedError()\",\n \"def getData(self, local_cache):\",\n \"def fetch_data(self):\\n\\n data_dict = {\\n 'price': self.get_current_price(),\\n }\\n\\n return self.save_data(data_dict)\",\n \"def run_calculation():\\n\\n print(\\\"Creating %d-process pool\\\" % mp.cpu_count())\\n\\n pool = mp.Pool(mp.cpu_count())\\n\\n f = h5py.File('/testdata/mandelbrot.hdf5', 'w')\\n\\n print(\\\"Creating output dataset with shape %s x %s\\\" % (NX, NY))\\n\\n dset = f.create_dataset('mandelbrot', (NX, NY), 'i')\\n dset.attrs['XSTART'] = XSTART\\n dset.attrs['YSTART'] = YSTART\\n dset.attrs['XEXTENT'] = XEXTENT\\n dset.attrs['YEXTENT'] = YEXTENT\\n\\n result = pool.imap(compute_row, (x * xincr for x in range(NX)))\\n\\n for idx, arr in enumerate(result):\\n if idx % 25 == 0: print(\\\"Recording row %s\\\" % idx)\\n dset[idx] = arr\\n\\n print(\\\"Closing HDF5 file\\\")\\n\\n f.close()\\n\\n print(\\\"Shutting down process pool\\\")\\n\\n pool.close()\\n pool.join()\",\n \"def get_building_complaint_results():\\n data = None\\n blob = BUCKET.blob(BUILDING_COMPLAINT_FNAME)\\n if blob.exists():\\n blob.reload(client=STORAGE_CLIENT)\\n if blob.time_created.strftime(\\\"%Y-%m-%d\\\") == datetime.now().strftime(\\\"%Y-%m-%d\\\"):\\n print(\\\"Getting cached file \\\" + BUILDING_COMPLAINT_FNAME)\\n return {\\\"data\\\": __retrieve_from_bucket(BUILDING_COMPLAINT_FNAME)}\\n \\n df = update_building_complaint_results()\\n data = df.to_dict(orient=\\\"records\\\")\\n return {\\\"data\\\": data}\",\n \"def _data(self):\\n cell = self._get_cell()\\n return deepcopy(cell.data)\",\n \"def data_fetch(self, curs, splat_table, mcl_table, crs_no=0, output_fname=None):\\n\\t\\tgene_no2gene_id = get_gene_no2gene_id(curs)\\t#08-31-05\\n\\t\\toutf = open(output_fname, 'w')\\t#08-31-05\\n\\t\\toutf.write(\\\"r:=[\\\")\\t#08-31-05\\n\\t\\t\\n\\t\\tmcl_id2cluster_dstructure = {}\\n\\t\\tno_of_total_genes = get_no_of_total_genes(curs)\\n\\t\\tsys.stderr.write(\\\"Getting the basic information for all clusters...\\\\n\\\")\\n\\t\\tcurs.execute(\\\"DECLARE crs%s CURSOR FOR select m.mcl_id, m.vertex_set, m.connectivity, 0,\\\\\\n\\t\\t\\tm.recurrence_array, s.edge_set, s.connectivity, m.cooccurrent_cluster_id from %s m, %s s where \\\\\\n\\t\\t\\tm.splat_id=s.splat_id\\\"\\\\\\n\\t\\t\\t%(crs_no, mcl_table, splat_table))\\t#06-20-05\\tconnectivity_original faked to be 0\\n\\t\\tcurs.execute(\\\"fetch 5000 from crs%s\\\"%crs_no)\\n\\t\\trows = curs.fetchall()\\n\\t\\twhile rows:\\n\\t\\t\\tfor row in rows:\\n\\t\\t\\t\\tunit = cluster_dstructure()\\n\\t\\t\\t\\tunit.cluster_id = row[0]\\n\\t\\t\\t\\tvertex_set = row[1][1:-1].split(',')\\n\\t\\t\\t\\tunit.vertex_set = map(int, vertex_set)\\n\\t\\t\\t\\tunit.connectivity = row[2]\\n\\t\\t\\t\\tunit.connectivity_original = row[3]\\n\\t\\t\\t\\trecurrence_array = row[4][1:-1].split(',')\\n\\t\\t\\t\\tunit.recurrence_array = map(float, recurrence_array)\\n\\t\\t\\t\\tunit.edge_set = parse_splat_table_edge_set(row[5])\\n\\t\\t\\t\\tunit.splat_connectivity = row[6]\\n\\t\\t\\t\\tunit.cooccurrent_cluster_id = row[7]\\n\\t\\t\\t\\tunit.go_no2association_genes = self.get_go_functions_of_this_gene_set(curs, unit.vertex_set)\\n\\t\\t\\t\\tunit.go_no2information = self.get_information_of_go_functions(curs, \\\\\\n\\t\\t\\t\\t\\tunit.go_no2association_genes, len(unit.vertex_set), no_of_total_genes, p_value_cut_off=0.05)\\t#jasmine wants to cut some go-nos.\\n\\t\\t\\t\\tunit.edge_cor_2d_list, unit.edge_sig_2d_list = self.get_cor_sig_2d_list(curs, unit.edge_set)\\n\\t\\t\\t\\t\\n\\t\\t\\t\\tstr_tmp = self.return_string_form_of_cluster_dstructure(unit, gene_no2gene_id)\\t#08-31-05\\n\\t\\t\\t\\toutf.write(\\\"%s,\\\"%str_tmp)\\n\\t\\t\\t\\t#mcl_id2cluster_dstructure[unit.cluster_id] = unit\\n\\t\\t\\t\\t\\\"\\\"\\\"\\n\\t\\t\\t\\torder_1st_id, order_2nd_id = map(int, unit.cooccurrent_cluster_id.split('.'))\\n\\t\\t\\t\\tif order_1st_id not in self.order_1st_id2all_clusters:\\n\\t\\t\\t\\t\\tself.order_1st_id2all_clusters[order_1st_id] = {}\\n\\t\\t\\t\\tif order_2nd_id not in self.order_1st_id2all_clusters[order_1st_id]:\\n\\t\\t\\t\\t\\tself.order_1st_id2all_clusters[order_1st_id][order_2nd_id] = []\\n\\t\\t\\t\\tself.order_1st_id2all_clusters[order_1st_id][order_2nd_id].append(unit)\\n\\t\\t\\t\\t\\\"\\\"\\\"\\n\\t\\t\\tcurs.execute(\\\"fetch 5000 from crs%s\\\"%crs_no)\\n\\t\\t\\trows = curs.fetchall()\\n\\t\\toutf.write(\\\"[]]:\\\")\\t#08-31-05, 09-01-05 add the last blank []\\n\\t\\tdel outf\\n\\t\\tsys.stderr.write(\\\"Done.\\\\n\\\")\\n\\t\\treturn mcl_id2cluster_dstructure\",\n \"def read(self):\\n self._load_metadata()\\n return self._df.compute()\",\n \"def _retrieveCachedData(self):\",\n \"def get_data(self):\",\n \"def cell(self):\\n return self._cell\",\n \"def get_data():\\n pass\",\n \"def get_data():\\n pass\",\n \"def get_data():\\n pass\",\n \"def get_data(self, data_to_cpu=False, *args):\\n raise NotImplementedError\",\n \"def fetch(self):\\n # type: () -> List[List[Any]]\\n if self._request.next_uri is None:\\n self._finished = True\\n return []\\n response = self._request.get(self._request.next_uri)\\n status = self._request.process(response)\\n if status.columns:\\n self._columns = status.columns\\n self._stats.update(status.stats)\\n if status.next_uri is None:\\n self._finished = True\\n return status.rows\",\n \"def fetch(self):\\n pass\",\n \"def fetch(self):\\n pass\",\n \"def get_cell():\\n q = request.query.decode()\\n worksheet = q['worksheetKey']\\n row = q['row']\\n col = q['col']\\n value = ss.read_cell(worksheet, row, col)\\n response.status = '200 OK'\\n response.content_type = 'application/json'\\n return {'value': value,\\n 'row': row,\\n 'col': col,\\n 'worksheetKey': worksheet}\",\n \"def computation_download_data(self, uid: str, data_file: str) -> int:\\n return self.context.download(\\\"/ckks/computation/data/%s\\\" % uid, data_file, None, None,\\n \\\"CKKS:: failed downloading computed data for uid: %s\\\" % uid\\n )\",\n \"def __get(self, sock=None):\\n\\n # -------------------------------------------\\n # Build command string\\n\\n cmd = dict()\\n\\n cmd[\\\"name_\\\"] = self.thisptr[\\\"df_name_\\\"]\\n cmd[\\\"type_\\\"] = \\\"Column.get\\\"\\n\\n cmd[\\\"col_\\\"] = self.thisptr\\n\\n # -------------------------------------------\\n # Establish communication with getml engine\\n\\n if sock is None:\\n s = comm.send_and_receive_socket(cmd)\\n else:\\n s = sock\\n comm.send_string(s, json.dumps(cmd))\\n\\n msg = comm.recv_string(s)\\n\\n # -------------------------------------------\\n # Make sure everything went well, receive data\\n # and close connection\\n\\n if msg != \\\"Found!\\\":\\n s.close()\\n raise Exception(msg)\\n\\n mat = comm.recv_matrix(s)\\n\\n # -------------------------------------------\\n # Close connection.\\n\\n if sock is None:\\n s.close()\\n\\n # -------------------------------------------\\n # If this is a time stamp, then transform to\\n # pd.Timestamp.\\n\\n if self.thisptr[\\\"type_\\\"] == \\\"Column\\\":\\n if self.thisptr[\\\"role_\\\"] == \\\"time_stamp\\\" or \\\"time stamp\\\" in self.thisptr[\\\"unit_\\\"]:\\n shape = mat.shape\\n mat = [pd.Timestamp(ts_input=ts, unit=\\\"D\\\") for ts in mat.ravel()]\\n mat = np.asarray(mat)\\n mat.reshape(shape[0], shape[1])\\n\\n # -------------------------------------------\\n\\n return mat.ravel()\\n\\n # -------------------------------------------\",\n \"def initialize(self) -> None:\\n all_queries: List[Union[DataQueryInfo, MeasureQueryInfo]] = []\\n entity_cells: List[DataCell] = []\\n current_row_group = None\\n\\n # Loop over rows, columns\\n for row_index, row in enumerate(self.rows):\\n if isinstance(row, RowSeparator):\\n current_row_group = row.name\\n continue\\n entity: Entity = row.entity\\n if isinstance(entity, Entity):\\n self.entity_map[entity.get_marquee_id()] = entity\\n else:\\n self.entity_map[''] = entity\\n cells: List[DataCell] = []\\n row_overrides = row.overrides\\n\\n for column_index, column in enumerate(self.columns):\\n column_name = column.name\\n column_processor = column.processor\\n\\n # Get all the data coordinate overrides and apply the processor override if it exists\\n data_overrides, value_override, processor_override = _get_overrides(row_overrides, column_name)\\n\\n # Create the cell\\n cell: DataCell = DataCell(column_name,\\n column_processor,\\n entity,\\n data_overrides,\\n column_index,\\n row_index,\\n current_row_group)\\n\\n if processor_override:\\n # Check if there is a processor override and apply if so\\n cell.processor = processor_override\\n if value_override:\\n cell.value = ProcessorResult(True, value_override.value)\\n cell.updated_time = get_utc_now()\\n elif isinstance(column_processor, EntityProcessor):\\n # store these cells to fetch entity data during poll\\n entity_cells.append(cell)\\n elif isinstance(column_processor, CoordinateProcessor):\\n # store these cells to fetch entity data during poll\\n if len(data_overrides):\\n # Get the last in the list if more than 1 override is given\\n cell.processor.children['a'].set_dimensions(data_overrides[-1].dimensions)\\n\\n self._coord_processor_cells.append(cell)\\n elif column_processor.measure_processor:\\n all_queries.append(MeasureQueryInfo(attr='', entity=entity, processor=column_processor))\\n else:\\n # append the required queries to the map\\n cell.build_cell_graph(all_queries, self.rdate_entity_map)\\n\\n cells.append(cell)\\n\\n self._cells.extend(cells)\\n self.results.append(cells)\\n\\n self._data_queries = all_queries\\n self._entity_cells = entity_cells\\n self.is_initialized = True\",\n \"def get_data(self):\\n pass\",\n \"def get_data(self):\\n pass\",\n \"def get_data(self, calc_throughput=False):\\n n_timestamp = datetime.datetime.now()\\n delta = n_timestamp - self.timestamp\\n\\n try:\\n ret = ds_client().call('bridge\\\\n')\\n except socket.error:\\n return self.data\\n\\n self.timestamp = n_timestamp\\n\\n for brdata in ret:\\n name = brdata[u'name']\\n req = 'bridge ' + name.encode() + ' stats\\\\n'\\n res = ds_client().call(req)[0]\\n\\n if calc_throughput == True:\\n try:\\n lps = calc_pps(res[u'flow-lookup-count'],\\n self.data[brdata[u'name']][u'flow-lookup-count'],\\n delta)\\n mps = calc_pps(res[u'flow-matched-count'],\\n self.data[brdata[u'name']][u'flow-matched-count'],\\n delta)\\n res.update({u'lookup_per_sec':lps, u'match_per_sec':mps})\\n except KeyError:\\n res.update({u'lookup_per_sec':0, u'match_per_sec':0})\\n try:\\n c_hit = (res[u'flowcache-hit']\\n - self.data[brdata[u'name']][u'flowcache-hit'])\\n lookup = (res[u'flow-lookup-count']\\n - self.data[brdata[u'name']][u'flow-lookup-count'])\\n c_rate = c_hit / lookup\\n res.update({u'cache_hitrate':str(c_rate)})\\n except (KeyError, ZeroDivisionError):\\n res.update({u'cache_hitrate':'-'})\\n self.data[res[u'name']] = res\\n return self.data\",\n \"def data(self):\\n\\t\\tself.dworker()\\n\\t\\treturn self.d\",\n \"def get_data(self):\\n return self._result\",\n \"def get_data_in_cell_area(self, cell_parameters, ttl):\\n\\n # query_guid = payload[\\\"query_guid\\\"]\\n this_lat = cell_parameters[\\\"latitude\\\"]\\n this_lon = cell_parameters[\\\"longitude\\\"]\\n # utility = cell_parameters[\\\"company\\\"]\\n groom_time = cell_parameters[\\\"outageTime\\\"]\\n # circuit_id = cell_parameters[\\\"circuitID\\\"]\\n # asset_id = cell_parameters[\\\"assetID\\\"]\\n # votes = cell_parameters[\\\"votes\\\"]\\n # spatial = cell_parameters[\\\"spatial\\\"]\\n # temporal = cell_parameters[\\\"temporal\\\"]\\n # reputation_ena = cell_parameters[\\\"reputationEnabled\\\"]\\n # zoom_t = cell_parameters[\\\"zoomT\\\"]\\n # zoom_r = cell_parameters[\\\"zoomR\\\"]\\n this_radius = cell_parameters[\\\"radius\\\"]\\n # units = cell_parameters[\\\"units\\\"]\\n\\n query_type = \\\"ALL\\\"\\n\\n ont_serial_number_set = set()\\n ont_items = {}\\n\\n asset_serial_number_set = set()\\n asset_items = {}\\n\\n circuit_serial_number_set = set()\\n circuit_items = {}\\n # The six neighbor cells are initially set to be empty\\n # This a string quid and an angle (in degrees)\\n neighbor_array = [[\\\"\\\", 0], [\\\"\\\", 60], [\\\"\\\", 120], [\\\"\\\", 180], [\\\"\\\", 240], [\\\"\\\", 300]]\\n this_cell = {'neighbors': neighbor_array,\\n 'assets': {},\\n 'onts': {},\\n 'circuits': {},\\n 'state': 'creating',\\n 'lat_lon': [this_lat, this_lon],\\n 'radius': this_radius,\\n 'groom_time': groom_time,\\n 'ttl': 0\\n }\\n page_number = 0\\n page_size = 20\\n query_parameter = json.dumps({\\\"itemType\\\": query_type,\\n \\\"circle\\\": {\\\"longitude\\\": this_lon,\\n \\\"latitude\\\": this_lat,\\n \\\"radius\\\": this_radius, \\\"unit\\\": g_config.RADIUS_UNITS},\\n \\\"pageParameter\\\": {\\\"page\\\": page_number, \\\"size\\\": page_size}})\\n self.my_local_logger.debug(\\\"Formed query parameter: %s\\\" % query_parameter)\\n dd = self.eon_api_bridge.query_post_eon_data_30(query_parameter=query_parameter)\\n more_pages = True\\n # Loop here until no more utility components of the first collection are found\\n while more_pages and dd is not None:\\n # This is the ONTs loop through them and find all the ONTs in the area\\n for this_ont in dd['eligibility']['dataItems']:\\n ont_dictionary_keyword = this_ont['ontSerialNumber']\\n ont_serial_number_set.add(ont_dictionary_keyword)\\n if ont_dictionary_keyword == \\\"[PENDING INSTALL]\\\":\\n self.my_local_logger.debug(\\\"skipping this ont in eligibility list\\\")\\n continue\\n ont_items[ont_dictionary_keyword] = {'lat_lon': [this_ont['latitude'], this_ont['longitude']]}\\n alarm_set_time = set()\\n alarm_clear_time = set()\\n ont_items[ont_dictionary_keyword]['events'] = [alarm_set_time, alarm_clear_time]\\n ont_items[ont_dictionary_keyword]['assets'] = set()\\n\\n for this_alarm in dd['alarm']['dataItems']:\\n alarm_dictionary_keyword = this_alarm['ontSerialNumber']\\n if alarm_dictionary_keyword not in ont_serial_number_set:\\n if alarm_dictionary_keyword == \\\"[PENDING INSTALL]\\\":\\n self.my_local_logger.debug(\\\"skipping this ONT in the alarm list\\\")\\n continue\\n ont_serial_number_set.add(alarm_dictionary_keyword)\\n ont_items[alarm_dictionary_keyword] = {'lat_lon': [this_alarm['latitude'], this_alarm['longitude']]}\\n alarm_set_time = set()\\n alarm_clear_time = set()\\n ont_items[alarm_dictionary_keyword]['events'] = [alarm_set_time, alarm_clear_time]\\n ont_items[alarm_dictionary_keyword]['assets'] = set()\\n\\n if this_alarm['alarmReceiveTime']:\\n alarm_set = float(this_alarm['alarmReceiveTime']) # * 1e-3) / 60\\n ont_items[alarm_dictionary_keyword]['events'][0].add(alarm_set)\\n\\n if this_alarm['alarmClearTime']:\\n alarm_clear = float(this_alarm['alarmClearTime']) # * 1e-3) / 60\\n ont_items[alarm_dictionary_keyword]['events'][1].add(alarm_clear)\\n\\n # Now go through the assets and associate the assets to the ONTs and the ONTs to the assets\\n for this_item in dd['utility']['dataItems']:\\n asset_dictionary_keyword = this_item['transformerID']\\n if asset_dictionary_keyword not in asset_serial_number_set:\\n asset_serial_number_set.add(asset_dictionary_keyword)\\n asset_items[asset_dictionary_keyword] = {'lat_lon': [this_item['latitude'], this_item['longitude']]}\\n asset_items[asset_dictionary_keyword]['events'] = [set(), set()]\\n asset_items[asset_dictionary_keyword]['onts'] = set()\\n asset_items[asset_dictionary_keyword]['guid'] = this_item['guid']\\n asset_items[asset_dictionary_keyword]['serviceAddress'] = this_item['serviceAddress']\\n for this_ont in this_item['eligibilityList']:\\n ont_dictionary_keyword = this_ont['ontSerialNumber']\\n if ont_dictionary_keyword not in ont_serial_number_set:\\n ont_serial_number_set.add(ont_dictionary_keyword)\\n ont_items[ont_dictionary_keyword] = {\\n 'lat_lon': [this_ont['latitude'], this_ont['longitude']]}\\n alarm_set_time = set()\\n alarm_clear_time = set()\\n ont_items[ont_dictionary_keyword]['events'] = [alarm_set_time, alarm_clear_time]\\n ont_items[ont_dictionary_keyword]['assets'] = set()\\n # Skip the ONTs that don't have an installation.\\n if ont_dictionary_keyword == \\\"[PENDING INSTALL]\\\":\\n self.my_local_logger.debug(\\\"skipping the ONT listed on eligibility list in asset_id=%s\\\" %\\n asset_dictionary_keyword)\\n self.my_local_logger.info(\\\"Skipping %s because it's status is PENDING INSTALL\\\" %\\n asset_dictionary_keyword)\\n continue\\n # Stitch up the assets in the onts\\n ont_items[ont_dictionary_keyword]['assets'].add(asset_dictionary_keyword)\\n # Stitch up the onts in the assets\\n asset_items[asset_dictionary_keyword]['onts'].add(ont_dictionary_keyword)\\n\\n circuit_dictionary_keyword = this_item['circuitID']\\n if circuit_dictionary_keyword not in circuit_serial_number_set:\\n # add the circuit item to the circuit_serial_number_set is needed\\n circuit_serial_number_set.add(circuit_dictionary_keyword)\\n # and create an empty set\\n circuit_items[circuit_dictionary_keyword] = {'connected_items': set()}\\n # Now add the data structure to the set\\n circuit_items[circuit_dictionary_keyword]['connected_items'].add(asset_dictionary_keyword)\\n\\n ###########################\\n # Look for the next page #\\n ###########################\\n if (dd['utility']['pageTotalItems'] == page_size) or \\\\\\n (dd['alarm']['pageTotalItems'] == page_size) or \\\\\\n (dd['eligibility']['pageTotalItems'] == page_size):\\n self.my_local_logger.debug(\\\"Collecting next page for this message\\\")\\n page_number += 1\\n more_pages = True\\n query_parameter = json.dumps({\\\"itemType\\\": query_type,\\n \\\"circle\\\": {\\\"longitude\\\": this_lon,\\n \\\"latitude\\\": this_lat,\\n \\\"radius\\\": this_radius,\\n \\\"unit\\\": g_config.RADIUS_UNITS},\\n \\\"pageParameter\\\": {\\\"page\\\": page_number, \\\"size\\\": page_size}})\\n dd = self.eon_api_bridge.query_post_eon_data_30(query_parameter=query_parameter)\\n else:\\n more_pages = False\\n this_cell['assets'] = asset_items\\n # Go over the ONT set and see it there are any that don't have alarms. This might happen if there were no alarms\\n # posted to this ONT because the main alarm injestion loop failed for some reason. There will still be alarms\\n # that are posted on the ONTs and those can be recovered here.\\n for this_ont in ont_items:\\n if len(ont_items[this_ont]['events'][0]) == 0 or len(ont_items[this_ont]['events'][1]) == 0:\\n # To find any ONTs that don't seem to have alarms make this call:\\n # where ONT_SERIAL_NUMBER is 00ABB96 in this example.\\n # http://10.123.0.27:8080/eon360/api/alarms?sortBy=alarmReceiveTime&ontSerialNumber=000ABB96&p=0&s=20\\n dd = self.eon_api_bridge.alarm_get_pons_nms_00(ont_serial_number=this_ont)\\n if dd:\\n if 'alarms' in dd.keys():\\n for this_alarm in dd['alarms']:\\n if this_alarm['alarmReceiveTime']:\\n alarm_set = float(this_alarm['alarmReceiveTime']) # * 1e-3) / 60\\n ont_items[this_ont]['events'][0].add(alarm_set)\\n self.my_local_logger.info(\\\"Adding an AlarmReceiveTime to the data\\\")\\n if this_alarm['alarmClearTime']:\\n alarm_clear = float(this_alarm['alarmClearTime']) # * 1e-3) / 60\\n ont_items[this_ont]['events'][1].add(alarm_clear)\\n else:\\n self.my_local_logger.warning(\\\"No alarms found in call to alarm_get_pons_nms_00(ont_serial_number=%s)\\\" % this_ont )\\n else:\\n self.my_local_logger.warning(\\\"Nothing returned from the API call\\\")\\n this_cell['onts'] = ont_items\\n this_cell['circuits'] = circuit_items\\n this_cell['state'] = 'populated'\\n this_cell['ttl'] = ttl\\n self.my_local_logger.info(\\\"This CELL (radius= %3.3f %s @ lat=%f, lon=%f) has %d circuits, %d assets and %d onts.\\\" %\\n (this_radius, g_config.RADIUS_UNITS, this_lat, this_lon,\\n len(circuit_items), len(asset_items), len(ont_items))\\n )\\n\\n\\n\\n # Note convert the time units into minutes by dividing by 60000\\n return this_cell\",\n \"def EvaluateCellDataField(self, *float, **kwargs):\\n ...\",\n \"def _compute_value(self, row_struct, par_struct):\\n if _computephotpars is None:\\n raise ImportError('_computephotpars C-extension not built')\\n return _computephotpars.compute_value(row_struct, par_struct)\",\n \"def get_data(self):\\n raise NotImplementedError(\\\"Not implemented!\\\")\",\n \"def fetch(self) -> None:\\n pass\",\n \"def get_data(self):\\r\\n pass\",\n \"def fetchColumnData():\\n if request.method ==\\\"GET\\\": \\n return {'data':db.get_columns() }\\n return{}\",\n \"def read(self, epsg=None):\\n if self.data is None:\\n self.process.compute()\\n self.data = self.process.output.data\\n out_data = self.data\\n if epsg and self.get_epsg() != epsg:\\n out_data = reproject(self.data, epsg)\\n return out_data\",\n \"def test_compute_node_get_all_uuid_marker(self):\\n # create the compute nodes in the non-cell0 cells\\n count = 0\\n for cell in self.cell_mappings[1:]:\\n for x in range(3):\\n compute_node_uuid = getattr(uuids, 'node_%s' % count)\\n with context.target_cell(self.ctxt, cell) as cctxt:\\n node = objects.ComputeNode(\\n cctxt, uuid=compute_node_uuid, host=compute_node_uuid,\\n vcpus=2, memory_mb=2048, local_gb=128, vcpus_used=0,\\n memory_mb_used=0, local_gb_used=0, cpu_info='{}',\\n hypervisor_type='fake', hypervisor_version=10)\\n node.create()\\n count += 1\\n\\n # create a host mapping for the compute to link it to the cell\\n host_mapping = objects.HostMapping(\\n self.ctxt, host=compute_node_uuid, cell_mapping=cell)\\n host_mapping.create()\\n\\n # now start paging with a limit of two per page; the first page starts\\n # with no marker\\n compute_nodes = self.host_api.compute_node_get_all(self.ctxt, limit=2)\\n # assert that we got two compute nodes from cell1\\n self.assertEqual(2, len(compute_nodes))\\n for compute_node in compute_nodes:\\n host_mapping = objects.HostMapping.get_by_host(\\n self.ctxt, compute_node.host)\\n self.assertEqual(uuids.cell1, host_mapping.cell_mapping.uuid)\\n\\n # now our marker is the last item in the first page\\n marker = compute_nodes[-1].uuid\\n compute_nodes = self.host_api.compute_node_get_all(\\n self.ctxt, limit=2, marker=marker)\\n # assert that we got the last compute node from cell1 and the first\\n # compute node from cell2\\n self.assertEqual(2, len(compute_nodes))\\n host_mapping = objects.HostMapping.get_by_host(\\n self.ctxt, compute_nodes[0].host)\\n self.assertEqual(uuids.cell1, host_mapping.cell_mapping.uuid)\\n host_mapping = objects.HostMapping.get_by_host(\\n self.ctxt, compute_nodes[1].host)\\n self.assertEqual(uuids.cell2, host_mapping.cell_mapping.uuid)\\n\\n # now our marker is the last item in the second page; make the limit=3\\n # so we make sure we've exhausted the pages\\n marker = compute_nodes[-1].uuid\\n compute_nodes = self.host_api.compute_node_get_all(\\n self.ctxt, limit=3, marker=marker)\\n # assert that we got two compute nodes from cell2\\n self.assertEqual(2, len(compute_nodes))\\n for compute_node in compute_nodes:\\n host_mapping = objects.HostMapping.get_by_host(\\n self.ctxt, compute_node.host)\\n self.assertEqual(uuids.cell2, host_mapping.cell_mapping.uuid)\",\n \"def get_compute_op_list(job_content):\\n op_list = job_content[\\\"op_list\\\"]\\n op_compute_list = []\\n for op in op_list:\\n if op[\\\"type\\\"] != \\\"Data\\\":\\n op_compute_list.append(op)\\n return op_compute_list\",\n \"def get(self):\\n\\n # -------------------------------------------\\n # Build command string\\n\\n cmd = dict()\\n\\n cmd[\\\"name_\\\"] = \\\"\\\"\\n cmd[\\\"type_\\\"] = \\\"Column.aggregate\\\"\\n\\n cmd[\\\"aggregation_\\\"] = self.thisptr\\n cmd[\\\"df_name_\\\"] = self.thisptr[\\\"col_\\\"][\\\"df_name_\\\"]\\n\\n # -------------------------------------------\\n # Create connection and send the command\\n\\n s = send_and_receive_socket(cmd)\\n\\n msg = comm.recv_string(s)\\n\\n # -------------------------------------------\\n # Make sure everything went well, receive data\\n # and close connection\\n\\n if msg != \\\"Success!\\\":\\n s.close()\\n raise Exception(msg)\\n\\n mat = comm.recv_matrix(s)\\n\\n # -------------------------------------------\\n # Close connection.\\n\\n s.close()\\n\\n # -------------------------------------------\\n\\n return mat.ravel()[0]\",\n \"def data(self):\\n cell = self._get_cell()\\n return deepcopy(cell.data)\",\n \"def fetch_gridcell(x, y, z, vtk_volume):\\n s = (vtk_volume.GetScalarComponentAsFloat(x, y, z, 0),\\n vtk_volume.GetScalarComponentAsFloat(x+1, y, z, 0),\\n vtk_volume.GetScalarComponentAsFloat(x, y+1, z, 0),\\n vtk_volume.GetScalarComponentAsFloat(x+1, y+1, z, 0),\\n vtk_volume.GetScalarComponentAsFloat(x, y, z+1, 0),\\n vtk_volume.GetScalarComponentAsFloat(x+1, y, z+1, 0),\\n vtk_volume.GetScalarComponentAsFloat(x, y+1, z+1, 0),\\n vtk_volume.GetScalarComponentAsFloat(x+1, y+1, z+1, 0))\\n return s\",\n \"def get_cell(self, device: bytes, row: int, column: int) -> c_void_p:\\n self.allocated_resources.append(self.lib.get_cell(device, row, column))\\n return self.allocated_resources[-1]\",\n \"def compute(self) -> Any:\\n self._check_for_increment(\\\"compute\\\")\\n return self[-1].compute()\",\n \"def __getitem__(self, index):\\n return self.cellData[index]\",\n \"def cache(self, end_user, input_data, output_data0, c_EC, Name_offloaded_data):\\n\\n print(\\\"Caching at EC server is done at %d%% \\\" % (random.randint(50, 99)))\\n c_km = c_EC/(1+((end_user-1) * (input_data + output_data0)))\\n cache_capacity_allocation_EC.append(c_km)\\n cached_content.insert(Name_offloaded_data, output_data0)\\n return cached_content, cache_capacity_allocation_EC\",\n \"def get_cell(self, key):\\n\\n val = 0\\n\\n # -------------------------------------------------------------------------\\n # YOUR CODE GOES HERE\\n #\\n \\n #h = key[0]\\n #e = key[1]\\n\\n val = self._table[(key[0], key[1])]\\n\\n \\n #\\n # END OF YOUR CODE\\n # ------------------------------------------------------------------------- \\n\\n return val\",\n \"def gen_compute_data(self):\\n\\n print \\\"\\\\t* Generating combined nova and neutron data\\\"\\n self.init_compute_clients()\\n self.compute_data[\\\"heat_template_version\\\"] = \\\"2013-05-23\\\"\\n self.compute_data[\\\"description\\\"] = \\\"Generated Template %s on Project %s\\\" % \\\\\\n (str(datetime.datetime.now().strftime(\\\"%A, %d. %B %Y %I:%M%p\\\")), str(self.tenant_name))\\n self.compute_data[\\\"parameters\\\"] = {}\\n self.compute_data[\\\"resources\\\"] = {}\\n self.gen_parameters()\\n self.gen_resources()\\n self.compute_template = self.compute_data\",\n \"def getCurentData(self):\\n if not self.labExperiment:\\n super().getCurentData()\\n else:\\n return np.array(self.connection.query('get_actuator_data'))\",\n \"def _receive_lymphocytes(self):\\n getter_thread = GetterThread(self.nodes_manager.get_next_node_address(),\\n self._set_lymphocytes_to_return)\\n getter_thread.start()\",\n \"def onGetCell(self):\\n\\t\\tDEBUG_MSG('Avatar::onGetCell: %i %s' % (self.id, self.cell))\",\n \"def compute(\\n self,\\n ) -> Dict[str, torch.Tensor]:\\n if self.task == \\\"demographic_parity\\\":\\n return _compute_binary_demographic_parity(self.tp, self.fp, self.tn, self.fn)\\n\\n if self.task == \\\"equal_opportunity\\\":\\n return _compute_binary_equal_opportunity(self.tp, self.fp, self.tn, self.fn)\\n\\n if self.task == \\\"all\\\":\\n return {\\n **_compute_binary_demographic_parity(self.tp, self.fp, self.tn, self.fn),\\n **_compute_binary_equal_opportunity(self.tp, self.fp, self.tn, self.fn),\\n }\\n return None\",\n \"def computeDataEnergy(self):\\n _cgco.gcoComputeDataEnergy(self.handle, self.energyTempArray)\\n return self._convertEnergyBack(self.energyTempArray[0])\",\n \"def master(client, data, column_name):\\n\\n # Info messages can help you when an algorithm crashes. These info\\n # messages are stored in a log file which is send to the server when\\n # either a task finished or crashes.\\n info('Collecting participating organizations')\\n\\n # Collect all organization that participate in this collaboration.\\n # These organizations will receive the task to compute the partial.\\n organizations = client.get_organizations_in_my_collaboration()\\n ids = [organization.get(\\\"id\\\") for organization in organizations]\\n\\n # Request all participating parties to compute their partial. This\\n # will create a new task at the central server for them to pick up.\\n # We've used a kwarg but is is also possible to use `args`. Although\\n # we prefer kwargs as it is clearer.\\n info('Requesting partial computation')\\n task = client.create_new_task(\\n input_={\\n 'method': 'average_partial',\\n 'kwargs': {\\n 'column_name': column_name\\n }\\n },\\n organization_ids=ids\\n )\\n\\n # Now we need to wait untill all organizations(/nodes) finished\\n # their partial. We do this by polling the server for results. It is\\n # also possible to subscribe to a websocket channel to get status\\n # updates.\\n info(\\\"Waiting for resuls\\\")\\n task_id = task.get(\\\"id\\\")\\n task = client.get_task(task_id)\\n while not task.get(\\\"complete\\\"):\\n task = client.get_task(task_id)\\n info(\\\"Waiting for results\\\")\\n time.sleep(1)\\n\\n # Once we now the partials are complete, we can collect them.\\n info(\\\"Obtaining results\\\")\\n results = client.get_results(task_id=task.get(\\\"id\\\"))\\n\\n # Now we can combine the partials to a global average.\\n global_sum = 0\\n global_count = 0\\n for output in results:\\n global_sum += output[\\\"sum\\\"]\\n global_count += output[\\\"count\\\"]\\n\\n return {\\\"average\\\": global_sum / global_count}\",\n \"def fetch(update, context):\\n message = f'fetching data, this should take few seconds... {EMOJI_SLEEP*3}'\\n update.message.reply_text(message)\\n try:\\n start = timer()\\n url = execute()\\n message = f'data fetched in {round(timer() - start, 1)}s. here is url to spreadsheet: {url}'\\n update.message.reply_text(message)\\n except Exception as e:\\n message = f'there was some error {EMOJI_SCREAM*3}\\\\nerror message: {e}'\\n update.message.reply_text(message)\",\n \"def get_data():\\r\\n spatial_expmat = np.load('/home/anniegao/spatial_magan/data/spatial_pca_with_coords.npz')['arr_0']\\r\\n spatial_expmat[:,100:] *= 5\\r\\n rna_expmat = np.load('/home/anniegao/spatial_magan/data/rna_pca_sampled.npz')['arr_0']\\r\\n spatial_pca_components = np.load('/home/anniegao/spatial_magan/data/spatial_pca_100components.npz')['arr_0']\\r\\n rna_pca_components = np.load('/home/anniegao/spatial_magan/data/rna_pca_100components.npz')['arr_0']\\r\\n spatial_cluster_labels = np.load('/home/anniegao/spatial_magan/data/spatial_cluster_3_labels_phate.npz')['arr_0']\\r\\n rna_cluster_labels = np.load('/home/anniegao/spatial_magan/data/rna_cluster_5_labels_sampled.npz')['arr_0']\\r\\n return spatial_expmat, rna_expmat, spatial_pca_components, rna_pca_components, spatial_cluster_labels, rna_cluster_labels\",\n \"def get_data(self): # TODO: add smooth possibility\\n return self.data\",\n \"def fetch(self):\\n raise NotImplementedError()\",\n \"def get_eval_data() -> GraphDataset:\\n _load_data_if_needed()\\n return eval_data\",\n \"def _get_data(self):\\n project_name, experiment_id = self.parent._get_parent_identifiers()\\n\\n self._data = self.repository.get_dataframe_data(\\n project_name, self.id, experiment_id=experiment_id\\n )\",\n \"def test_get_cell_info(self):\\n expected = { 'Experiment': \\\"20220101_EGS1_12345AA\\\",\\n 'Cell': '12345AA0018/20220101_1234_1-A1-A1_AAA66666_deadbeef',\\n 'Pool': '12345AA0018',\\n 'Date': '20220101',\\n 'Number': '1234',\\n 'Slot': '1-A1-A1',\\n 'CellID': 'AAA66666',\\n 'Checksum': 'deadbeef',\\n 'Project': '12345',\\n 'Base': '12345AA0018/20220101_1234_1-A1-A1_AAA66666_deadbeef/'\\n '20220101_EGS1_12345AA_12345AA0018_AAA66666_deadbeef',\\n 'Files in pass': 'unknown',\\n 'Files in fail': 1,\\n 'Files in fast5 fail': 1,\\n '_counts': [\\n {'_barcode': '.', '_label': 'All passed reads', '_part': 'pass', 'total_reads': 200},\\n {'_barcode': '.', '_label': 'Passed and lambda-filtered reads', '_part': 'nolambda'},\\n {'_barcode': '.', '_label': 'All failed reads', '_part': 'fail'} ],\\n '_blobs': ['../../__blob__'],\\n '_duplex' : [ ['Duplex pairs', 1],\\n ['from total passing reads', 200],\\n ['% of passing reads', '1.00%'] ],\\n '_filter_type': 'none',\\n '_final_summary': {'is_rna': False},\\n '_nanoplot': '../../__nanoplot__',\\n }\\n\\n\\n got = get_cell_info( experiment = \\\"20220101_EGS1_12345AA\\\",\\n cell = \\\"12345AA0018/20220101_1234_1-A1-A1_AAA66666_deadbeef\\\",\\n cell_content = { '.': dict( fast5_pass = ['x.fast5'],\\n fastq_fail = ['y.fastq'],\\n fast5_fail = ['y.fast5'] ) },\\n counts = { ('.','pass'): dict(total_reads = 200),\\n ('.','fail'): dict(),\\n ('.','nolambda'): dict() },\\n fin_summary = dict(is_rna = False),\\n blobs = ['__blob__'],\\n nanoplot = '__nanoplot__',\\n duplex = 1,\\n fast5_meta = dict() )\\n\\n if VERBOSE:\\n pprint(got)\\n\\n self.assertEqual( type(got), OrderedDict )\\n self.assertEqual( dict(got), expected )\",\n \"def get_cost_data(monitor, num_batches, num_epochs):\\n log = monitor.main_loop.log\\n\\n cost = np.zeros((2, num_epochs))\\n\\n for i in xrange(num_epochs):\\n cost[0, i] = log[(i + 1) * num_batches]['train_cost']\\n cost[1, i] = log[(i + 1) * num_batches]['test_cost']\\n\\n return cost\",\n \"def GetGeData(self, *args, **kwargs):\\n pass\",\n \"def compute(self) -> Dict[str, np.ndarray]:\\n return {name: self.metrics[name].compute() for name in self.metrics}\",\n \"def get(self):\\n\\n # -------------------------------------------\\n # Build command string\\n\\n cmd = dict()\\n\\n cmd[\\\"name_\\\"] = self.thisptr[\\\"df_name_\\\"]\\n cmd[\\\"type_\\\"] = \\\"BooleanColumn.get\\\"\\n\\n cmd[\\\"col_\\\"] = self.thisptr\\n\\n # -------------------------------------------\\n # Send command to engine\\n\\n s = comm.send_and_receive_socket(cmd)\\n\\n msg = comm.recv_string(s)\\n\\n # -------------------------------------------\\n # Make sure everything went well, receive data\\n # and close connection\\n\\n if msg != \\\"Found!\\\":\\n s.close()\\n raise Exception(msg)\\n\\n mat = comm.recv_boolean_matrix(s)\\n\\n # -------------------------------------------\\n # Close connection, if necessary.\\n\\n s.close()\\n\\n # -------------------------------------------\\n\\n return mat.ravel()\",\n \"def run(self):\\n\\t\\t(conn, curs) = db_connect(self.hostname, self.dbname, self.schema)\\n\\t\\t\\n\\t\\t\\n\\t\\t#e_splat_table = self.table+'e'\\n\\t\\t#e_mcl_table = self.mcl_table+'e'\\n\\t\\t#CoexprFromCooccu_instance = CoexprFromCooccu()\\n\\t\\t#pre_2nd_cc_hierarchy = CoexprFromCooccu_instance.data_fetch(curs, self.mcl_table, e_mcl_table)\\n\\t\\t#mcl_id2cluster_dstructure = self.data_fetch(curs, self.table, self.mcl_table, crs_no=1)\\n\\t\\t#mcl_id_2nd_order2cluster_dstructure = self.data_fetch(curs, e_splat_table, e_mcl_table, crs_no=2)\\n\\t\\t#self.cluster_dstructure_output_with_both_hierarchy(curs, self.output_fname, pre_2nd_cc_hierarchy,\\\\\\n\\t\\t#\\tmcl_id2cluster_dstructure, mcl_id_2nd_order2cluster_dstructure)\\n\\t\\t#self.cluster_dstructure_output(curs, self.output_fname, self.order_1st_id2all_clusters)\\n\\t\\tself.data_fetch(curs, self.table, self.mcl_table, crs_no=1, output_fname=self.output_fname)\",\n \"def get_host_data(self):\\n\\n raise NotImplementedError\",\n \"def data_fetcher(self, mp_ids: List[str] = None, mp_props: List[str] = None,\\n elasticity: bool = False) -> pd.DataFrame:\\n\\n print('Will fetch %s inorganic compounds from Materials Project' % len(mp_ids))\\n\\n def grouper(iterable, n, fillvalue=None):\\n \\\"\\\"\\\"\\\"\\n Split requests into fixed number groups\\n eg: grouper('ABCDEFG', 3, 'x') --> ABC DEF Gxx\\n Collect data_cluster into fixed-length chunks or blocks\\\"\\\"\\\"\\n args = [iter(iterable)] * n\\n return zip_longest(fillvalue=fillvalue, *args)\\n\\n if mp_ids is None:\\n mp_ids = self.ids\\n # the following props will be fetched\\n if mp_props is None:\\n mp_props = [\\n 'band_gap',\\n 'density',\\n \\\"icsd_ids\\\"\\n 'volume',\\n 'material_id',\\n 'pretty_formula',\\n 'elements',\\n \\\"energy\\\",\\n 'efermi',\\n 'e_above_hull',\\n 'formation_energy_per_atom',\\n 'final_energy_per_atom',\\n 'unit_cell_formula',\\n 'spacegroup',\\n 'nelements'\\n \\\"nsites\\\",\\n \\\"final_structure\\\",\\n \\\"cif\\\",\\n \\\"piezo\\\",\\n \\\"diel\\\"\\n ]\\n\\n if elasticity:\\n mp_props.append(\\\"elasticity\\\")\\n\\n entries = []\\n mpid_groups = [g for g in grouper(mp_ids, 40)]\\n\\n with self.m as mpr:\\n for group in tqdm(mpid_groups):\\n mpid_list = [ids for ids in filter(None, group)]\\n chunk = mpr.query({\\\"material_id\\\": {\\\"$in\\\": mpid_list}}, mp_props)\\n entries.extend(chunk)\\n\\n if 'material_id' in entries[0]:\\n df = pd.DataFrame(entries, index=[e['material_id'] for e in entries])\\n else:\\n df = pd.DataFrame(entries)\\n if 'unit_cell_formula' in df.columns:\\n df = df.rename(columns={'unit_cell_formula': 'composition'})\\n\\n df = df.T\\n\\n self.dff = df\\n\\n return df\",\n \"def get_data(self):\\n logging.info(\\n \\\"[IEXIntervalDataSource] started: fetching every %d seconds\\\",\\n self._interval,\\n )\\n\\n def _get(self):\\n data = self._iex_source(**self._iex_source_kwargs)\\n if data:\\n should_update = True\\n if self._should_hash:\\n hashed = str_to_float(json.dumps(data, sort_keys=True))\\n should_update = hashed != self._last_hash\\n if should_update:\\n self._last_hash = hashed\\n if should_update:\\n if self._data_cleaner:\\n data = self._data_cleaner(data)\\n self.table.update(data)\\n \\n callback = tornado.ioloop.PeriodicCallback(callback=partial(_get, self), callback_time=self._interval)\\n callback.start()\",\n \"def calc(self):\\n self.proc_blocks = [cluster.cells for cluster in self.clusters]\\n self.cell_loads = [sum([len(cell) for cell in self.proc_blocks])]\\n self.particle_loads = [cluster.np for cluster in self.clusters]\\n self.imbalance = LoadBalancer.get_load_imbalance(self.particle_loads)\",\n \"def fetch_data(data_url):\\n return requests.get(data_url).content\",\n \"def compute_api(self):\\n path = self.compute.path = '/'\\n res = self.compute.call('', 'GET', data='', \\n token=self.manager.identity.token)\\n self.logger.debug('Get openstack compute services: %s' % truncate(res))\\n self.compute.path = path\\n return res[0]\",\n \"def test_get_entity(self):\\n\\n storage = StringIO.StringIO()\\n c = pycurl.Curl()\\n c.setopt(c.URL,\\\"http://127.0.0.1:8090/compute/9930\\\")\\n c.setopt(c.HTTPHEADER, ['Accept:application/occi+json'])\\n c.setopt(c.VERBOSE, True)\\n c.setopt(c.CUSTOMREQUEST, 'GET')\\n c.setopt(c.WRITEFUNCTION, storage.write)\\n c.perform()\\n content = storage.getvalue()\\n print \\\" ===== Body content =====\\\\n \\\" + content + \\\" ==========\\\\n\\\"\",\n \"def get(self, fetch_number: int):\\n return self.results[fetch_number]\",\n \"def _get_io_data(self, cell_dim_only=None):\\n conns, mat_ids = [], []\\n if cell_dim_only is not None:\\n if not is_sequence(cell_dim_only):\\n cell_dim_only = [cell_dim_only]\\n descs = [ii for ii in self.descs if int(ii[0]) in cell_dim_only]\\n else:\\n descs = self.descs\\n for desc in descs:\\n cmesh = self.get_cmesh(desc)\\n conn, cells = self.get_conn(desc, ret_cells=True, tdim=cmesh.tdim)\\n conns.append(conn)\\n mat_ids.append(cmesh.cell_groups[cells])\\n\\n return cmesh.coors, cmesh.vertex_groups, conns, mat_ids, descs\",\n \"def getDataContaining(self, address: ghidra.program.model.address.Address) -> ghidra.program.model.listing.Data:\\n ...\",\n \"def getDataAt(self, address: ghidra.program.model.address.Address) -> ghidra.program.model.listing.Data:\\n ...\",\n \"def compute(self, end_user, input_data, computation_deadline, p_ec, c_ec, RC_EC_capacity,\\n computation_requirement_user, transm_delay_user_ru, Name_offloaded_data):\\n global execution_latency_dc_vector # Global need to be declared in the beginning of the function\\n global EC_m_cache_allocation_variable\\n global cache_capacity_allocation_EC\\n global DC_caching_decision_variable\\n global DC_computation_decision_variable\\n global output_data\\n\\n pkm = p_ec * (computation_requirement_user / ((end_user - 1) * computation_requirement_user))\\n p_EC_n = max(computation_capacity_EC)\\n pkn = p_EC_n * (computation_requirement_user / ((end_user - 1) * computation_requirement_user))\\n execution_latency_ec_n = (input_data * computation_requirement_user) / ((end_user - 1) * p_ec)\\n execution_latency_ec_m = (input_data * computation_requirement_user) / ((end_user - 1) * p_ec)\\n if computation_requirement_user <= pkm and execution_latency_ec_m <= computation_deadline:\\n execution_latency_mc = (input_data * computation_requirement_user) / ((end_user - 1) * p_ec)\\n total_executing_ec_m = execution_latency_mc\\n total_executing_ec_m_array.append(total_executing_ec_m)\\n total_executing_ec_n_array.append(0)\\n EC_m_EC_n_offloading_variable.append(0)\\n EC_n_cache_allocation_variable.append(0)\\n print(\\\" Computation at EC is done at %d%%\\\" % (random.randint(50, 99))) # We just print to see where the\\n # task is execute\\n EC_m_computation_allocation_variable.append(1)\\n EC_n_computation_allocation_variable.append(0)\\n output_data0 = self.input_data * (70 / 100) # We assume that after computation, the input can be reduced\\n # 30%\\n EC_m_cache_allocation_variable.append(1)\\n cached_content, cache_capacity_allocation_EC = \\\\\\n EC.cache(self, end_user, input_data, output_data0, c_ec, Name_offloaded_data)\\n computation_capacity_allocation_EC.append(pkm)\\n execution_latency_dc_vector.append(0)\\n cache_capacity_allocation_dc = []\\n computation_capacity_allocation_dc = []\\n DC_computation_decision_variable.append(0)\\n computation_capacity_allocation_dc.append(0)\\n DC_caching_decision_variable.append(0)\\n cache_capacity_allocation_dc.append(0)\\n output_data.append(output_data0)\\n transm_delay_BS_m= input_data / Fiber_Fronthaul_RU_EC[end_user]\\n transm_delay_between_ru.append(transm_delay_BS_m)\\n return transm_delay_between_ru, cached_content, EC_m_cloud_offloading_variable, \\\\\\n EC_m_computation_allocation_variable, EC_n_computation_allocation_variable, \\\\\\n total_executing_ec_m_array, total_executing_ec_n_array, \\\\\\n computation_capacity_allocation_EC, cache_capacity_allocation_EC, \\\\\\n EC_m_cache_allocation_variable, EC_n_cache_allocation_variable, \\\\\\n EC_m_EC_n_offloading_variable, output_data, execution_latency_dc_vector, \\\\\\n DC_computation_decision_variable, computation_capacity_allocation_dc, DC_caching_decision_variable, \\\\\\n cache_capacity_allocation_dc\\n elif computation_requirement_user <= pkn and execution_latency_ec_n <= computation_deadline:\\n EC_n_cache_allocation_variable.append(1)\\n EC_m_EC_n_offloading_variable.append(1)\\n EC_m_cache_allocation_variable.append(0)\\n EC_n_computation_allocation_variable.append(1)\\n EC_m_computation_allocation_variable.append(0)\\n # Offloading delay between EC m and EC n\\n transm_delay_ECm_ECn = input_data / Bandwidth_EC_m_EC_n\\n total_executing_ec_n = execution_latency_ec_n\\n print(\\\" Computation at EC n is done at %d%%\\\" % (random.randint(50, 99)))\\n output_data0 = input_data * (40 / 1000)\\n cached_content, cache_capacity_allocation_EC = EC.cache(\\n self, end_user, input_data, output_data0, c_ec, Name_offloaded_data)\\n computation_capacity_allocation_EC.append(0)\\n execution_latency_dc_vector.append(0)\\n cache_capacity_allocation_dc = []\\n output_data.append(output_data0)\\n computation_capacity_allocation_dc = []\\n DC_computation_decision_variable = []\\n DC_computation_decision_variable.append(0)\\n computation_capacity_allocation_dc.append(0)\\n DC_caching_decision_variable.append(0)\\n cache_capacity_allocation_dc.append(0)\\n EC_m_cloud_offloading_variable.append(0)\\n total_executing_ec_n_array.append(total_executing_ec_n)\\n total_executing_ec_m_array.append(0)\\n transm_delay_between_ru.append(transm_delay_ECm_ECn)\\n return transm_delay_between_ru, cached_content, EC_m_cloud_offloading_variable, \\\\\\n EC_m_computation_allocation_variable, EC_n_computation_allocation_variable, \\\\\\n total_executing_ec_m_array, total_executing_ec_n_array, \\\\\\n computation_capacity_allocation_EC, cache_capacity_allocation_EC, \\\\\\n EC_m_cache_allocation_variable, EC_n_cache_allocation_variable, \\\\\\n EC_m_EC_n_offloading_variable, output_data, execution_latency_dc_vector, \\\\\\n DC_computation_decision_variable, computation_capacity_allocation_dc, DC_caching_decision_variable, \\\\\\n cache_capacity_allocation_dc\\n else:\\n print(\\\"No available resources at EC server, task is offloaded to Data center\\\")\\n EC_n_computation_allocation_variable.append(0)\\n EC_m_computation_allocation_variable.append(0)\\n EC_m_cloud_offloading_variable.append(1)\\n EC_m_cache_allocation_variable.append(0)\\n cache_capacity_allocation_EC.append(0)\\n EC_n_cache_allocation_variable.append(0)\\n EC_m_EC_n_offloading_variable.append(0)\\n transm_delay_EC_dc = input_data / RC_EC_capacity\\n # Request Resource at Data center\\n datacenter = DataCenter(total_number_regional_cloud, computation_deadline, input_data, computation_deadline,\\n computation_requirement_user, p_d, c_d)\\n output_data, execution_latency_dc_vector, DC_computation_decision_variable, \\\\\\n computation_capacity_allocation_dc, cached_content, DC_caching_decision_variable, \\\\\\n cache_capacity_allocation_dc = datacenter.compute_dc(end_user, input_data, computation_requirement_user,\\n transm_delay_EC_dc, Name_offloaded_data)\\n\\n computation_capacity_allocation_EC.append(0)\\n total_executing_ec_m_array.append(0)\\n total_executing_ec_n_array.append(0)\\n transm_delay_bsm_dc = input_data / RC_EC_capacity\\n transm_delay_between_ru.append(transm_delay_bsm_dc)\\n\\n return transm_delay_between_ru, cached_content, EC_m_cloud_offloading_variable, \\\\\\n EC_m_computation_allocation_variable, EC_n_computation_allocation_variable, \\\\\\n total_executing_ec_m_array, total_executing_ec_n_array, \\\\\\n computation_capacity_allocation_EC, cache_capacity_allocation_EC, \\\\\\n EC_m_cache_allocation_variable, EC_n_cache_allocation_variable, \\\\\\n EC_m_EC_n_offloading_variable, output_data, execution_latency_dc_vector, \\\\\\n DC_computation_decision_variable, computation_capacity_allocation_dc, DC_caching_decision_variable, \\\\\\n cache_capacity_allocation_dc\",\n \"def query(self):\\n self._measurements[self.KEY_FMIX].df = self.fetch_data_fmix()\\n self._measurements[self.KEY_EMIS].df = self.co2_calc(self._measurements[self.KEY_FMIX].df)\",\n \"def value(self):\\n '''\\n if self.hash_pattern:\\n msg = \\\"\\\"\\\"It is too costly to construct the full value of a deep cell\\n Use cell.data instead.\\n \\n If you are really sure that the value of the deep cell fits in memory,\\n you can assign this cell to a normal cell, e.g:\\n \\n ctx.othercell = Cell()\\n ctx.othercell = ctx.thiscell\\n \\\"\\\"\\\"\\n raise AttributeError(msg)\\n '''\\n self._parent()\\n hcell = self._get_hcell()\\n if hcell.get(\\\"UNTRANSLATED\\\"):\\n if self._path[0] == \\\"HELP\\\":\\n return hcell.get(\\\"TEMP\\\")\\n return None\\n try:\\n cell = self._get_cell()\\n except Exception:\\n import traceback\\n\\n traceback.print_exc()\\n raise\\n if cell is None:\\n raise ValueError\\n value = cell.value\\n return value\",\n \"def data_from_ucr_query(self):\\n raise NotImplementedError\",\n \"def calculate():\\n print 'AJAX getJSON request to get current data and begin compute on new client'\\n global isBegin, CurrentIndex, isFinished, ComputationTime, N, Row, RowIndex\\n if isBegin:\\n generate_random_texts(N)\\n ComputationTime = time.time()\\n RowIndex = (RowN - 1) * 256\\n Row = FirstText[RowIndex:RowIndex + 255]\\n part = SecondText[CurrentIndex:CurrentIndex+1024]\\n isBegin = False\\n else:\\n Row = FirstText[RowIndex:RowIndex + 255]\\n part = SecondText[CurrentIndex:CurrentIndex+1024]\\n if isFinished:\\n Row = ''\\n part = ''\\n return jsonify(current_row=Row, current_part=part)\",\n \"def _compute_(self):\\n dic = \\\"data/sim/{dn}/{rad}/\\\".format(dn=self.event.strftime(\\\"%Y.%m.%d.%H.%M\\\"), rad=self.rad)\\n fbgc = \\\"data/sim/{dn}/{rad}/exp.bgc.bm({bm}).elv(<elv>).csv\\\".format(dn=self.event.strftime(\\\"%Y.%m.%d.%H.%M\\\"), \\n rad=self.rad, bm=self.bmnum)\\n fflare = \\\"data/sim/{dn}/{rad}/exp.flare.bm({bm}).elv(<elv>).csv\\\".format(dn=self.event.strftime(\\\"%Y.%m.%d.%H.%M\\\"),\\n rad=self.rad, bm=self.bmnum)\\n cmd = \\\"export DIR_MODELS_REF_DAT=/home/shibaji/Collaboration_NCAR/code_rt_sd/pharlap/pharlap_4.1.3/dat;\\\\\\n cd pharlap;\\\\\\n matlab -nodisplay -nodesktop -nosplash -nojvm -r \\\\\\\"UT=[{ut}];rad='{rad}';dic='{dic}';fbgc='{fbgc}';bm={bm};\\\\\\n fflare='{fflare}';rt_1D_sim;exit;\\\\\\\"\\\".format(ut=self.event.strftime(\\\"%Y %m %d %H %S\\\"), rad=self.rad,\\n dic=dic, bm=self.bmnum, fbgc=fbgc, fflare=fflare)\\n os.system(cmd)\\n return\",\n \"def get_data(self):\\n return self._results\",\n \"def get_distribution(self):\\n self.calc()\\n proc_blocks = self.proc_blocks\\n proc_num_particles = self.particle_loads\\n cell_proc = LoadBalancer.get_block_proc(proc_blocks=proc_blocks)\\n return cell_proc, proc_num_particles\",\n \"def peridym_get_neighbor_data(mesh, horizon, omega_fun, struct_grd=False):\\n\\n if(struct_grd):\\n cell_cent = structured_cell_centroids(mesh)\\n cell_vol = structured_cell_volumes(mesh)\\n else:\\n cell_cent = get_cell_centroids(mesh)\\n cell_vol = get_cell_volumes(mesh)\\n\\n tree = QuadTree()\\n extents = get_domain_bounding_box(mesh)\\n tree.put(extents, horizon)\\n linear_tree = tree.get_linear_tree()\\n nbr_lst, nbr_beta_lst = tree_nbr_search(tree.get_linear_tree(), cell_cent, horizon)\\n\\n start = tm.default_timer()\\n print(\\\"computing the remaining peridynamic neighbor data for the mesh with horizon: %4.2f\\\" %horizon)\\n\\n mw = peridym_compute_weighted_volume(mesh, nbr_lst, nbr_beta_lst, horizon, omega_fun, struct_grd)\\n\\n nbr_bnd_vector_lst = []\\n nbr_bnd_len_lst = []\\n nbr_infl_fld_lst = []\\n\\n for i in range(len(cell_cent)):\\n curr_node_coord = cell_cent[i]\\n \\n #declare empty lists for current node neighbor\\n #attributes like neighbor bond vector, bond len,\\n #and influence field \\n #refer ch5 algo1 of handbook of peridynamic modelling\\n #by silling etal \\n\\n curr_nbr_lst = nbr_lst[i] \\n curr_nbr_bnd_vct = cell_cent[curr_nbr_lst] - curr_node_coord\\n curr_nbr_bnd_len = la.norm(curr_nbr_bnd_vct, 2, axis=1)\\n\\n nbr_bnd_vector_lst.append(curr_nbr_bnd_vct)\\n nbr_bnd_len_lst.append(curr_nbr_bnd_len)\\n\\n print(\\\"time taken for computation of remaining neighbor data for the given mesh is %4.3f seconds\\\"%(tm.default_timer()-start))\\n \\n return nbr_lst, nbr_beta_lst, nbr_bnd_vector_lst, nbr_bnd_len_lst, mw\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6227691","0.6165168","0.61336154","0.6130058","0.6112285","0.60044104","0.5895254","0.57669675","0.57077354","0.5702612","0.56423634","0.5611894","0.56060374","0.559","0.5576882","0.5543976","0.552921","0.5527102","0.5463397","0.54594564","0.5458928","0.54355335","0.5432847","0.5414548","0.540815","0.5400971","0.5390855","0.5388424","0.5388424","0.5388424","0.53528005","0.53395724","0.5331791","0.5331791","0.5314333","0.53104645","0.5308058","0.52842003","0.52826226","0.52826226","0.52821624","0.528172","0.5280591","0.5263017","0.52609104","0.5254489","0.5254229","0.5232086","0.5199217","0.51736265","0.51722693","0.51696247","0.5164924","0.5163255","0.51604915","0.51455605","0.51377684","0.5128106","0.51194763","0.5117406","0.5113655","0.51040435","0.5102973","0.50986487","0.5091334","0.50897676","0.507726","0.5074079","0.50683516","0.5064876","0.5042893","0.5025646","0.50208473","0.5018224","0.50172216","0.50144064","0.5009787","0.50039965","0.5002621","0.49947912","0.4974796","0.4972667","0.4972033","0.4955964","0.49533322","0.4952988","0.49523073","0.495214","0.49431038","0.494297","0.49401543","0.49358034","0.49337363","0.49259186","0.49221888","0.49175745","0.49122757","0.4908661","0.49023637","0.49000478"],"string":"[\n \"0.6227691\",\n \"0.6165168\",\n \"0.61336154\",\n \"0.6130058\",\n \"0.6112285\",\n \"0.60044104\",\n \"0.5895254\",\n \"0.57669675\",\n \"0.57077354\",\n \"0.5702612\",\n \"0.56423634\",\n \"0.5611894\",\n \"0.56060374\",\n \"0.559\",\n \"0.5576882\",\n \"0.5543976\",\n \"0.552921\",\n \"0.5527102\",\n \"0.5463397\",\n \"0.54594564\",\n \"0.5458928\",\n \"0.54355335\",\n \"0.5432847\",\n \"0.5414548\",\n \"0.540815\",\n \"0.5400971\",\n \"0.5390855\",\n \"0.5388424\",\n \"0.5388424\",\n \"0.5388424\",\n \"0.53528005\",\n \"0.53395724\",\n \"0.5331791\",\n \"0.5331791\",\n \"0.5314333\",\n \"0.53104645\",\n \"0.5308058\",\n \"0.52842003\",\n \"0.52826226\",\n \"0.52826226\",\n \"0.52821624\",\n \"0.528172\",\n \"0.5280591\",\n \"0.5263017\",\n \"0.52609104\",\n \"0.5254489\",\n \"0.5254229\",\n \"0.5232086\",\n \"0.5199217\",\n \"0.51736265\",\n \"0.51722693\",\n \"0.51696247\",\n \"0.5164924\",\n \"0.5163255\",\n \"0.51604915\",\n \"0.51455605\",\n \"0.51377684\",\n \"0.5128106\",\n \"0.51194763\",\n \"0.5117406\",\n \"0.5113655\",\n \"0.51040435\",\n \"0.5102973\",\n \"0.50986487\",\n \"0.5091334\",\n \"0.50897676\",\n \"0.507726\",\n \"0.5074079\",\n \"0.50683516\",\n \"0.5064876\",\n \"0.5042893\",\n \"0.5025646\",\n \"0.50208473\",\n \"0.5018224\",\n \"0.50172216\",\n \"0.50144064\",\n \"0.5009787\",\n \"0.50039965\",\n \"0.5002621\",\n \"0.49947912\",\n \"0.4974796\",\n \"0.4972667\",\n \"0.4972033\",\n \"0.4955964\",\n \"0.49533322\",\n \"0.4952988\",\n \"0.49523073\",\n \"0.495214\",\n \"0.49431038\",\n \"0.494297\",\n \"0.49401543\",\n \"0.49358034\",\n \"0.49337363\",\n \"0.49259186\",\n \"0.49221888\",\n \"0.49175745\",\n \"0.49122757\",\n \"0.4908661\",\n \"0.49023637\",\n \"0.49000478\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6701156"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":14379,"cells":{"query":{"kind":"string","value":"Get count of resources for requested cluster and namespace"},"document":{"kind":"string","value":"def get_resource_count(cluster_id, namespace_id=None):\n # fetching namespaced resource count\n if namespace_id:\n # Deployment count\n deployment_count = len(client.AppsV1beta2Api().list_namespaced_deployment(namespace_id).items)\n # Pod count\n pod_items = client.CoreV1Api().list_namespaced_pod(namespace_id).items\n pod_count = len([pod_item for pod_item in pod_items if pod_item.status.phase == 'Running'])\n # Cell count\n cell_pod_map = get_cell_pod_map(cluster_id)\n pods_list = [pod_item.metadata.name for pod_item in pod_items]\n cell_count = len([cell_pod_map[pods] for pods in pods_list if pods in cell_pod_map])\n # fetching resource count for entire cluster\n else:\n # Deployment count\n deployment_count = len(client.AppsV1beta2Api().list_deployment_for_all_namespaces().items)\n # Pod count\n pod_count = len(client.CoreV1Api().list_pod_for_all_namespaces().items)\n # Cell count\n cell_count = len(get_compute_cell_data()['items'])\n # Removing resources related to the excluded namespace\n if namespace_is_hidden == 'true':\n resources_to_hide = get_hidden_namespace_resources(cluster_id, 'dashboard')\n deployment_count = 0 if deployment_count == 0 else deployment_count - resources_to_hide['deployments']\n pod_count = 0 if pod_count == 0 else pod_count - resources_to_hide['pods']\n cell_count = 0 if cell_count == 0 else cell_count - resources_to_hide['cells']\n return {\"deployments\": deployment_count,\n \"pods\": pod_count,\n 'cells': cell_count}"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_cluster_count(self) -> int:\n return len(self.get_all_cluster_ids())","def count(self, resource):\n return len(self.all(resource))","def get_count_all(cls, context, cluster_id):\n return cls.dbapi.get_cluster_nodegroup_count(context, cluster_id)","def test_get_resource_license_resource_count_list(self):\n pass","def count(cls, client) :\n try :\n obj = nshttpprofile()\n option_ = options()\n option_.count = True\n response = obj.get_resources(client, option_)\n if response :\n return response[0].__dict__['___count']\n return 0\n except Exception as e :\n raise e","def count(self):\n\n if self.cluster:\n return self.cluster.count()\n\n return super().count()","def getNumResources(self, *args):\n return _libsbml.CVTerm_getNumResources(self, *args)","def test_list_applied_cluster_resource_quota_for_all_namespaces(self):\n pass","def test_list_namespaced_applied_cluster_resource_quota(self):\n pass","def GetConceptCounts(self, request, context):\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')","def count(self):\r\n url = '{0}/{1}'.format(self.get_url(), 'count')\r\n\r\n return http.Request('GET', url), parsers.parse_json","def get_hidden_namespace_resources(cluster_id, namespace_id):\n # Deployment count\n deployment_count = len(client.AppsV1beta2Api().list_namespaced_deployment(namespace_id).items)\n # Pod count\n pod_items = client.CoreV1Api().list_namespaced_pod(namespace_id).items\n pod_count = len(pod_items)\n # Cell count\n cell_pod_map = get_cell_pod_map(cluster_id)\n pods_list = [pod_item.metadata.name for pod_item in pod_items]\n cell_count = len([cell_pod_map[pods] for pods in pods_list])\n return {'deployments': deployment_count,\n 'pods': pod_count,\n 'cells': cell_count\n }","def count(cls, client) :\n\t\ttry :\n\t\t\tobj = bfdsession()\n\t\t\toption_ = options()\n\t\t\toption_.count = True\n\t\t\tresponse = obj.get_resources(client, option_)\n\t\t\tif response :\n\t\t\t\treturn response[0].__dict__['___count']\n\t\t\treturn 0\n\t\texcept Exception as e :\n\t\t\traise e","def test_read_namespaced_applied_cluster_resource_quota(self):\n pass","def test_count_resources(ops_and_shots, expected_resources):\n ops, shots = ops_and_shots\n computed_resources = _count_resources(QuantumScript(ops=ops, shots=shots))\n assert computed_resources == expected_resources","def count(cls, client) :\n\t\ttry :\n\t\t\tobj = rewriteaction()\n\t\t\toption_ = options()\n\t\t\toption_.count = True\n\t\t\tresponse = obj.get_resources(client, option_)\n\t\t\tif response :\n\t\t\t\treturn response[0].__dict__['___count']\n\t\t\treturn 0\n\t\texcept Exception as e :\n\t\t\traise e","def get_resource_count(har_json):\n entries = har_json['log']['entries']\n\n resource_type_counts = Counter()\n\n for entry in entries:\n resource = entry['request']['url']\n dirty_resource_type = resource.split('.')[-1]\n resource_type = dirty_resource_type.split('?')[0] # Remove url params\n if len(resource_type) > 4:\n resource_type_counts['other'] += 1\n # print 'Found other resource type: {0}'.format(resource_type)\n else:\n resource_type_counts[resource_type] += 1\n\n return resource_type_counts","def count(cls, client) :\n\t\ttry :\n\t\t\tobj = lbprofile()\n\t\t\toption_ = options()\n\t\t\toption_.count = True\n\t\t\tresponse = obj.get_resources(client, option_)\n\t\t\tif response :\n\t\t\t\treturn response[0].__dict__['___count']\n\t\t\treturn 0\n\t\texcept Exception as e :\n\t\t\traise e","def count(cls, client) :\n\t\ttry :\n\t\t\tobj = lsntransportprofile()\n\t\t\toption_ = options()\n\t\t\toption_.count = True\n\t\t\tresponse = obj.get_resources(client, option_)\n\t\t\tif response :\n\t\t\t\treturn response[0].__dict__['___count']\n\t\t\treturn 0\n\t\texcept Exception as e :\n\t\t\traise e","def resource_discovery_association_count(self) -> pulumi.Output[int]:\n return pulumi.get(self, \"resource_discovery_association_count\")","def fetch_counts(swarming, start, end, state, tags, parallel):\n\n def process(data):\n return int(data['count'])\n delta = datetime.timedelta(days=1)\n return _fetch_daily_internal(delta, swarming, process, 'tasks/count', start,\n end, state, tags, parallel)","def getHostCount(self, **kwargs):\n\n allParams = ['scope']\n\n params = locals()\n for (key, val) in list(params['kwargs'].items()):\n if key not in allParams:\n raise TypeError(\"Got an unexpected keyword argument '%s' to method getHostCount\" % key)\n params[key] = val\n del params['kwargs']\n\n resourcePath = '/host/count'\n resourcePath = resourcePath.replace('{format}', 'json')\n method = 'GET'\n\n queryParams = {}\n headerParams = {}\n formParams = {}\n files = {}\n bodyParam = None\n\n headerParams['Accept'] = 'application/json'\n headerParams['Content-Type'] = 'application/json'\n\n\n\n\n if ('scope' in params):\n headerParams['scope'] = params['scope']\n\n\n\n\n\n\n\n\n postData = (formParams if formParams else bodyParam)\n\n response = self.apiClient.callAPI(resourcePath, method, queryParams,\n postData, headerParams, files=files)\n\n\n if not response:\n return None\n\n responseObject = self.apiClient.deserialize(response, 'CountResult')\n return responseObject","def get_public_narrative_count():\n client_workspace = MongoClient(mongoDB_metrics_connection+to_workspace)\n db_workspace = client_workspace.workspace\n public_narrative_count = db_workspace.workspaceACLs.find({\"user\" : \"*\"}).count()\n return public_narrative_count;","def aquire_cnts(self,n):\n return self.cnts.get_n(n)","def query_counts(\n self,\n nodes,\n project_id=None,\n chunk_size=2500,\n format=\"json\",\n args=None,\n ):\n\n counts = {}\n\n if isinstance(nodes,str):\n nodes = [nodes]\n\n for node in nodes:\n if project_id != None:\n program, project = project_id.split(\"-\", 1)\n if args == None:\n query_txt = \"\"\"{_%s_count (project_id:\"%s\")}\"\"\" % (node, project_id)\n else:\n query_txt = \"\"\"{_%s_count (project_id:\"%s\", %s)}\"\"\" % (node, project_id, args)\n else:\n if args == None:\n query_txt = \"\"\"{_%s_count}\"\"\" % (node)\n else:\n query_txt = \"\"\"{_%s_count (%s)}\"\"\" % (node, args)\n\n # First query the node count to get the expected number of results for the requested query:\n\n try:\n res = self.sub.query(query_txt)\n count_name = \"_\".join(map(str, [\"\", node, \"count\"]))\n qsize = res[\"data\"][count_name]\n counts[node] = qsize\n except:\n print(\"\\n\\tQuery to get _{}_count failed! {}\".format(node, query_txt))\n\n\n return counts","def _count(self):\n if self._count_valid:\n return self._total_results\n\n result = self._cb.get_object(self._doc_class.urlobject.format(self._cb.credentials.org_key))\n results = result.get(\"results\", [])\n\n self._total_results = len(results)\n self._count_valid = True\n\n return self._total_results","def resource_record_set_count(self) -> int:\n return pulumi.get(self, \"resource_record_set_count\")","def cluster_obs_count(self):\n return(self.merged_data.groupby(\n 'labels').count().transpose().iloc[0, :])","def count(cls, client) :\n\t\ttry :\n\t\t\tobj = appfwlearningsettings()\n\t\t\toption_ = options()\n\t\t\toption_.count = True\n\t\t\tresponse = obj.get_resources(client, option_)\n\t\t\tif response :\n\t\t\t\treturn response[0].__dict__['___count']\n\t\t\treturn 0\n\t\texcept Exception as e :\n\t\t\traise e","def get_desired_count(cluster_name, service_name):\n\n response = ecs_client.describe_services(\n cluster=cluster_name, services=[service_name],\n )\n\n for service in response[\"services\"]:\n return service[\"desiredCount\"]\n\n raise Exception(\n f\"desiredCount not found for cluster: {cluster_name} service: {service_name}\"\n )","def test_count_reads_per_cluster(self):\n \n bedtool = pybedtools.BedTool(clipper.test_file(\"clip_analysis_test_peak_results.bed\"))\n \n total_reads, reads_per_cluster = count_reads_per_cluster(bedtool, None)\n \n self.assertListEqual([147,52, 239, 85, 47, 119, 58, 588, 92, 59, 196, 36], reads_per_cluster)\n self.assertEqual(sum([147,52, 239, 85, 47, 119, 58, 588, 92, 59, 196, 36]), total_reads)","def get_count(cls):\n total = 0\n for counter in SimpleCounterShard.objects.all():\n total += counter.count\n return total","def get_cluster_sizes(self,sc):\n\n clusterSizes = []\n clusters = sc.labels\n for k in np.arange(clusters.max()):\n clusterSizes.append(len(np.where(clusters==k)[0])) \n \n return clusterSizes","def test_get_resource_license_resource_count_by_moid(self):\n pass","def test_list_cluster_resource_quota(self):\n pass","def get_count(name, key):\n total = 0\n query = CounterShard.all().filter('name = ', name).filter('reference_key = ', key)\n for counter in query:\n total += counter.count\n \n return total","def cluster_counter(self):\n return Counter(self.model.labels_.tolist())","def identify_cluster_namespaces():\n # Execute 'kubectl get namespaces' command, returned as CompletedProcess\n namespaces_completed_process = run([\"kubectl\", \"get\", \"namespaces\"], stdout=PIPE, stderr=PIPE)\n\n status = namespaces_completed_process.returncode\n # If return code is not 0\n if status:\n return {'status': status, 'value': namespaces_completed_process.stderr.decode('utf-8').strip()}\n else:\n return {'status': status, 'value': namespaces_completed_process.stdout.decode('utf-8').strip()}","def get_cluster_usage_info(cluster_id, kind, namespace_id=None, pods_list=None):\n if pods_list is None:\n pods_list = []\n else:\n logger.info('pod list not none')\n if pods_list == 'no_pod_resource':\n return {'cpu': 0,\n 'memory': 0}\n else:\n logger.info('resources no 0')\n # node usage stats if needed\n if kind == 'nodes':\n cpu_usage_info = client.CustomObjectsApi().list_cluster_custom_object('metrics.k8s.io', 'v1beta1', kind)\n cpu_usage_in_cores = sum([int(''.join(filter(\n str.isdigit, str(cpu_usage_item['usage']['cpu'].encode(\n 'utf-8'))))) for cpu_usage_item in cpu_usage_info['items']])\n cpu_usage_in_percentage = round(cpu_usage_in_cores / 10000000, 0)\n memory_usage = sum([unit_conversion(int(''.join(filter(\n str.isdigit, str(memory_usage_item['usage']['memory'].encode(\n 'utf-8'))))), ''.join(filter(str.isalpha, str(memory_usage_item['usage']['memory'].encode('utf-8')))))\n for memory_usage_item in cpu_usage_info['items']])\n # pods usage stats\n elif kind == 'pods':\n if namespace_id:\n cpu_usage_info = client.CustomObjectsApi().list_namespaced_custom_object('metrics.k8s.io', 'v1beta1',\n namespace_id, kind)\n else:\n cpu_usage_info = client.CustomObjectsApi().list_cluster_custom_object('metrics.k8s.io', 'v1beta1', kind)\n if len(pods_list) != 0:\n cpu_usage_in_cores = round(unit_conversion(sum([int(''.join(filter(\n str.isdigit, str(cpu_usage_item['containers'][0]['usage']['cpu'].encode(\n 'utf-8'))))) for cpu_usage_item in cpu_usage_info['items'] if cpu_usage_item['metadata']['name']\n in pods_list]), 'n'), 2)\n memory_usage = round(sum([unit_conversion(int(''.join(filter(\n str.isdigit, str(memory_usage_item['containers'][0]['usage']['memory'].encode(\n 'utf-8'))))),\n ''.join(\n filter(str.isalpha, str(memory_usage_item['containers'][0]['usage']['memory'].encode('utf-8')))))\n for memory_usage_item in cpu_usage_info['items'] if memory_usage_item['metadata']['name']\n in pods_list]), 2)\n else:\n cpu_usage_in_cores = round(unit_conversion(sum([int(''.join(filter(\n str.isdigit, str(cpu_usage_item['containers'][0]['usage']['cpu'].encode(\n 'utf-8'))))) for cpu_usage_item in cpu_usage_info['items']]), 'n'), 2)\n memory_usage = round(sum([unit_conversion(int(''.join(filter(\n str.isdigit, str(memory_usage_item['containers'][0]['usage']['memory'].encode(\n 'utf-8'))))),\n ''.join(filter(str.isalpha, str(memory_usage_item['containers'][0]['usage']['memory'].encode('utf-8')))))\n for memory_usage_item in cpu_usage_info['items']]), 2)\n return {'cpu': cpu_usage_in_cores,\n 'memory': memory_usage}","def device_count():\n apipath = \"/targets/devices\"\n url = SERVER + apipath\n params = {\n 'q': '(deviceType:ASA)',\n 'agg': 'count'}\n headers = {\n 'Accept': \"application/json\",\n 'Content-Type': \"application/json\",\n 'Authorization': \"bearer {}\".format(token)}\n response = requests.get(url, verify=False, stream=True, headers=headers, params=params)\n getstatuscode = response.status_code\n getresponse = response.json()\n if getstatuscode == 200:\n return getresponse\n else:\n response.raise_for_status()","def __len__(self, context=None):\n if context is not None:\n context = self._repair_context(context)\n uri = self.rest_services[\"size\"]\n payload=dict()\n if context:\n context = context.n3()\n payload[\"context\"] = context\n r = requests.get(uri, params = payload)\n return int(r.text)","def test_count_reads_per_cluster_merged(self):\n \n tool = pybedtools.BedTool(\"chr15 91512755 91512836 ENSMUSG00000025736_1_83;ENSMUSG00000091321_6_83 0 -\", from_string=True)\n total_reads, reads_per_cluster = count_reads_per_cluster(tool, None)\n \n self.assertListEqual([83], reads_per_cluster)","def count(options=None):\n if options is None:\n return requests.get(\"/count\")\n else:\n return requests.get(\"/count\", options)","def client_count(request):\n return request.param","def node_count(self) -> pulumi.Output[int]:\n return pulumi.get(self, \"node_count\")","def node_count(self) -> pulumi.Output[int]:\n return pulumi.get(self, \"node_count\")","def n_clusters(self):\n return len(self.clusters)","async def get_segment_count(lang: str):\n try:\n collection = get_collection(lang)\n return {\"count\": collection.count()}\n except KeyError as error:\n return error","def test_count_reads_in_region_total(self):\n self.c.skipZeros = False\n self.c.stepSize = 200\n self.c.binLength = 200\n resp, _ = self.c.count_reads_in_region(self.chrom, 0, 200)\n nt.assert_equal(resp, np.array([[2, 4.]]))","def GetCount(self):\n return self._server.get_count()","def count(cls, resq):\n first = MultipleBackend.classes[0]\n return first.count(resq)","def get_rsc_cnt(rsc):\n global __rsc_cnt\n if rsc in __rsc_cnt:\n return __rsc_cnt[rsc]\n else:\n return 0","def cluster_count(self) -> int:\n cluster_count = max(1, round(16**3 * (self.vein.purity / 100.0) / self.cluster_size))\n return self.distribution.scale_cluster_count(cluster_count)","def node_count(self) -> int:\n return pulumi.get(self, \"node_count\")","def gal_count(clusters):\n sum = 0\n for x in clusters:\n sum += x.ngal\n return sum","def count_all_cluster_instances(cluster_name, predictive=False, exclude_node_label_keys=app_config[\"EXCLUDE_NODE_LABEL_KEYS\"]):\n\n # Get the K8s nodes on the cluster, while excluding nodes with certain label keys\n k8s_nodes = get_k8s_nodes(exclude_node_label_keys)\n\n count = 0\n asgs = get_all_asgs(cluster_name)\n for asg in asgs:\n instances = asg['Instances']\n if predictive:\n count += asg['DesiredCapacity']\n else:\n # Use the get_node_by_instance_id() function as it only returns the node if it is not excluded by K8s labels\n for instance in instances:\n instance_id = instance['InstanceId']\n try:\n get_node_by_instance_id(k8s_nodes, instance_id)\n count += 1\n except Exception:\n logger.info(\"Skipping instance {}\".format(instance_id))\n logger.info(\"{} asg instance count in cluster is: {}. K8s node count should match this number\".format(\"*** Predicted\" if predictive else \"Current\", count))\n return count","def node_state_counts(self) -> pulumi.Output['outputs.NodeStateCountsResponse']:\n return pulumi.get(self, \"node_state_counts\")","def analysis_function_num_clusters(self,clustering):\n return len(clustering.clusters)","def get_count():\n _check_init()\n return _pypm.CountDevices()","def count(self, index):\n if isinstance(index, list):\n index = ','.join(index)\n req = requests.get(\n urljoin(self.base_url, '{0}/_count'.format(index)),\n verify=self.verify_certs)\n return req.json()['count']","def count(self):\n\t\treturn len(list(self.nodes))","def get_count(self):\n connection = self.__get_database_connection()\n response = self.__make_request(connection, '/%s' % (self.database_name))\n return response.get('doc_count', 0)","def getViewPortAppCount(self):\n logger.debug('Getting map view port app count...')\n elements = get_elements_by_css(\".leaflet-marker-icon.dstCluster\")\n users = 0\n for element in elements:\n users += int(get_text(element))\n return users","def get_expected_instance_count_for_namespace(\n service: str,\n namespace: str,\n cluster: str = None,\n instance_type_class: Type[LongRunningServiceConfig] = MarathonServiceConfig,\n soa_dir: str = DEFAULT_SOA_DIR,\n) -> int:\n total_expected = 0\n if not cluster:\n cluster = load_system_paasta_config().get_cluster()\n\n pscl = PaastaServiceConfigLoader(\n service=service, soa_dir=soa_dir, load_deployments=False\n )\n for job_config in pscl.instance_configs(\n cluster=cluster, instance_type_class=instance_type_class\n ):\n if f\"{service}.{namespace}\" in job_config.get_registrations():\n total_expected += job_config.get_instances()\n return total_expected","def num_links(self):\n count=0.0\n for cluster in self.clusters:\n if self.clusters[cluster] == self.clusters[cluster].antecessor:\n numberofmembers=self.clusters[cluster].number_of_members\n count+=numberofmembers\n return count","def get_count(self, cf_name, key, start='', finish='', keyspace_name=None):\n return self._Get_Count(\n cf_name=cf_name, key=key, start=start, finish=finish,\n keyspace_name=keyspace_name)","def carn_count(self):\n return len(self.carnivores)","def get_config_count():\n return jsonify(config_count=config_count(), config_limit=config_limit)","def count_nodes(self, term=None, labels: istr = None) -> int:","def _get_count(results):\n return len(results)","def count(context, namespace_name, session):\n namespace = namespace_api.get(context, namespace_name, session)\n query = (session.query(func.count(models.MetadefTag.id)).filter_by(\n namespace_id=namespace['id']))\n return query.scalar()","def get_node_count(self) -> Iterable:\n return len([i for i in self.all_nodes_as_iterable()])","def count(self):\n return {'count': self.collection.count()}","def count():","def count(self, where=\"\", parameters={}):\n return self._backend.count(where, parameters)","def getOperationCount():\n return _operationCount","def count_nodes(self):\n\t\treturn self.__count_nodes(self)","def _get_count(awsclient, function_name, alias_name='ACTIVE', version=None):\n client_lambda = awsclient.get_client('lambda')\n payload = '{\"ramuda_action\": \"count\"}'\n\n if version:\n response = client_lambda.invoke(\n FunctionName=function_name,\n InvocationType='RequestResponse',\n Payload=payload,\n Qualifier=version\n )\n else:\n response = client_lambda.invoke(\n FunctionName=function_name,\n InvocationType='RequestResponse',\n Payload=payload,\n Qualifier=alias_name\n )\n\n # print type(response['Payload'])\n results = response['Payload'].read() # payload is a 'StreamingBody'\n return results","def trace_region_count(self):\n cmd = enums.JLinkTraceCommand.GET_NUM_REGIONS\n data = ctypes.c_uint32(0)\n res = self._dll.JLINKARM_TRACE_Control(cmd, ctypes.byref(data))\n if (res == 1):\n raise errors.JLinkException('Failed to get trace region count.')\n return data.value","def count(self):\n return self._lift(\"count\")","def count(self,\n query: str,\n datatype: str = \"response\",\n indices: str = \"\") -> dict:\n endpoint = \"/api/responses_count/\"\n if datatype == \"cert\":\n endpoint = \"/api/certs_count/\"\n elif datatype == \"domain\":\n endpoint = \"/api/domains_count/\"\n ret = self._request(endpoint=endpoint,\n params={\n \"q\": query,\n \"indices\": indices\n })\n return ret","def count(self, *args, **kwargs):\r\n with base.extract_request():\r\n kwargs['per_page'] = 1\r\n request = self.get(*args, **kwargs)\r\n\r\n return request, parse_count","def get_datasets_count(request):\n organization_id = request.GET.get('organization_id', '')\n datasets_count = Organization.objects.get(\n pk=organization_id).import_records.all().distinct().count()\n\n return {'status': 'success', 'datasets_count': datasets_count}","def test_read_cluster_resource_quota(self):\n pass","def _get_request_count(self):\n\n joined_query = self._db.Request.join(\n self._db.UsersRequest, self._db.UsersRequest.c.requestId == self._db.Request.c.id_)\n joined_query = joined_query.select().with_only_columns([func.count()])\n res = joined_query.execute()\n return [_ for _ in res][0][0]","def count_containers(client, prefix=DOCK_CONTAINER_NAME_PREFIX):\n\n containers = get_containers_names(client)\n return sum([1 for c in containers if prefix in c])","def get_number_of_locations():\n count = 0\n tree = ET.parse('./devset_topics.xml')\n root = tree.getroot()\n for item in root.findall('./topic'):\n count = count + 1\n return count","def count(self):\n return self.data_container.count","def count(self, conn, key):\n return conn.llen(key)","def get_count(name):\n ndb_storage = NDBStorage.get_by_id(name, namespace=NAMESPACE)\n if ndb_storage is None:\n return 0\n return ndb_storage.count","def getSegmentCount(self) -> int:\n ...","def test_read_cluster_resource_quota_status(self):\n pass","def counts(self):\n counts = {\n 'total': self.app.db.jobs.count(),\n 'failed': self.app.db.jobs.find({'status': 'failed'}).count(),\n 'pending': self.app.db.jobs.find({'status': 'pending'}).count(),\n 'done': self.app.db.jobs.find({'status': 'done'}).count(),\n }\n return counts","def _get_hit_count(self, database, enquire):\n return self._get_enquire_mset(\n database, enquire, 0, database.get_doccount()\n ).size()","def count(self):\n return self.connection.llen(self.key)","def _compute_license_data_size(namespace_stats, set_stats, cluster_dict, ns_dict):\n\n if not namespace_stats:\n return\n\n cl_memory_data_size = 0\n cl_device_data_size = 0\n\n for ns, ns_stats in namespace_stats.iteritems():\n if not ns_stats or isinstance(ns_stats, Exception):\n continue\n repl_factor = max(\n util.get_value_from_second_level_of_dict(ns_stats, (\"repl-factor\", \"replication-factor\"), default_value=0,\n return_type=int).values())\n master_objects = sum(\n util.get_value_from_second_level_of_dict(ns_stats, (\"master_objects\", \"master-objects\"), default_value=0,\n return_type=int).values())\n devices_in_use = list(set(util.get_value_from_second_level_of_dict(ns_stats, (\n \"storage-engine.device\", \"device\", \"storage-engine.file\", \"file\", \"dev\"), default_value=None,\n return_type=str).values()))\n memory_data_size = None\n device_data_size = None\n\n if len(devices_in_use) == 0 or (len(devices_in_use) == 1 and devices_in_use[0] == None):\n # Data in memory only\n memory_data_size = sum(\n util.get_value_from_second_level_of_dict(ns_stats, (\"memory_used_data_bytes\", \"data-used-bytes-memory\"),\n default_value=0, return_type=int).values())\n memory_data_size = memory_data_size / repl_factor\n\n if memory_data_size > 0:\n memory_record_overhead = master_objects * 2\n memory_data_size = memory_data_size - memory_record_overhead\n\n else:\n # Data on disk\n device_data_size = sum(\n util.get_value_from_second_level_of_dict(ns_stats, (\"device_used_bytes\", \"used-bytes-disk\"),\n default_value=0, return_type=int).values())\n\n if device_data_size > 0:\n set_overhead = _compute_set_overhead_for_ns(set_stats, ns)\n device_data_size = device_data_size - set_overhead\n\n if device_data_size > 0:\n tombstones = sum(util.get_value_from_second_level_of_dict(ns_stats, (\"tombstones\",), default_value=0,\n return_type=int).values())\n tombstone_overhead = tombstones * 128\n device_data_size = device_data_size - tombstone_overhead\n\n device_data_size = device_data_size / repl_factor\n if device_data_size > 0:\n device_record_overhead = master_objects * 64\n device_data_size = device_data_size - device_record_overhead\n\n ns_dict[ns][\"license_data_in_memory\"] = 0\n ns_dict[ns][\"license_data_on_disk\"] = 0\n if memory_data_size is not None:\n ns_dict[ns][\"license_data_in_memory\"] = memory_data_size\n cl_memory_data_size += memory_data_size\n\n if device_data_size is not None:\n ns_dict[ns][\"license_data_on_disk\"] = device_data_size\n cl_device_data_size += device_data_size\n\n cluster_dict[\"license_data\"] = {}\n cluster_dict[\"license_data\"][\"memory_size\"] = cl_memory_data_size\n cluster_dict[\"license_data\"][\"device_size\"] = cl_device_data_size","def count_nodes(self, term=None, labels: istr = None):","def num_allocated_resources(self) -> int:\n return len(self.allocated_resources)","def all_client_number():\n\n url = CMX_URL + '/api/location/v2/clients/count'\n header = {'content-type': 'application/json', 'accept': 'application/json'}\n response = requests.get(url, headers=header, auth=CMX_AUTH, verify=False)\n response_json = response.json()\n clients_number = response_json['count']\n return clients_number","def LnsCount(self):\n if self.force_auto_sync:\n self.get('LnsCount')\n return self._LnsCount"],"string":"[\n \"def get_cluster_count(self) -> int:\\n return len(self.get_all_cluster_ids())\",\n \"def count(self, resource):\\n return len(self.all(resource))\",\n \"def get_count_all(cls, context, cluster_id):\\n return cls.dbapi.get_cluster_nodegroup_count(context, cluster_id)\",\n \"def test_get_resource_license_resource_count_list(self):\\n pass\",\n \"def count(cls, client) :\\n try :\\n obj = nshttpprofile()\\n option_ = options()\\n option_.count = True\\n response = obj.get_resources(client, option_)\\n if response :\\n return response[0].__dict__['___count']\\n return 0\\n except Exception as e :\\n raise e\",\n \"def count(self):\\n\\n if self.cluster:\\n return self.cluster.count()\\n\\n return super().count()\",\n \"def getNumResources(self, *args):\\n return _libsbml.CVTerm_getNumResources(self, *args)\",\n \"def test_list_applied_cluster_resource_quota_for_all_namespaces(self):\\n pass\",\n \"def test_list_namespaced_applied_cluster_resource_quota(self):\\n pass\",\n \"def GetConceptCounts(self, request, context):\\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\\n context.set_details('Method not implemented!')\\n raise NotImplementedError('Method not implemented!')\",\n \"def count(self):\\r\\n url = '{0}/{1}'.format(self.get_url(), 'count')\\r\\n\\r\\n return http.Request('GET', url), parsers.parse_json\",\n \"def get_hidden_namespace_resources(cluster_id, namespace_id):\\n # Deployment count\\n deployment_count = len(client.AppsV1beta2Api().list_namespaced_deployment(namespace_id).items)\\n # Pod count\\n pod_items = client.CoreV1Api().list_namespaced_pod(namespace_id).items\\n pod_count = len(pod_items)\\n # Cell count\\n cell_pod_map = get_cell_pod_map(cluster_id)\\n pods_list = [pod_item.metadata.name for pod_item in pod_items]\\n cell_count = len([cell_pod_map[pods] for pods in pods_list])\\n return {'deployments': deployment_count,\\n 'pods': pod_count,\\n 'cells': cell_count\\n }\",\n \"def count(cls, client) :\\n\\t\\ttry :\\n\\t\\t\\tobj = bfdsession()\\n\\t\\t\\toption_ = options()\\n\\t\\t\\toption_.count = True\\n\\t\\t\\tresponse = obj.get_resources(client, option_)\\n\\t\\t\\tif response :\\n\\t\\t\\t\\treturn response[0].__dict__['___count']\\n\\t\\t\\treturn 0\\n\\t\\texcept Exception as e :\\n\\t\\t\\traise e\",\n \"def test_read_namespaced_applied_cluster_resource_quota(self):\\n pass\",\n \"def test_count_resources(ops_and_shots, expected_resources):\\n ops, shots = ops_and_shots\\n computed_resources = _count_resources(QuantumScript(ops=ops, shots=shots))\\n assert computed_resources == expected_resources\",\n \"def count(cls, client) :\\n\\t\\ttry :\\n\\t\\t\\tobj = rewriteaction()\\n\\t\\t\\toption_ = options()\\n\\t\\t\\toption_.count = True\\n\\t\\t\\tresponse = obj.get_resources(client, option_)\\n\\t\\t\\tif response :\\n\\t\\t\\t\\treturn response[0].__dict__['___count']\\n\\t\\t\\treturn 0\\n\\t\\texcept Exception as e :\\n\\t\\t\\traise e\",\n \"def get_resource_count(har_json):\\n entries = har_json['log']['entries']\\n\\n resource_type_counts = Counter()\\n\\n for entry in entries:\\n resource = entry['request']['url']\\n dirty_resource_type = resource.split('.')[-1]\\n resource_type = dirty_resource_type.split('?')[0] # Remove url params\\n if len(resource_type) > 4:\\n resource_type_counts['other'] += 1\\n # print 'Found other resource type: {0}'.format(resource_type)\\n else:\\n resource_type_counts[resource_type] += 1\\n\\n return resource_type_counts\",\n \"def count(cls, client) :\\n\\t\\ttry :\\n\\t\\t\\tobj = lbprofile()\\n\\t\\t\\toption_ = options()\\n\\t\\t\\toption_.count = True\\n\\t\\t\\tresponse = obj.get_resources(client, option_)\\n\\t\\t\\tif response :\\n\\t\\t\\t\\treturn response[0].__dict__['___count']\\n\\t\\t\\treturn 0\\n\\t\\texcept Exception as e :\\n\\t\\t\\traise e\",\n \"def count(cls, client) :\\n\\t\\ttry :\\n\\t\\t\\tobj = lsntransportprofile()\\n\\t\\t\\toption_ = options()\\n\\t\\t\\toption_.count = True\\n\\t\\t\\tresponse = obj.get_resources(client, option_)\\n\\t\\t\\tif response :\\n\\t\\t\\t\\treturn response[0].__dict__['___count']\\n\\t\\t\\treturn 0\\n\\t\\texcept Exception as e :\\n\\t\\t\\traise e\",\n \"def resource_discovery_association_count(self) -> pulumi.Output[int]:\\n return pulumi.get(self, \\\"resource_discovery_association_count\\\")\",\n \"def fetch_counts(swarming, start, end, state, tags, parallel):\\n\\n def process(data):\\n return int(data['count'])\\n delta = datetime.timedelta(days=1)\\n return _fetch_daily_internal(delta, swarming, process, 'tasks/count', start,\\n end, state, tags, parallel)\",\n \"def getHostCount(self, **kwargs):\\n\\n allParams = ['scope']\\n\\n params = locals()\\n for (key, val) in list(params['kwargs'].items()):\\n if key not in allParams:\\n raise TypeError(\\\"Got an unexpected keyword argument '%s' to method getHostCount\\\" % key)\\n params[key] = val\\n del params['kwargs']\\n\\n resourcePath = '/host/count'\\n resourcePath = resourcePath.replace('{format}', 'json')\\n method = 'GET'\\n\\n queryParams = {}\\n headerParams = {}\\n formParams = {}\\n files = {}\\n bodyParam = None\\n\\n headerParams['Accept'] = 'application/json'\\n headerParams['Content-Type'] = 'application/json'\\n\\n\\n\\n\\n if ('scope' in params):\\n headerParams['scope'] = params['scope']\\n\\n\\n\\n\\n\\n\\n\\n\\n postData = (formParams if formParams else bodyParam)\\n\\n response = self.apiClient.callAPI(resourcePath, method, queryParams,\\n postData, headerParams, files=files)\\n\\n\\n if not response:\\n return None\\n\\n responseObject = self.apiClient.deserialize(response, 'CountResult')\\n return responseObject\",\n \"def get_public_narrative_count():\\n client_workspace = MongoClient(mongoDB_metrics_connection+to_workspace)\\n db_workspace = client_workspace.workspace\\n public_narrative_count = db_workspace.workspaceACLs.find({\\\"user\\\" : \\\"*\\\"}).count()\\n return public_narrative_count;\",\n \"def aquire_cnts(self,n):\\n return self.cnts.get_n(n)\",\n \"def query_counts(\\n self,\\n nodes,\\n project_id=None,\\n chunk_size=2500,\\n format=\\\"json\\\",\\n args=None,\\n ):\\n\\n counts = {}\\n\\n if isinstance(nodes,str):\\n nodes = [nodes]\\n\\n for node in nodes:\\n if project_id != None:\\n program, project = project_id.split(\\\"-\\\", 1)\\n if args == None:\\n query_txt = \\\"\\\"\\\"{_%s_count (project_id:\\\"%s\\\")}\\\"\\\"\\\" % (node, project_id)\\n else:\\n query_txt = \\\"\\\"\\\"{_%s_count (project_id:\\\"%s\\\", %s)}\\\"\\\"\\\" % (node, project_id, args)\\n else:\\n if args == None:\\n query_txt = \\\"\\\"\\\"{_%s_count}\\\"\\\"\\\" % (node)\\n else:\\n query_txt = \\\"\\\"\\\"{_%s_count (%s)}\\\"\\\"\\\" % (node, args)\\n\\n # First query the node count to get the expected number of results for the requested query:\\n\\n try:\\n res = self.sub.query(query_txt)\\n count_name = \\\"_\\\".join(map(str, [\\\"\\\", node, \\\"count\\\"]))\\n qsize = res[\\\"data\\\"][count_name]\\n counts[node] = qsize\\n except:\\n print(\\\"\\\\n\\\\tQuery to get _{}_count failed! {}\\\".format(node, query_txt))\\n\\n\\n return counts\",\n \"def _count(self):\\n if self._count_valid:\\n return self._total_results\\n\\n result = self._cb.get_object(self._doc_class.urlobject.format(self._cb.credentials.org_key))\\n results = result.get(\\\"results\\\", [])\\n\\n self._total_results = len(results)\\n self._count_valid = True\\n\\n return self._total_results\",\n \"def resource_record_set_count(self) -> int:\\n return pulumi.get(self, \\\"resource_record_set_count\\\")\",\n \"def cluster_obs_count(self):\\n return(self.merged_data.groupby(\\n 'labels').count().transpose().iloc[0, :])\",\n \"def count(cls, client) :\\n\\t\\ttry :\\n\\t\\t\\tobj = appfwlearningsettings()\\n\\t\\t\\toption_ = options()\\n\\t\\t\\toption_.count = True\\n\\t\\t\\tresponse = obj.get_resources(client, option_)\\n\\t\\t\\tif response :\\n\\t\\t\\t\\treturn response[0].__dict__['___count']\\n\\t\\t\\treturn 0\\n\\t\\texcept Exception as e :\\n\\t\\t\\traise e\",\n \"def get_desired_count(cluster_name, service_name):\\n\\n response = ecs_client.describe_services(\\n cluster=cluster_name, services=[service_name],\\n )\\n\\n for service in response[\\\"services\\\"]:\\n return service[\\\"desiredCount\\\"]\\n\\n raise Exception(\\n f\\\"desiredCount not found for cluster: {cluster_name} service: {service_name}\\\"\\n )\",\n \"def test_count_reads_per_cluster(self):\\n \\n bedtool = pybedtools.BedTool(clipper.test_file(\\\"clip_analysis_test_peak_results.bed\\\"))\\n \\n total_reads, reads_per_cluster = count_reads_per_cluster(bedtool, None)\\n \\n self.assertListEqual([147,52, 239, 85, 47, 119, 58, 588, 92, 59, 196, 36], reads_per_cluster)\\n self.assertEqual(sum([147,52, 239, 85, 47, 119, 58, 588, 92, 59, 196, 36]), total_reads)\",\n \"def get_count(cls):\\n total = 0\\n for counter in SimpleCounterShard.objects.all():\\n total += counter.count\\n return total\",\n \"def get_cluster_sizes(self,sc):\\n\\n clusterSizes = []\\n clusters = sc.labels\\n for k in np.arange(clusters.max()):\\n clusterSizes.append(len(np.where(clusters==k)[0])) \\n \\n return clusterSizes\",\n \"def test_get_resource_license_resource_count_by_moid(self):\\n pass\",\n \"def test_list_cluster_resource_quota(self):\\n pass\",\n \"def get_count(name, key):\\n total = 0\\n query = CounterShard.all().filter('name = ', name).filter('reference_key = ', key)\\n for counter in query:\\n total += counter.count\\n \\n return total\",\n \"def cluster_counter(self):\\n return Counter(self.model.labels_.tolist())\",\n \"def identify_cluster_namespaces():\\n # Execute 'kubectl get namespaces' command, returned as CompletedProcess\\n namespaces_completed_process = run([\\\"kubectl\\\", \\\"get\\\", \\\"namespaces\\\"], stdout=PIPE, stderr=PIPE)\\n\\n status = namespaces_completed_process.returncode\\n # If return code is not 0\\n if status:\\n return {'status': status, 'value': namespaces_completed_process.stderr.decode('utf-8').strip()}\\n else:\\n return {'status': status, 'value': namespaces_completed_process.stdout.decode('utf-8').strip()}\",\n \"def get_cluster_usage_info(cluster_id, kind, namespace_id=None, pods_list=None):\\n if pods_list is None:\\n pods_list = []\\n else:\\n logger.info('pod list not none')\\n if pods_list == 'no_pod_resource':\\n return {'cpu': 0,\\n 'memory': 0}\\n else:\\n logger.info('resources no 0')\\n # node usage stats if needed\\n if kind == 'nodes':\\n cpu_usage_info = client.CustomObjectsApi().list_cluster_custom_object('metrics.k8s.io', 'v1beta1', kind)\\n cpu_usage_in_cores = sum([int(''.join(filter(\\n str.isdigit, str(cpu_usage_item['usage']['cpu'].encode(\\n 'utf-8'))))) for cpu_usage_item in cpu_usage_info['items']])\\n cpu_usage_in_percentage = round(cpu_usage_in_cores / 10000000, 0)\\n memory_usage = sum([unit_conversion(int(''.join(filter(\\n str.isdigit, str(memory_usage_item['usage']['memory'].encode(\\n 'utf-8'))))), ''.join(filter(str.isalpha, str(memory_usage_item['usage']['memory'].encode('utf-8')))))\\n for memory_usage_item in cpu_usage_info['items']])\\n # pods usage stats\\n elif kind == 'pods':\\n if namespace_id:\\n cpu_usage_info = client.CustomObjectsApi().list_namespaced_custom_object('metrics.k8s.io', 'v1beta1',\\n namespace_id, kind)\\n else:\\n cpu_usage_info = client.CustomObjectsApi().list_cluster_custom_object('metrics.k8s.io', 'v1beta1', kind)\\n if len(pods_list) != 0:\\n cpu_usage_in_cores = round(unit_conversion(sum([int(''.join(filter(\\n str.isdigit, str(cpu_usage_item['containers'][0]['usage']['cpu'].encode(\\n 'utf-8'))))) for cpu_usage_item in cpu_usage_info['items'] if cpu_usage_item['metadata']['name']\\n in pods_list]), 'n'), 2)\\n memory_usage = round(sum([unit_conversion(int(''.join(filter(\\n str.isdigit, str(memory_usage_item['containers'][0]['usage']['memory'].encode(\\n 'utf-8'))))),\\n ''.join(\\n filter(str.isalpha, str(memory_usage_item['containers'][0]['usage']['memory'].encode('utf-8')))))\\n for memory_usage_item in cpu_usage_info['items'] if memory_usage_item['metadata']['name']\\n in pods_list]), 2)\\n else:\\n cpu_usage_in_cores = round(unit_conversion(sum([int(''.join(filter(\\n str.isdigit, str(cpu_usage_item['containers'][0]['usage']['cpu'].encode(\\n 'utf-8'))))) for cpu_usage_item in cpu_usage_info['items']]), 'n'), 2)\\n memory_usage = round(sum([unit_conversion(int(''.join(filter(\\n str.isdigit, str(memory_usage_item['containers'][0]['usage']['memory'].encode(\\n 'utf-8'))))),\\n ''.join(filter(str.isalpha, str(memory_usage_item['containers'][0]['usage']['memory'].encode('utf-8')))))\\n for memory_usage_item in cpu_usage_info['items']]), 2)\\n return {'cpu': cpu_usage_in_cores,\\n 'memory': memory_usage}\",\n \"def device_count():\\n apipath = \\\"/targets/devices\\\"\\n url = SERVER + apipath\\n params = {\\n 'q': '(deviceType:ASA)',\\n 'agg': 'count'}\\n headers = {\\n 'Accept': \\\"application/json\\\",\\n 'Content-Type': \\\"application/json\\\",\\n 'Authorization': \\\"bearer {}\\\".format(token)}\\n response = requests.get(url, verify=False, stream=True, headers=headers, params=params)\\n getstatuscode = response.status_code\\n getresponse = response.json()\\n if getstatuscode == 200:\\n return getresponse\\n else:\\n response.raise_for_status()\",\n \"def __len__(self, context=None):\\n if context is not None:\\n context = self._repair_context(context)\\n uri = self.rest_services[\\\"size\\\"]\\n payload=dict()\\n if context:\\n context = context.n3()\\n payload[\\\"context\\\"] = context\\n r = requests.get(uri, params = payload)\\n return int(r.text)\",\n \"def test_count_reads_per_cluster_merged(self):\\n \\n tool = pybedtools.BedTool(\\\"chr15 91512755 91512836 ENSMUSG00000025736_1_83;ENSMUSG00000091321_6_83 0 -\\\", from_string=True)\\n total_reads, reads_per_cluster = count_reads_per_cluster(tool, None)\\n \\n self.assertListEqual([83], reads_per_cluster)\",\n \"def count(options=None):\\n if options is None:\\n return requests.get(\\\"/count\\\")\\n else:\\n return requests.get(\\\"/count\\\", options)\",\n \"def client_count(request):\\n return request.param\",\n \"def node_count(self) -> pulumi.Output[int]:\\n return pulumi.get(self, \\\"node_count\\\")\",\n \"def node_count(self) -> pulumi.Output[int]:\\n return pulumi.get(self, \\\"node_count\\\")\",\n \"def n_clusters(self):\\n return len(self.clusters)\",\n \"async def get_segment_count(lang: str):\\n try:\\n collection = get_collection(lang)\\n return {\\\"count\\\": collection.count()}\\n except KeyError as error:\\n return error\",\n \"def test_count_reads_in_region_total(self):\\n self.c.skipZeros = False\\n self.c.stepSize = 200\\n self.c.binLength = 200\\n resp, _ = self.c.count_reads_in_region(self.chrom, 0, 200)\\n nt.assert_equal(resp, np.array([[2, 4.]]))\",\n \"def GetCount(self):\\n return self._server.get_count()\",\n \"def count(cls, resq):\\n first = MultipleBackend.classes[0]\\n return first.count(resq)\",\n \"def get_rsc_cnt(rsc):\\n global __rsc_cnt\\n if rsc in __rsc_cnt:\\n return __rsc_cnt[rsc]\\n else:\\n return 0\",\n \"def cluster_count(self) -> int:\\n cluster_count = max(1, round(16**3 * (self.vein.purity / 100.0) / self.cluster_size))\\n return self.distribution.scale_cluster_count(cluster_count)\",\n \"def node_count(self) -> int:\\n return pulumi.get(self, \\\"node_count\\\")\",\n \"def gal_count(clusters):\\n sum = 0\\n for x in clusters:\\n sum += x.ngal\\n return sum\",\n \"def count_all_cluster_instances(cluster_name, predictive=False, exclude_node_label_keys=app_config[\\\"EXCLUDE_NODE_LABEL_KEYS\\\"]):\\n\\n # Get the K8s nodes on the cluster, while excluding nodes with certain label keys\\n k8s_nodes = get_k8s_nodes(exclude_node_label_keys)\\n\\n count = 0\\n asgs = get_all_asgs(cluster_name)\\n for asg in asgs:\\n instances = asg['Instances']\\n if predictive:\\n count += asg['DesiredCapacity']\\n else:\\n # Use the get_node_by_instance_id() function as it only returns the node if it is not excluded by K8s labels\\n for instance in instances:\\n instance_id = instance['InstanceId']\\n try:\\n get_node_by_instance_id(k8s_nodes, instance_id)\\n count += 1\\n except Exception:\\n logger.info(\\\"Skipping instance {}\\\".format(instance_id))\\n logger.info(\\\"{} asg instance count in cluster is: {}. K8s node count should match this number\\\".format(\\\"*** Predicted\\\" if predictive else \\\"Current\\\", count))\\n return count\",\n \"def node_state_counts(self) -> pulumi.Output['outputs.NodeStateCountsResponse']:\\n return pulumi.get(self, \\\"node_state_counts\\\")\",\n \"def analysis_function_num_clusters(self,clustering):\\n return len(clustering.clusters)\",\n \"def get_count():\\n _check_init()\\n return _pypm.CountDevices()\",\n \"def count(self, index):\\n if isinstance(index, list):\\n index = ','.join(index)\\n req = requests.get(\\n urljoin(self.base_url, '{0}/_count'.format(index)),\\n verify=self.verify_certs)\\n return req.json()['count']\",\n \"def count(self):\\n\\t\\treturn len(list(self.nodes))\",\n \"def get_count(self):\\n connection = self.__get_database_connection()\\n response = self.__make_request(connection, '/%s' % (self.database_name))\\n return response.get('doc_count', 0)\",\n \"def getViewPortAppCount(self):\\n logger.debug('Getting map view port app count...')\\n elements = get_elements_by_css(\\\".leaflet-marker-icon.dstCluster\\\")\\n users = 0\\n for element in elements:\\n users += int(get_text(element))\\n return users\",\n \"def get_expected_instance_count_for_namespace(\\n service: str,\\n namespace: str,\\n cluster: str = None,\\n instance_type_class: Type[LongRunningServiceConfig] = MarathonServiceConfig,\\n soa_dir: str = DEFAULT_SOA_DIR,\\n) -> int:\\n total_expected = 0\\n if not cluster:\\n cluster = load_system_paasta_config().get_cluster()\\n\\n pscl = PaastaServiceConfigLoader(\\n service=service, soa_dir=soa_dir, load_deployments=False\\n )\\n for job_config in pscl.instance_configs(\\n cluster=cluster, instance_type_class=instance_type_class\\n ):\\n if f\\\"{service}.{namespace}\\\" in job_config.get_registrations():\\n total_expected += job_config.get_instances()\\n return total_expected\",\n \"def num_links(self):\\n count=0.0\\n for cluster in self.clusters:\\n if self.clusters[cluster] == self.clusters[cluster].antecessor:\\n numberofmembers=self.clusters[cluster].number_of_members\\n count+=numberofmembers\\n return count\",\n \"def get_count(self, cf_name, key, start='', finish='', keyspace_name=None):\\n return self._Get_Count(\\n cf_name=cf_name, key=key, start=start, finish=finish,\\n keyspace_name=keyspace_name)\",\n \"def carn_count(self):\\n return len(self.carnivores)\",\n \"def get_config_count():\\n return jsonify(config_count=config_count(), config_limit=config_limit)\",\n \"def count_nodes(self, term=None, labels: istr = None) -> int:\",\n \"def _get_count(results):\\n return len(results)\",\n \"def count(context, namespace_name, session):\\n namespace = namespace_api.get(context, namespace_name, session)\\n query = (session.query(func.count(models.MetadefTag.id)).filter_by(\\n namespace_id=namespace['id']))\\n return query.scalar()\",\n \"def get_node_count(self) -> Iterable:\\n return len([i for i in self.all_nodes_as_iterable()])\",\n \"def count(self):\\n return {'count': self.collection.count()}\",\n \"def count():\",\n \"def count(self, where=\\\"\\\", parameters={}):\\n return self._backend.count(where, parameters)\",\n \"def getOperationCount():\\n return _operationCount\",\n \"def count_nodes(self):\\n\\t\\treturn self.__count_nodes(self)\",\n \"def _get_count(awsclient, function_name, alias_name='ACTIVE', version=None):\\n client_lambda = awsclient.get_client('lambda')\\n payload = '{\\\"ramuda_action\\\": \\\"count\\\"}'\\n\\n if version:\\n response = client_lambda.invoke(\\n FunctionName=function_name,\\n InvocationType='RequestResponse',\\n Payload=payload,\\n Qualifier=version\\n )\\n else:\\n response = client_lambda.invoke(\\n FunctionName=function_name,\\n InvocationType='RequestResponse',\\n Payload=payload,\\n Qualifier=alias_name\\n )\\n\\n # print type(response['Payload'])\\n results = response['Payload'].read() # payload is a 'StreamingBody'\\n return results\",\n \"def trace_region_count(self):\\n cmd = enums.JLinkTraceCommand.GET_NUM_REGIONS\\n data = ctypes.c_uint32(0)\\n res = self._dll.JLINKARM_TRACE_Control(cmd, ctypes.byref(data))\\n if (res == 1):\\n raise errors.JLinkException('Failed to get trace region count.')\\n return data.value\",\n \"def count(self):\\n return self._lift(\\\"count\\\")\",\n \"def count(self,\\n query: str,\\n datatype: str = \\\"response\\\",\\n indices: str = \\\"\\\") -> dict:\\n endpoint = \\\"/api/responses_count/\\\"\\n if datatype == \\\"cert\\\":\\n endpoint = \\\"/api/certs_count/\\\"\\n elif datatype == \\\"domain\\\":\\n endpoint = \\\"/api/domains_count/\\\"\\n ret = self._request(endpoint=endpoint,\\n params={\\n \\\"q\\\": query,\\n \\\"indices\\\": indices\\n })\\n return ret\",\n \"def count(self, *args, **kwargs):\\r\\n with base.extract_request():\\r\\n kwargs['per_page'] = 1\\r\\n request = self.get(*args, **kwargs)\\r\\n\\r\\n return request, parse_count\",\n \"def get_datasets_count(request):\\n organization_id = request.GET.get('organization_id', '')\\n datasets_count = Organization.objects.get(\\n pk=organization_id).import_records.all().distinct().count()\\n\\n return {'status': 'success', 'datasets_count': datasets_count}\",\n \"def test_read_cluster_resource_quota(self):\\n pass\",\n \"def _get_request_count(self):\\n\\n joined_query = self._db.Request.join(\\n self._db.UsersRequest, self._db.UsersRequest.c.requestId == self._db.Request.c.id_)\\n joined_query = joined_query.select().with_only_columns([func.count()])\\n res = joined_query.execute()\\n return [_ for _ in res][0][0]\",\n \"def count_containers(client, prefix=DOCK_CONTAINER_NAME_PREFIX):\\n\\n containers = get_containers_names(client)\\n return sum([1 for c in containers if prefix in c])\",\n \"def get_number_of_locations():\\n count = 0\\n tree = ET.parse('./devset_topics.xml')\\n root = tree.getroot()\\n for item in root.findall('./topic'):\\n count = count + 1\\n return count\",\n \"def count(self):\\n return self.data_container.count\",\n \"def count(self, conn, key):\\n return conn.llen(key)\",\n \"def get_count(name):\\n ndb_storage = NDBStorage.get_by_id(name, namespace=NAMESPACE)\\n if ndb_storage is None:\\n return 0\\n return ndb_storage.count\",\n \"def getSegmentCount(self) -> int:\\n ...\",\n \"def test_read_cluster_resource_quota_status(self):\\n pass\",\n \"def counts(self):\\n counts = {\\n 'total': self.app.db.jobs.count(),\\n 'failed': self.app.db.jobs.find({'status': 'failed'}).count(),\\n 'pending': self.app.db.jobs.find({'status': 'pending'}).count(),\\n 'done': self.app.db.jobs.find({'status': 'done'}).count(),\\n }\\n return counts\",\n \"def _get_hit_count(self, database, enquire):\\n return self._get_enquire_mset(\\n database, enquire, 0, database.get_doccount()\\n ).size()\",\n \"def count(self):\\n return self.connection.llen(self.key)\",\n \"def _compute_license_data_size(namespace_stats, set_stats, cluster_dict, ns_dict):\\n\\n if not namespace_stats:\\n return\\n\\n cl_memory_data_size = 0\\n cl_device_data_size = 0\\n\\n for ns, ns_stats in namespace_stats.iteritems():\\n if not ns_stats or isinstance(ns_stats, Exception):\\n continue\\n repl_factor = max(\\n util.get_value_from_second_level_of_dict(ns_stats, (\\\"repl-factor\\\", \\\"replication-factor\\\"), default_value=0,\\n return_type=int).values())\\n master_objects = sum(\\n util.get_value_from_second_level_of_dict(ns_stats, (\\\"master_objects\\\", \\\"master-objects\\\"), default_value=0,\\n return_type=int).values())\\n devices_in_use = list(set(util.get_value_from_second_level_of_dict(ns_stats, (\\n \\\"storage-engine.device\\\", \\\"device\\\", \\\"storage-engine.file\\\", \\\"file\\\", \\\"dev\\\"), default_value=None,\\n return_type=str).values()))\\n memory_data_size = None\\n device_data_size = None\\n\\n if len(devices_in_use) == 0 or (len(devices_in_use) == 1 and devices_in_use[0] == None):\\n # Data in memory only\\n memory_data_size = sum(\\n util.get_value_from_second_level_of_dict(ns_stats, (\\\"memory_used_data_bytes\\\", \\\"data-used-bytes-memory\\\"),\\n default_value=0, return_type=int).values())\\n memory_data_size = memory_data_size / repl_factor\\n\\n if memory_data_size > 0:\\n memory_record_overhead = master_objects * 2\\n memory_data_size = memory_data_size - memory_record_overhead\\n\\n else:\\n # Data on disk\\n device_data_size = sum(\\n util.get_value_from_second_level_of_dict(ns_stats, (\\\"device_used_bytes\\\", \\\"used-bytes-disk\\\"),\\n default_value=0, return_type=int).values())\\n\\n if device_data_size > 0:\\n set_overhead = _compute_set_overhead_for_ns(set_stats, ns)\\n device_data_size = device_data_size - set_overhead\\n\\n if device_data_size > 0:\\n tombstones = sum(util.get_value_from_second_level_of_dict(ns_stats, (\\\"tombstones\\\",), default_value=0,\\n return_type=int).values())\\n tombstone_overhead = tombstones * 128\\n device_data_size = device_data_size - tombstone_overhead\\n\\n device_data_size = device_data_size / repl_factor\\n if device_data_size > 0:\\n device_record_overhead = master_objects * 64\\n device_data_size = device_data_size - device_record_overhead\\n\\n ns_dict[ns][\\\"license_data_in_memory\\\"] = 0\\n ns_dict[ns][\\\"license_data_on_disk\\\"] = 0\\n if memory_data_size is not None:\\n ns_dict[ns][\\\"license_data_in_memory\\\"] = memory_data_size\\n cl_memory_data_size += memory_data_size\\n\\n if device_data_size is not None:\\n ns_dict[ns][\\\"license_data_on_disk\\\"] = device_data_size\\n cl_device_data_size += device_data_size\\n\\n cluster_dict[\\\"license_data\\\"] = {}\\n cluster_dict[\\\"license_data\\\"][\\\"memory_size\\\"] = cl_memory_data_size\\n cluster_dict[\\\"license_data\\\"][\\\"device_size\\\"] = cl_device_data_size\",\n \"def count_nodes(self, term=None, labels: istr = None):\",\n \"def num_allocated_resources(self) -> int:\\n return len(self.allocated_resources)\",\n \"def all_client_number():\\n\\n url = CMX_URL + '/api/location/v2/clients/count'\\n header = {'content-type': 'application/json', 'accept': 'application/json'}\\n response = requests.get(url, headers=header, auth=CMX_AUTH, verify=False)\\n response_json = response.json()\\n clients_number = response_json['count']\\n return clients_number\",\n \"def LnsCount(self):\\n if self.force_auto_sync:\\n self.get('LnsCount')\\n return self._LnsCount\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.66817355","0.65607","0.6554999","0.6523906","0.65174896","0.64166987","0.6349572","0.6297506","0.6249817","0.6204213","0.6179697","0.6140397","0.6123831","0.6111716","0.61096334","0.6105647","0.6091557","0.6071596","0.6053722","0.6047418","0.6045339","0.60324806","0.6028506","0.60127497","0.60122764","0.6005237","0.596058","0.5957017","0.5947993","0.59468925","0.5935469","0.59275436","0.5909931","0.5906337","0.586649","0.5861168","0.58527017","0.5850914","0.5847752","0.58354485","0.58167976","0.58150154","0.57934123","0.57907176","0.57629544","0.57629544","0.5752445","0.5731759","0.57167286","0.56995237","0.56968766","0.5695761","0.56779057","0.5674959","0.56731665","0.5657521","0.5655439","0.5654658","0.5636613","0.5624337","0.56160814","0.56091684","0.56064945","0.5603154","0.5599449","0.55897105","0.55850565","0.55813336","0.5574597","0.5564649","0.55613375","0.55588895","0.5557888","0.55557436","0.5538605","0.55254406","0.5513742","0.5507654","0.5504424","0.5499027","0.5495719","0.549442","0.54877436","0.5485084","0.54826343","0.5472798","0.5471735","0.54648185","0.546303","0.5462258","0.5462122","0.54603624","0.5458374","0.5456119","0.5454606","0.5446145","0.5443416","0.5441479","0.5439656","0.54353327"],"string":"[\n \"0.66817355\",\n \"0.65607\",\n \"0.6554999\",\n \"0.6523906\",\n \"0.65174896\",\n \"0.64166987\",\n \"0.6349572\",\n \"0.6297506\",\n \"0.6249817\",\n \"0.6204213\",\n \"0.6179697\",\n \"0.6140397\",\n \"0.6123831\",\n \"0.6111716\",\n \"0.61096334\",\n \"0.6105647\",\n \"0.6091557\",\n \"0.6071596\",\n \"0.6053722\",\n \"0.6047418\",\n \"0.6045339\",\n \"0.60324806\",\n \"0.6028506\",\n \"0.60127497\",\n \"0.60122764\",\n \"0.6005237\",\n \"0.596058\",\n \"0.5957017\",\n \"0.5947993\",\n \"0.59468925\",\n \"0.5935469\",\n \"0.59275436\",\n \"0.5909931\",\n \"0.5906337\",\n \"0.586649\",\n \"0.5861168\",\n \"0.58527017\",\n \"0.5850914\",\n \"0.5847752\",\n \"0.58354485\",\n \"0.58167976\",\n \"0.58150154\",\n \"0.57934123\",\n \"0.57907176\",\n \"0.57629544\",\n \"0.57629544\",\n \"0.5752445\",\n \"0.5731759\",\n \"0.57167286\",\n \"0.56995237\",\n \"0.56968766\",\n \"0.5695761\",\n \"0.56779057\",\n \"0.5674959\",\n \"0.56731665\",\n \"0.5657521\",\n \"0.5655439\",\n \"0.5654658\",\n \"0.5636613\",\n \"0.5624337\",\n \"0.56160814\",\n \"0.56091684\",\n \"0.56064945\",\n \"0.5603154\",\n \"0.5599449\",\n \"0.55897105\",\n \"0.55850565\",\n \"0.55813336\",\n \"0.5574597\",\n \"0.5564649\",\n \"0.55613375\",\n \"0.55588895\",\n \"0.5557888\",\n \"0.55557436\",\n \"0.5538605\",\n \"0.55254406\",\n \"0.5513742\",\n \"0.5507654\",\n \"0.5504424\",\n \"0.5499027\",\n \"0.5495719\",\n \"0.549442\",\n \"0.54877436\",\n \"0.5485084\",\n \"0.54826343\",\n \"0.5472798\",\n \"0.5471735\",\n \"0.54648185\",\n \"0.546303\",\n \"0.5462258\",\n \"0.5462122\",\n \"0.54603624\",\n \"0.5458374\",\n \"0.5456119\",\n \"0.5454606\",\n \"0.5446145\",\n \"0.5443416\",\n \"0.5441479\",\n \"0.5439656\",\n \"0.54353327\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7759584"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":14380,"cells":{"query":{"kind":"string","value":"Get the resource details for the namespace to be excluded"},"document":{"kind":"string","value":"def get_hidden_namespace_resources(cluster_id, namespace_id):\n # Deployment count\n deployment_count = len(client.AppsV1beta2Api().list_namespaced_deployment(namespace_id).items)\n # Pod count\n pod_items = client.CoreV1Api().list_namespaced_pod(namespace_id).items\n pod_count = len(pod_items)\n # Cell count\n cell_pod_map = get_cell_pod_map(cluster_id)\n pods_list = [pod_item.metadata.name for pod_item in pod_items]\n cell_count = len([cell_pod_map[pods] for pods in pods_list])\n return {'deployments': deployment_count,\n 'pods': pod_count,\n 'cells': cell_count\n }"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def exclude_resource_rules(self) -> Optional[Sequence['outputs.NamedRuleWithOperations']]:\n return pulumi.get(self, \"exclude_resource_rules\")","def exclude_resource_ids_scope(self) -> str:\n return pulumi.get(self, \"exclude_resource_ids_scope\")","def exclude_resource_ids_scope(self) -> str:\n return pulumi.get(self, \"exclude_resource_ids_scope\")","def exclude_resource_rules(self) -> Optional[Sequence['outputs.NamedRuleWithOperationsPatch']]:\n return pulumi.get(self, \"exclude_resource_rules\")","def excluded_namespaces(self) -> Optional[Sequence[str]]:\n return pulumi.get(self, \"excluded_namespaces\")","def network_excludes(self) -> pulumi.Output[Optional[Sequence[str]]]:\n return pulumi.get(self, \"network_excludes\")","def exclude():\n data = list(request.files.values())[0].file.read() if len(request.files) else request.body.read()\n return excludeView(data, request.params)","def clean_resource(resource):\n return omit(resource, 'occupations', 'availability')","def get_resource_status(self, namespace: \"str\" = None):\n pass","def excluded_namespaces(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:\n return pulumi.get(self, \"excluded_namespaces\")","def excluded_namespaces(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:\n return pulumi.get(self, \"excluded_namespaces\")","def excluded_from_scan(self):\n return six.next(six.itervalues(self.zap._request(self.zap.base + 'spider/view/excludedFromScan/')))","def excluded_resource_types(self) -> Optional[Sequence[str]]:\n return pulumi.get(self, \"excluded_resource_types\")","def excluded(self):\n\n # A few variables for convenience of writing the method\n spec = self.spec\n conf = self.module.configuration(self.name)\n\n # Compute the list of include rules that match\n # DEPRECATED: remove 'whitelist' in v0.20\n include_rules = get_deprecated(conf, \"include\", \"whitelist\", [])\n include_matches = [x for x in include_rules if spec.satisfies(x)]\n\n # Compute the list of exclude rules that match\n # DEPRECATED: remove 'blacklist' in v0.20\n exclude_rules = get_deprecated(conf, \"exclude\", \"blacklist\", [])\n exclude_matches = [x for x in exclude_rules if spec.satisfies(x)]\n\n # Should I exclude the module because it's implicit?\n # DEPRECATED: remove 'blacklist_implicits' in v0.20\n exclude_implicits = get_deprecated(conf, \"exclude_implicits\", \"blacklist_implicits\", None)\n installed_implicitly = not spec._installed_explicitly()\n excluded_as_implicit = exclude_implicits and installed_implicitly\n\n def debug_info(line_header, match_list):\n if match_list:\n msg = \"\\t{0} : {1}\".format(line_header, spec.cshort_spec)\n tty.debug(msg)\n for rule in match_list:\n tty.debug(\"\\t\\tmatches rule: {0}\".format(rule))\n\n debug_info(\"INCLUDE\", include_matches)\n debug_info(\"EXCLUDE\", exclude_matches)\n\n if excluded_as_implicit:\n msg = \"\\tEXCLUDED_AS_IMPLICIT : {0}\".format(spec.cshort_spec)\n tty.debug(msg)\n\n is_excluded = exclude_matches or excluded_as_implicit\n if not include_matches and is_excluded:\n return True\n\n return False","def _filter_non_existing_namespaces(namespaces, k8s_cli):\n return_code, out = run_shell_command(\n \"{} get ns -o=custom-columns=\\\"DATA:metadata.name\\\" --no-headers=true\".format(k8s_cli))\n if return_code:\n return []\n res = []\n existing_namespaces = set(out.split())\n for namespace in namespaces:\n if namespace in existing_namespaces:\n res.append(namespace)\n else:\n logger.warning(\"Namespace %s doesn't exist - Skipping\", namespace)\n return res","def noAccessAllResources(self):\n return self._get_field(\"noAccessToAllResources\")","def non_resource_attributes(self) -> Optional[pulumi.Input['NonResourceAttributesArgs']]:\n return pulumi.get(self, \"non_resource_attributes\")","def non_resource_attributes(self) -> Optional[pulumi.Input['NonResourceAttributesArgs']]:\n return pulumi.get(self, \"non_resource_attributes\")","def non_resource_ur_ls(self):\n return self._non_resource_ur_ls","def excluded(cls):\n return []","def removeResource(self, *args):\n return _libsbml.XMLAttributes_removeResource(self, *args)","def _get_excludeFolders(self, dom):\n return self._get_new_module_root_manager(dom).getElementsByTagName('excludeFolder')","def resource_details(self) -> pulumi.Output[Any]:\n return pulumi.get(self, \"resource_details\")","def resource_details(self) -> pulumi.Output[Any]:\n return pulumi.get(self, \"resource_details\")","def namespace(self, namespace):\n return self.client.call('GET',\n self.name, params={'namespace': namespace})","def exclusion_rule(self) -> Optional['outputs.PreventionInspectTemplateInspectConfigRuleSetRuleExclusionRule']:\n return pulumi.get(self, \"exclusion_rule\")","def get_resources(self):\n return []","def resource_prefix(self):","def exclude_foreign_resources(returned_resource_set, expected_resource_set):\n expected_owners = {res.owner for res in expected_resource_set}\n return [\n res for res in returned_resource_set\n if res.owner in expected_owners\n ]","def get_resource(self):\n raise errors.Unimplemented()","def test_resource_collection_get_missing_resource(self):\n collection = fhir_package.ResourceCollection(\n self._valueset_cls, self._primitive_handler, 'Z'\n )\n resource = collection.get('missing-uri')\n\n self.assertIsNone(resource)","def getResourceAttributes(self, authenticationToken, guid):\r\n pass","def excluded_resource_types(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:\n return pulumi.get(self, \"excluded_resource_types\")","def excludes(self):\r\n\r\n return self._excludes","def resources(self):\n return self.__resources","def otherResources(self):\n return self._get_list_field(\"otherResources\")","def getResourceFromHydroshare(self, resid, destination=None):\n print('Not Implemented')","def network_excludes(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:\n return pulumi.get(self, \"network_excludes\")","def network_excludes(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:\n return pulumi.get(self, \"network_excludes\")","def isExcluded(self, http_resource):\n match = False\n for regexp in self.excluded:\n if self.__reWildcard(regexp, http_resource.url):\n match = True\n return match","def exclude(self, *args, **kwargs):","def remove_resources(self):\n return self._remove_resources","def is_excluded(self, attr_name, request):\n return False","def exclude(self: _R, *fn_exrps: str) -> _R:\n exclude_exprs = []\n exclude_exprs.extend(self.exclude_exprs)\n for fn_exrp in fn_exrps:\n if \"*\" not in fn_exrp and \".\" not in fn_exrp:\n fn_exrp = f\"{fn_exrp}/*\"\n exclude_exprs.append(fn_exrp)\n return self._copy(include_exprs=self.include_exprs, exclude_exprs=exclude_exprs)","def filter_excluded_paths(self) -> ConfigNodePropertyArray:\n return self._filter_excluded_paths","def test_delete_nonexistent_resource_rpc(self, mcg_obj):\n response = mcg_obj.send_rpc_query(\n \"pool_api\", \"delete_namespace_resource\", {\"name\": \"notexisting_resource\"}\n )\n assert \"error\" in response.json()","def fhir_resource_concepts(self) -> Set[URIRef]:\n return {subj for subj in self.graph.transitive_subjects(RDFS.subClassOf, FHIR.Resource)\n if isinstance(subj, URIRef) and not self.w5_ontology.is_w5_infrastructure(subj)}","def getResource(self):\n pass;","def test_get_unregistered_networks(self):\n pass","def inconclusive_detail(self) -> 'outputs.InconclusiveDetailResponse':\n return pulumi.get(self, \"inconclusive_detail\")","def get_extended_resources(self, version):\n return {}","def getExcludedDumpRegisterNames(self):\n pass","def discard_resources(data, presence_treshold=1.0):\n\n # we will discard stuff from this copy\n res = copy.deepcopy(data)\n\n # to compare easier\n good_one = copy.deepcopy(data[\"no_vpn\"])\n del data[\"no_vpn\"]\n\n to_del = []\n\n for extension in data:\n for webpage in data[extension]:\n for f in data[extension][webpage]:\n if f in good_one[webpage]:\n del res[extension][webpage][f]\n # this will repeat, but it shouldn't be a problem\n to_del.append((webpage, f))\n\n for webpage, file in to_del:\n try:\n del data[\"no_vpn\"][webpage][file]\n except KeyError:\n pass\n\n return res","def except_image_only(resource):\n if resource.image is None:\n raise FeatureExtractionError(resource, 400, 'Image resource is required')\n if resource.mask:\n raise FeatureExtractionError(resource, 400, 'Mask resource is not accepted')\n if resource.gobject:\n raise FeatureExtractionError(resource, 400, 'Gobject resource is not accepted')","def exclusions(self) -> Sequence['outputs.LogExclusionResponse']:\n return pulumi.get(self, \"exclusions\")","def exclude_from_scan(self, regex, apikey=''):\n return six.next(six.itervalues(self.zap._request(self.zap.base + 'spider/action/excludeFromScan/', {'regex': regex, 'apikey': apikey})))","def exclude_list(self):\n pass","def private_link_resource_region(self) -> Optional[str]:\n return pulumi.get(self, \"private_link_resource_region\")","def test_list_namespaced_egress_network_policy(self):\n pass","def get_exclusions(self: object, *args, parameters: dict = None, **kwargs) -> Dict[str, Union[int, dict]]:\n return process_service_request(\n calling_object=self,\n endpoints=EPS,\n operation_id=\"getSensorVisibilityExclusionsV1\",\n keywords=kwargs,\n params=handle_single_argument(args, parameters, \"ids\")\n )","def getResources(self, folder):\n\n #-------------------- \n # Get the resource JSON\n #-------------------- \n folder += \"/resources\"\n resources = self.__getJson(folder)\n #print(\"%s %s\"%(, folder))\n #print(\" Got resources: '%s'\"%(str(resources)))\n\n\n\n #-------------------- \n # Filter the JSONs\n #-------------------- \n resourceNames = []\n for r in resources:\n if 'label' in r:\n resourceNames.append(r['label'])\n #print(\"FOUND RESOURCE ('%s') : %s\"%(folder, r['label']))\n elif 'Name' in r:\n resourceNames.append(r['Name'])\n #print(\"FOUND RESOURCE ('%s') : %s\"%(folder, r['Name']))\n\n return resourceNames","def exclude(elem: etree.Element) -> bool:\n # Skip redirects\n if elem.find('redirect') is not None:\n return True\n\n # Skip non-article pages.(namespace 0 are article pages)\n # Note that you may need error handling\n if elem.find('ns') is None or elem.find('ns').text != '0':\n return True\n\n # We don't have any reason to exclude it, so return\n # False (keep it).\n return False","def ignore_non_actual_section(): # noqa: D416","def get_ing_from_ns(namespace):\n ing_list = get_all_ingress_from(namespace)\n ing_list_json = get_json_list(ing_list)\n return Response(response=ing_list_json, status=200, mimetype='application/json')","def evaluate_notresource_for_permission(statement: Dict, resource_arn: str) -> bool:\n if 'notresource' not in statement:\n return False\n for clause in statement['notresource']:\n if evaluate_clause(clause, resource_arn):\n return True\n return False","def test_read_namespaced_egress_network_policy(self):\n pass","def namespace_resource_blacklist(self, namespace_resource_blacklist):\n\n self._namespace_resource_blacklist = namespace_resource_blacklist","def getResource(self):\n return self.serviceClass.app.resource()","def test_resource_namespace(self, integrationtest, k8sconfig):\n # Fixtures.\n config = self.k8sconfig(integrationtest, k8sconfig)\n MM = MetaManifest\n\n for src in [\"\", \"v1\"]:\n # A particular Namespace.\n res, err = k8s.resource(config, MM(src, \"Namespace\", None, \"name\"))\n assert not err\n assert res == K8sResource(\n apiVersion=\"v1\",\n kind=\"Namespace\",\n name=\"namespaces\",\n namespaced=False,\n url=f\"{config.url}/api/v1/namespaces/name\",\n )\n\n # A particular Namespace in a particular namespace -> Invalid.\n assert k8s.resource(config, MM(src, \"Namespace\", \"ns\", \"name\")) == (res, err)\n\n # All Namespaces.\n res, err = k8s.resource(config, MM(src, \"Namespace\", None, None))\n assert not err\n assert res == K8sResource(\n apiVersion=\"v1\",\n kind=\"Namespace\",\n name=\"namespaces\",\n namespaced=False,\n url=f\"{config.url}/api/v1/namespaces\",\n )\n\n # Same as above because the \"namespace\" argument is ignored for Namespaces.\n assert k8s.resource(config, MM(src, \"Namespace\", \"name\", \"\")) == (res, err)","def excludes(self) -> Set:\n if self._excludes is None:\n manifest = self._get_manifest()\n self._excludes = manifest[\"files\"][\"excludes\"]\n\n return self._excludes","def __init__(__self__,\n resource_name: str,\n args: BillingAccountExclusionArgs,\n opts: Optional[pulumi.ResourceOptions] = None):\n ...","def get_bad_container(self):\n return XMLFile(\n FilePath(\"TrackerDash/snippets/no_dash_data_container.xml\"))","def _get_lsp_config_exclude_interfaces(self):\n return self.__lsp_config_exclude_interfaces","def GetExclusiveResources(self):\n res = set(self.exclusive_resources)\n if self.parent:\n res |= self.parent.GetExclusiveResources()\n return res","def GetResourceNames(self):\r\n return [x.name for x in self.resources]","def exclude_nodes(self, nodes):","def prune_namespace(self, properties):\n if self.default_namespace is None:\n # I have no default -- no change\n return properties\n if 'namespace' not in properties:\n # he has no namespace - no change\n return properties\n if properties['namespace'] != self.default_namespace:\n # we're different - leave his stuff alone\n return properties\n # we each have a namespace and it's redundant. delete his.\n prunable = properties.copy()\n del prunable['namespace']\n return prunable","def getResources(self):\n\t\treturn deepcopy(self.server.resources)","def _excludes(self, method, url):\n if method != 'GET':\n return False\n # maybe change to a dict?\n for excl in self.excludes:\n if excl == \"topInfo\" and \"info.json\" in url:\n return True\n if str(excl) + \".json\" in url:\n return True\n return False","def resource_arn(self) -> Optional[str]:\n return pulumi.get(self, \"resource_arn\")","def get_resource_information():\n\n\n # the resources we are allowed to use is easy. We just copy this...\n resource_limit_dict = _resources_allowed_dict.copy()\n\n \n # from the other dict, we only take the resource information. (this omits\n # locks and timing information that isn't needed)\n\n # first, let's do the easy thing, the quantity resources. These are just \n # floats\n resource_use_dict = {}\n for resourcename in resource_constants.quantity_resources:\n resource_use_dict[resourcename] = _resources_consumed_dict[resourcename]\n\n # for the fungible resources (files opened, etc,), we only need a count...\n for resourcename in resource_constants.fungible_item_resources:\n resource_use_dict[resourcename] = len(_resources_consumed_dict[resourcename])\n\n # for the individual item resources (ports, etc,), we copy the set...\n for resourcename in resource_constants.individual_item_resources:\n resource_use_dict[resourcename] = _resources_consumed_dict[resourcename].copy()\n\n # and that's it!\n return (resource_limit_dict, resource_use_dict)","def get_excluded_observations(self):\n\n return copy.deepcopy(self._excluded_observations)","def private_subnet(template, name):\n return template.resources[name]","def to_exclude(self):\n reporting_period = self.message_number - 3\n one_day_ago = now() - datetime.timedelta(hours=24)\n\n return PollingReport.objects.filter(\n period_number=reporting_period,\n creation_date__gte=one_day_ago,\n ).values_list('phone_number', flat=True)","def getResourceAlternateData(self, authenticationToken, guid):\r\n pass","def test_method_excludes(self):\n self.assertContains(self.response, \"<td>get_full_name</td>\")\n self.assertNotContains(self.response, \"<td>_get_full_name</td>\")\n self.assertNotContains(self.response, \"<td>add_image</td>\")\n self.assertNotContains(self.response, \"<td>delete_image</td>\")\n self.assertNotContains(self.response, \"<td>set_status</td>\")\n self.assertNotContains(self.response, \"<td>save_changes</td>\")","def attributes(self):\n attr = super(BaseDataRecord, self).attributes()\n return [a for a in attr if a not in self.excluded()]","def getResource(self):\n return self.__resource;","def getChild(self, path, request):\r\n # override this method if you want to serve dynamic child resources\r\n return resource.ForbiddenResource(\"This is a resource namespace.\")","def get_region(self, region, namespace, region_id, **filters):\n filters['namespace'] = namespace\n return self.get_resource('data/wow/region/{0}', region, *[region_id], **filters)","def collect_resources_list(namespace, output_dir, k8s_cli, mode):\n selector = \"\"\n if mode == MODE_RESTRICTED:\n selector = '--selector=\"{}\"'.format(OPERATOR_LABEL)\n collect_helper(output_dir,\n cmd=\"{} get all -o wide -n {} {}\".format(k8s_cli, namespace, selector),\n file_name=\"resources_list\",\n resource_name=\"resources list\",\n namespace=namespace)","def auditcontextnotfound(self) :\n\t\ttry :\n\t\t\treturn self._auditcontextnotfound\n\t\texcept Exception as e:\n\t\t\traise e","def getUnmoderated (self) :\n return self.getPictures (Event.objects.filter (approval = None))","def getResourceDef(url, user, pWd, resourceName):\n \n print(\"getting resource for catalog:-\" + url + \" resource=\" + resourceName +\n ' user=' + user)\n apiURL = url + '/access/1/catalog/resources/' + resourceName\n # print(\"\\turl=\" + apiURL)\n header = {\"Accept\": \"application/json\"} \n tResp = requests.get(apiURL, params={}, headers=header, auth=HTTPBasicAuth(user,pWd))\n print(\"\\tresponse=\" + str(tResp.status_code))\n if tResp.status_code == 200:\n # valid - return the jsom\n return tResp.status_code, json.loads(tResp.text)\n else:\n # not valid\n return tResp.status_code, None","def to_exclude(self):\n midnight = now().replace(hour=0, minute=0, microsecond=0)\n return CenterOpen.objects.filter(\n creation_date__gte=midnight,\n ).values_list('phone_number', flat=True)","def getResourceAttributes(self, authenticationToken, guid):\r\n self.send_getResourceAttributes(authenticationToken, guid)\r\n return self.recv_getResourceAttributes()","def pop_resources(self):\n resources = self.request_local.resources\n self.request_local.resources = {}\n # deal with aggregated resources\n if resources and \"head\" in resources:\n # This is lazy, because we otherwise run\n # into circular import issues\n if self.aggregation_config is not None:\n self._setup_aggregation_mapping()\n\n\n if self.aggregated_js_mapping:\n self._replace_resources_with_aggregates(resources,\n self.aggregated_js_mapping,\n JSLink,\n )\n if self.aggregated_css_mapping:\n self._replace_resources_with_aggregates(resources,\n self.aggregated_css_mapping,\n CSSLink,\n )\n return resources","def resources(self) -> Optional[Sequence[str]]:\n return pulumi.get(self, \"resources\")","def resources(self) -> Optional[Sequence[str]]:\n return pulumi.get(self, \"resources\")","def resources(self) -> Optional[Sequence[str]]:\n return pulumi.get(self, \"resources\")"],"string":"[\n \"def exclude_resource_rules(self) -> Optional[Sequence['outputs.NamedRuleWithOperations']]:\\n return pulumi.get(self, \\\"exclude_resource_rules\\\")\",\n \"def exclude_resource_ids_scope(self) -> str:\\n return pulumi.get(self, \\\"exclude_resource_ids_scope\\\")\",\n \"def exclude_resource_ids_scope(self) -> str:\\n return pulumi.get(self, \\\"exclude_resource_ids_scope\\\")\",\n \"def exclude_resource_rules(self) -> Optional[Sequence['outputs.NamedRuleWithOperationsPatch']]:\\n return pulumi.get(self, \\\"exclude_resource_rules\\\")\",\n \"def excluded_namespaces(self) -> Optional[Sequence[str]]:\\n return pulumi.get(self, \\\"excluded_namespaces\\\")\",\n \"def network_excludes(self) -> pulumi.Output[Optional[Sequence[str]]]:\\n return pulumi.get(self, \\\"network_excludes\\\")\",\n \"def exclude():\\n data = list(request.files.values())[0].file.read() if len(request.files) else request.body.read()\\n return excludeView(data, request.params)\",\n \"def clean_resource(resource):\\n return omit(resource, 'occupations', 'availability')\",\n \"def get_resource_status(self, namespace: \\\"str\\\" = None):\\n pass\",\n \"def excluded_namespaces(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:\\n return pulumi.get(self, \\\"excluded_namespaces\\\")\",\n \"def excluded_namespaces(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:\\n return pulumi.get(self, \\\"excluded_namespaces\\\")\",\n \"def excluded_from_scan(self):\\n return six.next(six.itervalues(self.zap._request(self.zap.base + 'spider/view/excludedFromScan/')))\",\n \"def excluded_resource_types(self) -> Optional[Sequence[str]]:\\n return pulumi.get(self, \\\"excluded_resource_types\\\")\",\n \"def excluded(self):\\n\\n # A few variables for convenience of writing the method\\n spec = self.spec\\n conf = self.module.configuration(self.name)\\n\\n # Compute the list of include rules that match\\n # DEPRECATED: remove 'whitelist' in v0.20\\n include_rules = get_deprecated(conf, \\\"include\\\", \\\"whitelist\\\", [])\\n include_matches = [x for x in include_rules if spec.satisfies(x)]\\n\\n # Compute the list of exclude rules that match\\n # DEPRECATED: remove 'blacklist' in v0.20\\n exclude_rules = get_deprecated(conf, \\\"exclude\\\", \\\"blacklist\\\", [])\\n exclude_matches = [x for x in exclude_rules if spec.satisfies(x)]\\n\\n # Should I exclude the module because it's implicit?\\n # DEPRECATED: remove 'blacklist_implicits' in v0.20\\n exclude_implicits = get_deprecated(conf, \\\"exclude_implicits\\\", \\\"blacklist_implicits\\\", None)\\n installed_implicitly = not spec._installed_explicitly()\\n excluded_as_implicit = exclude_implicits and installed_implicitly\\n\\n def debug_info(line_header, match_list):\\n if match_list:\\n msg = \\\"\\\\t{0} : {1}\\\".format(line_header, spec.cshort_spec)\\n tty.debug(msg)\\n for rule in match_list:\\n tty.debug(\\\"\\\\t\\\\tmatches rule: {0}\\\".format(rule))\\n\\n debug_info(\\\"INCLUDE\\\", include_matches)\\n debug_info(\\\"EXCLUDE\\\", exclude_matches)\\n\\n if excluded_as_implicit:\\n msg = \\\"\\\\tEXCLUDED_AS_IMPLICIT : {0}\\\".format(spec.cshort_spec)\\n tty.debug(msg)\\n\\n is_excluded = exclude_matches or excluded_as_implicit\\n if not include_matches and is_excluded:\\n return True\\n\\n return False\",\n \"def _filter_non_existing_namespaces(namespaces, k8s_cli):\\n return_code, out = run_shell_command(\\n \\\"{} get ns -o=custom-columns=\\\\\\\"DATA:metadata.name\\\\\\\" --no-headers=true\\\".format(k8s_cli))\\n if return_code:\\n return []\\n res = []\\n existing_namespaces = set(out.split())\\n for namespace in namespaces:\\n if namespace in existing_namespaces:\\n res.append(namespace)\\n else:\\n logger.warning(\\\"Namespace %s doesn't exist - Skipping\\\", namespace)\\n return res\",\n \"def noAccessAllResources(self):\\n return self._get_field(\\\"noAccessToAllResources\\\")\",\n \"def non_resource_attributes(self) -> Optional[pulumi.Input['NonResourceAttributesArgs']]:\\n return pulumi.get(self, \\\"non_resource_attributes\\\")\",\n \"def non_resource_attributes(self) -> Optional[pulumi.Input['NonResourceAttributesArgs']]:\\n return pulumi.get(self, \\\"non_resource_attributes\\\")\",\n \"def non_resource_ur_ls(self):\\n return self._non_resource_ur_ls\",\n \"def excluded(cls):\\n return []\",\n \"def removeResource(self, *args):\\n return _libsbml.XMLAttributes_removeResource(self, *args)\",\n \"def _get_excludeFolders(self, dom):\\n return self._get_new_module_root_manager(dom).getElementsByTagName('excludeFolder')\",\n \"def resource_details(self) -> pulumi.Output[Any]:\\n return pulumi.get(self, \\\"resource_details\\\")\",\n \"def resource_details(self) -> pulumi.Output[Any]:\\n return pulumi.get(self, \\\"resource_details\\\")\",\n \"def namespace(self, namespace):\\n return self.client.call('GET',\\n self.name, params={'namespace': namespace})\",\n \"def exclusion_rule(self) -> Optional['outputs.PreventionInspectTemplateInspectConfigRuleSetRuleExclusionRule']:\\n return pulumi.get(self, \\\"exclusion_rule\\\")\",\n \"def get_resources(self):\\n return []\",\n \"def resource_prefix(self):\",\n \"def exclude_foreign_resources(returned_resource_set, expected_resource_set):\\n expected_owners = {res.owner for res in expected_resource_set}\\n return [\\n res for res in returned_resource_set\\n if res.owner in expected_owners\\n ]\",\n \"def get_resource(self):\\n raise errors.Unimplemented()\",\n \"def test_resource_collection_get_missing_resource(self):\\n collection = fhir_package.ResourceCollection(\\n self._valueset_cls, self._primitive_handler, 'Z'\\n )\\n resource = collection.get('missing-uri')\\n\\n self.assertIsNone(resource)\",\n \"def getResourceAttributes(self, authenticationToken, guid):\\r\\n pass\",\n \"def excluded_resource_types(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:\\n return pulumi.get(self, \\\"excluded_resource_types\\\")\",\n \"def excludes(self):\\r\\n\\r\\n return self._excludes\",\n \"def resources(self):\\n return self.__resources\",\n \"def otherResources(self):\\n return self._get_list_field(\\\"otherResources\\\")\",\n \"def getResourceFromHydroshare(self, resid, destination=None):\\n print('Not Implemented')\",\n \"def network_excludes(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:\\n return pulumi.get(self, \\\"network_excludes\\\")\",\n \"def network_excludes(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:\\n return pulumi.get(self, \\\"network_excludes\\\")\",\n \"def isExcluded(self, http_resource):\\n match = False\\n for regexp in self.excluded:\\n if self.__reWildcard(regexp, http_resource.url):\\n match = True\\n return match\",\n \"def exclude(self, *args, **kwargs):\",\n \"def remove_resources(self):\\n return self._remove_resources\",\n \"def is_excluded(self, attr_name, request):\\n return False\",\n \"def exclude(self: _R, *fn_exrps: str) -> _R:\\n exclude_exprs = []\\n exclude_exprs.extend(self.exclude_exprs)\\n for fn_exrp in fn_exrps:\\n if \\\"*\\\" not in fn_exrp and \\\".\\\" not in fn_exrp:\\n fn_exrp = f\\\"{fn_exrp}/*\\\"\\n exclude_exprs.append(fn_exrp)\\n return self._copy(include_exprs=self.include_exprs, exclude_exprs=exclude_exprs)\",\n \"def filter_excluded_paths(self) -> ConfigNodePropertyArray:\\n return self._filter_excluded_paths\",\n \"def test_delete_nonexistent_resource_rpc(self, mcg_obj):\\n response = mcg_obj.send_rpc_query(\\n \\\"pool_api\\\", \\\"delete_namespace_resource\\\", {\\\"name\\\": \\\"notexisting_resource\\\"}\\n )\\n assert \\\"error\\\" in response.json()\",\n \"def fhir_resource_concepts(self) -> Set[URIRef]:\\n return {subj for subj in self.graph.transitive_subjects(RDFS.subClassOf, FHIR.Resource)\\n if isinstance(subj, URIRef) and not self.w5_ontology.is_w5_infrastructure(subj)}\",\n \"def getResource(self):\\n pass;\",\n \"def test_get_unregistered_networks(self):\\n pass\",\n \"def inconclusive_detail(self) -> 'outputs.InconclusiveDetailResponse':\\n return pulumi.get(self, \\\"inconclusive_detail\\\")\",\n \"def get_extended_resources(self, version):\\n return {}\",\n \"def getExcludedDumpRegisterNames(self):\\n pass\",\n \"def discard_resources(data, presence_treshold=1.0):\\n\\n # we will discard stuff from this copy\\n res = copy.deepcopy(data)\\n\\n # to compare easier\\n good_one = copy.deepcopy(data[\\\"no_vpn\\\"])\\n del data[\\\"no_vpn\\\"]\\n\\n to_del = []\\n\\n for extension in data:\\n for webpage in data[extension]:\\n for f in data[extension][webpage]:\\n if f in good_one[webpage]:\\n del res[extension][webpage][f]\\n # this will repeat, but it shouldn't be a problem\\n to_del.append((webpage, f))\\n\\n for webpage, file in to_del:\\n try:\\n del data[\\\"no_vpn\\\"][webpage][file]\\n except KeyError:\\n pass\\n\\n return res\",\n \"def except_image_only(resource):\\n if resource.image is None:\\n raise FeatureExtractionError(resource, 400, 'Image resource is required')\\n if resource.mask:\\n raise FeatureExtractionError(resource, 400, 'Mask resource is not accepted')\\n if resource.gobject:\\n raise FeatureExtractionError(resource, 400, 'Gobject resource is not accepted')\",\n \"def exclusions(self) -> Sequence['outputs.LogExclusionResponse']:\\n return pulumi.get(self, \\\"exclusions\\\")\",\n \"def exclude_from_scan(self, regex, apikey=''):\\n return six.next(six.itervalues(self.zap._request(self.zap.base + 'spider/action/excludeFromScan/', {'regex': regex, 'apikey': apikey})))\",\n \"def exclude_list(self):\\n pass\",\n \"def private_link_resource_region(self) -> Optional[str]:\\n return pulumi.get(self, \\\"private_link_resource_region\\\")\",\n \"def test_list_namespaced_egress_network_policy(self):\\n pass\",\n \"def get_exclusions(self: object, *args, parameters: dict = None, **kwargs) -> Dict[str, Union[int, dict]]:\\n return process_service_request(\\n calling_object=self,\\n endpoints=EPS,\\n operation_id=\\\"getSensorVisibilityExclusionsV1\\\",\\n keywords=kwargs,\\n params=handle_single_argument(args, parameters, \\\"ids\\\")\\n )\",\n \"def getResources(self, folder):\\n\\n #-------------------- \\n # Get the resource JSON\\n #-------------------- \\n folder += \\\"/resources\\\"\\n resources = self.__getJson(folder)\\n #print(\\\"%s %s\\\"%(, folder))\\n #print(\\\" Got resources: '%s'\\\"%(str(resources)))\\n\\n\\n\\n #-------------------- \\n # Filter the JSONs\\n #-------------------- \\n resourceNames = []\\n for r in resources:\\n if 'label' in r:\\n resourceNames.append(r['label'])\\n #print(\\\"FOUND RESOURCE ('%s') : %s\\\"%(folder, r['label']))\\n elif 'Name' in r:\\n resourceNames.append(r['Name'])\\n #print(\\\"FOUND RESOURCE ('%s') : %s\\\"%(folder, r['Name']))\\n\\n return resourceNames\",\n \"def exclude(elem: etree.Element) -> bool:\\n # Skip redirects\\n if elem.find('redirect') is not None:\\n return True\\n\\n # Skip non-article pages.(namespace 0 are article pages)\\n # Note that you may need error handling\\n if elem.find('ns') is None or elem.find('ns').text != '0':\\n return True\\n\\n # We don't have any reason to exclude it, so return\\n # False (keep it).\\n return False\",\n \"def ignore_non_actual_section(): # noqa: D416\",\n \"def get_ing_from_ns(namespace):\\n ing_list = get_all_ingress_from(namespace)\\n ing_list_json = get_json_list(ing_list)\\n return Response(response=ing_list_json, status=200, mimetype='application/json')\",\n \"def evaluate_notresource_for_permission(statement: Dict, resource_arn: str) -> bool:\\n if 'notresource' not in statement:\\n return False\\n for clause in statement['notresource']:\\n if evaluate_clause(clause, resource_arn):\\n return True\\n return False\",\n \"def test_read_namespaced_egress_network_policy(self):\\n pass\",\n \"def namespace_resource_blacklist(self, namespace_resource_blacklist):\\n\\n self._namespace_resource_blacklist = namespace_resource_blacklist\",\n \"def getResource(self):\\n return self.serviceClass.app.resource()\",\n \"def test_resource_namespace(self, integrationtest, k8sconfig):\\n # Fixtures.\\n config = self.k8sconfig(integrationtest, k8sconfig)\\n MM = MetaManifest\\n\\n for src in [\\\"\\\", \\\"v1\\\"]:\\n # A particular Namespace.\\n res, err = k8s.resource(config, MM(src, \\\"Namespace\\\", None, \\\"name\\\"))\\n assert not err\\n assert res == K8sResource(\\n apiVersion=\\\"v1\\\",\\n kind=\\\"Namespace\\\",\\n name=\\\"namespaces\\\",\\n namespaced=False,\\n url=f\\\"{config.url}/api/v1/namespaces/name\\\",\\n )\\n\\n # A particular Namespace in a particular namespace -> Invalid.\\n assert k8s.resource(config, MM(src, \\\"Namespace\\\", \\\"ns\\\", \\\"name\\\")) == (res, err)\\n\\n # All Namespaces.\\n res, err = k8s.resource(config, MM(src, \\\"Namespace\\\", None, None))\\n assert not err\\n assert res == K8sResource(\\n apiVersion=\\\"v1\\\",\\n kind=\\\"Namespace\\\",\\n name=\\\"namespaces\\\",\\n namespaced=False,\\n url=f\\\"{config.url}/api/v1/namespaces\\\",\\n )\\n\\n # Same as above because the \\\"namespace\\\" argument is ignored for Namespaces.\\n assert k8s.resource(config, MM(src, \\\"Namespace\\\", \\\"name\\\", \\\"\\\")) == (res, err)\",\n \"def excludes(self) -> Set:\\n if self._excludes is None:\\n manifest = self._get_manifest()\\n self._excludes = manifest[\\\"files\\\"][\\\"excludes\\\"]\\n\\n return self._excludes\",\n \"def __init__(__self__,\\n resource_name: str,\\n args: BillingAccountExclusionArgs,\\n opts: Optional[pulumi.ResourceOptions] = None):\\n ...\",\n \"def get_bad_container(self):\\n return XMLFile(\\n FilePath(\\\"TrackerDash/snippets/no_dash_data_container.xml\\\"))\",\n \"def _get_lsp_config_exclude_interfaces(self):\\n return self.__lsp_config_exclude_interfaces\",\n \"def GetExclusiveResources(self):\\n res = set(self.exclusive_resources)\\n if self.parent:\\n res |= self.parent.GetExclusiveResources()\\n return res\",\n \"def GetResourceNames(self):\\r\\n return [x.name for x in self.resources]\",\n \"def exclude_nodes(self, nodes):\",\n \"def prune_namespace(self, properties):\\n if self.default_namespace is None:\\n # I have no default -- no change\\n return properties\\n if 'namespace' not in properties:\\n # he has no namespace - no change\\n return properties\\n if properties['namespace'] != self.default_namespace:\\n # we're different - leave his stuff alone\\n return properties\\n # we each have a namespace and it's redundant. delete his.\\n prunable = properties.copy()\\n del prunable['namespace']\\n return prunable\",\n \"def getResources(self):\\n\\t\\treturn deepcopy(self.server.resources)\",\n \"def _excludes(self, method, url):\\n if method != 'GET':\\n return False\\n # maybe change to a dict?\\n for excl in self.excludes:\\n if excl == \\\"topInfo\\\" and \\\"info.json\\\" in url:\\n return True\\n if str(excl) + \\\".json\\\" in url:\\n return True\\n return False\",\n \"def resource_arn(self) -> Optional[str]:\\n return pulumi.get(self, \\\"resource_arn\\\")\",\n \"def get_resource_information():\\n\\n\\n # the resources we are allowed to use is easy. We just copy this...\\n resource_limit_dict = _resources_allowed_dict.copy()\\n\\n \\n # from the other dict, we only take the resource information. (this omits\\n # locks and timing information that isn't needed)\\n\\n # first, let's do the easy thing, the quantity resources. These are just \\n # floats\\n resource_use_dict = {}\\n for resourcename in resource_constants.quantity_resources:\\n resource_use_dict[resourcename] = _resources_consumed_dict[resourcename]\\n\\n # for the fungible resources (files opened, etc,), we only need a count...\\n for resourcename in resource_constants.fungible_item_resources:\\n resource_use_dict[resourcename] = len(_resources_consumed_dict[resourcename])\\n\\n # for the individual item resources (ports, etc,), we copy the set...\\n for resourcename in resource_constants.individual_item_resources:\\n resource_use_dict[resourcename] = _resources_consumed_dict[resourcename].copy()\\n\\n # and that's it!\\n return (resource_limit_dict, resource_use_dict)\",\n \"def get_excluded_observations(self):\\n\\n return copy.deepcopy(self._excluded_observations)\",\n \"def private_subnet(template, name):\\n return template.resources[name]\",\n \"def to_exclude(self):\\n reporting_period = self.message_number - 3\\n one_day_ago = now() - datetime.timedelta(hours=24)\\n\\n return PollingReport.objects.filter(\\n period_number=reporting_period,\\n creation_date__gte=one_day_ago,\\n ).values_list('phone_number', flat=True)\",\n \"def getResourceAlternateData(self, authenticationToken, guid):\\r\\n pass\",\n \"def test_method_excludes(self):\\n self.assertContains(self.response, \\\"<td>get_full_name</td>\\\")\\n self.assertNotContains(self.response, \\\"<td>_get_full_name</td>\\\")\\n self.assertNotContains(self.response, \\\"<td>add_image</td>\\\")\\n self.assertNotContains(self.response, \\\"<td>delete_image</td>\\\")\\n self.assertNotContains(self.response, \\\"<td>set_status</td>\\\")\\n self.assertNotContains(self.response, \\\"<td>save_changes</td>\\\")\",\n \"def attributes(self):\\n attr = super(BaseDataRecord, self).attributes()\\n return [a for a in attr if a not in self.excluded()]\",\n \"def getResource(self):\\n return self.__resource;\",\n \"def getChild(self, path, request):\\r\\n # override this method if you want to serve dynamic child resources\\r\\n return resource.ForbiddenResource(\\\"This is a resource namespace.\\\")\",\n \"def get_region(self, region, namespace, region_id, **filters):\\n filters['namespace'] = namespace\\n return self.get_resource('data/wow/region/{0}', region, *[region_id], **filters)\",\n \"def collect_resources_list(namespace, output_dir, k8s_cli, mode):\\n selector = \\\"\\\"\\n if mode == MODE_RESTRICTED:\\n selector = '--selector=\\\"{}\\\"'.format(OPERATOR_LABEL)\\n collect_helper(output_dir,\\n cmd=\\\"{} get all -o wide -n {} {}\\\".format(k8s_cli, namespace, selector),\\n file_name=\\\"resources_list\\\",\\n resource_name=\\\"resources list\\\",\\n namespace=namespace)\",\n \"def auditcontextnotfound(self) :\\n\\t\\ttry :\\n\\t\\t\\treturn self._auditcontextnotfound\\n\\t\\texcept Exception as e:\\n\\t\\t\\traise e\",\n \"def getUnmoderated (self) :\\n return self.getPictures (Event.objects.filter (approval = None))\",\n \"def getResourceDef(url, user, pWd, resourceName):\\n \\n print(\\\"getting resource for catalog:-\\\" + url + \\\" resource=\\\" + resourceName +\\n ' user=' + user)\\n apiURL = url + '/access/1/catalog/resources/' + resourceName\\n # print(\\\"\\\\turl=\\\" + apiURL)\\n header = {\\\"Accept\\\": \\\"application/json\\\"} \\n tResp = requests.get(apiURL, params={}, headers=header, auth=HTTPBasicAuth(user,pWd))\\n print(\\\"\\\\tresponse=\\\" + str(tResp.status_code))\\n if tResp.status_code == 200:\\n # valid - return the jsom\\n return tResp.status_code, json.loads(tResp.text)\\n else:\\n # not valid\\n return tResp.status_code, None\",\n \"def to_exclude(self):\\n midnight = now().replace(hour=0, minute=0, microsecond=0)\\n return CenterOpen.objects.filter(\\n creation_date__gte=midnight,\\n ).values_list('phone_number', flat=True)\",\n \"def getResourceAttributes(self, authenticationToken, guid):\\r\\n self.send_getResourceAttributes(authenticationToken, guid)\\r\\n return self.recv_getResourceAttributes()\",\n \"def pop_resources(self):\\n resources = self.request_local.resources\\n self.request_local.resources = {}\\n # deal with aggregated resources\\n if resources and \\\"head\\\" in resources:\\n # This is lazy, because we otherwise run\\n # into circular import issues\\n if self.aggregation_config is not None:\\n self._setup_aggregation_mapping()\\n\\n\\n if self.aggregated_js_mapping:\\n self._replace_resources_with_aggregates(resources,\\n self.aggregated_js_mapping,\\n JSLink,\\n )\\n if self.aggregated_css_mapping:\\n self._replace_resources_with_aggregates(resources,\\n self.aggregated_css_mapping,\\n CSSLink,\\n )\\n return resources\",\n \"def resources(self) -> Optional[Sequence[str]]:\\n return pulumi.get(self, \\\"resources\\\")\",\n \"def resources(self) -> Optional[Sequence[str]]:\\n return pulumi.get(self, \\\"resources\\\")\",\n \"def resources(self) -> Optional[Sequence[str]]:\\n return pulumi.get(self, \\\"resources\\\")\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.60753304","0.6006292","0.6006292","0.57762307","0.5728827","0.54888046","0.53958565","0.5395732","0.5374372","0.53347665","0.53347665","0.5316922","0.52620876","0.5223665","0.5212365","0.5199779","0.5170026","0.5170026","0.51522565","0.51229185","0.51103574","0.50885206","0.50169665","0.50169665","0.5011655","0.49566853","0.49293658","0.49011952","0.48986468","0.4875216","0.4872585","0.4867935","0.4865607","0.48416212","0.4840021","0.4837211","0.48336565","0.4809507","0.4809507","0.48084167","0.47991163","0.47966227","0.47796413","0.4775071","0.47724658","0.47567943","0.47507957","0.47439873","0.4742111","0.47361782","0.47351792","0.47343257","0.46741724","0.467038","0.4669759","0.4648868","0.46449992","0.46412072","0.4629717","0.46165633","0.461003","0.46061254","0.46037695","0.45952654","0.4593318","0.45923084","0.45869565","0.45836514","0.4580286","0.45741788","0.4562596","0.45499036","0.45424283","0.45420545","0.45377773","0.45355013","0.45257428","0.45234776","0.45194063","0.45165476","0.45163146","0.45035768","0.44956934","0.4494924","0.44932082","0.4491071","0.44910192","0.44759542","0.4473033","0.44688517","0.4461444","0.44589075","0.44579628","0.44475457","0.4445559","0.44427788","0.4440357","0.443865","0.443865","0.443865"],"string":"[\n \"0.60753304\",\n \"0.6006292\",\n \"0.6006292\",\n \"0.57762307\",\n \"0.5728827\",\n \"0.54888046\",\n \"0.53958565\",\n \"0.5395732\",\n \"0.5374372\",\n \"0.53347665\",\n \"0.53347665\",\n \"0.5316922\",\n \"0.52620876\",\n \"0.5223665\",\n \"0.5212365\",\n \"0.5199779\",\n \"0.5170026\",\n \"0.5170026\",\n \"0.51522565\",\n \"0.51229185\",\n \"0.51103574\",\n \"0.50885206\",\n \"0.50169665\",\n \"0.50169665\",\n \"0.5011655\",\n \"0.49566853\",\n \"0.49293658\",\n \"0.49011952\",\n \"0.48986468\",\n \"0.4875216\",\n \"0.4872585\",\n \"0.4867935\",\n \"0.4865607\",\n \"0.48416212\",\n \"0.4840021\",\n \"0.4837211\",\n \"0.48336565\",\n \"0.4809507\",\n \"0.4809507\",\n \"0.48084167\",\n \"0.47991163\",\n \"0.47966227\",\n \"0.47796413\",\n \"0.4775071\",\n \"0.47724658\",\n \"0.47567943\",\n \"0.47507957\",\n \"0.47439873\",\n \"0.4742111\",\n \"0.47361782\",\n \"0.47351792\",\n \"0.47343257\",\n \"0.46741724\",\n \"0.467038\",\n \"0.4669759\",\n \"0.4648868\",\n \"0.46449992\",\n \"0.46412072\",\n \"0.4629717\",\n \"0.46165633\",\n \"0.461003\",\n \"0.46061254\",\n \"0.46037695\",\n \"0.45952654\",\n \"0.4593318\",\n \"0.45923084\",\n \"0.45869565\",\n \"0.45836514\",\n \"0.4580286\",\n \"0.45741788\",\n \"0.4562596\",\n \"0.45499036\",\n \"0.45424283\",\n \"0.45420545\",\n \"0.45377773\",\n \"0.45355013\",\n \"0.45257428\",\n \"0.45234776\",\n \"0.45194063\",\n \"0.45165476\",\n \"0.45163146\",\n \"0.45035768\",\n \"0.44956934\",\n \"0.4494924\",\n \"0.44932082\",\n \"0.4491071\",\n \"0.44910192\",\n \"0.44759542\",\n \"0.4473033\",\n \"0.44688517\",\n \"0.4461444\",\n \"0.44589075\",\n \"0.44579628\",\n \"0.44475457\",\n \"0.4445559\",\n \"0.44427788\",\n \"0.4440357\",\n \"0.443865\",\n \"0.443865\",\n \"0.443865\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.53204346"},"document_rank":{"kind":"string","value":"11"}}},{"rowIdx":14381,"cells":{"query":{"kind":"string","value":"calculating capacity and usage for requested resources"},"document":{"kind":"string","value":"def get_resource_info(cluster_id, kind, namespace_id=None, pods_list=None):\n if pods_list is None:\n pods_list = []\n capacity = get_cluster_capacity_info(cluster_id),\n usage = get_cluster_usage_info(cluster_id, kind, namespace_id, pods_list)\n if capacity[0]['cpu'] != 0 and capacity[0]['memory'] != 0:\n resource_info = {\n \"capacity\": capacity[0],\n \"usage\": {\n \"cpu\": usage['cpu'],\n \"cpu_percentage\": float(round(decimal.Decimal(usage['cpu'] / capacity[0]['cpu'])*100, 2)),\n \"memory\": usage['memory'],\n \"memory_percentage\": float(round(decimal.Decimal(usage['memory'] / capacity[0]['memory'])*100, 2))\n }\n }\n else:\n resource_info = {\n \"capacity\": capacity[0],\n \"usage\": {\n \"cpu\": usage['cpu'],\n \"cpu_percentage\": 0.0,\n \"memory\": usage['memory'],\n \"memory_percentage\": 0.0\n }\n }\n\n return resource_info"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_capacity():\n fs.get_capacity()","def capacity_used(self):\n raise NotImplementedError()","def capacity(self):\n capacity = {}\n resources = self.nodes[0].capacity.keys()\n for r in resources:\n values = [n.capacity[r] for n in self.nodes]\n capacity[r] = mean(values) if len(values) > 0 else 0.0\n return capacity","def get_num_slots(self):\n # Your code here\n return self.capacity","def _get_capacity_info(self, nfs_share):\n nms = self.share2nms[nfs_share]\n ns_volume, ns_folder = self._get_share_datasets(nfs_share)\n folder_props = nms.folder.get_child_props('%s/%s' % (ns_volume,\n ns_folder),\n 'used|available')\n free = utils.str2size(folder_props['available'])\n allocated = utils.str2size(folder_props['used'])\n self.shares_with_capacities[nfs_share] = {\n 'free': utils.str2gib_size(free),\n 'total': utils.str2gib_size(free + allocated)}\n return free + allocated, free, allocated","def Capacity(self) -> int:","def test_rest_v20_dd_systems_systemid_stats_capacity_get(self):\n pass","def capacity(self):\n raise NotImplementedError()","def total_sdram_requirements(self):","def get_available_resources(threshold, usage, total):\n return dict((host, int(threshold * total[host] - resource))\n for host, resource in usage.items())","def _check_capacity_limit(self, res, amt, balance, meta, raven_vars, dispatch, t, level):\n # note \"amt\" has units of AMOUNT not RATE (resource, not resource per second)\n sign = np.sign(amt)\n # are we storing or providing?\n #print('DEBUGG supposed current level:', level)\n if sign < 0:\n # we are being asked to consume some\n cap, meta = self.get_capacity(meta, raven_vars, dispatch, t)\n available_amount = cap[res] - level\n #print('Supposed Capacity, Only calculated ins sign<0 (being asked to consumer)',cap)\n else:\n # we are being asked to produce some\n available_amount = level\n # the amount we can consume is the minimum of the requested or what's available\n delta = sign * min(available_amount, abs(amt))\n return {res: delta}, meta","def calculate_demand(flow, requested_sf, available_sf, service_functions):\n\n if requested_sf in available_sf:\n vnf_need_placement = False\n demanded_total_capacity = 0.0\n for sf_i, sf_data in available_sf.items():\n if requested_sf == sf_i:\n # Include flows data rate in requested sf capacity calculation\n demanded_total_capacity += service_functions[sf_i]['resource_function'](\n sf_data['load'] + flow.dr)\n else:\n demanded_total_capacity += service_functions[sf_i]['resource_function'](sf_data['load'])\n return demanded_total_capacity, vnf_need_placement\n else:\n vnf_need_placement = True\n available_sf[requested_sf] = {'load': 0.0}\n demanded_total_capacity = 0.0\n for sf_i, sf_data in available_sf.items():\n if requested_sf == sf_i:\n # Include flows data rate in requested sf capacity calculation\n demanded_total_capacity += service_functions[sf_i]['resource_function'](\n sf_data['load'] + flow.dr)\n else:\n demanded_total_capacity += service_functions[sf_i]['resource_function'](sf_data['load'])\n del available_sf[requested_sf]\n return demanded_total_capacity, vnf_need_placement","def available_capacity(self):\r\n return self.capacity - len(self.passengers)","def initialize_supply(self):\n unit_count = 0\n for i in range(self.start_allocation[0 ] -1, self.start_allocation[1]):\n for j in range(len(self.capacity_list[i][1])):\n self.capacity_list[i][1][j] = 1\n unit_count += 1\n self.total_supply -= unit_count","def _estimate_elasticsearch_requirement(\n instance: Instance,\n desires: CapacityDesires,\n working_set: float,\n reads_per_second: float,\n max_rps_to_disk: int,\n zones_per_region: int = 3,\n copies_per_region: int = 3,\n) -> CapacityRequirement:\n # Keep half of the cores free for background work (merging mostly)\n needed_cores = math.ceil(sqrt_staffed_cores(desires) * 1.5)\n # Keep half of the bandwidth available for backup\n needed_network_mbps = simple_network_mbps(desires) * 2\n\n needed_disk = math.ceil(\n (1.0 / desires.data_shape.estimated_compression_ratio.mid)\n * desires.data_shape.estimated_state_size_gib.mid\n * copies_per_region,\n )\n\n # Rough estimate of how many instances we would need just for the the CPU\n # Note that this is a lower bound, we might end up with more.\n needed_cores = math.ceil(\n max(1, needed_cores // (instance.cpu_ghz / desires.core_reference_ghz))\n )\n rough_count = math.ceil(needed_cores / instance.cpu)\n\n # Generally speaking we want fewer than some number of reads per second\n # hitting disk per instance. If we don't have many reads we don't need to\n # hold much data in memory.\n instance_rps = max(1, reads_per_second // rough_count)\n disk_rps = instance_rps * _es_io_per_read(max(1, needed_disk // rough_count))\n rps_working_set = min(1.0, disk_rps / max_rps_to_disk)\n\n # If disk RPS will be smaller than our target because there are no\n # reads, we don't need to hold as much data in memory\n needed_memory = min(working_set, rps_working_set) * needed_disk\n\n # Now convert to per zone\n needed_cores = needed_cores // zones_per_region\n needed_disk = needed_disk // zones_per_region\n needed_memory = int(needed_memory // zones_per_region)\n logger.debug(\n \"Need (cpu, mem, disk, working) = (%s, %s, %s, %f)\",\n needed_cores,\n needed_memory,\n needed_disk,\n working_set,\n )\n\n return CapacityRequirement(\n requirement_type=\"elasticsearch-data-zonal\",\n core_reference_ghz=desires.core_reference_ghz,\n cpu_cores=certain_int(needed_cores),\n mem_gib=certain_float(needed_memory),\n disk_gib=certain_float(needed_disk),\n network_mbps=certain_float(needed_network_mbps),\n context={\n \"working_set\": min(working_set, rps_working_set),\n \"rps_working_set\": rps_working_set,\n \"disk_slo_working_set\": working_set,\n \"replication_factor\": copies_per_region,\n \"compression_ratio\": round(\n 1.0 / desires.data_shape.estimated_compression_ratio.mid, 2\n ),\n \"read_per_second\": reads_per_second,\n },\n )","def _consume_resources(self, job_limits: Dict[str, int]) -> None:\n for limit_name, count in job_limits.items():\n self.limits_used[limit_name] += count","def get_capacity(self, meta, raven_vars, dispatch, t, raw=False):\n return self.get_interaction().get_capacity(meta, raven_vars, dispatch, t, raw=raw)","def compute_capacity(self, totalflow, i):\r\n self.capacity[i] = self.phi * self.flow[i] / (self.b[i] \\\r\n - self.beta*(self.flow[i]**self.theta) + np.log(1-totalflow) - np.log(self.flow[i]))","def _get_info_about_available_resources(self, min_ram, min_hdd, min_vcpus):\n vms_count = 0\n for hypervisor in self.nova_cli.hypervisors.list():\n if hypervisor.free_ram_mb >= min_ram:\n if hypervisor.free_disk_gb >= min_hdd:\n if hypervisor.vcpus - hypervisor.vcpus_used >= min_vcpus:\n # We need to determine how many VMs we can run\n # on this hypervisor\n free_cpu = hypervisor.vcpus - hypervisor.vcpus_used\n k1 = int(hypervisor.free_ram_mb / min_ram)\n k2 = int(hypervisor.free_disk_gb / min_hdd)\n k3 = int(free_cpu / min_vcpus)\n vms_count += min(k1, k2, k3)\n return vms_count","def calculate_total_capacity(**kwargs):\n members = kwargs.get(\"data\", [])\n total = 0\n if members:\n for member in members:\n capacity = Mapper.get_single_attribute(\n member, \"Capacity\", MappingTable.device.value, output_as_json=True\n )\n total += capacity.get(\"Capacity\", {}).get(\"Value\", 0)\n # returning value in MiB\n return total * 1024","def capacitygroup_group():","def capacity(self):\n return self._capacity","def get_capacity_param(self):\n intr = self.get_interaction()\n return intr.get_capacity(None, None, None, None, raw=True)","def _allocate(self, n_resource, beliefs):\n raise NotImplementedError","def get_resources():\n # Acquire the lock...\n get_resources_lock.acquire()\n\n # ...but always release it\n try:\n # Construct the dictionaries as copies from nanny\n (limits,usage) = nanny.get_resource_information()\n\n\n # Calculate all the usage's\n pid = os.getpid()\n\n # Get CPU and memory, this is thread specific\n if ostype in [\"Linux\", \"Darwin\"]:\n \n # Get CPU first, then memory\n usage[\"cpu\"] = os_api.get_process_cpu_time(pid)\n\n # This uses the cached PID data from the CPU check\n usage[\"memory\"] = os_api.get_process_rss()\n\n # Get the thread specific CPU usage\n usage[\"threadcpu\"] = os_api.get_current_thread_cpu_time() \n\n\n # Windows Specific versions\n elif ostype in [\"Windows\"]:\n \n # Get the CPU time\n usage[\"cpu\"] = windows_api.get_process_cpu_time(pid)\n\n # Get the memory, use the resident set size\n usage[\"memory\"] = windows_api.process_memory_info(pid)['WorkingSetSize'] \n\n # Get thread-level CPU \n usage[\"threadcpu\"] = windows_api.get_current_thread_cpu_time()\n\n # Unknown OS\n else:\n raise EnvironmentError(\"Unsupported Platform!\")\n\n # Use the cached disk used amount\n usage[\"diskused\"] = cached_disk_used\n\n finally:\n # Release the lock\n get_resources_lock.release()\n\n # Copy the stop times\n stoptimes = process_stopped_timeline[:]\n\n # Return the dictionaries and the stoptimes\n return (limits,usage,stoptimes)","def update_available_resource(self, context):\n # ask hypervisor for its view of resource availability &\n # usage:\n resources = self.driver.get_available_resource()\n if not resources:\n # The virt driver does not support this function\n LOG.warn(_(\"Virt driver does not support \"\n \"'get_available_resource' Compute tracking is disabled.\"))\n self.compute_node = None\n self.claims = {}\n return\n\n # Confirm resources dictionary contains expected keys:\n self._verify_resources(resources)\n\n resources['free_ram_mb'] = resources['memory_mb'] - \\\n resources['memory_mb_used']\n resources['free_disk_gb'] = resources['local_gb'] - \\\n resources['local_gb_used']\n\n LOG.audit(_(\"free_ram_mb: %s\") % resources['free_ram_mb'])\n LOG.audit(_(\"free_disk_gb: %s\") % resources['free_disk_gb'])\n # Apply resource claims representing in-progress operations to\n # 'resources'. This may over-estimate the amount of resources in use,\n # at least until the next time 'update_available_resource' runs.\n self._apply_claims(resources)\n\n # also generate all load stats:\n values = self._create_load_stats(context)\n resources.update(values)\n\n if not self.compute_node:\n # we need a copy of the ComputeNode record:\n service = self._get_service(context)\n if not service:\n # no service record, disable resource\n return\n\n compute_node_ref = service['compute_node']\n if compute_node_ref:\n self.compute_node = compute_node_ref[0]\n\n if not self.compute_node:\n # Need to create the ComputeNode record:\n resources['service_id'] = service['id']\n self.compute_node = self._create(context, resources)\n LOG.info(_('Compute_service record created for %s ') % self.host)\n\n else:\n # just update the record:\n self.compute_node = self._update(context, resources,\n prune_stats=True)\n LOG.info(_('Compute_service record updated for %s ') % self.host)","def get_resource_information():\n\n\n # the resources we are allowed to use is easy. We just copy this...\n resource_limit_dict = _resources_allowed_dict.copy()\n\n \n # from the other dict, we only take the resource information. (this omits\n # locks and timing information that isn't needed)\n\n # first, let's do the easy thing, the quantity resources. These are just \n # floats\n resource_use_dict = {}\n for resourcename in resource_constants.quantity_resources:\n resource_use_dict[resourcename] = _resources_consumed_dict[resourcename]\n\n # for the fungible resources (files opened, etc,), we only need a count...\n for resourcename in resource_constants.fungible_item_resources:\n resource_use_dict[resourcename] = len(_resources_consumed_dict[resourcename])\n\n # for the individual item resources (ports, etc,), we copy the set...\n for resourcename in resource_constants.individual_item_resources:\n resource_use_dict[resourcename] = _resources_consumed_dict[resourcename].copy()\n\n # and that's it!\n return (resource_limit_dict, resource_use_dict)","def capacity(self):\n return self._cap","def get_space_used():\n fs.get_space_used()","def calculate_worker_requirements(self, resource_name, queue_config):\n worker_cores = 1\n worker_memory = 1\n\n self.load_data()\n try:\n # retrieve the resource type definition\n resource_type = self.resource_types[resource_name]\n\n # retrieve the queue configuration\n site_maxrss = queue_config.get('maxrss', 0) or 0\n site_corecount = queue_config.get('corecount', 1) or 1\n\n unified_queue = queue_config.get('capability', '') == 'ucore'\n if not unified_queue:\n # site is not unified, just request whatever is configured in AGIS\n return site_maxrss, site_corecount\n\n if resource_type.max_core:\n worker_cores = min(resource_type.max_core, site_corecount)\n else:\n worker_cores = site_corecount\n\n if resource_type.max_ram_per_core:\n worker_memory = min(resource_type.max_ram_per_core * worker_cores,\n (site_maxrss / site_corecount) * worker_cores)\n else:\n worker_memory = (site_maxrss / site_corecount) * worker_cores\n\n except KeyError:\n pass\n\n return worker_cores, worker_memory","def _get_free_capacity(self):\n\n capacity = np.ones(len(self.grid.T)) * len(self.grid)\n capacity -= np.count_nonzero(self.grid, axis=0)\n return capacity","def capacity(self):\n return self._capacity","def capacity(self) -> Capacity:\n raw = self._call('GET', 'capacity')\n return Capacity.parse_raw(raw)","def __init__(self, capacity, units=0):\n self.capacity = capacity\n self.units = units","def get_load_factor(self):\n # Your code here\n return self.total_items / self.capacity","def test_capacity_factor(pudl_out_eia):\n print(\"\\nCalculating generator capacity factors...\")\n cf = pudl_out_eia.capacity_factor()\n print(f\" capacity_factor: {len(cf)} records\")","def capacity(self) -> pulumi.Input[int]:\n return pulumi.get(self, \"capacity\")","def knapsack(items, capacity):\r\n pass","def get_available_resource(self, nodename):\n curent_time = time.time()\n if curent_time - self.cleanup_time > CONF.azure.cleanup_span:\n self.cleanup_time = curent_time\n self._cleanup_deleted_os_disks()\n self._cleanup_deleted_nics()\n usage_family = 'basicAFamily'\n try:\n page = self.compute.usage.list(CONF.azure.location)\n except Exception as e:\n msg = six.text_type(e)\n LOG.exception(msg)\n ex = exception.ComputeUsageListFailure(reason=six.text_type(e))\n raise ex\n usages = [i for i in page]\n cores = 0\n cores_used = 0\n for i in usages:\n if hasattr(i, 'name') and hasattr(i.name, 'value'):\n if usage_family == i.name.value:\n cores = i.limit if hasattr(i, 'limit') else 0\n cores_used = i.current_value \\\n if hasattr(i, 'current_value') else 0\n break\n return {'vcpus': cores,\n 'memory_mb': 100000000,\n 'local_gb': 100000000,\n 'vcpus_used': cores_used,\n 'memory_mb_used': 0,\n 'local_gb_used': 0,\n 'hypervisor_type': hv_type.HYPERV,\n 'hypervisor_version': 300,\n 'hypervisor_hostname': nodename,\n 'cpu_info': '{\"model\": [\"Intel(R) Xeon(R) CPU E5-2670 0 @ '\n '2.60GHz\"], \"topology\": {\"cores\": 16, \"threads\": '\n '32}}',\n 'supported_instances': [(arch.I686, hv_type.HYPERV,\n vm_mode.HVM),\n (arch.X86_64, hv_type.HYPERV,\n vm_mode.HVM)],\n 'numa_topology': None\n }","def capacity(self):\n return self.buffer_capacity.mean(dim=1)","def new_capacity_rule(mod, g, p):\n return 0","def get_capacity_var(self):\n return self._capacity_var","def buy_resource(self, num_res):\n check_pur = self.poss_purchases()\n if num_res in check_pur.keys():\n update_res = num_res\n for bin in self.capacity_list:\n for i, res in enumerate(bin[1]):\n if res == 1 and update_res > 0:\n bin[1][i] = 0\n update_res -= 1\n return (num_res, check_pur[num_res])\n else:\n return 'not enough resources'","def new_capacity_rule(mod, prj, prd):\n return 0","def get_capacity(self, meta, raven_vars, dispatch, t, raw=False):\n if raw:\n return self._capacity\n request = {self._capacity_var: None}\n inputs = {'request': request,\n 'meta': meta,\n 'raven_vars': raven_vars,\n 'dispatch': dispatch,\n 't': t}\n evaluated, meta = self._capacity.evaluate(inputs, target_var=self._capacity_var)\n return evaluated, meta","def capacity(self):\n return str(int(self._properties.get('capacity')) * 1073741824)","def _get_share_capacity_info(self):\n lcfg = self.configuration\n share_details = self.zfssa.get_share(lcfg.zfssa_nfs_pool,\n lcfg.zfssa_nfs_project,\n lcfg.zfssa_nfs_share)\n\n free = share_details['space_available']\n used = share_details['space_total']\n return free, used","def getRepairCapacity(self):\n return int(self.myDesign.getSYCRequired() * (1-(self.strength/100.0)))","def do_stats(self, args):\n total_cpu = free_cpu = in_use_cpu = 0\n\n summary = self._qm.get_all_host_summary()\n for host_id, host_info in summary.viewitems():\n host_cpu = int(host_info['total cores'])\n total_cpu += host_cpu\n locked = host_info.get('locked by')\n if locked:\n # If host is locked then all CPUs are in use.\n in_use_cpu += host_cpu\n else:\n free_host_cpu = int(host_info['free cores'])\n in_use_cpu += (host_cpu - free_host_cpu)\n free_cpu += free_host_cpu\n\n print('total CPU: ', total_cpu)\n print('used/locked CPU: ', in_use_cpu)\n print('free CPU: ', free_cpu)\n capacity = float(in_use_cpu) / float(total_cpu)\n print('capacity used: %.1f%%' % (capacity * 100,))\n capacity = float(free_cpu) / float(total_cpu)\n print('capacity remaining: %.1f%%' % (capacity * 100,))","def _calc_resource_stats(self, interval):\n result = {}\n\n if 'mem' in self.metrics:\n result['mem'] = self._get_mem_info()\n\n if 'disk-space' in self.metrics:\n result['disk-space'] = self.__get_disk_usage(self.engine.artifacts_dir).percent\n\n if 'engine-loop' in self.metrics:\n result['engine-loop'] = self.engine.engine_loop_utilization\n\n if 'conn-all' in self.metrics:\n try:\n # take all connections without address resolution\n output = subprocess.check_output(['netstat', '-an'])\n output_lines = stream_decode(output).split('\\n') # in py3 stream has 'bytes' type\n est_lines = [line for line in output_lines if line.find('EST') != -1]\n result['conn-all'] = len(est_lines)\n except BaseException as exc:\n self.log.debug(\"Failed to get connections info: %s\", exc)\n result['conn-all'] = 0\n\n if 'cpu' in self.metrics:\n result['cpu'] = self._get_cpu_percent()\n\n if 'bytes-recv' in self.metrics or 'bytes-sent' in self.metrics:\n net = self.__get_net_counters()\n if net is not None:\n tx_bytes = int((net.bytes_sent - self._net_counters.bytes_sent) / float(interval))\n rx_bytes = int((net.bytes_recv - self._net_counters.bytes_recv) / float(interval))\n self._net_counters = net\n else:\n rx_bytes = 0.0\n tx_bytes = 0.0\n\n if 'bytes-recv' in self.metrics:\n result['bytes-recv'] = rx_bytes\n if 'bytes-sent' in self.metrics:\n result['bytes-sent'] = tx_bytes\n\n if 'disk-read' in self.metrics or 'disk-write' in self.metrics:\n disk = self.__get_disk_counters()\n if disk is not None:\n dru = int((disk.read_bytes - self._disk_counters.read_bytes) / float(interval))\n dwu = int((disk.write_bytes - self._disk_counters.write_bytes) / float(interval))\n self._disk_counters = disk\n else:\n dru = 0.0\n dwu = 0.0\n\n if 'disk-read' in self.metrics:\n result['disk-read'] = dru\n if 'disk-write' in self.metrics:\n result['disk-write'] = dwu\n\n return result","def su_cost(node_type, node_count, cpu, gpu, ram, duration):\r\n if node_type == 'std':\r\n node_factor = 1\r\n tot_cpu = 28\r\n tot_ram = 128\r\n if node_type == 'gpu':\r\n node_factor = 2\r\n tot_cpu = 28\r\n tot_ram = 256\r\n if node_type == 'fat':\r\n node_factor = 6\r\n tot_cpu = 56\r\n tot_ram = 1024\r\n # job_setup = \"current setup = {} node type + {} number of nodes + {} number of cpu # + {} number of ram + {} hrs duration of job + {} total cpu + {} total ram\".format(node_type, node_count, cpu, ram, duration, tot_cpu, tot_ram)\r\n # calculate service units\r\n max_resource = top_resource(\r\n alloc_CPU = cpu, cpu_denominator = tot_cpu,\r\n alloc_GPU = gpu, gpu_denominator = 4,\r\n alloc_RAM = ram, ram_denominator = tot_ram)\r\n su = ( (node_count * (max_resource * node_factor)) * 28 * duration )\r\n return(su)","def collect_metrics():\n p = os.path.join(os.sep, \"mnt\", \"glusterfs\")\n mount_stats = os.statvfs(p)\n # block size * total blocks\n total_space = mount_stats.f_blocks * mount_stats.f_bsize\n free_space = mount_stats.f_bfree * mount_stats.f_bsize\n # capsize only operates on i64 values\n used_space = total_space - free_space\n gb_used = used_space / 1024 / 1024 / 1024\n\n # log!(format!(\"Collecting metric gb-used {}\", gb_used), Info)\n add_metric(\"gb-used\", \"{}\".format(gb_used))","def get_capacity():\n\n try:\n raw_capacity = PLIST[\"TotalSize\"]\n raw_capacity = str(raw_capacity)\n\n except KeyError:\n return \"Unknown\", \"Unknown\"\n\n #Round the sizes to make them human-readable.\n unit_list = [None, \"B\", \"KB\", \"MB\", \"GB\", \"TB\", \"PB\", \"EB\"]\n unit = \"B\"\n human_readable_size = int(raw_capacity)\n\n try:\n while len(str(human_readable_size)) > 3:\n #Shift up one unit.\n unit = unit_list[unit_list.index(unit)+1]\n human_readable_size = human_readable_size//1000\n\n except IndexError:\n return \"Unknown\", \"Unknown\"\n\n #Include the unit in the result for both exact and human-readable sizes.\n return raw_capacity, str(human_readable_size)+\" \"+unit","def _create_capacity(self, m, comp, prod_name):\n name = comp.name\n cap_res = comp.get_capacity_var() # name of resource that defines capacity\n r = m.resource_index_map[comp][cap_res] # production index of the governing resource\n # production is always lower than capacity\n ## NOTE get_capacity returns (data, meta) and data is dict\n ## TODO does this work with, e.g., ARMA-based capacities?\n ### -> \"time\" is stored on \"m\" and could be used to correctly evaluate the capacity\n cap = comp.get_capacity(None, None, None, None)[0][cap_res] # value of capacity limit (units of governing resource)\n rule = partial(self._capacity_rule, prod_name, r, cap)\n constr = pyo.Constraint(m.T, rule=rule)\n setattr(m, '{c}_{r}_capacity_constr'.format(c=name, r=cap_res), constr)\n # minimum production\n print('DEBUGG dispatchable?', comp.name, comp.is_dispatchable())\n if comp.is_dispatchable() == 'fixed':\n minimum = cap\n var = getattr(m, prod_name)\n values = var.get_values()\n for k in values:\n values[k] = cap\n var.set_values(values)\n else:\n minimum = 0 # -> for now just use 0, but fix this! XXX\n print('DEBUGG ... min:', minimum)\n rule = partial(self._min_prod_rule, prod_name, r, cap, minimum)\n constr = pyo.Constraint(m.T, rule=rule)\n setattr(m, '{c}_{r}_minprod_constr'.format(c=name, r=cap_res), constr)","def calc_resources(self):\n self.popula = self.energy = self.popula_used = self.energy_used = 0\n self.cnt_public = self.cnt_shop = self.cnt_1 = self.cnt_2 = self.cnt_3 = self.cnt_4 = self.cnt_5 = self.cnt_office = 0\n self.popula += self.extra_pop\n for i in range(20):\n b = self.b[i]\n if b == 'T':\n self.popula += self.f[i] * 2\n self.energy_used += 1\n elif b == 'O':\n self.popula_used += 1\n self.energy_used += 1\n self.cnt_office += self.f[i]\n elif b == 'U':\n self.popula_used += 1\n self.cnt_public += 1\n elif b == 'S':\n self.energy_used += 1\n self.cnt_shop += 1\n elif b == '1':\n self.popula += 1\n self.energy += 1\n self.popula_used += 1\n self.cnt_1 += 1\n elif b == '2':\n self.popula_used += 1\n self.cnt_2 += 1\n elif b == '3':\n self.popula_used += 1\n self.cnt_3 += 1\n elif b == '4':\n self.popula += 2\n self.popula_used += 1\n self.cnt_4 += 1\n elif b == '5':\n self.energy += 2\n self.popula_used += 1\n self.cnt_5 += 1\n elif b == 'A':\n self.energy += 2\n self.popula_used += 1\n elif b == 'F':\n self.energy += 3\n self.popula_used += 1\n elif b == 'G':\n self.popula += 1\n if 'tvst' in args.exp:\n self.popula += self.cnt_shop\n if 'ward' in args.exp:\n self.popula += 3\n if 'elec' in args.exp:\n self.energy += 3\n if 'capi' in args.exp:\n self.popula_used += 2\n if 'fire' in args.exp:\n self.popula_used += 1\n if 'park' in args.exp:\n self.popula_used += 1","def capacity(self):\n return sum(f.capacity for f in self.filters)","def get_used_capacity(self,tot=\"50\"):\n data=self.at_cmd(\"CPMS?\")\n index=data[1].find(tot)-1\n if data[1][index-1]==',':\n return data[index]\n else:\n return data[1][index-1:index]","def cost(self):\n cost = {}\n if len(self.nodes) == 0:\n return cost\n resources = self.nodes[0].capacity.keys()\n for r in resources:\n values = [n.cost[r] for n in self.nodes]\n estimator = AvgAggregatorEstimator(values)\n cost[r] = estimator\n return cost","def _tattle_quantity(resource, quantity, resourcesalloweddict, resourcesuseddict):\n\n\n # I assume that the quantity will never be negative\n if quantity < 0:\n # This will cause the program to exit and log things if logging is\n # enabled. -Brent\n tracebackrepy.handle_internalerror(\"Resource '\" + resource + \n \"' has a negative quantity \" + str(quantity) + \"!\", 132)\n \n # get the lock for this resource\n resourcesuseddict['renewable_locks'][resource].acquire()\n \n # release the lock afterwards no matter what\n try: \n # update the resource counters based upon the current time.\n _update_resource_consumption_table(resource, resourcesalloweddict, resourcesuseddict)\n\n # It's renewable, so I can wait for it to clear\n if resource not in resource_constants.renewable_resources:\n # Should never have a quantity tattle for a non-renewable resource\n # This will cause the program to exit and log things if logging is\n # enabled. -Brent\n tracebackrepy.handle_internalerror(\"Resource '\" + resource + \n \"' is not renewable!\", 133)\n \n\n resourcesuseddict[resource] = resourcesuseddict[resource] + quantity\n # I'll block if I'm over...\n _sleep_until_resource_drains(resource, resourcesalloweddict, resourcesuseddict)\n \n finally:\n # release the lock for this resource\n resourcesuseddict['renewable_locks'][resource].release()","def test_capacity(self, space_each_type):\n tspace = build_required_space(space_each_type, type_requirement=\"real\")\n assert tspace.cardinality == numpy.inf\n\n space = Space()\n probs = (0.1, 0.2, 0.3, 0.4)\n categories = (\"asdfa\", 2, 3, 4)\n dim = Categorical(\"yolo\", OrderedDict(zip(categories, probs)), shape=2)\n space.register(dim)\n dim = Integer(\"yolo2\", \"uniform\", -3, 6)\n space.register(dim)\n tspace = build_required_space(space, type_requirement=\"integer\")\n assert tspace.cardinality == (4**2) * (6 + 1)\n\n dim = Integer(\"yolo3\", \"uniform\", -3, 6, shape=(2, 1))\n space.register(dim)\n tspace = build_required_space(space, type_requirement=\"integer\")\n assert tspace.cardinality == (4**2) * (6 + 1) * ((6 + 1) ** (2 * 1))\n\n tspace = build_required_space(\n space, type_requirement=\"integer\", shape_requirement=\"flattened\"\n )\n assert tspace.cardinality == (4**2) * (6 + 1) * ((6 + 1) ** (2 * 1))\n\n tspace = build_required_space(\n space, type_requirement=\"integer\", dist_requirement=\"linear\"\n )\n assert tspace.cardinality == (4**2) * (6 + 1) * ((6 + 1) ** (2 * 1))","def usedspace(self):\n self.log.info(\"freespace\")\n nbytes = 0\n keys = list(self.downloads.keys())\n keys.sort()\n for key in keys:\n download = self.downloads[key]\n nbytes += download['size']\n self.log.info(\"returning:\" + str(nbytes))\n return nbytes","def system_capacity_ac(self):\n return (self.sam_sys_inputs['system_capacity']\n / self.sam_sys_inputs['dc_ac_ratio'])","def _estimate_free(self):\n # Query the information we need for this task's channel and package.\n capacity_deferred = self.channel.total_capacity()\n open_tasks_deferred = self.channel.tasks(state=[task_states.OPEN])\n avg_delta_deferred = self.estimate_duration()\n deferreds = [capacity_deferred,\n open_tasks_deferred,\n avg_delta_deferred]\n results = yield defer.gatherResults(deferreds, consumeErrors=True)\n capacity, open_tasks, avg_delta = results\n # Ensure this task's channel has spare capacity for this task.\n open_weight = sum([task.weight for task in open_tasks])\n if open_weight >= capacity:\n # TODO: Evaluate all tasks in the channel and\n # determine when enough OPEN tasks will complete so that we can\n # get to OPEN.\n raise NotImplementedError('channel %d is at capacity' %\n self.channel_id)\n # A builder will pick up this task and start it within SLEEPTIME.\n # start_time is the maximum amount of time we expect to wait here.\n start_time = self.created + SLEEPTIME\n if avg_delta is None:\n defer.returnValue(None)\n est_completion = start_time + avg_delta\n defer.returnValue(est_completion)","def ok(self, results):\n return \"{:5.2f}% capacity used\".format(\n results[\"usage\"].resource.usage_ratio * 100.0\n )","def _get_host_utilization(context, host, ram_mb, disk_gb):\n instances = instance_get_all_by_host(context, host)\n vms = len(instances)\n free_ram_mb = ram_mb - FLAGS.reserved_host_memory_mb\n free_disk_gb = disk_gb - (FLAGS.reserved_host_disk_mb * 1024)\n\n work = 0\n for instance in instances:\n free_ram_mb -= instance.memory_mb\n free_disk_gb -= instance.root_gb\n free_disk_gb -= instance.ephemeral_gb\n if instance.vm_state in [vm_states.BUILDING, vm_states.REBUILDING,\n vm_states.MIGRATING, vm_states.RESIZING]:\n work += 1\n return dict(free_ram_mb=free_ram_mb,\n free_disk_gb=free_disk_gb,\n current_workload=work,\n running_vms=vms)","def get_memory_info(ssh):\r\n cmd04='wmic memorychip get capacity'\r\n retry_number1=3\r\n try:\r\n while True:\r\n if retry_number1 == 0:\r\n logger.writeLog(\"get memory sum size fail\",level='error')\r\n break\r\n stdin,stdout,stderr=ssh.exec_command(cmd04)\r\n data04=stdout.read().decode().strip('Capacity')\r\n print(data04)\r\n if data04 == \"\":\r\n retry_number1 -= 1\r\n logger.writeLog(\"get memory sum size data null\",level='error')\r\n continue\r\n else:\r\n result_list=data04.split()\r\n print(result_list)\r\n memory_size=float(int(result_list[0])+int(result_list[1]))/1024/1024/1024\r\n print(\"mem total Gb: \",memory_size)\r\n logger.writeLog(\"get memory sum size success\",level='info')\r\n # return memory_size\r\n break\r\n except:\r\n logger.writeLog(\"get memory size error\",level='error')\r\n return None\r\n\r\n#6.内存剩余量/Gb\r\n# def get_memory_surplus(ssh):\r\n \"\"\"get memory surplus\"\"\"\r\n cmd05='wmic OS get FreePhysicalMemory'\r\n retry_number2=3\r\n try:\r\n while True:\r\n if retry_number2 == 0:\r\n logger.writeLog(\"get memory surplus fail\",level='error')\r\n break\r\n stdin,stdout,stderr=ssh.exec_command(cmd05)\r\n data05=int(stdout.read().decode().split()[1])\r\n print(data05)\r\n if data05 == \"\":\r\n logger.writeLog(\"get memory surplus data null\",level='error')\r\n retry_number2 -= 1\r\n continue\r\n else:\r\n memory_surplus=round(float(data05)/1024/1024,4)\r\n print(\"mem free Gb: \",memory_surplus)\r\n logger.writeLog(\"get memory surplus data success\",level='info')\r\n # return memory_surplus\r\n break\r\n except:\r\n logger.writeLog(\"get memory surplus error\",level='error')\r\n return None\r\n\r\n#7.内存使用率\r\n# def get_memory_ratio(ssh):\r\n \"\"\"get memory ratio\"\"\"\r\n # memory_size=get_memory_size(ssh)\r\n # memory_surplus=get_memory_surplus(ssh)\r\n if memory_size == \"\" or memory_surplus == \"\":\r\n logger.writeLog(\"memory_szie is null or memory_surplus is null\",level='error')\r\n return None\r\n else:\r\n try:\r\n data06=round(float((memory_size-memory_surplus))/memory_size,4)\r\n print(\"mem use ratio: \",data06)\r\n logger.writeLog(\"get memory ratio success\",level='info')\r\n return (memory_size,memory_surplus,data06)\r\n except:\r\n logger.writeLog(\"get memory ratio error\",level='error')\r\n return None","def calculate_available_link_res (self, sg_hops_to_be_ignored, mode=MODE_ADD):\n # set availbandwidth to the maximal value\n for i, j, k, d in self.network.edges_iter(data=True, keys=True):\n if d.type == 'STATIC':\n setattr(self.network[i][j][k], 'availbandwidth', d.bandwidth)\n # subtract the reserved link and internal (inside Infras) bandwidth\n if mode == self.MODE_ADD:\n for d in self.infras:\n for p in d.ports:\n for fr in p.flowrules:\n if fr.id not in sg_hops_to_be_ignored and fr.bandwidth is not None:\n # Flowrules are cummulatively subtracted from the switching \n # capacity of the node.\n d.availres['bandwidth'] -= fr.bandwidth\n if d.availres['bandwidth'] < 0:\n raise RuntimeError(\"The node bandwidth of %s got below zero \"\n \"during available resource calculation!\" %\n d.id)\n # Get all the mapped paths of all SGHops from the NFFG\n sg_map = NFFGToolBox.get_all_sghop_info(self, return_paths=True)\n for sg_hop_id, data in sg_map.iteritems():\n src, dst, flowclass, bandwidth, delay, path = data\n if bandwidth is not None:\n for link in path:\n link.availbandwidth -= bandwidth\n if link.availbandwidth < 0:\n raise RuntimeError(\n \"The link bandwidth of %s got below zero during\"\n \"available resource calculation!\" % link.id)","def __len__(self):\n return self.capacity","def test_read_cluster_resource_quota(self):\n pass","def memory_get_usage():\n raise NotImplementedError()","def get_capacity_var(self):\n return self.get_interaction().get_capacity_var()","def _check_capacity_limit(self, res, amt, balance, meta, raven_vars, dispatch, t):\n cap = self.get_capacity(meta, raven_vars, dispatch, t)[0][self._capacity_var]\n try:\n if abs(balance[self._capacity_var]) > abs(cap):\n #ttttt\n # do the inverse problem: how much can we make?\n balance, meta = self.produce_max(meta, raven_vars, dispatch, t)\n print('The full requested amount ({res}: {req}) was not possible, so accessing maximum available instead ({res}: {blc}).'.format(res=res, req=amt, blc=balance[res]))\n except KeyError:\n raise SyntaxError('Resource \"{}\" is listed as capacity limiter, but not an output of the component! Got: {}'.format(self._capacity_var, balance))\n return balance, meta","def collect():\n\n command = \"cat /proc/meminfo |grep MemTotal|awk -F' ' '{print $2}'\"\n memTotal_f = round(float(os.popen(command).read())/1024/1000,0)\n memTotal = int(memTotal_f)\n cmd = 'df -h |grep \"/dev/s\"'\n metric_disk = os.popen(cmd).readlines()\n hardNum=[]\n for i in metric_disk:\n hard_space = float((i.strip().split()[1])[:-1])\n hardNum.append(hard_space)\n\n disk_info = sum(hardNum)\n disk_use = {}\n metric_disks=os.popen('df -x tmpfs -x devtmpfs | grep -Eo \" /\\S*$\" ').readlines()\n for disk in metric_disks:\n cmd = 'df|grep -E \"%s$\"' % disk.strip()\n disks = os.popen(cmd).readlines()[0]\n disk_list = disks.split()\n disk_use[disk_list[5]]=disk_list[4]\n hard = {\n \"disk_used\" : disk_use,\n \"disk_total\":disk_info,\n \"mem_total\":memTotal\n }\n\n return hard","def get_resource_usages(self, project_id):\n try:\n # The API call does not give usage for keypair, fixed ips &\n # metadata items. Have raised a bug for that.\n limits = self.nova_client.limits.get(\n tenant_id=project_id).to_dict()\n resource_usage = collections.defaultdict(dict)\n resource_usage['ram'] = limits['absolute']['totalRAMUsed']\n resource_usage['cores'] = limits['absolute']['totalCoresUsed']\n resource_usage['instances'] = \\\n limits['absolute']['totalInstancesUsed']\n # If neutron is not enabled, calculate below resources from nova\n if self.no_neutron:\n resource_usage['security_groups'] = \\\n limits['absolute']['totalSecurityGroupsUsed']\n resource_usage['floating_ips'] = \\\n limits['absolute']['totalFloatingIpsUsed']\n # For time being, keypair is calculated in below manner.\n resource_usage['key_pairs'] = \\\n len(self.nova_client.keypairs.list())\n return resource_usage\n except exceptions.InternalError:\n raise","def CalculateProcessingCapacity(self, problemManager, mineDataManager):\n \n self.oreProcessed = np.zeros(len(mineDataManager.theMiningSystem.oreMined)) \n self.processingPower = np.zeros(len(mineDataManager.theMiningSystem.oreMined)) \n self.processingCapacity = mineDataManager.theMiningSystem.mineOreProductionCapacity # ore is processed at a constant rate\n carryOver = 0.0\n for year in range( len(mineDataManager.theMiningSystem.oreMined )-1 ):\n processedOre = carryOver + mineDataManager.theMiningSystem.oreMined[year]\n \n if(processedOre > self.processingCapacity):\n carryOver = processedOre - self.processingCapacity\n processedOre = self.processingCapacity\n else:\n carryOver = 0.0\n self.oreProcessed[year] = processedOre\n \n self.oreProcessed[-1] = carryOver + mineDataManager.theMiningSystem.oreMined[-1] # final year\n \n \n # convert tonnes processed each year to the number of Mwh based on powerlaw fit\n self.processingPower = 3.96*(self.oreProcessed )**0.703 # in Mwh\n \n referenceMetalStr = mineDataManager.theOreBody.type[:2] \n # first two letters of orebody type is assumed to be reference metal for determining processing grade\n # eg AuCu -> gold is reference metal - note that user must select correct method\n \n \n referenceMetalOreConcentration = mineDataManager.theOreBody.metalGrades[referenceMetalStr]\n\n self.concentrateMetalConcentration = 1.0\n \n # lookup concentrateMetalConcentrations based on reference metal type\n \n concentrateConcentrations = {\"Au\":0.75,\"Ag\":0.85,\"Ni\":0.1,\"Cu\":0.25,\"Pb\":0.5}\n \n # find the minimum amount of concentration needed to bring concentrate to market\n minConcentrationFactor = 1e64\n \n for metal,metalOreGrade in mineDataManager.theOreBody.metalGrades.iteritems():\n if metal in concentrateConcentrations:\n concentrateGrade = concentrateConcentrations[metal]\n concFactor = concentrateGrade/(metalOreGrade/(1.0+ mineDataManager.theMiningSystem.dilution) +1e-64)\n if concFactor < 1.0:\n concFactor = 1.0\n #print \"concFactor\", metal, concFactor, metalOreGrade, concentrateGrade\n if(concFactor < minConcentrationFactor ):\n minConcentrationFactor = concFactor\n self.concentrateMetalConcentration = concentrateGrade\n \n # concentrate is calculated based on estimate of mineral content\n self.concentrateProduced = (1.0 - self.processingLoss) \\\n *np.array(mineDataManager.theMiningSystem.oreMined)/minConcentrationFactor \n \n \n return self.processingCapacity","def _calc_worker_assign_limits(self, initial_count, occupied=None):\n occupied = occupied or dict()\n actual_count = initial_count - sum(occupied.values())\n\n endpoint_res = sorted(self._worker_slots.items(), key=operator.itemgetter(1),\n reverse=True)\n\n endpoints = [t[0] for t in endpoint_res]\n endpoint_cores = np.array([t[1] for t in endpoint_res]).astype(np.float32)\n\n # remove assigned nodes from limitations\n counts = initial_count * endpoint_cores / endpoint_cores.sum()\n for idx, ep in enumerate(endpoints):\n counts[idx] = max(0, counts[idx] - occupied.get(ep, 0))\n\n # all assigned, nothing to do\n if counts.sum() == 0:\n return dict((ep, 0) for ep in endpoints)\n\n counts = (actual_count * counts / counts.sum()).astype(np.int32)\n\n # assign remaining nodes\n pos = 0\n rest = actual_count - counts.sum()\n while rest > 0:\n counts[pos] += 1\n rest -= 1\n pos = (pos + 1) % len(counts)\n return dict(zip(endpoints, counts))","def capacity(self) -> Optional[int]:\n return pulumi.get(self, \"capacity\")","def capacity(self) -> Optional[int]:\n return pulumi.get(self, \"capacity\")","def capacity(self) -> Optional[int]:\n return pulumi.get(self, \"capacity\")","def capacity(self) -> Optional[int]:\n return pulumi.get(self, \"capacity\")","def _calc_desc_and_queues(\n total_numa_nodes, total_ports_per_numa, total_rx_queues, ports_per_numa_value\n ):\n\n # Get the number of rx queues\n rx_queues = max(1, total_rx_queues)\n tx_queues = rx_queues * total_numa_nodes + 1\n\n # Get the descriptor entries\n desc_entries = 1024\n ports_per_numa_value[\"rx_queues\"] = rx_queues\n total_mbufs = (\n (rx_queues * desc_entries) + (tx_queues * desc_entries)\n ) * total_ports_per_numa\n\n return total_mbufs","def free_resources(self, context):\n self.update_available_resource(context.elevated())","def desired_capacity(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"desired_capacity\")","def fs_percent_used_capacity(self):\n return self._fs_percent_used_capacity","def resource_costs(self, fleet):\n base = (10, 15, 0, 0)\n ins = fleet.get_ship_instances()\n mult = sum([ship_stats.get_ship_type(x.base().stype)\n .resource_mult for x in ins])\n return tuple(map(lambda x: x * mult, base))","def _create_resource_consumption_dict():\n\n returned_resource_dict = {}\n\n # things that are quantities should start at 0.0\n for resource in resource_constants.quantity_resources:\n returned_resource_dict[resource] = 0.0\n\n for resource in resource_constants.item_resources:\n # double check there is no overlap...\n if resource in resource_constants.quantity_resources:\n raise InternalRepyError(\"Resource '\"+resource+\"' cannot be both quantity and item based!\")\n\n returned_resource_dict[resource] = set()\n\n # I need locks to protect races in accesses to some items...\n returned_resource_dict['fungible_locks'] = {}\n for init_resource in resource_constants.fungible_item_resources:\n returned_resource_dict['fungible_locks'][init_resource] = threading.Lock()\n\n returned_resource_dict['renewable_locks'] = {}\n for init_resource in resource_constants.renewable_resources:\n returned_resource_dict['renewable_locks'][init_resource] = threading.Lock()\n\n\n # I also need to track when the last update of a renewable resource occurred\n returned_resource_dict['renewable_update_time'] = {}\n\n # (Aside) JAC: I've thought about this and looked through the commit history.\n # I don't see any reason to initialize the renewable resources with the\n # current time (as was done before).\n for init_resource in resource_constants.renewable_resources:\n returned_resource_dict['renewable_update_time'][init_resource] = 0.0\n\n\n return returned_resource_dict","def capacity_gb(self) -> str:\n return pulumi.get(self, \"capacity_gb\")","def check_resources(reqs):\n logger.info(\"REQUIREMENTS: \" + str(reqs))\n free_cpu, free_mem = get_resources()\n return check_if_free_resources(free_mem, free_cpu, reqs)","def test_list_cluster_resource_quota(self):\n pass","def capacity(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"capacity\")","def capacity(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"capacity\")","def capacity(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"capacity\")","def capacity(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"capacity\")","def capacity(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"capacity\")","def capacity(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"capacity\")","def capacity(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"capacity\")","def compute_resources(instance: dict):\r\n\r\n # Retrieve usefull infos\r\n Interventions = instance[INTERVENTIONS_STR]\r\n T_max = instance[T_STR]\r\n Resources = instance[RESOURCES_STR]\r\n # Init resource usage dictionnary for each resource and time\r\n resources_usage = {}\r\n for resource_name in Resources.keys():\r\n resources_usage[resource_name] = np.zeros(T_max)\r\n # Compute value for each resource and time step\r\n for intervention_name, intervention in Interventions.items():\r\n # start time should be defined (already checked in scheduled constraint checker)\r\n if not START_STR in intervention:\r\n continue\r\n start_time = intervention[START_STR]\r\n start_time_idx = start_time - 1\r\n # index of list starts at 0\r\n intervention_worload = intervention[RESOURCE_CHARGE_STR]\r\n intervention_delta = int(intervention[DELTA_STR][start_time_idx])\r\n # compute effective worload\r\n for (\r\n resource_name,\r\n intervention_resource_worload,\r\n ) in intervention_worload.items():\r\n for time in range(start_time_idx, start_time_idx + intervention_delta):\r\n # null values are not available\r\n if (\r\n str(time + 1) in intervention_resource_worload\r\n and str(start_time) in intervention_resource_worload[str(time + 1)]\r\n ):\r\n resources_usage[resource_name][\r\n time\r\n ] += intervention_resource_worload[str(time + 1)][str(start_time)]\r\n\r\n return resources_usage","def _get_prod_bounds(self, comp):\n cap_res = comp.get_capacity_var() # name of resource that defines capacity\n maximum = comp.get_capacity(None, None, None, None)[0][cap_res]\n # TODO minimum!\n # producing or consuming the defining resource?\n if maximum > 0:\n return 0, maximum, pyo.NonNegativeReals\n else:\n return maximum, 0, pyo.NonPositiveReals","def get_cluster_capacity(self, query_data): # noqa: C901\n annotations = self._mapper.report_type_map.get(\"capacity_aggregate\")\n if not annotations:\n return query_data, {}\n\n cap_key = list(annotations.keys())[0]\n total_capacity = Decimal(0)\n daily_total_capacity = defaultdict(Decimal)\n capacity_by_cluster = defaultdict(Decimal)\n daily_capacity_by_cluster = defaultdict(lambda: defaultdict(Decimal))\n\n q_table = self._mapper.query_table\n query = q_table.objects.filter(self.query_filter)\n query_group_by = [\"usage_start\", \"cluster_id\"]\n\n with tenant_context(self.tenant):\n cap_data = query.values(*query_group_by).annotate(**annotations)\n for entry in cap_data:\n cluster_id = entry.get(\"cluster_id\", \"\")\n usage_start = entry.get(\"usage_start\", \"\")\n if isinstance(usage_start, datetime.date):\n usage_start = usage_start.isoformat()\n cap_value = entry.get(cap_key, 0)\n if cap_value is None:\n cap_value = 0\n capacity_by_cluster[cluster_id] += cap_value\n daily_capacity_by_cluster[usage_start][cluster_id] = cap_value\n daily_total_capacity[usage_start] += cap_value\n total_capacity += cap_value\n\n if self.resolution == \"daily\":\n for row in query_data:\n cluster_id = row.get(\"cluster\")\n date = row.get(\"date\")\n if cluster_id:\n row[cap_key] = daily_capacity_by_cluster.get(date, {}).get(cluster_id, Decimal(0))\n else:\n row[cap_key] = daily_total_capacity.get(date, Decimal(0))\n elif self.resolution == \"monthly\":\n for row in query_data:\n cluster_id = row.get(\"cluster\")\n if cluster_id:\n row[cap_key] = capacity_by_cluster.get(cluster_id, Decimal(0))\n else:\n row[cap_key] = total_capacity\n\n return query_data, {cap_key: total_capacity}","def calculate_task_volatile_size(**kwargs):\n task = kwargs.get(\"data\", {})\n memory_members = kwargs.get(\"resources\", {}).get(\"memory\", [])\n interleave_sets = task.get(\"Payload\").get(\"JsonBody\").get(\"InterleaveSets\", [])\n selected_members = []\n for interleave_set in interleave_sets:\n for pmem in memory_members:\n if PmemHelpers.compare_id(\n interleave_set.get(\"Memory\").get(\"@odata.id\"), pmem.get(\"@odata.id\")\n ):\n selected_members.append(pmem)\n # finding total capacity\n total_capacity = Mapper.get_single_attribute(\n selected_members,\n \"TotalCapacity\",\n MappingTable.summary.value,\n output_as_json=True,\n )\n total_capacity = total_capacity.get(\"TotalCapacity\", {}).get(\"Value\", 0)\n volatile_size = total_capacity\n # finding memory chunk size\n memory_chunk_size = Mapper.get_single_attribute(\n task, \"MemoryChunkSize\", MappingTable.tasks.value, output_as_json=True\n )\n memory_chunk_size = memory_chunk_size.get(\"MemoryChunkSize\", {}).get(\n \"Value\", None\n )\n if memory_chunk_size is not None:\n size = memory_chunk_size\n volatile_size = total_capacity - size\n else:\n # finding memory chunk size percentage\n memory_chunk_size_percentage = Mapper.get_single_attribute(\n task,\n \"MemoryChunkSizePercentage\",\n MappingTable.tasks.value,\n output_as_json=True,\n )\n memory_chunk_size_percentage = memory_chunk_size_percentage.get(\n \"MemoryChunkSizePercentage\", {}\n ).get(\"Value\", None)\n if memory_chunk_size_percentage is not None:\n size = total_capacity * memory_chunk_size_percentage / 100\n volatile_size = total_capacity - size\n # returning value in MiB\n return volatile_size * 1024","def _free_space() -> int:\n return disk_usage(realpath('/')).free"],"string":"[\n \"def get_capacity():\\n fs.get_capacity()\",\n \"def capacity_used(self):\\n raise NotImplementedError()\",\n \"def capacity(self):\\n capacity = {}\\n resources = self.nodes[0].capacity.keys()\\n for r in resources:\\n values = [n.capacity[r] for n in self.nodes]\\n capacity[r] = mean(values) if len(values) > 0 else 0.0\\n return capacity\",\n \"def get_num_slots(self):\\n # Your code here\\n return self.capacity\",\n \"def _get_capacity_info(self, nfs_share):\\n nms = self.share2nms[nfs_share]\\n ns_volume, ns_folder = self._get_share_datasets(nfs_share)\\n folder_props = nms.folder.get_child_props('%s/%s' % (ns_volume,\\n ns_folder),\\n 'used|available')\\n free = utils.str2size(folder_props['available'])\\n allocated = utils.str2size(folder_props['used'])\\n self.shares_with_capacities[nfs_share] = {\\n 'free': utils.str2gib_size(free),\\n 'total': utils.str2gib_size(free + allocated)}\\n return free + allocated, free, allocated\",\n \"def Capacity(self) -> int:\",\n \"def test_rest_v20_dd_systems_systemid_stats_capacity_get(self):\\n pass\",\n \"def capacity(self):\\n raise NotImplementedError()\",\n \"def total_sdram_requirements(self):\",\n \"def get_available_resources(threshold, usage, total):\\n return dict((host, int(threshold * total[host] - resource))\\n for host, resource in usage.items())\",\n \"def _check_capacity_limit(self, res, amt, balance, meta, raven_vars, dispatch, t, level):\\n # note \\\"amt\\\" has units of AMOUNT not RATE (resource, not resource per second)\\n sign = np.sign(amt)\\n # are we storing or providing?\\n #print('DEBUGG supposed current level:', level)\\n if sign < 0:\\n # we are being asked to consume some\\n cap, meta = self.get_capacity(meta, raven_vars, dispatch, t)\\n available_amount = cap[res] - level\\n #print('Supposed Capacity, Only calculated ins sign<0 (being asked to consumer)',cap)\\n else:\\n # we are being asked to produce some\\n available_amount = level\\n # the amount we can consume is the minimum of the requested or what's available\\n delta = sign * min(available_amount, abs(amt))\\n return {res: delta}, meta\",\n \"def calculate_demand(flow, requested_sf, available_sf, service_functions):\\n\\n if requested_sf in available_sf:\\n vnf_need_placement = False\\n demanded_total_capacity = 0.0\\n for sf_i, sf_data in available_sf.items():\\n if requested_sf == sf_i:\\n # Include flows data rate in requested sf capacity calculation\\n demanded_total_capacity += service_functions[sf_i]['resource_function'](\\n sf_data['load'] + flow.dr)\\n else:\\n demanded_total_capacity += service_functions[sf_i]['resource_function'](sf_data['load'])\\n return demanded_total_capacity, vnf_need_placement\\n else:\\n vnf_need_placement = True\\n available_sf[requested_sf] = {'load': 0.0}\\n demanded_total_capacity = 0.0\\n for sf_i, sf_data in available_sf.items():\\n if requested_sf == sf_i:\\n # Include flows data rate in requested sf capacity calculation\\n demanded_total_capacity += service_functions[sf_i]['resource_function'](\\n sf_data['load'] + flow.dr)\\n else:\\n demanded_total_capacity += service_functions[sf_i]['resource_function'](sf_data['load'])\\n del available_sf[requested_sf]\\n return demanded_total_capacity, vnf_need_placement\",\n \"def available_capacity(self):\\r\\n return self.capacity - len(self.passengers)\",\n \"def initialize_supply(self):\\n unit_count = 0\\n for i in range(self.start_allocation[0 ] -1, self.start_allocation[1]):\\n for j in range(len(self.capacity_list[i][1])):\\n self.capacity_list[i][1][j] = 1\\n unit_count += 1\\n self.total_supply -= unit_count\",\n \"def _estimate_elasticsearch_requirement(\\n instance: Instance,\\n desires: CapacityDesires,\\n working_set: float,\\n reads_per_second: float,\\n max_rps_to_disk: int,\\n zones_per_region: int = 3,\\n copies_per_region: int = 3,\\n) -> CapacityRequirement:\\n # Keep half of the cores free for background work (merging mostly)\\n needed_cores = math.ceil(sqrt_staffed_cores(desires) * 1.5)\\n # Keep half of the bandwidth available for backup\\n needed_network_mbps = simple_network_mbps(desires) * 2\\n\\n needed_disk = math.ceil(\\n (1.0 / desires.data_shape.estimated_compression_ratio.mid)\\n * desires.data_shape.estimated_state_size_gib.mid\\n * copies_per_region,\\n )\\n\\n # Rough estimate of how many instances we would need just for the the CPU\\n # Note that this is a lower bound, we might end up with more.\\n needed_cores = math.ceil(\\n max(1, needed_cores // (instance.cpu_ghz / desires.core_reference_ghz))\\n )\\n rough_count = math.ceil(needed_cores / instance.cpu)\\n\\n # Generally speaking we want fewer than some number of reads per second\\n # hitting disk per instance. If we don't have many reads we don't need to\\n # hold much data in memory.\\n instance_rps = max(1, reads_per_second // rough_count)\\n disk_rps = instance_rps * _es_io_per_read(max(1, needed_disk // rough_count))\\n rps_working_set = min(1.0, disk_rps / max_rps_to_disk)\\n\\n # If disk RPS will be smaller than our target because there are no\\n # reads, we don't need to hold as much data in memory\\n needed_memory = min(working_set, rps_working_set) * needed_disk\\n\\n # Now convert to per zone\\n needed_cores = needed_cores // zones_per_region\\n needed_disk = needed_disk // zones_per_region\\n needed_memory = int(needed_memory // zones_per_region)\\n logger.debug(\\n \\\"Need (cpu, mem, disk, working) = (%s, %s, %s, %f)\\\",\\n needed_cores,\\n needed_memory,\\n needed_disk,\\n working_set,\\n )\\n\\n return CapacityRequirement(\\n requirement_type=\\\"elasticsearch-data-zonal\\\",\\n core_reference_ghz=desires.core_reference_ghz,\\n cpu_cores=certain_int(needed_cores),\\n mem_gib=certain_float(needed_memory),\\n disk_gib=certain_float(needed_disk),\\n network_mbps=certain_float(needed_network_mbps),\\n context={\\n \\\"working_set\\\": min(working_set, rps_working_set),\\n \\\"rps_working_set\\\": rps_working_set,\\n \\\"disk_slo_working_set\\\": working_set,\\n \\\"replication_factor\\\": copies_per_region,\\n \\\"compression_ratio\\\": round(\\n 1.0 / desires.data_shape.estimated_compression_ratio.mid, 2\\n ),\\n \\\"read_per_second\\\": reads_per_second,\\n },\\n )\",\n \"def _consume_resources(self, job_limits: Dict[str, int]) -> None:\\n for limit_name, count in job_limits.items():\\n self.limits_used[limit_name] += count\",\n \"def get_capacity(self, meta, raven_vars, dispatch, t, raw=False):\\n return self.get_interaction().get_capacity(meta, raven_vars, dispatch, t, raw=raw)\",\n \"def compute_capacity(self, totalflow, i):\\r\\n self.capacity[i] = self.phi * self.flow[i] / (self.b[i] \\\\\\r\\n - self.beta*(self.flow[i]**self.theta) + np.log(1-totalflow) - np.log(self.flow[i]))\",\n \"def _get_info_about_available_resources(self, min_ram, min_hdd, min_vcpus):\\n vms_count = 0\\n for hypervisor in self.nova_cli.hypervisors.list():\\n if hypervisor.free_ram_mb >= min_ram:\\n if hypervisor.free_disk_gb >= min_hdd:\\n if hypervisor.vcpus - hypervisor.vcpus_used >= min_vcpus:\\n # We need to determine how many VMs we can run\\n # on this hypervisor\\n free_cpu = hypervisor.vcpus - hypervisor.vcpus_used\\n k1 = int(hypervisor.free_ram_mb / min_ram)\\n k2 = int(hypervisor.free_disk_gb / min_hdd)\\n k3 = int(free_cpu / min_vcpus)\\n vms_count += min(k1, k2, k3)\\n return vms_count\",\n \"def calculate_total_capacity(**kwargs):\\n members = kwargs.get(\\\"data\\\", [])\\n total = 0\\n if members:\\n for member in members:\\n capacity = Mapper.get_single_attribute(\\n member, \\\"Capacity\\\", MappingTable.device.value, output_as_json=True\\n )\\n total += capacity.get(\\\"Capacity\\\", {}).get(\\\"Value\\\", 0)\\n # returning value in MiB\\n return total * 1024\",\n \"def capacitygroup_group():\",\n \"def capacity(self):\\n return self._capacity\",\n \"def get_capacity_param(self):\\n intr = self.get_interaction()\\n return intr.get_capacity(None, None, None, None, raw=True)\",\n \"def _allocate(self, n_resource, beliefs):\\n raise NotImplementedError\",\n \"def get_resources():\\n # Acquire the lock...\\n get_resources_lock.acquire()\\n\\n # ...but always release it\\n try:\\n # Construct the dictionaries as copies from nanny\\n (limits,usage) = nanny.get_resource_information()\\n\\n\\n # Calculate all the usage's\\n pid = os.getpid()\\n\\n # Get CPU and memory, this is thread specific\\n if ostype in [\\\"Linux\\\", \\\"Darwin\\\"]:\\n \\n # Get CPU first, then memory\\n usage[\\\"cpu\\\"] = os_api.get_process_cpu_time(pid)\\n\\n # This uses the cached PID data from the CPU check\\n usage[\\\"memory\\\"] = os_api.get_process_rss()\\n\\n # Get the thread specific CPU usage\\n usage[\\\"threadcpu\\\"] = os_api.get_current_thread_cpu_time() \\n\\n\\n # Windows Specific versions\\n elif ostype in [\\\"Windows\\\"]:\\n \\n # Get the CPU time\\n usage[\\\"cpu\\\"] = windows_api.get_process_cpu_time(pid)\\n\\n # Get the memory, use the resident set size\\n usage[\\\"memory\\\"] = windows_api.process_memory_info(pid)['WorkingSetSize'] \\n\\n # Get thread-level CPU \\n usage[\\\"threadcpu\\\"] = windows_api.get_current_thread_cpu_time()\\n\\n # Unknown OS\\n else:\\n raise EnvironmentError(\\\"Unsupported Platform!\\\")\\n\\n # Use the cached disk used amount\\n usage[\\\"diskused\\\"] = cached_disk_used\\n\\n finally:\\n # Release the lock\\n get_resources_lock.release()\\n\\n # Copy the stop times\\n stoptimes = process_stopped_timeline[:]\\n\\n # Return the dictionaries and the stoptimes\\n return (limits,usage,stoptimes)\",\n \"def update_available_resource(self, context):\\n # ask hypervisor for its view of resource availability &\\n # usage:\\n resources = self.driver.get_available_resource()\\n if not resources:\\n # The virt driver does not support this function\\n LOG.warn(_(\\\"Virt driver does not support \\\"\\n \\\"'get_available_resource' Compute tracking is disabled.\\\"))\\n self.compute_node = None\\n self.claims = {}\\n return\\n\\n # Confirm resources dictionary contains expected keys:\\n self._verify_resources(resources)\\n\\n resources['free_ram_mb'] = resources['memory_mb'] - \\\\\\n resources['memory_mb_used']\\n resources['free_disk_gb'] = resources['local_gb'] - \\\\\\n resources['local_gb_used']\\n\\n LOG.audit(_(\\\"free_ram_mb: %s\\\") % resources['free_ram_mb'])\\n LOG.audit(_(\\\"free_disk_gb: %s\\\") % resources['free_disk_gb'])\\n # Apply resource claims representing in-progress operations to\\n # 'resources'. This may over-estimate the amount of resources in use,\\n # at least until the next time 'update_available_resource' runs.\\n self._apply_claims(resources)\\n\\n # also generate all load stats:\\n values = self._create_load_stats(context)\\n resources.update(values)\\n\\n if not self.compute_node:\\n # we need a copy of the ComputeNode record:\\n service = self._get_service(context)\\n if not service:\\n # no service record, disable resource\\n return\\n\\n compute_node_ref = service['compute_node']\\n if compute_node_ref:\\n self.compute_node = compute_node_ref[0]\\n\\n if not self.compute_node:\\n # Need to create the ComputeNode record:\\n resources['service_id'] = service['id']\\n self.compute_node = self._create(context, resources)\\n LOG.info(_('Compute_service record created for %s ') % self.host)\\n\\n else:\\n # just update the record:\\n self.compute_node = self._update(context, resources,\\n prune_stats=True)\\n LOG.info(_('Compute_service record updated for %s ') % self.host)\",\n \"def get_resource_information():\\n\\n\\n # the resources we are allowed to use is easy. We just copy this...\\n resource_limit_dict = _resources_allowed_dict.copy()\\n\\n \\n # from the other dict, we only take the resource information. (this omits\\n # locks and timing information that isn't needed)\\n\\n # first, let's do the easy thing, the quantity resources. These are just \\n # floats\\n resource_use_dict = {}\\n for resourcename in resource_constants.quantity_resources:\\n resource_use_dict[resourcename] = _resources_consumed_dict[resourcename]\\n\\n # for the fungible resources (files opened, etc,), we only need a count...\\n for resourcename in resource_constants.fungible_item_resources:\\n resource_use_dict[resourcename] = len(_resources_consumed_dict[resourcename])\\n\\n # for the individual item resources (ports, etc,), we copy the set...\\n for resourcename in resource_constants.individual_item_resources:\\n resource_use_dict[resourcename] = _resources_consumed_dict[resourcename].copy()\\n\\n # and that's it!\\n return (resource_limit_dict, resource_use_dict)\",\n \"def capacity(self):\\n return self._cap\",\n \"def get_space_used():\\n fs.get_space_used()\",\n \"def calculate_worker_requirements(self, resource_name, queue_config):\\n worker_cores = 1\\n worker_memory = 1\\n\\n self.load_data()\\n try:\\n # retrieve the resource type definition\\n resource_type = self.resource_types[resource_name]\\n\\n # retrieve the queue configuration\\n site_maxrss = queue_config.get('maxrss', 0) or 0\\n site_corecount = queue_config.get('corecount', 1) or 1\\n\\n unified_queue = queue_config.get('capability', '') == 'ucore'\\n if not unified_queue:\\n # site is not unified, just request whatever is configured in AGIS\\n return site_maxrss, site_corecount\\n\\n if resource_type.max_core:\\n worker_cores = min(resource_type.max_core, site_corecount)\\n else:\\n worker_cores = site_corecount\\n\\n if resource_type.max_ram_per_core:\\n worker_memory = min(resource_type.max_ram_per_core * worker_cores,\\n (site_maxrss / site_corecount) * worker_cores)\\n else:\\n worker_memory = (site_maxrss / site_corecount) * worker_cores\\n\\n except KeyError:\\n pass\\n\\n return worker_cores, worker_memory\",\n \"def _get_free_capacity(self):\\n\\n capacity = np.ones(len(self.grid.T)) * len(self.grid)\\n capacity -= np.count_nonzero(self.grid, axis=0)\\n return capacity\",\n \"def capacity(self):\\n return self._capacity\",\n \"def capacity(self) -> Capacity:\\n raw = self._call('GET', 'capacity')\\n return Capacity.parse_raw(raw)\",\n \"def __init__(self, capacity, units=0):\\n self.capacity = capacity\\n self.units = units\",\n \"def get_load_factor(self):\\n # Your code here\\n return self.total_items / self.capacity\",\n \"def test_capacity_factor(pudl_out_eia):\\n print(\\\"\\\\nCalculating generator capacity factors...\\\")\\n cf = pudl_out_eia.capacity_factor()\\n print(f\\\" capacity_factor: {len(cf)} records\\\")\",\n \"def capacity(self) -> pulumi.Input[int]:\\n return pulumi.get(self, \\\"capacity\\\")\",\n \"def knapsack(items, capacity):\\r\\n pass\",\n \"def get_available_resource(self, nodename):\\n curent_time = time.time()\\n if curent_time - self.cleanup_time > CONF.azure.cleanup_span:\\n self.cleanup_time = curent_time\\n self._cleanup_deleted_os_disks()\\n self._cleanup_deleted_nics()\\n usage_family = 'basicAFamily'\\n try:\\n page = self.compute.usage.list(CONF.azure.location)\\n except Exception as e:\\n msg = six.text_type(e)\\n LOG.exception(msg)\\n ex = exception.ComputeUsageListFailure(reason=six.text_type(e))\\n raise ex\\n usages = [i for i in page]\\n cores = 0\\n cores_used = 0\\n for i in usages:\\n if hasattr(i, 'name') and hasattr(i.name, 'value'):\\n if usage_family == i.name.value:\\n cores = i.limit if hasattr(i, 'limit') else 0\\n cores_used = i.current_value \\\\\\n if hasattr(i, 'current_value') else 0\\n break\\n return {'vcpus': cores,\\n 'memory_mb': 100000000,\\n 'local_gb': 100000000,\\n 'vcpus_used': cores_used,\\n 'memory_mb_used': 0,\\n 'local_gb_used': 0,\\n 'hypervisor_type': hv_type.HYPERV,\\n 'hypervisor_version': 300,\\n 'hypervisor_hostname': nodename,\\n 'cpu_info': '{\\\"model\\\": [\\\"Intel(R) Xeon(R) CPU E5-2670 0 @ '\\n '2.60GHz\\\"], \\\"topology\\\": {\\\"cores\\\": 16, \\\"threads\\\": '\\n '32}}',\\n 'supported_instances': [(arch.I686, hv_type.HYPERV,\\n vm_mode.HVM),\\n (arch.X86_64, hv_type.HYPERV,\\n vm_mode.HVM)],\\n 'numa_topology': None\\n }\",\n \"def capacity(self):\\n return self.buffer_capacity.mean(dim=1)\",\n \"def new_capacity_rule(mod, g, p):\\n return 0\",\n \"def get_capacity_var(self):\\n return self._capacity_var\",\n \"def buy_resource(self, num_res):\\n check_pur = self.poss_purchases()\\n if num_res in check_pur.keys():\\n update_res = num_res\\n for bin in self.capacity_list:\\n for i, res in enumerate(bin[1]):\\n if res == 1 and update_res > 0:\\n bin[1][i] = 0\\n update_res -= 1\\n return (num_res, check_pur[num_res])\\n else:\\n return 'not enough resources'\",\n \"def new_capacity_rule(mod, prj, prd):\\n return 0\",\n \"def get_capacity(self, meta, raven_vars, dispatch, t, raw=False):\\n if raw:\\n return self._capacity\\n request = {self._capacity_var: None}\\n inputs = {'request': request,\\n 'meta': meta,\\n 'raven_vars': raven_vars,\\n 'dispatch': dispatch,\\n 't': t}\\n evaluated, meta = self._capacity.evaluate(inputs, target_var=self._capacity_var)\\n return evaluated, meta\",\n \"def capacity(self):\\n return str(int(self._properties.get('capacity')) * 1073741824)\",\n \"def _get_share_capacity_info(self):\\n lcfg = self.configuration\\n share_details = self.zfssa.get_share(lcfg.zfssa_nfs_pool,\\n lcfg.zfssa_nfs_project,\\n lcfg.zfssa_nfs_share)\\n\\n free = share_details['space_available']\\n used = share_details['space_total']\\n return free, used\",\n \"def getRepairCapacity(self):\\n return int(self.myDesign.getSYCRequired() * (1-(self.strength/100.0)))\",\n \"def do_stats(self, args):\\n total_cpu = free_cpu = in_use_cpu = 0\\n\\n summary = self._qm.get_all_host_summary()\\n for host_id, host_info in summary.viewitems():\\n host_cpu = int(host_info['total cores'])\\n total_cpu += host_cpu\\n locked = host_info.get('locked by')\\n if locked:\\n # If host is locked then all CPUs are in use.\\n in_use_cpu += host_cpu\\n else:\\n free_host_cpu = int(host_info['free cores'])\\n in_use_cpu += (host_cpu - free_host_cpu)\\n free_cpu += free_host_cpu\\n\\n print('total CPU: ', total_cpu)\\n print('used/locked CPU: ', in_use_cpu)\\n print('free CPU: ', free_cpu)\\n capacity = float(in_use_cpu) / float(total_cpu)\\n print('capacity used: %.1f%%' % (capacity * 100,))\\n capacity = float(free_cpu) / float(total_cpu)\\n print('capacity remaining: %.1f%%' % (capacity * 100,))\",\n \"def _calc_resource_stats(self, interval):\\n result = {}\\n\\n if 'mem' in self.metrics:\\n result['mem'] = self._get_mem_info()\\n\\n if 'disk-space' in self.metrics:\\n result['disk-space'] = self.__get_disk_usage(self.engine.artifacts_dir).percent\\n\\n if 'engine-loop' in self.metrics:\\n result['engine-loop'] = self.engine.engine_loop_utilization\\n\\n if 'conn-all' in self.metrics:\\n try:\\n # take all connections without address resolution\\n output = subprocess.check_output(['netstat', '-an'])\\n output_lines = stream_decode(output).split('\\\\n') # in py3 stream has 'bytes' type\\n est_lines = [line for line in output_lines if line.find('EST') != -1]\\n result['conn-all'] = len(est_lines)\\n except BaseException as exc:\\n self.log.debug(\\\"Failed to get connections info: %s\\\", exc)\\n result['conn-all'] = 0\\n\\n if 'cpu' in self.metrics:\\n result['cpu'] = self._get_cpu_percent()\\n\\n if 'bytes-recv' in self.metrics or 'bytes-sent' in self.metrics:\\n net = self.__get_net_counters()\\n if net is not None:\\n tx_bytes = int((net.bytes_sent - self._net_counters.bytes_sent) / float(interval))\\n rx_bytes = int((net.bytes_recv - self._net_counters.bytes_recv) / float(interval))\\n self._net_counters = net\\n else:\\n rx_bytes = 0.0\\n tx_bytes = 0.0\\n\\n if 'bytes-recv' in self.metrics:\\n result['bytes-recv'] = rx_bytes\\n if 'bytes-sent' in self.metrics:\\n result['bytes-sent'] = tx_bytes\\n\\n if 'disk-read' in self.metrics or 'disk-write' in self.metrics:\\n disk = self.__get_disk_counters()\\n if disk is not None:\\n dru = int((disk.read_bytes - self._disk_counters.read_bytes) / float(interval))\\n dwu = int((disk.write_bytes - self._disk_counters.write_bytes) / float(interval))\\n self._disk_counters = disk\\n else:\\n dru = 0.0\\n dwu = 0.0\\n\\n if 'disk-read' in self.metrics:\\n result['disk-read'] = dru\\n if 'disk-write' in self.metrics:\\n result['disk-write'] = dwu\\n\\n return result\",\n \"def su_cost(node_type, node_count, cpu, gpu, ram, duration):\\r\\n if node_type == 'std':\\r\\n node_factor = 1\\r\\n tot_cpu = 28\\r\\n tot_ram = 128\\r\\n if node_type == 'gpu':\\r\\n node_factor = 2\\r\\n tot_cpu = 28\\r\\n tot_ram = 256\\r\\n if node_type == 'fat':\\r\\n node_factor = 6\\r\\n tot_cpu = 56\\r\\n tot_ram = 1024\\r\\n # job_setup = \\\"current setup = {} node type + {} number of nodes + {} number of cpu # + {} number of ram + {} hrs duration of job + {} total cpu + {} total ram\\\".format(node_type, node_count, cpu, ram, duration, tot_cpu, tot_ram)\\r\\n # calculate service units\\r\\n max_resource = top_resource(\\r\\n alloc_CPU = cpu, cpu_denominator = tot_cpu,\\r\\n alloc_GPU = gpu, gpu_denominator = 4,\\r\\n alloc_RAM = ram, ram_denominator = tot_ram)\\r\\n su = ( (node_count * (max_resource * node_factor)) * 28 * duration )\\r\\n return(su)\",\n \"def collect_metrics():\\n p = os.path.join(os.sep, \\\"mnt\\\", \\\"glusterfs\\\")\\n mount_stats = os.statvfs(p)\\n # block size * total blocks\\n total_space = mount_stats.f_blocks * mount_stats.f_bsize\\n free_space = mount_stats.f_bfree * mount_stats.f_bsize\\n # capsize only operates on i64 values\\n used_space = total_space - free_space\\n gb_used = used_space / 1024 / 1024 / 1024\\n\\n # log!(format!(\\\"Collecting metric gb-used {}\\\", gb_used), Info)\\n add_metric(\\\"gb-used\\\", \\\"{}\\\".format(gb_used))\",\n \"def get_capacity():\\n\\n try:\\n raw_capacity = PLIST[\\\"TotalSize\\\"]\\n raw_capacity = str(raw_capacity)\\n\\n except KeyError:\\n return \\\"Unknown\\\", \\\"Unknown\\\"\\n\\n #Round the sizes to make them human-readable.\\n unit_list = [None, \\\"B\\\", \\\"KB\\\", \\\"MB\\\", \\\"GB\\\", \\\"TB\\\", \\\"PB\\\", \\\"EB\\\"]\\n unit = \\\"B\\\"\\n human_readable_size = int(raw_capacity)\\n\\n try:\\n while len(str(human_readable_size)) > 3:\\n #Shift up one unit.\\n unit = unit_list[unit_list.index(unit)+1]\\n human_readable_size = human_readable_size//1000\\n\\n except IndexError:\\n return \\\"Unknown\\\", \\\"Unknown\\\"\\n\\n #Include the unit in the result for both exact and human-readable sizes.\\n return raw_capacity, str(human_readable_size)+\\\" \\\"+unit\",\n \"def _create_capacity(self, m, comp, prod_name):\\n name = comp.name\\n cap_res = comp.get_capacity_var() # name of resource that defines capacity\\n r = m.resource_index_map[comp][cap_res] # production index of the governing resource\\n # production is always lower than capacity\\n ## NOTE get_capacity returns (data, meta) and data is dict\\n ## TODO does this work with, e.g., ARMA-based capacities?\\n ### -> \\\"time\\\" is stored on \\\"m\\\" and could be used to correctly evaluate the capacity\\n cap = comp.get_capacity(None, None, None, None)[0][cap_res] # value of capacity limit (units of governing resource)\\n rule = partial(self._capacity_rule, prod_name, r, cap)\\n constr = pyo.Constraint(m.T, rule=rule)\\n setattr(m, '{c}_{r}_capacity_constr'.format(c=name, r=cap_res), constr)\\n # minimum production\\n print('DEBUGG dispatchable?', comp.name, comp.is_dispatchable())\\n if comp.is_dispatchable() == 'fixed':\\n minimum = cap\\n var = getattr(m, prod_name)\\n values = var.get_values()\\n for k in values:\\n values[k] = cap\\n var.set_values(values)\\n else:\\n minimum = 0 # -> for now just use 0, but fix this! XXX\\n print('DEBUGG ... min:', minimum)\\n rule = partial(self._min_prod_rule, prod_name, r, cap, minimum)\\n constr = pyo.Constraint(m.T, rule=rule)\\n setattr(m, '{c}_{r}_minprod_constr'.format(c=name, r=cap_res), constr)\",\n \"def calc_resources(self):\\n self.popula = self.energy = self.popula_used = self.energy_used = 0\\n self.cnt_public = self.cnt_shop = self.cnt_1 = self.cnt_2 = self.cnt_3 = self.cnt_4 = self.cnt_5 = self.cnt_office = 0\\n self.popula += self.extra_pop\\n for i in range(20):\\n b = self.b[i]\\n if b == 'T':\\n self.popula += self.f[i] * 2\\n self.energy_used += 1\\n elif b == 'O':\\n self.popula_used += 1\\n self.energy_used += 1\\n self.cnt_office += self.f[i]\\n elif b == 'U':\\n self.popula_used += 1\\n self.cnt_public += 1\\n elif b == 'S':\\n self.energy_used += 1\\n self.cnt_shop += 1\\n elif b == '1':\\n self.popula += 1\\n self.energy += 1\\n self.popula_used += 1\\n self.cnt_1 += 1\\n elif b == '2':\\n self.popula_used += 1\\n self.cnt_2 += 1\\n elif b == '3':\\n self.popula_used += 1\\n self.cnt_3 += 1\\n elif b == '4':\\n self.popula += 2\\n self.popula_used += 1\\n self.cnt_4 += 1\\n elif b == '5':\\n self.energy += 2\\n self.popula_used += 1\\n self.cnt_5 += 1\\n elif b == 'A':\\n self.energy += 2\\n self.popula_used += 1\\n elif b == 'F':\\n self.energy += 3\\n self.popula_used += 1\\n elif b == 'G':\\n self.popula += 1\\n if 'tvst' in args.exp:\\n self.popula += self.cnt_shop\\n if 'ward' in args.exp:\\n self.popula += 3\\n if 'elec' in args.exp:\\n self.energy += 3\\n if 'capi' in args.exp:\\n self.popula_used += 2\\n if 'fire' in args.exp:\\n self.popula_used += 1\\n if 'park' in args.exp:\\n self.popula_used += 1\",\n \"def capacity(self):\\n return sum(f.capacity for f in self.filters)\",\n \"def get_used_capacity(self,tot=\\\"50\\\"):\\n data=self.at_cmd(\\\"CPMS?\\\")\\n index=data[1].find(tot)-1\\n if data[1][index-1]==',':\\n return data[index]\\n else:\\n return data[1][index-1:index]\",\n \"def cost(self):\\n cost = {}\\n if len(self.nodes) == 0:\\n return cost\\n resources = self.nodes[0].capacity.keys()\\n for r in resources:\\n values = [n.cost[r] for n in self.nodes]\\n estimator = AvgAggregatorEstimator(values)\\n cost[r] = estimator\\n return cost\",\n \"def _tattle_quantity(resource, quantity, resourcesalloweddict, resourcesuseddict):\\n\\n\\n # I assume that the quantity will never be negative\\n if quantity < 0:\\n # This will cause the program to exit and log things if logging is\\n # enabled. -Brent\\n tracebackrepy.handle_internalerror(\\\"Resource '\\\" + resource + \\n \\\"' has a negative quantity \\\" + str(quantity) + \\\"!\\\", 132)\\n \\n # get the lock for this resource\\n resourcesuseddict['renewable_locks'][resource].acquire()\\n \\n # release the lock afterwards no matter what\\n try: \\n # update the resource counters based upon the current time.\\n _update_resource_consumption_table(resource, resourcesalloweddict, resourcesuseddict)\\n\\n # It's renewable, so I can wait for it to clear\\n if resource not in resource_constants.renewable_resources:\\n # Should never have a quantity tattle for a non-renewable resource\\n # This will cause the program to exit and log things if logging is\\n # enabled. -Brent\\n tracebackrepy.handle_internalerror(\\\"Resource '\\\" + resource + \\n \\\"' is not renewable!\\\", 133)\\n \\n\\n resourcesuseddict[resource] = resourcesuseddict[resource] + quantity\\n # I'll block if I'm over...\\n _sleep_until_resource_drains(resource, resourcesalloweddict, resourcesuseddict)\\n \\n finally:\\n # release the lock for this resource\\n resourcesuseddict['renewable_locks'][resource].release()\",\n \"def test_capacity(self, space_each_type):\\n tspace = build_required_space(space_each_type, type_requirement=\\\"real\\\")\\n assert tspace.cardinality == numpy.inf\\n\\n space = Space()\\n probs = (0.1, 0.2, 0.3, 0.4)\\n categories = (\\\"asdfa\\\", 2, 3, 4)\\n dim = Categorical(\\\"yolo\\\", OrderedDict(zip(categories, probs)), shape=2)\\n space.register(dim)\\n dim = Integer(\\\"yolo2\\\", \\\"uniform\\\", -3, 6)\\n space.register(dim)\\n tspace = build_required_space(space, type_requirement=\\\"integer\\\")\\n assert tspace.cardinality == (4**2) * (6 + 1)\\n\\n dim = Integer(\\\"yolo3\\\", \\\"uniform\\\", -3, 6, shape=(2, 1))\\n space.register(dim)\\n tspace = build_required_space(space, type_requirement=\\\"integer\\\")\\n assert tspace.cardinality == (4**2) * (6 + 1) * ((6 + 1) ** (2 * 1))\\n\\n tspace = build_required_space(\\n space, type_requirement=\\\"integer\\\", shape_requirement=\\\"flattened\\\"\\n )\\n assert tspace.cardinality == (4**2) * (6 + 1) * ((6 + 1) ** (2 * 1))\\n\\n tspace = build_required_space(\\n space, type_requirement=\\\"integer\\\", dist_requirement=\\\"linear\\\"\\n )\\n assert tspace.cardinality == (4**2) * (6 + 1) * ((6 + 1) ** (2 * 1))\",\n \"def usedspace(self):\\n self.log.info(\\\"freespace\\\")\\n nbytes = 0\\n keys = list(self.downloads.keys())\\n keys.sort()\\n for key in keys:\\n download = self.downloads[key]\\n nbytes += download['size']\\n self.log.info(\\\"returning:\\\" + str(nbytes))\\n return nbytes\",\n \"def system_capacity_ac(self):\\n return (self.sam_sys_inputs['system_capacity']\\n / self.sam_sys_inputs['dc_ac_ratio'])\",\n \"def _estimate_free(self):\\n # Query the information we need for this task's channel and package.\\n capacity_deferred = self.channel.total_capacity()\\n open_tasks_deferred = self.channel.tasks(state=[task_states.OPEN])\\n avg_delta_deferred = self.estimate_duration()\\n deferreds = [capacity_deferred,\\n open_tasks_deferred,\\n avg_delta_deferred]\\n results = yield defer.gatherResults(deferreds, consumeErrors=True)\\n capacity, open_tasks, avg_delta = results\\n # Ensure this task's channel has spare capacity for this task.\\n open_weight = sum([task.weight for task in open_tasks])\\n if open_weight >= capacity:\\n # TODO: Evaluate all tasks in the channel and\\n # determine when enough OPEN tasks will complete so that we can\\n # get to OPEN.\\n raise NotImplementedError('channel %d is at capacity' %\\n self.channel_id)\\n # A builder will pick up this task and start it within SLEEPTIME.\\n # start_time is the maximum amount of time we expect to wait here.\\n start_time = self.created + SLEEPTIME\\n if avg_delta is None:\\n defer.returnValue(None)\\n est_completion = start_time + avg_delta\\n defer.returnValue(est_completion)\",\n \"def ok(self, results):\\n return \\\"{:5.2f}% capacity used\\\".format(\\n results[\\\"usage\\\"].resource.usage_ratio * 100.0\\n )\",\n \"def _get_host_utilization(context, host, ram_mb, disk_gb):\\n instances = instance_get_all_by_host(context, host)\\n vms = len(instances)\\n free_ram_mb = ram_mb - FLAGS.reserved_host_memory_mb\\n free_disk_gb = disk_gb - (FLAGS.reserved_host_disk_mb * 1024)\\n\\n work = 0\\n for instance in instances:\\n free_ram_mb -= instance.memory_mb\\n free_disk_gb -= instance.root_gb\\n free_disk_gb -= instance.ephemeral_gb\\n if instance.vm_state in [vm_states.BUILDING, vm_states.REBUILDING,\\n vm_states.MIGRATING, vm_states.RESIZING]:\\n work += 1\\n return dict(free_ram_mb=free_ram_mb,\\n free_disk_gb=free_disk_gb,\\n current_workload=work,\\n running_vms=vms)\",\n \"def get_memory_info(ssh):\\r\\n cmd04='wmic memorychip get capacity'\\r\\n retry_number1=3\\r\\n try:\\r\\n while True:\\r\\n if retry_number1 == 0:\\r\\n logger.writeLog(\\\"get memory sum size fail\\\",level='error')\\r\\n break\\r\\n stdin,stdout,stderr=ssh.exec_command(cmd04)\\r\\n data04=stdout.read().decode().strip('Capacity')\\r\\n print(data04)\\r\\n if data04 == \\\"\\\":\\r\\n retry_number1 -= 1\\r\\n logger.writeLog(\\\"get memory sum size data null\\\",level='error')\\r\\n continue\\r\\n else:\\r\\n result_list=data04.split()\\r\\n print(result_list)\\r\\n memory_size=float(int(result_list[0])+int(result_list[1]))/1024/1024/1024\\r\\n print(\\\"mem total Gb: \\\",memory_size)\\r\\n logger.writeLog(\\\"get memory sum size success\\\",level='info')\\r\\n # return memory_size\\r\\n break\\r\\n except:\\r\\n logger.writeLog(\\\"get memory size error\\\",level='error')\\r\\n return None\\r\\n\\r\\n#6.内存剩余量/Gb\\r\\n# def get_memory_surplus(ssh):\\r\\n \\\"\\\"\\\"get memory surplus\\\"\\\"\\\"\\r\\n cmd05='wmic OS get FreePhysicalMemory'\\r\\n retry_number2=3\\r\\n try:\\r\\n while True:\\r\\n if retry_number2 == 0:\\r\\n logger.writeLog(\\\"get memory surplus fail\\\",level='error')\\r\\n break\\r\\n stdin,stdout,stderr=ssh.exec_command(cmd05)\\r\\n data05=int(stdout.read().decode().split()[1])\\r\\n print(data05)\\r\\n if data05 == \\\"\\\":\\r\\n logger.writeLog(\\\"get memory surplus data null\\\",level='error')\\r\\n retry_number2 -= 1\\r\\n continue\\r\\n else:\\r\\n memory_surplus=round(float(data05)/1024/1024,4)\\r\\n print(\\\"mem free Gb: \\\",memory_surplus)\\r\\n logger.writeLog(\\\"get memory surplus data success\\\",level='info')\\r\\n # return memory_surplus\\r\\n break\\r\\n except:\\r\\n logger.writeLog(\\\"get memory surplus error\\\",level='error')\\r\\n return None\\r\\n\\r\\n#7.内存使用率\\r\\n# def get_memory_ratio(ssh):\\r\\n \\\"\\\"\\\"get memory ratio\\\"\\\"\\\"\\r\\n # memory_size=get_memory_size(ssh)\\r\\n # memory_surplus=get_memory_surplus(ssh)\\r\\n if memory_size == \\\"\\\" or memory_surplus == \\\"\\\":\\r\\n logger.writeLog(\\\"memory_szie is null or memory_surplus is null\\\",level='error')\\r\\n return None\\r\\n else:\\r\\n try:\\r\\n data06=round(float((memory_size-memory_surplus))/memory_size,4)\\r\\n print(\\\"mem use ratio: \\\",data06)\\r\\n logger.writeLog(\\\"get memory ratio success\\\",level='info')\\r\\n return (memory_size,memory_surplus,data06)\\r\\n except:\\r\\n logger.writeLog(\\\"get memory ratio error\\\",level='error')\\r\\n return None\",\n \"def calculate_available_link_res (self, sg_hops_to_be_ignored, mode=MODE_ADD):\\n # set availbandwidth to the maximal value\\n for i, j, k, d in self.network.edges_iter(data=True, keys=True):\\n if d.type == 'STATIC':\\n setattr(self.network[i][j][k], 'availbandwidth', d.bandwidth)\\n # subtract the reserved link and internal (inside Infras) bandwidth\\n if mode == self.MODE_ADD:\\n for d in self.infras:\\n for p in d.ports:\\n for fr in p.flowrules:\\n if fr.id not in sg_hops_to_be_ignored and fr.bandwidth is not None:\\n # Flowrules are cummulatively subtracted from the switching \\n # capacity of the node.\\n d.availres['bandwidth'] -= fr.bandwidth\\n if d.availres['bandwidth'] < 0:\\n raise RuntimeError(\\\"The node bandwidth of %s got below zero \\\"\\n \\\"during available resource calculation!\\\" %\\n d.id)\\n # Get all the mapped paths of all SGHops from the NFFG\\n sg_map = NFFGToolBox.get_all_sghop_info(self, return_paths=True)\\n for sg_hop_id, data in sg_map.iteritems():\\n src, dst, flowclass, bandwidth, delay, path = data\\n if bandwidth is not None:\\n for link in path:\\n link.availbandwidth -= bandwidth\\n if link.availbandwidth < 0:\\n raise RuntimeError(\\n \\\"The link bandwidth of %s got below zero during\\\"\\n \\\"available resource calculation!\\\" % link.id)\",\n \"def __len__(self):\\n return self.capacity\",\n \"def test_read_cluster_resource_quota(self):\\n pass\",\n \"def memory_get_usage():\\n raise NotImplementedError()\",\n \"def get_capacity_var(self):\\n return self.get_interaction().get_capacity_var()\",\n \"def _check_capacity_limit(self, res, amt, balance, meta, raven_vars, dispatch, t):\\n cap = self.get_capacity(meta, raven_vars, dispatch, t)[0][self._capacity_var]\\n try:\\n if abs(balance[self._capacity_var]) > abs(cap):\\n #ttttt\\n # do the inverse problem: how much can we make?\\n balance, meta = self.produce_max(meta, raven_vars, dispatch, t)\\n print('The full requested amount ({res}: {req}) was not possible, so accessing maximum available instead ({res}: {blc}).'.format(res=res, req=amt, blc=balance[res]))\\n except KeyError:\\n raise SyntaxError('Resource \\\"{}\\\" is listed as capacity limiter, but not an output of the component! Got: {}'.format(self._capacity_var, balance))\\n return balance, meta\",\n \"def collect():\\n\\n command = \\\"cat /proc/meminfo |grep MemTotal|awk -F' ' '{print $2}'\\\"\\n memTotal_f = round(float(os.popen(command).read())/1024/1000,0)\\n memTotal = int(memTotal_f)\\n cmd = 'df -h |grep \\\"/dev/s\\\"'\\n metric_disk = os.popen(cmd).readlines()\\n hardNum=[]\\n for i in metric_disk:\\n hard_space = float((i.strip().split()[1])[:-1])\\n hardNum.append(hard_space)\\n\\n disk_info = sum(hardNum)\\n disk_use = {}\\n metric_disks=os.popen('df -x tmpfs -x devtmpfs | grep -Eo \\\" /\\\\S*$\\\" ').readlines()\\n for disk in metric_disks:\\n cmd = 'df|grep -E \\\"%s$\\\"' % disk.strip()\\n disks = os.popen(cmd).readlines()[0]\\n disk_list = disks.split()\\n disk_use[disk_list[5]]=disk_list[4]\\n hard = {\\n \\\"disk_used\\\" : disk_use,\\n \\\"disk_total\\\":disk_info,\\n \\\"mem_total\\\":memTotal\\n }\\n\\n return hard\",\n \"def get_resource_usages(self, project_id):\\n try:\\n # The API call does not give usage for keypair, fixed ips &\\n # metadata items. Have raised a bug for that.\\n limits = self.nova_client.limits.get(\\n tenant_id=project_id).to_dict()\\n resource_usage = collections.defaultdict(dict)\\n resource_usage['ram'] = limits['absolute']['totalRAMUsed']\\n resource_usage['cores'] = limits['absolute']['totalCoresUsed']\\n resource_usage['instances'] = \\\\\\n limits['absolute']['totalInstancesUsed']\\n # If neutron is not enabled, calculate below resources from nova\\n if self.no_neutron:\\n resource_usage['security_groups'] = \\\\\\n limits['absolute']['totalSecurityGroupsUsed']\\n resource_usage['floating_ips'] = \\\\\\n limits['absolute']['totalFloatingIpsUsed']\\n # For time being, keypair is calculated in below manner.\\n resource_usage['key_pairs'] = \\\\\\n len(self.nova_client.keypairs.list())\\n return resource_usage\\n except exceptions.InternalError:\\n raise\",\n \"def CalculateProcessingCapacity(self, problemManager, mineDataManager):\\n \\n self.oreProcessed = np.zeros(len(mineDataManager.theMiningSystem.oreMined)) \\n self.processingPower = np.zeros(len(mineDataManager.theMiningSystem.oreMined)) \\n self.processingCapacity = mineDataManager.theMiningSystem.mineOreProductionCapacity # ore is processed at a constant rate\\n carryOver = 0.0\\n for year in range( len(mineDataManager.theMiningSystem.oreMined )-1 ):\\n processedOre = carryOver + mineDataManager.theMiningSystem.oreMined[year]\\n \\n if(processedOre > self.processingCapacity):\\n carryOver = processedOre - self.processingCapacity\\n processedOre = self.processingCapacity\\n else:\\n carryOver = 0.0\\n self.oreProcessed[year] = processedOre\\n \\n self.oreProcessed[-1] = carryOver + mineDataManager.theMiningSystem.oreMined[-1] # final year\\n \\n \\n # convert tonnes processed each year to the number of Mwh based on powerlaw fit\\n self.processingPower = 3.96*(self.oreProcessed )**0.703 # in Mwh\\n \\n referenceMetalStr = mineDataManager.theOreBody.type[:2] \\n # first two letters of orebody type is assumed to be reference metal for determining processing grade\\n # eg AuCu -> gold is reference metal - note that user must select correct method\\n \\n \\n referenceMetalOreConcentration = mineDataManager.theOreBody.metalGrades[referenceMetalStr]\\n\\n self.concentrateMetalConcentration = 1.0\\n \\n # lookup concentrateMetalConcentrations based on reference metal type\\n \\n concentrateConcentrations = {\\\"Au\\\":0.75,\\\"Ag\\\":0.85,\\\"Ni\\\":0.1,\\\"Cu\\\":0.25,\\\"Pb\\\":0.5}\\n \\n # find the minimum amount of concentration needed to bring concentrate to market\\n minConcentrationFactor = 1e64\\n \\n for metal,metalOreGrade in mineDataManager.theOreBody.metalGrades.iteritems():\\n if metal in concentrateConcentrations:\\n concentrateGrade = concentrateConcentrations[metal]\\n concFactor = concentrateGrade/(metalOreGrade/(1.0+ mineDataManager.theMiningSystem.dilution) +1e-64)\\n if concFactor < 1.0:\\n concFactor = 1.0\\n #print \\\"concFactor\\\", metal, concFactor, metalOreGrade, concentrateGrade\\n if(concFactor < minConcentrationFactor ):\\n minConcentrationFactor = concFactor\\n self.concentrateMetalConcentration = concentrateGrade\\n \\n # concentrate is calculated based on estimate of mineral content\\n self.concentrateProduced = (1.0 - self.processingLoss) \\\\\\n *np.array(mineDataManager.theMiningSystem.oreMined)/minConcentrationFactor \\n \\n \\n return self.processingCapacity\",\n \"def _calc_worker_assign_limits(self, initial_count, occupied=None):\\n occupied = occupied or dict()\\n actual_count = initial_count - sum(occupied.values())\\n\\n endpoint_res = sorted(self._worker_slots.items(), key=operator.itemgetter(1),\\n reverse=True)\\n\\n endpoints = [t[0] for t in endpoint_res]\\n endpoint_cores = np.array([t[1] for t in endpoint_res]).astype(np.float32)\\n\\n # remove assigned nodes from limitations\\n counts = initial_count * endpoint_cores / endpoint_cores.sum()\\n for idx, ep in enumerate(endpoints):\\n counts[idx] = max(0, counts[idx] - occupied.get(ep, 0))\\n\\n # all assigned, nothing to do\\n if counts.sum() == 0:\\n return dict((ep, 0) for ep in endpoints)\\n\\n counts = (actual_count * counts / counts.sum()).astype(np.int32)\\n\\n # assign remaining nodes\\n pos = 0\\n rest = actual_count - counts.sum()\\n while rest > 0:\\n counts[pos] += 1\\n rest -= 1\\n pos = (pos + 1) % len(counts)\\n return dict(zip(endpoints, counts))\",\n \"def capacity(self) -> Optional[int]:\\n return pulumi.get(self, \\\"capacity\\\")\",\n \"def capacity(self) -> Optional[int]:\\n return pulumi.get(self, \\\"capacity\\\")\",\n \"def capacity(self) -> Optional[int]:\\n return pulumi.get(self, \\\"capacity\\\")\",\n \"def capacity(self) -> Optional[int]:\\n return pulumi.get(self, \\\"capacity\\\")\",\n \"def _calc_desc_and_queues(\\n total_numa_nodes, total_ports_per_numa, total_rx_queues, ports_per_numa_value\\n ):\\n\\n # Get the number of rx queues\\n rx_queues = max(1, total_rx_queues)\\n tx_queues = rx_queues * total_numa_nodes + 1\\n\\n # Get the descriptor entries\\n desc_entries = 1024\\n ports_per_numa_value[\\\"rx_queues\\\"] = rx_queues\\n total_mbufs = (\\n (rx_queues * desc_entries) + (tx_queues * desc_entries)\\n ) * total_ports_per_numa\\n\\n return total_mbufs\",\n \"def free_resources(self, context):\\n self.update_available_resource(context.elevated())\",\n \"def desired_capacity(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"desired_capacity\\\")\",\n \"def fs_percent_used_capacity(self):\\n return self._fs_percent_used_capacity\",\n \"def resource_costs(self, fleet):\\n base = (10, 15, 0, 0)\\n ins = fleet.get_ship_instances()\\n mult = sum([ship_stats.get_ship_type(x.base().stype)\\n .resource_mult for x in ins])\\n return tuple(map(lambda x: x * mult, base))\",\n \"def _create_resource_consumption_dict():\\n\\n returned_resource_dict = {}\\n\\n # things that are quantities should start at 0.0\\n for resource in resource_constants.quantity_resources:\\n returned_resource_dict[resource] = 0.0\\n\\n for resource in resource_constants.item_resources:\\n # double check there is no overlap...\\n if resource in resource_constants.quantity_resources:\\n raise InternalRepyError(\\\"Resource '\\\"+resource+\\\"' cannot be both quantity and item based!\\\")\\n\\n returned_resource_dict[resource] = set()\\n\\n # I need locks to protect races in accesses to some items...\\n returned_resource_dict['fungible_locks'] = {}\\n for init_resource in resource_constants.fungible_item_resources:\\n returned_resource_dict['fungible_locks'][init_resource] = threading.Lock()\\n\\n returned_resource_dict['renewable_locks'] = {}\\n for init_resource in resource_constants.renewable_resources:\\n returned_resource_dict['renewable_locks'][init_resource] = threading.Lock()\\n\\n\\n # I also need to track when the last update of a renewable resource occurred\\n returned_resource_dict['renewable_update_time'] = {}\\n\\n # (Aside) JAC: I've thought about this and looked through the commit history.\\n # I don't see any reason to initialize the renewable resources with the\\n # current time (as was done before).\\n for init_resource in resource_constants.renewable_resources:\\n returned_resource_dict['renewable_update_time'][init_resource] = 0.0\\n\\n\\n return returned_resource_dict\",\n \"def capacity_gb(self) -> str:\\n return pulumi.get(self, \\\"capacity_gb\\\")\",\n \"def check_resources(reqs):\\n logger.info(\\\"REQUIREMENTS: \\\" + str(reqs))\\n free_cpu, free_mem = get_resources()\\n return check_if_free_resources(free_mem, free_cpu, reqs)\",\n \"def test_list_cluster_resource_quota(self):\\n pass\",\n \"def capacity(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"capacity\\\")\",\n \"def capacity(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"capacity\\\")\",\n \"def capacity(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"capacity\\\")\",\n \"def capacity(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"capacity\\\")\",\n \"def capacity(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"capacity\\\")\",\n \"def capacity(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"capacity\\\")\",\n \"def capacity(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"capacity\\\")\",\n \"def compute_resources(instance: dict):\\r\\n\\r\\n # Retrieve usefull infos\\r\\n Interventions = instance[INTERVENTIONS_STR]\\r\\n T_max = instance[T_STR]\\r\\n Resources = instance[RESOURCES_STR]\\r\\n # Init resource usage dictionnary for each resource and time\\r\\n resources_usage = {}\\r\\n for resource_name in Resources.keys():\\r\\n resources_usage[resource_name] = np.zeros(T_max)\\r\\n # Compute value for each resource and time step\\r\\n for intervention_name, intervention in Interventions.items():\\r\\n # start time should be defined (already checked in scheduled constraint checker)\\r\\n if not START_STR in intervention:\\r\\n continue\\r\\n start_time = intervention[START_STR]\\r\\n start_time_idx = start_time - 1\\r\\n # index of list starts at 0\\r\\n intervention_worload = intervention[RESOURCE_CHARGE_STR]\\r\\n intervention_delta = int(intervention[DELTA_STR][start_time_idx])\\r\\n # compute effective worload\\r\\n for (\\r\\n resource_name,\\r\\n intervention_resource_worload,\\r\\n ) in intervention_worload.items():\\r\\n for time in range(start_time_idx, start_time_idx + intervention_delta):\\r\\n # null values are not available\\r\\n if (\\r\\n str(time + 1) in intervention_resource_worload\\r\\n and str(start_time) in intervention_resource_worload[str(time + 1)]\\r\\n ):\\r\\n resources_usage[resource_name][\\r\\n time\\r\\n ] += intervention_resource_worload[str(time + 1)][str(start_time)]\\r\\n\\r\\n return resources_usage\",\n \"def _get_prod_bounds(self, comp):\\n cap_res = comp.get_capacity_var() # name of resource that defines capacity\\n maximum = comp.get_capacity(None, None, None, None)[0][cap_res]\\n # TODO minimum!\\n # producing or consuming the defining resource?\\n if maximum > 0:\\n return 0, maximum, pyo.NonNegativeReals\\n else:\\n return maximum, 0, pyo.NonPositiveReals\",\n \"def get_cluster_capacity(self, query_data): # noqa: C901\\n annotations = self._mapper.report_type_map.get(\\\"capacity_aggregate\\\")\\n if not annotations:\\n return query_data, {}\\n\\n cap_key = list(annotations.keys())[0]\\n total_capacity = Decimal(0)\\n daily_total_capacity = defaultdict(Decimal)\\n capacity_by_cluster = defaultdict(Decimal)\\n daily_capacity_by_cluster = defaultdict(lambda: defaultdict(Decimal))\\n\\n q_table = self._mapper.query_table\\n query = q_table.objects.filter(self.query_filter)\\n query_group_by = [\\\"usage_start\\\", \\\"cluster_id\\\"]\\n\\n with tenant_context(self.tenant):\\n cap_data = query.values(*query_group_by).annotate(**annotations)\\n for entry in cap_data:\\n cluster_id = entry.get(\\\"cluster_id\\\", \\\"\\\")\\n usage_start = entry.get(\\\"usage_start\\\", \\\"\\\")\\n if isinstance(usage_start, datetime.date):\\n usage_start = usage_start.isoformat()\\n cap_value = entry.get(cap_key, 0)\\n if cap_value is None:\\n cap_value = 0\\n capacity_by_cluster[cluster_id] += cap_value\\n daily_capacity_by_cluster[usage_start][cluster_id] = cap_value\\n daily_total_capacity[usage_start] += cap_value\\n total_capacity += cap_value\\n\\n if self.resolution == \\\"daily\\\":\\n for row in query_data:\\n cluster_id = row.get(\\\"cluster\\\")\\n date = row.get(\\\"date\\\")\\n if cluster_id:\\n row[cap_key] = daily_capacity_by_cluster.get(date, {}).get(cluster_id, Decimal(0))\\n else:\\n row[cap_key] = daily_total_capacity.get(date, Decimal(0))\\n elif self.resolution == \\\"monthly\\\":\\n for row in query_data:\\n cluster_id = row.get(\\\"cluster\\\")\\n if cluster_id:\\n row[cap_key] = capacity_by_cluster.get(cluster_id, Decimal(0))\\n else:\\n row[cap_key] = total_capacity\\n\\n return query_data, {cap_key: total_capacity}\",\n \"def calculate_task_volatile_size(**kwargs):\\n task = kwargs.get(\\\"data\\\", {})\\n memory_members = kwargs.get(\\\"resources\\\", {}).get(\\\"memory\\\", [])\\n interleave_sets = task.get(\\\"Payload\\\").get(\\\"JsonBody\\\").get(\\\"InterleaveSets\\\", [])\\n selected_members = []\\n for interleave_set in interleave_sets:\\n for pmem in memory_members:\\n if PmemHelpers.compare_id(\\n interleave_set.get(\\\"Memory\\\").get(\\\"@odata.id\\\"), pmem.get(\\\"@odata.id\\\")\\n ):\\n selected_members.append(pmem)\\n # finding total capacity\\n total_capacity = Mapper.get_single_attribute(\\n selected_members,\\n \\\"TotalCapacity\\\",\\n MappingTable.summary.value,\\n output_as_json=True,\\n )\\n total_capacity = total_capacity.get(\\\"TotalCapacity\\\", {}).get(\\\"Value\\\", 0)\\n volatile_size = total_capacity\\n # finding memory chunk size\\n memory_chunk_size = Mapper.get_single_attribute(\\n task, \\\"MemoryChunkSize\\\", MappingTable.tasks.value, output_as_json=True\\n )\\n memory_chunk_size = memory_chunk_size.get(\\\"MemoryChunkSize\\\", {}).get(\\n \\\"Value\\\", None\\n )\\n if memory_chunk_size is not None:\\n size = memory_chunk_size\\n volatile_size = total_capacity - size\\n else:\\n # finding memory chunk size percentage\\n memory_chunk_size_percentage = Mapper.get_single_attribute(\\n task,\\n \\\"MemoryChunkSizePercentage\\\",\\n MappingTable.tasks.value,\\n output_as_json=True,\\n )\\n memory_chunk_size_percentage = memory_chunk_size_percentage.get(\\n \\\"MemoryChunkSizePercentage\\\", {}\\n ).get(\\\"Value\\\", None)\\n if memory_chunk_size_percentage is not None:\\n size = total_capacity * memory_chunk_size_percentage / 100\\n volatile_size = total_capacity - size\\n # returning value in MiB\\n return volatile_size * 1024\",\n \"def _free_space() -> int:\\n return disk_usage(realpath('/')).free\"\n]","isConversational":false},"negative_scores":{"kind":"list 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\"0.76844007\",\n \"0.7094635\",\n \"0.6953356\",\n \"0.6724627\",\n \"0.6691189\",\n \"0.6664577\",\n \"0.6574017\",\n \"0.6568174\",\n \"0.6502811\",\n \"0.646026\",\n \"0.64514846\",\n \"0.64033794\",\n \"0.6387363\",\n \"0.6367259\",\n \"0.6345023\",\n \"0.6332639\",\n \"0.6331895\",\n \"0.6319946\",\n \"0.6291326\",\n \"0.62830526\",\n \"0.62749445\",\n \"0.62568116\",\n \"0.62541866\",\n \"0.6253994\",\n \"0.6242477\",\n \"0.6226798\",\n \"0.6226002\",\n \"0.62046057\",\n \"0.61957145\",\n \"0.61602813\",\n \"0.61508226\",\n \"0.6148572\",\n \"0.612171\",\n \"0.6121634\",\n \"0.6107987\",\n \"0.60920036\",\n \"0.6086564\",\n \"0.60837996\",\n \"0.6083594\",\n \"0.6080138\",\n \"0.60786915\",\n \"0.6078596\",\n \"0.60743433\",\n \"0.6070887\",\n \"0.6062269\",\n \"0.60598695\",\n \"0.60483056\",\n \"0.6047566\",\n \"0.6029476\",\n \"0.60261023\",\n \"0.60218346\",\n \"0.60210115\",\n \"0.59923756\",\n \"0.5986638\",\n \"0.5982413\",\n \"0.5977498\",\n \"0.5961603\",\n \"0.595302\",\n \"0.59491235\",\n \"0.5928472\",\n \"0.5917898\",\n \"0.5888614\",\n \"0.58703053\",\n \"0.5856351\",\n \"0.5841719\",\n \"0.5835721\",\n \"0.58341324\",\n \"0.58340454\",\n \"0.5818788\",\n \"0.58109504\",\n \"0.5807411\",\n \"0.58016294\",\n \"0.5800296\",\n \"0.57907176\",\n \"0.57809615\",\n \"0.5780702\",\n \"0.5772088\",\n \"0.5772088\",\n \"0.5772088\",\n \"0.5772088\",\n \"0.57676446\",\n \"0.5767164\",\n \"0.5760549\",\n \"0.57537735\",\n \"0.5746239\",\n \"0.57368165\",\n \"0.5731679\",\n \"0.5729836\",\n \"0.5720344\",\n \"0.5718333\",\n \"0.5718333\",\n \"0.5718333\",\n \"0.5718333\",\n \"0.5718333\",\n \"0.5718333\",\n \"0.5718333\",\n \"0.5703814\",\n \"0.570158\",\n \"0.56996727\",\n \"0.5680534\",\n \"0.5678901\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":14382,"cells":{"query":{"kind":"string","value":"Get cluster capacity from node detail"},"document":{"kind":"string","value":"def get_cluster_capacity_info(cluster_id):\n cpu_capacity_info = get_node_data(cluster_id)\n cpu_capacity_in_cores = round(unit_conversion(sum([int(''.join(filter(\n str.isdigit, str(item['status']['allocatable']['cpu'])))) for item in cpu_capacity_info]), 'm'), 2)\n memory_capacity_in_gib = round(sum(\n [unit_conversion(int(''.join(filter(str.isdigit, str(item['status']['allocatable']['memory'])))),\n ''.join(filter(str.isalpha, str(item['status']['allocatable']['memory']))))\n for item in cpu_capacity_info]), 2)\n return {'cpu': cpu_capacity_in_cores, 'memory': memory_capacity_in_gib}"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_capacity():\n fs.get_capacity()","def capacity(self):\n capacity = {}\n resources = self.nodes[0].capacity.keys()\n for r in resources:\n values = [n.capacity[r] for n in self.nodes]\n capacity[r] = mean(values) if len(values) > 0 else 0.0\n return capacity","def get_capacity_param(self):\n intr = self.get_interaction()\n return intr.get_capacity(None, None, None, None, raw=True)","def get_capacity_var(self):\n return self._capacity_var","def get_capacity_var(self):\n return self.get_interaction().get_capacity_var()","def Capacity(self) -> int:","def capacity(self) -> Capacity:\n raw = self._call('GET', 'capacity')\n return Capacity.parse_raw(raw)","def get_num_slots(self):\n # Your code here\n return self.capacity","def test_rest_v20_dd_systems_systemid_stats_capacity_get(self):\n pass","def get_capacity(self, meta, raven_vars, dispatch, t, raw=False):\n return self.get_interaction().get_capacity(meta, raven_vars, dispatch, t, raw=raw)","def test_get_cluster_capacity_monthly_resolution(self):\n url = \"?filter[time_scope_units]=month&filter[time_scope_value]=-1&filter[resolution]=monthly\"\n query_params = self.mocked_query_params(url, OCPCpuView)\n handler = OCPReportQueryHandler(query_params)\n query_data = [{\"row\": 1}]\n query_data, total_capacity = handler.get_cluster_capacity(query_data)\n self.assertTrue(\"capacity\" in total_capacity)\n self.assertTrue(isinstance(total_capacity[\"capacity\"], Decimal))\n self.assertTrue(\"capacity\" in query_data[0])\n self.assertIsNotNone(query_data[0].get(\"capacity\"))\n self.assertIsNotNone(total_capacity.get(\"capacity\"))\n self.assertEqual(query_data[0].get(\"capacity\"), total_capacity.get(\"capacity\"))","def get_used_capacity(self,tot=\"50\"):\n data=self.at_cmd(\"CPMS?\")\n index=data[1].find(tot)-1\n if data[1][index-1]==',':\n return data[index]\n else:\n return data[1][index-1:index]","def node_memory_allocatable(self) -> units.Quantity:\n stdout, _, _ = RunKubectlCommand(\n # TODO(pclay): Take a minimum of all nodes?\n [\n 'get', 'nodes', '-o',\n 'jsonpath={.items[0].status.allocatable.memory}'\n ])\n return units.ParseExpression(stdout)","def test_read_cluster_resource_quota(self):\n pass","def capacity(self):\n return self._cap","def capacity(self):\n return self._capacity","def capacity(self):\n return str(int(self._properties.get('capacity')) * 1073741824)","def capacity(self):\n raise NotImplementedError()","def capacity(self):\n return self._capacity","def capacity(self) -> Optional[int]:\n return pulumi.get(self, \"capacity\")","def capacity(self) -> Optional[int]:\n return pulumi.get(self, \"capacity\")","def capacity(self) -> Optional[int]:\n return pulumi.get(self, \"capacity\")","def capacity(self) -> Optional[int]:\n return pulumi.get(self, \"capacity\")","def capacity_used(self):\n raise NotImplementedError()","def capacity_gb(self) -> str:\n return pulumi.get(self, \"capacity_gb\")","def get_heap_cap(self):\r\n return self.capacity","def get_cluster_capacity(self, query_data): # noqa: C901\n annotations = self._mapper.report_type_map.get(\"capacity_aggregate\")\n if not annotations:\n return query_data, {}\n\n cap_key = list(annotations.keys())[0]\n total_capacity = Decimal(0)\n daily_total_capacity = defaultdict(Decimal)\n capacity_by_cluster = defaultdict(Decimal)\n daily_capacity_by_cluster = defaultdict(lambda: defaultdict(Decimal))\n\n q_table = self._mapper.query_table\n query = q_table.objects.filter(self.query_filter)\n query_group_by = [\"usage_start\", \"cluster_id\"]\n\n with tenant_context(self.tenant):\n cap_data = query.values(*query_group_by).annotate(**annotations)\n for entry in cap_data:\n cluster_id = entry.get(\"cluster_id\", \"\")\n usage_start = entry.get(\"usage_start\", \"\")\n if isinstance(usage_start, datetime.date):\n usage_start = usage_start.isoformat()\n cap_value = entry.get(cap_key, 0)\n if cap_value is None:\n cap_value = 0\n capacity_by_cluster[cluster_id] += cap_value\n daily_capacity_by_cluster[usage_start][cluster_id] = cap_value\n daily_total_capacity[usage_start] += cap_value\n total_capacity += cap_value\n\n if self.resolution == \"daily\":\n for row in query_data:\n cluster_id = row.get(\"cluster\")\n date = row.get(\"date\")\n if cluster_id:\n row[cap_key] = daily_capacity_by_cluster.get(date, {}).get(cluster_id, Decimal(0))\n else:\n row[cap_key] = daily_total_capacity.get(date, Decimal(0))\n elif self.resolution == \"monthly\":\n for row in query_data:\n cluster_id = row.get(\"cluster\")\n if cluster_id:\n row[cap_key] = capacity_by_cluster.get(cluster_id, Decimal(0))\n else:\n row[cap_key] = total_capacity\n\n return query_data, {cap_key: total_capacity}","def current_capacity(self, capacity=None):\n if capacity:\n if self._request('SC', str(capacity))[0]:\n return capacity\n else:\n done, data = self._request('GE')\n if done:\n return int(data[0])\n\n raise EvseError","def test_list_cluster_resource_quota(self):\n pass","def current_capacity_range(self):\n done, data = self._request('GC')\n if done:\n return int(data[0]), int(data[1])\n\n raise EvseError","def node_num_cpu(self) -> int:\n stdout, _, _ = RunKubectlCommand(\n ['get', 'nodes', '-o', 'jsonpath={.items[0].status.capacity.cpu}'])\n return int(stdout)","def capacity(self) -> pulumi.Input[int]:\n return pulumi.get(self, \"capacity\")","def _get_capacity_info(self, nfs_share):\n nms = self.share2nms[nfs_share]\n ns_volume, ns_folder = self._get_share_datasets(nfs_share)\n folder_props = nms.folder.get_child_props('%s/%s' % (ns_volume,\n ns_folder),\n 'used|available')\n free = utils.str2size(folder_props['available'])\n allocated = utils.str2size(folder_props['used'])\n self.shares_with_capacities[nfs_share] = {\n 'free': utils.str2gib_size(free),\n 'total': utils.str2gib_size(free + allocated)}\n return free + allocated, free, allocated","def active_capacity(self) -> int:\n return pulumi.get(self, \"active_capacity\")","def get_capacity(self, channel):\n for pattern, capacity in self.channel_capacity:\n if pattern.match(channel):\n return capacity\n return self.capacity","def test_get_cluster_capacity_monthly_resolution_group_by_cluster(self):\n url = \"?filter[time_scope_units]=month&filter[time_scope_value]=-1&filter[resolution]=monthly&group_by[cluster]=*\" # noqa: E501\n query_params = self.mocked_query_params(url, OCPCpuView)\n handler = OCPReportQueryHandler(query_params)\n query_data = handler.execute_query()\n\n capacity_by_cluster = defaultdict(Decimal)\n total_capacity = Decimal(0)\n query_filter = handler.query_filter\n query_group_by = [\"usage_start\", \"cluster_id\"]\n annotations = {\"capacity\": Max(\"cluster_capacity_cpu_core_hours\")}\n cap_key = list(annotations.keys())[0]\n\n q_table = handler._mapper.provider_map.get(\"tables\").get(\"query\")\n query = q_table.objects.filter(query_filter)\n\n with tenant_context(self.tenant):\n cap_data = query.values(*query_group_by).annotate(**annotations)\n for entry in cap_data:\n cluster_id = entry.get(\"cluster_id\", \"\")\n capacity_by_cluster[cluster_id] += entry.get(cap_key, 0)\n total_capacity += entry.get(cap_key, 0)\n\n for entry in query_data.get(\"data\", []):\n for cluster in entry.get(\"clusters\", []):\n cluster_name = cluster.get(\"cluster\", \"\")\n capacity = cluster.get(\"values\")[0].get(\"capacity\", {}).get(\"value\")\n self.assertEqual(capacity, capacity_by_cluster[cluster_name])\n\n self.assertEqual(query_data.get(\"total\", {}).get(\"capacity\", {}).get(\"value\"), total_capacity)","def __len__(self):\n return self.capacity","def get_cluster_sizes(self,sc):\n\n clusterSizes = []\n clusters = sc.labels\n for k in np.arange(clusters.max()):\n clusterSizes.append(len(np.where(clusters==k)[0])) \n \n return clusterSizes","def test_read_cluster_resource_quota_status(self):\n pass","def capacitygroup_group():","def desired_capacity(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"desired_capacity\")","def su_cost(node_type, node_count, cpu, gpu, ram, duration):\r\n if node_type == 'std':\r\n node_factor = 1\r\n tot_cpu = 28\r\n tot_ram = 128\r\n if node_type == 'gpu':\r\n node_factor = 2\r\n tot_cpu = 28\r\n tot_ram = 256\r\n if node_type == 'fat':\r\n node_factor = 6\r\n tot_cpu = 56\r\n tot_ram = 1024\r\n # job_setup = \"current setup = {} node type + {} number of nodes + {} number of cpu # + {} number of ram + {} hrs duration of job + {} total cpu + {} total ram\".format(node_type, node_count, cpu, ram, duration, tot_cpu, tot_ram)\r\n # calculate service units\r\n max_resource = top_resource(\r\n alloc_CPU = cpu, cpu_denominator = tot_cpu,\r\n alloc_GPU = gpu, gpu_denominator = 4,\r\n alloc_RAM = ram, ram_denominator = tot_ram)\r\n su = ( (node_count * (max_resource * node_factor)) * 28 * duration )\r\n return(su)","def get_available_resource(self, nodename):\n curent_time = time.time()\n if curent_time - self.cleanup_time > CONF.azure.cleanup_span:\n self.cleanup_time = curent_time\n self._cleanup_deleted_os_disks()\n self._cleanup_deleted_nics()\n usage_family = 'basicAFamily'\n try:\n page = self.compute.usage.list(CONF.azure.location)\n except Exception as e:\n msg = six.text_type(e)\n LOG.exception(msg)\n ex = exception.ComputeUsageListFailure(reason=six.text_type(e))\n raise ex\n usages = [i for i in page]\n cores = 0\n cores_used = 0\n for i in usages:\n if hasattr(i, 'name') and hasattr(i.name, 'value'):\n if usage_family == i.name.value:\n cores = i.limit if hasattr(i, 'limit') else 0\n cores_used = i.current_value \\\n if hasattr(i, 'current_value') else 0\n break\n return {'vcpus': cores,\n 'memory_mb': 100000000,\n 'local_gb': 100000000,\n 'vcpus_used': cores_used,\n 'memory_mb_used': 0,\n 'local_gb_used': 0,\n 'hypervisor_type': hv_type.HYPERV,\n 'hypervisor_version': 300,\n 'hypervisor_hostname': nodename,\n 'cpu_info': '{\"model\": [\"Intel(R) Xeon(R) CPU E5-2670 0 @ '\n '2.60GHz\"], \"topology\": {\"cores\": 16, \"threads\": '\n '32}}',\n 'supported_instances': [(arch.I686, hv_type.HYPERV,\n vm_mode.HVM),\n (arch.X86_64, hv_type.HYPERV,\n vm_mode.HVM)],\n 'numa_topology': None\n }","def test_get_cluster_capacity_daily_resolution(self):\n url = \"?filter[time_scope_units]=month&filter[time_scope_value]=-1&filter[resolution]=daily\"\n query_params = self.mocked_query_params(url, OCPCpuView)\n handler = OCPReportQueryHandler(query_params)\n query_data = handler.execute_query()\n\n daily_capacity = defaultdict(Decimal)\n total_capacity = Decimal(0)\n query_filter = handler.query_filter\n query_group_by = [\"usage_start\", \"cluster_id\"]\n annotations = {\"capacity\": Max(\"cluster_capacity_cpu_core_hours\")}\n cap_key = list(annotations.keys())[0]\n\n q_table = handler._mapper.provider_map.get(\"tables\").get(\"query\")\n query = q_table.objects.filter(query_filter)\n\n with tenant_context(self.tenant):\n cap_data = query.values(*query_group_by).annotate(**annotations)\n for entry in cap_data:\n date = handler.date_to_string(entry.get(\"usage_start\"))\n daily_capacity[date] += entry.get(cap_key, 0)\n cap_data = query.values(*query_group_by).annotate(**annotations)\n for entry in cap_data:\n total_capacity += entry.get(cap_key, 0)\n\n self.assertEqual(query_data.get(\"total\", {}).get(\"capacity\", {}).get(\"value\"), total_capacity)\n for entry in query_data.get(\"data\", []):\n date = entry.get(\"date\")\n values = entry.get(\"values\")\n if values:\n capacity = values[0].get(\"capacity\", {}).get(\"value\")\n self.assertEqual(capacity, daily_capacity[date])","def capacity(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"capacity\")","def capacity(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"capacity\")","def capacity(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"capacity\")","def capacity(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"capacity\")","def capacity(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"capacity\")","def capacity(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"capacity\")","def capacity(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"capacity\")","def test_capacity_factor(pudl_out_eia):\n print(\"\\nCalculating generator capacity factors...\")\n cf = pudl_out_eia.capacity_factor()\n print(f\" capacity_factor: {len(cf)} records\")","def CacheCapacity(self):\n if self.force_auto_sync:\n self.get('CacheCapacity')\n return self._CacheCapacity","def energy_capacity_rule(mod, g, p):\n return mod.stor_spec_energy_capacity_mwh[g, p]","def max_node_size(self):\n return self.max_node_capacity","def test_read_namespaced_applied_cluster_resource_quota(self):\n pass","def cluster_node_get(self, node_name, desired_attributes=None):\n return self.request( \"cluster-node-get\", {\n 'node_name': [ node_name, 'node-name', [ basestring, 'node-name' ], False ],\n 'desired_attributes': [ desired_attributes, 'desired-attributes', [ ClusterNodeInfo, 'None' ], False ],\n }, {\n 'attributes': [ ClusterNodeInfo, False ],\n } )","def get_capacity(self, meta, raven_vars, dispatch, t, raw=False):\n if raw:\n return self._capacity\n request = {self._capacity_var: None}\n inputs = {'request': request,\n 'meta': meta,\n 'raven_vars': raven_vars,\n 'dispatch': dispatch,\n 't': t}\n evaluated, meta = self._capacity.evaluate(inputs, target_var=self._capacity_var)\n return evaluated, meta","def cluster_size(result, var):\n df=calculate_cluster_size(result, var)\n df['cus']=df.index\n return df","def capacity_provider(self) -> str:\n return pulumi.get(self, \"capacity_provider\")","def test_create_cluster_resource_quota(self):\n pass","def _get_free_capacity(self):\n\n capacity = np.ones(len(self.grid.T)) * len(self.grid)\n capacity -= np.count_nonzero(self.grid, axis=0)\n return capacity","def capacity(self) -> typing.Optional[str]:\n value = self._properties.get(\"capacity\")\n return f\"{value}\" if value is not None else None","def _GetDiskCapacity(self, device):\n args = ['-l', device]\n stdout = self._tools.Run('fdisk', args, sudo=True)\n if stdout:\n # Seach for the line with capacity information.\n re_capacity = re.compile('Disk .*: (\\d+) \\w+,')\n lines = filter(re_capacity.match, stdout.splitlines())\n if len(lines):\n m = re_capacity.match(lines[0])\n\n # We get something like 7859 MB, so turn into bytes, then GB\n return int(m.group(1)) * 1024 * 1024 / 1e9\n return 0","def available_capacity(self):\r\n return self.capacity - len(self.passengers)","def system_capacity_ac(self):\n return (self.sam_sys_inputs['system_capacity']\n / self.sam_sys_inputs['dc_ac_ratio'])","def getCapacity(self):\n\n return self._dfa._capacity","def calculate_total_capacity(**kwargs):\n members = kwargs.get(\"data\", [])\n total = 0\n if members:\n for member in members:\n capacity = Mapper.get_single_attribute(\n member, \"Capacity\", MappingTable.device.value, output_as_json=True\n )\n total += capacity.get(\"Capacity\", {}).get(\"Value\", 0)\n # returning value in MiB\n return total * 1024","def cost(self):\n cost = {}\n if len(self.nodes) == 0:\n return cost\n resources = self.nodes[0].capacity.keys()\n for r in resources:\n values = [n.cost[r] for n in self.nodes]\n estimator = AvgAggregatorEstimator(values)\n cost[r] = estimator\n return cost","def _get_new_capacity(self):\n for prime in primes:\n if prime > 2 * self.size:\n return prime\n raise ValueError(\"Error: Table size overflow!\")","def capacity_factor(self, update=False,\n min_cap_fact=None, max_cap_fact=None):\n if update or self._dfs['capacity_factor'] is None:\n self._dfs['capacity_factor'] = pudl.analysis.mcoe.capacity_factor(\n self, min_cap_fact=min_cap_fact, max_cap_fact=max_cap_fact)\n return self._dfs['capacity_factor']","def test_get_cluster_capacity_daily_resolution_group_by_clusters(self):\n url = (\n \"?filter[time_scope_units]=month&filter[time_scope_value]=-1&filter[resolution]=daily&group_by[cluster]=*\"\n )\n query_params = self.mocked_query_params(url, OCPCpuView)\n handler = OCPReportQueryHandler(query_params)\n query_data = handler.execute_query()\n\n daily_capacity_by_cluster = defaultdict(dict)\n total_capacity = Decimal(0)\n query_filter = handler.query_filter\n query_group_by = [\"usage_start\", \"cluster_id\"]\n annotations = {\"capacity\": Max(\"cluster_capacity_cpu_core_hours\")}\n cap_key = list(annotations.keys())[0]\n\n q_table = handler._mapper.query_table\n query = q_table.objects.filter(query_filter)\n\n with tenant_context(self.tenant):\n cap_data = query.values(*query_group_by).annotate(**annotations)\n for entry in cap_data:\n date = handler.date_to_string(entry.get(\"usage_start\"))\n cluster_id = entry.get(\"cluster_id\", \"\")\n if cluster_id in daily_capacity_by_cluster[date]:\n daily_capacity_by_cluster[date][cluster_id] += entry.get(cap_key, 0)\n else:\n daily_capacity_by_cluster[date][cluster_id] = entry.get(cap_key, 0)\n total_capacity += entry.get(cap_key, 0)\n\n for entry in query_data.get(\"data\", []):\n date = entry.get(\"date\")\n for cluster in entry.get(\"clusters\", []):\n cluster_name = cluster.get(\"cluster\", \"\")\n capacity = cluster.get(\"values\")[0].get(\"capacity\", {}).get(\"value\")\n self.assertEqual(capacity, daily_capacity_by_cluster[date][cluster_name])\n\n self.assertEqual(query_data.get(\"total\", {}).get(\"capacity\", {}).get(\"value\"), total_capacity)","def get_capacity():\n\n try:\n raw_capacity = PLIST[\"TotalSize\"]\n raw_capacity = str(raw_capacity)\n\n except KeyError:\n return \"Unknown\", \"Unknown\"\n\n #Round the sizes to make them human-readable.\n unit_list = [None, \"B\", \"KB\", \"MB\", \"GB\", \"TB\", \"PB\", \"EB\"]\n unit = \"B\"\n human_readable_size = int(raw_capacity)\n\n try:\n while len(str(human_readable_size)) > 3:\n #Shift up one unit.\n unit = unit_list[unit_list.index(unit)+1]\n human_readable_size = human_readable_size//1000\n\n except IndexError:\n return \"Unknown\", \"Unknown\"\n\n #Include the unit in the result for both exact and human-readable sizes.\n return raw_capacity, str(human_readable_size)+\" \"+unit","def getRepairCapacity(self):\n return int(self.myDesign.getSYCRequired() * (1-(self.strength/100.0)))","def target_capacity(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"target_capacity\")","def target_capacity(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"target_capacity\")","def get_cardinality(self, node=None):\n\n if node:\n return self.cardinalities[node]\n else:\n cardinalities = defaultdict(int)\n for cpd in self.cpds:\n cardinalities[cpd.variable] = cpd.cardinality[0]\n return cardinalities","def capacity(self):\n return self.buffer_capacity.mean(dim=1)","def get_link_capacity(self, src_port_ofid, dst_port_ofid):\n src_node_id = src_port_ofid.rsplit(\":\", 1)[-2]\n for neighbor in self.neighbors[src_node_id]:\n if neighbor[\"dst_port\"] == dst_port_ofid:\n return neighbor[\"bps_capacity\"]\n\n # Link not found\n raise KeyError","def node_size(graph):\n adj = nx.betweenness_centrality(graph)\n return np.array([x * 1e3 for x in adj.values()])","def capacity_rule(mod, g, p):\n return mod.stor_spec_power_capacity_mw[g, p]","def extract_cluster_size(line):\r\n cluster_size = line.split(\":\")[-1]\r\n\r\n try:\r\n cluster_size = int(cluster_size)\r\n except ValueError:\r\n return 0\r\n return cluster_size","def cpus(self):\n return self.__cpus","def _get_share_capacity_info(self):\n lcfg = self.configuration\n share_details = self.zfssa.get_share(lcfg.zfssa_nfs_pool,\n lcfg.zfssa_nfs_project,\n lcfg.zfssa_nfs_share)\n\n free = share_details['space_available']\n used = share_details['space_total']\n return free, used","def user_capacity(self) -> SmartSsdUserCapacity:\n return self._user_capacity","def get_cluster_usage_info(cluster_id, kind, namespace_id=None, pods_list=None):\n if pods_list is None:\n pods_list = []\n else:\n logger.info('pod list not none')\n if pods_list == 'no_pod_resource':\n return {'cpu': 0,\n 'memory': 0}\n else:\n logger.info('resources no 0')\n # node usage stats if needed\n if kind == 'nodes':\n cpu_usage_info = client.CustomObjectsApi().list_cluster_custom_object('metrics.k8s.io', 'v1beta1', kind)\n cpu_usage_in_cores = sum([int(''.join(filter(\n str.isdigit, str(cpu_usage_item['usage']['cpu'].encode(\n 'utf-8'))))) for cpu_usage_item in cpu_usage_info['items']])\n cpu_usage_in_percentage = round(cpu_usage_in_cores / 10000000, 0)\n memory_usage = sum([unit_conversion(int(''.join(filter(\n str.isdigit, str(memory_usage_item['usage']['memory'].encode(\n 'utf-8'))))), ''.join(filter(str.isalpha, str(memory_usage_item['usage']['memory'].encode('utf-8')))))\n for memory_usage_item in cpu_usage_info['items']])\n # pods usage stats\n elif kind == 'pods':\n if namespace_id:\n cpu_usage_info = client.CustomObjectsApi().list_namespaced_custom_object('metrics.k8s.io', 'v1beta1',\n namespace_id, kind)\n else:\n cpu_usage_info = client.CustomObjectsApi().list_cluster_custom_object('metrics.k8s.io', 'v1beta1', kind)\n if len(pods_list) != 0:\n cpu_usage_in_cores = round(unit_conversion(sum([int(''.join(filter(\n str.isdigit, str(cpu_usage_item['containers'][0]['usage']['cpu'].encode(\n 'utf-8'))))) for cpu_usage_item in cpu_usage_info['items'] if cpu_usage_item['metadata']['name']\n in pods_list]), 'n'), 2)\n memory_usage = round(sum([unit_conversion(int(''.join(filter(\n str.isdigit, str(memory_usage_item['containers'][0]['usage']['memory'].encode(\n 'utf-8'))))),\n ''.join(\n filter(str.isalpha, str(memory_usage_item['containers'][0]['usage']['memory'].encode('utf-8')))))\n for memory_usage_item in cpu_usage_info['items'] if memory_usage_item['metadata']['name']\n in pods_list]), 2)\n else:\n cpu_usage_in_cores = round(unit_conversion(sum([int(''.join(filter(\n str.isdigit, str(cpu_usage_item['containers'][0]['usage']['cpu'].encode(\n 'utf-8'))))) for cpu_usage_item in cpu_usage_info['items']]), 'n'), 2)\n memory_usage = round(sum([unit_conversion(int(''.join(filter(\n str.isdigit, str(memory_usage_item['containers'][0]['usage']['memory'].encode(\n 'utf-8'))))),\n ''.join(filter(str.isalpha, str(memory_usage_item['containers'][0]['usage']['memory'].encode('utf-8')))))\n for memory_usage_item in cpu_usage_info['items']]), 2)\n return {'cpu': cpu_usage_in_cores,\n 'memory': memory_usage}","def clustering_factor(self):\n return self.unpack_dword(0x2C)","def get_compute_nodes_allocation(scheduler_commands, region, stack_name, max_monitoring_time):\n ec2_capacity_time_series = []\n compute_nodes_time_series = []\n timestamps = []\n\n @retry(\n # Retry until EC2 and Scheduler capacities scale down to 0\n # Also make sure cluster scaled up before scaling down\n retry_on_result=lambda _: ec2_capacity_time_series[-1] != 0\n or compute_nodes_time_series[-1] != 0\n or max(ec2_capacity_time_series) == 0\n or max(compute_nodes_time_series) == 0,\n wait_fixed=seconds(20),\n stop_max_delay=max_monitoring_time,\n )\n def _watch_compute_nodes_allocation():\n compute_nodes = scheduler_commands.compute_nodes_count()\n ec2_capacity = get_compute_nodes_count(stack_name, region)\n timestamp = time.time()\n\n # add values only if there is a transition.\n if (\n len(ec2_capacity_time_series) == 0\n or ec2_capacity_time_series[-1] != ec2_capacity\n or compute_nodes_time_series[-1] != compute_nodes\n ):\n ec2_capacity_time_series.append(ec2_capacity)\n compute_nodes_time_series.append(compute_nodes)\n timestamps.append(timestamp)\n\n try:\n _watch_compute_nodes_allocation()\n except RetryError:\n # ignoring this error in order to perform assertions on the collected data.\n pass\n\n logging.info(\n \"Monitoring completed: %s, %s, %s\",\n \"ec2_capacity_time_series [\" + \" \".join(map(str, ec2_capacity_time_series)) + \"]\",\n \"compute_nodes_time_series [\" + \" \".join(map(str, compute_nodes_time_series)) + \"]\",\n \"timestamps [\" + \" \".join(map(str, timestamps)) + \"]\",\n )\n return ec2_capacity_time_series, compute_nodes_time_series, timestamps","def getHeatCapacity(self, Tlist, V=1.0):\n\t\treturn _modes.translation_heatcapacity(Tlist, self.mass, self.dimension, V)","def consumed_spice_capacity_in_bytes(self) -> pulumi.Output[float]:\n return pulumi.get(self, \"consumed_spice_capacity_in_bytes\")","def vcpus(self):\n return self._vcpus","def get_cluster_info(self) -> Dict[str, Any]:\n pass","def read_resource(self, namespace: str = None):\n names = [\n \"read_namespaced_csistorage_capacity\",\n \"read_csistorage_capacity\",\n ]\n return _kube_api.execute(\n action=\"read\",\n resource=self,\n names=names,\n namespace=namespace,\n api_client=None,\n api_args={\"name\": self.metadata.name},\n )","def max_capacity_gb(self) -> str:\n return pulumi.get(self, \"max_capacity_gb\")","def get_bmc(node):\n if get_cluster_vendor() == \"sgi\":\n return sgi_cluster.get_bmc()\n elif get_cluster_vendor() == \"ibm\": \n return ibm_cluster.get_bmc()\n return None","def assign_cluster(node):\n if (node not in attrs_by_node):\n return -1\n else:\n attrs = list(attrs_by_node[node])\n if (len(attrs) == 1):\n return labels[indices_by_vocab[attrs[0]]]\n else:\n vec = np.zeros(k, dtype = float)\n for attr in attrs:\n vec += features[indices_by_vocab[attr]]\n vec /= len(attrs)\n if sphere:\n vec = normalize(vec)\n sims = [np.dot(vec, center) for center in centers]\n else:\n sims = [-np.linalg.norm(vec - center) for center in centers]\n max_index, max_sim = -1, -float('inf')\n for (i, sim) in enumerate(sims):\n if (sim > max_sim):\n max_index = i\n max_sim = sim\n return max_index","def get_minimum_platform_reserved_memory(dbapi, ihost, numa_node):\n reserved = 0\n\n system = dbapi.isystem_get_one()\n ihost_inodes = dbapi.inode_get_by_ihost(ihost['uuid'])\n numa_node_count = len(ihost_inodes)\n\n if is_virtual() or is_virtual_worker(ihost):\n # minimal memory requirements for VirtualBox\n if host_has_function(ihost, constants.WORKER):\n if numa_node == 0:\n reserved += 1200\n if host_has_function(ihost, constants.CONTROLLER):\n reserved += 5000\n else:\n reserved += 500\n elif (system.distributed_cloud_role ==\n constants.DISTRIBUTED_CLOUD_ROLE_SYSTEMCONTROLLER and\n ihost['personality'] == constants.CONTROLLER):\n reserved += \\\n constants.DISTRIBUTED_CLOUD_CONTROLLER_MEMORY_RESERVED_MIB // numa_node_count\n elif host_has_function(ihost, constants.WORKER):\n # Engineer 1G per numa node for disk IO RSS overhead\n reserved += constants.DISK_IO_RESIDENT_SET_SIZE_MIB\n elif ihost['personality'] == constants.CONTROLLER:\n # Standard controller\n reserved += constants.STANDARD_CONTROLLER_MEMORY_RESERVED_MIB // numa_node_count\n\n return reserved","def new_capacity_rule(mod, g, p):\n return 0","def cluster_constraint(self):\n return self._cluster_constraint","def canopy_heat_capacity(states: ClimateStates) -> float:\n return CAP_LEAF * states.leaf_area_index"],"string":"[\n \"def get_capacity():\\n fs.get_capacity()\",\n \"def capacity(self):\\n capacity = {}\\n resources = self.nodes[0].capacity.keys()\\n for r in resources:\\n values = [n.capacity[r] for n in self.nodes]\\n capacity[r] = mean(values) if len(values) > 0 else 0.0\\n return capacity\",\n \"def get_capacity_param(self):\\n intr = self.get_interaction()\\n return intr.get_capacity(None, None, None, None, raw=True)\",\n \"def get_capacity_var(self):\\n return self._capacity_var\",\n \"def get_capacity_var(self):\\n return self.get_interaction().get_capacity_var()\",\n \"def Capacity(self) -> int:\",\n \"def capacity(self) -> Capacity:\\n raw = self._call('GET', 'capacity')\\n return Capacity.parse_raw(raw)\",\n \"def get_num_slots(self):\\n # Your code here\\n return self.capacity\",\n \"def test_rest_v20_dd_systems_systemid_stats_capacity_get(self):\\n pass\",\n \"def get_capacity(self, meta, raven_vars, dispatch, t, raw=False):\\n return self.get_interaction().get_capacity(meta, raven_vars, dispatch, t, raw=raw)\",\n \"def test_get_cluster_capacity_monthly_resolution(self):\\n url = \\\"?filter[time_scope_units]=month&filter[time_scope_value]=-1&filter[resolution]=monthly\\\"\\n query_params = self.mocked_query_params(url, OCPCpuView)\\n handler = OCPReportQueryHandler(query_params)\\n query_data = [{\\\"row\\\": 1}]\\n query_data, total_capacity = handler.get_cluster_capacity(query_data)\\n self.assertTrue(\\\"capacity\\\" in total_capacity)\\n self.assertTrue(isinstance(total_capacity[\\\"capacity\\\"], Decimal))\\n self.assertTrue(\\\"capacity\\\" in query_data[0])\\n self.assertIsNotNone(query_data[0].get(\\\"capacity\\\"))\\n self.assertIsNotNone(total_capacity.get(\\\"capacity\\\"))\\n self.assertEqual(query_data[0].get(\\\"capacity\\\"), total_capacity.get(\\\"capacity\\\"))\",\n \"def get_used_capacity(self,tot=\\\"50\\\"):\\n data=self.at_cmd(\\\"CPMS?\\\")\\n index=data[1].find(tot)-1\\n if data[1][index-1]==',':\\n return data[index]\\n else:\\n return data[1][index-1:index]\",\n \"def node_memory_allocatable(self) -> units.Quantity:\\n stdout, _, _ = RunKubectlCommand(\\n # TODO(pclay): Take a minimum of all nodes?\\n [\\n 'get', 'nodes', '-o',\\n 'jsonpath={.items[0].status.allocatable.memory}'\\n ])\\n return units.ParseExpression(stdout)\",\n \"def test_read_cluster_resource_quota(self):\\n pass\",\n \"def capacity(self):\\n return self._cap\",\n \"def capacity(self):\\n return self._capacity\",\n \"def capacity(self):\\n return str(int(self._properties.get('capacity')) * 1073741824)\",\n \"def capacity(self):\\n raise NotImplementedError()\",\n \"def capacity(self):\\n return self._capacity\",\n \"def capacity(self) -> Optional[int]:\\n return pulumi.get(self, \\\"capacity\\\")\",\n \"def capacity(self) -> Optional[int]:\\n return pulumi.get(self, \\\"capacity\\\")\",\n \"def capacity(self) -> Optional[int]:\\n return pulumi.get(self, \\\"capacity\\\")\",\n \"def capacity(self) -> Optional[int]:\\n return pulumi.get(self, \\\"capacity\\\")\",\n \"def capacity_used(self):\\n raise NotImplementedError()\",\n \"def capacity_gb(self) -> str:\\n return pulumi.get(self, \\\"capacity_gb\\\")\",\n \"def get_heap_cap(self):\\r\\n return self.capacity\",\n \"def get_cluster_capacity(self, query_data): # noqa: C901\\n annotations = self._mapper.report_type_map.get(\\\"capacity_aggregate\\\")\\n if not annotations:\\n return query_data, {}\\n\\n cap_key = list(annotations.keys())[0]\\n total_capacity = Decimal(0)\\n daily_total_capacity = defaultdict(Decimal)\\n capacity_by_cluster = defaultdict(Decimal)\\n daily_capacity_by_cluster = defaultdict(lambda: defaultdict(Decimal))\\n\\n q_table = self._mapper.query_table\\n query = q_table.objects.filter(self.query_filter)\\n query_group_by = [\\\"usage_start\\\", \\\"cluster_id\\\"]\\n\\n with tenant_context(self.tenant):\\n cap_data = query.values(*query_group_by).annotate(**annotations)\\n for entry in cap_data:\\n cluster_id = entry.get(\\\"cluster_id\\\", \\\"\\\")\\n usage_start = entry.get(\\\"usage_start\\\", \\\"\\\")\\n if isinstance(usage_start, datetime.date):\\n usage_start = usage_start.isoformat()\\n cap_value = entry.get(cap_key, 0)\\n if cap_value is None:\\n cap_value = 0\\n capacity_by_cluster[cluster_id] += cap_value\\n daily_capacity_by_cluster[usage_start][cluster_id] = cap_value\\n daily_total_capacity[usage_start] += cap_value\\n total_capacity += cap_value\\n\\n if self.resolution == \\\"daily\\\":\\n for row in query_data:\\n cluster_id = row.get(\\\"cluster\\\")\\n date = row.get(\\\"date\\\")\\n if cluster_id:\\n row[cap_key] = daily_capacity_by_cluster.get(date, {}).get(cluster_id, Decimal(0))\\n else:\\n row[cap_key] = daily_total_capacity.get(date, Decimal(0))\\n elif self.resolution == \\\"monthly\\\":\\n for row in query_data:\\n cluster_id = row.get(\\\"cluster\\\")\\n if cluster_id:\\n row[cap_key] = capacity_by_cluster.get(cluster_id, Decimal(0))\\n else:\\n row[cap_key] = total_capacity\\n\\n return query_data, {cap_key: total_capacity}\",\n \"def current_capacity(self, capacity=None):\\n if capacity:\\n if self._request('SC', str(capacity))[0]:\\n return capacity\\n else:\\n done, data = self._request('GE')\\n if done:\\n return int(data[0])\\n\\n raise EvseError\",\n \"def test_list_cluster_resource_quota(self):\\n pass\",\n \"def current_capacity_range(self):\\n done, data = self._request('GC')\\n if done:\\n return int(data[0]), int(data[1])\\n\\n raise EvseError\",\n \"def node_num_cpu(self) -> int:\\n stdout, _, _ = RunKubectlCommand(\\n ['get', 'nodes', '-o', 'jsonpath={.items[0].status.capacity.cpu}'])\\n return int(stdout)\",\n \"def capacity(self) -> pulumi.Input[int]:\\n return pulumi.get(self, \\\"capacity\\\")\",\n \"def _get_capacity_info(self, nfs_share):\\n nms = self.share2nms[nfs_share]\\n ns_volume, ns_folder = self._get_share_datasets(nfs_share)\\n folder_props = nms.folder.get_child_props('%s/%s' % (ns_volume,\\n ns_folder),\\n 'used|available')\\n free = utils.str2size(folder_props['available'])\\n allocated = utils.str2size(folder_props['used'])\\n self.shares_with_capacities[nfs_share] = {\\n 'free': utils.str2gib_size(free),\\n 'total': utils.str2gib_size(free + allocated)}\\n return free + allocated, free, allocated\",\n \"def active_capacity(self) -> int:\\n return pulumi.get(self, \\\"active_capacity\\\")\",\n \"def get_capacity(self, channel):\\n for pattern, capacity in self.channel_capacity:\\n if pattern.match(channel):\\n return capacity\\n return self.capacity\",\n \"def test_get_cluster_capacity_monthly_resolution_group_by_cluster(self):\\n url = \\\"?filter[time_scope_units]=month&filter[time_scope_value]=-1&filter[resolution]=monthly&group_by[cluster]=*\\\" # noqa: E501\\n query_params = self.mocked_query_params(url, OCPCpuView)\\n handler = OCPReportQueryHandler(query_params)\\n query_data = handler.execute_query()\\n\\n capacity_by_cluster = defaultdict(Decimal)\\n total_capacity = Decimal(0)\\n query_filter = handler.query_filter\\n query_group_by = [\\\"usage_start\\\", \\\"cluster_id\\\"]\\n annotations = {\\\"capacity\\\": Max(\\\"cluster_capacity_cpu_core_hours\\\")}\\n cap_key = list(annotations.keys())[0]\\n\\n q_table = handler._mapper.provider_map.get(\\\"tables\\\").get(\\\"query\\\")\\n query = q_table.objects.filter(query_filter)\\n\\n with tenant_context(self.tenant):\\n cap_data = query.values(*query_group_by).annotate(**annotations)\\n for entry in cap_data:\\n cluster_id = entry.get(\\\"cluster_id\\\", \\\"\\\")\\n capacity_by_cluster[cluster_id] += entry.get(cap_key, 0)\\n total_capacity += entry.get(cap_key, 0)\\n\\n for entry in query_data.get(\\\"data\\\", []):\\n for cluster in entry.get(\\\"clusters\\\", []):\\n cluster_name = cluster.get(\\\"cluster\\\", \\\"\\\")\\n capacity = cluster.get(\\\"values\\\")[0].get(\\\"capacity\\\", {}).get(\\\"value\\\")\\n self.assertEqual(capacity, capacity_by_cluster[cluster_name])\\n\\n self.assertEqual(query_data.get(\\\"total\\\", {}).get(\\\"capacity\\\", {}).get(\\\"value\\\"), total_capacity)\",\n \"def __len__(self):\\n return self.capacity\",\n \"def get_cluster_sizes(self,sc):\\n\\n clusterSizes = []\\n clusters = sc.labels\\n for k in np.arange(clusters.max()):\\n clusterSizes.append(len(np.where(clusters==k)[0])) \\n \\n return clusterSizes\",\n \"def test_read_cluster_resource_quota_status(self):\\n pass\",\n \"def capacitygroup_group():\",\n \"def desired_capacity(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"desired_capacity\\\")\",\n \"def su_cost(node_type, node_count, cpu, gpu, ram, duration):\\r\\n if node_type == 'std':\\r\\n node_factor = 1\\r\\n tot_cpu = 28\\r\\n tot_ram = 128\\r\\n if node_type == 'gpu':\\r\\n node_factor = 2\\r\\n tot_cpu = 28\\r\\n tot_ram = 256\\r\\n if node_type == 'fat':\\r\\n node_factor = 6\\r\\n tot_cpu = 56\\r\\n tot_ram = 1024\\r\\n # job_setup = \\\"current setup = {} node type + {} number of nodes + {} number of cpu # + {} number of ram + {} hrs duration of job + {} total cpu + {} total ram\\\".format(node_type, node_count, cpu, ram, duration, tot_cpu, tot_ram)\\r\\n # calculate service units\\r\\n max_resource = top_resource(\\r\\n alloc_CPU = cpu, cpu_denominator = tot_cpu,\\r\\n alloc_GPU = gpu, gpu_denominator = 4,\\r\\n alloc_RAM = ram, ram_denominator = tot_ram)\\r\\n su = ( (node_count * (max_resource * node_factor)) * 28 * duration )\\r\\n return(su)\",\n \"def get_available_resource(self, nodename):\\n curent_time = time.time()\\n if curent_time - self.cleanup_time > CONF.azure.cleanup_span:\\n self.cleanup_time = curent_time\\n self._cleanup_deleted_os_disks()\\n self._cleanup_deleted_nics()\\n usage_family = 'basicAFamily'\\n try:\\n page = self.compute.usage.list(CONF.azure.location)\\n except Exception as e:\\n msg = six.text_type(e)\\n LOG.exception(msg)\\n ex = exception.ComputeUsageListFailure(reason=six.text_type(e))\\n raise ex\\n usages = [i for i in page]\\n cores = 0\\n cores_used = 0\\n for i in usages:\\n if hasattr(i, 'name') and hasattr(i.name, 'value'):\\n if usage_family == i.name.value:\\n cores = i.limit if hasattr(i, 'limit') else 0\\n cores_used = i.current_value \\\\\\n if hasattr(i, 'current_value') else 0\\n break\\n return {'vcpus': cores,\\n 'memory_mb': 100000000,\\n 'local_gb': 100000000,\\n 'vcpus_used': cores_used,\\n 'memory_mb_used': 0,\\n 'local_gb_used': 0,\\n 'hypervisor_type': hv_type.HYPERV,\\n 'hypervisor_version': 300,\\n 'hypervisor_hostname': nodename,\\n 'cpu_info': '{\\\"model\\\": [\\\"Intel(R) Xeon(R) CPU E5-2670 0 @ '\\n '2.60GHz\\\"], \\\"topology\\\": {\\\"cores\\\": 16, \\\"threads\\\": '\\n '32}}',\\n 'supported_instances': [(arch.I686, hv_type.HYPERV,\\n vm_mode.HVM),\\n (arch.X86_64, hv_type.HYPERV,\\n vm_mode.HVM)],\\n 'numa_topology': None\\n }\",\n \"def test_get_cluster_capacity_daily_resolution(self):\\n url = \\\"?filter[time_scope_units]=month&filter[time_scope_value]=-1&filter[resolution]=daily\\\"\\n query_params = self.mocked_query_params(url, OCPCpuView)\\n handler = OCPReportQueryHandler(query_params)\\n query_data = handler.execute_query()\\n\\n daily_capacity = defaultdict(Decimal)\\n total_capacity = Decimal(0)\\n query_filter = handler.query_filter\\n query_group_by = [\\\"usage_start\\\", \\\"cluster_id\\\"]\\n annotations = {\\\"capacity\\\": Max(\\\"cluster_capacity_cpu_core_hours\\\")}\\n cap_key = list(annotations.keys())[0]\\n\\n q_table = handler._mapper.provider_map.get(\\\"tables\\\").get(\\\"query\\\")\\n query = q_table.objects.filter(query_filter)\\n\\n with tenant_context(self.tenant):\\n cap_data = query.values(*query_group_by).annotate(**annotations)\\n for entry in cap_data:\\n date = handler.date_to_string(entry.get(\\\"usage_start\\\"))\\n daily_capacity[date] += entry.get(cap_key, 0)\\n cap_data = query.values(*query_group_by).annotate(**annotations)\\n for entry in cap_data:\\n total_capacity += entry.get(cap_key, 0)\\n\\n self.assertEqual(query_data.get(\\\"total\\\", {}).get(\\\"capacity\\\", {}).get(\\\"value\\\"), total_capacity)\\n for entry in query_data.get(\\\"data\\\", []):\\n date = entry.get(\\\"date\\\")\\n values = entry.get(\\\"values\\\")\\n if values:\\n capacity = values[0].get(\\\"capacity\\\", {}).get(\\\"value\\\")\\n self.assertEqual(capacity, daily_capacity[date])\",\n \"def capacity(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"capacity\\\")\",\n \"def capacity(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"capacity\\\")\",\n \"def capacity(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"capacity\\\")\",\n \"def capacity(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"capacity\\\")\",\n \"def capacity(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"capacity\\\")\",\n \"def capacity(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"capacity\\\")\",\n \"def capacity(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"capacity\\\")\",\n \"def test_capacity_factor(pudl_out_eia):\\n print(\\\"\\\\nCalculating generator capacity factors...\\\")\\n cf = pudl_out_eia.capacity_factor()\\n print(f\\\" capacity_factor: {len(cf)} records\\\")\",\n \"def CacheCapacity(self):\\n if self.force_auto_sync:\\n self.get('CacheCapacity')\\n return self._CacheCapacity\",\n \"def energy_capacity_rule(mod, g, p):\\n return mod.stor_spec_energy_capacity_mwh[g, p]\",\n \"def max_node_size(self):\\n return self.max_node_capacity\",\n \"def test_read_namespaced_applied_cluster_resource_quota(self):\\n pass\",\n \"def cluster_node_get(self, node_name, desired_attributes=None):\\n return self.request( \\\"cluster-node-get\\\", {\\n 'node_name': [ node_name, 'node-name', [ basestring, 'node-name' ], False ],\\n 'desired_attributes': [ desired_attributes, 'desired-attributes', [ ClusterNodeInfo, 'None' ], False ],\\n }, {\\n 'attributes': [ ClusterNodeInfo, False ],\\n } )\",\n \"def get_capacity(self, meta, raven_vars, dispatch, t, raw=False):\\n if raw:\\n return self._capacity\\n request = {self._capacity_var: None}\\n inputs = {'request': request,\\n 'meta': meta,\\n 'raven_vars': raven_vars,\\n 'dispatch': dispatch,\\n 't': t}\\n evaluated, meta = self._capacity.evaluate(inputs, target_var=self._capacity_var)\\n return evaluated, meta\",\n \"def cluster_size(result, var):\\n df=calculate_cluster_size(result, var)\\n df['cus']=df.index\\n return df\",\n \"def capacity_provider(self) -> str:\\n return pulumi.get(self, \\\"capacity_provider\\\")\",\n \"def test_create_cluster_resource_quota(self):\\n pass\",\n \"def _get_free_capacity(self):\\n\\n capacity = np.ones(len(self.grid.T)) * len(self.grid)\\n capacity -= np.count_nonzero(self.grid, axis=0)\\n return capacity\",\n \"def capacity(self) -> typing.Optional[str]:\\n value = self._properties.get(\\\"capacity\\\")\\n return f\\\"{value}\\\" if value is not None else None\",\n \"def _GetDiskCapacity(self, device):\\n args = ['-l', device]\\n stdout = self._tools.Run('fdisk', args, sudo=True)\\n if stdout:\\n # Seach for the line with capacity information.\\n re_capacity = re.compile('Disk .*: (\\\\d+) \\\\w+,')\\n lines = filter(re_capacity.match, stdout.splitlines())\\n if len(lines):\\n m = re_capacity.match(lines[0])\\n\\n # We get something like 7859 MB, so turn into bytes, then GB\\n return int(m.group(1)) * 1024 * 1024 / 1e9\\n return 0\",\n \"def available_capacity(self):\\r\\n return self.capacity - len(self.passengers)\",\n \"def system_capacity_ac(self):\\n return (self.sam_sys_inputs['system_capacity']\\n / self.sam_sys_inputs['dc_ac_ratio'])\",\n \"def getCapacity(self):\\n\\n return self._dfa._capacity\",\n \"def calculate_total_capacity(**kwargs):\\n members = kwargs.get(\\\"data\\\", [])\\n total = 0\\n if members:\\n for member in members:\\n capacity = Mapper.get_single_attribute(\\n member, \\\"Capacity\\\", MappingTable.device.value, output_as_json=True\\n )\\n total += capacity.get(\\\"Capacity\\\", {}).get(\\\"Value\\\", 0)\\n # returning value in MiB\\n return total * 1024\",\n \"def cost(self):\\n cost = {}\\n if len(self.nodes) == 0:\\n return cost\\n resources = self.nodes[0].capacity.keys()\\n for r in resources:\\n values = [n.cost[r] for n in self.nodes]\\n estimator = AvgAggregatorEstimator(values)\\n cost[r] = estimator\\n return cost\",\n \"def _get_new_capacity(self):\\n for prime in primes:\\n if prime > 2 * self.size:\\n return prime\\n raise ValueError(\\\"Error: Table size overflow!\\\")\",\n \"def capacity_factor(self, update=False,\\n min_cap_fact=None, max_cap_fact=None):\\n if update or self._dfs['capacity_factor'] is None:\\n self._dfs['capacity_factor'] = pudl.analysis.mcoe.capacity_factor(\\n self, min_cap_fact=min_cap_fact, max_cap_fact=max_cap_fact)\\n return self._dfs['capacity_factor']\",\n \"def test_get_cluster_capacity_daily_resolution_group_by_clusters(self):\\n url = (\\n \\\"?filter[time_scope_units]=month&filter[time_scope_value]=-1&filter[resolution]=daily&group_by[cluster]=*\\\"\\n )\\n query_params = self.mocked_query_params(url, OCPCpuView)\\n handler = OCPReportQueryHandler(query_params)\\n query_data = handler.execute_query()\\n\\n daily_capacity_by_cluster = defaultdict(dict)\\n total_capacity = Decimal(0)\\n query_filter = handler.query_filter\\n query_group_by = [\\\"usage_start\\\", \\\"cluster_id\\\"]\\n annotations = {\\\"capacity\\\": Max(\\\"cluster_capacity_cpu_core_hours\\\")}\\n cap_key = list(annotations.keys())[0]\\n\\n q_table = handler._mapper.query_table\\n query = q_table.objects.filter(query_filter)\\n\\n with tenant_context(self.tenant):\\n cap_data = query.values(*query_group_by).annotate(**annotations)\\n for entry in cap_data:\\n date = handler.date_to_string(entry.get(\\\"usage_start\\\"))\\n cluster_id = entry.get(\\\"cluster_id\\\", \\\"\\\")\\n if cluster_id in daily_capacity_by_cluster[date]:\\n daily_capacity_by_cluster[date][cluster_id] += entry.get(cap_key, 0)\\n else:\\n daily_capacity_by_cluster[date][cluster_id] = entry.get(cap_key, 0)\\n total_capacity += entry.get(cap_key, 0)\\n\\n for entry in query_data.get(\\\"data\\\", []):\\n date = entry.get(\\\"date\\\")\\n for cluster in entry.get(\\\"clusters\\\", []):\\n cluster_name = cluster.get(\\\"cluster\\\", \\\"\\\")\\n capacity = cluster.get(\\\"values\\\")[0].get(\\\"capacity\\\", {}).get(\\\"value\\\")\\n self.assertEqual(capacity, daily_capacity_by_cluster[date][cluster_name])\\n\\n self.assertEqual(query_data.get(\\\"total\\\", {}).get(\\\"capacity\\\", {}).get(\\\"value\\\"), total_capacity)\",\n \"def get_capacity():\\n\\n try:\\n raw_capacity = PLIST[\\\"TotalSize\\\"]\\n raw_capacity = str(raw_capacity)\\n\\n except KeyError:\\n return \\\"Unknown\\\", \\\"Unknown\\\"\\n\\n #Round the sizes to make them human-readable.\\n unit_list = [None, \\\"B\\\", \\\"KB\\\", \\\"MB\\\", \\\"GB\\\", \\\"TB\\\", \\\"PB\\\", \\\"EB\\\"]\\n unit = \\\"B\\\"\\n human_readable_size = int(raw_capacity)\\n\\n try:\\n while len(str(human_readable_size)) > 3:\\n #Shift up one unit.\\n unit = unit_list[unit_list.index(unit)+1]\\n human_readable_size = human_readable_size//1000\\n\\n except IndexError:\\n return \\\"Unknown\\\", \\\"Unknown\\\"\\n\\n #Include the unit in the result for both exact and human-readable sizes.\\n return raw_capacity, str(human_readable_size)+\\\" \\\"+unit\",\n \"def getRepairCapacity(self):\\n return int(self.myDesign.getSYCRequired() * (1-(self.strength/100.0)))\",\n \"def target_capacity(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"target_capacity\\\")\",\n \"def target_capacity(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"target_capacity\\\")\",\n \"def get_cardinality(self, node=None):\\n\\n if node:\\n return self.cardinalities[node]\\n else:\\n cardinalities = defaultdict(int)\\n for cpd in self.cpds:\\n cardinalities[cpd.variable] = cpd.cardinality[0]\\n return cardinalities\",\n \"def capacity(self):\\n return self.buffer_capacity.mean(dim=1)\",\n \"def get_link_capacity(self, src_port_ofid, dst_port_ofid):\\n src_node_id = src_port_ofid.rsplit(\\\":\\\", 1)[-2]\\n for neighbor in self.neighbors[src_node_id]:\\n if neighbor[\\\"dst_port\\\"] == dst_port_ofid:\\n return neighbor[\\\"bps_capacity\\\"]\\n\\n # Link not found\\n raise KeyError\",\n \"def node_size(graph):\\n adj = nx.betweenness_centrality(graph)\\n return np.array([x * 1e3 for x in adj.values()])\",\n \"def capacity_rule(mod, g, p):\\n return mod.stor_spec_power_capacity_mw[g, p]\",\n \"def extract_cluster_size(line):\\r\\n cluster_size = line.split(\\\":\\\")[-1]\\r\\n\\r\\n try:\\r\\n cluster_size = int(cluster_size)\\r\\n except ValueError:\\r\\n return 0\\r\\n return cluster_size\",\n \"def cpus(self):\\n return self.__cpus\",\n \"def _get_share_capacity_info(self):\\n lcfg = self.configuration\\n share_details = self.zfssa.get_share(lcfg.zfssa_nfs_pool,\\n lcfg.zfssa_nfs_project,\\n lcfg.zfssa_nfs_share)\\n\\n free = share_details['space_available']\\n used = share_details['space_total']\\n return free, used\",\n \"def user_capacity(self) -> SmartSsdUserCapacity:\\n return self._user_capacity\",\n \"def get_cluster_usage_info(cluster_id, kind, namespace_id=None, pods_list=None):\\n if pods_list is None:\\n pods_list = []\\n else:\\n logger.info('pod list not none')\\n if pods_list == 'no_pod_resource':\\n return {'cpu': 0,\\n 'memory': 0}\\n else:\\n logger.info('resources no 0')\\n # node usage stats if needed\\n if kind == 'nodes':\\n cpu_usage_info = client.CustomObjectsApi().list_cluster_custom_object('metrics.k8s.io', 'v1beta1', kind)\\n cpu_usage_in_cores = sum([int(''.join(filter(\\n str.isdigit, str(cpu_usage_item['usage']['cpu'].encode(\\n 'utf-8'))))) for cpu_usage_item in cpu_usage_info['items']])\\n cpu_usage_in_percentage = round(cpu_usage_in_cores / 10000000, 0)\\n memory_usage = sum([unit_conversion(int(''.join(filter(\\n str.isdigit, str(memory_usage_item['usage']['memory'].encode(\\n 'utf-8'))))), ''.join(filter(str.isalpha, str(memory_usage_item['usage']['memory'].encode('utf-8')))))\\n for memory_usage_item in cpu_usage_info['items']])\\n # pods usage stats\\n elif kind == 'pods':\\n if namespace_id:\\n cpu_usage_info = client.CustomObjectsApi().list_namespaced_custom_object('metrics.k8s.io', 'v1beta1',\\n namespace_id, kind)\\n else:\\n cpu_usage_info = client.CustomObjectsApi().list_cluster_custom_object('metrics.k8s.io', 'v1beta1', kind)\\n if len(pods_list) != 0:\\n cpu_usage_in_cores = round(unit_conversion(sum([int(''.join(filter(\\n str.isdigit, str(cpu_usage_item['containers'][0]['usage']['cpu'].encode(\\n 'utf-8'))))) for cpu_usage_item in cpu_usage_info['items'] if cpu_usage_item['metadata']['name']\\n in pods_list]), 'n'), 2)\\n memory_usage = round(sum([unit_conversion(int(''.join(filter(\\n str.isdigit, str(memory_usage_item['containers'][0]['usage']['memory'].encode(\\n 'utf-8'))))),\\n ''.join(\\n filter(str.isalpha, str(memory_usage_item['containers'][0]['usage']['memory'].encode('utf-8')))))\\n for memory_usage_item in cpu_usage_info['items'] if memory_usage_item['metadata']['name']\\n in pods_list]), 2)\\n else:\\n cpu_usage_in_cores = round(unit_conversion(sum([int(''.join(filter(\\n str.isdigit, str(cpu_usage_item['containers'][0]['usage']['cpu'].encode(\\n 'utf-8'))))) for cpu_usage_item in cpu_usage_info['items']]), 'n'), 2)\\n memory_usage = round(sum([unit_conversion(int(''.join(filter(\\n str.isdigit, str(memory_usage_item['containers'][0]['usage']['memory'].encode(\\n 'utf-8'))))),\\n ''.join(filter(str.isalpha, str(memory_usage_item['containers'][0]['usage']['memory'].encode('utf-8')))))\\n for memory_usage_item in cpu_usage_info['items']]), 2)\\n return {'cpu': cpu_usage_in_cores,\\n 'memory': memory_usage}\",\n \"def clustering_factor(self):\\n return self.unpack_dword(0x2C)\",\n \"def get_compute_nodes_allocation(scheduler_commands, region, stack_name, max_monitoring_time):\\n ec2_capacity_time_series = []\\n compute_nodes_time_series = []\\n timestamps = []\\n\\n @retry(\\n # Retry until EC2 and Scheduler capacities scale down to 0\\n # Also make sure cluster scaled up before scaling down\\n retry_on_result=lambda _: ec2_capacity_time_series[-1] != 0\\n or compute_nodes_time_series[-1] != 0\\n or max(ec2_capacity_time_series) == 0\\n or max(compute_nodes_time_series) == 0,\\n wait_fixed=seconds(20),\\n stop_max_delay=max_monitoring_time,\\n )\\n def _watch_compute_nodes_allocation():\\n compute_nodes = scheduler_commands.compute_nodes_count()\\n ec2_capacity = get_compute_nodes_count(stack_name, region)\\n timestamp = time.time()\\n\\n # add values only if there is a transition.\\n if (\\n len(ec2_capacity_time_series) == 0\\n or ec2_capacity_time_series[-1] != ec2_capacity\\n or compute_nodes_time_series[-1] != compute_nodes\\n ):\\n ec2_capacity_time_series.append(ec2_capacity)\\n compute_nodes_time_series.append(compute_nodes)\\n timestamps.append(timestamp)\\n\\n try:\\n _watch_compute_nodes_allocation()\\n except RetryError:\\n # ignoring this error in order to perform assertions on the collected data.\\n pass\\n\\n logging.info(\\n \\\"Monitoring completed: %s, %s, %s\\\",\\n \\\"ec2_capacity_time_series [\\\" + \\\" \\\".join(map(str, ec2_capacity_time_series)) + \\\"]\\\",\\n \\\"compute_nodes_time_series [\\\" + \\\" \\\".join(map(str, compute_nodes_time_series)) + \\\"]\\\",\\n \\\"timestamps [\\\" + \\\" \\\".join(map(str, timestamps)) + \\\"]\\\",\\n )\\n return ec2_capacity_time_series, compute_nodes_time_series, timestamps\",\n \"def getHeatCapacity(self, Tlist, V=1.0):\\n\\t\\treturn _modes.translation_heatcapacity(Tlist, self.mass, self.dimension, V)\",\n \"def consumed_spice_capacity_in_bytes(self) -> pulumi.Output[float]:\\n return pulumi.get(self, \\\"consumed_spice_capacity_in_bytes\\\")\",\n \"def vcpus(self):\\n return self._vcpus\",\n \"def get_cluster_info(self) -> Dict[str, Any]:\\n pass\",\n \"def read_resource(self, namespace: str = None):\\n names = [\\n \\\"read_namespaced_csistorage_capacity\\\",\\n \\\"read_csistorage_capacity\\\",\\n ]\\n return _kube_api.execute(\\n action=\\\"read\\\",\\n resource=self,\\n names=names,\\n namespace=namespace,\\n api_client=None,\\n api_args={\\\"name\\\": self.metadata.name},\\n )\",\n \"def max_capacity_gb(self) -> str:\\n return pulumi.get(self, \\\"max_capacity_gb\\\")\",\n \"def get_bmc(node):\\n if get_cluster_vendor() == \\\"sgi\\\":\\n return sgi_cluster.get_bmc()\\n elif get_cluster_vendor() == \\\"ibm\\\": \\n return ibm_cluster.get_bmc()\\n return None\",\n \"def assign_cluster(node):\\n if (node not in attrs_by_node):\\n return -1\\n else:\\n attrs = list(attrs_by_node[node])\\n if (len(attrs) == 1):\\n return labels[indices_by_vocab[attrs[0]]]\\n else:\\n vec = np.zeros(k, dtype = float)\\n for attr in attrs:\\n vec += features[indices_by_vocab[attr]]\\n vec /= len(attrs)\\n if sphere:\\n vec = normalize(vec)\\n sims = [np.dot(vec, center) for center in centers]\\n else:\\n sims = [-np.linalg.norm(vec - center) for center in centers]\\n max_index, max_sim = -1, -float('inf')\\n for (i, sim) in enumerate(sims):\\n if (sim > max_sim):\\n max_index = i\\n max_sim = sim\\n return max_index\",\n \"def get_minimum_platform_reserved_memory(dbapi, ihost, numa_node):\\n reserved = 0\\n\\n system = dbapi.isystem_get_one()\\n ihost_inodes = dbapi.inode_get_by_ihost(ihost['uuid'])\\n numa_node_count = len(ihost_inodes)\\n\\n if is_virtual() or is_virtual_worker(ihost):\\n # minimal memory requirements for VirtualBox\\n if host_has_function(ihost, constants.WORKER):\\n if numa_node == 0:\\n reserved += 1200\\n if host_has_function(ihost, constants.CONTROLLER):\\n reserved += 5000\\n else:\\n reserved += 500\\n elif (system.distributed_cloud_role ==\\n constants.DISTRIBUTED_CLOUD_ROLE_SYSTEMCONTROLLER and\\n ihost['personality'] == constants.CONTROLLER):\\n reserved += \\\\\\n constants.DISTRIBUTED_CLOUD_CONTROLLER_MEMORY_RESERVED_MIB // numa_node_count\\n elif host_has_function(ihost, constants.WORKER):\\n # Engineer 1G per numa node for disk IO RSS overhead\\n reserved += constants.DISK_IO_RESIDENT_SET_SIZE_MIB\\n elif ihost['personality'] == constants.CONTROLLER:\\n # Standard controller\\n reserved += constants.STANDARD_CONTROLLER_MEMORY_RESERVED_MIB // numa_node_count\\n\\n return reserved\",\n \"def new_capacity_rule(mod, g, p):\\n return 0\",\n \"def cluster_constraint(self):\\n return self._cluster_constraint\",\n \"def canopy_heat_capacity(states: ClimateStates) -> float:\\n return CAP_LEAF * states.leaf_area_index\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7165189","0.67714345","0.67460614","0.66856956","0.6549141","0.6534968","0.65045047","0.64937866","0.648811","0.64549166","0.64503264","0.63680506","0.6324158","0.6307951","0.6224518","0.6187836","0.61583894","0.6155459","0.6146542","0.6124433","0.6124433","0.6124433","0.6124433","0.6105483","0.60947037","0.60880065","0.60655963","0.60592383","0.6057764","0.60521275","0.6051045","0.6012345","0.59798217","0.595911","0.59267837","0.59223074","0.5908598","0.5895209","0.58950984","0.5859143","0.583487","0.5798298","0.5784493","0.5777997","0.57306135","0.57306135","0.57306135","0.57306135","0.57306135","0.57306135","0.57306135","0.5724238","0.57219756","0.5683836","0.5681725","0.56738275","0.56601167","0.5659705","0.5651907","0.56328464","0.5632299","0.5612483","0.5608395","0.55957925","0.5579367","0.5574743","0.5537441","0.5531073","0.552605","0.550918","0.55041265","0.5496215","0.5484921","0.5481962","0.5465952","0.5465952","0.54645175","0.5464072","0.5439034","0.54335773","0.54293025","0.5408993","0.5397936","0.5386781","0.5379377","0.53766656","0.537345","0.5370812","0.5363781","0.53592676","0.53575104","0.5351935","0.534127","0.5338002","0.5335424","0.5322522","0.5317662","0.5314788","0.53117836","0.53072196"],"string":"[\n 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\"0.5659705\",\n \"0.5651907\",\n \"0.56328464\",\n \"0.5632299\",\n \"0.5612483\",\n \"0.5608395\",\n \"0.55957925\",\n \"0.5579367\",\n \"0.5574743\",\n \"0.5537441\",\n \"0.5531073\",\n \"0.552605\",\n \"0.550918\",\n \"0.55041265\",\n \"0.5496215\",\n \"0.5484921\",\n \"0.5481962\",\n \"0.5465952\",\n \"0.5465952\",\n \"0.54645175\",\n \"0.5464072\",\n \"0.5439034\",\n \"0.54335773\",\n \"0.54293025\",\n \"0.5408993\",\n \"0.5397936\",\n \"0.5386781\",\n \"0.5379377\",\n \"0.53766656\",\n \"0.537345\",\n \"0.5370812\",\n \"0.5363781\",\n \"0.53592676\",\n \"0.53575104\",\n \"0.5351935\",\n \"0.534127\",\n \"0.5338002\",\n \"0.5335424\",\n \"0.5322522\",\n \"0.5317662\",\n \"0.5314788\",\n \"0.53117836\",\n \"0.53072196\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7078413"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":14383,"cells":{"query":{"kind":"string","value":"get resource usage information from pods usage"},"document":{"kind":"string","value":"def get_cluster_usage_info(cluster_id, kind, namespace_id=None, pods_list=None):\n if pods_list is None:\n pods_list = []\n else:\n logger.info('pod list not none')\n if pods_list == 'no_pod_resource':\n return {'cpu': 0,\n 'memory': 0}\n else:\n logger.info('resources no 0')\n # node usage stats if needed\n if kind == 'nodes':\n cpu_usage_info = client.CustomObjectsApi().list_cluster_custom_object('metrics.k8s.io', 'v1beta1', kind)\n cpu_usage_in_cores = sum([int(''.join(filter(\n str.isdigit, str(cpu_usage_item['usage']['cpu'].encode(\n 'utf-8'))))) for cpu_usage_item in cpu_usage_info['items']])\n cpu_usage_in_percentage = round(cpu_usage_in_cores / 10000000, 0)\n memory_usage = sum([unit_conversion(int(''.join(filter(\n str.isdigit, str(memory_usage_item['usage']['memory'].encode(\n 'utf-8'))))), ''.join(filter(str.isalpha, str(memory_usage_item['usage']['memory'].encode('utf-8')))))\n for memory_usage_item in cpu_usage_info['items']])\n # pods usage stats\n elif kind == 'pods':\n if namespace_id:\n cpu_usage_info = client.CustomObjectsApi().list_namespaced_custom_object('metrics.k8s.io', 'v1beta1',\n namespace_id, kind)\n else:\n cpu_usage_info = client.CustomObjectsApi().list_cluster_custom_object('metrics.k8s.io', 'v1beta1', kind)\n if len(pods_list) != 0:\n cpu_usage_in_cores = round(unit_conversion(sum([int(''.join(filter(\n str.isdigit, str(cpu_usage_item['containers'][0]['usage']['cpu'].encode(\n 'utf-8'))))) for cpu_usage_item in cpu_usage_info['items'] if cpu_usage_item['metadata']['name']\n in pods_list]), 'n'), 2)\n memory_usage = round(sum([unit_conversion(int(''.join(filter(\n str.isdigit, str(memory_usage_item['containers'][0]['usage']['memory'].encode(\n 'utf-8'))))),\n ''.join(\n filter(str.isalpha, str(memory_usage_item['containers'][0]['usage']['memory'].encode('utf-8')))))\n for memory_usage_item in cpu_usage_info['items'] if memory_usage_item['metadata']['name']\n in pods_list]), 2)\n else:\n cpu_usage_in_cores = round(unit_conversion(sum([int(''.join(filter(\n str.isdigit, str(cpu_usage_item['containers'][0]['usage']['cpu'].encode(\n 'utf-8'))))) for cpu_usage_item in cpu_usage_info['items']]), 'n'), 2)\n memory_usage = round(sum([unit_conversion(int(''.join(filter(\n str.isdigit, str(memory_usage_item['containers'][0]['usage']['memory'].encode(\n 'utf-8'))))),\n ''.join(filter(str.isalpha, str(memory_usage_item['containers'][0]['usage']['memory'].encode('utf-8')))))\n for memory_usage_item in cpu_usage_info['items']]), 2)\n return {'cpu': cpu_usage_in_cores,\n 'memory': memory_usage}"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_resource_info(cluster_id, kind, namespace_id=None, pods_list=None):\n if pods_list is None:\n pods_list = []\n capacity = get_cluster_capacity_info(cluster_id),\n usage = get_cluster_usage_info(cluster_id, kind, namespace_id, pods_list)\n if capacity[0]['cpu'] != 0 and capacity[0]['memory'] != 0:\n resource_info = {\n \"capacity\": capacity[0],\n \"usage\": {\n \"cpu\": usage['cpu'],\n \"cpu_percentage\": float(round(decimal.Decimal(usage['cpu'] / capacity[0]['cpu'])*100, 2)),\n \"memory\": usage['memory'],\n \"memory_percentage\": float(round(decimal.Decimal(usage['memory'] / capacity[0]['memory'])*100, 2))\n }\n }\n else:\n resource_info = {\n \"capacity\": capacity[0],\n \"usage\": {\n \"cpu\": usage['cpu'],\n \"cpu_percentage\": 0.0,\n \"memory\": usage['memory'],\n \"memory_percentage\": 0.0\n }\n }\n\n return resource_info","def get_resource_usages(self, project_id):\n try:\n # The API call does not give usage for keypair, fixed ips &\n # metadata items. Have raised a bug for that.\n limits = self.nova_client.limits.get(\n tenant_id=project_id).to_dict()\n resource_usage = collections.defaultdict(dict)\n resource_usage['ram'] = limits['absolute']['totalRAMUsed']\n resource_usage['cores'] = limits['absolute']['totalCoresUsed']\n resource_usage['instances'] = \\\n limits['absolute']['totalInstancesUsed']\n # If neutron is not enabled, calculate below resources from nova\n if self.no_neutron:\n resource_usage['security_groups'] = \\\n limits['absolute']['totalSecurityGroupsUsed']\n resource_usage['floating_ips'] = \\\n limits['absolute']['totalFloatingIpsUsed']\n # For time being, keypair is calculated in below manner.\n resource_usage['key_pairs'] = \\\n len(self.nova_client.keypairs.list())\n return resource_usage\n except exceptions.InternalError:\n raise","def getUsageInfo(self):\n return self.jsonRequest(\"/api/v1/usage\", { \"apiKey\": self._apiKey })","def get_kong_node_usage_metrics(opts):\n\n url = \"{0}/status\".format(opts['base_url'])\n\n r = requests.get(url)\n try:\n r.raise_for_status()\n except requests.exceptions.RequestException as e:\n logging.debug(\"http response body - %s\", r.text)\n logging.error(\"An exception occurred: (%s)\", e)\n sys.exit(2)\n\n print r.text\n\n return True","def get_usage_info(self):\n\n usage_info = resource.getrusage(resource.RUSAGE_SELF)\n user_cpu = usage_info[0]\n system_cpu = usage_info[1]\n rss_size = usage_info[2]\n\n return user_cpu, system_cpu, rss_size","def get_usages(self):\n return self.client._perform_json(\"GET\", \"/projects/%s/managedfolders/%s/usages\" % (self.project_key, self.odb_id))","def usage_metrics(self) -> Sequence['outputs.GetServiceQuotaUsageMetricResult']:\n return pulumi.get(self, \"usage_metrics\")","def get_available_resource(self, nodename):\n curent_time = time.time()\n if curent_time - self.cleanup_time > CONF.azure.cleanup_span:\n self.cleanup_time = curent_time\n self._cleanup_deleted_os_disks()\n self._cleanup_deleted_nics()\n usage_family = 'basicAFamily'\n try:\n page = self.compute.usage.list(CONF.azure.location)\n except Exception as e:\n msg = six.text_type(e)\n LOG.exception(msg)\n ex = exception.ComputeUsageListFailure(reason=six.text_type(e))\n raise ex\n usages = [i for i in page]\n cores = 0\n cores_used = 0\n for i in usages:\n if hasattr(i, 'name') and hasattr(i.name, 'value'):\n if usage_family == i.name.value:\n cores = i.limit if hasattr(i, 'limit') else 0\n cores_used = i.current_value \\\n if hasattr(i, 'current_value') else 0\n break\n return {'vcpus': cores,\n 'memory_mb': 100000000,\n 'local_gb': 100000000,\n 'vcpus_used': cores_used,\n 'memory_mb_used': 0,\n 'local_gb_used': 0,\n 'hypervisor_type': hv_type.HYPERV,\n 'hypervisor_version': 300,\n 'hypervisor_hostname': nodename,\n 'cpu_info': '{\"model\": [\"Intel(R) Xeon(R) CPU E5-2670 0 @ '\n '2.60GHz\"], \"topology\": {\"cores\": 16, \"threads\": '\n '32}}',\n 'supported_instances': [(arch.I686, hv_type.HYPERV,\n vm_mode.HVM),\n (arch.X86_64, hv_type.HYPERV,\n vm_mode.HVM)],\n 'numa_topology': None\n }","def memory_get_usage():\n raise NotImplementedError()","def resource_usage_export_config(self) -> 'outputs.ResourceUsageExportConfigResponse':\n return pulumi.get(self, \"resource_usage_export_config\")","def get_usage(self, start=None, end=None):\n return self.manager.get_usage(self, start=start, end=end)","def get_resources():\n # Acquire the lock...\n get_resources_lock.acquire()\n\n # ...but always release it\n try:\n # Construct the dictionaries as copies from nanny\n (limits,usage) = nanny.get_resource_information()\n\n\n # Calculate all the usage's\n pid = os.getpid()\n\n # Get CPU and memory, this is thread specific\n if ostype in [\"Linux\", \"Darwin\"]:\n \n # Get CPU first, then memory\n usage[\"cpu\"] = os_api.get_process_cpu_time(pid)\n\n # This uses the cached PID data from the CPU check\n usage[\"memory\"] = os_api.get_process_rss()\n\n # Get the thread specific CPU usage\n usage[\"threadcpu\"] = os_api.get_current_thread_cpu_time() \n\n\n # Windows Specific versions\n elif ostype in [\"Windows\"]:\n \n # Get the CPU time\n usage[\"cpu\"] = windows_api.get_process_cpu_time(pid)\n\n # Get the memory, use the resident set size\n usage[\"memory\"] = windows_api.process_memory_info(pid)['WorkingSetSize'] \n\n # Get thread-level CPU \n usage[\"threadcpu\"] = windows_api.get_current_thread_cpu_time()\n\n # Unknown OS\n else:\n raise EnvironmentError(\"Unsupported Platform!\")\n\n # Use the cached disk used amount\n usage[\"diskused\"] = cached_disk_used\n\n finally:\n # Release the lock\n get_resources_lock.release()\n\n # Copy the stop times\n stoptimes = process_stopped_timeline[:]\n\n # Return the dictionaries and the stoptimes\n return (limits,usage,stoptimes)","def get_resource_information():\n\n\n # the resources we are allowed to use is easy. We just copy this...\n resource_limit_dict = _resources_allowed_dict.copy()\n\n \n # from the other dict, we only take the resource information. (this omits\n # locks and timing information that isn't needed)\n\n # first, let's do the easy thing, the quantity resources. These are just \n # floats\n resource_use_dict = {}\n for resourcename in resource_constants.quantity_resources:\n resource_use_dict[resourcename] = _resources_consumed_dict[resourcename]\n\n # for the fungible resources (files opened, etc,), we only need a count...\n for resourcename in resource_constants.fungible_item_resources:\n resource_use_dict[resourcename] = len(_resources_consumed_dict[resourcename])\n\n # for the individual item resources (ports, etc,), we copy the set...\n for resourcename in resource_constants.individual_item_resources:\n resource_use_dict[resourcename] = _resources_consumed_dict[resourcename].copy()\n\n # and that's it!\n return (resource_limit_dict, resource_use_dict)","def get_cpu_usage():\n process_details = RU_OBJ.get_curr_processes()\n return json.dumps(sorted(process_details, key=lambda k: k['name']))","def ok(self, results):\n return \"{:5.2f}% capacity used\".format(\n results[\"usage\"].resource.usage_ratio * 100.0\n )","def get_used_resource_overview(self, resource_name):\n page_overview = self._page_overview()\n return page_overview.row_avaible_resource.value(resource_name)","def get_cpu_usage(conn):\n prev_idle = 0\n prev_total = 0\n cpu = conn.getCPUStats(-1, 0)\n if type(cpu) == dict:\n for num in range(2):\n idle = list(conn.getCPUStats(-1, 0).values())[1]\n total = sum(list(conn.getCPUStats(-1, 0).values()))\n diff_idle = idle - prev_idle\n diff_total = total - prev_total\n diff_usage = (1000 * (diff_total - diff_idle) / diff_total + 5) / 10\n prev_total = total\n prev_idle = idle\n if num == 0:\n time.sleep(1)\n else:\n if diff_usage < 0:\n diff_usage = 0\n else:\n return {'usage': None}\n return {'usage': diff_usage}","def usage_information(self):\n return self._usage_information","def get_cpu_usage(self):\n\t\treturn call_sdk_function('PrlStatCpu_GetCpuUsage', self.handle)","def resource_collect(pid=None):\n try:\n import psutil\n except ImportError:\n return {}\n\n p = psutil.Process(pid or os.getpid())\n return {'cpu_percent': psutil.cpu_percent(),\n 'status': p.status(),\n 'memory_percent': p.memory_percent(),\n 'memory_info_ex': p.memory_info_ex(),\n 'disk_io_counters': metrics.disk_io_counters(),\n 'net_io_counters': metrics.net_io_counters()}","def get_usage(self, loadbalancer=None, start=None, end=None):\n return self._manager.get_usage(loadbalancer=loadbalancer, start=start,\n end=end)","def ListPodMetrics(self, request, context):\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')","def get_pods(self, **kw):\n resource = self.pods\n\n try:\n pod_data = resource.get(**kw)\n log.info(pod_data)\n except exceptions.NotFoundError:\n log.error(\"Failed to get pods: resource not found.\")\n raise\n except Exception:\n log.error(\"Unexpected error.\")\n raise\n\n return [item.metadata.name for item in pod_data.items]","def get(cls, service, name=\"\", option_=\"\") :\n\t\ttry :\n\t\t\tobj = qos_stats()\n\t\t\tif not name :\n\t\t\t\tresponse = obj.stat_resources(service, option_)\n\t\t\treturn response\n\t\texcept Exception as e:\n\t\t\traise e","def get_usage(self):\r\n return self.box_usage","def get_usage_stats(self) -> UsageStats:\n return self._usage","def get_usage(self):\n return self.box_usage","def test_get_deployment_metric(self):\n pass","def _calc_resource_stats(self, interval):\n result = {}\n\n if 'mem' in self.metrics:\n result['mem'] = self._get_mem_info()\n\n if 'disk-space' in self.metrics:\n result['disk-space'] = self.__get_disk_usage(self.engine.artifacts_dir).percent\n\n if 'engine-loop' in self.metrics:\n result['engine-loop'] = self.engine.engine_loop_utilization\n\n if 'conn-all' in self.metrics:\n try:\n # take all connections without address resolution\n output = subprocess.check_output(['netstat', '-an'])\n output_lines = stream_decode(output).split('\\n') # in py3 stream has 'bytes' type\n est_lines = [line for line in output_lines if line.find('EST') != -1]\n result['conn-all'] = len(est_lines)\n except BaseException as exc:\n self.log.debug(\"Failed to get connections info: %s\", exc)\n result['conn-all'] = 0\n\n if 'cpu' in self.metrics:\n result['cpu'] = self._get_cpu_percent()\n\n if 'bytes-recv' in self.metrics or 'bytes-sent' in self.metrics:\n net = self.__get_net_counters()\n if net is not None:\n tx_bytes = int((net.bytes_sent - self._net_counters.bytes_sent) / float(interval))\n rx_bytes = int((net.bytes_recv - self._net_counters.bytes_recv) / float(interval))\n self._net_counters = net\n else:\n rx_bytes = 0.0\n tx_bytes = 0.0\n\n if 'bytes-recv' in self.metrics:\n result['bytes-recv'] = rx_bytes\n if 'bytes-sent' in self.metrics:\n result['bytes-sent'] = tx_bytes\n\n if 'disk-read' in self.metrics or 'disk-write' in self.metrics:\n disk = self.__get_disk_counters()\n if disk is not None:\n dru = int((disk.read_bytes - self._disk_counters.read_bytes) / float(interval))\n dwu = int((disk.write_bytes - self._disk_counters.write_bytes) / float(interval))\n self._disk_counters = disk\n else:\n dru = 0.0\n dwu = 0.0\n\n if 'disk-read' in self.metrics:\n result['disk-read'] = dru\n if 'disk-write' in self.metrics:\n result['disk-write'] = dwu\n\n return result","def get_pool_stats(self, pool):\n svc = self.pool_path % pool\n ret = self.rclient.get(svc)\n if ret.status != restclient.Status.OK:\n exception_msg = (_('Error getting pool stats: '\n 'pool: %(pool)s '\n 'return code: %(ret.status)d '\n 'message: %(ret.data)s.')\n % {'pool': pool,\n 'ret.status': ret.status,\n 'ret.data': ret.data})\n raise exception.InvalidInput(reason=exception_msg)\n val = jsonutils.loads(ret.data)\n if not self._is_pool_owned(val):\n exception_msg = (_('Error pool ownership: '\n 'pool %(pool)s is not owned '\n 'by %(host)s.')\n % {'pool': pool,\n 'host': self.host})\n raise exception.InvalidInput(reason=pool)\n avail = val['pool']['usage']['available']\n used = val['pool']['usage']['used']\n return avail, used","def get_available_resources(threshold, usage, total):\n return dict((host, int(threshold * total[host] - resource))\n for host, resource in usage.items())","def _base_stats(self):\n usage = resource.getrusage(resource.RUSAGE_SELF)\n return {'host': self.application.host,\n 'port': self.application.port,\n 'requests': self.application.counters,\n 'timestamp': int(time.time()),\n 'block': {'input': usage.ru_inblock,\n 'output': usage.ru_oublock},\n 'context_switches': usage.ru_nvcsw + usage.ru_nivcsw,\n 'cpu_time': {'user': usage.ru_utime,\n 'system': usage.ru_stime},\n 'memory_usage': usage.ru_maxrss,\n 'page_faults': {'minor': usage.ru_minflt,\n 'major': usage.ru_majflt},\n 'page_size': resource.getpagesize(),\n 'signals_received': usage.ru_nsignals,\n 'swap_outs': usage.ru_nswap}","def usage(self):\n self.process = subprocess.Popen(\n# \"ps -u %s -o rss | awk '{sum+=$1} END {print sum}'\" % self.username,\n \"ps -p %s -o rss | awk '{sum+=$1} END {print sum}'\" % self.pid,\n shell=True, stdout=subprocess.PIPE)\n self.stdout_list = self.process.communicate()[0].split('\\n')\n return int(self.stdout_list[0])","def cpu_usage():\n return str(_cpu_usage())","def usage():\n return _usage","def test_getusage(self):\n ret = {\"message\": \"No Random.org api key or api version found.\", \"res\": False}\n self.assertDictEqual(random_org.getUsage(), ret)\n\n self.assertDictEqual(\n random_org.getUsage(api_key=\"peW\", api_version=\"1\"),\n {\n \"bitsLeft\": None,\n \"requestsLeft\": None,\n \"res\": True,\n \"totalBits\": None,\n \"totalRequests\": None,\n },\n )","def get_application_api_usage_get(self, applicationId, end, start):\n # TODO: Assuming first server is good - need to make fallback logic\n return self.session.get_any(\"{base}{request_url}\".format(base=self.servers[0],\n request_url=F\"/App/ApiUsage/{applicationId}/\"))","def get_discovery_summary():\n pass","def print_mem_usage(usage):\n for region in usage.keys():\n used = usage[region][\"used\"]\n free = usage[region][\"free\"]\n usage_msg = \"{region}:\\n used: {used} bytes\\n free: {free} bytes\"\n usage_msg = usage_msg.format(region=region, used=used, free=free)\n print(usage_msg)","def usage(self, start, end):\n # Returns a usage dict, based on regions.\n vms = {}\n vm_to_region = {}\n ports = {}\n\n usage_by_dc = {}\n\n writing_to = None\n\n vms = []\n networks = []\n storage = []\n volumes = []\n\n # Object storage is mapped by project_id\n\n for resource in self.resources(start, end):\n # print dir(resource)\n rels = [link[\"rel\"] for link in resource.links if link[\"rel\"] != 'self' ]\n if \"storage.objects\" in rels:\n # Unknown how this data layout happens yet.\n storage.append(Resource(resource, self.conn))\n pass\n elif \"network\" in rels:\n # Have we seen the VM that owns this yet?\n networks.append(Resource(resource , self.conn))\n elif \"volumne\" in rels:\n volumes.append( Resource(resource, self.conn) )\n elif 'instance' in rels:\n vms.append(Resource(resource, self.conn ))\n\n datacenters = {}\n region_tmpl = {\n \"vms\": vms,\n \"network\": networks,\n \"objects\": storage,\n \"volumes\": volumes\n }\n\n return Usage(region_tmpl, start, end, self.conn)","def _get_network_utilization(self):\n options = self.scenario_cfg[\"options\"]\n interval = options.get('interval', 1)\n count = options.get('count', 1)\n\n cmd = \"sudo sar -n DEV %d %d\" % (interval, count)\n\n raw_result = self._execute_command(cmd)\n result = self._filtrate_result(raw_result)\n\n return result","def get_instance_cpu_usage(self, resource, period,\n aggregate, granularity=None):\n pass","def get_disk_usage():\n query = {\n \"type\": \"op\",\n \"cmd\": \"<show><system><disk-space></disk-space></system></show>\",\n }\n\n return __proxy__[\"panos.call\"](query)","def get_cpu_usage():\n cpuInfo1 = read_cpu_usage()\n if not cpuInfo1:\n return None\n\n time.sleep(2)\n\n cpuInfo2 = read_cpu_usage()\n if not cpuInfo2:\n return None\n\n cpuUsage = OrderedDict()\n\n for key in cpuInfo1.keys():\n cpustr1 = cpuInfo1[key]\n cpustr2 = cpuInfo2[key]\n\n if len(cpustr1) >= 7 and len(cpustr2) >= 7:\n\n totalCPUTime1 = long(cpustr1[1]) + long(cpustr1[2]) + long(cpustr1[3]) + long(cpustr1[4]) + long(cpustr1[5]) + long(cpustr1[6]) + long(\n cpustr1[7])\n usedCPUTime1 = long(cpustr1[1]) + long(cpustr1[2]) + long(cpustr1[3])\n\n totalCPUTime2 = float(cpustr2[1]) + long(cpustr2[2]) + long(cpustr2[3]) + long(cpustr2[4]) + long(cpustr2[5]) + long(cpustr2[6]) + long(\n cpustr2[7])\n usedCPUTime2 = float(cpustr2[1]) + long(cpustr2[2]) + long(cpustr2[3])\n\n cpuPct = round((usedCPUTime2 - usedCPUTime1) * 100 / (totalCPUTime2 - totalCPUTime1), 2)\n cpuUsage[key] = cpuPct\n\n return cpuUsage","def get_usage(metric: str, interval_time: int):\n\n count, ignored = check_scale(metric, f'{interval_time}s')\n param = {\n 'start': f'{interval_time}s-ago',\n 'm': f'sum:{metric}' + '{host=*}',\n }\n\n start = time.time()\n resp = urlopen(f'http://{OPENTSDB_HOST}:{OPENTSDB_PORT}/api/query?', param)\n if resp.status == 200:\n _total = json.load(resp)\n else:\n pass\n\n # remove the elements that should be ignored\n valid_source = [i for i in _total if i['tags'] not in ignored]\n\n valid_last_time = []\n for i in valid_source:\n last = sorted(i['dps'].keys())[-1]\n if (start - interval_time) <= int(last) <= (start + interval_time):\n valid_last_time.append(i)\n else:\n pass\n # elements in valid_last_time mean it should be aggregated.\n total = [i['dps'][sorted(i['dps'].keys())[-1]] for i in valid_last_time]\n\n return count, sum(total)","def resource_details(self) -> pulumi.Output[Any]:\n return pulumi.get(self, \"resource_details\")","def resource_details(self) -> pulumi.Output[Any]:\n return pulumi.get(self, \"resource_details\")","def get_cpu_usage():\n\n # use the global 'before' variable\n global before\n\n # take the current CPU usage information\n now = prepare_cpu_usage()\n # compute deltas between current and previous measurements\n delta = [now[i]-before[i] for i in range(len(now))]\n # compute the total (needed for percentages calculation)\n total = sum(delta)\n # save the current measurement to before object\n before = now\n # return the percentage of CPU usage for our 4 categories\n return [(100.0*dt)/total for dt in delta]","def cpuInfo(self, json, i3status_config):\n response = {'full_text': '', 'name': 'cpu_usage'}\n cpu_total, cpu_idle = self.data.cpu()\n used_cpu_percent = 1 - float(cpu_idle-self.cpu_idle)/float(cpu_total-self.cpu_total)\n self.cpu_total = cpu_total\n self.cpu_idle = cpu_idle\n\n \"\"\"\n if used_cpu_percent <= 40/100.0:\n response['color'] = i3status_config['color_good']\n elif used_cpu_percent <= 75/100.0:\n response['color'] = i3status_config['color_degraded']\n else:\n response['color'] = i3status_config['color_bad']\n \"\"\"\n response['color'] = \"#6c71c4\"\n #cpu temp\n CPUTEMP=False\n if CPUTEMP:\n cputemp=subprocess.check_output('sensors | grep \"CPU Temp\" | cut -f 2 -d \"+\" | cut -f 1 -d \" \"',shell=True)\n cputemp=cputemp[:-1].decode('utf-8')\n response['full_text'] = \" %.2f%%\" % (used_cpu_percent*100) +\" \"+cputemp\n else:\n \tresponse['full_text'] = \" %.2f%%\" % (used_cpu_percent*100)\n\n #cache the status for 10 seconds\n response['cached_until'] = time() + 10\n\n return (0, response)","def backend_usages(service):\n\n return service.backend_usages.list()","def test_rest_v20_dd_systems_systemid_stats_capacity_get(self):\n pass","def show_resource_pool(client, private_cloud, resource_pool, location):\n return client.get(location, private_cloud, resource_pool)","def project_usage(selfself, manager: str, package: str) -> Any:\n\n return search_api(\"pproject_usage\", manager, package)","def get_host_cpu_usage(self, resource, period, aggregate,\n granularity=None):\n pass","def gather_metric(self):\n result = self._shell.run(self.KERNEL_COMMAND).stdout\n response = {self.KERNEL_RELEASE: result}\n return response","def get_cpu_usage(cls):\n\n cpu_stats = psutil.cpu_times_percent(percpu=False)\n cpu_stats_dict = { StatsKeys.CPU :\n {\n StatsKeys.IDLE : cpu_stats.idle,\n StatsKeys.SYSTEM : cpu_stats.system,\n StatsKeys.USER : cpu_stats.user,\n StatsKeys.COUNT : len(psutil.cpu_times(percpu=True))\n }\n }\n logger.debug(\"CPU stats: {}\".format(cpu_stats_dict))\n\n return cpu_stats_dict","def test_read_cluster_resource_quota_status(self):\n pass","def get_mem_usage():\n \n with open('/proc/meminfo') as f:\n for line in f:\n if line.startswith('MemTotal:'):\n mem_total = int(line.split()[1])\n elif line.startswith('MemFree:'):\n mem_free = int(line.split()[1])\n elif line.startswith('VmallocTotal:'):\n vm_total = int(line.split()[1])\n elif line.startswith('Cached:'):\n mem_cached = int(line.split()[1])\n \n return {\n 'total': mem_total,\n 'res': mem_total - mem_free,\n 'virt': vm_total,\n 'cached': mem_cached\n }","def collect_metrics():\n p = os.path.join(os.sep, \"mnt\", \"glusterfs\")\n mount_stats = os.statvfs(p)\n # block size * total blocks\n total_space = mount_stats.f_blocks * mount_stats.f_bsize\n free_space = mount_stats.f_bfree * mount_stats.f_bsize\n # capsize only operates on i64 values\n used_space = total_space - free_space\n gb_used = used_space / 1024 / 1024 / 1024\n\n # log!(format!(\"Collecting metric gb-used {}\", gb_used), Info)\n add_metric(\"gb-used\", \"{}\".format(gb_used))","def get_usage(self, loadbalancer=None, start=None, end=None):\n if start is end is None:\n period = None\n else:\n parts = []\n startStr = utils.iso_time_string(start)\n if startStr:\n parts.append(\"startTime=%s\" % startStr)\n endStr = utils.iso_time_string(end)\n if endStr:\n parts.append(\"endTime=%s\" % endStr)\n period = \"&\".join(parts).strip(\"&\")\n if loadbalancer is None:\n uri = \"/loadbalancers/usage\"\n else:\n uri = \"/loadbalancers/%s/usage\" % utils.get_id(loadbalancer)\n if period:\n uri = \"%s?%s\" % (uri, period)\n resp, body = self.api.method_get(uri)\n return body","def read_cpu_usage():\n cpuInfo = OrderedDict()\n\n with open('/proc/stat') as f:\n for line in f:\n l = line.split()\n if len(l) < 5:\n continue\n if l[0].startswith('cpu'):\n cpuInfo[l[0]] = l\n\n return cpuInfo","def get_statistics(self) -> Optional[ResourceStatistics]:\n return None","def gather_info_and_display():\n # Obtain total rss displayed in memory.stat for each group,\n # container and service.\n try:\n output_mem = pipe_command(GREP_CMD, AWK_CMD, cwd=MEMPATH)\n LOG.debug(\n 'command: %s\\n%s',\n \"grep -rs total_rss '/sys/fs/cgroup/memory/' \"\n \"| awk '$2>0{print$0}' \",\n output_mem)\n except subprocess.CalledProcessError as error:\n LOG.error('Could not get total_rss memory, error=%s', error)\n return 1\n\n mem_info = get_meminfo()\n pt_groups = gather_groups_memory(output_mem)\n pt_cont = gather_containers_memory(output_mem)\n pt_serv = sys_service_memory()\n\n # Dump the tables out\n print('\\nPer groups memory usage:')\n\n # Get string to be printed and create list of elements separated by \\n\n list_of_table_lines = pt_groups.get_string().split('\\n')\n\n # Use the first line (+---+-- ...) as horizontal rule to insert later\n horizontal_line = list_of_table_lines[0]\n\n # Print the table, except last two lines ( \"Total\" row + final separator).\n print(\"\\n\".join(list_of_table_lines[:-2]))\n # Print separator, and finally the \"Total\" row.\n print(horizontal_line)\n print(\"\\n\".join(list_of_table_lines[-2:]))\n\n pt_namespc = prettytable.PrettyTable(\n ['Namespace',\n 'Resident Set Size (MiB)',\n ], caching=False)\n pt_namespc.align = 'l'\n pt_namespc.align['Resident Set Size (MiB)'] = 'r'\n\n print('\\nPer namespace memory usage:')\n for n_s in MEMORY['namespaces']:\n pt_namespc.add_row(\n [n_s,\n MEMORY['namespaces'][n_s],\n ])\n print(pt_namespc)\n\n print('\\nPer container memory usage:')\n print(pt_cont)\n\n print('\\nPer service memory usage:')\n print(pt_serv)\n\n base_mebib = 0.0\n k8s_system = 0.0\n k8s_addon = 0.0\n platform_memory_percent = 0.0\n\n # Calculate base memory usage (i.e., normal memory, exclude K8S and VMs)\n # e.g., docker, system.slice, user.slice\n for group in MEMORY['cgroups']:\n if group in BASE_GROUPS:\n base_mebib += float(MEMORY['cgroups'][group])\n\n # K8S platform system usage (essential) and addons usage (non-essential)\n for n_s in MEMORY['namespaces']:\n if n_s in K8S_NAMESPACE_SYSTEM:\n k8s_system += MEMORY['namespaces'][n_s]\n elif n_s in K8S_NAMESPACE_ADDON:\n k8s_addon += MEMORY['namespaces'][n_s]\n\n # Calculate platform memory usage\n platform_mebib = base_mebib + k8s_system\n\n anon_mebib = float(mem_to_mebibytes(\n mem_info['Active(anon)'] + mem_info['Inactive(anon)'])) * KBYTE\n avail_mebib = float(mem_to_mebibytes(\n mem_info['MemAvailable'])) * KBYTE\n total_mebib = float(anon_mebib + avail_mebib)\n\n anon_percent = py2_round(100 * anon_mebib / total_mebib, DECIMAL_DIGITS) # pylint: disable=W1619\n\n reserved_mebib = get_platform_reserved_memory()\n # Calculate platform memory in terms of percent reserved\n if reserved_mebib > 0.0:\n platform_memory_percent = py2_round(\n 100 * platform_mebib / reserved_mebib, DECIMAL_DIGITS) # pylint: disable=W1619\n\n pt_platf = prettytable.PrettyTable(\n ['Reserved',\n 'Platform',\n 'Base',\n 'K8s Platform system',\n 'k8s-addon'\n ], caching=False)\n pt_platf.align = 'l'\n\n pt_platf.add_row(\n [reserved_mebib,\n '{} ({}%)'.format(platform_mebib, platform_memory_percent),\n base_mebib,\n k8s_system,\n k8s_addon\n ])\n print('\\nPlatform memory usage in MiB:')\n print(pt_platf)\n\n pt_4k = prettytable.PrettyTable(\n ['Anon',\n 'Cgroup-rss',\n 'Available',\n 'Total'\n ], caching=False)\n pt_4k.align = 'l'\n\n pt_4k.add_row(\n ['{} ({}%)'.format(anon_mebib, anon_percent),\n MEMORY['cgroups']['total_rss'],\n avail_mebib,\n total_mebib\n ])\n\n print('\\n4K memory usage in MiB:')\n print(pt_4k)\n\n return 0","def get_resource_status(type, full_name=\"\", label=\"\", must_exist=True):\n cmd = \"kubectl get {} --no-headers --all-namespaces \" \\\n \"-o wide --selector \\\"{}\\\" \" \\\n \"| grep \\\"{}\\\" | awk '{{print $1 \\\" \\\" $2}}'\"\n cmd = cmd.format(type, label, full_name)\n try:\n encoded_output = subprocess.check_output(\n cmd, shell=True, stderr=subprocess.STDOUT,\n )\n except subprocess.CalledProcessError as exc:\n log.warning(\"command to get status of {} has \"\n \"failed. error code: \"\n \"{} {}\".format(full_name,\n exc.returncode, exc.output))\n raise RuntimeError(\n \"command to get status of {} has \"\n \"failed. error code: \"\n \"{} {}\".format(full_name,\n exc.returncode, exc.output))\n output = encoded_output.decode()\n if output == \"\":\n if not must_exist:\n return None\n log.warning(\n \"{} \\\"{}\\\" with label \\\"{}\\\" can't be found in \"\n \"the cluster\".format(type, full_name, label))\n raise RuntimeError(\n \"{} {} with label {} can't be found in the cluster\".format(\n type, full_name, label))\n # Example line:\n # kube-system cilium\n split_line = output.split(' ')\n return ResourceStatus(namespace=split_line[0],\n name=split_line[1])","def get_mem_usage(**kwargs):\n try:\n con_mem_data_list = kwargs[\"con\"]._client.get_memory(\n session=kwargs[\"con\"]._session, memory_level=kwargs[\"mem_type\"]\n )\n usedram = 0\n freeram = 0\n for con_mem_data in con_mem_data_list:\n page_size = con_mem_data.page_size\n node_memory_data_list = con_mem_data.node_memory_data\n for node_memory_data in node_memory_data_list:\n ram = node_memory_data.num_pages * page_size\n is_free = node_memory_data.is_free\n if is_free:\n freeram += ram\n else:\n usedram += ram\n totalallocated = usedram + freeram\n if totalallocated > 0:\n totalallocated = round(totalallocated / 1024 / 1024, 1)\n usedram = round(usedram / 1024 / 1024, 1)\n freeram = round(freeram / 1024 / 1024, 1)\n ramusage = {}\n ramusage[\"usedram\"] = usedram\n ramusage[\"freeram\"] = freeram\n ramusage[\"totalallocated\"] = totalallocated\n ramusage[\"errormessage\"] = \"\"\n except Exception as e:\n errormessage = \"Get memory failed with error: \" + str(e)\n logging.error(errormessage)\n ramusage[\"errormessage\"] = errormessage\n return ramusage","def describe_resource(namespace, resource_type, k8s_cli, selector=\"\", resource_name=\"\"):\n cmd = \"{} describe -n {} {} {} {}\".format(k8s_cli, namespace, resource_type, resource_name, selector)\n error_template = \"Namespace '{}': Failed to describe {} resource: {{}}.\".format(namespace, resource_type)\n missing_resource_template = f\"Namespace '{namespace}': Skip collecting description for {resource_type}. \" \\\n f\"Server has no resource of type {resource_type}\"\n return run_shell_command_with_retries(cmd, KUBCTL_GET_YAML_RETRIES, error_template, missing_resource_template)","def get_cpu_usage():\n return psutil.cpu_percent()","def get_swap_usage():\n\n swap_usage = {'total' : 0, 'used' : 0}\n swapinfo = subprocess.Popen(['free', '-m'], shell=False, stdout=subprocess.PIPE)\n swapinfo.stdout.readline()\n swapinfo.stdout.readline()\n swapinfo.stdout.readline()\n total_used = swapinfo.stdout.readline()\n swap_usage['total'] += float(total_used.split()[1])\n swap_usage['used'] += float(total_used.split()[2])\n return swap_usage","def usage(self):\r\n return usage.Usage(self)","def metrics(self, request):\n return OtterMetrics(self.store).app.resource()","def health():\n return jsonify(hostname=hostname, uptime=uptime(), \\\n cpu_percent=int(cpu_percent(interval=None, percpu=False)))","def _usage_report_dict(self, root):\n details = {}\n selector = 'table > tbody > tr'\n for (resource, unit, used) in root.cssselect(selector):\n name = resource.findtext('strong').strip()\n details[name] = (used.text.strip(), unit.text.strip())\n return details","def test_get_resource_license_resource_count_list(self):\n pass","def get_pods(namespace, k8s_cli, selector=\"\"):\n if selector:\n selector = '--selector=\"{}\"'.format(selector)\n cmd = '{} get pod -n {} {} -o json '.format(k8s_cli, namespace, selector)\n return_code, out = run_shell_command(cmd)\n if return_code:\n logger.warning(\"Failed to get pods: %s\", out)\n return None\n return json.loads(out)['items']","def get_cpu_usage(*args):\n \n keys = ['us', 'ni', 'sy', 'id', 'wa', 'hi', 'si', 'st'] #usage % to be returned\n \n with open('/proc/stat') as f1:\n with open('/proc/stat') as f2:\n content1 = f1.read() #first collection\n yield {} #yield so that caller can put delay before sampling again\n content2 = f2.read() #second collection\n \n cpu_count = multiprocessing.cpu_count() #total number of cpu cores available\n lines1, lines2 = content1.splitlines(), content2.splitlines()\n data, deltas = {}, {}\n \n #if only one cpu available, read only the first line, else read total cpu count lines starting from the second line\n i, cpu_count = (1, cpu_count + 1) if cpu_count > 1 else (0, 1)\n \n #extract deltas\n while i < cpu_count:\n line_split1 = lines1[i].split()\n line_split2 = lines2[i].split()\n deltas[line_split1[0]] = [int(b) - int(a) for a, b in zip(line_split1[1:], line_split2[1:])]\n i += 1\n \n for key in deltas:\n #calculate the percentage\n total = sum(deltas[key])\n data[key] = dict(zip(keys, [100 - (100 * (float(total - x) / total)) for x in deltas[key]]))\n \n yield data","def test_read_namespaced_applied_cluster_resource_quota(self):\n pass","def sys_service_memory():\n sort_cmd = [\"sort\", \"-k\", \"2nr\"]\n\n p_table = prettytable.PrettyTable(\n ['Service',\n 'Resident Set Size (MiB)',\n ], caching=False)\n p_table.align = 'l'\n p_table.align['Resident Set Size (MiB)'] = 'r'\n\n try:\n output = pipe_command(GREP_CMD, AWK_CMD, sort_cmd,\n cwd=MEMPATH + \"system.slice\")\n LOG.debug(\n 'command: %s\\n%s',\n ' '.join(GREP_CMD + [MEMPATH] + AWK_CMD + sort_cmd), output)\n except subprocess.CalledProcessError as error:\n LOG.error('Could not get total_rss memory, error=%s', error)\n return 1\n\n for line in output.split(\"\\n\"):\n service = line.split(\"memory.stat:total_rss \")[0]\n rss_mem = line.split(\"memory.stat:total_rss \")[-1]\n p_table.add_row(\n [service,\n mem_to_mebibytes(rss_mem),\n ])\n\n # Delete first row wich display total system.slice rss\n p_table.del_row(0)\n return p_table","def getcpuusage(self):\n return ord(self.reg(0x11, write=1))","def watch():\n\n try:\n headers = ('CONTAINER ID', 'NAME', 'CPU %', 'MEM USAGE / LIMIT',\n 'MEM %', 'NET I/O', 'BLOCK I/O', 'PIDS')\n column_width = 20\n for element in headers:\n print(element.ljust(column_width)),\n print('')\n\n for container in CLIENT.containers.list():\n column_width = 20\n stats = container.stats(stream=False)\n\n # Block I/O stats\n blkio = stats.get('blkio_stats').get('io_service_bytes_recursive')\n # in case blkio is empty --> IndexError: list index out of range\n if not blkio:\n blkio_read = '0'\n blkio_write = '0'\n else:\n blkio_read = size(blkio[0].get('value'), system=si)\n blkio_write = size(blkio[1].get('value'), system=si)\n\n # Network stats\n rx_stats = size(stats.get('networks').get('eth0').get('rx_bytes'), system=si)\n tx_stats = size(stats.get('networks').get('eth0').get('tx_bytes'), system=si)\n\n # Memory stats\n mem = stats.get('memory_stats')\n mem_usage = mem.get('stats').get('active_anon')\n mem_limit = mem.get('limit')\n mem_percent = (\"%.2f\"%((mem_usage / mem_limit)*100))\n\n # CPU stats\n # this is taken directly from docker CLIENT:\n # https://github.com/docker/docker/blob/28a7577a029780e4533faf3d057ec9f6c7a10948/api/CLIENT/stats.go#L309\n cpu_percent = 0.0\n cpu = stats.get('cpu_stats')\n pre_cpu = stats.get('precpu_stats')\n cpu_total = cpu.get('cpu_usage').get('total_usage')\n pre_cpu_total = pre_cpu.get('cpu_usage').get('total_usage')\n cpu_count = cpu.get('online_cpus')\n\n cpu_delta = cpu_total - pre_cpu_total\n system_delta = cpu.get('system_cpu_usage') - pre_cpu.get('system_cpu_usage')\n\n if system_delta > 0.0 and cpu_delta > 0.0:\n cpu_percent = (\"%.2f\"%(cpu_delta / system_delta * 100.0 * cpu_count))\n\n # container attributes\n attrs = [(str(container.short_id), str(container.name), str(cpu_percent),\n str(size((mem_usage), system=si) + \" / \" + size((mem_limit), system=si)),\n str(mem_percent), str(rx_stats + \" / \" + tx_stats),\n str(blkio_read + \" / \" + blkio_write),\n str(stats.get('pids_stats').get('current')))]\n\n for row in attrs:\n for element in row:\n print(element.ljust(column_width)),\n print('')\n\n except (docker.errors.NotFound, KeyError, AttributeError):\n print('No such container or container not running!')","def test_list_cluster_resource_quota(self):\n pass","def _get_info_about_available_resources(self, min_ram, min_hdd, min_vcpus):\n vms_count = 0\n for hypervisor in self.nova_cli.hypervisors.list():\n if hypervisor.free_ram_mb >= min_ram:\n if hypervisor.free_disk_gb >= min_hdd:\n if hypervisor.vcpus - hypervisor.vcpus_used >= min_vcpus:\n # We need to determine how many VMs we can run\n # on this hypervisor\n free_cpu = hypervisor.vcpus - hypervisor.vcpus_used\n k1 = int(hypervisor.free_ram_mb / min_ram)\n k2 = int(hypervisor.free_disk_gb / min_hdd)\n k3 = int(free_cpu / min_vcpus)\n vms_count += min(k1, k2, k3)\n return vms_count","def test_read_cluster_resource_quota(self):\n pass","def get_instance_ram_usage(self, resource, period,\n aggregate, granularity=None):\n pass","def cpu_usage(self):\n dsp = c_float()\n stream = c_float()\n geometry = c_float()\n update = c_float()\n total = c_float()\n ckresult(\n _dll.FMOD_System_GetCPUUsage(\n self._ptr,\n byref(dsp),\n byref(stream),\n byref(geometry),\n byref(update),\n byref(total),\n )\n )\n return so(\n dsp=dsp.value,\n stream=stream.value,\n geometry=geometry.value,\n update=update.value,\n total=total.value,\n )","def handle_api_resources_docker(self, http_context):\n\n command = [self.docker, 'stats', '--format', '\\'{{json .}}\\'', '--no-stream', '-a']\n return [json.loads(line) for line in subprocess.check_output(command).decode().splitlines()]","async def get(self):\n config = self.settings['nbresuse_display_config']\n cur_process = psutil.Process()\n all_processes = [cur_process] + cur_process.children(recursive=True)\n limits = {}\n\n mem_usage = dict(line.split() for line in list(open('/sys/fs/cgroup/memory/memory.stat').readlines()))\n\n # Get memory information\n rss = int(mem_usage['rss'])\n mem_limit = int(memory_stats['hierarchical_memory_limit'])\n\n # A better approach would use cpu_affinity to account for the\n # fact that the number of logical CPUs in the system is not\n # necessarily the same as the number of CPUs the process\n # can actually use. But cpu_affinity isn't available for OS X.\n cpu_count = psutil.cpu_count()\n\n if config.track_cpu_percent:\n self.cpu_percent = await self.update_cpu_percent(all_processes)\n\n limits['memory'] = {\n 'rss': mem_limit\n }\n if config.mem_warning_threshold != 0:\n limits['memory']['warn'] = (mem_limit - rss) < (mem_limit * config.mem_warning_threshold)\n\n # Optionally get CPU information\n if config.track_cpu_percent:\n self.cpu_percent = await self.update_cpu_percent(all_processes)\n\n if config.cpu_limit != 0:\n limits['cpu'] = {\n 'cpu': config.cpu_limit\n }\n if config.cpu_warning_threshold != 0:\n limits['cpu']['warn'] = (config.cpu_limit - self.cpu_percent) < (config.cpu_limit * config.cpu_warning_threshold)\n\n metrics = {\n 'rss': rss,\n 'limits': limits,\n }\n if config.track_cpu_percent:\n metrics.update(cpu_percent=self.cpu_percent,\n cpu_count=self.cpu_count)\n\n self.log.debug(\"NBResuse metrics: %s\", metrics)\n self.write(json.dumps(metrics))","def print_usage(self):\r\n print 'Total Usage: %f compute seconds' % self.box_usage\r\n cost = self.box_usage * 0.14\r\n print 'Approximate Cost: $%f' % cost","def get_memory_usage():\n\n memory_usage = {'total' : 0, 'used' : 0}\n meminfo = subprocess.Popen(['free', '-m'], shell=False, stdout=subprocess.PIPE)\n meminfo.stdout.readline()\n total_used = meminfo.stdout.readline()\n memory_usage['total'] = total_used.split()[1]\n memory_usage['used'] = total_used.split()[2]\n return memory_usage","def print_usage(self):\n print('Total Usage: %f compute seconds' % self.box_usage)\n cost = self.box_usage * 0.14\n print('Approximate Cost: $%f' % cost)","def get_resources_output(name: Optional[pulumi.Input[Optional[str]]] = None,\n required_tags: Optional[pulumi.Input[Optional[Mapping[str, str]]]] = None,\n resource_group_name: Optional[pulumi.Input[Optional[str]]] = None,\n type: Optional[pulumi.Input[Optional[str]]] = None,\n opts: Optional[pulumi.InvokeOptions] = None) -> pulumi.Output[GetResourcesResult]:\n ...","def get_space_used():\n fs.get_space_used()","def service_load_metrics(self) -> pulumi.Output[Optional[Sequence['outputs.ServiceLoadMetricDescriptionResponse']]]:\n return pulumi.get(self, \"service_load_metrics\")","def _GetPod(self) -> Dict[str, Any]:\n stdout, _, _ = RunKubectlCommand(['get', 'pod', self.name, '-o', 'yaml'])\n pod = yaml.safe_load(stdout)\n self.ip_address = pod.get('status', {}).get('podIP')\n return pod","def allocation_resource_tags(self) -> pulumi.Output[Optional[Mapping[str, str]]]:\n return pulumi.get(self, \"allocation_resource_tags\")","def get_capacity():\n fs.get_capacity()","def usage(self, host):","def ip_allocations(self) -> Optional[Sequence['outputs.SubResourceResponse']]:\n return pulumi.get(self, \"ip_allocations\")","def get_info(self):\n return {'q_ref': self.q_ref, 'v_ref': self.v_ref, 'U': self.U, 'type': 'POD'}","def get_config_metrics():\n\n metrics = {'disk_usage': 'YES',\n 'cpu_percent': 'YES',\n 'memory_info': 'YES',\n 'cpu_stats': 'YES'}\n\n return metrics","def get_usage(self, source):\n return sum(i.quantity for i in self.items.all() if i.source == source)"],"string":"[\n \"def get_resource_info(cluster_id, kind, namespace_id=None, pods_list=None):\\n if pods_list is None:\\n pods_list = []\\n capacity = get_cluster_capacity_info(cluster_id),\\n usage = get_cluster_usage_info(cluster_id, kind, namespace_id, pods_list)\\n if capacity[0]['cpu'] != 0 and capacity[0]['memory'] != 0:\\n resource_info = {\\n \\\"capacity\\\": capacity[0],\\n \\\"usage\\\": {\\n \\\"cpu\\\": usage['cpu'],\\n \\\"cpu_percentage\\\": float(round(decimal.Decimal(usage['cpu'] / capacity[0]['cpu'])*100, 2)),\\n \\\"memory\\\": usage['memory'],\\n \\\"memory_percentage\\\": float(round(decimal.Decimal(usage['memory'] / capacity[0]['memory'])*100, 2))\\n }\\n }\\n else:\\n resource_info = {\\n \\\"capacity\\\": capacity[0],\\n \\\"usage\\\": {\\n \\\"cpu\\\": usage['cpu'],\\n \\\"cpu_percentage\\\": 0.0,\\n \\\"memory\\\": usage['memory'],\\n \\\"memory_percentage\\\": 0.0\\n }\\n }\\n\\n return resource_info\",\n \"def get_resource_usages(self, project_id):\\n try:\\n # The API call does not give usage for keypair, fixed ips &\\n # metadata items. Have raised a bug for that.\\n limits = self.nova_client.limits.get(\\n tenant_id=project_id).to_dict()\\n resource_usage = collections.defaultdict(dict)\\n resource_usage['ram'] = limits['absolute']['totalRAMUsed']\\n resource_usage['cores'] = limits['absolute']['totalCoresUsed']\\n resource_usage['instances'] = \\\\\\n limits['absolute']['totalInstancesUsed']\\n # If neutron is not enabled, calculate below resources from nova\\n if self.no_neutron:\\n resource_usage['security_groups'] = \\\\\\n limits['absolute']['totalSecurityGroupsUsed']\\n resource_usage['floating_ips'] = \\\\\\n limits['absolute']['totalFloatingIpsUsed']\\n # For time being, keypair is calculated in below manner.\\n resource_usage['key_pairs'] = \\\\\\n len(self.nova_client.keypairs.list())\\n return resource_usage\\n except exceptions.InternalError:\\n raise\",\n \"def getUsageInfo(self):\\n return self.jsonRequest(\\\"/api/v1/usage\\\", { \\\"apiKey\\\": self._apiKey })\",\n \"def get_kong_node_usage_metrics(opts):\\n\\n url = \\\"{0}/status\\\".format(opts['base_url'])\\n\\n r = requests.get(url)\\n try:\\n r.raise_for_status()\\n except requests.exceptions.RequestException as e:\\n logging.debug(\\\"http response body - %s\\\", r.text)\\n logging.error(\\\"An exception occurred: (%s)\\\", e)\\n sys.exit(2)\\n\\n print r.text\\n\\n return True\",\n \"def get_usage_info(self):\\n\\n usage_info = resource.getrusage(resource.RUSAGE_SELF)\\n user_cpu = usage_info[0]\\n system_cpu = usage_info[1]\\n rss_size = usage_info[2]\\n\\n return user_cpu, system_cpu, rss_size\",\n \"def get_usages(self):\\n return self.client._perform_json(\\\"GET\\\", \\\"/projects/%s/managedfolders/%s/usages\\\" % (self.project_key, self.odb_id))\",\n \"def usage_metrics(self) -> Sequence['outputs.GetServiceQuotaUsageMetricResult']:\\n return pulumi.get(self, \\\"usage_metrics\\\")\",\n \"def get_available_resource(self, nodename):\\n curent_time = time.time()\\n if curent_time - self.cleanup_time > CONF.azure.cleanup_span:\\n self.cleanup_time = curent_time\\n self._cleanup_deleted_os_disks()\\n self._cleanup_deleted_nics()\\n usage_family = 'basicAFamily'\\n try:\\n page = self.compute.usage.list(CONF.azure.location)\\n except Exception as e:\\n msg = six.text_type(e)\\n LOG.exception(msg)\\n ex = exception.ComputeUsageListFailure(reason=six.text_type(e))\\n raise ex\\n usages = [i for i in page]\\n cores = 0\\n cores_used = 0\\n for i in usages:\\n if hasattr(i, 'name') and hasattr(i.name, 'value'):\\n if usage_family == i.name.value:\\n cores = i.limit if hasattr(i, 'limit') else 0\\n cores_used = i.current_value \\\\\\n if hasattr(i, 'current_value') else 0\\n break\\n return {'vcpus': cores,\\n 'memory_mb': 100000000,\\n 'local_gb': 100000000,\\n 'vcpus_used': cores_used,\\n 'memory_mb_used': 0,\\n 'local_gb_used': 0,\\n 'hypervisor_type': hv_type.HYPERV,\\n 'hypervisor_version': 300,\\n 'hypervisor_hostname': nodename,\\n 'cpu_info': '{\\\"model\\\": [\\\"Intel(R) Xeon(R) CPU E5-2670 0 @ '\\n '2.60GHz\\\"], \\\"topology\\\": {\\\"cores\\\": 16, \\\"threads\\\": '\\n '32}}',\\n 'supported_instances': [(arch.I686, hv_type.HYPERV,\\n vm_mode.HVM),\\n (arch.X86_64, hv_type.HYPERV,\\n vm_mode.HVM)],\\n 'numa_topology': None\\n }\",\n \"def memory_get_usage():\\n raise NotImplementedError()\",\n \"def resource_usage_export_config(self) -> 'outputs.ResourceUsageExportConfigResponse':\\n return pulumi.get(self, \\\"resource_usage_export_config\\\")\",\n \"def get_usage(self, start=None, end=None):\\n return self.manager.get_usage(self, start=start, end=end)\",\n \"def get_resources():\\n # Acquire the lock...\\n get_resources_lock.acquire()\\n\\n # ...but always release it\\n try:\\n # Construct the dictionaries as copies from nanny\\n (limits,usage) = nanny.get_resource_information()\\n\\n\\n # Calculate all the usage's\\n pid = os.getpid()\\n\\n # Get CPU and memory, this is thread specific\\n if ostype in [\\\"Linux\\\", \\\"Darwin\\\"]:\\n \\n # Get CPU first, then memory\\n usage[\\\"cpu\\\"] = os_api.get_process_cpu_time(pid)\\n\\n # This uses the cached PID data from the CPU check\\n usage[\\\"memory\\\"] = os_api.get_process_rss()\\n\\n # Get the thread specific CPU usage\\n usage[\\\"threadcpu\\\"] = os_api.get_current_thread_cpu_time() \\n\\n\\n # Windows Specific versions\\n elif ostype in [\\\"Windows\\\"]:\\n \\n # Get the CPU time\\n usage[\\\"cpu\\\"] = windows_api.get_process_cpu_time(pid)\\n\\n # Get the memory, use the resident set size\\n usage[\\\"memory\\\"] = windows_api.process_memory_info(pid)['WorkingSetSize'] \\n\\n # Get thread-level CPU \\n usage[\\\"threadcpu\\\"] = windows_api.get_current_thread_cpu_time()\\n\\n # Unknown OS\\n else:\\n raise EnvironmentError(\\\"Unsupported Platform!\\\")\\n\\n # Use the cached disk used amount\\n usage[\\\"diskused\\\"] = cached_disk_used\\n\\n finally:\\n # Release the lock\\n get_resources_lock.release()\\n\\n # Copy the stop times\\n stoptimes = process_stopped_timeline[:]\\n\\n # Return the dictionaries and the stoptimes\\n return (limits,usage,stoptimes)\",\n \"def get_resource_information():\\n\\n\\n # the resources we are allowed to use is easy. We just copy this...\\n resource_limit_dict = _resources_allowed_dict.copy()\\n\\n \\n # from the other dict, we only take the resource information. (this omits\\n # locks and timing information that isn't needed)\\n\\n # first, let's do the easy thing, the quantity resources. These are just \\n # floats\\n resource_use_dict = {}\\n for resourcename in resource_constants.quantity_resources:\\n resource_use_dict[resourcename] = _resources_consumed_dict[resourcename]\\n\\n # for the fungible resources (files opened, etc,), we only need a count...\\n for resourcename in resource_constants.fungible_item_resources:\\n resource_use_dict[resourcename] = len(_resources_consumed_dict[resourcename])\\n\\n # for the individual item resources (ports, etc,), we copy the set...\\n for resourcename in resource_constants.individual_item_resources:\\n resource_use_dict[resourcename] = _resources_consumed_dict[resourcename].copy()\\n\\n # and that's it!\\n return (resource_limit_dict, resource_use_dict)\",\n \"def get_cpu_usage():\\n process_details = RU_OBJ.get_curr_processes()\\n return json.dumps(sorted(process_details, key=lambda k: k['name']))\",\n \"def ok(self, results):\\n return \\\"{:5.2f}% capacity used\\\".format(\\n results[\\\"usage\\\"].resource.usage_ratio * 100.0\\n )\",\n \"def get_used_resource_overview(self, resource_name):\\n page_overview = self._page_overview()\\n return page_overview.row_avaible_resource.value(resource_name)\",\n \"def get_cpu_usage(conn):\\n prev_idle = 0\\n prev_total = 0\\n cpu = conn.getCPUStats(-1, 0)\\n if type(cpu) == dict:\\n for num in range(2):\\n idle = list(conn.getCPUStats(-1, 0).values())[1]\\n total = sum(list(conn.getCPUStats(-1, 0).values()))\\n diff_idle = idle - prev_idle\\n diff_total = total - prev_total\\n diff_usage = (1000 * (diff_total - diff_idle) / diff_total + 5) / 10\\n prev_total = total\\n prev_idle = idle\\n if num == 0:\\n time.sleep(1)\\n else:\\n if diff_usage < 0:\\n diff_usage = 0\\n else:\\n return {'usage': None}\\n return {'usage': diff_usage}\",\n \"def usage_information(self):\\n return self._usage_information\",\n \"def get_cpu_usage(self):\\n\\t\\treturn call_sdk_function('PrlStatCpu_GetCpuUsage', self.handle)\",\n \"def resource_collect(pid=None):\\n try:\\n import psutil\\n except ImportError:\\n return {}\\n\\n p = psutil.Process(pid or os.getpid())\\n return {'cpu_percent': psutil.cpu_percent(),\\n 'status': p.status(),\\n 'memory_percent': p.memory_percent(),\\n 'memory_info_ex': p.memory_info_ex(),\\n 'disk_io_counters': metrics.disk_io_counters(),\\n 'net_io_counters': metrics.net_io_counters()}\",\n \"def get_usage(self, loadbalancer=None, start=None, end=None):\\n return self._manager.get_usage(loadbalancer=loadbalancer, start=start,\\n end=end)\",\n \"def ListPodMetrics(self, request, context):\\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\\n context.set_details('Method not implemented!')\\n raise NotImplementedError('Method not implemented!')\",\n \"def get_pods(self, **kw):\\n resource = self.pods\\n\\n try:\\n pod_data = resource.get(**kw)\\n log.info(pod_data)\\n except exceptions.NotFoundError:\\n log.error(\\\"Failed to get pods: resource not found.\\\")\\n raise\\n except Exception:\\n log.error(\\\"Unexpected error.\\\")\\n raise\\n\\n return [item.metadata.name for item in pod_data.items]\",\n \"def get(cls, service, name=\\\"\\\", option_=\\\"\\\") :\\n\\t\\ttry :\\n\\t\\t\\tobj = qos_stats()\\n\\t\\t\\tif not name :\\n\\t\\t\\t\\tresponse = obj.stat_resources(service, option_)\\n\\t\\t\\treturn response\\n\\t\\texcept Exception as e:\\n\\t\\t\\traise e\",\n \"def get_usage(self):\\r\\n return self.box_usage\",\n \"def get_usage_stats(self) -> UsageStats:\\n return self._usage\",\n \"def get_usage(self):\\n return self.box_usage\",\n \"def test_get_deployment_metric(self):\\n pass\",\n \"def _calc_resource_stats(self, interval):\\n result = {}\\n\\n if 'mem' in self.metrics:\\n result['mem'] = self._get_mem_info()\\n\\n if 'disk-space' in self.metrics:\\n result['disk-space'] = self.__get_disk_usage(self.engine.artifacts_dir).percent\\n\\n if 'engine-loop' in self.metrics:\\n result['engine-loop'] = self.engine.engine_loop_utilization\\n\\n if 'conn-all' in self.metrics:\\n try:\\n # take all connections without address resolution\\n output = subprocess.check_output(['netstat', '-an'])\\n output_lines = stream_decode(output).split('\\\\n') # in py3 stream has 'bytes' type\\n est_lines = [line for line in output_lines if line.find('EST') != -1]\\n result['conn-all'] = len(est_lines)\\n except BaseException as exc:\\n self.log.debug(\\\"Failed to get connections info: %s\\\", exc)\\n result['conn-all'] = 0\\n\\n if 'cpu' in self.metrics:\\n result['cpu'] = self._get_cpu_percent()\\n\\n if 'bytes-recv' in self.metrics or 'bytes-sent' in self.metrics:\\n net = self.__get_net_counters()\\n if net is not None:\\n tx_bytes = int((net.bytes_sent - self._net_counters.bytes_sent) / float(interval))\\n rx_bytes = int((net.bytes_recv - self._net_counters.bytes_recv) / float(interval))\\n self._net_counters = net\\n else:\\n rx_bytes = 0.0\\n tx_bytes = 0.0\\n\\n if 'bytes-recv' in self.metrics:\\n result['bytes-recv'] = rx_bytes\\n if 'bytes-sent' in self.metrics:\\n result['bytes-sent'] = tx_bytes\\n\\n if 'disk-read' in self.metrics or 'disk-write' in self.metrics:\\n disk = self.__get_disk_counters()\\n if disk is not None:\\n dru = int((disk.read_bytes - self._disk_counters.read_bytes) / float(interval))\\n dwu = int((disk.write_bytes - self._disk_counters.write_bytes) / float(interval))\\n self._disk_counters = disk\\n else:\\n dru = 0.0\\n dwu = 0.0\\n\\n if 'disk-read' in self.metrics:\\n result['disk-read'] = dru\\n if 'disk-write' in self.metrics:\\n result['disk-write'] = dwu\\n\\n return result\",\n \"def get_pool_stats(self, pool):\\n svc = self.pool_path % pool\\n ret = self.rclient.get(svc)\\n if ret.status != restclient.Status.OK:\\n exception_msg = (_('Error getting pool stats: '\\n 'pool: %(pool)s '\\n 'return code: %(ret.status)d '\\n 'message: %(ret.data)s.')\\n % {'pool': pool,\\n 'ret.status': ret.status,\\n 'ret.data': ret.data})\\n raise exception.InvalidInput(reason=exception_msg)\\n val = jsonutils.loads(ret.data)\\n if not self._is_pool_owned(val):\\n exception_msg = (_('Error pool ownership: '\\n 'pool %(pool)s is not owned '\\n 'by %(host)s.')\\n % {'pool': pool,\\n 'host': self.host})\\n raise exception.InvalidInput(reason=pool)\\n avail = val['pool']['usage']['available']\\n used = val['pool']['usage']['used']\\n return avail, used\",\n \"def get_available_resources(threshold, usage, total):\\n return dict((host, int(threshold * total[host] - resource))\\n for host, resource in usage.items())\",\n \"def _base_stats(self):\\n usage = resource.getrusage(resource.RUSAGE_SELF)\\n return {'host': self.application.host,\\n 'port': self.application.port,\\n 'requests': self.application.counters,\\n 'timestamp': int(time.time()),\\n 'block': {'input': usage.ru_inblock,\\n 'output': usage.ru_oublock},\\n 'context_switches': usage.ru_nvcsw + usage.ru_nivcsw,\\n 'cpu_time': {'user': usage.ru_utime,\\n 'system': usage.ru_stime},\\n 'memory_usage': usage.ru_maxrss,\\n 'page_faults': {'minor': usage.ru_minflt,\\n 'major': usage.ru_majflt},\\n 'page_size': resource.getpagesize(),\\n 'signals_received': usage.ru_nsignals,\\n 'swap_outs': usage.ru_nswap}\",\n \"def usage(self):\\n self.process = subprocess.Popen(\\n# \\\"ps -u %s -o rss | awk '{sum+=$1} END {print sum}'\\\" % self.username,\\n \\\"ps -p %s -o rss | awk '{sum+=$1} END {print sum}'\\\" % self.pid,\\n shell=True, stdout=subprocess.PIPE)\\n self.stdout_list = self.process.communicate()[0].split('\\\\n')\\n return int(self.stdout_list[0])\",\n \"def cpu_usage():\\n return str(_cpu_usage())\",\n \"def usage():\\n return _usage\",\n \"def test_getusage(self):\\n ret = {\\\"message\\\": \\\"No Random.org api key or api version found.\\\", \\\"res\\\": False}\\n self.assertDictEqual(random_org.getUsage(), ret)\\n\\n self.assertDictEqual(\\n random_org.getUsage(api_key=\\\"peW\\\", api_version=\\\"1\\\"),\\n {\\n \\\"bitsLeft\\\": None,\\n \\\"requestsLeft\\\": None,\\n \\\"res\\\": True,\\n \\\"totalBits\\\": None,\\n \\\"totalRequests\\\": None,\\n },\\n )\",\n \"def get_application_api_usage_get(self, applicationId, end, start):\\n # TODO: Assuming first server is good - need to make fallback logic\\n return self.session.get_any(\\\"{base}{request_url}\\\".format(base=self.servers[0],\\n request_url=F\\\"/App/ApiUsage/{applicationId}/\\\"))\",\n \"def get_discovery_summary():\\n pass\",\n \"def print_mem_usage(usage):\\n for region in usage.keys():\\n used = usage[region][\\\"used\\\"]\\n free = usage[region][\\\"free\\\"]\\n usage_msg = \\\"{region}:\\\\n used: {used} bytes\\\\n free: {free} bytes\\\"\\n usage_msg = usage_msg.format(region=region, used=used, free=free)\\n print(usage_msg)\",\n \"def usage(self, start, end):\\n # Returns a usage dict, based on regions.\\n vms = {}\\n vm_to_region = {}\\n ports = {}\\n\\n usage_by_dc = {}\\n\\n writing_to = None\\n\\n vms = []\\n networks = []\\n storage = []\\n volumes = []\\n\\n # Object storage is mapped by project_id\\n\\n for resource in self.resources(start, end):\\n # print dir(resource)\\n rels = [link[\\\"rel\\\"] for link in resource.links if link[\\\"rel\\\"] != 'self' ]\\n if \\\"storage.objects\\\" in rels:\\n # Unknown how this data layout happens yet.\\n storage.append(Resource(resource, self.conn))\\n pass\\n elif \\\"network\\\" in rels:\\n # Have we seen the VM that owns this yet?\\n networks.append(Resource(resource , self.conn))\\n elif \\\"volumne\\\" in rels:\\n volumes.append( Resource(resource, self.conn) )\\n elif 'instance' in rels:\\n vms.append(Resource(resource, self.conn ))\\n\\n datacenters = {}\\n region_tmpl = {\\n \\\"vms\\\": vms,\\n \\\"network\\\": networks,\\n \\\"objects\\\": storage,\\n \\\"volumes\\\": volumes\\n }\\n\\n return Usage(region_tmpl, start, end, self.conn)\",\n \"def _get_network_utilization(self):\\n options = self.scenario_cfg[\\\"options\\\"]\\n interval = options.get('interval', 1)\\n count = options.get('count', 1)\\n\\n cmd = \\\"sudo sar -n DEV %d %d\\\" % (interval, count)\\n\\n raw_result = self._execute_command(cmd)\\n result = self._filtrate_result(raw_result)\\n\\n return result\",\n \"def get_instance_cpu_usage(self, resource, period,\\n aggregate, granularity=None):\\n pass\",\n \"def get_disk_usage():\\n query = {\\n \\\"type\\\": \\\"op\\\",\\n \\\"cmd\\\": \\\"<show><system><disk-space></disk-space></system></show>\\\",\\n }\\n\\n return __proxy__[\\\"panos.call\\\"](query)\",\n \"def get_cpu_usage():\\n cpuInfo1 = read_cpu_usage()\\n if not cpuInfo1:\\n return None\\n\\n time.sleep(2)\\n\\n cpuInfo2 = read_cpu_usage()\\n if not cpuInfo2:\\n return None\\n\\n cpuUsage = OrderedDict()\\n\\n for key in cpuInfo1.keys():\\n cpustr1 = cpuInfo1[key]\\n cpustr2 = cpuInfo2[key]\\n\\n if len(cpustr1) >= 7 and len(cpustr2) >= 7:\\n\\n totalCPUTime1 = long(cpustr1[1]) + long(cpustr1[2]) + long(cpustr1[3]) + long(cpustr1[4]) + long(cpustr1[5]) + long(cpustr1[6]) + long(\\n cpustr1[7])\\n usedCPUTime1 = long(cpustr1[1]) + long(cpustr1[2]) + long(cpustr1[3])\\n\\n totalCPUTime2 = float(cpustr2[1]) + long(cpustr2[2]) + long(cpustr2[3]) + long(cpustr2[4]) + long(cpustr2[5]) + long(cpustr2[6]) + long(\\n cpustr2[7])\\n usedCPUTime2 = float(cpustr2[1]) + long(cpustr2[2]) + long(cpustr2[3])\\n\\n cpuPct = round((usedCPUTime2 - usedCPUTime1) * 100 / (totalCPUTime2 - totalCPUTime1), 2)\\n cpuUsage[key] = cpuPct\\n\\n return cpuUsage\",\n \"def get_usage(metric: str, interval_time: int):\\n\\n count, ignored = check_scale(metric, f'{interval_time}s')\\n param = {\\n 'start': f'{interval_time}s-ago',\\n 'm': f'sum:{metric}' + '{host=*}',\\n }\\n\\n start = time.time()\\n resp = urlopen(f'http://{OPENTSDB_HOST}:{OPENTSDB_PORT}/api/query?', param)\\n if resp.status == 200:\\n _total = json.load(resp)\\n else:\\n pass\\n\\n # remove the elements that should be ignored\\n valid_source = [i for i in _total if i['tags'] not in ignored]\\n\\n valid_last_time = []\\n for i in valid_source:\\n last = sorted(i['dps'].keys())[-1]\\n if (start - interval_time) <= int(last) <= (start + interval_time):\\n valid_last_time.append(i)\\n else:\\n pass\\n # elements in valid_last_time mean it should be aggregated.\\n total = [i['dps'][sorted(i['dps'].keys())[-1]] for i in valid_last_time]\\n\\n return count, sum(total)\",\n \"def resource_details(self) -> pulumi.Output[Any]:\\n return pulumi.get(self, \\\"resource_details\\\")\",\n \"def resource_details(self) -> pulumi.Output[Any]:\\n return pulumi.get(self, \\\"resource_details\\\")\",\n \"def get_cpu_usage():\\n\\n # use the global 'before' variable\\n global before\\n\\n # take the current CPU usage information\\n now = prepare_cpu_usage()\\n # compute deltas between current and previous measurements\\n delta = [now[i]-before[i] for i in range(len(now))]\\n # compute the total (needed for percentages calculation)\\n total = sum(delta)\\n # save the current measurement to before object\\n before = now\\n # return the percentage of CPU usage for our 4 categories\\n return [(100.0*dt)/total for dt in delta]\",\n \"def cpuInfo(self, json, i3status_config):\\n response = {'full_text': '', 'name': 'cpu_usage'}\\n cpu_total, cpu_idle = self.data.cpu()\\n used_cpu_percent = 1 - float(cpu_idle-self.cpu_idle)/float(cpu_total-self.cpu_total)\\n self.cpu_total = cpu_total\\n self.cpu_idle = cpu_idle\\n\\n \\\"\\\"\\\"\\n if used_cpu_percent <= 40/100.0:\\n response['color'] = i3status_config['color_good']\\n elif used_cpu_percent <= 75/100.0:\\n response['color'] = i3status_config['color_degraded']\\n else:\\n response['color'] = i3status_config['color_bad']\\n \\\"\\\"\\\"\\n response['color'] = \\\"#6c71c4\\\"\\n #cpu temp\\n CPUTEMP=False\\n if CPUTEMP:\\n cputemp=subprocess.check_output('sensors | grep \\\"CPU Temp\\\" | cut -f 2 -d \\\"+\\\" | cut -f 1 -d \\\" \\\"',shell=True)\\n cputemp=cputemp[:-1].decode('utf-8')\\n response['full_text'] = \\\" %.2f%%\\\" % (used_cpu_percent*100) +\\\" \\\"+cputemp\\n else:\\n \\tresponse['full_text'] = \\\" %.2f%%\\\" % (used_cpu_percent*100)\\n\\n #cache the status for 10 seconds\\n response['cached_until'] = time() + 10\\n\\n return (0, response)\",\n \"def backend_usages(service):\\n\\n return service.backend_usages.list()\",\n \"def test_rest_v20_dd_systems_systemid_stats_capacity_get(self):\\n pass\",\n \"def show_resource_pool(client, private_cloud, resource_pool, location):\\n return client.get(location, private_cloud, resource_pool)\",\n \"def project_usage(selfself, manager: str, package: str) -> Any:\\n\\n return search_api(\\\"pproject_usage\\\", manager, package)\",\n \"def get_host_cpu_usage(self, resource, period, aggregate,\\n granularity=None):\\n pass\",\n \"def gather_metric(self):\\n result = self._shell.run(self.KERNEL_COMMAND).stdout\\n response = {self.KERNEL_RELEASE: result}\\n return response\",\n \"def get_cpu_usage(cls):\\n\\n cpu_stats = psutil.cpu_times_percent(percpu=False)\\n cpu_stats_dict = { StatsKeys.CPU :\\n {\\n StatsKeys.IDLE : cpu_stats.idle,\\n StatsKeys.SYSTEM : cpu_stats.system,\\n StatsKeys.USER : cpu_stats.user,\\n StatsKeys.COUNT : len(psutil.cpu_times(percpu=True))\\n }\\n }\\n logger.debug(\\\"CPU stats: {}\\\".format(cpu_stats_dict))\\n\\n return cpu_stats_dict\",\n \"def test_read_cluster_resource_quota_status(self):\\n pass\",\n \"def get_mem_usage():\\n \\n with open('/proc/meminfo') as f:\\n for line in f:\\n if line.startswith('MemTotal:'):\\n mem_total = int(line.split()[1])\\n elif line.startswith('MemFree:'):\\n mem_free = int(line.split()[1])\\n elif line.startswith('VmallocTotal:'):\\n vm_total = int(line.split()[1])\\n elif line.startswith('Cached:'):\\n mem_cached = int(line.split()[1])\\n \\n return {\\n 'total': mem_total,\\n 'res': mem_total - mem_free,\\n 'virt': vm_total,\\n 'cached': mem_cached\\n }\",\n \"def collect_metrics():\\n p = os.path.join(os.sep, \\\"mnt\\\", \\\"glusterfs\\\")\\n mount_stats = os.statvfs(p)\\n # block size * total blocks\\n total_space = mount_stats.f_blocks * mount_stats.f_bsize\\n free_space = mount_stats.f_bfree * mount_stats.f_bsize\\n # capsize only operates on i64 values\\n used_space = total_space - free_space\\n gb_used = used_space / 1024 / 1024 / 1024\\n\\n # log!(format!(\\\"Collecting metric gb-used {}\\\", gb_used), Info)\\n add_metric(\\\"gb-used\\\", \\\"{}\\\".format(gb_used))\",\n \"def get_usage(self, loadbalancer=None, start=None, end=None):\\n if start is end is None:\\n period = None\\n else:\\n parts = []\\n startStr = utils.iso_time_string(start)\\n if startStr:\\n parts.append(\\\"startTime=%s\\\" % startStr)\\n endStr = utils.iso_time_string(end)\\n if endStr:\\n parts.append(\\\"endTime=%s\\\" % endStr)\\n period = \\\"&\\\".join(parts).strip(\\\"&\\\")\\n if loadbalancer is None:\\n uri = \\\"/loadbalancers/usage\\\"\\n else:\\n uri = \\\"/loadbalancers/%s/usage\\\" % utils.get_id(loadbalancer)\\n if period:\\n uri = \\\"%s?%s\\\" % (uri, period)\\n resp, body = self.api.method_get(uri)\\n return body\",\n \"def read_cpu_usage():\\n cpuInfo = OrderedDict()\\n\\n with open('/proc/stat') as f:\\n for line in f:\\n l = line.split()\\n if len(l) < 5:\\n continue\\n if l[0].startswith('cpu'):\\n cpuInfo[l[0]] = l\\n\\n return cpuInfo\",\n \"def get_statistics(self) -> Optional[ResourceStatistics]:\\n return None\",\n \"def gather_info_and_display():\\n # Obtain total rss displayed in memory.stat for each group,\\n # container and service.\\n try:\\n output_mem = pipe_command(GREP_CMD, AWK_CMD, cwd=MEMPATH)\\n LOG.debug(\\n 'command: %s\\\\n%s',\\n \\\"grep -rs total_rss '/sys/fs/cgroup/memory/' \\\"\\n \\\"| awk '$2>0{print$0}' \\\",\\n output_mem)\\n except subprocess.CalledProcessError as error:\\n LOG.error('Could not get total_rss memory, error=%s', error)\\n return 1\\n\\n mem_info = get_meminfo()\\n pt_groups = gather_groups_memory(output_mem)\\n pt_cont = gather_containers_memory(output_mem)\\n pt_serv = sys_service_memory()\\n\\n # Dump the tables out\\n print('\\\\nPer groups memory usage:')\\n\\n # Get string to be printed and create list of elements separated by \\\\n\\n list_of_table_lines = pt_groups.get_string().split('\\\\n')\\n\\n # Use the first line (+---+-- ...) as horizontal rule to insert later\\n horizontal_line = list_of_table_lines[0]\\n\\n # Print the table, except last two lines ( \\\"Total\\\" row + final separator).\\n print(\\\"\\\\n\\\".join(list_of_table_lines[:-2]))\\n # Print separator, and finally the \\\"Total\\\" row.\\n print(horizontal_line)\\n print(\\\"\\\\n\\\".join(list_of_table_lines[-2:]))\\n\\n pt_namespc = prettytable.PrettyTable(\\n ['Namespace',\\n 'Resident Set Size (MiB)',\\n ], caching=False)\\n pt_namespc.align = 'l'\\n pt_namespc.align['Resident Set Size (MiB)'] = 'r'\\n\\n print('\\\\nPer namespace memory usage:')\\n for n_s in MEMORY['namespaces']:\\n pt_namespc.add_row(\\n [n_s,\\n MEMORY['namespaces'][n_s],\\n ])\\n print(pt_namespc)\\n\\n print('\\\\nPer container memory usage:')\\n print(pt_cont)\\n\\n print('\\\\nPer service memory usage:')\\n print(pt_serv)\\n\\n base_mebib = 0.0\\n k8s_system = 0.0\\n k8s_addon = 0.0\\n platform_memory_percent = 0.0\\n\\n # Calculate base memory usage (i.e., normal memory, exclude K8S and VMs)\\n # e.g., docker, system.slice, user.slice\\n for group in MEMORY['cgroups']:\\n if group in BASE_GROUPS:\\n base_mebib += float(MEMORY['cgroups'][group])\\n\\n # K8S platform system usage (essential) and addons usage (non-essential)\\n for n_s in MEMORY['namespaces']:\\n if n_s in K8S_NAMESPACE_SYSTEM:\\n k8s_system += MEMORY['namespaces'][n_s]\\n elif n_s in K8S_NAMESPACE_ADDON:\\n k8s_addon += MEMORY['namespaces'][n_s]\\n\\n # Calculate platform memory usage\\n platform_mebib = base_mebib + k8s_system\\n\\n anon_mebib = float(mem_to_mebibytes(\\n mem_info['Active(anon)'] + mem_info['Inactive(anon)'])) * KBYTE\\n avail_mebib = float(mem_to_mebibytes(\\n mem_info['MemAvailable'])) * KBYTE\\n total_mebib = float(anon_mebib + avail_mebib)\\n\\n anon_percent = py2_round(100 * anon_mebib / total_mebib, DECIMAL_DIGITS) # pylint: disable=W1619\\n\\n reserved_mebib = get_platform_reserved_memory()\\n # Calculate platform memory in terms of percent reserved\\n if reserved_mebib > 0.0:\\n platform_memory_percent = py2_round(\\n 100 * platform_mebib / reserved_mebib, DECIMAL_DIGITS) # pylint: disable=W1619\\n\\n pt_platf = prettytable.PrettyTable(\\n ['Reserved',\\n 'Platform',\\n 'Base',\\n 'K8s Platform system',\\n 'k8s-addon'\\n ], caching=False)\\n pt_platf.align = 'l'\\n\\n pt_platf.add_row(\\n [reserved_mebib,\\n '{} ({}%)'.format(platform_mebib, platform_memory_percent),\\n base_mebib,\\n k8s_system,\\n k8s_addon\\n ])\\n print('\\\\nPlatform memory usage in MiB:')\\n print(pt_platf)\\n\\n pt_4k = prettytable.PrettyTable(\\n ['Anon',\\n 'Cgroup-rss',\\n 'Available',\\n 'Total'\\n ], caching=False)\\n pt_4k.align = 'l'\\n\\n pt_4k.add_row(\\n ['{} ({}%)'.format(anon_mebib, anon_percent),\\n MEMORY['cgroups']['total_rss'],\\n avail_mebib,\\n total_mebib\\n ])\\n\\n print('\\\\n4K memory usage in MiB:')\\n print(pt_4k)\\n\\n return 0\",\n \"def get_resource_status(type, full_name=\\\"\\\", label=\\\"\\\", must_exist=True):\\n cmd = \\\"kubectl get {} --no-headers --all-namespaces \\\" \\\\\\n \\\"-o wide --selector \\\\\\\"{}\\\\\\\" \\\" \\\\\\n \\\"| grep \\\\\\\"{}\\\\\\\" | awk '{{print $1 \\\\\\\" \\\\\\\" $2}}'\\\"\\n cmd = cmd.format(type, label, full_name)\\n try:\\n encoded_output = subprocess.check_output(\\n cmd, shell=True, stderr=subprocess.STDOUT,\\n )\\n except subprocess.CalledProcessError as exc:\\n log.warning(\\\"command to get status of {} has \\\"\\n \\\"failed. error code: \\\"\\n \\\"{} {}\\\".format(full_name,\\n exc.returncode, exc.output))\\n raise RuntimeError(\\n \\\"command to get status of {} has \\\"\\n \\\"failed. error code: \\\"\\n \\\"{} {}\\\".format(full_name,\\n exc.returncode, exc.output))\\n output = encoded_output.decode()\\n if output == \\\"\\\":\\n if not must_exist:\\n return None\\n log.warning(\\n \\\"{} \\\\\\\"{}\\\\\\\" with label \\\\\\\"{}\\\\\\\" can't be found in \\\"\\n \\\"the cluster\\\".format(type, full_name, label))\\n raise RuntimeError(\\n \\\"{} {} with label {} can't be found in the cluster\\\".format(\\n type, full_name, label))\\n # Example line:\\n # kube-system cilium\\n split_line = output.split(' ')\\n return ResourceStatus(namespace=split_line[0],\\n name=split_line[1])\",\n \"def get_mem_usage(**kwargs):\\n try:\\n con_mem_data_list = kwargs[\\\"con\\\"]._client.get_memory(\\n session=kwargs[\\\"con\\\"]._session, memory_level=kwargs[\\\"mem_type\\\"]\\n )\\n usedram = 0\\n freeram = 0\\n for con_mem_data in con_mem_data_list:\\n page_size = con_mem_data.page_size\\n node_memory_data_list = con_mem_data.node_memory_data\\n for node_memory_data in node_memory_data_list:\\n ram = node_memory_data.num_pages * page_size\\n is_free = node_memory_data.is_free\\n if is_free:\\n freeram += ram\\n else:\\n usedram += ram\\n totalallocated = usedram + freeram\\n if totalallocated > 0:\\n totalallocated = round(totalallocated / 1024 / 1024, 1)\\n usedram = round(usedram / 1024 / 1024, 1)\\n freeram = round(freeram / 1024 / 1024, 1)\\n ramusage = {}\\n ramusage[\\\"usedram\\\"] = usedram\\n ramusage[\\\"freeram\\\"] = freeram\\n ramusage[\\\"totalallocated\\\"] = totalallocated\\n ramusage[\\\"errormessage\\\"] = \\\"\\\"\\n except Exception as e:\\n errormessage = \\\"Get memory failed with error: \\\" + str(e)\\n logging.error(errormessage)\\n ramusage[\\\"errormessage\\\"] = errormessage\\n return ramusage\",\n \"def describe_resource(namespace, resource_type, k8s_cli, selector=\\\"\\\", resource_name=\\\"\\\"):\\n cmd = \\\"{} describe -n {} {} {} {}\\\".format(k8s_cli, namespace, resource_type, resource_name, selector)\\n error_template = \\\"Namespace '{}': Failed to describe {} resource: {{}}.\\\".format(namespace, resource_type)\\n missing_resource_template = f\\\"Namespace '{namespace}': Skip collecting description for {resource_type}. \\\" \\\\\\n f\\\"Server has no resource of type {resource_type}\\\"\\n return run_shell_command_with_retries(cmd, KUBCTL_GET_YAML_RETRIES, error_template, missing_resource_template)\",\n \"def get_cpu_usage():\\n return psutil.cpu_percent()\",\n \"def get_swap_usage():\\n\\n swap_usage = {'total' : 0, 'used' : 0}\\n swapinfo = subprocess.Popen(['free', '-m'], shell=False, stdout=subprocess.PIPE)\\n swapinfo.stdout.readline()\\n swapinfo.stdout.readline()\\n swapinfo.stdout.readline()\\n total_used = swapinfo.stdout.readline()\\n swap_usage['total'] += float(total_used.split()[1])\\n swap_usage['used'] += float(total_used.split()[2])\\n return swap_usage\",\n \"def usage(self):\\r\\n return usage.Usage(self)\",\n \"def metrics(self, request):\\n return OtterMetrics(self.store).app.resource()\",\n \"def health():\\n return jsonify(hostname=hostname, uptime=uptime(), \\\\\\n cpu_percent=int(cpu_percent(interval=None, percpu=False)))\",\n \"def _usage_report_dict(self, root):\\n details = {}\\n selector = 'table > tbody > tr'\\n for (resource, unit, used) in root.cssselect(selector):\\n name = resource.findtext('strong').strip()\\n details[name] = (used.text.strip(), unit.text.strip())\\n return details\",\n \"def test_get_resource_license_resource_count_list(self):\\n pass\",\n \"def get_pods(namespace, k8s_cli, selector=\\\"\\\"):\\n if selector:\\n selector = '--selector=\\\"{}\\\"'.format(selector)\\n cmd = '{} get pod -n {} {} -o json '.format(k8s_cli, namespace, selector)\\n return_code, out = run_shell_command(cmd)\\n if return_code:\\n logger.warning(\\\"Failed to get pods: %s\\\", out)\\n return None\\n return json.loads(out)['items']\",\n \"def get_cpu_usage(*args):\\n \\n keys = ['us', 'ni', 'sy', 'id', 'wa', 'hi', 'si', 'st'] #usage % to be returned\\n \\n with open('/proc/stat') as f1:\\n with open('/proc/stat') as f2:\\n content1 = f1.read() #first collection\\n yield {} #yield so that caller can put delay before sampling again\\n content2 = f2.read() #second collection\\n \\n cpu_count = multiprocessing.cpu_count() #total number of cpu cores available\\n lines1, lines2 = content1.splitlines(), content2.splitlines()\\n data, deltas = {}, {}\\n \\n #if only one cpu available, read only the first line, else read total cpu count lines starting from the second line\\n i, cpu_count = (1, cpu_count + 1) if cpu_count > 1 else (0, 1)\\n \\n #extract deltas\\n while i < cpu_count:\\n line_split1 = lines1[i].split()\\n line_split2 = lines2[i].split()\\n deltas[line_split1[0]] = [int(b) - int(a) for a, b in zip(line_split1[1:], line_split2[1:])]\\n i += 1\\n \\n for key in deltas:\\n #calculate the percentage\\n total = sum(deltas[key])\\n data[key] = dict(zip(keys, [100 - (100 * (float(total - x) / total)) for x in deltas[key]]))\\n \\n yield data\",\n \"def test_read_namespaced_applied_cluster_resource_quota(self):\\n pass\",\n \"def sys_service_memory():\\n sort_cmd = [\\\"sort\\\", \\\"-k\\\", \\\"2nr\\\"]\\n\\n p_table = prettytable.PrettyTable(\\n ['Service',\\n 'Resident Set Size (MiB)',\\n ], caching=False)\\n p_table.align = 'l'\\n p_table.align['Resident Set Size (MiB)'] = 'r'\\n\\n try:\\n output = pipe_command(GREP_CMD, AWK_CMD, sort_cmd,\\n cwd=MEMPATH + \\\"system.slice\\\")\\n LOG.debug(\\n 'command: %s\\\\n%s',\\n ' '.join(GREP_CMD + [MEMPATH] + AWK_CMD + sort_cmd), output)\\n except subprocess.CalledProcessError as error:\\n LOG.error('Could not get total_rss memory, error=%s', error)\\n return 1\\n\\n for line in output.split(\\\"\\\\n\\\"):\\n service = line.split(\\\"memory.stat:total_rss \\\")[0]\\n rss_mem = line.split(\\\"memory.stat:total_rss \\\")[-1]\\n p_table.add_row(\\n [service,\\n mem_to_mebibytes(rss_mem),\\n ])\\n\\n # Delete first row wich display total system.slice rss\\n p_table.del_row(0)\\n return p_table\",\n \"def getcpuusage(self):\\n return ord(self.reg(0x11, write=1))\",\n \"def watch():\\n\\n try:\\n headers = ('CONTAINER ID', 'NAME', 'CPU %', 'MEM USAGE / LIMIT',\\n 'MEM %', 'NET I/O', 'BLOCK I/O', 'PIDS')\\n column_width = 20\\n for element in headers:\\n print(element.ljust(column_width)),\\n print('')\\n\\n for container in CLIENT.containers.list():\\n column_width = 20\\n stats = container.stats(stream=False)\\n\\n # Block I/O stats\\n blkio = stats.get('blkio_stats').get('io_service_bytes_recursive')\\n # in case blkio is empty --> IndexError: list index out of range\\n if not blkio:\\n blkio_read = '0'\\n blkio_write = '0'\\n else:\\n blkio_read = size(blkio[0].get('value'), system=si)\\n blkio_write = size(blkio[1].get('value'), system=si)\\n\\n # Network stats\\n rx_stats = size(stats.get('networks').get('eth0').get('rx_bytes'), system=si)\\n tx_stats = size(stats.get('networks').get('eth0').get('tx_bytes'), system=si)\\n\\n # Memory stats\\n mem = stats.get('memory_stats')\\n mem_usage = mem.get('stats').get('active_anon')\\n mem_limit = mem.get('limit')\\n mem_percent = (\\\"%.2f\\\"%((mem_usage / mem_limit)*100))\\n\\n # CPU stats\\n # this is taken directly from docker CLIENT:\\n # https://github.com/docker/docker/blob/28a7577a029780e4533faf3d057ec9f6c7a10948/api/CLIENT/stats.go#L309\\n cpu_percent = 0.0\\n cpu = stats.get('cpu_stats')\\n pre_cpu = stats.get('precpu_stats')\\n cpu_total = cpu.get('cpu_usage').get('total_usage')\\n pre_cpu_total = pre_cpu.get('cpu_usage').get('total_usage')\\n cpu_count = cpu.get('online_cpus')\\n\\n cpu_delta = cpu_total - pre_cpu_total\\n system_delta = cpu.get('system_cpu_usage') - pre_cpu.get('system_cpu_usage')\\n\\n if system_delta > 0.0 and cpu_delta > 0.0:\\n cpu_percent = (\\\"%.2f\\\"%(cpu_delta / system_delta * 100.0 * cpu_count))\\n\\n # container attributes\\n attrs = [(str(container.short_id), str(container.name), str(cpu_percent),\\n str(size((mem_usage), system=si) + \\\" / \\\" + size((mem_limit), system=si)),\\n str(mem_percent), str(rx_stats + \\\" / \\\" + tx_stats),\\n str(blkio_read + \\\" / \\\" + blkio_write),\\n str(stats.get('pids_stats').get('current')))]\\n\\n for row in attrs:\\n for element in row:\\n print(element.ljust(column_width)),\\n print('')\\n\\n except (docker.errors.NotFound, KeyError, AttributeError):\\n print('No such container or container not running!')\",\n \"def test_list_cluster_resource_quota(self):\\n pass\",\n \"def _get_info_about_available_resources(self, min_ram, min_hdd, min_vcpus):\\n vms_count = 0\\n for hypervisor in self.nova_cli.hypervisors.list():\\n if hypervisor.free_ram_mb >= min_ram:\\n if hypervisor.free_disk_gb >= min_hdd:\\n if hypervisor.vcpus - hypervisor.vcpus_used >= min_vcpus:\\n # We need to determine how many VMs we can run\\n # on this hypervisor\\n free_cpu = hypervisor.vcpus - hypervisor.vcpus_used\\n k1 = int(hypervisor.free_ram_mb / min_ram)\\n k2 = int(hypervisor.free_disk_gb / min_hdd)\\n k3 = int(free_cpu / min_vcpus)\\n vms_count += min(k1, k2, k3)\\n return vms_count\",\n \"def test_read_cluster_resource_quota(self):\\n pass\",\n \"def get_instance_ram_usage(self, resource, period,\\n aggregate, granularity=None):\\n pass\",\n \"def cpu_usage(self):\\n dsp = c_float()\\n stream = c_float()\\n geometry = c_float()\\n update = c_float()\\n total = c_float()\\n ckresult(\\n _dll.FMOD_System_GetCPUUsage(\\n self._ptr,\\n byref(dsp),\\n byref(stream),\\n byref(geometry),\\n byref(update),\\n byref(total),\\n )\\n )\\n return so(\\n dsp=dsp.value,\\n stream=stream.value,\\n geometry=geometry.value,\\n update=update.value,\\n total=total.value,\\n )\",\n \"def handle_api_resources_docker(self, http_context):\\n\\n command = [self.docker, 'stats', '--format', '\\\\'{{json .}}\\\\'', '--no-stream', '-a']\\n return [json.loads(line) for line in subprocess.check_output(command).decode().splitlines()]\",\n \"async def get(self):\\n config = self.settings['nbresuse_display_config']\\n cur_process = psutil.Process()\\n all_processes = [cur_process] + cur_process.children(recursive=True)\\n limits = {}\\n\\n mem_usage = dict(line.split() for line in list(open('/sys/fs/cgroup/memory/memory.stat').readlines()))\\n\\n # Get memory information\\n rss = int(mem_usage['rss'])\\n mem_limit = int(memory_stats['hierarchical_memory_limit'])\\n\\n # A better approach would use cpu_affinity to account for the\\n # fact that the number of logical CPUs in the system is not\\n # necessarily the same as the number of CPUs the process\\n # can actually use. But cpu_affinity isn't available for OS X.\\n cpu_count = psutil.cpu_count()\\n\\n if config.track_cpu_percent:\\n self.cpu_percent = await self.update_cpu_percent(all_processes)\\n\\n limits['memory'] = {\\n 'rss': mem_limit\\n }\\n if config.mem_warning_threshold != 0:\\n limits['memory']['warn'] = (mem_limit - rss) < (mem_limit * config.mem_warning_threshold)\\n\\n # Optionally get CPU information\\n if config.track_cpu_percent:\\n self.cpu_percent = await self.update_cpu_percent(all_processes)\\n\\n if config.cpu_limit != 0:\\n limits['cpu'] = {\\n 'cpu': config.cpu_limit\\n }\\n if config.cpu_warning_threshold != 0:\\n limits['cpu']['warn'] = (config.cpu_limit - self.cpu_percent) < (config.cpu_limit * config.cpu_warning_threshold)\\n\\n metrics = {\\n 'rss': rss,\\n 'limits': limits,\\n }\\n if config.track_cpu_percent:\\n metrics.update(cpu_percent=self.cpu_percent,\\n cpu_count=self.cpu_count)\\n\\n self.log.debug(\\\"NBResuse metrics: %s\\\", metrics)\\n self.write(json.dumps(metrics))\",\n \"def print_usage(self):\\r\\n print 'Total Usage: %f compute seconds' % self.box_usage\\r\\n cost = self.box_usage * 0.14\\r\\n print 'Approximate Cost: $%f' % cost\",\n \"def get_memory_usage():\\n\\n memory_usage = {'total' : 0, 'used' : 0}\\n meminfo = subprocess.Popen(['free', '-m'], shell=False, stdout=subprocess.PIPE)\\n meminfo.stdout.readline()\\n total_used = meminfo.stdout.readline()\\n memory_usage['total'] = total_used.split()[1]\\n memory_usage['used'] = total_used.split()[2]\\n return memory_usage\",\n \"def print_usage(self):\\n print('Total Usage: %f compute seconds' % self.box_usage)\\n cost = self.box_usage * 0.14\\n print('Approximate Cost: $%f' % cost)\",\n \"def get_resources_output(name: Optional[pulumi.Input[Optional[str]]] = None,\\n required_tags: Optional[pulumi.Input[Optional[Mapping[str, str]]]] = None,\\n resource_group_name: Optional[pulumi.Input[Optional[str]]] = None,\\n type: Optional[pulumi.Input[Optional[str]]] = None,\\n opts: Optional[pulumi.InvokeOptions] = None) -> pulumi.Output[GetResourcesResult]:\\n ...\",\n \"def get_space_used():\\n fs.get_space_used()\",\n \"def service_load_metrics(self) -> pulumi.Output[Optional[Sequence['outputs.ServiceLoadMetricDescriptionResponse']]]:\\n return pulumi.get(self, \\\"service_load_metrics\\\")\",\n \"def _GetPod(self) -> Dict[str, Any]:\\n stdout, _, _ = RunKubectlCommand(['get', 'pod', self.name, '-o', 'yaml'])\\n pod = yaml.safe_load(stdout)\\n self.ip_address = pod.get('status', {}).get('podIP')\\n return pod\",\n \"def allocation_resource_tags(self) -> pulumi.Output[Optional[Mapping[str, str]]]:\\n return pulumi.get(self, \\\"allocation_resource_tags\\\")\",\n \"def get_capacity():\\n fs.get_capacity()\",\n \"def usage(self, host):\",\n \"def ip_allocations(self) -> Optional[Sequence['outputs.SubResourceResponse']]:\\n return pulumi.get(self, \\\"ip_allocations\\\")\",\n \"def get_info(self):\\n return {'q_ref': self.q_ref, 'v_ref': self.v_ref, 'U': self.U, 'type': 'POD'}\",\n \"def get_config_metrics():\\n\\n metrics = {'disk_usage': 'YES',\\n 'cpu_percent': 'YES',\\n 'memory_info': 'YES',\\n 'cpu_stats': 'YES'}\\n\\n return metrics\",\n \"def get_usage(self, source):\\n return sum(i.quantity for i in self.items.all() if i.source == source)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.684437","0.6337999","0.6151427","0.6085964","0.6063859","0.6056686","0.60112286","0.5989251","0.5980302","0.5904598","0.5870229","0.58482635","0.58432084","0.5836446","0.5833565","0.582723","0.57628006","0.57566756","0.5720972","0.5699034","0.5695292","0.56908196","0.5646332","0.56459475","0.56391704","0.56358427","0.5618438","0.56183475","0.5572603","0.55669135","0.55405647","0.5540511","0.5506407","0.54828966","0.5463227","0.5460275","0.54567343","0.54503757","0.54500294","0.54471904","0.54380333","0.5437231","0.5369876","0.5361598","0.53558636","0.5344846","0.5344846","0.5326155","0.5325162","0.5322191","0.52974623","0.529124","0.5284402","0.52779174","0.527577","0.52698094","0.52636045","0.5257175","0.5251362","0.5250602","0.5249094","0.5235953","0.52359074","0.5234463","0.52317077","0.52265996","0.5225988","0.5218711","0.5201103","0.5192328","0.51913977","0.5188824","0.5187784","0.51809984","0.5178616","0.51754177","0.5172188","0.5163022","0.5156598","0.5150611","0.51388913","0.5138436","0.51244485","0.511939","0.5118312","0.51173687","0.5116804","0.5111679","0.51071256","0.51034445","0.50955814","0.5093985","0.50895435","0.50771314","0.50726473","0.5068265","0.50613797","0.50603384","0.504807","0.5046829"],"string":"[\n \"0.684437\",\n \"0.6337999\",\n \"0.6151427\",\n \"0.6085964\",\n \"0.6063859\",\n \"0.6056686\",\n \"0.60112286\",\n \"0.5989251\",\n \"0.5980302\",\n \"0.5904598\",\n \"0.5870229\",\n \"0.58482635\",\n \"0.58432084\",\n \"0.5836446\",\n \"0.5833565\",\n \"0.582723\",\n \"0.57628006\",\n \"0.57566756\",\n \"0.5720972\",\n \"0.5699034\",\n \"0.5695292\",\n \"0.56908196\",\n \"0.5646332\",\n \"0.56459475\",\n \"0.56391704\",\n \"0.56358427\",\n \"0.5618438\",\n \"0.56183475\",\n \"0.5572603\",\n \"0.55669135\",\n \"0.55405647\",\n \"0.5540511\",\n \"0.5506407\",\n \"0.54828966\",\n \"0.5463227\",\n \"0.5460275\",\n \"0.54567343\",\n \"0.54503757\",\n \"0.54500294\",\n \"0.54471904\",\n \"0.54380333\",\n \"0.5437231\",\n \"0.5369876\",\n \"0.5361598\",\n \"0.53558636\",\n \"0.5344846\",\n \"0.5344846\",\n \"0.5326155\",\n \"0.5325162\",\n \"0.5322191\",\n \"0.52974623\",\n \"0.529124\",\n \"0.5284402\",\n \"0.52779174\",\n \"0.527577\",\n \"0.52698094\",\n \"0.52636045\",\n \"0.5257175\",\n \"0.5251362\",\n \"0.5250602\",\n \"0.5249094\",\n \"0.5235953\",\n \"0.52359074\",\n \"0.5234463\",\n \"0.52317077\",\n \"0.52265996\",\n \"0.5225988\",\n \"0.5218711\",\n \"0.5201103\",\n \"0.5192328\",\n \"0.51913977\",\n \"0.5188824\",\n \"0.5187784\",\n \"0.51809984\",\n \"0.5178616\",\n \"0.51754177\",\n \"0.5172188\",\n \"0.5163022\",\n \"0.5156598\",\n \"0.5150611\",\n \"0.51388913\",\n \"0.5138436\",\n \"0.51244485\",\n \"0.511939\",\n \"0.5118312\",\n \"0.51173687\",\n \"0.5116804\",\n \"0.5111679\",\n \"0.51071256\",\n \"0.51034445\",\n \"0.50955814\",\n \"0.5093985\",\n \"0.50895435\",\n \"0.50771314\",\n \"0.50726473\",\n \"0.5068265\",\n \"0.50613797\",\n \"0.50603384\",\n \"0.504807\",\n \"0.5046829\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.730197"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":14384,"cells":{"query":{"kind":"string","value":"Creating a cellpod mapping"},"document":{"kind":"string","value":"def get_cell_pod_map(cluster_id):\n # creating a pod-cell mapping\n cell_pod_map = dict()\n for cell_info in get_compute_cell_data(cluster_id)['items']:\n cell_pod_map[cell_info['status']['podName']] = {'cell_name': cell_info['metadata']['name'],\n 'podNamespace': cell_info['status']['podNamespace']\n }\n return cell_pod_map"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def makeMapping(globalMap):\n \n from memops.xml.Implementation import bool2str, str2bool\n\n # Set up top level dictionaries\n loadMaps = globalMap.get('loadMaps')\n mapsByGuid = globalMap.get('mapsByGuid')\n\n abstractTypes = globalMap.get('CCLB').get('abstractTypes')\n exolinks = globalMap.get('CCLB').get('exolinks')\n\n # Class AtomLabel\n currentMap = {}\n abstractTypes['AtomLabel'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-14:25:09_00018'] = currentMap\n loadMaps['CCLB.AtomLabel'] = currentMap\n currentMap['tag'] = 'CCLB.AtomLabel'\n currentMap['type'] = 'class'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:25:09_00018'\n currentMap['eType'] = 'cplx'\n currentMap['fromParent'] = 'atomLabels'\n currentMap['class'] = ccp.api.molecule.ChemCompLabel.AtomLabel\n contentMap = {}\n currentMap['content'] = contentMap\n\n # Attribute AtomLabel.applicationData\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\n\n # Attribute AtomLabel.isotopeCode\n currentMap = {}\n contentMap['isotopeCode'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:37_00092'] = currentMap\n loadMaps['CCLB.AtomLabel.isotopeCode'] = currentMap\n currentMap['tag'] = 'CCLB.AtomLabel.isotopeCode'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:37_00092'\n currentMap['name'] = 'isotopeCode'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00037')\n\n # Attribute AtomLabel.name\n currentMap = {}\n contentMap['name'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-10-24-11:30:14_00003'] = currentMap\n loadMaps['CCLB.AtomLabel.name'] = currentMap\n currentMap['tag'] = 'CCLB.AtomLabel.name'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-10-24-11:30:14_00003'\n currentMap['name'] = 'name'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00037')\n\n # Attribute AtomLabel.subType\n currentMap = {}\n contentMap['subType'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-10-24-11:30:14_00004'] = currentMap\n loadMaps['CCLB.AtomLabel.subType'] = currentMap\n currentMap['tag'] = 'CCLB.AtomLabel.subType'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-10-24-11:30:14_00004'\n currentMap['name'] = 'subType'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['proc'] = 'direct'\n currentMap['default'] = 1\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00032')\n\n # Attribute AtomLabel.weight\n currentMap = {}\n contentMap['weight'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:37_00093'] = currentMap\n loadMaps['CCLB.AtomLabel.weight'] = currentMap\n currentMap['tag'] = 'CCLB.AtomLabel.weight'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:37_00093'\n currentMap['name'] = 'weight'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['default'] = 1.0\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:54_00009')\n\n # Role AtomLabel.access\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\n # End of AtomLabel\n\n currentMap = abstractTypes.get('AtomLabel')\n aList = ['isotopeCode', 'name', 'subType', 'weight']\n currentMap['headerAttrs'] = aList\n aList = ['access', 'applicationData']\n currentMap['cplxAttrs'] = aList\n\n # Class ChemCompLabel\n currentMap = {}\n abstractTypes['ChemCompLabel'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-14:25:09_00014'] = currentMap\n loadMaps['CCLB.ChemCompLabel'] = currentMap\n currentMap['tag'] = 'CCLB.ChemCompLabel'\n currentMap['type'] = 'class'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:25:09_00014'\n currentMap['eType'] = 'cplx'\n currentMap['fromParent'] = 'chemCompLabels'\n currentMap['class'] = ccp.api.molecule.ChemCompLabel.ChemCompLabel\n contentMap = {}\n currentMap['content'] = contentMap\n\n # Attribute ChemCompLabel.applicationData\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\n\n # Attribute ChemCompLabel.ccpCode\n currentMap = {}\n contentMap['ccpCode'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:37_00073'] = currentMap\n loadMaps['CCLB.ChemCompLabel.ccpCode'] = currentMap\n currentMap['tag'] = 'CCLB.ChemCompLabel.ccpCode'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:37_00073'\n currentMap['name'] = 'ccpCode'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2007-09-12-18:31:28_00003')\n\n # Attribute ChemCompLabel.molType\n currentMap = {}\n contentMap['molType'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:37_00072'] = currentMap\n loadMaps['CCLB.ChemCompLabel.molType'] = currentMap\n currentMap['tag'] = 'CCLB.ChemCompLabel.molType'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:37_00072'\n currentMap['name'] = 'molType'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:52_00024')\n\n # Role ChemCompLabel.access\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\n\n # Role ChemCompLabel.isotopomers\n currentMap = {}\n contentMap['isotopomers'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-10-24-11:30:13_00001'] = currentMap\n loadMaps['CCLB.ChemCompLabel.isotopomers'] = currentMap\n currentMap['tag'] = 'CCLB.ChemCompLabel.isotopomers'\n currentMap['type'] = 'child'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-10-24-11:30:13_00001'\n currentMap['name'] = 'isotopomers'\n currentMap['hicard'] = -1\n currentMap['locard'] = 0\n currentMap['eType'] = 'cplx'\n currentMap['content'] = globalMap.get('CCLB').get('abstractTypes')\n # End of ChemCompLabel\n\n currentMap = abstractTypes.get('ChemCompLabel')\n aList = ['ccpCode', 'molType']\n currentMap['headerAttrs'] = aList\n aList = ['isotopomers', 'access', 'applicationData']\n currentMap['cplxAttrs'] = aList\n aList = ['isotopomers']\n currentMap['children'] = aList\n\n # Class Isotopomer\n currentMap = {}\n abstractTypes['Isotopomer'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-10-24-11:28:54_00001'] = currentMap\n loadMaps['CCLB.Isotopomer'] = currentMap\n currentMap['tag'] = 'CCLB.Isotopomer'\n currentMap['type'] = 'class'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-10-24-11:28:54_00001'\n currentMap['eType'] = 'cplx'\n currentMap['fromParent'] = 'isotopomers'\n currentMap['objkey'] = 'serial'\n currentMap['class'] = ccp.api.molecule.ChemCompLabel.Isotopomer\n contentMap = {}\n currentMap['content'] = contentMap\n\n # Attribute Isotopomer.applicationData\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\n\n # Attribute Isotopomer.serial\n currentMap = {}\n contentMap['serial'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-10-24-11:30:14_00005'] = currentMap\n loadMaps['CCLB.Isotopomer.serial'] = currentMap\n currentMap['tag'] = 'CCLB.Isotopomer.serial'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-10-24-11:30:14_00005'\n currentMap['name'] = 'serial'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00032')\n\n # Attribute Isotopomer.weight\n currentMap = {}\n contentMap['weight'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-10-24-11:30:14_00006'] = currentMap\n loadMaps['CCLB.Isotopomer.weight'] = currentMap\n currentMap['tag'] = 'CCLB.Isotopomer.weight'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-10-24-11:30:14_00006'\n currentMap['name'] = 'weight'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['default'] = 1.0\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:54_00009')\n\n # Role Isotopomer.access\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\n\n # Role Isotopomer.atomLabels\n currentMap = {}\n contentMap['atomLabels'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-10-24-11:30:14_00001'] = currentMap\n loadMaps['CCLB.Isotopomer.atomLabels'] = currentMap\n currentMap['tag'] = 'CCLB.Isotopomer.atomLabels'\n currentMap['type'] = 'child'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-10-24-11:30:14_00001'\n currentMap['name'] = 'atomLabels'\n currentMap['hicard'] = -1\n currentMap['locard'] = 0\n currentMap['eType'] = 'cplx'\n currentMap['content'] = globalMap.get('CCLB').get('abstractTypes')\n # End of Isotopomer\n\n currentMap = abstractTypes.get('Isotopomer')\n aList = ['serial', 'weight']\n currentMap['headerAttrs'] = aList\n aList = ['atomLabels', 'access', 'applicationData']\n currentMap['cplxAttrs'] = aList\n aList = ['atomLabels']\n currentMap['children'] = aList\n\n # Class LabelingScheme\n currentMap = {}\n abstractTypes['LabelingScheme'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2007-01-24-12:23:14_00001'] = currentMap\n loadMaps['CCLB.LabelingScheme'] = currentMap\n currentMap['tag'] = 'CCLB.LabelingScheme'\n currentMap['type'] = 'class'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2007-01-24-12:23:14_00001'\n currentMap['eType'] = 'cplx'\n currentMap['fromParent'] = 'labelingSchemes'\n currentMap['isTop'] = True\n currentMap['objkey'] = 'name'\n currentMap['class'] = ccp.api.molecule.ChemCompLabel.LabelingScheme\n contentMap = {}\n currentMap['content'] = contentMap\n\n # Attribute LabelingScheme.applicationData\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\n\n # Attribute LabelingScheme.createdBy\n contentMap['createdBy'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:00:59_00002__www.ccpn.ac.uk_Fogh_2007-10-03-14:53:27_00001__www.ccpn.ac.uk_Fogh_2006-09-14-16:28:57_00002')\n\n # Attribute LabelingScheme.details\n currentMap = {}\n contentMap['details'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2007-01-24-12:23:55_00005'] = currentMap\n loadMaps['CCLB.LabelingScheme.details'] = currentMap\n currentMap['tag'] = 'CCLB.LabelingScheme.details'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2007-01-24-12:23:55_00005'\n currentMap['name'] = 'details'\n currentMap['hicard'] = 1\n currentMap['locard'] = 0\n currentMap['eType'] = 'cplx'\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00036')\n\n # Attribute LabelingScheme.guid\n contentMap['guid'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:26_00002')\n\n # Attribute LabelingScheme.isModifiable\n contentMap['isModifiable'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-17-14:16:26_00010__www.ccpn.ac.uk_Fogh_2007-10-03-14:53:27_00001__www.ccpn.ac.uk_Fogh_2006-09-14-16:28:57_00002')\n\n # Attribute LabelingScheme.lastUnlockedBy\n contentMap['lastUnlockedBy'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:00:59_00003__www.ccpn.ac.uk_Fogh_2007-10-03-14:53:27_00001__www.ccpn.ac.uk_Fogh_2006-09-14-16:28:57_00002')\n\n # Attribute LabelingScheme.longName\n currentMap = {}\n contentMap['longName'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2007-01-24-12:23:55_00004'] = currentMap\n loadMaps['CCLB.LabelingScheme.longName'] = currentMap\n currentMap['tag'] = 'CCLB.LabelingScheme.longName'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2007-01-24-12:23:55_00004'\n currentMap['name'] = 'longName'\n currentMap['hicard'] = 1\n currentMap['locard'] = 0\n currentMap['eType'] = 'cplx'\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033')\n\n # Attribute LabelingScheme.name\n currentMap = {}\n contentMap['name'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2007-01-24-12:23:55_00003'] = currentMap\n loadMaps['CCLB.LabelingScheme.name'] = currentMap\n currentMap['tag'] = 'CCLB.LabelingScheme.name'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2007-01-24-12:23:55_00003'\n currentMap['name'] = 'name'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00037')\n\n # Role LabelingScheme.access\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\n\n # Role LabelingScheme.chemCompLabels\n currentMap = {}\n contentMap['chemCompLabels'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2007-01-24-12:23:55_00002'] = currentMap\n loadMaps['CCLB.LabelingScheme.chemCompLabels'] = currentMap\n currentMap['tag'] = 'CCLB.LabelingScheme.chemCompLabels'\n currentMap['type'] = 'child'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2007-01-24-12:23:55_00002'\n currentMap['name'] = 'chemCompLabels'\n currentMap['hicard'] = -1\n currentMap['locard'] = 0\n currentMap['eType'] = 'cplx'\n currentMap['implSkip'] = True\n currentMap['content'] = globalMap.get('CCLB').get('abstractTypes')\n # End of LabelingScheme\n\n currentMap = abstractTypes.get('LabelingScheme')\n aList = ['createdBy', 'guid', 'isModifiable', 'lastUnlockedBy', 'name']\n currentMap['headerAttrs'] = aList\n aList = ['details', 'longName']\n currentMap['simpleAttrs'] = aList\n aList = ['chemCompLabels', 'access', 'applicationData']\n currentMap['cplxAttrs'] = aList\n aList = ['chemCompLabels']\n currentMap['children'] = aList\n\n # Out-of-package link to AtomLabel\n currentMap = {}\n exolinks['AtomLabel'] = currentMap\n loadMaps['CCLB.exo-AtomLabel'] = currentMap\n currentMap['tag'] = 'CCLB.exo-AtomLabel'\n currentMap['type'] = 'exo'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:25:09_00018'\n currentMap['name'] = 'AtomLabel'\n currentMap['eType'] = 'cplx'\n currentMap['class'] = ccp.api.molecule.ChemCompLabel.AtomLabel\n aList = list()\n currentMap['keyMaps'] = aList\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:52_00024'))\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2007-09-12-18:31:28_00003'))\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00032'))\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00037'))\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00032'))\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00037'))\n\n # Out-of-package link to ChemCompLabel\n currentMap = {}\n exolinks['ChemCompLabel'] = currentMap\n loadMaps['CCLB.exo-ChemCompLabel'] = currentMap\n currentMap['tag'] = 'CCLB.exo-ChemCompLabel'\n currentMap['type'] = 'exo'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:25:09_00014'\n currentMap['name'] = 'ChemCompLabel'\n currentMap['eType'] = 'cplx'\n currentMap['class'] = ccp.api.molecule.ChemCompLabel.ChemCompLabel\n aList = list()\n currentMap['keyMaps'] = aList\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:52_00024'))\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2007-09-12-18:31:28_00003'))\n\n # Out-of-package link to Isotopomer\n currentMap = {}\n exolinks['Isotopomer'] = currentMap\n loadMaps['CCLB.exo-Isotopomer'] = currentMap\n currentMap['tag'] = 'CCLB.exo-Isotopomer'\n currentMap['type'] = 'exo'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-10-24-11:28:54_00001'\n currentMap['name'] = 'Isotopomer'\n currentMap['eType'] = 'cplx'\n currentMap['class'] = ccp.api.molecule.ChemCompLabel.Isotopomer\n aList = list()\n currentMap['keyMaps'] = aList\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:52_00024'))\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2007-09-12-18:31:28_00003'))\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00032'))\n\n # Out-of-package link to LabelingScheme\n currentMap = {}\n exolinks['LabelingScheme'] = currentMap\n loadMaps['CCLB.exo-LabelingScheme'] = currentMap\n currentMap['tag'] = 'CCLB.exo-LabelingScheme'\n currentMap['type'] = 'exo'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2007-01-24-12:23:14_00001'\n currentMap['name'] = 'LabelingScheme'\n currentMap['eType'] = 'cplx'\n currentMap['class'] = ccp.api.molecule.ChemCompLabel.LabelingScheme\n aList = list()\n currentMap['keyMaps'] = aList\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))","def node_mapping(self):\n ...","def create_cell_map(dim):\n for cell, faces in cell_face_map.iteritems():\n \n for face in faces:\n nds = face_list[face - 1][1]\n \n if not cell in cell_map:\n cell_map[cell] = copy(nds)\n \n else:\n cell_map[cell] = list(Set(cell_map[cell] + nds))","def makeMapping(globalMap):\n \n from memops.xml.Implementation import bool2str, str2bool\n\n # Set up top level dictionaries\n loadMaps = globalMap.get('loadMaps')\n mapsByGuid = globalMap.get('mapsByGuid')\n\n abstractTypes = globalMap.get('CHEL').get('abstractTypes')\n exolinks = globalMap.get('CHEL').get('exolinks')\n\n # DataType HalfLifeType\n currentMap = {}\n abstractTypes['HalfLifeType'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2007-06-07-18:18:10_00002'] = currentMap\n loadMaps['CHEL.HalfLifeType'] = currentMap\n currentMap['tag'] = 'CHEL.HalfLifeType'\n currentMap['type'] = 'simple'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2007-06-07-18:18:10_00002'\n currentMap['toStr'] = 'text'\n currentMap['cnvrt'] = 'text'\n\n # Class ChemElement\n currentMap = {}\n abstractTypes['ChemElement'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:19:49_00004'] = currentMap\n loadMaps['CHEL.ChemElement'] = currentMap\n currentMap['tag'] = 'CHEL.ChemElement'\n currentMap['type'] = 'class'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:19:49_00004'\n currentMap['eType'] = 'cplx'\n currentMap['fromParent'] = 'chemElements'\n currentMap['objkey'] = 'symbol'\n currentMap['class'] = ccp.api.molecule.ChemElement.ChemElement\n contentMap = {}\n currentMap['content'] = contentMap\n\n # Attribute ChemElement.applicationData\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\n\n # Attribute ChemElement.atomNumber\n currentMap = {}\n contentMap['atomNumber'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00017'] = currentMap\n loadMaps['CHEL.ChemElement.atomNumber'] = currentMap\n currentMap['tag'] = 'CHEL.ChemElement.atomNumber'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00017'\n currentMap['name'] = 'atomNumber'\n currentMap['hicard'] = 1\n currentMap['locard'] = 0\n currentMap['proc'] = 'direct'\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00032')\n\n # Attribute ChemElement.atomicRadius\n currentMap = {}\n contentMap['atomicRadius'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00018'] = currentMap\n loadMaps['CHEL.ChemElement.atomicRadius'] = currentMap\n currentMap['tag'] = 'CHEL.ChemElement.atomicRadius'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00018'\n currentMap['name'] = 'atomicRadius'\n currentMap['hicard'] = 1\n currentMap['locard'] = 0\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00031')\n\n # Attribute ChemElement.covalentRadius\n currentMap = {}\n contentMap['covalentRadius'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00019'] = currentMap\n loadMaps['CHEL.ChemElement.covalentRadius'] = currentMap\n currentMap['tag'] = 'CHEL.ChemElement.covalentRadius'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00019'\n currentMap['name'] = 'covalentRadius'\n currentMap['hicard'] = 1\n currentMap['locard'] = 0\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00031')\n\n # Attribute ChemElement.mass\n currentMap = {}\n contentMap['mass'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00020'] = currentMap\n loadMaps['CHEL.ChemElement.mass'] = currentMap\n currentMap['tag'] = 'CHEL.ChemElement.mass'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00020'\n currentMap['name'] = 'mass'\n currentMap['hicard'] = 1\n currentMap['locard'] = 0\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00031')\n\n # Attribute ChemElement.name\n currentMap = {}\n contentMap['name'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00021'] = currentMap\n loadMaps['CHEL.ChemElement.name'] = currentMap\n currentMap['tag'] = 'CHEL.ChemElement.name'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00021'\n currentMap['name'] = 'name'\n currentMap['hicard'] = 1\n currentMap['locard'] = 0\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00055')\n\n # Attribute ChemElement.symbol\n currentMap = {}\n contentMap['symbol'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00015'] = currentMap\n loadMaps['CHEL.ChemElement.symbol'] = currentMap\n currentMap['tag'] = 'CHEL.ChemElement.symbol'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00015'\n currentMap['name'] = 'symbol'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00055')\n\n # Role ChemElement.access\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\n\n # Role ChemElement.isotopes\n currentMap = {}\n contentMap['isotopes'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00004'] = currentMap\n loadMaps['CHEL.ChemElement.isotopes'] = currentMap\n currentMap['tag'] = 'CHEL.ChemElement.isotopes'\n currentMap['type'] = 'child'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00004'\n currentMap['name'] = 'isotopes'\n currentMap['hicard'] = -1\n currentMap['locard'] = 0\n currentMap['eType'] = 'cplx'\n currentMap['content'] = globalMap.get('CHEL').get('abstractTypes')\n # End of ChemElement\n\n currentMap = abstractTypes.get('ChemElement')\n aList = ['atomNumber', 'atomicRadius', 'covalentRadius', 'mass', 'name', 'symbol']\n currentMap['headerAttrs'] = aList\n aList = ['isotopes', 'access', 'applicationData']\n currentMap['cplxAttrs'] = aList\n aList = ['isotopes']\n currentMap['children'] = aList\n\n # Class ChemElementStore\n currentMap = {}\n abstractTypes['ChemElementStore'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:19:49_00005'] = currentMap\n loadMaps['CHEL.ChemElementStore'] = currentMap\n currentMap['tag'] = 'CHEL.ChemElementStore'\n currentMap['type'] = 'class'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:19:49_00005'\n currentMap['eType'] = 'cplx'\n currentMap['fromParent'] = 'chemElementStores'\n currentMap['isTop'] = True\n currentMap['objkey'] = 'name'\n currentMap['class'] = ccp.api.molecule.ChemElement.ChemElementStore\n contentMap = {}\n currentMap['content'] = contentMap\n\n # Attribute ChemElementStore.applicationData\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\n\n # Attribute ChemElementStore.createdBy\n contentMap['createdBy'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:00:59_00002__www.ccpn.ac.uk_Fogh_2007-10-03-14:53:27_00001__www.ccpn.ac.uk_Fogh_2006-09-14-16:28:57_00002')\n\n # Attribute ChemElementStore.guid\n contentMap['guid'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:26_00002')\n\n # Attribute ChemElementStore.isModifiable\n contentMap['isModifiable'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-17-14:16:26_00010__www.ccpn.ac.uk_Fogh_2007-10-03-14:53:27_00001__www.ccpn.ac.uk_Fogh_2006-09-14-16:28:57_00002')\n\n # Attribute ChemElementStore.lastUnlockedBy\n contentMap['lastUnlockedBy'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:00:59_00003__www.ccpn.ac.uk_Fogh_2007-10-03-14:53:27_00001__www.ccpn.ac.uk_Fogh_2006-09-14-16:28:57_00002')\n\n # Attribute ChemElementStore.name\n currentMap = {}\n contentMap['name'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00024'] = currentMap\n loadMaps['CHEL.ChemElementStore.name'] = currentMap\n currentMap['tag'] = 'CHEL.ChemElementStore.name'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00024'\n currentMap['name'] = 'name'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['eType'] = 'cplx'\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033')\n\n # Role ChemElementStore.access\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\n\n # Role ChemElementStore.chemElements\n currentMap = {}\n contentMap['chemElements'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00023'] = currentMap\n loadMaps['CHEL.ChemElementStore.chemElements'] = currentMap\n currentMap['tag'] = 'CHEL.ChemElementStore.chemElements'\n currentMap['type'] = 'child'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00023'\n currentMap['name'] = 'chemElements'\n currentMap['hicard'] = -1\n currentMap['locard'] = 0\n currentMap['eType'] = 'cplx'\n currentMap['implSkip'] = True\n currentMap['content'] = globalMap.get('CHEL').get('abstractTypes')\n # End of ChemElementStore\n\n currentMap = abstractTypes.get('ChemElementStore')\n aList = ['createdBy', 'guid', 'isModifiable', 'lastUnlockedBy']\n currentMap['headerAttrs'] = aList\n aList = ['name']\n currentMap['simpleAttrs'] = aList\n aList = ['chemElements', 'access', 'applicationData']\n currentMap['cplxAttrs'] = aList\n aList = ['chemElements']\n currentMap['children'] = aList\n\n # Class Isotope\n currentMap = {}\n abstractTypes['Isotope'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:19:49_00003'] = currentMap\n loadMaps['CHEL.Isotope'] = currentMap\n currentMap['tag'] = 'CHEL.Isotope'\n currentMap['type'] = 'class'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:19:49_00003'\n currentMap['eType'] = 'cplx'\n currentMap['fromParent'] = 'isotopes'\n currentMap['objkey'] = 'massNumber'\n currentMap['class'] = ccp.api.molecule.ChemElement.Isotope\n contentMap = {}\n currentMap['content'] = contentMap\n\n # Attribute Isotope.abundance\n currentMap = {}\n contentMap['abundance'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00011'] = currentMap\n loadMaps['CHEL.Isotope.abundance'] = currentMap\n currentMap['tag'] = 'CHEL.Isotope.abundance'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00011'\n currentMap['name'] = 'abundance'\n currentMap['hicard'] = 1\n currentMap['locard'] = 0\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00058')\n\n # Attribute Isotope.applicationData\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\n\n # Attribute Isotope.gyroMagneticRatio\n currentMap = {}\n contentMap['gyroMagneticRatio'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00008'] = currentMap\n loadMaps['CHEL.Isotope.gyroMagneticRatio'] = currentMap\n currentMap['tag'] = 'CHEL.Isotope.gyroMagneticRatio'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00008'\n currentMap['name'] = 'gyroMagneticRatio'\n currentMap['hicard'] = 1\n currentMap['locard'] = 0\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00031')\n\n # Attribute Isotope.halfLife\n currentMap = {}\n contentMap['halfLife'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2007-06-07-18:18:13_00001'] = currentMap\n loadMaps['CHEL.Isotope.halfLife'] = currentMap\n currentMap['tag'] = 'CHEL.Isotope.halfLife'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2007-06-07-18:18:13_00001'\n currentMap['name'] = 'halfLife'\n currentMap['hicard'] = 1\n currentMap['locard'] = 0\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:54_00007')\n\n # Attribute Isotope.halfLifeError\n currentMap = {}\n contentMap['halfLifeError'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2007-06-07-18:18:13_00002'] = currentMap\n loadMaps['CHEL.Isotope.halfLifeError'] = currentMap\n currentMap['tag'] = 'CHEL.Isotope.halfLifeError'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2007-06-07-18:18:13_00002'\n currentMap['name'] = 'halfLifeError'\n currentMap['hicard'] = 1\n currentMap['locard'] = 0\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:54_00007')\n\n # Attribute Isotope.halfLifeType\n currentMap = {}\n contentMap['halfLifeType'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2007-06-07-18:18:13_00003'] = currentMap\n loadMaps['CHEL.Isotope.halfLifeType'] = currentMap\n currentMap['tag'] = 'CHEL.Isotope.halfLifeType'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2007-06-07-18:18:13_00003'\n currentMap['name'] = 'halfLifeType'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['default'] = 'unknown'\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2007-06-07-18:18:10_00002')\n\n # Attribute Isotope.magneticMoment\n currentMap = {}\n contentMap['magneticMoment'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00013'] = currentMap\n loadMaps['CHEL.Isotope.magneticMoment'] = currentMap\n currentMap['tag'] = 'CHEL.Isotope.magneticMoment'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00013'\n currentMap['name'] = 'magneticMoment'\n currentMap['hicard'] = 1\n currentMap['locard'] = 0\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00031')\n\n # Attribute Isotope.mass\n currentMap = {}\n contentMap['mass'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00010'] = currentMap\n loadMaps['CHEL.Isotope.mass'] = currentMap\n currentMap['tag'] = 'CHEL.Isotope.mass'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00010'\n currentMap['name'] = 'mass'\n currentMap['hicard'] = 1\n currentMap['locard'] = 0\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00031')\n\n # Attribute Isotope.massNumber\n currentMap = {}\n contentMap['massNumber'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00007'] = currentMap\n loadMaps['CHEL.Isotope.massNumber'] = currentMap\n currentMap['tag'] = 'CHEL.Isotope.massNumber'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00007'\n currentMap['name'] = 'massNumber'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['proc'] = 'direct'\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00032')\n\n # Attribute Isotope.quadrupoleMoment\n currentMap = {}\n contentMap['quadrupoleMoment'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00014'] = currentMap\n loadMaps['CHEL.Isotope.quadrupoleMoment'] = currentMap\n currentMap['tag'] = 'CHEL.Isotope.quadrupoleMoment'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00014'\n currentMap['name'] = 'quadrupoleMoment'\n currentMap['hicard'] = 1\n currentMap['locard'] = 0\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00031')\n\n # Attribute Isotope.receptivity\n currentMap = {}\n contentMap['receptivity'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00012'] = currentMap\n loadMaps['CHEL.Isotope.receptivity'] = currentMap\n currentMap['tag'] = 'CHEL.Isotope.receptivity'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00012'\n currentMap['name'] = 'receptivity'\n currentMap['hicard'] = 1\n currentMap['locard'] = 0\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00031')\n\n # Attribute Isotope.spin\n currentMap = {}\n contentMap['spin'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00009'] = currentMap\n loadMaps['CHEL.Isotope.spin'] = currentMap\n currentMap['tag'] = 'CHEL.Isotope.spin'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00009'\n currentMap['name'] = 'spin'\n currentMap['hicard'] = 1\n currentMap['locard'] = 0\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00037')\n\n # Role Isotope.access\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\n # End of Isotope\n\n currentMap = abstractTypes.get('Isotope')\n aList = ['abundance', 'gyroMagneticRatio', 'halfLife', 'halfLifeError', 'halfLifeType', 'magneticMoment', 'mass', 'massNumber', 'quadrupoleMoment', 'receptivity', 'spin']\n currentMap['headerAttrs'] = aList\n aList = ['access', 'applicationData']\n currentMap['cplxAttrs'] = aList\n\n # Out-of-package link to ChemElement\n currentMap = {}\n exolinks['ChemElement'] = currentMap\n loadMaps['CHEL.exo-ChemElement'] = currentMap\n currentMap['tag'] = 'CHEL.exo-ChemElement'\n currentMap['type'] = 'exo'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:19:49_00004'\n currentMap['name'] = 'ChemElement'\n currentMap['eType'] = 'cplx'\n currentMap['class'] = ccp.api.molecule.ChemElement.ChemElement\n aList = list()\n currentMap['keyMaps'] = aList\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00055'))\n\n # Out-of-package link to ChemElementStore\n currentMap = {}\n exolinks['ChemElementStore'] = currentMap\n loadMaps['CHEL.exo-ChemElementStore'] = currentMap\n currentMap['tag'] = 'CHEL.exo-ChemElementStore'\n currentMap['type'] = 'exo'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:19:49_00005'\n currentMap['name'] = 'ChemElementStore'\n currentMap['eType'] = 'cplx'\n currentMap['class'] = ccp.api.molecule.ChemElement.ChemElementStore\n aList = list()\n currentMap['keyMaps'] = aList\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\n\n # Out-of-package link to Isotope\n currentMap = {}\n exolinks['Isotope'] = currentMap\n loadMaps['CHEL.exo-Isotope'] = currentMap\n currentMap['tag'] = 'CHEL.exo-Isotope'\n currentMap['type'] = 'exo'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:19:49_00003'\n currentMap['name'] = 'Isotope'\n currentMap['eType'] = 'cplx'\n currentMap['class'] = ccp.api.molecule.ChemElement.Isotope\n aList = list()\n currentMap['keyMaps'] = aList\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00055'))\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00032'))","def create_map():\n pass\n # for line in range(0, shared.lines):\n # map_data[line][0] = (1, -1)\n # map_data[line][shared.columns - 1] = (1, -1)\n #\n # for column in range(0, shared.columns):\n # map_data[0, column] = (-1, 1)\n # # if column <= shared.left_space or column > shared.columns - shared.left_space:\n # map_data[shared.lines - 1, column] = (-1, 1)","def _do_mapping(self):\n pass","def create_mapping(self):\n\n indice = client.IndicesClient(self.es)\n\n indice.put_mapping(index=self.es_main_index,\n doc_type=self.es_main_type,\n body=self.mapping)","def cell_map_from_database(self) -> None:\n for row in self.session.query(DatamapItem).all():\n self.cell_map.append(\n Cell(\n datamap_id=row.id,\n cell_key=row.key,\n cell_value=None,\n template_sheet=row.bicc_sheet,\n bg_colour=None,\n fg_colour=None,\n number_format=None,\n verification_list=None,\n cell_reference=row.bicc_cellref))","def _part_group_cell_mapper(bd_type):\n js, iss = np.meshgrid(range(smt.cols), range(smt.rows)) # zero indexed to agree with python interpretation\n idx = bd_type.flatten() != -1\n out = dict(zip(range(1, idx.sum() + 1), list(zip(iss.flatten()[idx], js.flatten()[idx]))))\n return out","def _makeimap(self):\n self.map_['source'] = 'GOES'\n self.map_['provider'] = 'NOAA'\n self.map_['instrument'] = 'SUVI'\n self.map_['physobs'] = 'flux'","def _makeimap(self):\n self.map_[\"source\"] = \"nasa\"\n self.map_[\"instrument\"] = \"goes\"\n self.map_[\"physobs\"] = \"irradiance\"\n self.map_[\"provider\"] = \"sdac\"","def create_periodic_cell_face_map():\n for f0, f1 in periodic_face_map.iteritems():\n # f0, f1 = periodic face0 - face1\n face0 = face_list[f0 - 1]\n face1 = face_list[f1 - 1] # shadow\n nd, nds, cells, bc_type, zone_id = [0,]*2, [0,]*2, [0,]*2, [0,]*2, [0,]*2\n for i, ff in enumerate([face0, face1]):\n nd[i], nds[i], cells[i], bc_type[i], zone_id[i] = ff\n \n cell_face_pair = []\n for i in range(2):\n c = max(cells[i])\n if len(nds[i]) == 2:\n cell_face_pair.append((c, face_map[c][(nds[i][0], nds[i][1])]))\n else:\n cell_face_pair.append((c, face_map[c][eval('f'+str(i))]))\n \n periodic_cell_face_map[cell_face_pair[0]] = cell_face_pair[1]\n periodic_cell_face_map[cell_face_pair[1]] = cell_face_pair[0]","def makeMapping(globalMap):\n \n from memops.xml.Implementation import bool2str, str2bool\n\n # Set up top level dictionaries\n loadMaps = globalMap.get('loadMaps')\n mapsByGuid = globalMap.get('mapsByGuid')\n\n abstractTypes = globalMap.get('CLAS').get('abstractTypes')\n exolinks = globalMap.get('CLAS').get('exolinks')\n\n # Class AbstractCategory\n currentMap = {}\n abstractTypes['AbstractCategory'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-09-04-17:19:17_00001'] = currentMap\n currentMap['type'] = 'class'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-09-04-17:19:17_00001'\n currentMap['eType'] = 'cplx'\n currentMap['objkey'] = 'name'\n currentMap['class'] = ccp.api.lims.Classification.AbstractCategory\n contentMap = {}\n currentMap['content'] = contentMap\n\n # Attribute AbstractCategory.applicationData\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\n\n # Attribute AbstractCategory.details\n currentMap = {}\n contentMap['details'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-09-04-17:21:35_00001'] = currentMap\n loadMaps['CLAS.AbstractCategory.details'] = currentMap\n currentMap['tag'] = 'CLAS.AbstractCategory.details'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-09-04-17:21:35_00001'\n currentMap['name'] = 'details'\n currentMap['hicard'] = 1\n currentMap['locard'] = 0\n currentMap['eType'] = 'cplx'\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00036')\n\n # Attribute AbstractCategory.name\n currentMap = {}\n contentMap['name'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00014'] = currentMap\n loadMaps['CLAS.AbstractCategory.name'] = currentMap\n currentMap['tag'] = 'CLAS.AbstractCategory.name'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00014'\n currentMap['name'] = 'name'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['eType'] = 'cplx'\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033')\n\n # Role AbstractCategory.access\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\n # End of AbstractCategory\n\n currentMap = abstractTypes.get('AbstractCategory')\n aList = ['details', 'name']\n currentMap['simpleAttrs'] = aList\n aList = ['access', 'applicationData']\n currentMap['cplxAttrs'] = aList\n\n # Class Classification\n currentMap = {}\n abstractTypes['Classification'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-09-04-17:18:54_00002'] = currentMap\n loadMaps['CLAS.Classification'] = currentMap\n currentMap['tag'] = 'CLAS.Classification'\n currentMap['type'] = 'class'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-09-04-17:18:54_00002'\n currentMap['eType'] = 'cplx'\n currentMap['fromParent'] = 'classifications'\n currentMap['isTop'] = True\n currentMap['objkey'] = 'namingSystem'\n currentMap['class'] = ccp.api.lims.Classification.Classification\n contentMap = {}\n currentMap['content'] = contentMap\n\n # Attribute Classification.applicationData\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\n\n # Attribute Classification.createdBy\n contentMap['createdBy'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:00:59_00002__www.ccpn.ac.uk_Fogh_2007-10-03-14:53:27_00001__www.ccpn.ac.uk_Fogh_2006-09-14-16:28:57_00002')\n\n # Attribute Classification.guid\n contentMap['guid'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:26_00002')\n\n # Attribute Classification.isModifiable\n contentMap['isModifiable'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-17-14:16:26_00010__www.ccpn.ac.uk_Fogh_2007-10-03-14:53:27_00001__www.ccpn.ac.uk_Fogh_2006-09-14-16:28:57_00002')\n\n # Attribute Classification.lastUnlockedBy\n contentMap['lastUnlockedBy'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:00:59_00003__www.ccpn.ac.uk_Fogh_2007-10-03-14:53:27_00001__www.ccpn.ac.uk_Fogh_2006-09-14-16:28:57_00002')\n\n # Attribute Classification.namingSystem\n currentMap = {}\n contentMap['namingSystem'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00013'] = currentMap\n loadMaps['CLAS.Classification.namingSystem'] = currentMap\n currentMap['tag'] = 'CLAS.Classification.namingSystem'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00013'\n currentMap['name'] = 'namingSystem'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['eType'] = 'cplx'\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033')\n\n # Role Classification.access\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\n\n # Role Classification.experimentTypes\n currentMap = {}\n contentMap['experimentTypes'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00002'] = currentMap\n loadMaps['CLAS.Classification.experimentTypes'] = currentMap\n currentMap['tag'] = 'CLAS.Classification.experimentTypes'\n currentMap['type'] = 'child'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00002'\n currentMap['name'] = 'experimentTypes'\n currentMap['hicard'] = -1\n currentMap['locard'] = 0\n currentMap['eType'] = 'cplx'\n currentMap['implSkip'] = True\n currentMap['content'] = globalMap.get('CLAS').get('abstractTypes')\n\n # Role Classification.hazardPhrases\n currentMap = {}\n contentMap['hazardPhrases'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00006'] = currentMap\n loadMaps['CLAS.Classification.hazardPhrases'] = currentMap\n currentMap['tag'] = 'CLAS.Classification.hazardPhrases'\n currentMap['type'] = 'child'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00006'\n currentMap['name'] = 'hazardPhrases'\n currentMap['hicard'] = -1\n currentMap['locard'] = 0\n currentMap['eType'] = 'cplx'\n currentMap['implSkip'] = True\n currentMap['content'] = globalMap.get('CLAS').get('abstractTypes')\n\n # Role Classification.holderCategorys\n currentMap = {}\n contentMap['holderCategorys'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-09-04-17:33:23_00002'] = currentMap\n loadMaps['CLAS.Classification.holderCategorys'] = currentMap\n currentMap['tag'] = 'CLAS.Classification.holderCategorys'\n currentMap['type'] = 'child'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-09-04-17:33:23_00002'\n currentMap['name'] = 'holderCategorys'\n currentMap['hicard'] = -1\n currentMap['locard'] = 0\n currentMap['eType'] = 'cplx'\n currentMap['implSkip'] = True\n currentMap['content'] = globalMap.get('CLAS').get('abstractTypes')\n\n # Role Classification.instrumentTypes\n currentMap = {}\n contentMap['instrumentTypes'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-09-04-17:21:33_00001'] = currentMap\n loadMaps['CLAS.Classification.instrumentTypes'] = currentMap\n currentMap['tag'] = 'CLAS.Classification.instrumentTypes'\n currentMap['type'] = 'child'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-09-04-17:21:33_00001'\n currentMap['name'] = 'instrumentTypes'\n currentMap['hicard'] = -1\n currentMap['locard'] = 0\n currentMap['eType'] = 'cplx'\n currentMap['implSkip'] = True\n currentMap['content'] = globalMap.get('CLAS').get('abstractTypes')\n\n # Role Classification.sampleCategories\n currentMap = {}\n contentMap['sampleCategories'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00008'] = currentMap\n loadMaps['CLAS.Classification.sampleCategories'] = currentMap\n currentMap['tag'] = 'CLAS.Classification.sampleCategories'\n currentMap['type'] = 'child'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00008'\n currentMap['name'] = 'sampleCategories'\n currentMap['hicard'] = -1\n currentMap['locard'] = 0\n currentMap['eType'] = 'cplx'\n currentMap['implSkip'] = True\n currentMap['content'] = globalMap.get('CLAS').get('abstractTypes')\n\n # Role Classification.sampleComponentCategory\n currentMap = {}\n contentMap['sampleComponentCategory'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00004'] = currentMap\n loadMaps['CLAS.Classification.sampleComponentCategory'] = currentMap\n currentMap['tag'] = 'CLAS.Classification.sampleComponentCategory'\n currentMap['type'] = 'child'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00004'\n currentMap['name'] = 'sampleComponentCategory'\n currentMap['hicard'] = -1\n currentMap['locard'] = 0\n currentMap['eType'] = 'cplx'\n currentMap['implSkip'] = True\n currentMap['content'] = globalMap.get('CLAS').get('abstractTypes')\n\n # Role Classification.targetScoreboards\n currentMap = {}\n contentMap['targetScoreboards'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00012'] = currentMap\n loadMaps['CLAS.Classification.targetScoreboards'] = currentMap\n currentMap['tag'] = 'CLAS.Classification.targetScoreboards'\n currentMap['type'] = 'child'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00012'\n currentMap['name'] = 'targetScoreboards'\n currentMap['hicard'] = -1\n currentMap['locard'] = 0\n currentMap['eType'] = 'cplx'\n currentMap['implSkip'] = True\n currentMap['content'] = globalMap.get('CLAS').get('abstractTypes')\n\n # Role Classification.targetStatus\n currentMap = {}\n contentMap['targetStatus'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00010'] = currentMap\n loadMaps['CLAS.Classification.targetStatus'] = currentMap\n currentMap['tag'] = 'CLAS.Classification.targetStatus'\n currentMap['type'] = 'child'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00010'\n currentMap['name'] = 'targetStatus'\n currentMap['hicard'] = -1\n currentMap['locard'] = 0\n currentMap['eType'] = 'cplx'\n currentMap['implSkip'] = True\n currentMap['content'] = globalMap.get('CLAS').get('abstractTypes')\n # End of Classification\n\n currentMap = abstractTypes.get('Classification')\n aList = ['createdBy', 'guid', 'isModifiable', 'lastUnlockedBy']\n currentMap['headerAttrs'] = aList\n aList = ['namingSystem']\n currentMap['simpleAttrs'] = aList\n aList = ['targetStatus', 'targetScoreboards', 'sampleComponentCategory', 'sampleCategories', 'instrumentTypes', 'holderCategorys', 'hazardPhrases', 'experimentTypes', 'access', 'applicationData']\n currentMap['cplxAttrs'] = aList\n aList = ['experimentTypes', 'hazardPhrases', 'holderCategorys', 'instrumentTypes', 'sampleCategories', 'sampleComponentCategory', 'targetScoreboards', 'targetStatus']\n currentMap['children'] = aList\n\n # Class SampleComponentCategory\n currentMap = {}\n abstractTypes['SampleComponentCategory'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00014'] = currentMap\n loadMaps['CLAS.SampleComponentCategory'] = currentMap\n currentMap['tag'] = 'CLAS.SampleComponentCategory'\n currentMap['type'] = 'class'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00014'\n currentMap['eType'] = 'cplx'\n currentMap['fromParent'] = 'sampleComponentCategory'\n currentMap['objkey'] = 'name'\n currentMap['class'] = ccp.api.lims.Classification.SampleComponentCategory\n contentMap = {}\n currentMap['content'] = contentMap\n\n # Attribute SampleComponentCategory.applicationData\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\n\n # Attribute SampleComponentCategory.details\n contentMap['details'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-04-17:21:35_00001')\n\n # Attribute SampleComponentCategory.name\n contentMap['name'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00014')\n\n # Role SampleComponentCategory.access\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\n # End of SampleComponentCategory\n\n currentMap = abstractTypes.get('SampleComponentCategory')\n aList = ['details', 'name']\n currentMap['simpleAttrs'] = aList\n aList = ['access', 'applicationData']\n currentMap['cplxAttrs'] = aList\n\n # Class ExperimentType\n currentMap = {}\n abstractTypes['ExperimentType'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-14:22:45_00014'] = currentMap\n loadMaps['CLAS.ExperimentType'] = currentMap\n currentMap['tag'] = 'CLAS.ExperimentType'\n currentMap['type'] = 'class'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:45_00014'\n currentMap['eType'] = 'cplx'\n currentMap['fromParent'] = 'experimentTypes'\n currentMap['objkey'] = 'name'\n currentMap['class'] = ccp.api.lims.Classification.ExperimentType\n contentMap = {}\n currentMap['content'] = contentMap\n\n # Attribute ExperimentType.applicationData\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\n\n # Attribute ExperimentType.details\n contentMap['details'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-04-17:21:35_00001')\n\n # Attribute ExperimentType.name\n contentMap['name'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00014')\n\n # Role ExperimentType.access\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\n\n # Role ExperimentType.instrumentTypes\n currentMap = {}\n contentMap['instrumentTypes'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:22:57_00002'] = currentMap\n loadMaps['CLAS.ExperimentType.instrumentTypes'] = currentMap\n currentMap['tag'] = 'CLAS.ExperimentType.instrumentTypes'\n currentMap['type'] = 'link'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:22:57_00002'\n currentMap['name'] = 'instrumentTypes'\n currentMap['hicard'] = -1\n currentMap['locard'] = 0\n currentMap['copyOverride'] = True\n\n # Role ExperimentType.sampleCategories\n currentMap = {}\n contentMap['sampleCategories'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:22:56_00031'] = currentMap\n loadMaps['CLAS.ExperimentType.sampleCategories'] = currentMap\n currentMap['tag'] = 'CLAS.ExperimentType.sampleCategories'\n currentMap['type'] = 'link'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:22:56_00031'\n currentMap['name'] = 'sampleCategories'\n currentMap['hicard'] = -1\n currentMap['locard'] = 0\n currentMap['copyOverride'] = True\n # End of ExperimentType\n\n currentMap = abstractTypes.get('ExperimentType')\n aList = ['details', 'name', 'instrumentTypes', 'sampleCategories']\n currentMap['simpleAttrs'] = aList\n aList = ['access', 'applicationData']\n currentMap['cplxAttrs'] = aList\n\n # Class TargetScoreboard\n currentMap = {}\n abstractTypes['TargetScoreboard'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-14:22:51_00040'] = currentMap\n loadMaps['CLAS.TargetScoreboard'] = currentMap\n currentMap['tag'] = 'CLAS.TargetScoreboard'\n currentMap['type'] = 'class'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:51_00040'\n currentMap['eType'] = 'cplx'\n currentMap['fromParent'] = 'targetScoreboards'\n currentMap['objkey'] = 'name'\n currentMap['class'] = ccp.api.lims.Classification.TargetScoreboard\n contentMap = {}\n currentMap['content'] = contentMap\n\n # Attribute TargetScoreboard.applicationData\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\n\n # Attribute TargetScoreboard.details\n contentMap['details'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-04-17:21:35_00001')\n\n # Attribute TargetScoreboard.name\n contentMap['name'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00014')\n\n # Role TargetScoreboard.access\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\n\n # Role TargetScoreboard.targetStatus\n currentMap = {}\n contentMap['targetStatus'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:18_00039'] = currentMap\n loadMaps['CLAS.TargetScoreboard.targetStatus'] = currentMap\n currentMap['tag'] = 'CLAS.TargetScoreboard.targetStatus'\n currentMap['type'] = 'link'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:18_00039'\n currentMap['name'] = 'targetStatus'\n currentMap['hicard'] = -1\n currentMap['locard'] = 0\n currentMap['copyOverride'] = True\n # End of TargetScoreboard\n\n currentMap = abstractTypes.get('TargetScoreboard')\n aList = ['details', 'name', 'targetStatus']\n currentMap['simpleAttrs'] = aList\n aList = ['access', 'applicationData']\n currentMap['cplxAttrs'] = aList\n\n # Class HolderCategory\n currentMap = {}\n abstractTypes['HolderCategory'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-14:22:52_00049'] = currentMap\n loadMaps['CLAS.HolderCategory'] = currentMap\n currentMap['tag'] = 'CLAS.HolderCategory'\n currentMap['type'] = 'class'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:52_00049'\n currentMap['eType'] = 'cplx'\n currentMap['fromParent'] = 'holderCategorys'\n currentMap['objkey'] = 'name'\n currentMap['class'] = ccp.api.lims.Classification.HolderCategory\n contentMap = {}\n currentMap['content'] = contentMap\n\n # Attribute HolderCategory.applicationData\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\n\n # Attribute HolderCategory.details\n contentMap['details'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-04-17:21:35_00001')\n\n # Attribute HolderCategory.name\n contentMap['name'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00014')\n\n # Role HolderCategory.access\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\n # End of HolderCategory\n\n currentMap = abstractTypes.get('HolderCategory')\n aList = ['details', 'name']\n currentMap['simpleAttrs'] = aList\n aList = ['access', 'applicationData']\n currentMap['cplxAttrs'] = aList\n\n # Class HazardPhrase\n currentMap = {}\n abstractTypes['HazardPhrase'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-14:22:52_00052'] = currentMap\n loadMaps['CLAS.HazardPhrase'] = currentMap\n currentMap['tag'] = 'CLAS.HazardPhrase'\n currentMap['type'] = 'class'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:52_00052'\n currentMap['eType'] = 'cplx'\n currentMap['fromParent'] = 'hazardPhrases'\n currentMap['objkey'] = 'name'\n currentMap['class'] = ccp.api.lims.Classification.HazardPhrase\n contentMap = {}\n currentMap['content'] = contentMap\n\n # Attribute HazardPhrase.applicationData\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\n\n # Attribute HazardPhrase.details\n contentMap['details'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-04-17:21:35_00001')\n\n # Attribute HazardPhrase.name\n contentMap['name'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00014')\n\n # Attribute HazardPhrase.phrase\n currentMap = {}\n contentMap['phrase'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:28_00017'] = currentMap\n loadMaps['CLAS.HazardPhrase.phrase'] = currentMap\n currentMap['tag'] = 'CLAS.HazardPhrase.phrase'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:28_00017'\n currentMap['name'] = 'phrase'\n currentMap['hicard'] = 1\n currentMap['locard'] = 0\n currentMap['eType'] = 'cplx'\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00036')\n\n # Role HazardPhrase.access\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\n # End of HazardPhrase\n\n currentMap = abstractTypes.get('HazardPhrase')\n aList = ['details', 'name', 'phrase']\n currentMap['simpleAttrs'] = aList\n aList = ['access', 'applicationData']\n currentMap['cplxAttrs'] = aList\n\n # Class InstrumentType\n currentMap = {}\n abstractTypes['InstrumentType'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-14:22:46_00005'] = currentMap\n loadMaps['CLAS.InstrumentType'] = currentMap\n currentMap['tag'] = 'CLAS.InstrumentType'\n currentMap['type'] = 'class'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:46_00005'\n currentMap['eType'] = 'cplx'\n currentMap['fromParent'] = 'instrumentTypes'\n currentMap['objkey'] = 'name'\n currentMap['class'] = ccp.api.lims.Classification.InstrumentType\n contentMap = {}\n currentMap['content'] = contentMap\n\n # Attribute InstrumentType.applicationData\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\n\n # Attribute InstrumentType.details\n contentMap['details'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-04-17:21:35_00001')\n\n # Attribute InstrumentType.name\n contentMap['name'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00014')\n\n # Role InstrumentType.access\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\n\n # Role InstrumentType.experimentTypes\n currentMap = {}\n contentMap['experimentTypes'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:22:57_00001'] = currentMap\n loadMaps['CLAS.InstrumentType.experimentTypes'] = currentMap\n currentMap['tag'] = 'CLAS.InstrumentType.experimentTypes'\n currentMap['type'] = 'link'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:22:57_00001'\n currentMap['name'] = 'experimentTypes'\n currentMap['hicard'] = -1\n currentMap['locard'] = 0\n currentMap['copyOverride'] = True\n # End of InstrumentType\n\n currentMap = abstractTypes.get('InstrumentType')\n aList = ['details', 'name', 'experimentTypes']\n currentMap['simpleAttrs'] = aList\n aList = ['access', 'applicationData']\n currentMap['cplxAttrs'] = aList\n\n # Class SampleCategory\n currentMap = {}\n abstractTypes['SampleCategory'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-14:22:44_00009'] = currentMap\n loadMaps['CLAS.SampleCategory'] = currentMap\n currentMap['tag'] = 'CLAS.SampleCategory'\n currentMap['type'] = 'class'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:44_00009'\n currentMap['eType'] = 'cplx'\n currentMap['fromParent'] = 'sampleCategories'\n currentMap['objkey'] = 'name'\n currentMap['class'] = ccp.api.lims.Classification.SampleCategory\n contentMap = {}\n currentMap['content'] = contentMap\n\n # Attribute SampleCategory.applicationData\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\n\n # Attribute SampleCategory.details\n contentMap['details'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-04-17:21:35_00001')\n\n # Attribute SampleCategory.name\n contentMap['name'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00014')\n\n # Role SampleCategory.access\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\n\n # Role SampleCategory.experimentTypes\n currentMap = {}\n contentMap['experimentTypes'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:22:56_00030'] = currentMap\n loadMaps['CLAS.SampleCategory.experimentTypes'] = currentMap\n currentMap['tag'] = 'CLAS.SampleCategory.experimentTypes'\n currentMap['type'] = 'link'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:22:56_00030'\n currentMap['name'] = 'experimentTypes'\n currentMap['hicard'] = -1\n currentMap['locard'] = 0\n currentMap['copyOverride'] = True\n # End of SampleCategory\n\n currentMap = abstractTypes.get('SampleCategory')\n aList = ['details', 'name', 'experimentTypes']\n currentMap['simpleAttrs'] = aList\n aList = ['access', 'applicationData']\n currentMap['cplxAttrs'] = aList\n\n # Class TargetStatus\n currentMap = {}\n abstractTypes['TargetStatus'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-14:22:51_00039'] = currentMap\n loadMaps['CLAS.TargetStatus'] = currentMap\n currentMap['tag'] = 'CLAS.TargetStatus'\n currentMap['type'] = 'class'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:51_00039'\n currentMap['eType'] = 'cplx'\n currentMap['fromParent'] = 'targetStatus'\n currentMap['objkey'] = 'name'\n currentMap['class'] = ccp.api.lims.Classification.TargetStatus\n contentMap = {}\n currentMap['content'] = contentMap\n\n # Attribute TargetStatus.applicationData\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\n\n # Attribute TargetStatus.details\n contentMap['details'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-04-17:21:35_00001')\n\n # Attribute TargetStatus.name\n contentMap['name'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00014')\n\n # Role TargetStatus.access\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\n\n # Role TargetStatus.targetScoreboards\n currentMap = {}\n contentMap['targetScoreboards'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:18_00040'] = currentMap\n loadMaps['CLAS.TargetStatus.targetScoreboards'] = currentMap\n currentMap['tag'] = 'CLAS.TargetStatus.targetScoreboards'\n currentMap['type'] = 'link'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:18_00040'\n currentMap['name'] = 'targetScoreboards'\n currentMap['hicard'] = -1\n currentMap['locard'] = 0\n currentMap['copyOverride'] = True\n # End of TargetStatus\n\n currentMap = abstractTypes.get('TargetStatus')\n aList = ['details', 'name', 'targetScoreboards']\n currentMap['simpleAttrs'] = aList\n aList = ['access', 'applicationData']\n currentMap['cplxAttrs'] = aList\n\n # Out-of-package link to Classification\n currentMap = {}\n exolinks['Classification'] = currentMap\n loadMaps['CLAS.exo-Classification'] = currentMap\n currentMap['tag'] = 'CLAS.exo-Classification'\n currentMap['type'] = 'exo'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-09-04-17:18:54_00002'\n currentMap['name'] = 'Classification'\n currentMap['eType'] = 'cplx'\n currentMap['class'] = ccp.api.lims.Classification.Classification\n aList = list()\n currentMap['keyMaps'] = aList\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\n\n # Out-of-package link to SampleComponentCategory\n currentMap = {}\n exolinks['SampleComponentCategory'] = currentMap\n loadMaps['CLAS.exo-SampleComponentCategory'] = currentMap\n currentMap['tag'] = 'CLAS.exo-SampleComponentCategory'\n currentMap['type'] = 'exo'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00014'\n currentMap['name'] = 'SampleComponentCategory'\n currentMap['eType'] = 'cplx'\n currentMap['class'] = ccp.api.lims.Classification.SampleComponentCategory\n aList = list()\n currentMap['keyMaps'] = aList\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033'))\n\n # Out-of-package link to ExperimentType\n currentMap = {}\n exolinks['ExperimentType'] = currentMap\n loadMaps['CLAS.exo-ExperimentType'] = currentMap\n currentMap['tag'] = 'CLAS.exo-ExperimentType'\n currentMap['type'] = 'exo'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:45_00014'\n currentMap['name'] = 'ExperimentType'\n currentMap['eType'] = 'cplx'\n currentMap['class'] = ccp.api.lims.Classification.ExperimentType\n aList = list()\n currentMap['keyMaps'] = aList\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033'))\n\n # Out-of-package link to TargetScoreboard\n currentMap = {}\n exolinks['TargetScoreboard'] = currentMap\n loadMaps['CLAS.exo-TargetScoreboard'] = currentMap\n currentMap['tag'] = 'CLAS.exo-TargetScoreboard'\n currentMap['type'] = 'exo'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:51_00040'\n currentMap['name'] = 'TargetScoreboard'\n currentMap['eType'] = 'cplx'\n currentMap['class'] = ccp.api.lims.Classification.TargetScoreboard\n aList = list()\n currentMap['keyMaps'] = aList\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033'))\n\n # Out-of-package link to HolderCategory\n currentMap = {}\n exolinks['HolderCategory'] = currentMap\n loadMaps['CLAS.exo-HolderCategory'] = currentMap\n currentMap['tag'] = 'CLAS.exo-HolderCategory'\n currentMap['type'] = 'exo'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:52_00049'\n currentMap['name'] = 'HolderCategory'\n currentMap['eType'] = 'cplx'\n currentMap['class'] = ccp.api.lims.Classification.HolderCategory\n aList = list()\n currentMap['keyMaps'] = aList\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033'))\n\n # Out-of-package link to HazardPhrase\n currentMap = {}\n exolinks['HazardPhrase'] = currentMap\n loadMaps['CLAS.exo-HazardPhrase'] = currentMap\n currentMap['tag'] = 'CLAS.exo-HazardPhrase'\n currentMap['type'] = 'exo'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:52_00052'\n currentMap['name'] = 'HazardPhrase'\n currentMap['eType'] = 'cplx'\n currentMap['class'] = ccp.api.lims.Classification.HazardPhrase\n aList = list()\n currentMap['keyMaps'] = aList\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033'))\n\n # Out-of-package link to InstrumentType\n currentMap = {}\n exolinks['InstrumentType'] = currentMap\n loadMaps['CLAS.exo-InstrumentType'] = currentMap\n currentMap['tag'] = 'CLAS.exo-InstrumentType'\n currentMap['type'] = 'exo'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:46_00005'\n currentMap['name'] = 'InstrumentType'\n currentMap['eType'] = 'cplx'\n currentMap['class'] = ccp.api.lims.Classification.InstrumentType\n aList = list()\n currentMap['keyMaps'] = aList\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033'))\n\n # Out-of-package link to SampleCategory\n currentMap = {}\n exolinks['SampleCategory'] = currentMap\n loadMaps['CLAS.exo-SampleCategory'] = currentMap\n currentMap['tag'] = 'CLAS.exo-SampleCategory'\n currentMap['type'] = 'exo'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:44_00009'\n currentMap['name'] = 'SampleCategory'\n currentMap['eType'] = 'cplx'\n currentMap['class'] = ccp.api.lims.Classification.SampleCategory\n aList = list()\n currentMap['keyMaps'] = aList\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033'))\n\n # Out-of-package link to TargetStatus\n currentMap = {}\n exolinks['TargetStatus'] = currentMap\n loadMaps['CLAS.exo-TargetStatus'] = currentMap\n currentMap['tag'] = 'CLAS.exo-TargetStatus'\n currentMap['type'] = 'exo'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:51_00039'\n currentMap['name'] = 'TargetStatus'\n currentMap['eType'] = 'cplx'\n currentMap['class'] = ccp.api.lims.Classification.TargetStatus\n aList = list()\n currentMap['keyMaps'] = aList\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033'))","def make_hp_map(data, hp_indices, Nside = 2048, nest = True):\n \n print(\"len hp_indices: {}\".format(len(hp_indices)))\n hp_indices = np.array(hp_indices)\n print(\"shape of hp_indices: {}\".format(hp_indices.shape))\n hp_indices = np.squeeze(hp_indices)\n \n Npix = 12*Nside**2\n map_data = np.zeros(Npix, np.float_)\n map_data[hp_indices] = data\n \n return map_data","def _makeimap(self):\n self.map_['source'] = 'NAOJ'\n self.map_['provider'] = 'NRO'\n self.map_['instrument'] = 'NORH'\n self.map_['phyobs'] = ''","def applyMapping(self):\n pass","def _create_room(new_map, room):\n for x in range(room.x1 + 1, room.x2):\n for y in range(room.y1 + 1, room.y2):\n new_map.terrain[x][y] = 1","def map():","def build_I_map(self):\n raise NotImplementedError","def base_mappings():\n return {\n 'from_1': {\n 'to_1': {\n 'mol_1': ({}, {}, []),\n 'mol_2': ({}, {}, []),\n },\n },\n }","def remap_particle_to_cell_nv(particle_cell_map, input_particle):\n return particle_cell_map[input_particle]","def make_mapping(self) -> None:\n start_mark = StreamMark('generated node', 0, 0, 0)\n end_mark = StreamMark('generated node', 0, 0, 0)\n self.yaml_node = yaml.MappingNode('tag:yaml.org,2002:map', list(),\n start_mark, end_mark)","def makeMapping(globalMap):\n \n from memops.xml.Implementation import bool2str, str2bool\n\n # Set up top level dictionaries\n loadMaps = globalMap.get('loadMaps')\n mapsByGuid = globalMap.get('mapsByGuid')\n\n abstractTypes = globalMap.get('ANAP').get('abstractTypes')\n exolinks = globalMap.get('ANAP').get('exolinks')\n\n # DataType GraphicsHandlerType\n currentMap = {}\n abstractTypes['GraphicsHandlerType'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-10-03-11:26:03_00001'] = currentMap\n loadMaps['ANAP.GraphicsHandlerType'] = currentMap\n currentMap['tag'] = 'ANAP.GraphicsHandlerType'\n currentMap['type'] = 'simple'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-10-03-11:26:03_00001'\n currentMap['toStr'] = 'text'\n currentMap['cnvrt'] = 'text'\n\n # Class AnalysisProfile\n currentMap = {}\n abstractTypes['AnalysisProfile'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:50_00004'] = currentMap\n loadMaps['ANAP.AnalysisProfile'] = currentMap\n currentMap['tag'] = 'ANAP.AnalysisProfile'\n currentMap['type'] = 'class'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:50_00004'\n currentMap['eType'] = 'cplx'\n currentMap['fromParent'] = 'analysisProfiles'\n currentMap['isTop'] = True\n currentMap['objkey'] = 'name'\n currentMap['class'] = ccpnmr.api.AnalysisProfile.AnalysisProfile\n contentMap = {}\n currentMap['content'] = contentMap\n\n # Attribute AnalysisProfile.applicationData\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\n\n # Attribute AnalysisProfile.bgColor\n currentMap = {}\n contentMap['bgColor'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00031'] = currentMap\n loadMaps['ANAP.AnalysisProfile.bgColor'] = currentMap\n currentMap['tag'] = 'ANAP.AnalysisProfile.bgColor'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00031'\n currentMap['name'] = 'bgColor'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['eType'] = 'cplx'\n currentMap['default'] = '#FFFFFF'\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-05-05-15:12:50_00007')\n\n # Attribute AnalysisProfile.createdBy\n contentMap['createdBy'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:00:59_00002__www.ccpn.ac.uk_Fogh_2007-10-03-14:53:27_00001__www.ccpn.ac.uk_Fogh_2006-09-14-16:28:57_00002')\n\n # Attribute AnalysisProfile.fgColor\n currentMap = {}\n contentMap['fgColor'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00032'] = currentMap\n loadMaps['ANAP.AnalysisProfile.fgColor'] = currentMap\n currentMap['tag'] = 'ANAP.AnalysisProfile.fgColor'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00032'\n currentMap['name'] = 'fgColor'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['eType'] = 'cplx'\n currentMap['default'] = '#000000'\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-05-05-15:12:50_00007')\n\n # Attribute AnalysisProfile.font\n currentMap = {}\n contentMap['font'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00030'] = currentMap\n loadMaps['ANAP.AnalysisProfile.font'] = currentMap\n currentMap['tag'] = 'ANAP.AnalysisProfile.font'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00030'\n currentMap['name'] = 'font'\n currentMap['hicard'] = 1\n currentMap['locard'] = 0\n currentMap['eType'] = 'cplx'\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033')\n\n # Attribute AnalysisProfile.graphicsHandler\n currentMap = {}\n contentMap['graphicsHandler'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00040'] = currentMap\n loadMaps['ANAP.AnalysisProfile.graphicsHandler'] = currentMap\n currentMap['tag'] = 'ANAP.AnalysisProfile.graphicsHandler'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00040'\n currentMap['name'] = 'graphicsHandler'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['default'] = 'Tk'\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-10-03-11:26:03_00001')\n\n # Attribute AnalysisProfile.guid\n contentMap['guid'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:26_00002')\n\n # Attribute AnalysisProfile.isModifiable\n contentMap['isModifiable'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-17-14:16:26_00010__www.ccpn.ac.uk_Fogh_2007-10-03-14:53:27_00001__www.ccpn.ac.uk_Fogh_2006-09-14-16:28:57_00002')\n\n # Attribute AnalysisProfile.lastUnlockedBy\n contentMap['lastUnlockedBy'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:00:59_00003__www.ccpn.ac.uk_Fogh_2007-10-03-14:53:27_00001__www.ccpn.ac.uk_Fogh_2006-09-14-16:28:57_00002')\n\n # Attribute AnalysisProfile.name\n currentMap = {}\n contentMap['name'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00029'] = currentMap\n loadMaps['ANAP.AnalysisProfile.name'] = currentMap\n currentMap['tag'] = 'ANAP.AnalysisProfile.name'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00029'\n currentMap['name'] = 'name'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00037')\n\n # Attribute AnalysisProfile.panView\n currentMap = {}\n contentMap['panView'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00036'] = currentMap\n loadMaps['ANAP.AnalysisProfile.panView'] = currentMap\n currentMap['tag'] = 'ANAP.AnalysisProfile.panView'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00036'\n currentMap['name'] = 'panView'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['default'] = True\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00028')\n\n # Attribute AnalysisProfile.sendBugReports\n currentMap = {}\n contentMap['sendBugReports'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2010-11-17-16:21:37_00004'] = currentMap\n loadMaps['ANAP.AnalysisProfile.sendBugReports'] = currentMap\n currentMap['tag'] = 'ANAP.AnalysisProfile.sendBugReports'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2010-11-17-16:21:37_00004'\n currentMap['name'] = 'sendBugReports'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['default'] = 'maybe'\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2010-11-17-16:21:33_00001')\n\n # Attribute AnalysisProfile.transientDialogs\n currentMap = {}\n contentMap['transientDialogs'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00037'] = currentMap\n loadMaps['ANAP.AnalysisProfile.transientDialogs'] = currentMap\n currentMap['tag'] = 'ANAP.AnalysisProfile.transientDialogs'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00037'\n currentMap['name'] = 'transientDialogs'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['default'] = True\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00028')\n\n # Attribute AnalysisProfile.transientWindows\n currentMap = {}\n contentMap['transientWindows'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00038'] = currentMap\n loadMaps['ANAP.AnalysisProfile.transientWindows'] = currentMap\n currentMap['tag'] = 'ANAP.AnalysisProfile.transientWindows'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00038'\n currentMap['name'] = 'transientWindows'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['default'] = False\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00028')\n\n # Attribute AnalysisProfile.twoCharShortcuts\n currentMap = {}\n contentMap['twoCharShortcuts'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00039'] = currentMap\n loadMaps['ANAP.AnalysisProfile.twoCharShortcuts'] = currentMap\n currentMap['tag'] = 'ANAP.AnalysisProfile.twoCharShortcuts'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00039'\n currentMap['name'] = 'twoCharShortcuts'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['default'] = False\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00028')\n\n # Attribute AnalysisProfile.useCrosshair\n currentMap = {}\n contentMap['useCrosshair'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00034'] = currentMap\n loadMaps['ANAP.AnalysisProfile.useCrosshair'] = currentMap\n currentMap['tag'] = 'ANAP.AnalysisProfile.useCrosshair'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00034'\n currentMap['name'] = 'useCrosshair'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['default'] = True\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00028')\n\n # Attribute AnalysisProfile.useGlobalShortcuts\n currentMap = {}\n contentMap['useGlobalShortcuts'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00035'] = currentMap\n loadMaps['ANAP.AnalysisProfile.useGlobalShortcuts'] = currentMap\n currentMap['tag'] = 'ANAP.AnalysisProfile.useGlobalShortcuts'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00035'\n currentMap['name'] = 'useGlobalShortcuts'\n currentMap['hicard'] = 1\n currentMap['locard'] = 0\n currentMap['default'] = False\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00028')\n\n # Attribute AnalysisProfile.userEmail\n currentMap = {}\n contentMap['userEmail'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2010-11-17-16:21:37_00003'] = currentMap\n loadMaps['ANAP.AnalysisProfile.userEmail'] = currentMap\n currentMap['tag'] = 'ANAP.AnalysisProfile.userEmail'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2010-11-17-16:21:37_00003'\n currentMap['name'] = 'userEmail'\n currentMap['hicard'] = 1\n currentMap['locard'] = 0\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2007-09-12-18:31:28_00003')\n\n # Attribute AnalysisProfile.userName\n currentMap = {}\n contentMap['userName'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2010-11-17-16:21:37_00001'] = currentMap\n loadMaps['ANAP.AnalysisProfile.userName'] = currentMap\n currentMap['tag'] = 'ANAP.AnalysisProfile.userName'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2010-11-17-16:21:37_00001'\n currentMap['name'] = 'userName'\n currentMap['hicard'] = 1\n currentMap['locard'] = 0\n currentMap['eType'] = 'cplx'\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033')\n\n # Attribute AnalysisProfile.userOrganisation\n currentMap = {}\n contentMap['userOrganisation'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2010-11-17-16:21:37_00002'] = currentMap\n loadMaps['ANAP.AnalysisProfile.userOrganisation'] = currentMap\n currentMap['tag'] = 'ANAP.AnalysisProfile.userOrganisation'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2010-11-17-16:21:37_00002'\n currentMap['name'] = 'userOrganisation'\n currentMap['hicard'] = 1\n currentMap['locard'] = 0\n currentMap['eType'] = 'cplx'\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033')\n\n # Attribute AnalysisProfile.webBrowser\n currentMap = {}\n contentMap['webBrowser'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00033'] = currentMap\n loadMaps['ANAP.AnalysisProfile.webBrowser'] = currentMap\n currentMap['tag'] = 'ANAP.AnalysisProfile.webBrowser'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00033'\n currentMap['name'] = 'webBrowser'\n currentMap['hicard'] = 1\n currentMap['locard'] = 0\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00037')\n\n # Role AnalysisProfile.access\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\n\n # Role AnalysisProfile.colorSchemes\n currentMap = {}\n contentMap['colorSchemes'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00026'] = currentMap\n loadMaps['ANAP.AnalysisProfile.colorSchemes'] = currentMap\n currentMap['tag'] = 'ANAP.AnalysisProfile.colorSchemes'\n currentMap['type'] = 'child'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00026'\n currentMap['name'] = 'colorSchemes'\n currentMap['hicard'] = -1\n currentMap['locard'] = 0\n currentMap['eType'] = 'cplx'\n currentMap['implSkip'] = True\n currentMap['content'] = globalMap.get('ANAP').get('abstractTypes')\n\n # Role AnalysisProfile.macros\n currentMap = {}\n contentMap['macros'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00022'] = currentMap\n loadMaps['ANAP.AnalysisProfile.macros'] = currentMap\n currentMap['tag'] = 'ANAP.AnalysisProfile.macros'\n currentMap['type'] = 'child'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00022'\n currentMap['name'] = 'macros'\n currentMap['hicard'] = -1\n currentMap['locard'] = 0\n currentMap['eType'] = 'cplx'\n currentMap['implSkip'] = True\n currentMap['content'] = globalMap.get('ANAP').get('abstractTypes')\n\n # Role AnalysisProfile.marksColor\n currentMap = {}\n contentMap['marksColor'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00028'] = currentMap\n loadMaps['ANAP.AnalysisProfile.marksColor'] = currentMap\n currentMap['tag'] = 'ANAP.AnalysisProfile.marksColor'\n currentMap['type'] = 'link'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00028'\n currentMap['name'] = 'marksColor'\n currentMap['hicard'] = 1\n currentMap['locard'] = 0\n currentMap['implSkip'] = True\n currentMap['copyOverride'] = True\n\n # Role AnalysisProfile.refExpProfiles\n currentMap = {}\n contentMap['refExpProfiles'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00024'] = currentMap\n loadMaps['ANAP.AnalysisProfile.refExpProfiles'] = currentMap\n currentMap['tag'] = 'ANAP.AnalysisProfile.refExpProfiles'\n currentMap['type'] = 'child'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00024'\n currentMap['name'] = 'refExpProfiles'\n currentMap['hicard'] = -1\n currentMap['locard'] = 0\n currentMap['eType'] = 'cplx'\n currentMap['implSkip'] = True\n currentMap['content'] = globalMap.get('ANAP').get('abstractTypes')\n\n # Role AnalysisProfile.residueProfiles\n currentMap = {}\n contentMap['residueProfiles'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00020'] = currentMap\n loadMaps['ANAP.AnalysisProfile.residueProfiles'] = currentMap\n currentMap['tag'] = 'ANAP.AnalysisProfile.residueProfiles'\n currentMap['type'] = 'child'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00020'\n currentMap['name'] = 'residueProfiles'\n currentMap['hicard'] = -1\n currentMap['locard'] = 0\n currentMap['eType'] = 'cplx'\n currentMap['implSkip'] = True\n currentMap['content'] = globalMap.get('ANAP').get('abstractTypes')\n\n # Role AnalysisProfile.rulersColor\n currentMap = {}\n contentMap['rulersColor'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00027'] = currentMap\n loadMaps['ANAP.AnalysisProfile.rulersColor'] = currentMap\n currentMap['tag'] = 'ANAP.AnalysisProfile.rulersColor'\n currentMap['type'] = 'link'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00027'\n currentMap['name'] = 'rulersColor'\n currentMap['hicard'] = 1\n currentMap['locard'] = 0\n currentMap['implSkip'] = True\n currentMap['copyOverride'] = True\n # End of AnalysisProfile\n\n currentMap = abstractTypes.get('AnalysisProfile')\n aList = ['createdBy', 'graphicsHandler', 'guid', 'isModifiable', 'lastUnlockedBy', 'name', 'panView', 'sendBugReports', 'transientDialogs', 'transientWindows', 'twoCharShortcuts', 'useCrosshair', 'useGlobalShortcuts', 'userEmail', 'webBrowser']\n currentMap['headerAttrs'] = aList\n aList = ['bgColor', 'fgColor', 'font', 'userName', 'userOrganisation', 'marksColor', 'rulersColor']\n currentMap['simpleAttrs'] = aList\n aList = ['residueProfiles', 'refExpProfiles', 'macros', 'colorSchemes', 'access', 'applicationData']\n currentMap['cplxAttrs'] = aList\n aList = ['colorSchemes', 'macros', 'refExpProfiles', 'residueProfiles']\n currentMap['children'] = aList\n\n # Class ColorScheme\n currentMap = {}\n abstractTypes['ColorScheme'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-08-11:06:22_00002'] = currentMap\n loadMaps['ANAP.ColorScheme'] = currentMap\n currentMap['tag'] = 'ANAP.ColorScheme'\n currentMap['type'] = 'class'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-08-11:06:22_00002'\n currentMap['eType'] = 'cplx'\n currentMap['fromParent'] = 'colorSchemes'\n currentMap['objkey'] = 'name'\n currentMap['class'] = ccpnmr.api.AnalysisProfile.ColorScheme\n contentMap = {}\n currentMap['content'] = contentMap\n\n # Attribute ColorScheme.applicationData\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\n\n # Attribute ColorScheme.colors\n currentMap = {}\n contentMap['colors'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00043'] = currentMap\n loadMaps['ANAP.ColorScheme.colors'] = currentMap\n currentMap['tag'] = 'ANAP.ColorScheme.colors'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00043'\n currentMap['name'] = 'colors'\n currentMap['hicard'] = -1\n currentMap['locard'] = 0\n currentMap['eType'] = 'cplx'\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-05-05-15:12:50_00007')\n\n # Attribute ColorScheme.name\n currentMap = {}\n contentMap['name'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-15:11:12_00007'] = currentMap\n loadMaps['ANAP.ColorScheme.name'] = currentMap\n currentMap['tag'] = 'ANAP.ColorScheme.name'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-15:11:12_00007'\n currentMap['name'] = 'name'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['eType'] = 'cplx'\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033')\n\n # Role ColorScheme.access\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\n # End of ColorScheme\n\n currentMap = abstractTypes.get('ColorScheme')\n aList = ['colors', 'name']\n currentMap['simpleAttrs'] = aList\n aList = ['access', 'applicationData']\n currentMap['cplxAttrs'] = aList\n\n # Class Macro\n currentMap = {}\n abstractTypes['Macro'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-08-11:06:22_00001'] = currentMap\n loadMaps['ANAP.Macro'] = currentMap\n currentMap['tag'] = 'ANAP.Macro'\n currentMap['type'] = 'class'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-08-11:06:22_00001'\n currentMap['eType'] = 'cplx'\n currentMap['fromParent'] = 'macros'\n currentMap['objkey'] = 'serial'\n currentMap['class'] = ccpnmr.api.AnalysisProfile.Macro\n contentMap = {}\n currentMap['content'] = contentMap\n\n # Attribute Macro.applicationData\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\n\n # Attribute Macro.details\n currentMap = {}\n contentMap['details'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-15:11:12_00005'] = currentMap\n loadMaps['ANAP.Macro.details'] = currentMap\n currentMap['tag'] = 'ANAP.Macro.details'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-15:11:12_00005'\n currentMap['name'] = 'details'\n currentMap['hicard'] = 1\n currentMap['locard'] = 0\n currentMap['eType'] = 'cplx'\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00036')\n\n # Attribute Macro.function\n currentMap = {}\n contentMap['function'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-15:11:12_00002'] = currentMap\n loadMaps['ANAP.Macro.function'] = currentMap\n currentMap['tag'] = 'ANAP.Macro.function'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-15:11:12_00002'\n currentMap['name'] = 'function'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00037')\n\n # Attribute Macro.isInMenu\n currentMap = {}\n contentMap['isInMenu'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-09-29-13:48:16_00005'] = currentMap\n loadMaps['ANAP.Macro.isInMenu'] = currentMap\n currentMap['tag'] = 'ANAP.Macro.isInMenu'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-09-29-13:48:16_00005'\n currentMap['name'] = 'isInMenu'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['default'] = False\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00028')\n\n # Attribute Macro.isInMouseMenu\n currentMap = {}\n contentMap['isInMouseMenu'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-09-29-13:48:16_00006'] = currentMap\n loadMaps['ANAP.Macro.isInMouseMenu'] = currentMap\n currentMap['tag'] = 'ANAP.Macro.isInMouseMenu'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-09-29-13:48:16_00006'\n currentMap['name'] = 'isInMouseMenu'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['default'] = False\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00028')\n\n # Attribute Macro.module\n currentMap = {}\n contentMap['module'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-15:11:12_00003'] = currentMap\n loadMaps['ANAP.Macro.module'] = currentMap\n currentMap['tag'] = 'ANAP.Macro.module'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-15:11:12_00003'\n currentMap['name'] = 'module'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00037')\n\n # Attribute Macro.name\n currentMap = {}\n contentMap['name'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-15:11:10_00001'] = currentMap\n loadMaps['ANAP.Macro.name'] = currentMap\n currentMap['tag'] = 'ANAP.Macro.name'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-15:11:10_00001'\n currentMap['name'] = 'name'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['eType'] = 'cplx'\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033')\n\n # Attribute Macro.ordering\n currentMap = {}\n contentMap['ordering'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-15:11:12_00004'] = currentMap\n loadMaps['ANAP.Macro.ordering'] = currentMap\n currentMap['tag'] = 'ANAP.Macro.ordering'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-15:11:12_00004'\n currentMap['name'] = 'ordering'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['proc'] = 'direct'\n currentMap['default'] = 0\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00032')\n\n # Attribute Macro.path\n currentMap = {}\n contentMap['path'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-15:11:12_00001'] = currentMap\n loadMaps['ANAP.Macro.path'] = currentMap\n currentMap['tag'] = 'ANAP.Macro.path'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-15:11:12_00001'\n currentMap['name'] = 'path'\n currentMap['hicard'] = 1\n currentMap['locard'] = 0\n currentMap['eType'] = 'cplx'\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:54_00003')\n\n # Attribute Macro.serial\n currentMap = {}\n contentMap['serial'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-15:11:09_00001'] = currentMap\n loadMaps['ANAP.Macro.serial'] = currentMap\n currentMap['tag'] = 'ANAP.Macro.serial'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-15:11:09_00001'\n currentMap['name'] = 'serial'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00032')\n\n # Attribute Macro.shortcut\n currentMap = {}\n contentMap['shortcut'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-15:11:12_00006'] = currentMap\n loadMaps['ANAP.Macro.shortcut'] = currentMap\n currentMap['tag'] = 'ANAP.Macro.shortcut'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-15:11:12_00006'\n currentMap['name'] = 'shortcut'\n currentMap['hicard'] = 1\n currentMap['locard'] = 0\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00037')\n\n # Role Macro.access\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\n # End of Macro\n\n currentMap = abstractTypes.get('Macro')\n aList = ['function', 'isInMenu', 'isInMouseMenu', 'module', 'ordering', 'serial', 'shortcut']\n currentMap['headerAttrs'] = aList\n aList = ['details', 'name', 'path']\n currentMap['simpleAttrs'] = aList\n aList = ['access', 'applicationData']\n currentMap['cplxAttrs'] = aList\n\n # Class RefExpProfile\n currentMap = {}\n abstractTypes['RefExpProfile'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:50_00006'] = currentMap\n loadMaps['ANAP.RefExpProfile'] = currentMap\n currentMap['tag'] = 'ANAP.RefExpProfile'\n currentMap['type'] = 'class'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:50_00006'\n currentMap['eType'] = 'cplx'\n currentMap['fromParent'] = 'refExpProfiles'\n currentMap['objkey'] = 'name'\n currentMap['class'] = ccpnmr.api.AnalysisProfile.RefExpProfile\n contentMap = {}\n currentMap['content'] = contentMap\n\n # Attribute RefExpProfile.applicationData\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\n\n # Attribute RefExpProfile.name\n currentMap = {}\n contentMap['name'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00046'] = currentMap\n loadMaps['ANAP.RefExpProfile.name'] = currentMap\n currentMap['tag'] = 'ANAP.RefExpProfile.name'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00046'\n currentMap['name'] = 'name'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00037')\n\n # Attribute RefExpProfile.peakSymbolColors\n currentMap = {}\n contentMap['peakSymbolColors'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00048'] = currentMap\n loadMaps['ANAP.RefExpProfile.peakSymbolColors'] = currentMap\n currentMap['tag'] = 'ANAP.RefExpProfile.peakSymbolColors'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00048'\n currentMap['name'] = 'peakSymbolColors'\n currentMap['hicard'] = -1\n currentMap['locard'] = 0\n currentMap['eType'] = 'cplx'\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-05-05-15:12:50_00007')\n\n # Attribute RefExpProfile.peakTextColors\n currentMap = {}\n contentMap['peakTextColors'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00049'] = currentMap\n loadMaps['ANAP.RefExpProfile.peakTextColors'] = currentMap\n currentMap['tag'] = 'ANAP.RefExpProfile.peakTextColors'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00049'\n currentMap['name'] = 'peakTextColors'\n currentMap['hicard'] = -1\n currentMap['locard'] = 0\n currentMap['eType'] = 'cplx'\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-05-05-15:12:50_00007')\n\n # Attribute RefExpProfile.refExpNames\n currentMap = {}\n contentMap['refExpNames'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00047'] = currentMap\n loadMaps['ANAP.RefExpProfile.refExpNames'] = currentMap\n currentMap['tag'] = 'ANAP.RefExpProfile.refExpNames'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00047'\n currentMap['name'] = 'refExpNames'\n currentMap['hicard'] = -1\n currentMap['locard'] = 0\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00037')\n\n # Role RefExpProfile.access\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\n\n # Role RefExpProfile.negColorSchemes\n currentMap = {}\n contentMap['negColorSchemes'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00045'] = currentMap\n loadMaps['ANAP.RefExpProfile.negColorSchemes'] = currentMap\n currentMap['tag'] = 'ANAP.RefExpProfile.negColorSchemes'\n currentMap['type'] = 'link'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00045'\n currentMap['name'] = 'negColorSchemes'\n currentMap['hicard'] = -1\n currentMap['locard'] = 0\n currentMap['copyOverride'] = True\n\n # Role RefExpProfile.posColorSchemes\n currentMap = {}\n contentMap['posColorSchemes'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00041'] = currentMap\n loadMaps['ANAP.RefExpProfile.posColorSchemes'] = currentMap\n currentMap['tag'] = 'ANAP.RefExpProfile.posColorSchemes'\n currentMap['type'] = 'link'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00041'\n currentMap['name'] = 'posColorSchemes'\n currentMap['hicard'] = -1\n currentMap['locard'] = 0\n currentMap['copyOverride'] = True\n # End of RefExpProfile\n\n currentMap = abstractTypes.get('RefExpProfile')\n aList = ['name']\n currentMap['headerAttrs'] = aList\n aList = ['peakSymbolColors', 'peakTextColors', 'refExpNames', 'negColorSchemes', 'posColorSchemes']\n currentMap['simpleAttrs'] = aList\n aList = ['access', 'applicationData']\n currentMap['cplxAttrs'] = aList\n\n # Class ResidueProfile\n currentMap = {}\n abstractTypes['ResidueProfile'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:50_00005'] = currentMap\n loadMaps['ANAP.ResidueProfile'] = currentMap\n currentMap['tag'] = 'ANAP.ResidueProfile'\n currentMap['type'] = 'class'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:50_00005'\n currentMap['eType'] = 'cplx'\n currentMap['fromParent'] = 'residueProfiles'\n currentMap['class'] = ccpnmr.api.AnalysisProfile.ResidueProfile\n contentMap = {}\n currentMap['content'] = contentMap\n\n # Attribute ResidueProfile.applicationData\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\n\n # Attribute ResidueProfile.ccpCode\n currentMap = {}\n contentMap['ccpCode'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00051'] = currentMap\n loadMaps['ANAP.ResidueProfile.ccpCode'] = currentMap\n currentMap['tag'] = 'ANAP.ResidueProfile.ccpCode'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00051'\n currentMap['name'] = 'ccpCode'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2007-09-12-18:31:28_00003')\n\n # Attribute ResidueProfile.guiName\n currentMap = {}\n contentMap['guiName'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00052'] = currentMap\n loadMaps['ANAP.ResidueProfile.guiName'] = currentMap\n currentMap['tag'] = 'ANAP.ResidueProfile.guiName'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00052'\n currentMap['name'] = 'guiName'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2007-09-12-18:31:28_00003')\n\n # Attribute ResidueProfile.molType\n currentMap = {}\n contentMap['molType'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00050'] = currentMap\n loadMaps['ANAP.ResidueProfile.molType'] = currentMap\n currentMap['tag'] = 'ANAP.ResidueProfile.molType'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00050'\n currentMap['name'] = 'molType'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:52_00024')\n\n # Role ResidueProfile.access\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\n # End of ResidueProfile\n\n currentMap = abstractTypes.get('ResidueProfile')\n aList = ['ccpCode', 'guiName', 'molType']\n currentMap['headerAttrs'] = aList\n aList = ['access', 'applicationData']\n currentMap['cplxAttrs'] = aList\n\n # Out-of-package link to AnalysisProfile\n currentMap = {}\n exolinks['AnalysisProfile'] = currentMap\n loadMaps['ANAP.exo-AnalysisProfile'] = currentMap\n currentMap['tag'] = 'ANAP.exo-AnalysisProfile'\n currentMap['type'] = 'exo'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:50_00004'\n currentMap['name'] = 'AnalysisProfile'\n currentMap['eType'] = 'cplx'\n currentMap['class'] = ccpnmr.api.AnalysisProfile.AnalysisProfile\n aList = list()\n currentMap['keyMaps'] = aList\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\n\n # Out-of-package link to ColorScheme\n currentMap = {}\n exolinks['ColorScheme'] = currentMap\n loadMaps['ANAP.exo-ColorScheme'] = currentMap\n currentMap['tag'] = 'ANAP.exo-ColorScheme'\n currentMap['type'] = 'exo'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-08-11:06:22_00002'\n currentMap['name'] = 'ColorScheme'\n currentMap['eType'] = 'cplx'\n currentMap['class'] = ccpnmr.api.AnalysisProfile.ColorScheme\n aList = list()\n currentMap['keyMaps'] = aList\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033'))\n\n # Out-of-package link to Macro\n currentMap = {}\n exolinks['Macro'] = currentMap\n loadMaps['ANAP.exo-Macro'] = currentMap\n currentMap['tag'] = 'ANAP.exo-Macro'\n currentMap['type'] = 'exo'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-08-11:06:22_00001'\n currentMap['name'] = 'Macro'\n currentMap['eType'] = 'cplx'\n currentMap['class'] = ccpnmr.api.AnalysisProfile.Macro\n aList = list()\n currentMap['keyMaps'] = aList\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00032'))\n\n # Out-of-package link to RefExpProfile\n currentMap = {}\n exolinks['RefExpProfile'] = currentMap\n loadMaps['ANAP.exo-RefExpProfile'] = currentMap\n currentMap['tag'] = 'ANAP.exo-RefExpProfile'\n currentMap['type'] = 'exo'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:50_00006'\n currentMap['name'] = 'RefExpProfile'\n currentMap['eType'] = 'cplx'\n currentMap['class'] = ccpnmr.api.AnalysisProfile.RefExpProfile\n aList = list()\n currentMap['keyMaps'] = aList\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00037'))\n\n # Out-of-package link to ResidueProfile\n currentMap = {}\n exolinks['ResidueProfile'] = currentMap\n loadMaps['ANAP.exo-ResidueProfile'] = currentMap\n currentMap['tag'] = 'ANAP.exo-ResidueProfile'\n currentMap['type'] = 'exo'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:50_00005'\n currentMap['name'] = 'ResidueProfile'\n currentMap['eType'] = 'cplx'\n currentMap['class'] = ccpnmr.api.AnalysisProfile.ResidueProfile\n aList = list()\n currentMap['keyMaps'] = aList\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:52_00024'))\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2007-09-12-18:31:28_00003'))","def makeMapping(globalMap):\n \n from memops.xml.Implementation import bool2str, str2bool\n\n # Set up top level dictionaries\n loadMaps = globalMap.get('loadMaps')\n mapsByGuid = globalMap.get('mapsByGuid')\n\n abstractTypes = globalMap.get('ACCO').get('abstractTypes')\n exolinks = globalMap.get('ACCO').get('exolinks')\n\n # Class AccessControlStore\n currentMap = {}\n abstractTypes['AccessControlStore'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-09-04-17:18:10_00001'] = currentMap\n loadMaps['ACCO.AccessControlStore'] = currentMap\n currentMap['tag'] = 'ACCO.AccessControlStore'\n currentMap['type'] = 'class'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-09-04-17:18:10_00001'\n currentMap['eType'] = 'cplx'\n currentMap['fromParent'] = 'accessControlStores'\n currentMap['isTop'] = True\n currentMap['objkey'] = 'name'\n currentMap['class'] = memops.api.AccessControl.AccessControlStore\n contentMap = {}\n currentMap['content'] = contentMap\n\n # Attribute AccessControlStore.applicationData\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\n\n # Attribute AccessControlStore.createdBy\n contentMap['createdBy'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:00:59_00002__www.ccpn.ac.uk_Fogh_2007-10-03-14:53:27_00001__www.ccpn.ac.uk_Fogh_2006-09-14-16:28:57_00002')\n\n # Attribute AccessControlStore.guid\n contentMap['guid'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:26_00002')\n\n # Attribute AccessControlStore.isModifiable\n contentMap['isModifiable'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-17-14:16:26_00010__www.ccpn.ac.uk_Fogh_2007-10-03-14:53:27_00001__www.ccpn.ac.uk_Fogh_2006-09-14-16:28:57_00002')\n\n # Attribute AccessControlStore.lastUnlockedBy\n contentMap['lastUnlockedBy'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:00:59_00003__www.ccpn.ac.uk_Fogh_2007-10-03-14:53:27_00001__www.ccpn.ac.uk_Fogh_2006-09-14-16:28:57_00002')\n\n # Attribute AccessControlStore.name\n currentMap = {}\n contentMap['name'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-09-04-17:21:38_00006'] = currentMap\n loadMaps['ACCO.AccessControlStore.name'] = currentMap\n currentMap['tag'] = 'ACCO.AccessControlStore.name'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-09-04-17:21:38_00006'\n currentMap['name'] = 'name'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['eType'] = 'cplx'\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033')\n\n # Role AccessControlStore.access\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\n\n # Role AccessControlStore.accessObjects\n currentMap = {}\n contentMap['accessObjects'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00013'] = currentMap\n loadMaps['ACCO.AccessControlStore.accessObjects'] = currentMap\n currentMap['tag'] = 'ACCO.AccessControlStore.accessObjects'\n currentMap['type'] = 'child'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00013'\n currentMap['name'] = 'accessObjects'\n currentMap['hicard'] = -1\n currentMap['locard'] = 0\n currentMap['eType'] = 'cplx'\n currentMap['implSkip'] = True\n currentMap['content'] = globalMap.get('ACCO').get('abstractTypes')\n\n # Role AccessControlStore.userGroups\n currentMap = {}\n contentMap['userGroups'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-09-04-17:21:38_00003'] = currentMap\n loadMaps['ACCO.AccessControlStore.userGroups'] = currentMap\n currentMap['tag'] = 'ACCO.AccessControlStore.userGroups'\n currentMap['type'] = 'child'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-09-04-17:21:38_00003'\n currentMap['name'] = 'userGroups'\n currentMap['hicard'] = -1\n currentMap['locard'] = 0\n currentMap['eType'] = 'cplx'\n currentMap['implSkip'] = True\n currentMap['content'] = globalMap.get('ACCO').get('abstractTypes')\n\n # Role AccessControlStore.users\n currentMap = {}\n contentMap['users'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-09-04-17:21:38_00001'] = currentMap\n loadMaps['ACCO.AccessControlStore.users'] = currentMap\n currentMap['tag'] = 'ACCO.AccessControlStore.users'\n currentMap['type'] = 'child'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-09-04-17:21:38_00001'\n currentMap['name'] = 'users'\n currentMap['hicard'] = -1\n currentMap['locard'] = 0\n currentMap['eType'] = 'cplx'\n currentMap['implSkip'] = True\n currentMap['content'] = globalMap.get('ACCO').get('abstractTypes')\n # End of AccessControlStore\n\n currentMap = abstractTypes.get('AccessControlStore')\n aList = ['createdBy', 'guid', 'isModifiable', 'lastUnlockedBy']\n currentMap['headerAttrs'] = aList\n aList = ['name']\n currentMap['simpleAttrs'] = aList\n aList = ['users', 'userGroups', 'accessObjects', 'access', 'applicationData']\n currentMap['cplxAttrs'] = aList\n aList = ['accessObjects', 'userGroups', 'users']\n currentMap['children'] = aList\n\n # Class AccessObject\n currentMap = {}\n abstractTypes['AccessObject'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-14:22:54_00014'] = currentMap\n loadMaps['ACCO.AccessObject'] = currentMap\n currentMap['tag'] = 'ACCO.AccessObject'\n currentMap['type'] = 'class'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:54_00014'\n currentMap['eType'] = 'cplx'\n currentMap['fromParent'] = 'accessObjects'\n currentMap['objkey'] = 'name'\n currentMap['class'] = memops.api.AccessControl.AccessObject\n contentMap = {}\n currentMap['content'] = contentMap\n\n # Attribute AccessObject.applicationData\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\n\n # Attribute AccessObject.description\n currentMap = {}\n contentMap['description'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-14:16:27_00005'] = currentMap\n loadMaps['ACCO.AccessObject.description'] = currentMap\n currentMap['tag'] = 'ACCO.AccessObject.description'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-14:16:27_00005'\n currentMap['name'] = 'description'\n currentMap['hicard'] = 1\n currentMap['locard'] = 0\n currentMap['eType'] = 'cplx'\n currentMap['proc'] = 'direct'\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00035')\n\n # Attribute AccessObject.name\n currentMap = {}\n contentMap['name'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-14:16:27_00004'] = currentMap\n loadMaps['ACCO.AccessObject.name'] = currentMap\n currentMap['tag'] = 'ACCO.AccessObject.name'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-14:16:27_00004'\n currentMap['name'] = 'name'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['eType'] = 'cplx'\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033')\n\n # Role AccessObject.access\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\n\n # Role AccessObject.permissions\n currentMap = {}\n contentMap['permissions'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-14:16:27_00001'] = currentMap\n loadMaps['ACCO.AccessObject.permissions'] = currentMap\n currentMap['tag'] = 'ACCO.AccessObject.permissions'\n currentMap['type'] = 'child'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-14:16:27_00001'\n currentMap['name'] = 'permissions'\n currentMap['hicard'] = -1\n currentMap['locard'] = 0\n currentMap['eType'] = 'cplx'\n currentMap['content'] = globalMap.get('ACCO').get('abstractTypes')\n # End of AccessObject\n\n currentMap = abstractTypes.get('AccessObject')\n aList = ['description', 'name']\n currentMap['simpleAttrs'] = aList\n aList = ['permissions', 'access', 'applicationData']\n currentMap['cplxAttrs'] = aList\n aList = ['permissions']\n currentMap['children'] = aList\n\n # Class Permission\n currentMap = {}\n abstractTypes['Permission'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-14:22:54_00018'] = currentMap\n loadMaps['ACCO.Permission'] = currentMap\n currentMap['tag'] = 'ACCO.Permission'\n currentMap['type'] = 'class'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:54_00018'\n currentMap['eType'] = 'cplx'\n currentMap['fromParent'] = 'permissions'\n currentMap['class'] = memops.api.AccessControl.Permission\n contentMap = {}\n currentMap['content'] = contentMap\n\n # Attribute Permission.applicationData\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\n\n # Attribute Permission.opType\n currentMap = {}\n contentMap['opType'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-14:16:23_00021'] = currentMap\n loadMaps['ACCO.Permission.opType'] = currentMap\n currentMap['tag'] = 'ACCO.Permission.opType'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-14:16:23_00021'\n currentMap['name'] = 'opType'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['default'] = 'any'\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00037')\n\n # Attribute Permission.permission\n currentMap = {}\n contentMap['permission'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-14:16:23_00023'] = currentMap\n loadMaps['ACCO.Permission.permission'] = currentMap\n currentMap['tag'] = 'ACCO.Permission.permission'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-14:16:23_00023'\n currentMap['name'] = 'permission'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['default'] = True\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00028')\n\n # Attribute Permission.permissionClass\n currentMap = {}\n contentMap['permissionClass'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-14:16:23_00020'] = currentMap\n loadMaps['ACCO.Permission.permissionClass'] = currentMap\n currentMap['tag'] = 'ACCO.Permission.permissionClass'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-14:16:23_00020'\n currentMap['name'] = 'permissionClass'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['default'] = 'any'\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00037')\n\n # Attribute Permission.roleName\n currentMap = {}\n contentMap['roleName'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-14:16:23_00022'] = currentMap\n loadMaps['ACCO.Permission.roleName'] = currentMap\n currentMap['tag'] = 'ACCO.Permission.roleName'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-14:16:23_00022'\n currentMap['name'] = 'roleName'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['default'] = 'any'\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00037')\n\n # Role Permission.access\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\n\n # Role Permission.userGroup\n currentMap = {}\n contentMap['userGroup'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-14:16:23_00016'] = currentMap\n loadMaps['ACCO.Permission.userGroup'] = currentMap\n currentMap['tag'] = 'ACCO.Permission.userGroup'\n currentMap['type'] = 'link'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-14:16:23_00016'\n currentMap['name'] = 'userGroup'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['copyOverride'] = True\n # End of Permission\n\n currentMap = abstractTypes.get('Permission')\n aList = ['opType', 'permission', 'permissionClass', 'roleName']\n currentMap['headerAttrs'] = aList\n aList = ['userGroup']\n currentMap['optLinks'] = aList\n aList = ['access', 'applicationData']\n currentMap['cplxAttrs'] = aList\n\n # Class User\n currentMap = {}\n abstractTypes['User'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-14:22:54_00017'] = currentMap\n loadMaps['ACCO.User'] = currentMap\n currentMap['tag'] = 'ACCO.User'\n currentMap['type'] = 'class'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:54_00017'\n currentMap['eType'] = 'cplx'\n currentMap['fromParent'] = 'users'\n currentMap['objkey'] = 'name'\n currentMap['class'] = memops.api.AccessControl.User\n contentMap = {}\n currentMap['content'] = contentMap\n\n # Attribute User.applicationData\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\n\n # Attribute User.isSuperuser\n currentMap = {}\n contentMap['isSuperuser'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2010-05-06-13:30:17_00060'] = currentMap\n loadMaps['ACCO.User.isSuperuser'] = currentMap\n currentMap['tag'] = 'ACCO.User.isSuperuser'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2010-05-06-13:30:17_00060'\n currentMap['name'] = 'isSuperuser'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['default'] = False\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00028')\n\n # Attribute User.name\n currentMap = {}\n contentMap['name'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-14:16:23_00019'] = currentMap\n loadMaps['ACCO.User.name'] = currentMap\n currentMap['tag'] = 'ACCO.User.name'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-14:16:23_00019'\n currentMap['name'] = 'name'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['eType'] = 'cplx'\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033')\n\n # Attribute User.passwordHashed\n currentMap = {}\n contentMap['passwordHashed'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2009-08-19-17:31:11_00005'] = currentMap\n loadMaps['ACCO.User.passwordHashed'] = currentMap\n currentMap['tag'] = 'ACCO.User.passwordHashed'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2009-08-19-17:31:11_00005'\n currentMap['name'] = 'passwordHashed'\n currentMap['hicard'] = 1\n currentMap['locard'] = 0\n currentMap['eType'] = 'cplx'\n currentMap['proc'] = 'direct'\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00035')\n\n # Role User.access\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\n\n # Role User.ledGroups\n currentMap = {}\n contentMap['ledGroups'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-14:16:23_00014'] = currentMap\n loadMaps['ACCO.User.ledGroups'] = currentMap\n currentMap['tag'] = 'ACCO.User.ledGroups'\n currentMap['type'] = 'link'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-14:16:23_00014'\n currentMap['name'] = 'ledGroups'\n currentMap['hicard'] = -1\n currentMap['locard'] = 0\n currentMap['copyOverride'] = True\n\n # Role User.userGroups\n currentMap = {}\n contentMap['userGroups'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-14:16:23_00012'] = currentMap\n loadMaps['ACCO.User.userGroups'] = currentMap\n currentMap['tag'] = 'ACCO.User.userGroups'\n currentMap['type'] = 'link'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-14:16:23_00012'\n currentMap['name'] = 'userGroups'\n currentMap['hicard'] = -1\n currentMap['locard'] = 0\n currentMap['copyOverride'] = True\n # End of User\n\n currentMap = abstractTypes.get('User')\n aList = ['isSuperuser']\n currentMap['headerAttrs'] = aList\n aList = ['name', 'passwordHashed', 'ledGroups', 'userGroups']\n currentMap['simpleAttrs'] = aList\n aList = ['access', 'applicationData']\n currentMap['cplxAttrs'] = aList\n\n # Class UserGroup\n currentMap = {}\n abstractTypes['UserGroup'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-14:22:54_00016'] = currentMap\n loadMaps['ACCO.UserGroup'] = currentMap\n currentMap['tag'] = 'ACCO.UserGroup'\n currentMap['type'] = 'class'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:54_00016'\n currentMap['eType'] = 'cplx'\n currentMap['fromParent'] = 'userGroups'\n currentMap['objkey'] = 'name'\n currentMap['class'] = memops.api.AccessControl.UserGroup\n contentMap = {}\n currentMap['content'] = contentMap\n\n # Attribute UserGroup.applicationData\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\n\n # Attribute UserGroup.name\n currentMap = {}\n contentMap['name'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-14:16:23_00018'] = currentMap\n loadMaps['ACCO.UserGroup.name'] = currentMap\n currentMap['tag'] = 'ACCO.UserGroup.name'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-14:16:23_00018'\n currentMap['name'] = 'name'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['eType'] = 'cplx'\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033')\n\n # Role UserGroup.access\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\n\n # Role UserGroup.leaders\n currentMap = {}\n contentMap['leaders'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-14:16:23_00015'] = currentMap\n loadMaps['ACCO.UserGroup.leaders'] = currentMap\n currentMap['tag'] = 'ACCO.UserGroup.leaders'\n currentMap['type'] = 'link'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-14:16:23_00015'\n currentMap['name'] = 'leaders'\n currentMap['hicard'] = -1\n currentMap['locard'] = 0\n currentMap['copyOverride'] = True\n\n # Role UserGroup.members\n currentMap = {}\n contentMap['members'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-14:16:23_00013'] = currentMap\n loadMaps['ACCO.UserGroup.members'] = currentMap\n currentMap['tag'] = 'ACCO.UserGroup.members'\n currentMap['type'] = 'link'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-14:16:23_00013'\n currentMap['name'] = 'members'\n currentMap['hicard'] = -1\n currentMap['locard'] = 0\n currentMap['copyOverride'] = False\n\n # Role UserGroup.permissions\n currentMap = {}\n contentMap['permissions'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-14:16:23_00017'] = currentMap\n loadMaps['ACCO.UserGroup.permissions'] = currentMap\n currentMap['tag'] = 'ACCO.UserGroup.permissions'\n currentMap['type'] = 'link'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-14:16:23_00017'\n currentMap['name'] = 'permissions'\n currentMap['hicard'] = -1\n currentMap['locard'] = 0\n currentMap['copyOverride'] = False\n # End of UserGroup\n\n currentMap = abstractTypes.get('UserGroup')\n aList = ['name', 'leaders', 'members', 'permissions']\n currentMap['simpleAttrs'] = aList\n aList = ['access', 'applicationData']\n currentMap['cplxAttrs'] = aList\n\n # Out-of-package link to AccessControlStore\n currentMap = {}\n exolinks['AccessControlStore'] = currentMap\n loadMaps['ACCO.exo-AccessControlStore'] = currentMap\n currentMap['tag'] = 'ACCO.exo-AccessControlStore'\n currentMap['type'] = 'exo'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-09-04-17:18:10_00001'\n currentMap['name'] = 'AccessControlStore'\n currentMap['eType'] = 'cplx'\n currentMap['class'] = memops.api.AccessControl.AccessControlStore\n aList = list()\n currentMap['keyMaps'] = aList\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\n\n # Out-of-package link to AccessObject\n currentMap = {}\n exolinks['AccessObject'] = currentMap\n loadMaps['ACCO.exo-AccessObject'] = currentMap\n currentMap['tag'] = 'ACCO.exo-AccessObject'\n currentMap['type'] = 'exo'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:54_00014'\n currentMap['name'] = 'AccessObject'\n currentMap['eType'] = 'cplx'\n currentMap['class'] = memops.api.AccessControl.AccessObject\n aList = list()\n currentMap['keyMaps'] = aList\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033'))\n\n # Out-of-package link to Permission\n currentMap = {}\n exolinks['Permission'] = currentMap\n loadMaps['ACCO.exo-Permission'] = currentMap\n currentMap['tag'] = 'ACCO.exo-Permission'\n currentMap['type'] = 'exo'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:54_00018'\n currentMap['name'] = 'Permission'\n currentMap['eType'] = 'cplx'\n currentMap['class'] = memops.api.AccessControl.Permission\n aList = list()\n currentMap['keyMaps'] = aList\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033'))\n aList.append(globalMap.get('ACCO').get('exolinks'))\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00037'))\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00037'))\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00037'))\n\n # Out-of-package link to User\n currentMap = {}\n exolinks['User'] = currentMap\n loadMaps['ACCO.exo-User'] = currentMap\n currentMap['tag'] = 'ACCO.exo-User'\n currentMap['type'] = 'exo'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:54_00017'\n currentMap['name'] = 'User'\n currentMap['eType'] = 'cplx'\n currentMap['class'] = memops.api.AccessControl.User\n aList = list()\n currentMap['keyMaps'] = aList\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033'))\n\n # Out-of-package link to UserGroup\n currentMap = {}\n exolinks['UserGroup'] = currentMap\n loadMaps['ACCO.exo-UserGroup'] = currentMap\n currentMap['tag'] = 'ACCO.exo-UserGroup'\n currentMap['type'] = 'exo'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:54_00016'\n currentMap['name'] = 'UserGroup'\n currentMap['eType'] = 'cplx'\n currentMap['class'] = memops.api.AccessControl.UserGroup\n aList = list()\n currentMap['keyMaps'] = aList\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033'))","def create_cell_face_map(dim, mesh_format):\n if mesh_format == 'fenics':\n create_cell_map(dim)\n else:\n eval('create_cell_face_map_' + str(dim) + 'D()')","def ref_mapping_directory(tmpdir_factory):\n basedir = tmpdir_factory.mktemp('data')\n mapdir = basedir.mkdir('mappings')\n\n template = textwrap.dedent(\"\"\"\n [ molecule ]\n dummy_{0}\n\n [ from ]\n {1}\n\n [ to ]\n {2}\n\n [ atoms ]\n 0 X1{0} A{0} B{0}\n 1 X2{0} C{0} D{0}\n \"\"\")\n\n mappings = collections.defaultdict(lambda: collections.defaultdict(dict))\n\n force_fields_from = ['ff{}'.format(i) for i in range(4)]\n force_fields_to = force_fields_from + ['only_to']\n force_fields_from = force_fields_from + ['only_from']\n iterate_on = itertools.product(force_fields_from, force_fields_to, range(3))\n for idx, (from_ff, to_ff, _) in enumerate(iterate_on):\n mapfile = mapdir / 'file{}.map'.format(idx)\n with open(str(mapfile), 'w') as outfile:\n outfile.write(template.format(idx, from_ff, to_ff))\n\n mapping = {\n (0, 'X1{}'.format(idx)): [(0, 'A{}'.format(idx)), (0, 'B{}'.format(idx))],\n (0, 'X2{}'.format(idx)): [(0, 'C{}'.format(idx)), (0, 'D{}'.format(idx))],\n }\n weights = {\n (0, 'A{}'.format(idx)): {(0, 'X1{}'.format(idx)): 0.5},\n (0, 'B{}'.format(idx)): {(0, 'X1{}'.format(idx)): 0.5},\n (0, 'C{}'.format(idx)): {(0, 'X2{}'.format(idx)): 0.5},\n (0, 'D{}'.format(idx)): {(0, 'X2{}'.format(idx)): 0.5},\n }\n extra = []\n mappings[from_ff][to_ff]['dummy_{}'.format(idx)] = (mapping, weights, extra)\n\n mappings = {from_ff: dict(to_ff) for from_ff, to_ff in mappings.items()}\n\n return Path(str(basedir)), mappings","def _createMap(self):\n width = self.map_size[0] * self.chunk_size\n height = self.map_size[1] * self.chunk_size\n map_array = np.zeros((height, width), dtype=float)\n chunks = {}\n clist = []\n for i in range(0, self.map_size[0]*self.map_size[1]):\n chunks[i+1] = Chunk(self)\n chunk_array = np.asarray(list(chunks.keys()))\n chunk_array.resize(self.map_size[0], self.map_size[1])\n return map_array, chunk_array, chunks","def components_map(self):\r\n raise NotImplementedError","def receiverMapping():","def getObjectMap(self,oldRefs):\n objMap = {} #--objMap[cellId][oldIObj] = newIObj\n #--Old cells\n for oldCell in oldRefs.cells:\n cellId = oldCell.getId()\n newCell = self.cells_id.get(cellId)\n #--Cell deleted?\n if not newCell:\n objMap[cellId] = -1\n continue\n cellObjMap = {}\n newObjects = newCell.getObjects().list()\n nextObjectIndex = {}\n #--Old Objects\n for oldObject in oldCell.getObjects().list():\n (iMod,oldIObj,objId) = oldObject[:3]\n if iMod: continue #--Skip mods to masters\n #--New Objects\n objIndex = nextObjectIndex.get(objId,0)\n newIObj = -1 #--Delete by default\n while objIndex < len(newObjects):\n newObject = newObjects[objIndex]\n objIndex += 1\n if newObject[0]: continue #--Skip mods to masters\n if newObject[2] == objId:\n newIObj = newObject[1]\n break\n nextObjectIndex[objId] = objIndex\n #--Obj map has changed?\n if newIObj != oldIObj:\n cellObjMap[oldIObj] = (newIObj,objId)\n #--Save mapping for this cell?\n if cellObjMap: objMap[cellId] = cellObjMap\n #--Done\n return objMap","def __init__(self, mapping: Dict[int, int]):\n self.mapping = mapping","def geo2cell(geofile, posfile):","def create_cell(self, cid):\n self.cells[cid] = Cell()","def map(self):\r\n pass","def create_label_map():\n\n cnt = 1\n tmp_array = np.array([10, 15, 25, 30, 40, 47, 57, 63, 69, 74, 81])\n dictionary = dict()\n dictionary[1] = 1\n for idx, val in enumerate(tmp_array):\n for j in range(cnt + 1, val):\n dictionary[j] = int(idx + 2)\n cnt = j\n return dictionary","def __build_map(self):\n columns = []\n\n for i in range(self.__dimensions):\n columns.append([])\n\n for i in range(self.__dimensions):\n self.map.append(columns)","def requires_mapping(self):","def createMap(self):\n map = {}\n for rows in xrange(0,(size[1]/50)):\n for columns in xrange(0,(size[0]/50)):\n if rows == (size[1]/50)-1 or rows == 0 or columns== (size[0]/50)-1 or columns==0:\n map.update({(rows,columns):\"block\"})\n elif(rows%3 == 0):\n map.update({(rows,columns):random.choice(map_options)})\n else:\n map.update({(rows,columns):random.choice(map_options[:1])})\n\n self.map = map","def construct_new_ref_map(labels: np.ndarray, samples: list, ref_map_shape: list):\n new_ref_map = np.zeros(shape=ref_map_shape) + BG_CLASS\n for i, indexes in enumerate(samples):\n new_ref_map[indexes[ROW_AXIS], indexes[COLUMNS_AXIS]] = labels[i]\n return new_ref_map.astype(int)","def edge_mapping(self):\n ...","def to_mapping(self, dim):\n mim = cifti2.Cifti2MatrixIndicesMap([dim], 'CIFTI_INDEX_TYPE_LABELS')\n for name, label, meta in zip(self.name, self.label, self.meta):\n label_table = cifti2.Cifti2LabelTable()\n for key, value in label.items():\n label_table[key] = (value[0],) + tuple(value[1])\n named_map = cifti2.Cifti2NamedMap(name, cifti2.Cifti2MetaData(meta), label_table)\n mim.append(named_map)\n return mim","def __init__(self):\n self.map = {} #key: val, val, idx\n self.slot = []","def map(self, app, node):","def __init__(self):\n self.map = {}","def k_map(self):\n\t\tt1 = time.time()\n\t\tmapping_matrix = [] \n\t\tfor index in self.mapping:\n\t\t\tvector = np.zeros(len(self.unique_char),dtype=float)\n\t\t\tvector[index] = 1.0\n\t\t\tmapping_matrix.append(vector)\n\t\tprint(\"Time creating k map {:.3f} sec\".format(time.time()-t1))\n\t\tself.mapping_matrix = mapping_matrix\n\t\treturn mapping_matrix","def __init__(self):\n self.map = defaultdict(list)","def _create_subscript_mapping():\n # Create the normal and subscript digits list.\n normal_digits = [i for i in range(10)]\n subscript_digits = [chr(0x2080 + i) for i in range(10)]\n\n # Convert the normal digits to strings.\n normal_digits = [str(i) for i in normal_digits]\n\n # Create a dict mapping the two.\n return DefaultDictionary(zip(normal_digits, subscript_digits))","def nodes_mapped(instance):\n G, mapping = instance.network()\n node_dict = instance.network_nodes_species()\n\n node_dict_mapped = {}\n\n for old_label, new_label in mapping.items():\n for node, ammentity in node_dict.items():\n if old_label == node:\n node_dict_mapped[new_label] = ammentity\n\n return node_dict_mapped","def __init__(self, mappings):\n self.mappings = mappings","def __init__(self):\n self.map = [None] * 103","def map(pointer, objfile=\"\"):\n ei_class, ehdr = get_ehdr(pointer)\n return map_inner(ei_class, ehdr, objfile)","def create_task_dof_maps(field, cell_tasks, inter_facets, is_overlap=True,\n use_expand_dofs=False, save_inter_regions=False,\n output_dir=None):\n domain = field.domain\n cmesh = domain.cmesh\n\n if use_expand_dofs:\n id_map = nm.zeros(field.n_nod * field.n_components, dtype=nm.uint32)\n\n else:\n id_map = nm.zeros(field.n_nod, dtype=nm.uint32)\n\n def _get_dofs_region(field, region):\n dofs = field.get_dofs_in_region(region)\n if use_expand_dofs:\n dofs = expand_dofs(dofs, field.n_components)\n return dofs\n\n def _get_dofs_conn(field, conn):\n dofs = nm.unique(conn)\n if use_expand_dofs:\n dofs = expand_dofs(dofs, field.n_components)\n return dofs\n\n dof_maps = {}\n count = 0\n inter_count = 0\n ocs = nm.zeros(0, dtype=nm.int32)\n cell_parts = []\n for ir, ntasks in ordered_iteritems(inter_facets):\n cells = nm.where(cell_tasks == ir)[0].astype(nm.int32)\n cell_parts.append(cells)\n\n cregion = Region.from_cells(cells, domain, name='task_%d' % ir)\n domain.regions.append(cregion)\n dofs = _get_dofs_region(field, cregion)\n\n rdof_map = dof_maps.setdefault(ir, [None, [], [], 0, 0])\n inter_dofs = []\n for ic, facets in ordered_iteritems(ntasks):\n cdof_map = dof_maps.setdefault(ic, [None, [], [], 0, 0])\n\n name = 'inter_%d_%d' % (ir, ic)\n ii = ir\n\n region = Region.from_facets(facets, domain, name,\n parent=cregion.name)\n region.update_shape()\n\n if save_inter_regions:\n output_dir = output_dir if output_dir is not None else '.'\n filename = os.path.join(output_dir, '%s.mesh' % name)\n\n aux = domain.mesh.from_region(region, domain.mesh,\n is_surface=True)\n aux.write(filename, io='auto')\n\n mask = nm.zeros((domain.mesh.n_nod, 1), dtype=nm.float64)\n mask[region.vertices] = 1\n out = {name : Struct(name=name, mode='vertex', data=mask)}\n filename = os.path.join(output_dir, '%s.h5' % name)\n domain.mesh.write(filename, out=out, io='auto')\n\n inter_dofs.append(_get_dofs_region(field, region))\n\n sd = FESurface('surface_data_%s' % region.name, region,\n field.efaces, field.econn, field.region)\n econn = sd.get_connectivity()\n n_facet = econn.shape[0]\n\n ii2 = max(int(n_facet / 2), 1)\n\n dr = _get_dofs_conn(field, econn[:ii2])\n ii = nm.where((id_map[dr] == 0))[0]\n n_new = len(ii)\n if n_new:\n rdof_map[1].append(dr[ii])\n rdof_map[3] += n_new\n id_map[dr[ii]] = 1\n inter_count += n_new\n count += n_new\n\n if is_overlap:\n ovs = cmesh.get_incident(0, region.facets[:ii2],\n cmesh.tdim - 1)\n ocs = cmesh.get_incident(cmesh.tdim, ovs, 0)\n rdof_map[2].append(ocs.astype(nm.int32))\n\n dc = _get_dofs_conn(field, econn[ii2:])\n ii = nm.where((id_map[dc] == 0))[0]\n n_new = len(ii)\n if n_new:\n cdof_map[1].append(dc[ii])\n cdof_map[3] += n_new\n id_map[dc[ii]] = 1\n inter_count += n_new\n count += n_new\n\n if is_overlap:\n ovs = cmesh.get_incident(0, region.facets[ii2:],\n cmesh.tdim - 1)\n ocs = cmesh.get_incident(cmesh.tdim, ovs, 0)\n cdof_map[2].append(ocs.astype(nm.int32))\n\n domain.regions.pop() # Remove the cell region.\n\n inner_dofs = nm.setdiff1d(dofs, nm.concatenate(inter_dofs))\n n_inner = len(inner_dofs)\n rdof_map[3] += n_inner\n assert_(nm.all(id_map[inner_dofs] == 0))\n id_map[inner_dofs] = 1\n count += n_inner\n\n rdof_map[0] = inner_dofs\n\n offset = 0\n overlap_cells = []\n for ir, dof_map in ordered_iteritems(dof_maps):\n n_owned = dof_map[3]\n\n i0 = len(dof_map[0])\n id_map[dof_map[0]] = nm.arange(offset, offset + i0, dtype=nm.uint32)\n for aux in dof_map[1]:\n i1 = len(aux)\n id_map[aux] = nm.arange(offset + i0, offset + i0 + i1,\n dtype=nm.uint32)\n i0 += i1\n\n if len(dof_map[2]):\n ocs = nm.unique(nm.concatenate(dof_map[2]))\n\n else:\n ocs = nm.zeros(0, dtype=nm.int32)\n\n overlap_cells.append(ocs)\n\n assert_(i0 == n_owned)\n\n dof_map[4] = offset\n offset += n_owned\n\n if not len(dof_maps):\n dofs = _get_dofs_region(field, field.region)\n dof_maps[0] = [dofs, [], [], len(dofs), 0]\n id_map[:] = nm.arange(len(dofs), dtype=nm.uint32)\n\n if not len(cell_parts):\n cell_parts.append(nm.arange(len(cell_tasks), dtype=nm.int32))\n\n if not len(overlap_cells):\n overlap_cells.append(nm.zeros(0, dtype=nm.int32))\n\n return dof_maps, id_map, cell_parts, overlap_cells","def create_cells(self):\n raise NotImplementedError(\n \"create_cells function not reimplemented from base class\")","def _create_cells(self):\n\t\tcellId=0\n\t\t# Iterate over all dictionaries\n\t\tfor muscle,muscAfferentDelay in self._infoMuscles:\n\t\t\tfor cellInfo in self._infoCommonCellsInMuscles:\n\t\t\t\tcellClass = cellInfo[0]\n\t\t\t\tcellName = cellInfo[1]\n\t\t\t\tcellNumber = cellInfo[2]\n\t\t\t\tif len(cellInfo)>=4: neuronParam = cellInfo[3]\n\t\t\t\telse: neuronParam = None\n\t\t\t\tcellId = self._create_cell_population(cellId,muscle,muscAfferentDelay,cellClass,cellName,cellNumber,neuronParam)\n\t\t# Add special cells\n\t\tfor cellInfo in self._infoSpecialCells:\n\t\t\tgroupOrMuscle = cellInfo[0]\n\t\t\tcellClass = cellInfo[1]\n\t\t\tcellName = cellInfo[2]\n\t\t\tcellNumber = cellInfo[3]\n\t\t\tif len(cellInfo)>=5: neuronParam = cellInfo[4]\n\t\t\telse: neuronParam = None\n\t\t\tmuscAfferentDelay = None\n\t\t\tcellId = self._create_cell_population(cellId,groupOrMuscle,muscAfferentDelay,cellClass,cellName,cellNumber,neuronParam)\n\n\t\tself._motoneuronsNames = self._intMotoneuronsNames+self._realMotoneuronsNames\n\t\tself._afferentsNames = self._primaryAfferentsNames+self._secondaryAfferentsNames","def __init__(self):\n self._map = {}","def build_occupancy_map_torch(human: torch.Tensor, other_agents: torch.Tensor, cell_num: int, cell_size: float,\n om_channel_size: int) -> torch.Tensor:\n other_px = other_agents[:, 0] - human[0]\n other_py = other_agents[:, 1] - human[1]\n\n # new x-axis is in the direction of human's velocity\n human_velocity_angle = torch.atan2(human[1], human[0])\n other_human_orientation = torch.atan2(other_py, other_px)\n rotation = other_human_orientation - human_velocity_angle\n distance = torch.norm(torch.cat([other_px.view(-1, 1), other_py.view(-1, 1)], axis=1), dim=1)\n other_px = torch.cos(rotation) * distance\n other_py = torch.sin(rotation) * distance\n\n # compute indices of humans in the grid\n other_x_index = torch.floor(other_px / cell_size + cell_num / 2)\n other_y_index = torch.floor(other_py / cell_size + cell_num / 2)\n other_x_index[other_x_index < 0] = float('-inf')\n other_x_index[other_x_index >= cell_num] = float('-inf')\n other_y_index[other_y_index < 0] = float('-inf')\n other_y_index[other_y_index >= cell_num] = float('-inf')\n grid_indices = cell_num * other_y_index + other_x_index\n\n # calculate relative velocity for other agents\n other_human_velocity_angles = torch.atan2(other_agents[:, 3], other_agents[:, 2])\n rotation = other_human_velocity_angles - human_velocity_angle\n speed = torch.norm(other_agents[:, 2:4],dim=1)\n other_vx = torch.cos(rotation) * speed\n other_vy = torch.sin(rotation) * speed\n\n # occupy map\n dm = torch.zeros(3, cell_num ** 2)\n for i, index in np.ndenumerate(grid_indices):\n if index in range(cell_num ** 2):\n dm[0, int(index)] += 1\n dm[1, int(index)] += other_vx[i]\n dm[2, int(index)] += other_vy[i]\n\n mask = dm[0, :] != 0\n count = dm[0, mask]\n dm[0:3, mask] /= count\n\n if om_channel_size == 1:\n return dm[0, :].view(-1)\n elif om_channel_size == 2:\n return dm[1:3, :].view(-1)\n else:\n return dm.view(-1)","def dimensionizing_mapper(self, names=None):\n\n def fix(string):\n tags = [\n \"'\", '\"', ' ', '&', '.', '/', '-',\n '(', ')', '[', ']', '{', '}'\n ]\n for tag in tags:\n string = string.replace(tag, '_')\n return string\n\n masks = self._meta['masks']\n columns = self._meta['columns']\n suffix = self._dimensions_suffix\n\n if not names: names = self.variables()\n mapper = {}\n for org_mn, mask in masks.items():\n if org_mn in names:\n mask_name = fix(org_mn)\n new_mask_name = '{mn}.{mn}{s}'.format(mn=mask_name, s=suffix)\n mapper[org_mn] = new_mask_name\n\n mask_mapper = 'masks@{mn}'.format(mn=org_mn)\n new_mask_mapper = 'masks@{nmn}'.format(nmn=new_mask_name)\n mapper[mask_mapper] = new_mask_mapper\n\n values_mapper = 'lib@values@{mn}'.format(mn=org_mn)\n new_values_mapper = 'lib@values@{nmn}'.format(nmn=new_mask_name)\n mapper[values_mapper] = new_values_mapper\n\n items = masks[org_mn]['items']\n for i, item in enumerate(items):\n org_cn = item['source'].split('@')[-1]\n col_name = fix(org_cn)\n new_col_name = '{mn}[{{{cn}}}].{mn}{s}'.format(\n mn=mask_name, cn=col_name, s=suffix\n )\n mapper[org_cn] = new_col_name\n\n col_mapper = 'columns@{cn}'.format(cn=org_cn)\n new_col_mapper = 'columns@{ncn}'.format(ncn=new_col_name)\n mapper[col_mapper] = new_col_mapper\n\n for col_name, col in columns.items():\n if col_name in names and not self._is_array_item(col_name):\n new_col_name = fix(col_name)\n if new_col_name == col_name: continue\n mapper[col_name] = new_col_name\n\n col_mapper = 'columns@{cn}'.format(cn=col_name)\n new_col_mapper = 'columns@{ncn}'.format(ncn=new_col_name)\n mapper[col_mapper] = new_col_mapper\n\n return mapper","def to_mapping(self, dim):\n mim = cifti2.Cifti2MatrixIndicesMap([dim], 'CIFTI_INDEX_TYPE_PARCELS')\n if self.affine is not None:\n affine = cifti2.Cifti2TransformationMatrixVoxelIndicesIJKtoXYZ(-3, matrix=self.affine)\n mim.volume = cifti2.Cifti2Volume(self.volume_shape, affine)\n for name, nvertex in self.nvertices.items():\n mim.append(cifti2.Cifti2Surface(name, nvertex))\n for name, voxels, vertices in zip(self.name, self.voxels, self.vertices):\n cifti_voxels = cifti2.Cifti2VoxelIndicesIJK(voxels)\n element = cifti2.Cifti2Parcel(name, cifti_voxels)\n for name_vertex, idx_vertices in vertices.items():\n element.vertices.append(cifti2.Cifti2Vertices(name_vertex, idx_vertices))\n mim.append(element)\n return mim","def setup_mapping():\n # Mapping describes how elasticsearch handles a document during indexing.\n # Most fields are detected and mapped automatically.\n m = {\n # Turn off analysis on name so we can sort by it.\n 'name': {'index': 'not_analyzed', 'type': 'string'},\n }\n es = elasticutils.get_es()\n try:\n es.create_index_if_missing(settings.ES_INDEX)\n es.put_mapping(Addon._meta.app_label, {'properties': m},\n settings.ES_INDEX)\n except pyes.ElasticSearchException:\n pass","def test_make_ec_map():\n pass","def _get_mapper_0(model):\n # build the maps\n eids_all = (\n list(model.elements.keys()) +\n list(model.masses.keys()) +\n list(model.rigid_elements.keys())\n )\n eid_map = {eid : eid for eid in eids_all}\n nid_map = {nid : nid for nid in model.point_ids}\n cid_map = {cid : cid for cid in model.coord_ids}\n mid_map = {mid : mid for mid in model.material_ids}\n spc_map = _dicts_key_to_key((model.spcs, model.spcadds))\n mpc_map = _dicts_key_to_key((model.mpcs, model.mpcadds))\n method_map = _dict_key_to_key(model.methods)\n properties_map = _dict_key_to_key(model.properties)\n rigid_elements_map = _dict_key_to_key(model.rigid_elements)\n cmethod_map = _dict_key_to_key(model.cMethods)\n flfact_map = _dict_key_to_key(model.flfacts)\n flutter_map = _dict_key_to_key(model.flutters)\n caero_map = _dict_key_to_key(model.caeros)\n freq_map = _dict_key_to_key(model.frequencies)\n\n dload_map = _dicts_key_to_key((model.dload_entries, model.dloads))\n load_map = _dicts_key_to_key((model.loads, model.load_combinations))\n lseq_map = load_map # wrong???\n temp_map = load_map # wrong???\n\n tstep_map = _dict_key_to_key(model.tsteps)\n tstepnl_map = _dict_key_to_key(model.tstepnls)\n suport1_map = _dict_key_to_key(model.suport1)\n #suport_map = {}\n\n nlparm_map = _dict_key_to_key(model.nlparms)\n #nlpci_map = _dict_key_to_key(model.nlpcis)\n table_sdamping_map = _dict_key_to_key(model.tables_sdamping)\n dconadd_map = _dict_key_to_key(model.dconadds)\n dconstr_map = _dict_key_to_key(model.dconstrs)\n dessub_map = dconadd_map\n for key, value in dconstr_map.items():\n if key in dessub_map:\n raise NotImplementedError()\n dessub_map[key] = value\n dresp_map = _dict_key_to_key(model.dresps)\n gust_map = _dict_key_to_key(model.gusts)\n trim_map = _dict_key_to_key(model.trims)\n tic_map = _dict_key_to_key(model.tics)\n csschd_map = _dict_key_to_key(model.csschds)\n tranfer_function_map = _dict_key_to_key(model.transfer_functions)\n\n mapper = {\n 'elements' : eid_map,\n 'nodes' : nid_map,\n 'coords' : cid_map,\n 'materials' : mid_map,\n 'properties' : properties_map,\n 'rigid_elements': rigid_elements_map,\n 'spcs' : spc_map,\n 'mpcs' : mpc_map,\n 'METHOD' : method_map,\n 'CMETHOD' : cmethod_map,\n 'FLFACT' : flfact_map,\n 'FMETHOD' : flutter_map,\n 'caeros' : caero_map,\n 'FREQUENCY' : freq_map,\n\n 'DLOAD' : dload_map,\n 'LOAD' : load_map,\n 'LOADSET' : lseq_map,\n 'TSTEP' : tstep_map,\n 'TSTEPNL' : tstepnl_map,\n 'SUPORT1' : suport1_map,\n 'NLPARM' : nlparm_map,\n 'SDAMPING' : table_sdamping_map,\n 'DESSUB' : dessub_map,\n 'DESOBJ' : dresp_map,\n 'GUST' : gust_map,\n 'TRIM' : trim_map,\n 'IC' : tic_map,\n 'CSSCHD' : csschd_map,\n 'TFL' : tranfer_function_map,\n #'DESSUB' : dessub_map,\n # bad...\n 'TEMPERATURE(LOAD)' : temp_map,\n 'TEMPERATURE(INITIAL)' : temp_map,\n #'DATAREC' : datarec_map,\n #'ADAPT' : adapt_map,\n #'SUPER' : super_map,\n #'BOUTPUT' : boutput_map,\n #'OUTRCV' : outrcv_map,\n }\n\n return mapper","def CreateIndex(self, arg0: 'unsigned long long') -> \"void\":\n return _itkQuadEdgeCellTraitsInfoPython.itkMapContainerULLQEMPF2GQEULLULLBBT_CreateIndex(self, arg0)","def _create_labels_and_mapping(self, labels, mapping):\n numbered_classes = list(enumerate(list(labels), start=0))\n if mapping:\n new_mapping = {number: str(mapping[label]) for number, label in numbered_classes}\n else:\n new_mapping = {number: str(label) for number, label in numbered_classes}\n new_labels = [new_mapping[numbered[0]] for numbered in numbered_classes]\n\n return new_labels, new_mapping","def gen_CASTEP_supercell(CASTEP_cell,na,nb,nc):\n nruter=dict()\n nruter[\"na\"]=na\n nruter[\"nb\"]=nb\n nruter[\"nc\"]=nc\n nruter[\"lattvec\"]=np.array(CASTEP_cell[\"lattvec\"])\n nruter[\"lattvec\"][:,0]*=na\n nruter[\"lattvec\"][:,1]*=nb\n nruter[\"lattvec\"][:,2]*=nc\n nruter[\"elements\"]=copy.copy(CASTEP_cell[\"elements\"])\n nruter[\"numbers\"]=na*nb*nc*CASTEP_cell[\"numbers\"]\n nruter[\"positions\"]=np.empty((3,CASTEP_cell[\"positions\"].shape[1]*na*nb*nc))\n pos=0\n for pos,(k,j,i,iat) in enumerate(itertools.product(xrange(nc),\n xrange(nb),\n xrange(na),\n xrange(\n CASTEP_cell[\"positions\"].shape[1]))):\n nruter[\"positions\"][:,pos]=(CASTEP_cell[\"positions\"][:,iat]+[i,j,k])/[\n na,nb,nc]\n nruter[\"types\"]=[]\n for i in xrange(na*nb*nc):\n nruter[\"types\"].extend(CASTEP_cell[\"types\"])\n # print \"supercell\", nruter\n return nruter","def __init___0(self, map):\n super(LongObjectHashMap, self).__init__()\n self.__init__()\n putAll(map)","def celltypesref(pl):\n ppl = set(pl)\n refnd = dict(zip(ppl, range(len(ppl))))\n return refnd","def update_id2idx(self):\n self._id2idx = {}\n for n, cell in enumerate(self._cell_list):\n self._id2idx[cell.id()] = n","def create_board(self):\n board = dict()\n cell_names = self.create_cell_names()\n\n for cell_name in cell_names:\n if cell_name in self.given_cells:\n is_given = True\n value = self.given_cells[cell_name]\n else:\n is_given = False\n value = 0\n new_cell = c.Cell(cell_name, is_given, value, self.size)\n board[cell_name] = new_cell\n return board","def build_occupancy_map(human : Human, other_agents : np.array, cell_num : int, cell_size : float, om_channel_size : int) -> np.array:\n other_px = other_agents[:, 0] - human.px\n other_py = other_agents[:, 1] - human.py\n\n # new x-axis is in the direction of human's velocity\n human_velocity_angle = np.arctan2(human.vy, human.vx)\n other_human_orientation = np.arctan2(other_py, other_px)\n rotation = other_human_orientation - human_velocity_angle\n distance = np.linalg.norm([other_px, other_py], axis=0)\n other_px = np.cos(rotation) * distance\n other_py = np.sin(rotation) * distance\n\n # compute indices of humans in the grid\n other_x_index = np.floor(other_px / cell_size + cell_num / 2)\n other_y_index = np.floor(other_py / cell_size + cell_num / 2)\n other_x_index[other_x_index < 0] = float('-inf')\n other_x_index[other_x_index >= cell_num] = float('-inf')\n other_y_index[other_y_index < 0] = float('-inf')\n other_y_index[other_y_index >= cell_num] = float('-inf')\n grid_indices = cell_num * other_y_index + other_x_index\n occupancy_map = np.isin(range(cell_num ** 2), grid_indices)\n if om_channel_size == 1:\n return occupancy_map.astype(int)\n else:\n # calculate relative velocity for other agents\n other_human_velocity_angles = np.arctan2(other_agents[:, 3], other_agents[:, 2])\n rotation = other_human_velocity_angles - human_velocity_angle\n speed = np.linalg.norm(other_agents[:, 2:4], axis=1)\n other_vx = np.cos(rotation) * speed\n other_vy = np.sin(rotation) * speed\n dm = [list() for _ in range(cell_num ** 2 * om_channel_size)]\n for i, index in np.ndenumerate(grid_indices):\n if index in range(cell_num ** 2):\n if om_channel_size == 2:\n dm[2 * int(index)].append(other_vx[i])\n dm[2 * int(index) + 1].append(other_vy[i])\n elif om_channel_size == 3:\n dm[int(index)].append(1)\n dm[int(index) + cell_num ** 2].append(other_vx[i])\n dm[int(index) + cell_num ** 2 * 2].append(other_vy[i])\n else:\n raise NotImplementedError\n for i, cell in enumerate(dm):\n dm[i] = sum(dm[i]) / len(dm[i]) if len(dm[i]) != 0 else 0\n return dm","def update_cell_membranes(cells):\r\n membrane_polys = [Polygon(cell['membrane']).buffer(0) for cell in cells]\r\n membrane_bounds = [p.bounds for p in membrane_polys]\r\n\r\n # Get normal vectors for membrane vertices\r\n vert_normals = [geometry.get_vert_normals(\r\n geometry.get_edge_normals(cell['membrane'])) for cell in cells]\r\n\r\n all_membranes = np.concatenate([cell['membrane'] for cell in cells], axis=0)\r\n # [(cell idx, vert idx), ...]\r\n all_membrane_map = np.concatenate([\r\n np.stack([\r\n np.repeat([i], cell['membrane'].shape[0]),\r\n np.arange(cell['membrane'].shape[0],)\r\n ], axis=1)\r\n for i, cell in enumerate(cells)\r\n ], axis=0).astype(np.int32)\r\n\r\n # Find inter-cell membrane vertices that are close enough for adhesion/diffusion\r\n nearby_membranes, nearby_membrane_map = find_nearby_membranes(\r\n all_membranes, all_membrane_map, vert_normals)\r\n\r\n # Change membrane rest length according with the cell volume\r\n membrane_rdists = []\r\n for i, cell in enumerate(cells):\r\n # Get all the pairwise distances between membrane vertices\r\n membrane_dists = scipy.spatial.distance.squareform(\r\n scipy.spatial.distance.pdist(cell['membrane']))\r\n membrane_rdists_i = 1.0 / (membrane_dists + 1e-6)\r\n membrane_rdists_i[np.where(membrane_dists == 0)] = 0\r\n membrane_rdists.append(membrane_rdists_i)\r\n\r\n return membrane_bounds, membrane_polys, vert_normals, \\\r\n all_membranes, all_membrane_map, \\\r\n nearby_membranes, nearby_membrane_map, \\\r\n membrane_rdists","def map(inputs, e0,e1,k):\r\n codebook = tf.cast(inputs[0][0:2 ** k], tf.float32)\r\n soft_map = tf.TensorArray(tf.float32, size=0, dynamic_size=True)\r\n for y in inputs[1]:\r\n Pxy_map = pyx(y, codebook, e0, e1)\r\n soft_map = soft_map.write(soft_map.size(), Pxy_map)\r\n\r\n soft_map = soft_map.stack()\r\n return soft_map","def create_matrix_mapping(train_mh, unk_vec_id):\n mh_index_map = {}\n matrix_idx = 0\n for vector_idx in train_mh:\n if vector_idx == unk_vec_id:\n unk_matrix_id = matrix_idx\n mh_index_map[vector_idx] = matrix_idx\n matrix_idx += 1\n return mh_index_map, unk_matrix_id","def dict() -> Dict[str, Pin]:","def templateMappings(self):\n raise NotImplementedError","def CreateIndex(self, arg0: 'unsigned long long') -> \"void\":\n return _itkQuadEdgeCellTraitsInfoPython.itkMapContainerULLQEMPF3GQEULLULLBBT_CreateIndex(self, arg0)","def create_single_map(self,tod,x,y,x0,y0):\n maps = {'map':np.zeros((self.Nx*self.Ny)),\n 'cov':np.zeros((self.Nx*self.Ny))}\n\n\n pixels,xp,yp,r_x, r_y = self.get_pixel_positions(x,y,x0,y0,0,invertx=True)\n mask = np.ones(pixels.size,dtype=int)\n\n mask[(pixels == -1) | np.isnan(tod) | np.isinf(tod)] = 0\n rms = stats.AutoRMS(tod)\n weights = {'map':tod.astype(np.float64)/rms**2,\n 'cov':np.ones(tod.size)/rms**2}\n for k in maps.keys():\n binFuncs.binValues(maps[k],\n pixels,\n weights=weights[k],mask=mask)\n maps[k] = np.reshape(maps[k],(self.Ny,self.Nx))\n return maps","def new(num_buckets=256):\n aMap=[]","def makemap(d,x,y,ra0=0,dec0=0, cd=1./60., nxpix=600, nypix=600):\n\n xy = np.zeros((x.size,2))\n xy[:,0] = x.flatten()\n xy[:,1] = y.flatten()\n\n from astropy import wcs\n\n w = wcs.WCS(naxis=2)\n w.wcs.crval = [ra0, dec0]\n w.wcs.cdelt = [cd,cd]\n w.wcs.crpix = [nxpix/2., nypix/2.]\n w.wcs.ctype = ['RA---TAN', 'DEC--TAN']\n\n pixels = w.wcs_world2pix(xy,0)\n ygrid, xgrid = np.meshgrid(np.arange(nypix),np.arange(nxpix))\n\n pixCens = w.wcs_pix2world(np.array([xgrid.flatten(), ygrid.flatten()]).T,0)\n pixCens[:,0] += 1./2.*cd\n pixCens[:,1] += 1./2.*cd\n pflat = (pixels[:,1].astype(int) + (nypix)*pixels[:,0].astype(int)).astype(int)\n\n\n pEdges = np.arange(nxpix*nypix+1)\n m = np.histogram(pflat,pEdges, weights=d)[0]\n h = np.histogram(pflat,pEdges)[0]\n m = m/h\n return m,pixCens,w","def _generate_character_map(self):\n self._ct = [-1] * 256\n index = 0\n for c_range in self._meta.character_ranges:\n for c_pos in range(c_range['min'], c_range['max'] + 1):\n self._ct[c_pos] = index\n index += 1","def cellannotation2ID(self, annotation_type):\n annotations = list(self.adata.obs[annotation_type])\n annotations_set = sorted(set(annotations))\n \n mapping = {a:idx for idx,a in enumerate(annotations_set)}\n \n truth_labels = [mapping[a] for a in annotations]\n self.adata.obs['label'] = pd.Categorical(values=truth_labels)\n #18m-unannotated\n # \n return mapping","def test_create_new_mapper(self):\n GraphMapper()","def to_mapping(self, dim):\n mim = cifti2.Cifti2MatrixIndicesMap([dim], 'CIFTI_INDEX_TYPE_SCALARS')\n for name, meta in zip(self.name, self.meta):\n named_map = cifti2.Cifti2NamedMap(name, cifti2.Cifti2MetaData(meta))\n mim.append(named_map)\n return mim","def __init__(self, nx, dx, map=None, ny=None, dy=None):\n super(rmap, self).__init__(nx, dx, ny=ny, dy=dy)\n if map == None:\n self.map = np.zeros((self.ny, self.nx))\n else:\n self.map = map\n assert ((self.ny, self.nx) == self.map.shape)","def assign(self, gateObj, mapping):\n copied = deepcopy(gateObj)\n if isinstance(mapping, dict):\n for key, idx_tag in mapping.items():\n qubitIdx = self.get_index(idx_tag[0])\n blockNum = idx_tag[1] - len(self.diagram[0, :]) + 1\n if blockNum > 0:\n self.diagram = np.concatenate((\n self.diagram, np.asarray(\n [[np.nan] * blockNum] * len(self.diagram[:, 0])\n )\n ), axis=1)\n self.diagram[qubitIdx, idx_tag[1]] = copied._qubitDict[key]\n else:\n qubitIdx = self.get_index(mapping[0])\n blockNum = mapping[1] - len(self.diagram[0, :]) + 1\n if blockNum > 0:\n self.diagram = np.concatenate((\n self.diagram, np.asarray(\n [[np.nan] * blockNum] * len(self.diagram[:, 0])\n )\n ), axis=1)\n self.diagram[qubitIdx, mapping[1]] = copied","def constructCell():\n\t\tself.weightGenerate()","def physical_maps(x, y):\n assert x.shape == (3,) and y.shape == (3,)\n assert x.dtype == np.float64 and y.dtype == np.float64\n\n C = np.empty((21,21), dtype=np.float64)\n B = np.empty((2,2), dtype=np.float64)\n b = np.empty((2,), dtype=np.float64)\n _ap.ap_physical_maps(x, y, C, B, b)\n return (C, B, b)","def generate_map(nrows, ncols, nrooms, max_col_size, max_row_size):\n arr = np.zeros((nrows, ncols), dtype=np.int8)\n\n for i in range(nrooms):\n rand_row_start = np.random.randint(nrows)\n rand_col_start = np.random.randint(ncols)\n\n rand_row_size = np.random.randint(max_row_size / 2, max_row_size)\n rand_col_size = np.random.randint(max_col_size / 2, max_col_size)\n\n arr[rand_row_start:rand_row_start + rand_row_size, rand_col_start:rand_col_start + rand_col_size] = 1\n\n labels = measure.label(arr)\n regions = measure.regionprops(labels)\n\n centroids = list()\n for region in regions:\n centroids.append(region.centroid)\n\n num_centroids = len(centroids)\n\n # get distances between every pair of centroids\n dists = scipy.spatial.distance.cdist(centroids, centroids)\n\n # get a distance that is greater than all current distances\n max_dist = np.max(dists) + 1\n\n # make sure upper triangle is at least max_dist so that when picking closest\n # pairs, we won't choose a diagonal element or a duplicate connection\n dists = dists + np.triu(np.ones((num_centroids, num_centroids))) * max_dist\n\n for i in range(num_centroids - 1):\n min_dist_idx = np.argmin(dists)\n min_dist_idx = np.unravel_index(min_dist_idx, dists.shape)\n\n # create a hallway between regionprops\n centroid1 = np.array(centroids[min_dist_idx[0]], dtype=np.int)\n centroid2 = np.array(centroids[min_dist_idx[1]], dtype=np.int)\n\n [row_centroid_1, row_centroid_2] = sorted([centroid1, centroid2], key=lambda x: x[0])\n [col_centroid_1, col_centroid_2] = sorted([centroid1, centroid2], key=lambda x: x[1])\n\n arr[row_centroid_1[0]:row_centroid_2[0] + 1, row_centroid_1[1]] = 1\n arr[row_centroid_2[0], col_centroid_1[1]:col_centroid_2[1] + 1] = 1\n\n dists[:, min_dist_idx[1]] += max_dist\n\n return arr","def __init__(self, *args):\n _itkQuadEdgeCellTraitsInfoPython.itkMapContainerULLQEMPF2GQEULLULLBBT_swiginit(self, _itkQuadEdgeCellTraitsInfoPython.new_itkMapContainerULLQEMPF2GQEULLULLBBT(*args))","def uvmap(self, p):\n pass","def _declare_auto_variable_mapping(self):\n if self.name not in self.nlp.variable_mappings:\n self.nlp.variable_mappings[self.name] = BiMapping(\n range(len(self.name_elements)), range(len(self.name_elements))\n )","def __init__(self):\n self.map = {}\n self.vec = []","def create_map(width, height, pixels):\n\n\n\n\n def index_to_xy(i, width, height):\n \"\"\" Takes 0 based index going line wise from top\n left to bottom right, returns x, y coordinates so\n that 0,0 is on bottom left corner\n \"\"\"\n x = i % width\n y = i // width\n y*= -1\n y+= height - 1\n return (x,y)\n\n def place_terrain(type, i):\n \"\"\"This won't return anything, just do side effects\n\n The object \"gameLogic\" is used to place the object\n initially. It doesn't matter where this object is,\n as long as it exists. There must be an easier way,\n but this works.\n \"\"\"\n x,y = index_to_xy(i, width, height)\n\n object_name = terrain_types.get(type, \"water\")\n\n if ob[\"fast_create\"] > 0 and not (x%ob[\"fast_create\"] == 0 and y%ob[\"fast_create\"] == 0):\n return\n\n if object_name != \"water\":\n object = scene.addObject(object_name, \"gameLogic\")\n object.worldPosition = (x,y,0)\n\n\n list(map( (lambda tup : place_terrain(tup[1], tup[0])), list(enumerate(pixels)) ))","def prepare_map(self):\n for y_coord, row in enumerate(self.contents):\n for x_coord, tile in enumerate(row):\n bit_map = self.get_tile_bitmap(tile)\n self.image[y_coord * TILE_SIZE:(y_coord+1) * TILE_SIZE,\n x_coord * TILE_SIZE:(x_coord+1) * TILE_SIZE] = bit_map","def map_to_homo_nid(self, ids, ntype):\n ...","def map_to_homo_nid(self, ids, ntype):\n ...","def create_map(grid_size):\n STATUS['game_grid'] = [] # Could be a tuple?\n STATUS['grid_size'] = grid_size\n x_coord = 1\n y_coord = 1\n grid_size_counter = grid_size * grid_size\n while grid_size_counter:\n STATUS['game_grid'].append([x_coord, y_coord])\n x_coord += 1\n if x_coord == grid_size + 1:\n y_coord += 1\n x_coord = 1\n grid_size_counter -= 1","def _cellTopology(self):\n facesPerCell = self.mesh._facesPerCell\n nodesPerFace = self.mesh._nodesPerFace\n\n def faceCountsMatch(targetCounts):\n if len(targetCounts) > nodesPerFace.shape[0]:\n # pad nodesPerFace with zeros\n paddedNodesPerFace = numerix.zeros((len(targetCounts), nodesPerFace.shape[1]), dtype=numerix.INT_DTYPE)\n paddedNodesPerFace[:nodesPerFace.shape[0],:] = nodesPerFace\n\n paddedTargetCounts = numerix.array(targetCounts)[..., numerix.newaxis]\n else:\n # pad target face node count with zeros\n paddedTargetCounts = numerix.concatenate((targetCounts,\n [0] * (self.mesh._maxFacesPerCell - len(targetCounts))))\n paddedTargetCounts = paddedTargetCounts[..., numerix.newaxis]\n\n paddedNodesPerFace = nodesPerFace\n\n return ((facesPerCell == len(targetCounts))\n & (paddedNodesPerFace == paddedTargetCounts).all(axis=0))\n\n cellTopology = numerix.empty((self.mesh.numberOfCells,), dtype=numerix.ubyte)\n\n t = self._elementTopology\n\n if self.mesh.dim == 1:\n cellTopology[:] = t[\"line\"]\n elif self.mesh.dim == 2:\n cellTopology[:] = t[\"polygon\"]\n cellTopology[faceCountsMatch([2, 2, 2])] = t[\"triangle\"]\n cellTopology[faceCountsMatch([2, 2, 2, 2])] = t[\"quadrangle\"]\n else:\n cellTopology[:] = t[\"unknown\"]\n cellTopology[faceCountsMatch([3, 3, 3, 3])] = t[\"tetrahedron\"]\n cellTopology[faceCountsMatch([4, 4, 4, 4, 4, 4])] = t[\"hexahedron\"]\n cellTopology[faceCountsMatch([4, 4, 4, 3, 3])] = t[\"prism\"]\n cellTopology[faceCountsMatch([4, 3, 3, 3, 3])] = t[\"pyramid\"]\n\n return cellTopology","def generate_bitmap_to_linear_index_map(bladeTupList, firstIdx):\n bitmap_map = np.zeros(len(bladeTupList), dtype=int)\n for ind, blade in enumerate(bladeTupList):\n bitmap_map[compute_bitmap_representation(blade, firstIdx)] = ind\n return bitmap_map","def get_tm_mapping(mapping):\n df = mapping[['slot', 'row', 'col', 'xpix', 'ypix']]\n f_rowcol = lambda v: v.values[0] // 8\n f = dict(row=f_rowcol, col=f_rowcol, xpix='mean', ypix='mean')\n df = df.groupby('slot').agg(f, as_index=False).reset_index()\n with warnings.catch_warnings():\n warnings.simplefilter('ignore', UserWarning)\n df.metadata = mapping.metadata\n df.metadata['n_rows'] = df['row'].max() + 1\n df.metadata['n_columns'] = df['col'].max() + 1\n df.metadata['size'] *= 8\n return df","def _fill_maps(maps, map_counts, paths, pixels, mapper, ctx):\n xy_ind = 0\n with RestructuredTODStore(paths) as store:\n for i, tod_pixel in enumerate(pixels):\n re_data = store.get(tod_pixel)\n values, counts = mapper.get_mapped_values(re_data, ctx)\n maps[:, xy_ind, i] = values\n map_counts[:, xy_ind, i] = counts"],"string":"[\n \"def makeMapping(globalMap):\\n \\n from memops.xml.Implementation import bool2str, str2bool\\n\\n # Set up top level dictionaries\\n loadMaps = globalMap.get('loadMaps')\\n mapsByGuid = globalMap.get('mapsByGuid')\\n\\n abstractTypes = globalMap.get('CCLB').get('abstractTypes')\\n exolinks = globalMap.get('CCLB').get('exolinks')\\n\\n # Class AtomLabel\\n currentMap = {}\\n abstractTypes['AtomLabel'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-14:25:09_00018'] = currentMap\\n loadMaps['CCLB.AtomLabel'] = currentMap\\n currentMap['tag'] = 'CCLB.AtomLabel'\\n currentMap['type'] = 'class'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:25:09_00018'\\n currentMap['eType'] = 'cplx'\\n currentMap['fromParent'] = 'atomLabels'\\n currentMap['class'] = ccp.api.molecule.ChemCompLabel.AtomLabel\\n contentMap = {}\\n currentMap['content'] = contentMap\\n\\n # Attribute AtomLabel.applicationData\\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\\n\\n # Attribute AtomLabel.isotopeCode\\n currentMap = {}\\n contentMap['isotopeCode'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:37_00092'] = currentMap\\n loadMaps['CCLB.AtomLabel.isotopeCode'] = currentMap\\n currentMap['tag'] = 'CCLB.AtomLabel.isotopeCode'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:37_00092'\\n currentMap['name'] = 'isotopeCode'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00037')\\n\\n # Attribute AtomLabel.name\\n currentMap = {}\\n contentMap['name'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-10-24-11:30:14_00003'] = currentMap\\n loadMaps['CCLB.AtomLabel.name'] = currentMap\\n currentMap['tag'] = 'CCLB.AtomLabel.name'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-10-24-11:30:14_00003'\\n currentMap['name'] = 'name'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00037')\\n\\n # Attribute AtomLabel.subType\\n currentMap = {}\\n contentMap['subType'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-10-24-11:30:14_00004'] = currentMap\\n loadMaps['CCLB.AtomLabel.subType'] = currentMap\\n currentMap['tag'] = 'CCLB.AtomLabel.subType'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-10-24-11:30:14_00004'\\n currentMap['name'] = 'subType'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['proc'] = 'direct'\\n currentMap['default'] = 1\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00032')\\n\\n # Attribute AtomLabel.weight\\n currentMap = {}\\n contentMap['weight'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:37_00093'] = currentMap\\n loadMaps['CCLB.AtomLabel.weight'] = currentMap\\n currentMap['tag'] = 'CCLB.AtomLabel.weight'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:37_00093'\\n currentMap['name'] = 'weight'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['default'] = 1.0\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:54_00009')\\n\\n # Role AtomLabel.access\\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\\n # End of AtomLabel\\n\\n currentMap = abstractTypes.get('AtomLabel')\\n aList = ['isotopeCode', 'name', 'subType', 'weight']\\n currentMap['headerAttrs'] = aList\\n aList = ['access', 'applicationData']\\n currentMap['cplxAttrs'] = aList\\n\\n # Class ChemCompLabel\\n currentMap = {}\\n abstractTypes['ChemCompLabel'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-14:25:09_00014'] = currentMap\\n loadMaps['CCLB.ChemCompLabel'] = currentMap\\n currentMap['tag'] = 'CCLB.ChemCompLabel'\\n currentMap['type'] = 'class'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:25:09_00014'\\n currentMap['eType'] = 'cplx'\\n currentMap['fromParent'] = 'chemCompLabels'\\n currentMap['class'] = ccp.api.molecule.ChemCompLabel.ChemCompLabel\\n contentMap = {}\\n currentMap['content'] = contentMap\\n\\n # Attribute ChemCompLabel.applicationData\\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\\n\\n # Attribute ChemCompLabel.ccpCode\\n currentMap = {}\\n contentMap['ccpCode'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:37_00073'] = currentMap\\n loadMaps['CCLB.ChemCompLabel.ccpCode'] = currentMap\\n currentMap['tag'] = 'CCLB.ChemCompLabel.ccpCode'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:37_00073'\\n currentMap['name'] = 'ccpCode'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2007-09-12-18:31:28_00003')\\n\\n # Attribute ChemCompLabel.molType\\n currentMap = {}\\n contentMap['molType'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:37_00072'] = currentMap\\n loadMaps['CCLB.ChemCompLabel.molType'] = currentMap\\n currentMap['tag'] = 'CCLB.ChemCompLabel.molType'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:37_00072'\\n currentMap['name'] = 'molType'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:52_00024')\\n\\n # Role ChemCompLabel.access\\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\\n\\n # Role ChemCompLabel.isotopomers\\n currentMap = {}\\n contentMap['isotopomers'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-10-24-11:30:13_00001'] = currentMap\\n loadMaps['CCLB.ChemCompLabel.isotopomers'] = currentMap\\n currentMap['tag'] = 'CCLB.ChemCompLabel.isotopomers'\\n currentMap['type'] = 'child'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-10-24-11:30:13_00001'\\n currentMap['name'] = 'isotopomers'\\n currentMap['hicard'] = -1\\n currentMap['locard'] = 0\\n currentMap['eType'] = 'cplx'\\n currentMap['content'] = globalMap.get('CCLB').get('abstractTypes')\\n # End of ChemCompLabel\\n\\n currentMap = abstractTypes.get('ChemCompLabel')\\n aList = ['ccpCode', 'molType']\\n currentMap['headerAttrs'] = aList\\n aList = ['isotopomers', 'access', 'applicationData']\\n currentMap['cplxAttrs'] = aList\\n aList = ['isotopomers']\\n currentMap['children'] = aList\\n\\n # Class Isotopomer\\n currentMap = {}\\n abstractTypes['Isotopomer'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-10-24-11:28:54_00001'] = currentMap\\n loadMaps['CCLB.Isotopomer'] = currentMap\\n currentMap['tag'] = 'CCLB.Isotopomer'\\n currentMap['type'] = 'class'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-10-24-11:28:54_00001'\\n currentMap['eType'] = 'cplx'\\n currentMap['fromParent'] = 'isotopomers'\\n currentMap['objkey'] = 'serial'\\n currentMap['class'] = ccp.api.molecule.ChemCompLabel.Isotopomer\\n contentMap = {}\\n currentMap['content'] = contentMap\\n\\n # Attribute Isotopomer.applicationData\\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\\n\\n # Attribute Isotopomer.serial\\n currentMap = {}\\n contentMap['serial'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-10-24-11:30:14_00005'] = currentMap\\n loadMaps['CCLB.Isotopomer.serial'] = currentMap\\n currentMap['tag'] = 'CCLB.Isotopomer.serial'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-10-24-11:30:14_00005'\\n currentMap['name'] = 'serial'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00032')\\n\\n # Attribute Isotopomer.weight\\n currentMap = {}\\n contentMap['weight'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-10-24-11:30:14_00006'] = currentMap\\n loadMaps['CCLB.Isotopomer.weight'] = currentMap\\n currentMap['tag'] = 'CCLB.Isotopomer.weight'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-10-24-11:30:14_00006'\\n currentMap['name'] = 'weight'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['default'] = 1.0\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:54_00009')\\n\\n # Role Isotopomer.access\\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\\n\\n # Role Isotopomer.atomLabels\\n currentMap = {}\\n contentMap['atomLabels'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-10-24-11:30:14_00001'] = currentMap\\n loadMaps['CCLB.Isotopomer.atomLabels'] = currentMap\\n currentMap['tag'] = 'CCLB.Isotopomer.atomLabels'\\n currentMap['type'] = 'child'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-10-24-11:30:14_00001'\\n currentMap['name'] = 'atomLabels'\\n currentMap['hicard'] = -1\\n currentMap['locard'] = 0\\n currentMap['eType'] = 'cplx'\\n currentMap['content'] = globalMap.get('CCLB').get('abstractTypes')\\n # End of Isotopomer\\n\\n currentMap = abstractTypes.get('Isotopomer')\\n aList = ['serial', 'weight']\\n currentMap['headerAttrs'] = aList\\n aList = ['atomLabels', 'access', 'applicationData']\\n currentMap['cplxAttrs'] = aList\\n aList = ['atomLabels']\\n currentMap['children'] = aList\\n\\n # Class LabelingScheme\\n currentMap = {}\\n abstractTypes['LabelingScheme'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2007-01-24-12:23:14_00001'] = currentMap\\n loadMaps['CCLB.LabelingScheme'] = currentMap\\n currentMap['tag'] = 'CCLB.LabelingScheme'\\n currentMap['type'] = 'class'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2007-01-24-12:23:14_00001'\\n currentMap['eType'] = 'cplx'\\n currentMap['fromParent'] = 'labelingSchemes'\\n currentMap['isTop'] = True\\n currentMap['objkey'] = 'name'\\n currentMap['class'] = ccp.api.molecule.ChemCompLabel.LabelingScheme\\n contentMap = {}\\n currentMap['content'] = contentMap\\n\\n # Attribute LabelingScheme.applicationData\\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\\n\\n # Attribute LabelingScheme.createdBy\\n contentMap['createdBy'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:00:59_00002__www.ccpn.ac.uk_Fogh_2007-10-03-14:53:27_00001__www.ccpn.ac.uk_Fogh_2006-09-14-16:28:57_00002')\\n\\n # Attribute LabelingScheme.details\\n currentMap = {}\\n contentMap['details'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2007-01-24-12:23:55_00005'] = currentMap\\n loadMaps['CCLB.LabelingScheme.details'] = currentMap\\n currentMap['tag'] = 'CCLB.LabelingScheme.details'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2007-01-24-12:23:55_00005'\\n currentMap['name'] = 'details'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 0\\n currentMap['eType'] = 'cplx'\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00036')\\n\\n # Attribute LabelingScheme.guid\\n contentMap['guid'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:26_00002')\\n\\n # Attribute LabelingScheme.isModifiable\\n contentMap['isModifiable'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-17-14:16:26_00010__www.ccpn.ac.uk_Fogh_2007-10-03-14:53:27_00001__www.ccpn.ac.uk_Fogh_2006-09-14-16:28:57_00002')\\n\\n # Attribute LabelingScheme.lastUnlockedBy\\n contentMap['lastUnlockedBy'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:00:59_00003__www.ccpn.ac.uk_Fogh_2007-10-03-14:53:27_00001__www.ccpn.ac.uk_Fogh_2006-09-14-16:28:57_00002')\\n\\n # Attribute LabelingScheme.longName\\n currentMap = {}\\n contentMap['longName'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2007-01-24-12:23:55_00004'] = currentMap\\n loadMaps['CCLB.LabelingScheme.longName'] = currentMap\\n currentMap['tag'] = 'CCLB.LabelingScheme.longName'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2007-01-24-12:23:55_00004'\\n currentMap['name'] = 'longName'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 0\\n currentMap['eType'] = 'cplx'\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033')\\n\\n # Attribute LabelingScheme.name\\n currentMap = {}\\n contentMap['name'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2007-01-24-12:23:55_00003'] = currentMap\\n loadMaps['CCLB.LabelingScheme.name'] = currentMap\\n currentMap['tag'] = 'CCLB.LabelingScheme.name'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2007-01-24-12:23:55_00003'\\n currentMap['name'] = 'name'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00037')\\n\\n # Role LabelingScheme.access\\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\\n\\n # Role LabelingScheme.chemCompLabels\\n currentMap = {}\\n contentMap['chemCompLabels'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2007-01-24-12:23:55_00002'] = currentMap\\n loadMaps['CCLB.LabelingScheme.chemCompLabels'] = currentMap\\n currentMap['tag'] = 'CCLB.LabelingScheme.chemCompLabels'\\n currentMap['type'] = 'child'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2007-01-24-12:23:55_00002'\\n currentMap['name'] = 'chemCompLabels'\\n currentMap['hicard'] = -1\\n currentMap['locard'] = 0\\n currentMap['eType'] = 'cplx'\\n currentMap['implSkip'] = True\\n currentMap['content'] = globalMap.get('CCLB').get('abstractTypes')\\n # End of LabelingScheme\\n\\n currentMap = abstractTypes.get('LabelingScheme')\\n aList = ['createdBy', 'guid', 'isModifiable', 'lastUnlockedBy', 'name']\\n currentMap['headerAttrs'] = aList\\n aList = ['details', 'longName']\\n currentMap['simpleAttrs'] = aList\\n aList = ['chemCompLabels', 'access', 'applicationData']\\n currentMap['cplxAttrs'] = aList\\n aList = ['chemCompLabels']\\n currentMap['children'] = aList\\n\\n # Out-of-package link to AtomLabel\\n currentMap = {}\\n exolinks['AtomLabel'] = currentMap\\n loadMaps['CCLB.exo-AtomLabel'] = currentMap\\n currentMap['tag'] = 'CCLB.exo-AtomLabel'\\n currentMap['type'] = 'exo'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:25:09_00018'\\n currentMap['name'] = 'AtomLabel'\\n currentMap['eType'] = 'cplx'\\n currentMap['class'] = ccp.api.molecule.ChemCompLabel.AtomLabel\\n aList = list()\\n currentMap['keyMaps'] = aList\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:52_00024'))\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2007-09-12-18:31:28_00003'))\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00032'))\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00037'))\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00032'))\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00037'))\\n\\n # Out-of-package link to ChemCompLabel\\n currentMap = {}\\n exolinks['ChemCompLabel'] = currentMap\\n loadMaps['CCLB.exo-ChemCompLabel'] = currentMap\\n currentMap['tag'] = 'CCLB.exo-ChemCompLabel'\\n currentMap['type'] = 'exo'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:25:09_00014'\\n currentMap['name'] = 'ChemCompLabel'\\n currentMap['eType'] = 'cplx'\\n currentMap['class'] = ccp.api.molecule.ChemCompLabel.ChemCompLabel\\n aList = list()\\n currentMap['keyMaps'] = aList\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:52_00024'))\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2007-09-12-18:31:28_00003'))\\n\\n # Out-of-package link to Isotopomer\\n currentMap = {}\\n exolinks['Isotopomer'] = currentMap\\n loadMaps['CCLB.exo-Isotopomer'] = currentMap\\n currentMap['tag'] = 'CCLB.exo-Isotopomer'\\n currentMap['type'] = 'exo'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-10-24-11:28:54_00001'\\n currentMap['name'] = 'Isotopomer'\\n currentMap['eType'] = 'cplx'\\n currentMap['class'] = ccp.api.molecule.ChemCompLabel.Isotopomer\\n aList = list()\\n currentMap['keyMaps'] = aList\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:52_00024'))\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2007-09-12-18:31:28_00003'))\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00032'))\\n\\n # Out-of-package link to LabelingScheme\\n currentMap = {}\\n exolinks['LabelingScheme'] = currentMap\\n loadMaps['CCLB.exo-LabelingScheme'] = currentMap\\n currentMap['tag'] = 'CCLB.exo-LabelingScheme'\\n currentMap['type'] = 'exo'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2007-01-24-12:23:14_00001'\\n currentMap['name'] = 'LabelingScheme'\\n currentMap['eType'] = 'cplx'\\n currentMap['class'] = ccp.api.molecule.ChemCompLabel.LabelingScheme\\n aList = list()\\n currentMap['keyMaps'] = aList\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\",\n \"def node_mapping(self):\\n ...\",\n \"def create_cell_map(dim):\\n for cell, faces in cell_face_map.iteritems():\\n \\n for face in faces:\\n nds = face_list[face - 1][1]\\n \\n if not cell in cell_map:\\n cell_map[cell] = copy(nds)\\n \\n else:\\n cell_map[cell] = list(Set(cell_map[cell] + nds))\",\n \"def makeMapping(globalMap):\\n \\n from memops.xml.Implementation import bool2str, str2bool\\n\\n # Set up top level dictionaries\\n loadMaps = globalMap.get('loadMaps')\\n mapsByGuid = globalMap.get('mapsByGuid')\\n\\n abstractTypes = globalMap.get('CHEL').get('abstractTypes')\\n exolinks = globalMap.get('CHEL').get('exolinks')\\n\\n # DataType HalfLifeType\\n currentMap = {}\\n abstractTypes['HalfLifeType'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2007-06-07-18:18:10_00002'] = currentMap\\n loadMaps['CHEL.HalfLifeType'] = currentMap\\n currentMap['tag'] = 'CHEL.HalfLifeType'\\n currentMap['type'] = 'simple'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2007-06-07-18:18:10_00002'\\n currentMap['toStr'] = 'text'\\n currentMap['cnvrt'] = 'text'\\n\\n # Class ChemElement\\n currentMap = {}\\n abstractTypes['ChemElement'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:19:49_00004'] = currentMap\\n loadMaps['CHEL.ChemElement'] = currentMap\\n currentMap['tag'] = 'CHEL.ChemElement'\\n currentMap['type'] = 'class'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:19:49_00004'\\n currentMap['eType'] = 'cplx'\\n currentMap['fromParent'] = 'chemElements'\\n currentMap['objkey'] = 'symbol'\\n currentMap['class'] = ccp.api.molecule.ChemElement.ChemElement\\n contentMap = {}\\n currentMap['content'] = contentMap\\n\\n # Attribute ChemElement.applicationData\\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\\n\\n # Attribute ChemElement.atomNumber\\n currentMap = {}\\n contentMap['atomNumber'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00017'] = currentMap\\n loadMaps['CHEL.ChemElement.atomNumber'] = currentMap\\n currentMap['tag'] = 'CHEL.ChemElement.atomNumber'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00017'\\n currentMap['name'] = 'atomNumber'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 0\\n currentMap['proc'] = 'direct'\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00032')\\n\\n # Attribute ChemElement.atomicRadius\\n currentMap = {}\\n contentMap['atomicRadius'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00018'] = currentMap\\n loadMaps['CHEL.ChemElement.atomicRadius'] = currentMap\\n currentMap['tag'] = 'CHEL.ChemElement.atomicRadius'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00018'\\n currentMap['name'] = 'atomicRadius'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 0\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00031')\\n\\n # Attribute ChemElement.covalentRadius\\n currentMap = {}\\n contentMap['covalentRadius'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00019'] = currentMap\\n loadMaps['CHEL.ChemElement.covalentRadius'] = currentMap\\n currentMap['tag'] = 'CHEL.ChemElement.covalentRadius'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00019'\\n currentMap['name'] = 'covalentRadius'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 0\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00031')\\n\\n # Attribute ChemElement.mass\\n currentMap = {}\\n contentMap['mass'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00020'] = currentMap\\n loadMaps['CHEL.ChemElement.mass'] = currentMap\\n currentMap['tag'] = 'CHEL.ChemElement.mass'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00020'\\n currentMap['name'] = 'mass'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 0\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00031')\\n\\n # Attribute ChemElement.name\\n currentMap = {}\\n contentMap['name'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00021'] = currentMap\\n loadMaps['CHEL.ChemElement.name'] = currentMap\\n currentMap['tag'] = 'CHEL.ChemElement.name'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00021'\\n currentMap['name'] = 'name'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 0\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00055')\\n\\n # Attribute ChemElement.symbol\\n currentMap = {}\\n contentMap['symbol'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00015'] = currentMap\\n loadMaps['CHEL.ChemElement.symbol'] = currentMap\\n currentMap['tag'] = 'CHEL.ChemElement.symbol'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00015'\\n currentMap['name'] = 'symbol'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00055')\\n\\n # Role ChemElement.access\\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\\n\\n # Role ChemElement.isotopes\\n currentMap = {}\\n contentMap['isotopes'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00004'] = currentMap\\n loadMaps['CHEL.ChemElement.isotopes'] = currentMap\\n currentMap['tag'] = 'CHEL.ChemElement.isotopes'\\n currentMap['type'] = 'child'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00004'\\n currentMap['name'] = 'isotopes'\\n currentMap['hicard'] = -1\\n currentMap['locard'] = 0\\n currentMap['eType'] = 'cplx'\\n currentMap['content'] = globalMap.get('CHEL').get('abstractTypes')\\n # End of ChemElement\\n\\n currentMap = abstractTypes.get('ChemElement')\\n aList = ['atomNumber', 'atomicRadius', 'covalentRadius', 'mass', 'name', 'symbol']\\n currentMap['headerAttrs'] = aList\\n aList = ['isotopes', 'access', 'applicationData']\\n currentMap['cplxAttrs'] = aList\\n aList = ['isotopes']\\n currentMap['children'] = aList\\n\\n # Class ChemElementStore\\n currentMap = {}\\n abstractTypes['ChemElementStore'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:19:49_00005'] = currentMap\\n loadMaps['CHEL.ChemElementStore'] = currentMap\\n currentMap['tag'] = 'CHEL.ChemElementStore'\\n currentMap['type'] = 'class'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:19:49_00005'\\n currentMap['eType'] = 'cplx'\\n currentMap['fromParent'] = 'chemElementStores'\\n currentMap['isTop'] = True\\n currentMap['objkey'] = 'name'\\n currentMap['class'] = ccp.api.molecule.ChemElement.ChemElementStore\\n contentMap = {}\\n currentMap['content'] = contentMap\\n\\n # Attribute ChemElementStore.applicationData\\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\\n\\n # Attribute ChemElementStore.createdBy\\n contentMap['createdBy'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:00:59_00002__www.ccpn.ac.uk_Fogh_2007-10-03-14:53:27_00001__www.ccpn.ac.uk_Fogh_2006-09-14-16:28:57_00002')\\n\\n # Attribute ChemElementStore.guid\\n contentMap['guid'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:26_00002')\\n\\n # Attribute ChemElementStore.isModifiable\\n contentMap['isModifiable'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-17-14:16:26_00010__www.ccpn.ac.uk_Fogh_2007-10-03-14:53:27_00001__www.ccpn.ac.uk_Fogh_2006-09-14-16:28:57_00002')\\n\\n # Attribute ChemElementStore.lastUnlockedBy\\n contentMap['lastUnlockedBy'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:00:59_00003__www.ccpn.ac.uk_Fogh_2007-10-03-14:53:27_00001__www.ccpn.ac.uk_Fogh_2006-09-14-16:28:57_00002')\\n\\n # Attribute ChemElementStore.name\\n currentMap = {}\\n contentMap['name'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00024'] = currentMap\\n loadMaps['CHEL.ChemElementStore.name'] = currentMap\\n currentMap['tag'] = 'CHEL.ChemElementStore.name'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00024'\\n currentMap['name'] = 'name'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['eType'] = 'cplx'\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033')\\n\\n # Role ChemElementStore.access\\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\\n\\n # Role ChemElementStore.chemElements\\n currentMap = {}\\n contentMap['chemElements'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00023'] = currentMap\\n loadMaps['CHEL.ChemElementStore.chemElements'] = currentMap\\n currentMap['tag'] = 'CHEL.ChemElementStore.chemElements'\\n currentMap['type'] = 'child'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00023'\\n currentMap['name'] = 'chemElements'\\n currentMap['hicard'] = -1\\n currentMap['locard'] = 0\\n currentMap['eType'] = 'cplx'\\n currentMap['implSkip'] = True\\n currentMap['content'] = globalMap.get('CHEL').get('abstractTypes')\\n # End of ChemElementStore\\n\\n currentMap = abstractTypes.get('ChemElementStore')\\n aList = ['createdBy', 'guid', 'isModifiable', 'lastUnlockedBy']\\n currentMap['headerAttrs'] = aList\\n aList = ['name']\\n currentMap['simpleAttrs'] = aList\\n aList = ['chemElements', 'access', 'applicationData']\\n currentMap['cplxAttrs'] = aList\\n aList = ['chemElements']\\n currentMap['children'] = aList\\n\\n # Class Isotope\\n currentMap = {}\\n abstractTypes['Isotope'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:19:49_00003'] = currentMap\\n loadMaps['CHEL.Isotope'] = currentMap\\n currentMap['tag'] = 'CHEL.Isotope'\\n currentMap['type'] = 'class'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:19:49_00003'\\n currentMap['eType'] = 'cplx'\\n currentMap['fromParent'] = 'isotopes'\\n currentMap['objkey'] = 'massNumber'\\n currentMap['class'] = ccp.api.molecule.ChemElement.Isotope\\n contentMap = {}\\n currentMap['content'] = contentMap\\n\\n # Attribute Isotope.abundance\\n currentMap = {}\\n contentMap['abundance'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00011'] = currentMap\\n loadMaps['CHEL.Isotope.abundance'] = currentMap\\n currentMap['tag'] = 'CHEL.Isotope.abundance'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00011'\\n currentMap['name'] = 'abundance'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 0\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00058')\\n\\n # Attribute Isotope.applicationData\\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\\n\\n # Attribute Isotope.gyroMagneticRatio\\n currentMap = {}\\n contentMap['gyroMagneticRatio'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00008'] = currentMap\\n loadMaps['CHEL.Isotope.gyroMagneticRatio'] = currentMap\\n currentMap['tag'] = 'CHEL.Isotope.gyroMagneticRatio'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00008'\\n currentMap['name'] = 'gyroMagneticRatio'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 0\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00031')\\n\\n # Attribute Isotope.halfLife\\n currentMap = {}\\n contentMap['halfLife'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2007-06-07-18:18:13_00001'] = currentMap\\n loadMaps['CHEL.Isotope.halfLife'] = currentMap\\n currentMap['tag'] = 'CHEL.Isotope.halfLife'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2007-06-07-18:18:13_00001'\\n currentMap['name'] = 'halfLife'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 0\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:54_00007')\\n\\n # Attribute Isotope.halfLifeError\\n currentMap = {}\\n contentMap['halfLifeError'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2007-06-07-18:18:13_00002'] = currentMap\\n loadMaps['CHEL.Isotope.halfLifeError'] = currentMap\\n currentMap['tag'] = 'CHEL.Isotope.halfLifeError'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2007-06-07-18:18:13_00002'\\n currentMap['name'] = 'halfLifeError'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 0\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:54_00007')\\n\\n # Attribute Isotope.halfLifeType\\n currentMap = {}\\n contentMap['halfLifeType'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2007-06-07-18:18:13_00003'] = currentMap\\n loadMaps['CHEL.Isotope.halfLifeType'] = currentMap\\n currentMap['tag'] = 'CHEL.Isotope.halfLifeType'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2007-06-07-18:18:13_00003'\\n currentMap['name'] = 'halfLifeType'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['default'] = 'unknown'\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2007-06-07-18:18:10_00002')\\n\\n # Attribute Isotope.magneticMoment\\n currentMap = {}\\n contentMap['magneticMoment'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00013'] = currentMap\\n loadMaps['CHEL.Isotope.magneticMoment'] = currentMap\\n currentMap['tag'] = 'CHEL.Isotope.magneticMoment'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00013'\\n currentMap['name'] = 'magneticMoment'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 0\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00031')\\n\\n # Attribute Isotope.mass\\n currentMap = {}\\n contentMap['mass'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00010'] = currentMap\\n loadMaps['CHEL.Isotope.mass'] = currentMap\\n currentMap['tag'] = 'CHEL.Isotope.mass'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00010'\\n currentMap['name'] = 'mass'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 0\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00031')\\n\\n # Attribute Isotope.massNumber\\n currentMap = {}\\n contentMap['massNumber'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00007'] = currentMap\\n loadMaps['CHEL.Isotope.massNumber'] = currentMap\\n currentMap['tag'] = 'CHEL.Isotope.massNumber'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00007'\\n currentMap['name'] = 'massNumber'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['proc'] = 'direct'\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00032')\\n\\n # Attribute Isotope.quadrupoleMoment\\n currentMap = {}\\n contentMap['quadrupoleMoment'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00014'] = currentMap\\n loadMaps['CHEL.Isotope.quadrupoleMoment'] = currentMap\\n currentMap['tag'] = 'CHEL.Isotope.quadrupoleMoment'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00014'\\n currentMap['name'] = 'quadrupoleMoment'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 0\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00031')\\n\\n # Attribute Isotope.receptivity\\n currentMap = {}\\n contentMap['receptivity'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00012'] = currentMap\\n loadMaps['CHEL.Isotope.receptivity'] = currentMap\\n currentMap['tag'] = 'CHEL.Isotope.receptivity'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00012'\\n currentMap['name'] = 'receptivity'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 0\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00031')\\n\\n # Attribute Isotope.spin\\n currentMap = {}\\n contentMap['spin'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00009'] = currentMap\\n loadMaps['CHEL.Isotope.spin'] = currentMap\\n currentMap['tag'] = 'CHEL.Isotope.spin'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00009'\\n currentMap['name'] = 'spin'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 0\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00037')\\n\\n # Role Isotope.access\\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\\n # End of Isotope\\n\\n currentMap = abstractTypes.get('Isotope')\\n aList = ['abundance', 'gyroMagneticRatio', 'halfLife', 'halfLifeError', 'halfLifeType', 'magneticMoment', 'mass', 'massNumber', 'quadrupoleMoment', 'receptivity', 'spin']\\n currentMap['headerAttrs'] = aList\\n aList = ['access', 'applicationData']\\n currentMap['cplxAttrs'] = aList\\n\\n # Out-of-package link to ChemElement\\n currentMap = {}\\n exolinks['ChemElement'] = currentMap\\n loadMaps['CHEL.exo-ChemElement'] = currentMap\\n currentMap['tag'] = 'CHEL.exo-ChemElement'\\n currentMap['type'] = 'exo'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:19:49_00004'\\n currentMap['name'] = 'ChemElement'\\n currentMap['eType'] = 'cplx'\\n currentMap['class'] = ccp.api.molecule.ChemElement.ChemElement\\n aList = list()\\n currentMap['keyMaps'] = aList\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00055'))\\n\\n # Out-of-package link to ChemElementStore\\n currentMap = {}\\n exolinks['ChemElementStore'] = currentMap\\n loadMaps['CHEL.exo-ChemElementStore'] = currentMap\\n currentMap['tag'] = 'CHEL.exo-ChemElementStore'\\n currentMap['type'] = 'exo'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:19:49_00005'\\n currentMap['name'] = 'ChemElementStore'\\n currentMap['eType'] = 'cplx'\\n currentMap['class'] = ccp.api.molecule.ChemElement.ChemElementStore\\n aList = list()\\n currentMap['keyMaps'] = aList\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\\n\\n # Out-of-package link to Isotope\\n currentMap = {}\\n exolinks['Isotope'] = currentMap\\n loadMaps['CHEL.exo-Isotope'] = currentMap\\n currentMap['tag'] = 'CHEL.exo-Isotope'\\n currentMap['type'] = 'exo'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:19:49_00003'\\n currentMap['name'] = 'Isotope'\\n currentMap['eType'] = 'cplx'\\n currentMap['class'] = ccp.api.molecule.ChemElement.Isotope\\n aList = list()\\n currentMap['keyMaps'] = aList\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00055'))\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00032'))\",\n \"def create_map():\\n pass\\n # for line in range(0, shared.lines):\\n # map_data[line][0] = (1, -1)\\n # map_data[line][shared.columns - 1] = (1, -1)\\n #\\n # for column in range(0, shared.columns):\\n # map_data[0, column] = (-1, 1)\\n # # if column <= shared.left_space or column > shared.columns - shared.left_space:\\n # map_data[shared.lines - 1, column] = (-1, 1)\",\n \"def _do_mapping(self):\\n pass\",\n \"def create_mapping(self):\\n\\n indice = client.IndicesClient(self.es)\\n\\n indice.put_mapping(index=self.es_main_index,\\n doc_type=self.es_main_type,\\n body=self.mapping)\",\n \"def cell_map_from_database(self) -> None:\\n for row in self.session.query(DatamapItem).all():\\n self.cell_map.append(\\n Cell(\\n datamap_id=row.id,\\n cell_key=row.key,\\n cell_value=None,\\n template_sheet=row.bicc_sheet,\\n bg_colour=None,\\n fg_colour=None,\\n number_format=None,\\n verification_list=None,\\n cell_reference=row.bicc_cellref))\",\n \"def _part_group_cell_mapper(bd_type):\\n js, iss = np.meshgrid(range(smt.cols), range(smt.rows)) # zero indexed to agree with python interpretation\\n idx = bd_type.flatten() != -1\\n out = dict(zip(range(1, idx.sum() + 1), list(zip(iss.flatten()[idx], js.flatten()[idx]))))\\n return out\",\n \"def _makeimap(self):\\n self.map_['source'] = 'GOES'\\n self.map_['provider'] = 'NOAA'\\n self.map_['instrument'] = 'SUVI'\\n self.map_['physobs'] = 'flux'\",\n \"def _makeimap(self):\\n self.map_[\\\"source\\\"] = \\\"nasa\\\"\\n self.map_[\\\"instrument\\\"] = \\\"goes\\\"\\n self.map_[\\\"physobs\\\"] = \\\"irradiance\\\"\\n self.map_[\\\"provider\\\"] = \\\"sdac\\\"\",\n \"def create_periodic_cell_face_map():\\n for f0, f1 in periodic_face_map.iteritems():\\n # f0, f1 = periodic face0 - face1\\n face0 = face_list[f0 - 1]\\n face1 = face_list[f1 - 1] # shadow\\n nd, nds, cells, bc_type, zone_id = [0,]*2, [0,]*2, [0,]*2, [0,]*2, [0,]*2\\n for i, ff in enumerate([face0, face1]):\\n nd[i], nds[i], cells[i], bc_type[i], zone_id[i] = ff\\n \\n cell_face_pair = []\\n for i in range(2):\\n c = max(cells[i])\\n if len(nds[i]) == 2:\\n cell_face_pair.append((c, face_map[c][(nds[i][0], nds[i][1])]))\\n else:\\n cell_face_pair.append((c, face_map[c][eval('f'+str(i))]))\\n \\n periodic_cell_face_map[cell_face_pair[0]] = cell_face_pair[1]\\n periodic_cell_face_map[cell_face_pair[1]] = cell_face_pair[0]\",\n \"def makeMapping(globalMap):\\n \\n from memops.xml.Implementation import bool2str, str2bool\\n\\n # Set up top level dictionaries\\n loadMaps = globalMap.get('loadMaps')\\n mapsByGuid = globalMap.get('mapsByGuid')\\n\\n abstractTypes = globalMap.get('CLAS').get('abstractTypes')\\n exolinks = globalMap.get('CLAS').get('exolinks')\\n\\n # Class AbstractCategory\\n currentMap = {}\\n abstractTypes['AbstractCategory'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-09-04-17:19:17_00001'] = currentMap\\n currentMap['type'] = 'class'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-09-04-17:19:17_00001'\\n currentMap['eType'] = 'cplx'\\n currentMap['objkey'] = 'name'\\n currentMap['class'] = ccp.api.lims.Classification.AbstractCategory\\n contentMap = {}\\n currentMap['content'] = contentMap\\n\\n # Attribute AbstractCategory.applicationData\\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\\n\\n # Attribute AbstractCategory.details\\n currentMap = {}\\n contentMap['details'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-09-04-17:21:35_00001'] = currentMap\\n loadMaps['CLAS.AbstractCategory.details'] = currentMap\\n currentMap['tag'] = 'CLAS.AbstractCategory.details'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-09-04-17:21:35_00001'\\n currentMap['name'] = 'details'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 0\\n currentMap['eType'] = 'cplx'\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00036')\\n\\n # Attribute AbstractCategory.name\\n currentMap = {}\\n contentMap['name'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00014'] = currentMap\\n loadMaps['CLAS.AbstractCategory.name'] = currentMap\\n currentMap['tag'] = 'CLAS.AbstractCategory.name'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00014'\\n currentMap['name'] = 'name'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['eType'] = 'cplx'\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033')\\n\\n # Role AbstractCategory.access\\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\\n # End of AbstractCategory\\n\\n currentMap = abstractTypes.get('AbstractCategory')\\n aList = ['details', 'name']\\n currentMap['simpleAttrs'] = aList\\n aList = ['access', 'applicationData']\\n currentMap['cplxAttrs'] = aList\\n\\n # Class Classification\\n currentMap = {}\\n abstractTypes['Classification'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-09-04-17:18:54_00002'] = currentMap\\n loadMaps['CLAS.Classification'] = currentMap\\n currentMap['tag'] = 'CLAS.Classification'\\n currentMap['type'] = 'class'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-09-04-17:18:54_00002'\\n currentMap['eType'] = 'cplx'\\n currentMap['fromParent'] = 'classifications'\\n currentMap['isTop'] = True\\n currentMap['objkey'] = 'namingSystem'\\n currentMap['class'] = ccp.api.lims.Classification.Classification\\n contentMap = {}\\n currentMap['content'] = contentMap\\n\\n # Attribute Classification.applicationData\\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\\n\\n # Attribute Classification.createdBy\\n contentMap['createdBy'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:00:59_00002__www.ccpn.ac.uk_Fogh_2007-10-03-14:53:27_00001__www.ccpn.ac.uk_Fogh_2006-09-14-16:28:57_00002')\\n\\n # Attribute Classification.guid\\n contentMap['guid'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:26_00002')\\n\\n # Attribute Classification.isModifiable\\n contentMap['isModifiable'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-17-14:16:26_00010__www.ccpn.ac.uk_Fogh_2007-10-03-14:53:27_00001__www.ccpn.ac.uk_Fogh_2006-09-14-16:28:57_00002')\\n\\n # Attribute Classification.lastUnlockedBy\\n contentMap['lastUnlockedBy'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:00:59_00003__www.ccpn.ac.uk_Fogh_2007-10-03-14:53:27_00001__www.ccpn.ac.uk_Fogh_2006-09-14-16:28:57_00002')\\n\\n # Attribute Classification.namingSystem\\n currentMap = {}\\n contentMap['namingSystem'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00013'] = currentMap\\n loadMaps['CLAS.Classification.namingSystem'] = currentMap\\n currentMap['tag'] = 'CLAS.Classification.namingSystem'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00013'\\n currentMap['name'] = 'namingSystem'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['eType'] = 'cplx'\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033')\\n\\n # Role Classification.access\\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\\n\\n # Role Classification.experimentTypes\\n currentMap = {}\\n contentMap['experimentTypes'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00002'] = currentMap\\n loadMaps['CLAS.Classification.experimentTypes'] = currentMap\\n currentMap['tag'] = 'CLAS.Classification.experimentTypes'\\n currentMap['type'] = 'child'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00002'\\n currentMap['name'] = 'experimentTypes'\\n currentMap['hicard'] = -1\\n currentMap['locard'] = 0\\n currentMap['eType'] = 'cplx'\\n currentMap['implSkip'] = True\\n currentMap['content'] = globalMap.get('CLAS').get('abstractTypes')\\n\\n # Role Classification.hazardPhrases\\n currentMap = {}\\n contentMap['hazardPhrases'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00006'] = currentMap\\n loadMaps['CLAS.Classification.hazardPhrases'] = currentMap\\n currentMap['tag'] = 'CLAS.Classification.hazardPhrases'\\n currentMap['type'] = 'child'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00006'\\n currentMap['name'] = 'hazardPhrases'\\n currentMap['hicard'] = -1\\n currentMap['locard'] = 0\\n currentMap['eType'] = 'cplx'\\n currentMap['implSkip'] = True\\n currentMap['content'] = globalMap.get('CLAS').get('abstractTypes')\\n\\n # Role Classification.holderCategorys\\n currentMap = {}\\n contentMap['holderCategorys'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-09-04-17:33:23_00002'] = currentMap\\n loadMaps['CLAS.Classification.holderCategorys'] = currentMap\\n currentMap['tag'] = 'CLAS.Classification.holderCategorys'\\n currentMap['type'] = 'child'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-09-04-17:33:23_00002'\\n currentMap['name'] = 'holderCategorys'\\n currentMap['hicard'] = -1\\n currentMap['locard'] = 0\\n currentMap['eType'] = 'cplx'\\n currentMap['implSkip'] = True\\n currentMap['content'] = globalMap.get('CLAS').get('abstractTypes')\\n\\n # Role Classification.instrumentTypes\\n currentMap = {}\\n contentMap['instrumentTypes'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-09-04-17:21:33_00001'] = currentMap\\n loadMaps['CLAS.Classification.instrumentTypes'] = currentMap\\n currentMap['tag'] = 'CLAS.Classification.instrumentTypes'\\n currentMap['type'] = 'child'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-09-04-17:21:33_00001'\\n currentMap['name'] = 'instrumentTypes'\\n currentMap['hicard'] = -1\\n currentMap['locard'] = 0\\n currentMap['eType'] = 'cplx'\\n currentMap['implSkip'] = True\\n currentMap['content'] = globalMap.get('CLAS').get('abstractTypes')\\n\\n # Role Classification.sampleCategories\\n currentMap = {}\\n contentMap['sampleCategories'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00008'] = currentMap\\n loadMaps['CLAS.Classification.sampleCategories'] = currentMap\\n currentMap['tag'] = 'CLAS.Classification.sampleCategories'\\n currentMap['type'] = 'child'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00008'\\n currentMap['name'] = 'sampleCategories'\\n currentMap['hicard'] = -1\\n currentMap['locard'] = 0\\n currentMap['eType'] = 'cplx'\\n currentMap['implSkip'] = True\\n currentMap['content'] = globalMap.get('CLAS').get('abstractTypes')\\n\\n # Role Classification.sampleComponentCategory\\n currentMap = {}\\n contentMap['sampleComponentCategory'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00004'] = currentMap\\n loadMaps['CLAS.Classification.sampleComponentCategory'] = currentMap\\n currentMap['tag'] = 'CLAS.Classification.sampleComponentCategory'\\n currentMap['type'] = 'child'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00004'\\n currentMap['name'] = 'sampleComponentCategory'\\n currentMap['hicard'] = -1\\n currentMap['locard'] = 0\\n currentMap['eType'] = 'cplx'\\n currentMap['implSkip'] = True\\n currentMap['content'] = globalMap.get('CLAS').get('abstractTypes')\\n\\n # Role Classification.targetScoreboards\\n currentMap = {}\\n contentMap['targetScoreboards'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00012'] = currentMap\\n loadMaps['CLAS.Classification.targetScoreboards'] = currentMap\\n currentMap['tag'] = 'CLAS.Classification.targetScoreboards'\\n currentMap['type'] = 'child'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00012'\\n currentMap['name'] = 'targetScoreboards'\\n currentMap['hicard'] = -1\\n currentMap['locard'] = 0\\n currentMap['eType'] = 'cplx'\\n currentMap['implSkip'] = True\\n currentMap['content'] = globalMap.get('CLAS').get('abstractTypes')\\n\\n # Role Classification.targetStatus\\n currentMap = {}\\n contentMap['targetStatus'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00010'] = currentMap\\n loadMaps['CLAS.Classification.targetStatus'] = currentMap\\n currentMap['tag'] = 'CLAS.Classification.targetStatus'\\n currentMap['type'] = 'child'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00010'\\n currentMap['name'] = 'targetStatus'\\n currentMap['hicard'] = -1\\n currentMap['locard'] = 0\\n currentMap['eType'] = 'cplx'\\n currentMap['implSkip'] = True\\n currentMap['content'] = globalMap.get('CLAS').get('abstractTypes')\\n # End of Classification\\n\\n currentMap = abstractTypes.get('Classification')\\n aList = ['createdBy', 'guid', 'isModifiable', 'lastUnlockedBy']\\n currentMap['headerAttrs'] = aList\\n aList = ['namingSystem']\\n currentMap['simpleAttrs'] = aList\\n aList = ['targetStatus', 'targetScoreboards', 'sampleComponentCategory', 'sampleCategories', 'instrumentTypes', 'holderCategorys', 'hazardPhrases', 'experimentTypes', 'access', 'applicationData']\\n currentMap['cplxAttrs'] = aList\\n aList = ['experimentTypes', 'hazardPhrases', 'holderCategorys', 'instrumentTypes', 'sampleCategories', 'sampleComponentCategory', 'targetScoreboards', 'targetStatus']\\n currentMap['children'] = aList\\n\\n # Class SampleComponentCategory\\n currentMap = {}\\n abstractTypes['SampleComponentCategory'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00014'] = currentMap\\n loadMaps['CLAS.SampleComponentCategory'] = currentMap\\n currentMap['tag'] = 'CLAS.SampleComponentCategory'\\n currentMap['type'] = 'class'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00014'\\n currentMap['eType'] = 'cplx'\\n currentMap['fromParent'] = 'sampleComponentCategory'\\n currentMap['objkey'] = 'name'\\n currentMap['class'] = ccp.api.lims.Classification.SampleComponentCategory\\n contentMap = {}\\n currentMap['content'] = contentMap\\n\\n # Attribute SampleComponentCategory.applicationData\\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\\n\\n # Attribute SampleComponentCategory.details\\n contentMap['details'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-04-17:21:35_00001')\\n\\n # Attribute SampleComponentCategory.name\\n contentMap['name'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00014')\\n\\n # Role SampleComponentCategory.access\\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\\n # End of SampleComponentCategory\\n\\n currentMap = abstractTypes.get('SampleComponentCategory')\\n aList = ['details', 'name']\\n currentMap['simpleAttrs'] = aList\\n aList = ['access', 'applicationData']\\n currentMap['cplxAttrs'] = aList\\n\\n # Class ExperimentType\\n currentMap = {}\\n abstractTypes['ExperimentType'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-14:22:45_00014'] = currentMap\\n loadMaps['CLAS.ExperimentType'] = currentMap\\n currentMap['tag'] = 'CLAS.ExperimentType'\\n currentMap['type'] = 'class'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:45_00014'\\n currentMap['eType'] = 'cplx'\\n currentMap['fromParent'] = 'experimentTypes'\\n currentMap['objkey'] = 'name'\\n currentMap['class'] = ccp.api.lims.Classification.ExperimentType\\n contentMap = {}\\n currentMap['content'] = contentMap\\n\\n # Attribute ExperimentType.applicationData\\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\\n\\n # Attribute ExperimentType.details\\n contentMap['details'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-04-17:21:35_00001')\\n\\n # Attribute ExperimentType.name\\n contentMap['name'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00014')\\n\\n # Role ExperimentType.access\\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\\n\\n # Role ExperimentType.instrumentTypes\\n currentMap = {}\\n contentMap['instrumentTypes'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:22:57_00002'] = currentMap\\n loadMaps['CLAS.ExperimentType.instrumentTypes'] = currentMap\\n currentMap['tag'] = 'CLAS.ExperimentType.instrumentTypes'\\n currentMap['type'] = 'link'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:22:57_00002'\\n currentMap['name'] = 'instrumentTypes'\\n currentMap['hicard'] = -1\\n currentMap['locard'] = 0\\n currentMap['copyOverride'] = True\\n\\n # Role ExperimentType.sampleCategories\\n currentMap = {}\\n contentMap['sampleCategories'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:22:56_00031'] = currentMap\\n loadMaps['CLAS.ExperimentType.sampleCategories'] = currentMap\\n currentMap['tag'] = 'CLAS.ExperimentType.sampleCategories'\\n currentMap['type'] = 'link'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:22:56_00031'\\n currentMap['name'] = 'sampleCategories'\\n currentMap['hicard'] = -1\\n currentMap['locard'] = 0\\n currentMap['copyOverride'] = True\\n # End of ExperimentType\\n\\n currentMap = abstractTypes.get('ExperimentType')\\n aList = ['details', 'name', 'instrumentTypes', 'sampleCategories']\\n currentMap['simpleAttrs'] = aList\\n aList = ['access', 'applicationData']\\n currentMap['cplxAttrs'] = aList\\n\\n # Class TargetScoreboard\\n currentMap = {}\\n abstractTypes['TargetScoreboard'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-14:22:51_00040'] = currentMap\\n loadMaps['CLAS.TargetScoreboard'] = currentMap\\n currentMap['tag'] = 'CLAS.TargetScoreboard'\\n currentMap['type'] = 'class'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:51_00040'\\n currentMap['eType'] = 'cplx'\\n currentMap['fromParent'] = 'targetScoreboards'\\n currentMap['objkey'] = 'name'\\n currentMap['class'] = ccp.api.lims.Classification.TargetScoreboard\\n contentMap = {}\\n currentMap['content'] = contentMap\\n\\n # Attribute TargetScoreboard.applicationData\\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\\n\\n # Attribute TargetScoreboard.details\\n contentMap['details'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-04-17:21:35_00001')\\n\\n # Attribute TargetScoreboard.name\\n contentMap['name'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00014')\\n\\n # Role TargetScoreboard.access\\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\\n\\n # Role TargetScoreboard.targetStatus\\n currentMap = {}\\n contentMap['targetStatus'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:18_00039'] = currentMap\\n loadMaps['CLAS.TargetScoreboard.targetStatus'] = currentMap\\n currentMap['tag'] = 'CLAS.TargetScoreboard.targetStatus'\\n currentMap['type'] = 'link'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:18_00039'\\n currentMap['name'] = 'targetStatus'\\n currentMap['hicard'] = -1\\n currentMap['locard'] = 0\\n currentMap['copyOverride'] = True\\n # End of TargetScoreboard\\n\\n currentMap = abstractTypes.get('TargetScoreboard')\\n aList = ['details', 'name', 'targetStatus']\\n currentMap['simpleAttrs'] = aList\\n aList = ['access', 'applicationData']\\n currentMap['cplxAttrs'] = aList\\n\\n # Class HolderCategory\\n currentMap = {}\\n abstractTypes['HolderCategory'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-14:22:52_00049'] = currentMap\\n loadMaps['CLAS.HolderCategory'] = currentMap\\n currentMap['tag'] = 'CLAS.HolderCategory'\\n currentMap['type'] = 'class'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:52_00049'\\n currentMap['eType'] = 'cplx'\\n currentMap['fromParent'] = 'holderCategorys'\\n currentMap['objkey'] = 'name'\\n currentMap['class'] = ccp.api.lims.Classification.HolderCategory\\n contentMap = {}\\n currentMap['content'] = contentMap\\n\\n # Attribute HolderCategory.applicationData\\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\\n\\n # Attribute HolderCategory.details\\n contentMap['details'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-04-17:21:35_00001')\\n\\n # Attribute HolderCategory.name\\n contentMap['name'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00014')\\n\\n # Role HolderCategory.access\\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\\n # End of HolderCategory\\n\\n currentMap = abstractTypes.get('HolderCategory')\\n aList = ['details', 'name']\\n currentMap['simpleAttrs'] = aList\\n aList = ['access', 'applicationData']\\n currentMap['cplxAttrs'] = aList\\n\\n # Class HazardPhrase\\n currentMap = {}\\n abstractTypes['HazardPhrase'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-14:22:52_00052'] = currentMap\\n loadMaps['CLAS.HazardPhrase'] = currentMap\\n currentMap['tag'] = 'CLAS.HazardPhrase'\\n currentMap['type'] = 'class'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:52_00052'\\n currentMap['eType'] = 'cplx'\\n currentMap['fromParent'] = 'hazardPhrases'\\n currentMap['objkey'] = 'name'\\n currentMap['class'] = ccp.api.lims.Classification.HazardPhrase\\n contentMap = {}\\n currentMap['content'] = contentMap\\n\\n # Attribute HazardPhrase.applicationData\\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\\n\\n # Attribute HazardPhrase.details\\n contentMap['details'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-04-17:21:35_00001')\\n\\n # Attribute HazardPhrase.name\\n contentMap['name'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00014')\\n\\n # Attribute HazardPhrase.phrase\\n currentMap = {}\\n contentMap['phrase'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:28_00017'] = currentMap\\n loadMaps['CLAS.HazardPhrase.phrase'] = currentMap\\n currentMap['tag'] = 'CLAS.HazardPhrase.phrase'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:28_00017'\\n currentMap['name'] = 'phrase'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 0\\n currentMap['eType'] = 'cplx'\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00036')\\n\\n # Role HazardPhrase.access\\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\\n # End of HazardPhrase\\n\\n currentMap = abstractTypes.get('HazardPhrase')\\n aList = ['details', 'name', 'phrase']\\n currentMap['simpleAttrs'] = aList\\n aList = ['access', 'applicationData']\\n currentMap['cplxAttrs'] = aList\\n\\n # Class InstrumentType\\n currentMap = {}\\n abstractTypes['InstrumentType'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-14:22:46_00005'] = currentMap\\n loadMaps['CLAS.InstrumentType'] = currentMap\\n currentMap['tag'] = 'CLAS.InstrumentType'\\n currentMap['type'] = 'class'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:46_00005'\\n currentMap['eType'] = 'cplx'\\n currentMap['fromParent'] = 'instrumentTypes'\\n currentMap['objkey'] = 'name'\\n currentMap['class'] = ccp.api.lims.Classification.InstrumentType\\n contentMap = {}\\n currentMap['content'] = contentMap\\n\\n # Attribute InstrumentType.applicationData\\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\\n\\n # Attribute InstrumentType.details\\n contentMap['details'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-04-17:21:35_00001')\\n\\n # Attribute InstrumentType.name\\n contentMap['name'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00014')\\n\\n # Role InstrumentType.access\\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\\n\\n # Role InstrumentType.experimentTypes\\n currentMap = {}\\n contentMap['experimentTypes'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:22:57_00001'] = currentMap\\n loadMaps['CLAS.InstrumentType.experimentTypes'] = currentMap\\n currentMap['tag'] = 'CLAS.InstrumentType.experimentTypes'\\n currentMap['type'] = 'link'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:22:57_00001'\\n currentMap['name'] = 'experimentTypes'\\n currentMap['hicard'] = -1\\n currentMap['locard'] = 0\\n currentMap['copyOverride'] = True\\n # End of InstrumentType\\n\\n currentMap = abstractTypes.get('InstrumentType')\\n aList = ['details', 'name', 'experimentTypes']\\n currentMap['simpleAttrs'] = aList\\n aList = ['access', 'applicationData']\\n currentMap['cplxAttrs'] = aList\\n\\n # Class SampleCategory\\n currentMap = {}\\n abstractTypes['SampleCategory'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-14:22:44_00009'] = currentMap\\n loadMaps['CLAS.SampleCategory'] = currentMap\\n currentMap['tag'] = 'CLAS.SampleCategory'\\n currentMap['type'] = 'class'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:44_00009'\\n currentMap['eType'] = 'cplx'\\n currentMap['fromParent'] = 'sampleCategories'\\n currentMap['objkey'] = 'name'\\n currentMap['class'] = ccp.api.lims.Classification.SampleCategory\\n contentMap = {}\\n currentMap['content'] = contentMap\\n\\n # Attribute SampleCategory.applicationData\\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\\n\\n # Attribute SampleCategory.details\\n contentMap['details'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-04-17:21:35_00001')\\n\\n # Attribute SampleCategory.name\\n contentMap['name'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00014')\\n\\n # Role SampleCategory.access\\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\\n\\n # Role SampleCategory.experimentTypes\\n currentMap = {}\\n contentMap['experimentTypes'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:22:56_00030'] = currentMap\\n loadMaps['CLAS.SampleCategory.experimentTypes'] = currentMap\\n currentMap['tag'] = 'CLAS.SampleCategory.experimentTypes'\\n currentMap['type'] = 'link'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:22:56_00030'\\n currentMap['name'] = 'experimentTypes'\\n currentMap['hicard'] = -1\\n currentMap['locard'] = 0\\n currentMap['copyOverride'] = True\\n # End of SampleCategory\\n\\n currentMap = abstractTypes.get('SampleCategory')\\n aList = ['details', 'name', 'experimentTypes']\\n currentMap['simpleAttrs'] = aList\\n aList = ['access', 'applicationData']\\n currentMap['cplxAttrs'] = aList\\n\\n # Class TargetStatus\\n currentMap = {}\\n abstractTypes['TargetStatus'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-14:22:51_00039'] = currentMap\\n loadMaps['CLAS.TargetStatus'] = currentMap\\n currentMap['tag'] = 'CLAS.TargetStatus'\\n currentMap['type'] = 'class'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:51_00039'\\n currentMap['eType'] = 'cplx'\\n currentMap['fromParent'] = 'targetStatus'\\n currentMap['objkey'] = 'name'\\n currentMap['class'] = ccp.api.lims.Classification.TargetStatus\\n contentMap = {}\\n currentMap['content'] = contentMap\\n\\n # Attribute TargetStatus.applicationData\\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\\n\\n # Attribute TargetStatus.details\\n contentMap['details'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-04-17:21:35_00001')\\n\\n # Attribute TargetStatus.name\\n contentMap['name'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00014')\\n\\n # Role TargetStatus.access\\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\\n\\n # Role TargetStatus.targetScoreboards\\n currentMap = {}\\n contentMap['targetScoreboards'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:18_00040'] = currentMap\\n loadMaps['CLAS.TargetStatus.targetScoreboards'] = currentMap\\n currentMap['tag'] = 'CLAS.TargetStatus.targetScoreboards'\\n currentMap['type'] = 'link'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:18_00040'\\n currentMap['name'] = 'targetScoreboards'\\n currentMap['hicard'] = -1\\n currentMap['locard'] = 0\\n currentMap['copyOverride'] = True\\n # End of TargetStatus\\n\\n currentMap = abstractTypes.get('TargetStatus')\\n aList = ['details', 'name', 'targetScoreboards']\\n currentMap['simpleAttrs'] = aList\\n aList = ['access', 'applicationData']\\n currentMap['cplxAttrs'] = aList\\n\\n # Out-of-package link to Classification\\n currentMap = {}\\n exolinks['Classification'] = currentMap\\n loadMaps['CLAS.exo-Classification'] = currentMap\\n currentMap['tag'] = 'CLAS.exo-Classification'\\n currentMap['type'] = 'exo'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-09-04-17:18:54_00002'\\n currentMap['name'] = 'Classification'\\n currentMap['eType'] = 'cplx'\\n currentMap['class'] = ccp.api.lims.Classification.Classification\\n aList = list()\\n currentMap['keyMaps'] = aList\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\\n\\n # Out-of-package link to SampleComponentCategory\\n currentMap = {}\\n exolinks['SampleComponentCategory'] = currentMap\\n loadMaps['CLAS.exo-SampleComponentCategory'] = currentMap\\n currentMap['tag'] = 'CLAS.exo-SampleComponentCategory'\\n currentMap['type'] = 'exo'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00014'\\n currentMap['name'] = 'SampleComponentCategory'\\n currentMap['eType'] = 'cplx'\\n currentMap['class'] = ccp.api.lims.Classification.SampleComponentCategory\\n aList = list()\\n currentMap['keyMaps'] = aList\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033'))\\n\\n # Out-of-package link to ExperimentType\\n currentMap = {}\\n exolinks['ExperimentType'] = currentMap\\n loadMaps['CLAS.exo-ExperimentType'] = currentMap\\n currentMap['tag'] = 'CLAS.exo-ExperimentType'\\n currentMap['type'] = 'exo'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:45_00014'\\n currentMap['name'] = 'ExperimentType'\\n currentMap['eType'] = 'cplx'\\n currentMap['class'] = ccp.api.lims.Classification.ExperimentType\\n aList = list()\\n currentMap['keyMaps'] = aList\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033'))\\n\\n # Out-of-package link to TargetScoreboard\\n currentMap = {}\\n exolinks['TargetScoreboard'] = currentMap\\n loadMaps['CLAS.exo-TargetScoreboard'] = currentMap\\n currentMap['tag'] = 'CLAS.exo-TargetScoreboard'\\n currentMap['type'] = 'exo'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:51_00040'\\n currentMap['name'] = 'TargetScoreboard'\\n currentMap['eType'] = 'cplx'\\n currentMap['class'] = ccp.api.lims.Classification.TargetScoreboard\\n aList = list()\\n currentMap['keyMaps'] = aList\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033'))\\n\\n # Out-of-package link to HolderCategory\\n currentMap = {}\\n exolinks['HolderCategory'] = currentMap\\n loadMaps['CLAS.exo-HolderCategory'] = currentMap\\n currentMap['tag'] = 'CLAS.exo-HolderCategory'\\n currentMap['type'] = 'exo'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:52_00049'\\n currentMap['name'] = 'HolderCategory'\\n currentMap['eType'] = 'cplx'\\n currentMap['class'] = ccp.api.lims.Classification.HolderCategory\\n aList = list()\\n currentMap['keyMaps'] = aList\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033'))\\n\\n # Out-of-package link to HazardPhrase\\n currentMap = {}\\n exolinks['HazardPhrase'] = currentMap\\n loadMaps['CLAS.exo-HazardPhrase'] = currentMap\\n currentMap['tag'] = 'CLAS.exo-HazardPhrase'\\n currentMap['type'] = 'exo'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:52_00052'\\n currentMap['name'] = 'HazardPhrase'\\n currentMap['eType'] = 'cplx'\\n currentMap['class'] = ccp.api.lims.Classification.HazardPhrase\\n aList = list()\\n currentMap['keyMaps'] = aList\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033'))\\n\\n # Out-of-package link to InstrumentType\\n currentMap = {}\\n exolinks['InstrumentType'] = currentMap\\n loadMaps['CLAS.exo-InstrumentType'] = currentMap\\n currentMap['tag'] = 'CLAS.exo-InstrumentType'\\n currentMap['type'] = 'exo'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:46_00005'\\n currentMap['name'] = 'InstrumentType'\\n currentMap['eType'] = 'cplx'\\n currentMap['class'] = ccp.api.lims.Classification.InstrumentType\\n aList = list()\\n currentMap['keyMaps'] = aList\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033'))\\n\\n # Out-of-package link to SampleCategory\\n currentMap = {}\\n exolinks['SampleCategory'] = currentMap\\n loadMaps['CLAS.exo-SampleCategory'] = currentMap\\n currentMap['tag'] = 'CLAS.exo-SampleCategory'\\n currentMap['type'] = 'exo'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:44_00009'\\n currentMap['name'] = 'SampleCategory'\\n currentMap['eType'] = 'cplx'\\n currentMap['class'] = ccp.api.lims.Classification.SampleCategory\\n aList = list()\\n currentMap['keyMaps'] = aList\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033'))\\n\\n # Out-of-package link to TargetStatus\\n currentMap = {}\\n exolinks['TargetStatus'] = currentMap\\n loadMaps['CLAS.exo-TargetStatus'] = currentMap\\n currentMap['tag'] = 'CLAS.exo-TargetStatus'\\n currentMap['type'] = 'exo'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:51_00039'\\n currentMap['name'] = 'TargetStatus'\\n currentMap['eType'] = 'cplx'\\n currentMap['class'] = ccp.api.lims.Classification.TargetStatus\\n aList = list()\\n currentMap['keyMaps'] = aList\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033'))\",\n \"def make_hp_map(data, hp_indices, Nside = 2048, nest = True):\\n \\n print(\\\"len hp_indices: {}\\\".format(len(hp_indices)))\\n hp_indices = np.array(hp_indices)\\n print(\\\"shape of hp_indices: {}\\\".format(hp_indices.shape))\\n hp_indices = np.squeeze(hp_indices)\\n \\n Npix = 12*Nside**2\\n map_data = np.zeros(Npix, np.float_)\\n map_data[hp_indices] = data\\n \\n return map_data\",\n \"def _makeimap(self):\\n self.map_['source'] = 'NAOJ'\\n self.map_['provider'] = 'NRO'\\n self.map_['instrument'] = 'NORH'\\n self.map_['phyobs'] = ''\",\n \"def applyMapping(self):\\n pass\",\n \"def _create_room(new_map, room):\\n for x in range(room.x1 + 1, room.x2):\\n for y in range(room.y1 + 1, room.y2):\\n new_map.terrain[x][y] = 1\",\n \"def map():\",\n \"def build_I_map(self):\\n raise NotImplementedError\",\n \"def base_mappings():\\n return {\\n 'from_1': {\\n 'to_1': {\\n 'mol_1': ({}, {}, []),\\n 'mol_2': ({}, {}, []),\\n },\\n },\\n }\",\n \"def remap_particle_to_cell_nv(particle_cell_map, input_particle):\\n return particle_cell_map[input_particle]\",\n \"def make_mapping(self) -> None:\\n start_mark = StreamMark('generated node', 0, 0, 0)\\n end_mark = StreamMark('generated node', 0, 0, 0)\\n self.yaml_node = yaml.MappingNode('tag:yaml.org,2002:map', list(),\\n start_mark, end_mark)\",\n \"def makeMapping(globalMap):\\n \\n from memops.xml.Implementation import bool2str, str2bool\\n\\n # Set up top level dictionaries\\n loadMaps = globalMap.get('loadMaps')\\n mapsByGuid = globalMap.get('mapsByGuid')\\n\\n abstractTypes = globalMap.get('ANAP').get('abstractTypes')\\n exolinks = globalMap.get('ANAP').get('exolinks')\\n\\n # DataType GraphicsHandlerType\\n currentMap = {}\\n abstractTypes['GraphicsHandlerType'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-10-03-11:26:03_00001'] = currentMap\\n loadMaps['ANAP.GraphicsHandlerType'] = currentMap\\n currentMap['tag'] = 'ANAP.GraphicsHandlerType'\\n currentMap['type'] = 'simple'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-10-03-11:26:03_00001'\\n currentMap['toStr'] = 'text'\\n currentMap['cnvrt'] = 'text'\\n\\n # Class AnalysisProfile\\n currentMap = {}\\n abstractTypes['AnalysisProfile'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:50_00004'] = currentMap\\n loadMaps['ANAP.AnalysisProfile'] = currentMap\\n currentMap['tag'] = 'ANAP.AnalysisProfile'\\n currentMap['type'] = 'class'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:50_00004'\\n currentMap['eType'] = 'cplx'\\n currentMap['fromParent'] = 'analysisProfiles'\\n currentMap['isTop'] = True\\n currentMap['objkey'] = 'name'\\n currentMap['class'] = ccpnmr.api.AnalysisProfile.AnalysisProfile\\n contentMap = {}\\n currentMap['content'] = contentMap\\n\\n # Attribute AnalysisProfile.applicationData\\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\\n\\n # Attribute AnalysisProfile.bgColor\\n currentMap = {}\\n contentMap['bgColor'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00031'] = currentMap\\n loadMaps['ANAP.AnalysisProfile.bgColor'] = currentMap\\n currentMap['tag'] = 'ANAP.AnalysisProfile.bgColor'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00031'\\n currentMap['name'] = 'bgColor'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['eType'] = 'cplx'\\n currentMap['default'] = '#FFFFFF'\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-05-05-15:12:50_00007')\\n\\n # Attribute AnalysisProfile.createdBy\\n contentMap['createdBy'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:00:59_00002__www.ccpn.ac.uk_Fogh_2007-10-03-14:53:27_00001__www.ccpn.ac.uk_Fogh_2006-09-14-16:28:57_00002')\\n\\n # Attribute AnalysisProfile.fgColor\\n currentMap = {}\\n contentMap['fgColor'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00032'] = currentMap\\n loadMaps['ANAP.AnalysisProfile.fgColor'] = currentMap\\n currentMap['tag'] = 'ANAP.AnalysisProfile.fgColor'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00032'\\n currentMap['name'] = 'fgColor'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['eType'] = 'cplx'\\n currentMap['default'] = '#000000'\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-05-05-15:12:50_00007')\\n\\n # Attribute AnalysisProfile.font\\n currentMap = {}\\n contentMap['font'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00030'] = currentMap\\n loadMaps['ANAP.AnalysisProfile.font'] = currentMap\\n currentMap['tag'] = 'ANAP.AnalysisProfile.font'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00030'\\n currentMap['name'] = 'font'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 0\\n currentMap['eType'] = 'cplx'\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033')\\n\\n # Attribute AnalysisProfile.graphicsHandler\\n currentMap = {}\\n contentMap['graphicsHandler'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00040'] = currentMap\\n loadMaps['ANAP.AnalysisProfile.graphicsHandler'] = currentMap\\n currentMap['tag'] = 'ANAP.AnalysisProfile.graphicsHandler'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00040'\\n currentMap['name'] = 'graphicsHandler'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['default'] = 'Tk'\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-10-03-11:26:03_00001')\\n\\n # Attribute AnalysisProfile.guid\\n contentMap['guid'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:26_00002')\\n\\n # Attribute AnalysisProfile.isModifiable\\n contentMap['isModifiable'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-17-14:16:26_00010__www.ccpn.ac.uk_Fogh_2007-10-03-14:53:27_00001__www.ccpn.ac.uk_Fogh_2006-09-14-16:28:57_00002')\\n\\n # Attribute AnalysisProfile.lastUnlockedBy\\n contentMap['lastUnlockedBy'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:00:59_00003__www.ccpn.ac.uk_Fogh_2007-10-03-14:53:27_00001__www.ccpn.ac.uk_Fogh_2006-09-14-16:28:57_00002')\\n\\n # Attribute AnalysisProfile.name\\n currentMap = {}\\n contentMap['name'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00029'] = currentMap\\n loadMaps['ANAP.AnalysisProfile.name'] = currentMap\\n currentMap['tag'] = 'ANAP.AnalysisProfile.name'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00029'\\n currentMap['name'] = 'name'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00037')\\n\\n # Attribute AnalysisProfile.panView\\n currentMap = {}\\n contentMap['panView'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00036'] = currentMap\\n loadMaps['ANAP.AnalysisProfile.panView'] = currentMap\\n currentMap['tag'] = 'ANAP.AnalysisProfile.panView'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00036'\\n currentMap['name'] = 'panView'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['default'] = True\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00028')\\n\\n # Attribute AnalysisProfile.sendBugReports\\n currentMap = {}\\n contentMap['sendBugReports'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2010-11-17-16:21:37_00004'] = currentMap\\n loadMaps['ANAP.AnalysisProfile.sendBugReports'] = currentMap\\n currentMap['tag'] = 'ANAP.AnalysisProfile.sendBugReports'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2010-11-17-16:21:37_00004'\\n currentMap['name'] = 'sendBugReports'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['default'] = 'maybe'\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2010-11-17-16:21:33_00001')\\n\\n # Attribute AnalysisProfile.transientDialogs\\n currentMap = {}\\n contentMap['transientDialogs'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00037'] = currentMap\\n loadMaps['ANAP.AnalysisProfile.transientDialogs'] = currentMap\\n currentMap['tag'] = 'ANAP.AnalysisProfile.transientDialogs'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00037'\\n currentMap['name'] = 'transientDialogs'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['default'] = True\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00028')\\n\\n # Attribute AnalysisProfile.transientWindows\\n currentMap = {}\\n contentMap['transientWindows'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00038'] = currentMap\\n loadMaps['ANAP.AnalysisProfile.transientWindows'] = currentMap\\n currentMap['tag'] = 'ANAP.AnalysisProfile.transientWindows'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00038'\\n currentMap['name'] = 'transientWindows'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['default'] = False\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00028')\\n\\n # Attribute AnalysisProfile.twoCharShortcuts\\n currentMap = {}\\n contentMap['twoCharShortcuts'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00039'] = currentMap\\n loadMaps['ANAP.AnalysisProfile.twoCharShortcuts'] = currentMap\\n currentMap['tag'] = 'ANAP.AnalysisProfile.twoCharShortcuts'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00039'\\n currentMap['name'] = 'twoCharShortcuts'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['default'] = False\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00028')\\n\\n # Attribute AnalysisProfile.useCrosshair\\n currentMap = {}\\n contentMap['useCrosshair'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00034'] = currentMap\\n loadMaps['ANAP.AnalysisProfile.useCrosshair'] = currentMap\\n currentMap['tag'] = 'ANAP.AnalysisProfile.useCrosshair'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00034'\\n currentMap['name'] = 'useCrosshair'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['default'] = True\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00028')\\n\\n # Attribute AnalysisProfile.useGlobalShortcuts\\n currentMap = {}\\n contentMap['useGlobalShortcuts'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00035'] = currentMap\\n loadMaps['ANAP.AnalysisProfile.useGlobalShortcuts'] = currentMap\\n currentMap['tag'] = 'ANAP.AnalysisProfile.useGlobalShortcuts'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00035'\\n currentMap['name'] = 'useGlobalShortcuts'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 0\\n currentMap['default'] = False\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00028')\\n\\n # Attribute AnalysisProfile.userEmail\\n currentMap = {}\\n contentMap['userEmail'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2010-11-17-16:21:37_00003'] = currentMap\\n loadMaps['ANAP.AnalysisProfile.userEmail'] = currentMap\\n currentMap['tag'] = 'ANAP.AnalysisProfile.userEmail'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2010-11-17-16:21:37_00003'\\n currentMap['name'] = 'userEmail'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 0\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2007-09-12-18:31:28_00003')\\n\\n # Attribute AnalysisProfile.userName\\n currentMap = {}\\n contentMap['userName'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2010-11-17-16:21:37_00001'] = currentMap\\n loadMaps['ANAP.AnalysisProfile.userName'] = currentMap\\n currentMap['tag'] = 'ANAP.AnalysisProfile.userName'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2010-11-17-16:21:37_00001'\\n currentMap['name'] = 'userName'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 0\\n currentMap['eType'] = 'cplx'\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033')\\n\\n # Attribute AnalysisProfile.userOrganisation\\n currentMap = {}\\n contentMap['userOrganisation'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2010-11-17-16:21:37_00002'] = currentMap\\n loadMaps['ANAP.AnalysisProfile.userOrganisation'] = currentMap\\n currentMap['tag'] = 'ANAP.AnalysisProfile.userOrganisation'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2010-11-17-16:21:37_00002'\\n currentMap['name'] = 'userOrganisation'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 0\\n currentMap['eType'] = 'cplx'\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033')\\n\\n # Attribute AnalysisProfile.webBrowser\\n currentMap = {}\\n contentMap['webBrowser'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00033'] = currentMap\\n loadMaps['ANAP.AnalysisProfile.webBrowser'] = currentMap\\n currentMap['tag'] = 'ANAP.AnalysisProfile.webBrowser'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00033'\\n currentMap['name'] = 'webBrowser'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 0\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00037')\\n\\n # Role AnalysisProfile.access\\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\\n\\n # Role AnalysisProfile.colorSchemes\\n currentMap = {}\\n contentMap['colorSchemes'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00026'] = currentMap\\n loadMaps['ANAP.AnalysisProfile.colorSchemes'] = currentMap\\n currentMap['tag'] = 'ANAP.AnalysisProfile.colorSchemes'\\n currentMap['type'] = 'child'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00026'\\n currentMap['name'] = 'colorSchemes'\\n currentMap['hicard'] = -1\\n currentMap['locard'] = 0\\n currentMap['eType'] = 'cplx'\\n currentMap['implSkip'] = True\\n currentMap['content'] = globalMap.get('ANAP').get('abstractTypes')\\n\\n # Role AnalysisProfile.macros\\n currentMap = {}\\n contentMap['macros'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00022'] = currentMap\\n loadMaps['ANAP.AnalysisProfile.macros'] = currentMap\\n currentMap['tag'] = 'ANAP.AnalysisProfile.macros'\\n currentMap['type'] = 'child'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00022'\\n currentMap['name'] = 'macros'\\n currentMap['hicard'] = -1\\n currentMap['locard'] = 0\\n currentMap['eType'] = 'cplx'\\n currentMap['implSkip'] = True\\n currentMap['content'] = globalMap.get('ANAP').get('abstractTypes')\\n\\n # Role AnalysisProfile.marksColor\\n currentMap = {}\\n contentMap['marksColor'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00028'] = currentMap\\n loadMaps['ANAP.AnalysisProfile.marksColor'] = currentMap\\n currentMap['tag'] = 'ANAP.AnalysisProfile.marksColor'\\n currentMap['type'] = 'link'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00028'\\n currentMap['name'] = 'marksColor'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 0\\n currentMap['implSkip'] = True\\n currentMap['copyOverride'] = True\\n\\n # Role AnalysisProfile.refExpProfiles\\n currentMap = {}\\n contentMap['refExpProfiles'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00024'] = currentMap\\n loadMaps['ANAP.AnalysisProfile.refExpProfiles'] = currentMap\\n currentMap['tag'] = 'ANAP.AnalysisProfile.refExpProfiles'\\n currentMap['type'] = 'child'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00024'\\n currentMap['name'] = 'refExpProfiles'\\n currentMap['hicard'] = -1\\n currentMap['locard'] = 0\\n currentMap['eType'] = 'cplx'\\n currentMap['implSkip'] = True\\n currentMap['content'] = globalMap.get('ANAP').get('abstractTypes')\\n\\n # Role AnalysisProfile.residueProfiles\\n currentMap = {}\\n contentMap['residueProfiles'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00020'] = currentMap\\n loadMaps['ANAP.AnalysisProfile.residueProfiles'] = currentMap\\n currentMap['tag'] = 'ANAP.AnalysisProfile.residueProfiles'\\n currentMap['type'] = 'child'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00020'\\n currentMap['name'] = 'residueProfiles'\\n currentMap['hicard'] = -1\\n currentMap['locard'] = 0\\n currentMap['eType'] = 'cplx'\\n currentMap['implSkip'] = True\\n currentMap['content'] = globalMap.get('ANAP').get('abstractTypes')\\n\\n # Role AnalysisProfile.rulersColor\\n currentMap = {}\\n contentMap['rulersColor'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00027'] = currentMap\\n loadMaps['ANAP.AnalysisProfile.rulersColor'] = currentMap\\n currentMap['tag'] = 'ANAP.AnalysisProfile.rulersColor'\\n currentMap['type'] = 'link'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00027'\\n currentMap['name'] = 'rulersColor'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 0\\n currentMap['implSkip'] = True\\n currentMap['copyOverride'] = True\\n # End of AnalysisProfile\\n\\n currentMap = abstractTypes.get('AnalysisProfile')\\n aList = ['createdBy', 'graphicsHandler', 'guid', 'isModifiable', 'lastUnlockedBy', 'name', 'panView', 'sendBugReports', 'transientDialogs', 'transientWindows', 'twoCharShortcuts', 'useCrosshair', 'useGlobalShortcuts', 'userEmail', 'webBrowser']\\n currentMap['headerAttrs'] = aList\\n aList = ['bgColor', 'fgColor', 'font', 'userName', 'userOrganisation', 'marksColor', 'rulersColor']\\n currentMap['simpleAttrs'] = aList\\n aList = ['residueProfiles', 'refExpProfiles', 'macros', 'colorSchemes', 'access', 'applicationData']\\n currentMap['cplxAttrs'] = aList\\n aList = ['colorSchemes', 'macros', 'refExpProfiles', 'residueProfiles']\\n currentMap['children'] = aList\\n\\n # Class ColorScheme\\n currentMap = {}\\n abstractTypes['ColorScheme'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-08-11:06:22_00002'] = currentMap\\n loadMaps['ANAP.ColorScheme'] = currentMap\\n currentMap['tag'] = 'ANAP.ColorScheme'\\n currentMap['type'] = 'class'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-08-11:06:22_00002'\\n currentMap['eType'] = 'cplx'\\n currentMap['fromParent'] = 'colorSchemes'\\n currentMap['objkey'] = 'name'\\n currentMap['class'] = ccpnmr.api.AnalysisProfile.ColorScheme\\n contentMap = {}\\n currentMap['content'] = contentMap\\n\\n # Attribute ColorScheme.applicationData\\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\\n\\n # Attribute ColorScheme.colors\\n currentMap = {}\\n contentMap['colors'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00043'] = currentMap\\n loadMaps['ANAP.ColorScheme.colors'] = currentMap\\n currentMap['tag'] = 'ANAP.ColorScheme.colors'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00043'\\n currentMap['name'] = 'colors'\\n currentMap['hicard'] = -1\\n currentMap['locard'] = 0\\n currentMap['eType'] = 'cplx'\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-05-05-15:12:50_00007')\\n\\n # Attribute ColorScheme.name\\n currentMap = {}\\n contentMap['name'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-15:11:12_00007'] = currentMap\\n loadMaps['ANAP.ColorScheme.name'] = currentMap\\n currentMap['tag'] = 'ANAP.ColorScheme.name'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-15:11:12_00007'\\n currentMap['name'] = 'name'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['eType'] = 'cplx'\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033')\\n\\n # Role ColorScheme.access\\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\\n # End of ColorScheme\\n\\n currentMap = abstractTypes.get('ColorScheme')\\n aList = ['colors', 'name']\\n currentMap['simpleAttrs'] = aList\\n aList = ['access', 'applicationData']\\n currentMap['cplxAttrs'] = aList\\n\\n # Class Macro\\n currentMap = {}\\n abstractTypes['Macro'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-08-11:06:22_00001'] = currentMap\\n loadMaps['ANAP.Macro'] = currentMap\\n currentMap['tag'] = 'ANAP.Macro'\\n currentMap['type'] = 'class'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-08-11:06:22_00001'\\n currentMap['eType'] = 'cplx'\\n currentMap['fromParent'] = 'macros'\\n currentMap['objkey'] = 'serial'\\n currentMap['class'] = ccpnmr.api.AnalysisProfile.Macro\\n contentMap = {}\\n currentMap['content'] = contentMap\\n\\n # Attribute Macro.applicationData\\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\\n\\n # Attribute Macro.details\\n currentMap = {}\\n contentMap['details'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-15:11:12_00005'] = currentMap\\n loadMaps['ANAP.Macro.details'] = currentMap\\n currentMap['tag'] = 'ANAP.Macro.details'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-15:11:12_00005'\\n currentMap['name'] = 'details'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 0\\n currentMap['eType'] = 'cplx'\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00036')\\n\\n # Attribute Macro.function\\n currentMap = {}\\n contentMap['function'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-15:11:12_00002'] = currentMap\\n loadMaps['ANAP.Macro.function'] = currentMap\\n currentMap['tag'] = 'ANAP.Macro.function'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-15:11:12_00002'\\n currentMap['name'] = 'function'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00037')\\n\\n # Attribute Macro.isInMenu\\n currentMap = {}\\n contentMap['isInMenu'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-09-29-13:48:16_00005'] = currentMap\\n loadMaps['ANAP.Macro.isInMenu'] = currentMap\\n currentMap['tag'] = 'ANAP.Macro.isInMenu'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-09-29-13:48:16_00005'\\n currentMap['name'] = 'isInMenu'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['default'] = False\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00028')\\n\\n # Attribute Macro.isInMouseMenu\\n currentMap = {}\\n contentMap['isInMouseMenu'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-09-29-13:48:16_00006'] = currentMap\\n loadMaps['ANAP.Macro.isInMouseMenu'] = currentMap\\n currentMap['tag'] = 'ANAP.Macro.isInMouseMenu'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-09-29-13:48:16_00006'\\n currentMap['name'] = 'isInMouseMenu'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['default'] = False\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00028')\\n\\n # Attribute Macro.module\\n currentMap = {}\\n contentMap['module'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-15:11:12_00003'] = currentMap\\n loadMaps['ANAP.Macro.module'] = currentMap\\n currentMap['tag'] = 'ANAP.Macro.module'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-15:11:12_00003'\\n currentMap['name'] = 'module'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00037')\\n\\n # Attribute Macro.name\\n currentMap = {}\\n contentMap['name'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-15:11:10_00001'] = currentMap\\n loadMaps['ANAP.Macro.name'] = currentMap\\n currentMap['tag'] = 'ANAP.Macro.name'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-15:11:10_00001'\\n currentMap['name'] = 'name'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['eType'] = 'cplx'\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033')\\n\\n # Attribute Macro.ordering\\n currentMap = {}\\n contentMap['ordering'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-15:11:12_00004'] = currentMap\\n loadMaps['ANAP.Macro.ordering'] = currentMap\\n currentMap['tag'] = 'ANAP.Macro.ordering'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-15:11:12_00004'\\n currentMap['name'] = 'ordering'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['proc'] = 'direct'\\n currentMap['default'] = 0\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00032')\\n\\n # Attribute Macro.path\\n currentMap = {}\\n contentMap['path'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-15:11:12_00001'] = currentMap\\n loadMaps['ANAP.Macro.path'] = currentMap\\n currentMap['tag'] = 'ANAP.Macro.path'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-15:11:12_00001'\\n currentMap['name'] = 'path'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 0\\n currentMap['eType'] = 'cplx'\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:54_00003')\\n\\n # Attribute Macro.serial\\n currentMap = {}\\n contentMap['serial'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-15:11:09_00001'] = currentMap\\n loadMaps['ANAP.Macro.serial'] = currentMap\\n currentMap['tag'] = 'ANAP.Macro.serial'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-15:11:09_00001'\\n currentMap['name'] = 'serial'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00032')\\n\\n # Attribute Macro.shortcut\\n currentMap = {}\\n contentMap['shortcut'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-15:11:12_00006'] = currentMap\\n loadMaps['ANAP.Macro.shortcut'] = currentMap\\n currentMap['tag'] = 'ANAP.Macro.shortcut'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-15:11:12_00006'\\n currentMap['name'] = 'shortcut'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 0\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00037')\\n\\n # Role Macro.access\\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\\n # End of Macro\\n\\n currentMap = abstractTypes.get('Macro')\\n aList = ['function', 'isInMenu', 'isInMouseMenu', 'module', 'ordering', 'serial', 'shortcut']\\n currentMap['headerAttrs'] = aList\\n aList = ['details', 'name', 'path']\\n currentMap['simpleAttrs'] = aList\\n aList = ['access', 'applicationData']\\n currentMap['cplxAttrs'] = aList\\n\\n # Class RefExpProfile\\n currentMap = {}\\n abstractTypes['RefExpProfile'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:50_00006'] = currentMap\\n loadMaps['ANAP.RefExpProfile'] = currentMap\\n currentMap['tag'] = 'ANAP.RefExpProfile'\\n currentMap['type'] = 'class'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:50_00006'\\n currentMap['eType'] = 'cplx'\\n currentMap['fromParent'] = 'refExpProfiles'\\n currentMap['objkey'] = 'name'\\n currentMap['class'] = ccpnmr.api.AnalysisProfile.RefExpProfile\\n contentMap = {}\\n currentMap['content'] = contentMap\\n\\n # Attribute RefExpProfile.applicationData\\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\\n\\n # Attribute RefExpProfile.name\\n currentMap = {}\\n contentMap['name'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00046'] = currentMap\\n loadMaps['ANAP.RefExpProfile.name'] = currentMap\\n currentMap['tag'] = 'ANAP.RefExpProfile.name'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00046'\\n currentMap['name'] = 'name'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00037')\\n\\n # Attribute RefExpProfile.peakSymbolColors\\n currentMap = {}\\n contentMap['peakSymbolColors'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00048'] = currentMap\\n loadMaps['ANAP.RefExpProfile.peakSymbolColors'] = currentMap\\n currentMap['tag'] = 'ANAP.RefExpProfile.peakSymbolColors'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00048'\\n currentMap['name'] = 'peakSymbolColors'\\n currentMap['hicard'] = -1\\n currentMap['locard'] = 0\\n currentMap['eType'] = 'cplx'\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-05-05-15:12:50_00007')\\n\\n # Attribute RefExpProfile.peakTextColors\\n currentMap = {}\\n contentMap['peakTextColors'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00049'] = currentMap\\n loadMaps['ANAP.RefExpProfile.peakTextColors'] = currentMap\\n currentMap['tag'] = 'ANAP.RefExpProfile.peakTextColors'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00049'\\n currentMap['name'] = 'peakTextColors'\\n currentMap['hicard'] = -1\\n currentMap['locard'] = 0\\n currentMap['eType'] = 'cplx'\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-05-05-15:12:50_00007')\\n\\n # Attribute RefExpProfile.refExpNames\\n currentMap = {}\\n contentMap['refExpNames'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00047'] = currentMap\\n loadMaps['ANAP.RefExpProfile.refExpNames'] = currentMap\\n currentMap['tag'] = 'ANAP.RefExpProfile.refExpNames'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00047'\\n currentMap['name'] = 'refExpNames'\\n currentMap['hicard'] = -1\\n currentMap['locard'] = 0\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00037')\\n\\n # Role RefExpProfile.access\\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\\n\\n # Role RefExpProfile.negColorSchemes\\n currentMap = {}\\n contentMap['negColorSchemes'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00045'] = currentMap\\n loadMaps['ANAP.RefExpProfile.negColorSchemes'] = currentMap\\n currentMap['tag'] = 'ANAP.RefExpProfile.negColorSchemes'\\n currentMap['type'] = 'link'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00045'\\n currentMap['name'] = 'negColorSchemes'\\n currentMap['hicard'] = -1\\n currentMap['locard'] = 0\\n currentMap['copyOverride'] = True\\n\\n # Role RefExpProfile.posColorSchemes\\n currentMap = {}\\n contentMap['posColorSchemes'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00041'] = currentMap\\n loadMaps['ANAP.RefExpProfile.posColorSchemes'] = currentMap\\n currentMap['tag'] = 'ANAP.RefExpProfile.posColorSchemes'\\n currentMap['type'] = 'link'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00041'\\n currentMap['name'] = 'posColorSchemes'\\n currentMap['hicard'] = -1\\n currentMap['locard'] = 0\\n currentMap['copyOverride'] = True\\n # End of RefExpProfile\\n\\n currentMap = abstractTypes.get('RefExpProfile')\\n aList = ['name']\\n currentMap['headerAttrs'] = aList\\n aList = ['peakSymbolColors', 'peakTextColors', 'refExpNames', 'negColorSchemes', 'posColorSchemes']\\n currentMap['simpleAttrs'] = aList\\n aList = ['access', 'applicationData']\\n currentMap['cplxAttrs'] = aList\\n\\n # Class ResidueProfile\\n currentMap = {}\\n abstractTypes['ResidueProfile'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:50_00005'] = currentMap\\n loadMaps['ANAP.ResidueProfile'] = currentMap\\n currentMap['tag'] = 'ANAP.ResidueProfile'\\n currentMap['type'] = 'class'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:50_00005'\\n currentMap['eType'] = 'cplx'\\n currentMap['fromParent'] = 'residueProfiles'\\n currentMap['class'] = ccpnmr.api.AnalysisProfile.ResidueProfile\\n contentMap = {}\\n currentMap['content'] = contentMap\\n\\n # Attribute ResidueProfile.applicationData\\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\\n\\n # Attribute ResidueProfile.ccpCode\\n currentMap = {}\\n contentMap['ccpCode'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00051'] = currentMap\\n loadMaps['ANAP.ResidueProfile.ccpCode'] = currentMap\\n currentMap['tag'] = 'ANAP.ResidueProfile.ccpCode'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00051'\\n currentMap['name'] = 'ccpCode'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2007-09-12-18:31:28_00003')\\n\\n # Attribute ResidueProfile.guiName\\n currentMap = {}\\n contentMap['guiName'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00052'] = currentMap\\n loadMaps['ANAP.ResidueProfile.guiName'] = currentMap\\n currentMap['tag'] = 'ANAP.ResidueProfile.guiName'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00052'\\n currentMap['name'] = 'guiName'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2007-09-12-18:31:28_00003')\\n\\n # Attribute ResidueProfile.molType\\n currentMap = {}\\n contentMap['molType'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00050'] = currentMap\\n loadMaps['ANAP.ResidueProfile.molType'] = currentMap\\n currentMap['tag'] = 'ANAP.ResidueProfile.molType'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00050'\\n currentMap['name'] = 'molType'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:52_00024')\\n\\n # Role ResidueProfile.access\\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\\n # End of ResidueProfile\\n\\n currentMap = abstractTypes.get('ResidueProfile')\\n aList = ['ccpCode', 'guiName', 'molType']\\n currentMap['headerAttrs'] = aList\\n aList = ['access', 'applicationData']\\n currentMap['cplxAttrs'] = aList\\n\\n # Out-of-package link to AnalysisProfile\\n currentMap = {}\\n exolinks['AnalysisProfile'] = currentMap\\n loadMaps['ANAP.exo-AnalysisProfile'] = currentMap\\n currentMap['tag'] = 'ANAP.exo-AnalysisProfile'\\n currentMap['type'] = 'exo'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:50_00004'\\n currentMap['name'] = 'AnalysisProfile'\\n currentMap['eType'] = 'cplx'\\n currentMap['class'] = ccpnmr.api.AnalysisProfile.AnalysisProfile\\n aList = list()\\n currentMap['keyMaps'] = aList\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\\n\\n # Out-of-package link to ColorScheme\\n currentMap = {}\\n exolinks['ColorScheme'] = currentMap\\n loadMaps['ANAP.exo-ColorScheme'] = currentMap\\n currentMap['tag'] = 'ANAP.exo-ColorScheme'\\n currentMap['type'] = 'exo'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-08-11:06:22_00002'\\n currentMap['name'] = 'ColorScheme'\\n currentMap['eType'] = 'cplx'\\n currentMap['class'] = ccpnmr.api.AnalysisProfile.ColorScheme\\n aList = list()\\n currentMap['keyMaps'] = aList\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033'))\\n\\n # Out-of-package link to Macro\\n currentMap = {}\\n exolinks['Macro'] = currentMap\\n loadMaps['ANAP.exo-Macro'] = currentMap\\n currentMap['tag'] = 'ANAP.exo-Macro'\\n currentMap['type'] = 'exo'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-08-11:06:22_00001'\\n currentMap['name'] = 'Macro'\\n currentMap['eType'] = 'cplx'\\n currentMap['class'] = ccpnmr.api.AnalysisProfile.Macro\\n aList = list()\\n currentMap['keyMaps'] = aList\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00032'))\\n\\n # Out-of-package link to RefExpProfile\\n currentMap = {}\\n exolinks['RefExpProfile'] = currentMap\\n loadMaps['ANAP.exo-RefExpProfile'] = currentMap\\n currentMap['tag'] = 'ANAP.exo-RefExpProfile'\\n currentMap['type'] = 'exo'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:50_00006'\\n currentMap['name'] = 'RefExpProfile'\\n currentMap['eType'] = 'cplx'\\n currentMap['class'] = ccpnmr.api.AnalysisProfile.RefExpProfile\\n aList = list()\\n currentMap['keyMaps'] = aList\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00037'))\\n\\n # Out-of-package link to ResidueProfile\\n currentMap = {}\\n exolinks['ResidueProfile'] = currentMap\\n loadMaps['ANAP.exo-ResidueProfile'] = currentMap\\n currentMap['tag'] = 'ANAP.exo-ResidueProfile'\\n currentMap['type'] = 'exo'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:50_00005'\\n currentMap['name'] = 'ResidueProfile'\\n currentMap['eType'] = 'cplx'\\n currentMap['class'] = ccpnmr.api.AnalysisProfile.ResidueProfile\\n aList = list()\\n currentMap['keyMaps'] = aList\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:52_00024'))\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2007-09-12-18:31:28_00003'))\",\n \"def makeMapping(globalMap):\\n \\n from memops.xml.Implementation import bool2str, str2bool\\n\\n # Set up top level dictionaries\\n loadMaps = globalMap.get('loadMaps')\\n mapsByGuid = globalMap.get('mapsByGuid')\\n\\n abstractTypes = globalMap.get('ACCO').get('abstractTypes')\\n exolinks = globalMap.get('ACCO').get('exolinks')\\n\\n # Class AccessControlStore\\n currentMap = {}\\n abstractTypes['AccessControlStore'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-09-04-17:18:10_00001'] = currentMap\\n loadMaps['ACCO.AccessControlStore'] = currentMap\\n currentMap['tag'] = 'ACCO.AccessControlStore'\\n currentMap['type'] = 'class'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-09-04-17:18:10_00001'\\n currentMap['eType'] = 'cplx'\\n currentMap['fromParent'] = 'accessControlStores'\\n currentMap['isTop'] = True\\n currentMap['objkey'] = 'name'\\n currentMap['class'] = memops.api.AccessControl.AccessControlStore\\n contentMap = {}\\n currentMap['content'] = contentMap\\n\\n # Attribute AccessControlStore.applicationData\\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\\n\\n # Attribute AccessControlStore.createdBy\\n contentMap['createdBy'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:00:59_00002__www.ccpn.ac.uk_Fogh_2007-10-03-14:53:27_00001__www.ccpn.ac.uk_Fogh_2006-09-14-16:28:57_00002')\\n\\n # Attribute AccessControlStore.guid\\n contentMap['guid'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:26_00002')\\n\\n # Attribute AccessControlStore.isModifiable\\n contentMap['isModifiable'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-17-14:16:26_00010__www.ccpn.ac.uk_Fogh_2007-10-03-14:53:27_00001__www.ccpn.ac.uk_Fogh_2006-09-14-16:28:57_00002')\\n\\n # Attribute AccessControlStore.lastUnlockedBy\\n contentMap['lastUnlockedBy'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:00:59_00003__www.ccpn.ac.uk_Fogh_2007-10-03-14:53:27_00001__www.ccpn.ac.uk_Fogh_2006-09-14-16:28:57_00002')\\n\\n # Attribute AccessControlStore.name\\n currentMap = {}\\n contentMap['name'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-09-04-17:21:38_00006'] = currentMap\\n loadMaps['ACCO.AccessControlStore.name'] = currentMap\\n currentMap['tag'] = 'ACCO.AccessControlStore.name'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-09-04-17:21:38_00006'\\n currentMap['name'] = 'name'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['eType'] = 'cplx'\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033')\\n\\n # Role AccessControlStore.access\\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\\n\\n # Role AccessControlStore.accessObjects\\n currentMap = {}\\n contentMap['accessObjects'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00013'] = currentMap\\n loadMaps['ACCO.AccessControlStore.accessObjects'] = currentMap\\n currentMap['tag'] = 'ACCO.AccessControlStore.accessObjects'\\n currentMap['type'] = 'child'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00013'\\n currentMap['name'] = 'accessObjects'\\n currentMap['hicard'] = -1\\n currentMap['locard'] = 0\\n currentMap['eType'] = 'cplx'\\n currentMap['implSkip'] = True\\n currentMap['content'] = globalMap.get('ACCO').get('abstractTypes')\\n\\n # Role AccessControlStore.userGroups\\n currentMap = {}\\n contentMap['userGroups'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-09-04-17:21:38_00003'] = currentMap\\n loadMaps['ACCO.AccessControlStore.userGroups'] = currentMap\\n currentMap['tag'] = 'ACCO.AccessControlStore.userGroups'\\n currentMap['type'] = 'child'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-09-04-17:21:38_00003'\\n currentMap['name'] = 'userGroups'\\n currentMap['hicard'] = -1\\n currentMap['locard'] = 0\\n currentMap['eType'] = 'cplx'\\n currentMap['implSkip'] = True\\n currentMap['content'] = globalMap.get('ACCO').get('abstractTypes')\\n\\n # Role AccessControlStore.users\\n currentMap = {}\\n contentMap['users'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-09-04-17:21:38_00001'] = currentMap\\n loadMaps['ACCO.AccessControlStore.users'] = currentMap\\n currentMap['tag'] = 'ACCO.AccessControlStore.users'\\n currentMap['type'] = 'child'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-09-04-17:21:38_00001'\\n currentMap['name'] = 'users'\\n currentMap['hicard'] = -1\\n currentMap['locard'] = 0\\n currentMap['eType'] = 'cplx'\\n currentMap['implSkip'] = True\\n currentMap['content'] = globalMap.get('ACCO').get('abstractTypes')\\n # End of AccessControlStore\\n\\n currentMap = abstractTypes.get('AccessControlStore')\\n aList = ['createdBy', 'guid', 'isModifiable', 'lastUnlockedBy']\\n currentMap['headerAttrs'] = aList\\n aList = ['name']\\n currentMap['simpleAttrs'] = aList\\n aList = ['users', 'userGroups', 'accessObjects', 'access', 'applicationData']\\n currentMap['cplxAttrs'] = aList\\n aList = ['accessObjects', 'userGroups', 'users']\\n currentMap['children'] = aList\\n\\n # Class AccessObject\\n currentMap = {}\\n abstractTypes['AccessObject'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-14:22:54_00014'] = currentMap\\n loadMaps['ACCO.AccessObject'] = currentMap\\n currentMap['tag'] = 'ACCO.AccessObject'\\n currentMap['type'] = 'class'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:54_00014'\\n currentMap['eType'] = 'cplx'\\n currentMap['fromParent'] = 'accessObjects'\\n currentMap['objkey'] = 'name'\\n currentMap['class'] = memops.api.AccessControl.AccessObject\\n contentMap = {}\\n currentMap['content'] = contentMap\\n\\n # Attribute AccessObject.applicationData\\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\\n\\n # Attribute AccessObject.description\\n currentMap = {}\\n contentMap['description'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-14:16:27_00005'] = currentMap\\n loadMaps['ACCO.AccessObject.description'] = currentMap\\n currentMap['tag'] = 'ACCO.AccessObject.description'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-14:16:27_00005'\\n currentMap['name'] = 'description'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 0\\n currentMap['eType'] = 'cplx'\\n currentMap['proc'] = 'direct'\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00035')\\n\\n # Attribute AccessObject.name\\n currentMap = {}\\n contentMap['name'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-14:16:27_00004'] = currentMap\\n loadMaps['ACCO.AccessObject.name'] = currentMap\\n currentMap['tag'] = 'ACCO.AccessObject.name'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-14:16:27_00004'\\n currentMap['name'] = 'name'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['eType'] = 'cplx'\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033')\\n\\n # Role AccessObject.access\\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\\n\\n # Role AccessObject.permissions\\n currentMap = {}\\n contentMap['permissions'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-14:16:27_00001'] = currentMap\\n loadMaps['ACCO.AccessObject.permissions'] = currentMap\\n currentMap['tag'] = 'ACCO.AccessObject.permissions'\\n currentMap['type'] = 'child'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-14:16:27_00001'\\n currentMap['name'] = 'permissions'\\n currentMap['hicard'] = -1\\n currentMap['locard'] = 0\\n currentMap['eType'] = 'cplx'\\n currentMap['content'] = globalMap.get('ACCO').get('abstractTypes')\\n # End of AccessObject\\n\\n currentMap = abstractTypes.get('AccessObject')\\n aList = ['description', 'name']\\n currentMap['simpleAttrs'] = aList\\n aList = ['permissions', 'access', 'applicationData']\\n currentMap['cplxAttrs'] = aList\\n aList = ['permissions']\\n currentMap['children'] = aList\\n\\n # Class Permission\\n currentMap = {}\\n abstractTypes['Permission'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-14:22:54_00018'] = currentMap\\n loadMaps['ACCO.Permission'] = currentMap\\n currentMap['tag'] = 'ACCO.Permission'\\n currentMap['type'] = 'class'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:54_00018'\\n currentMap['eType'] = 'cplx'\\n currentMap['fromParent'] = 'permissions'\\n currentMap['class'] = memops.api.AccessControl.Permission\\n contentMap = {}\\n currentMap['content'] = contentMap\\n\\n # Attribute Permission.applicationData\\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\\n\\n # Attribute Permission.opType\\n currentMap = {}\\n contentMap['opType'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-14:16:23_00021'] = currentMap\\n loadMaps['ACCO.Permission.opType'] = currentMap\\n currentMap['tag'] = 'ACCO.Permission.opType'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-14:16:23_00021'\\n currentMap['name'] = 'opType'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['default'] = 'any'\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00037')\\n\\n # Attribute Permission.permission\\n currentMap = {}\\n contentMap['permission'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-14:16:23_00023'] = currentMap\\n loadMaps['ACCO.Permission.permission'] = currentMap\\n currentMap['tag'] = 'ACCO.Permission.permission'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-14:16:23_00023'\\n currentMap['name'] = 'permission'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['default'] = True\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00028')\\n\\n # Attribute Permission.permissionClass\\n currentMap = {}\\n contentMap['permissionClass'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-14:16:23_00020'] = currentMap\\n loadMaps['ACCO.Permission.permissionClass'] = currentMap\\n currentMap['tag'] = 'ACCO.Permission.permissionClass'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-14:16:23_00020'\\n currentMap['name'] = 'permissionClass'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['default'] = 'any'\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00037')\\n\\n # Attribute Permission.roleName\\n currentMap = {}\\n contentMap['roleName'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-14:16:23_00022'] = currentMap\\n loadMaps['ACCO.Permission.roleName'] = currentMap\\n currentMap['tag'] = 'ACCO.Permission.roleName'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-14:16:23_00022'\\n currentMap['name'] = 'roleName'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['default'] = 'any'\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00037')\\n\\n # Role Permission.access\\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\\n\\n # Role Permission.userGroup\\n currentMap = {}\\n contentMap['userGroup'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-14:16:23_00016'] = currentMap\\n loadMaps['ACCO.Permission.userGroup'] = currentMap\\n currentMap['tag'] = 'ACCO.Permission.userGroup'\\n currentMap['type'] = 'link'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-14:16:23_00016'\\n currentMap['name'] = 'userGroup'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['copyOverride'] = True\\n # End of Permission\\n\\n currentMap = abstractTypes.get('Permission')\\n aList = ['opType', 'permission', 'permissionClass', 'roleName']\\n currentMap['headerAttrs'] = aList\\n aList = ['userGroup']\\n currentMap['optLinks'] = aList\\n aList = ['access', 'applicationData']\\n currentMap['cplxAttrs'] = aList\\n\\n # Class User\\n currentMap = {}\\n abstractTypes['User'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-14:22:54_00017'] = currentMap\\n loadMaps['ACCO.User'] = currentMap\\n currentMap['tag'] = 'ACCO.User'\\n currentMap['type'] = 'class'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:54_00017'\\n currentMap['eType'] = 'cplx'\\n currentMap['fromParent'] = 'users'\\n currentMap['objkey'] = 'name'\\n currentMap['class'] = memops.api.AccessControl.User\\n contentMap = {}\\n currentMap['content'] = contentMap\\n\\n # Attribute User.applicationData\\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\\n\\n # Attribute User.isSuperuser\\n currentMap = {}\\n contentMap['isSuperuser'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2010-05-06-13:30:17_00060'] = currentMap\\n loadMaps['ACCO.User.isSuperuser'] = currentMap\\n currentMap['tag'] = 'ACCO.User.isSuperuser'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2010-05-06-13:30:17_00060'\\n currentMap['name'] = 'isSuperuser'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['default'] = False\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00028')\\n\\n # Attribute User.name\\n currentMap = {}\\n contentMap['name'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-14:16:23_00019'] = currentMap\\n loadMaps['ACCO.User.name'] = currentMap\\n currentMap['tag'] = 'ACCO.User.name'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-14:16:23_00019'\\n currentMap['name'] = 'name'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['eType'] = 'cplx'\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033')\\n\\n # Attribute User.passwordHashed\\n currentMap = {}\\n contentMap['passwordHashed'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2009-08-19-17:31:11_00005'] = currentMap\\n loadMaps['ACCO.User.passwordHashed'] = currentMap\\n currentMap['tag'] = 'ACCO.User.passwordHashed'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2009-08-19-17:31:11_00005'\\n currentMap['name'] = 'passwordHashed'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 0\\n currentMap['eType'] = 'cplx'\\n currentMap['proc'] = 'direct'\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00035')\\n\\n # Role User.access\\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\\n\\n # Role User.ledGroups\\n currentMap = {}\\n contentMap['ledGroups'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-14:16:23_00014'] = currentMap\\n loadMaps['ACCO.User.ledGroups'] = currentMap\\n currentMap['tag'] = 'ACCO.User.ledGroups'\\n currentMap['type'] = 'link'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-14:16:23_00014'\\n currentMap['name'] = 'ledGroups'\\n currentMap['hicard'] = -1\\n currentMap['locard'] = 0\\n currentMap['copyOverride'] = True\\n\\n # Role User.userGroups\\n currentMap = {}\\n contentMap['userGroups'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-14:16:23_00012'] = currentMap\\n loadMaps['ACCO.User.userGroups'] = currentMap\\n currentMap['tag'] = 'ACCO.User.userGroups'\\n currentMap['type'] = 'link'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-14:16:23_00012'\\n currentMap['name'] = 'userGroups'\\n currentMap['hicard'] = -1\\n currentMap['locard'] = 0\\n currentMap['copyOverride'] = True\\n # End of User\\n\\n currentMap = abstractTypes.get('User')\\n aList = ['isSuperuser']\\n currentMap['headerAttrs'] = aList\\n aList = ['name', 'passwordHashed', 'ledGroups', 'userGroups']\\n currentMap['simpleAttrs'] = aList\\n aList = ['access', 'applicationData']\\n currentMap['cplxAttrs'] = aList\\n\\n # Class UserGroup\\n currentMap = {}\\n abstractTypes['UserGroup'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-14:22:54_00016'] = currentMap\\n loadMaps['ACCO.UserGroup'] = currentMap\\n currentMap['tag'] = 'ACCO.UserGroup'\\n currentMap['type'] = 'class'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:54_00016'\\n currentMap['eType'] = 'cplx'\\n currentMap['fromParent'] = 'userGroups'\\n currentMap['objkey'] = 'name'\\n currentMap['class'] = memops.api.AccessControl.UserGroup\\n contentMap = {}\\n currentMap['content'] = contentMap\\n\\n # Attribute UserGroup.applicationData\\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\\n\\n # Attribute UserGroup.name\\n currentMap = {}\\n contentMap['name'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-14:16:23_00018'] = currentMap\\n loadMaps['ACCO.UserGroup.name'] = currentMap\\n currentMap['tag'] = 'ACCO.UserGroup.name'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-14:16:23_00018'\\n currentMap['name'] = 'name'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['eType'] = 'cplx'\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033')\\n\\n # Role UserGroup.access\\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\\n\\n # Role UserGroup.leaders\\n currentMap = {}\\n contentMap['leaders'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-14:16:23_00015'] = currentMap\\n loadMaps['ACCO.UserGroup.leaders'] = currentMap\\n currentMap['tag'] = 'ACCO.UserGroup.leaders'\\n currentMap['type'] = 'link'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-14:16:23_00015'\\n currentMap['name'] = 'leaders'\\n currentMap['hicard'] = -1\\n currentMap['locard'] = 0\\n currentMap['copyOverride'] = True\\n\\n # Role UserGroup.members\\n currentMap = {}\\n contentMap['members'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-14:16:23_00013'] = currentMap\\n loadMaps['ACCO.UserGroup.members'] = currentMap\\n currentMap['tag'] = 'ACCO.UserGroup.members'\\n currentMap['type'] = 'link'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-14:16:23_00013'\\n currentMap['name'] = 'members'\\n currentMap['hicard'] = -1\\n currentMap['locard'] = 0\\n currentMap['copyOverride'] = False\\n\\n # Role UserGroup.permissions\\n currentMap = {}\\n contentMap['permissions'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-14:16:23_00017'] = currentMap\\n loadMaps['ACCO.UserGroup.permissions'] = currentMap\\n currentMap['tag'] = 'ACCO.UserGroup.permissions'\\n currentMap['type'] = 'link'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-14:16:23_00017'\\n currentMap['name'] = 'permissions'\\n currentMap['hicard'] = -1\\n currentMap['locard'] = 0\\n currentMap['copyOverride'] = False\\n # End of UserGroup\\n\\n currentMap = abstractTypes.get('UserGroup')\\n aList = ['name', 'leaders', 'members', 'permissions']\\n currentMap['simpleAttrs'] = aList\\n aList = ['access', 'applicationData']\\n currentMap['cplxAttrs'] = aList\\n\\n # Out-of-package link to AccessControlStore\\n currentMap = {}\\n exolinks['AccessControlStore'] = currentMap\\n loadMaps['ACCO.exo-AccessControlStore'] = currentMap\\n currentMap['tag'] = 'ACCO.exo-AccessControlStore'\\n currentMap['type'] = 'exo'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-09-04-17:18:10_00001'\\n currentMap['name'] = 'AccessControlStore'\\n currentMap['eType'] = 'cplx'\\n currentMap['class'] = memops.api.AccessControl.AccessControlStore\\n aList = list()\\n currentMap['keyMaps'] = aList\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\\n\\n # Out-of-package link to AccessObject\\n currentMap = {}\\n exolinks['AccessObject'] = currentMap\\n loadMaps['ACCO.exo-AccessObject'] = currentMap\\n currentMap['tag'] = 'ACCO.exo-AccessObject'\\n currentMap['type'] = 'exo'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:54_00014'\\n currentMap['name'] = 'AccessObject'\\n currentMap['eType'] = 'cplx'\\n currentMap['class'] = memops.api.AccessControl.AccessObject\\n aList = list()\\n currentMap['keyMaps'] = aList\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033'))\\n\\n # Out-of-package link to Permission\\n currentMap = {}\\n exolinks['Permission'] = currentMap\\n loadMaps['ACCO.exo-Permission'] = currentMap\\n currentMap['tag'] = 'ACCO.exo-Permission'\\n currentMap['type'] = 'exo'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:54_00018'\\n currentMap['name'] = 'Permission'\\n currentMap['eType'] = 'cplx'\\n currentMap['class'] = memops.api.AccessControl.Permission\\n aList = list()\\n currentMap['keyMaps'] = aList\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033'))\\n aList.append(globalMap.get('ACCO').get('exolinks'))\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00037'))\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00037'))\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00037'))\\n\\n # Out-of-package link to User\\n currentMap = {}\\n exolinks['User'] = currentMap\\n loadMaps['ACCO.exo-User'] = currentMap\\n currentMap['tag'] = 'ACCO.exo-User'\\n currentMap['type'] = 'exo'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:54_00017'\\n currentMap['name'] = 'User'\\n currentMap['eType'] = 'cplx'\\n currentMap['class'] = memops.api.AccessControl.User\\n aList = list()\\n currentMap['keyMaps'] = aList\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033'))\\n\\n # Out-of-package link to UserGroup\\n currentMap = {}\\n exolinks['UserGroup'] = currentMap\\n loadMaps['ACCO.exo-UserGroup'] = currentMap\\n currentMap['tag'] = 'ACCO.exo-UserGroup'\\n currentMap['type'] = 'exo'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:54_00016'\\n currentMap['name'] = 'UserGroup'\\n currentMap['eType'] = 'cplx'\\n currentMap['class'] = memops.api.AccessControl.UserGroup\\n aList = list()\\n currentMap['keyMaps'] = aList\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033'))\",\n \"def create_cell_face_map(dim, mesh_format):\\n if mesh_format == 'fenics':\\n create_cell_map(dim)\\n else:\\n eval('create_cell_face_map_' + str(dim) + 'D()')\",\n \"def ref_mapping_directory(tmpdir_factory):\\n basedir = tmpdir_factory.mktemp('data')\\n mapdir = basedir.mkdir('mappings')\\n\\n template = textwrap.dedent(\\\"\\\"\\\"\\n [ molecule ]\\n dummy_{0}\\n\\n [ from ]\\n {1}\\n\\n [ to ]\\n {2}\\n\\n [ atoms ]\\n 0 X1{0} A{0} B{0}\\n 1 X2{0} C{0} D{0}\\n \\\"\\\"\\\")\\n\\n mappings = collections.defaultdict(lambda: collections.defaultdict(dict))\\n\\n force_fields_from = ['ff{}'.format(i) for i in range(4)]\\n force_fields_to = force_fields_from + ['only_to']\\n force_fields_from = force_fields_from + ['only_from']\\n iterate_on = itertools.product(force_fields_from, force_fields_to, range(3))\\n for idx, (from_ff, to_ff, _) in enumerate(iterate_on):\\n mapfile = mapdir / 'file{}.map'.format(idx)\\n with open(str(mapfile), 'w') as outfile:\\n outfile.write(template.format(idx, from_ff, to_ff))\\n\\n mapping = {\\n (0, 'X1{}'.format(idx)): [(0, 'A{}'.format(idx)), (0, 'B{}'.format(idx))],\\n (0, 'X2{}'.format(idx)): [(0, 'C{}'.format(idx)), (0, 'D{}'.format(idx))],\\n }\\n weights = {\\n (0, 'A{}'.format(idx)): {(0, 'X1{}'.format(idx)): 0.5},\\n (0, 'B{}'.format(idx)): {(0, 'X1{}'.format(idx)): 0.5},\\n (0, 'C{}'.format(idx)): {(0, 'X2{}'.format(idx)): 0.5},\\n (0, 'D{}'.format(idx)): {(0, 'X2{}'.format(idx)): 0.5},\\n }\\n extra = []\\n mappings[from_ff][to_ff]['dummy_{}'.format(idx)] = (mapping, weights, extra)\\n\\n mappings = {from_ff: dict(to_ff) for from_ff, to_ff in mappings.items()}\\n\\n return Path(str(basedir)), mappings\",\n \"def _createMap(self):\\n width = self.map_size[0] * self.chunk_size\\n height = self.map_size[1] * self.chunk_size\\n map_array = np.zeros((height, width), dtype=float)\\n chunks = {}\\n clist = []\\n for i in range(0, self.map_size[0]*self.map_size[1]):\\n chunks[i+1] = Chunk(self)\\n chunk_array = np.asarray(list(chunks.keys()))\\n chunk_array.resize(self.map_size[0], self.map_size[1])\\n return map_array, chunk_array, chunks\",\n \"def components_map(self):\\r\\n raise NotImplementedError\",\n \"def receiverMapping():\",\n \"def getObjectMap(self,oldRefs):\\n objMap = {} #--objMap[cellId][oldIObj] = newIObj\\n #--Old cells\\n for oldCell in oldRefs.cells:\\n cellId = oldCell.getId()\\n newCell = self.cells_id.get(cellId)\\n #--Cell deleted?\\n if not newCell:\\n objMap[cellId] = -1\\n continue\\n cellObjMap = {}\\n newObjects = newCell.getObjects().list()\\n nextObjectIndex = {}\\n #--Old Objects\\n for oldObject in oldCell.getObjects().list():\\n (iMod,oldIObj,objId) = oldObject[:3]\\n if iMod: continue #--Skip mods to masters\\n #--New Objects\\n objIndex = nextObjectIndex.get(objId,0)\\n newIObj = -1 #--Delete by default\\n while objIndex < len(newObjects):\\n newObject = newObjects[objIndex]\\n objIndex += 1\\n if newObject[0]: continue #--Skip mods to masters\\n if newObject[2] == objId:\\n newIObj = newObject[1]\\n break\\n nextObjectIndex[objId] = objIndex\\n #--Obj map has changed?\\n if newIObj != oldIObj:\\n cellObjMap[oldIObj] = (newIObj,objId)\\n #--Save mapping for this cell?\\n if cellObjMap: objMap[cellId] = cellObjMap\\n #--Done\\n return objMap\",\n \"def __init__(self, mapping: Dict[int, int]):\\n self.mapping = mapping\",\n \"def geo2cell(geofile, posfile):\",\n \"def create_cell(self, cid):\\n self.cells[cid] = Cell()\",\n \"def map(self):\\r\\n pass\",\n \"def create_label_map():\\n\\n cnt = 1\\n tmp_array = np.array([10, 15, 25, 30, 40, 47, 57, 63, 69, 74, 81])\\n dictionary = dict()\\n dictionary[1] = 1\\n for idx, val in enumerate(tmp_array):\\n for j in range(cnt + 1, val):\\n dictionary[j] = int(idx + 2)\\n cnt = j\\n return dictionary\",\n \"def __build_map(self):\\n columns = []\\n\\n for i in range(self.__dimensions):\\n columns.append([])\\n\\n for i in range(self.__dimensions):\\n self.map.append(columns)\",\n \"def requires_mapping(self):\",\n \"def createMap(self):\\n map = {}\\n for rows in xrange(0,(size[1]/50)):\\n for columns in xrange(0,(size[0]/50)):\\n if rows == (size[1]/50)-1 or rows == 0 or columns== (size[0]/50)-1 or columns==0:\\n map.update({(rows,columns):\\\"block\\\"})\\n elif(rows%3 == 0):\\n map.update({(rows,columns):random.choice(map_options)})\\n else:\\n map.update({(rows,columns):random.choice(map_options[:1])})\\n\\n self.map = map\",\n \"def construct_new_ref_map(labels: np.ndarray, samples: list, ref_map_shape: list):\\n new_ref_map = np.zeros(shape=ref_map_shape) + BG_CLASS\\n for i, indexes in enumerate(samples):\\n new_ref_map[indexes[ROW_AXIS], indexes[COLUMNS_AXIS]] = labels[i]\\n return new_ref_map.astype(int)\",\n \"def edge_mapping(self):\\n ...\",\n \"def to_mapping(self, dim):\\n mim = cifti2.Cifti2MatrixIndicesMap([dim], 'CIFTI_INDEX_TYPE_LABELS')\\n for name, label, meta in zip(self.name, self.label, self.meta):\\n label_table = cifti2.Cifti2LabelTable()\\n for key, value in label.items():\\n label_table[key] = (value[0],) + tuple(value[1])\\n named_map = cifti2.Cifti2NamedMap(name, cifti2.Cifti2MetaData(meta), label_table)\\n mim.append(named_map)\\n return mim\",\n \"def __init__(self):\\n self.map = {} #key: val, val, idx\\n self.slot = []\",\n \"def map(self, app, node):\",\n \"def __init__(self):\\n self.map = {}\",\n \"def k_map(self):\\n\\t\\tt1 = time.time()\\n\\t\\tmapping_matrix = [] \\n\\t\\tfor index in self.mapping:\\n\\t\\t\\tvector = np.zeros(len(self.unique_char),dtype=float)\\n\\t\\t\\tvector[index] = 1.0\\n\\t\\t\\tmapping_matrix.append(vector)\\n\\t\\tprint(\\\"Time creating k map {:.3f} sec\\\".format(time.time()-t1))\\n\\t\\tself.mapping_matrix = mapping_matrix\\n\\t\\treturn mapping_matrix\",\n \"def __init__(self):\\n self.map = defaultdict(list)\",\n \"def _create_subscript_mapping():\\n # Create the normal and subscript digits list.\\n normal_digits = [i for i in range(10)]\\n subscript_digits = [chr(0x2080 + i) for i in range(10)]\\n\\n # Convert the normal digits to strings.\\n normal_digits = [str(i) for i in normal_digits]\\n\\n # Create a dict mapping the two.\\n return DefaultDictionary(zip(normal_digits, subscript_digits))\",\n \"def nodes_mapped(instance):\\n G, mapping = instance.network()\\n node_dict = instance.network_nodes_species()\\n\\n node_dict_mapped = {}\\n\\n for old_label, new_label in mapping.items():\\n for node, ammentity in node_dict.items():\\n if old_label == node:\\n node_dict_mapped[new_label] = ammentity\\n\\n return node_dict_mapped\",\n \"def __init__(self, mappings):\\n self.mappings = mappings\",\n \"def __init__(self):\\n self.map = [None] * 103\",\n \"def map(pointer, objfile=\\\"\\\"):\\n ei_class, ehdr = get_ehdr(pointer)\\n return map_inner(ei_class, ehdr, objfile)\",\n \"def create_task_dof_maps(field, cell_tasks, inter_facets, is_overlap=True,\\n use_expand_dofs=False, save_inter_regions=False,\\n output_dir=None):\\n domain = field.domain\\n cmesh = domain.cmesh\\n\\n if use_expand_dofs:\\n id_map = nm.zeros(field.n_nod * field.n_components, dtype=nm.uint32)\\n\\n else:\\n id_map = nm.zeros(field.n_nod, dtype=nm.uint32)\\n\\n def _get_dofs_region(field, region):\\n dofs = field.get_dofs_in_region(region)\\n if use_expand_dofs:\\n dofs = expand_dofs(dofs, field.n_components)\\n return dofs\\n\\n def _get_dofs_conn(field, conn):\\n dofs = nm.unique(conn)\\n if use_expand_dofs:\\n dofs = expand_dofs(dofs, field.n_components)\\n return dofs\\n\\n dof_maps = {}\\n count = 0\\n inter_count = 0\\n ocs = nm.zeros(0, dtype=nm.int32)\\n cell_parts = []\\n for ir, ntasks in ordered_iteritems(inter_facets):\\n cells = nm.where(cell_tasks == ir)[0].astype(nm.int32)\\n cell_parts.append(cells)\\n\\n cregion = Region.from_cells(cells, domain, name='task_%d' % ir)\\n domain.regions.append(cregion)\\n dofs = _get_dofs_region(field, cregion)\\n\\n rdof_map = dof_maps.setdefault(ir, [None, [], [], 0, 0])\\n inter_dofs = []\\n for ic, facets in ordered_iteritems(ntasks):\\n cdof_map = dof_maps.setdefault(ic, [None, [], [], 0, 0])\\n\\n name = 'inter_%d_%d' % (ir, ic)\\n ii = ir\\n\\n region = Region.from_facets(facets, domain, name,\\n parent=cregion.name)\\n region.update_shape()\\n\\n if save_inter_regions:\\n output_dir = output_dir if output_dir is not None else '.'\\n filename = os.path.join(output_dir, '%s.mesh' % name)\\n\\n aux = domain.mesh.from_region(region, domain.mesh,\\n is_surface=True)\\n aux.write(filename, io='auto')\\n\\n mask = nm.zeros((domain.mesh.n_nod, 1), dtype=nm.float64)\\n mask[region.vertices] = 1\\n out = {name : Struct(name=name, mode='vertex', data=mask)}\\n filename = os.path.join(output_dir, '%s.h5' % name)\\n domain.mesh.write(filename, out=out, io='auto')\\n\\n inter_dofs.append(_get_dofs_region(field, region))\\n\\n sd = FESurface('surface_data_%s' % region.name, region,\\n field.efaces, field.econn, field.region)\\n econn = sd.get_connectivity()\\n n_facet = econn.shape[0]\\n\\n ii2 = max(int(n_facet / 2), 1)\\n\\n dr = _get_dofs_conn(field, econn[:ii2])\\n ii = nm.where((id_map[dr] == 0))[0]\\n n_new = len(ii)\\n if n_new:\\n rdof_map[1].append(dr[ii])\\n rdof_map[3] += n_new\\n id_map[dr[ii]] = 1\\n inter_count += n_new\\n count += n_new\\n\\n if is_overlap:\\n ovs = cmesh.get_incident(0, region.facets[:ii2],\\n cmesh.tdim - 1)\\n ocs = cmesh.get_incident(cmesh.tdim, ovs, 0)\\n rdof_map[2].append(ocs.astype(nm.int32))\\n\\n dc = _get_dofs_conn(field, econn[ii2:])\\n ii = nm.where((id_map[dc] == 0))[0]\\n n_new = len(ii)\\n if n_new:\\n cdof_map[1].append(dc[ii])\\n cdof_map[3] += n_new\\n id_map[dc[ii]] = 1\\n inter_count += n_new\\n count += n_new\\n\\n if is_overlap:\\n ovs = cmesh.get_incident(0, region.facets[ii2:],\\n cmesh.tdim - 1)\\n ocs = cmesh.get_incident(cmesh.tdim, ovs, 0)\\n cdof_map[2].append(ocs.astype(nm.int32))\\n\\n domain.regions.pop() # Remove the cell region.\\n\\n inner_dofs = nm.setdiff1d(dofs, nm.concatenate(inter_dofs))\\n n_inner = len(inner_dofs)\\n rdof_map[3] += n_inner\\n assert_(nm.all(id_map[inner_dofs] == 0))\\n id_map[inner_dofs] = 1\\n count += n_inner\\n\\n rdof_map[0] = inner_dofs\\n\\n offset = 0\\n overlap_cells = []\\n for ir, dof_map in ordered_iteritems(dof_maps):\\n n_owned = dof_map[3]\\n\\n i0 = len(dof_map[0])\\n id_map[dof_map[0]] = nm.arange(offset, offset + i0, dtype=nm.uint32)\\n for aux in dof_map[1]:\\n i1 = len(aux)\\n id_map[aux] = nm.arange(offset + i0, offset + i0 + i1,\\n dtype=nm.uint32)\\n i0 += i1\\n\\n if len(dof_map[2]):\\n ocs = nm.unique(nm.concatenate(dof_map[2]))\\n\\n else:\\n ocs = nm.zeros(0, dtype=nm.int32)\\n\\n overlap_cells.append(ocs)\\n\\n assert_(i0 == n_owned)\\n\\n dof_map[4] = offset\\n offset += n_owned\\n\\n if not len(dof_maps):\\n dofs = _get_dofs_region(field, field.region)\\n dof_maps[0] = [dofs, [], [], len(dofs), 0]\\n id_map[:] = nm.arange(len(dofs), dtype=nm.uint32)\\n\\n if not len(cell_parts):\\n cell_parts.append(nm.arange(len(cell_tasks), dtype=nm.int32))\\n\\n if not len(overlap_cells):\\n overlap_cells.append(nm.zeros(0, dtype=nm.int32))\\n\\n return dof_maps, id_map, cell_parts, overlap_cells\",\n \"def create_cells(self):\\n raise NotImplementedError(\\n \\\"create_cells function not reimplemented from base class\\\")\",\n \"def _create_cells(self):\\n\\t\\tcellId=0\\n\\t\\t# Iterate over all dictionaries\\n\\t\\tfor muscle,muscAfferentDelay in self._infoMuscles:\\n\\t\\t\\tfor cellInfo in self._infoCommonCellsInMuscles:\\n\\t\\t\\t\\tcellClass = cellInfo[0]\\n\\t\\t\\t\\tcellName = cellInfo[1]\\n\\t\\t\\t\\tcellNumber = cellInfo[2]\\n\\t\\t\\t\\tif len(cellInfo)>=4: neuronParam = cellInfo[3]\\n\\t\\t\\t\\telse: neuronParam = None\\n\\t\\t\\t\\tcellId = self._create_cell_population(cellId,muscle,muscAfferentDelay,cellClass,cellName,cellNumber,neuronParam)\\n\\t\\t# Add special cells\\n\\t\\tfor cellInfo in self._infoSpecialCells:\\n\\t\\t\\tgroupOrMuscle = cellInfo[0]\\n\\t\\t\\tcellClass = cellInfo[1]\\n\\t\\t\\tcellName = cellInfo[2]\\n\\t\\t\\tcellNumber = cellInfo[3]\\n\\t\\t\\tif len(cellInfo)>=5: neuronParam = cellInfo[4]\\n\\t\\t\\telse: neuronParam = None\\n\\t\\t\\tmuscAfferentDelay = None\\n\\t\\t\\tcellId = self._create_cell_population(cellId,groupOrMuscle,muscAfferentDelay,cellClass,cellName,cellNumber,neuronParam)\\n\\n\\t\\tself._motoneuronsNames = self._intMotoneuronsNames+self._realMotoneuronsNames\\n\\t\\tself._afferentsNames = self._primaryAfferentsNames+self._secondaryAfferentsNames\",\n \"def __init__(self):\\n self._map = {}\",\n \"def build_occupancy_map_torch(human: torch.Tensor, other_agents: torch.Tensor, cell_num: int, cell_size: float,\\n om_channel_size: int) -> torch.Tensor:\\n other_px = other_agents[:, 0] - human[0]\\n other_py = other_agents[:, 1] - human[1]\\n\\n # new x-axis is in the direction of human's velocity\\n human_velocity_angle = torch.atan2(human[1], human[0])\\n other_human_orientation = torch.atan2(other_py, other_px)\\n rotation = other_human_orientation - human_velocity_angle\\n distance = torch.norm(torch.cat([other_px.view(-1, 1), other_py.view(-1, 1)], axis=1), dim=1)\\n other_px = torch.cos(rotation) * distance\\n other_py = torch.sin(rotation) * distance\\n\\n # compute indices of humans in the grid\\n other_x_index = torch.floor(other_px / cell_size + cell_num / 2)\\n other_y_index = torch.floor(other_py / cell_size + cell_num / 2)\\n other_x_index[other_x_index < 0] = float('-inf')\\n other_x_index[other_x_index >= cell_num] = float('-inf')\\n other_y_index[other_y_index < 0] = float('-inf')\\n other_y_index[other_y_index >= cell_num] = float('-inf')\\n grid_indices = cell_num * other_y_index + other_x_index\\n\\n # calculate relative velocity for other agents\\n other_human_velocity_angles = torch.atan2(other_agents[:, 3], other_agents[:, 2])\\n rotation = other_human_velocity_angles - human_velocity_angle\\n speed = torch.norm(other_agents[:, 2:4],dim=1)\\n other_vx = torch.cos(rotation) * speed\\n other_vy = torch.sin(rotation) * speed\\n\\n # occupy map\\n dm = torch.zeros(3, cell_num ** 2)\\n for i, index in np.ndenumerate(grid_indices):\\n if index in range(cell_num ** 2):\\n dm[0, int(index)] += 1\\n dm[1, int(index)] += other_vx[i]\\n dm[2, int(index)] += other_vy[i]\\n\\n mask = dm[0, :] != 0\\n count = dm[0, mask]\\n dm[0:3, mask] /= count\\n\\n if om_channel_size == 1:\\n return dm[0, :].view(-1)\\n elif om_channel_size == 2:\\n return dm[1:3, :].view(-1)\\n else:\\n return dm.view(-1)\",\n \"def dimensionizing_mapper(self, names=None):\\n\\n def fix(string):\\n tags = [\\n \\\"'\\\", '\\\"', ' ', '&', '.', '/', '-',\\n '(', ')', '[', ']', '{', '}'\\n ]\\n for tag in tags:\\n string = string.replace(tag, '_')\\n return string\\n\\n masks = self._meta['masks']\\n columns = self._meta['columns']\\n suffix = self._dimensions_suffix\\n\\n if not names: names = self.variables()\\n mapper = {}\\n for org_mn, mask in masks.items():\\n if org_mn in names:\\n mask_name = fix(org_mn)\\n new_mask_name = '{mn}.{mn}{s}'.format(mn=mask_name, s=suffix)\\n mapper[org_mn] = new_mask_name\\n\\n mask_mapper = 'masks@{mn}'.format(mn=org_mn)\\n new_mask_mapper = 'masks@{nmn}'.format(nmn=new_mask_name)\\n mapper[mask_mapper] = new_mask_mapper\\n\\n values_mapper = 'lib@values@{mn}'.format(mn=org_mn)\\n new_values_mapper = 'lib@values@{nmn}'.format(nmn=new_mask_name)\\n mapper[values_mapper] = new_values_mapper\\n\\n items = masks[org_mn]['items']\\n for i, item in enumerate(items):\\n org_cn = item['source'].split('@')[-1]\\n col_name = fix(org_cn)\\n new_col_name = '{mn}[{{{cn}}}].{mn}{s}'.format(\\n mn=mask_name, cn=col_name, s=suffix\\n )\\n mapper[org_cn] = new_col_name\\n\\n col_mapper = 'columns@{cn}'.format(cn=org_cn)\\n new_col_mapper = 'columns@{ncn}'.format(ncn=new_col_name)\\n mapper[col_mapper] = new_col_mapper\\n\\n for col_name, col in columns.items():\\n if col_name in names and not self._is_array_item(col_name):\\n new_col_name = fix(col_name)\\n if new_col_name == col_name: continue\\n mapper[col_name] = new_col_name\\n\\n col_mapper = 'columns@{cn}'.format(cn=col_name)\\n new_col_mapper = 'columns@{ncn}'.format(ncn=new_col_name)\\n mapper[col_mapper] = new_col_mapper\\n\\n return mapper\",\n \"def to_mapping(self, dim):\\n mim = cifti2.Cifti2MatrixIndicesMap([dim], 'CIFTI_INDEX_TYPE_PARCELS')\\n if self.affine is not None:\\n affine = cifti2.Cifti2TransformationMatrixVoxelIndicesIJKtoXYZ(-3, matrix=self.affine)\\n mim.volume = cifti2.Cifti2Volume(self.volume_shape, affine)\\n for name, nvertex in self.nvertices.items():\\n mim.append(cifti2.Cifti2Surface(name, nvertex))\\n for name, voxels, vertices in zip(self.name, self.voxels, self.vertices):\\n cifti_voxels = cifti2.Cifti2VoxelIndicesIJK(voxels)\\n element = cifti2.Cifti2Parcel(name, cifti_voxels)\\n for name_vertex, idx_vertices in vertices.items():\\n element.vertices.append(cifti2.Cifti2Vertices(name_vertex, idx_vertices))\\n mim.append(element)\\n return mim\",\n \"def setup_mapping():\\n # Mapping describes how elasticsearch handles a document during indexing.\\n # Most fields are detected and mapped automatically.\\n m = {\\n # Turn off analysis on name so we can sort by it.\\n 'name': {'index': 'not_analyzed', 'type': 'string'},\\n }\\n es = elasticutils.get_es()\\n try:\\n es.create_index_if_missing(settings.ES_INDEX)\\n es.put_mapping(Addon._meta.app_label, {'properties': m},\\n settings.ES_INDEX)\\n except pyes.ElasticSearchException:\\n pass\",\n \"def test_make_ec_map():\\n pass\",\n \"def _get_mapper_0(model):\\n # build the maps\\n eids_all = (\\n list(model.elements.keys()) +\\n list(model.masses.keys()) +\\n list(model.rigid_elements.keys())\\n )\\n eid_map = {eid : eid for eid in eids_all}\\n nid_map = {nid : nid for nid in model.point_ids}\\n cid_map = {cid : cid for cid in model.coord_ids}\\n mid_map = {mid : mid for mid in model.material_ids}\\n spc_map = _dicts_key_to_key((model.spcs, model.spcadds))\\n mpc_map = _dicts_key_to_key((model.mpcs, model.mpcadds))\\n method_map = _dict_key_to_key(model.methods)\\n properties_map = _dict_key_to_key(model.properties)\\n rigid_elements_map = _dict_key_to_key(model.rigid_elements)\\n cmethod_map = _dict_key_to_key(model.cMethods)\\n flfact_map = _dict_key_to_key(model.flfacts)\\n flutter_map = _dict_key_to_key(model.flutters)\\n caero_map = _dict_key_to_key(model.caeros)\\n freq_map = _dict_key_to_key(model.frequencies)\\n\\n dload_map = _dicts_key_to_key((model.dload_entries, model.dloads))\\n load_map = _dicts_key_to_key((model.loads, model.load_combinations))\\n lseq_map = load_map # wrong???\\n temp_map = load_map # wrong???\\n\\n tstep_map = _dict_key_to_key(model.tsteps)\\n tstepnl_map = _dict_key_to_key(model.tstepnls)\\n suport1_map = _dict_key_to_key(model.suport1)\\n #suport_map = {}\\n\\n nlparm_map = _dict_key_to_key(model.nlparms)\\n #nlpci_map = _dict_key_to_key(model.nlpcis)\\n table_sdamping_map = _dict_key_to_key(model.tables_sdamping)\\n dconadd_map = _dict_key_to_key(model.dconadds)\\n dconstr_map = _dict_key_to_key(model.dconstrs)\\n dessub_map = dconadd_map\\n for key, value in dconstr_map.items():\\n if key in dessub_map:\\n raise NotImplementedError()\\n dessub_map[key] = value\\n dresp_map = _dict_key_to_key(model.dresps)\\n gust_map = _dict_key_to_key(model.gusts)\\n trim_map = _dict_key_to_key(model.trims)\\n tic_map = _dict_key_to_key(model.tics)\\n csschd_map = _dict_key_to_key(model.csschds)\\n tranfer_function_map = _dict_key_to_key(model.transfer_functions)\\n\\n mapper = {\\n 'elements' : eid_map,\\n 'nodes' : nid_map,\\n 'coords' : cid_map,\\n 'materials' : mid_map,\\n 'properties' : properties_map,\\n 'rigid_elements': rigid_elements_map,\\n 'spcs' : spc_map,\\n 'mpcs' : mpc_map,\\n 'METHOD' : method_map,\\n 'CMETHOD' : cmethod_map,\\n 'FLFACT' : flfact_map,\\n 'FMETHOD' : flutter_map,\\n 'caeros' : caero_map,\\n 'FREQUENCY' : freq_map,\\n\\n 'DLOAD' : dload_map,\\n 'LOAD' : load_map,\\n 'LOADSET' : lseq_map,\\n 'TSTEP' : tstep_map,\\n 'TSTEPNL' : tstepnl_map,\\n 'SUPORT1' : suport1_map,\\n 'NLPARM' : nlparm_map,\\n 'SDAMPING' : table_sdamping_map,\\n 'DESSUB' : dessub_map,\\n 'DESOBJ' : dresp_map,\\n 'GUST' : gust_map,\\n 'TRIM' : trim_map,\\n 'IC' : tic_map,\\n 'CSSCHD' : csschd_map,\\n 'TFL' : tranfer_function_map,\\n #'DESSUB' : dessub_map,\\n # bad...\\n 'TEMPERATURE(LOAD)' : temp_map,\\n 'TEMPERATURE(INITIAL)' : temp_map,\\n #'DATAREC' : datarec_map,\\n #'ADAPT' : adapt_map,\\n #'SUPER' : super_map,\\n #'BOUTPUT' : boutput_map,\\n #'OUTRCV' : outrcv_map,\\n }\\n\\n return mapper\",\n \"def CreateIndex(self, arg0: 'unsigned long long') -> \\\"void\\\":\\n return _itkQuadEdgeCellTraitsInfoPython.itkMapContainerULLQEMPF2GQEULLULLBBT_CreateIndex(self, arg0)\",\n \"def _create_labels_and_mapping(self, labels, mapping):\\n numbered_classes = list(enumerate(list(labels), start=0))\\n if mapping:\\n new_mapping = {number: str(mapping[label]) for number, label in numbered_classes}\\n else:\\n new_mapping = {number: str(label) for number, label in numbered_classes}\\n new_labels = [new_mapping[numbered[0]] for numbered in numbered_classes]\\n\\n return new_labels, new_mapping\",\n \"def gen_CASTEP_supercell(CASTEP_cell,na,nb,nc):\\n nruter=dict()\\n nruter[\\\"na\\\"]=na\\n nruter[\\\"nb\\\"]=nb\\n nruter[\\\"nc\\\"]=nc\\n nruter[\\\"lattvec\\\"]=np.array(CASTEP_cell[\\\"lattvec\\\"])\\n nruter[\\\"lattvec\\\"][:,0]*=na\\n nruter[\\\"lattvec\\\"][:,1]*=nb\\n nruter[\\\"lattvec\\\"][:,2]*=nc\\n nruter[\\\"elements\\\"]=copy.copy(CASTEP_cell[\\\"elements\\\"])\\n nruter[\\\"numbers\\\"]=na*nb*nc*CASTEP_cell[\\\"numbers\\\"]\\n nruter[\\\"positions\\\"]=np.empty((3,CASTEP_cell[\\\"positions\\\"].shape[1]*na*nb*nc))\\n pos=0\\n for pos,(k,j,i,iat) in enumerate(itertools.product(xrange(nc),\\n xrange(nb),\\n xrange(na),\\n xrange(\\n CASTEP_cell[\\\"positions\\\"].shape[1]))):\\n nruter[\\\"positions\\\"][:,pos]=(CASTEP_cell[\\\"positions\\\"][:,iat]+[i,j,k])/[\\n na,nb,nc]\\n nruter[\\\"types\\\"]=[]\\n for i in xrange(na*nb*nc):\\n nruter[\\\"types\\\"].extend(CASTEP_cell[\\\"types\\\"])\\n # print \\\"supercell\\\", nruter\\n return nruter\",\n \"def __init___0(self, map):\\n super(LongObjectHashMap, self).__init__()\\n self.__init__()\\n putAll(map)\",\n \"def celltypesref(pl):\\n ppl = set(pl)\\n refnd = dict(zip(ppl, range(len(ppl))))\\n return refnd\",\n \"def update_id2idx(self):\\n self._id2idx = {}\\n for n, cell in enumerate(self._cell_list):\\n self._id2idx[cell.id()] = n\",\n \"def create_board(self):\\n board = dict()\\n cell_names = self.create_cell_names()\\n\\n for cell_name in cell_names:\\n if cell_name in self.given_cells:\\n is_given = True\\n value = self.given_cells[cell_name]\\n else:\\n is_given = False\\n value = 0\\n new_cell = c.Cell(cell_name, is_given, value, self.size)\\n board[cell_name] = new_cell\\n return board\",\n \"def build_occupancy_map(human : Human, other_agents : np.array, cell_num : int, cell_size : float, om_channel_size : int) -> np.array:\\n other_px = other_agents[:, 0] - human.px\\n other_py = other_agents[:, 1] - human.py\\n\\n # new x-axis is in the direction of human's velocity\\n human_velocity_angle = np.arctan2(human.vy, human.vx)\\n other_human_orientation = np.arctan2(other_py, other_px)\\n rotation = other_human_orientation - human_velocity_angle\\n distance = np.linalg.norm([other_px, other_py], axis=0)\\n other_px = np.cos(rotation) * distance\\n other_py = np.sin(rotation) * distance\\n\\n # compute indices of humans in the grid\\n other_x_index = np.floor(other_px / cell_size + cell_num / 2)\\n other_y_index = np.floor(other_py / cell_size + cell_num / 2)\\n other_x_index[other_x_index < 0] = float('-inf')\\n other_x_index[other_x_index >= cell_num] = float('-inf')\\n other_y_index[other_y_index < 0] = float('-inf')\\n other_y_index[other_y_index >= cell_num] = float('-inf')\\n grid_indices = cell_num * other_y_index + other_x_index\\n occupancy_map = np.isin(range(cell_num ** 2), grid_indices)\\n if om_channel_size == 1:\\n return occupancy_map.astype(int)\\n else:\\n # calculate relative velocity for other agents\\n other_human_velocity_angles = np.arctan2(other_agents[:, 3], other_agents[:, 2])\\n rotation = other_human_velocity_angles - human_velocity_angle\\n speed = np.linalg.norm(other_agents[:, 2:4], axis=1)\\n other_vx = np.cos(rotation) * speed\\n other_vy = np.sin(rotation) * speed\\n dm = [list() for _ in range(cell_num ** 2 * om_channel_size)]\\n for i, index in np.ndenumerate(grid_indices):\\n if index in range(cell_num ** 2):\\n if om_channel_size == 2:\\n dm[2 * int(index)].append(other_vx[i])\\n dm[2 * int(index) + 1].append(other_vy[i])\\n elif om_channel_size == 3:\\n dm[int(index)].append(1)\\n dm[int(index) + cell_num ** 2].append(other_vx[i])\\n dm[int(index) + cell_num ** 2 * 2].append(other_vy[i])\\n else:\\n raise NotImplementedError\\n for i, cell in enumerate(dm):\\n dm[i] = sum(dm[i]) / len(dm[i]) if len(dm[i]) != 0 else 0\\n return dm\",\n \"def update_cell_membranes(cells):\\r\\n membrane_polys = [Polygon(cell['membrane']).buffer(0) for cell in cells]\\r\\n membrane_bounds = [p.bounds for p in membrane_polys]\\r\\n\\r\\n # Get normal vectors for membrane vertices\\r\\n vert_normals = [geometry.get_vert_normals(\\r\\n geometry.get_edge_normals(cell['membrane'])) for cell in cells]\\r\\n\\r\\n all_membranes = np.concatenate([cell['membrane'] for cell in cells], axis=0)\\r\\n # [(cell idx, vert idx), ...]\\r\\n all_membrane_map = np.concatenate([\\r\\n np.stack([\\r\\n np.repeat([i], cell['membrane'].shape[0]),\\r\\n np.arange(cell['membrane'].shape[0],)\\r\\n ], axis=1)\\r\\n for i, cell in enumerate(cells)\\r\\n ], axis=0).astype(np.int32)\\r\\n\\r\\n # Find inter-cell membrane vertices that are close enough for adhesion/diffusion\\r\\n nearby_membranes, nearby_membrane_map = find_nearby_membranes(\\r\\n all_membranes, all_membrane_map, vert_normals)\\r\\n\\r\\n # Change membrane rest length according with the cell volume\\r\\n membrane_rdists = []\\r\\n for i, cell in enumerate(cells):\\r\\n # Get all the pairwise distances between membrane vertices\\r\\n membrane_dists = scipy.spatial.distance.squareform(\\r\\n scipy.spatial.distance.pdist(cell['membrane']))\\r\\n membrane_rdists_i = 1.0 / (membrane_dists + 1e-6)\\r\\n membrane_rdists_i[np.where(membrane_dists == 0)] = 0\\r\\n membrane_rdists.append(membrane_rdists_i)\\r\\n\\r\\n return membrane_bounds, membrane_polys, vert_normals, \\\\\\r\\n all_membranes, all_membrane_map, \\\\\\r\\n nearby_membranes, nearby_membrane_map, \\\\\\r\\n membrane_rdists\",\n \"def map(inputs, e0,e1,k):\\r\\n codebook = tf.cast(inputs[0][0:2 ** k], tf.float32)\\r\\n soft_map = tf.TensorArray(tf.float32, size=0, dynamic_size=True)\\r\\n for y in inputs[1]:\\r\\n Pxy_map = pyx(y, codebook, e0, e1)\\r\\n soft_map = soft_map.write(soft_map.size(), Pxy_map)\\r\\n\\r\\n soft_map = soft_map.stack()\\r\\n return soft_map\",\n \"def create_matrix_mapping(train_mh, unk_vec_id):\\n mh_index_map = {}\\n matrix_idx = 0\\n for vector_idx in train_mh:\\n if vector_idx == unk_vec_id:\\n unk_matrix_id = matrix_idx\\n mh_index_map[vector_idx] = matrix_idx\\n matrix_idx += 1\\n return mh_index_map, unk_matrix_id\",\n \"def dict() -> Dict[str, Pin]:\",\n \"def templateMappings(self):\\n raise NotImplementedError\",\n \"def CreateIndex(self, arg0: 'unsigned long long') -> \\\"void\\\":\\n return _itkQuadEdgeCellTraitsInfoPython.itkMapContainerULLQEMPF3GQEULLULLBBT_CreateIndex(self, arg0)\",\n \"def create_single_map(self,tod,x,y,x0,y0):\\n maps = {'map':np.zeros((self.Nx*self.Ny)),\\n 'cov':np.zeros((self.Nx*self.Ny))}\\n\\n\\n pixels,xp,yp,r_x, r_y = self.get_pixel_positions(x,y,x0,y0,0,invertx=True)\\n mask = np.ones(pixels.size,dtype=int)\\n\\n mask[(pixels == -1) | np.isnan(tod) | np.isinf(tod)] = 0\\n rms = stats.AutoRMS(tod)\\n weights = {'map':tod.astype(np.float64)/rms**2,\\n 'cov':np.ones(tod.size)/rms**2}\\n for k in maps.keys():\\n binFuncs.binValues(maps[k],\\n pixels,\\n weights=weights[k],mask=mask)\\n maps[k] = np.reshape(maps[k],(self.Ny,self.Nx))\\n return maps\",\n \"def new(num_buckets=256):\\n aMap=[]\",\n \"def makemap(d,x,y,ra0=0,dec0=0, cd=1./60., nxpix=600, nypix=600):\\n\\n xy = np.zeros((x.size,2))\\n xy[:,0] = x.flatten()\\n xy[:,1] = y.flatten()\\n\\n from astropy import wcs\\n\\n w = wcs.WCS(naxis=2)\\n w.wcs.crval = [ra0, dec0]\\n w.wcs.cdelt = [cd,cd]\\n w.wcs.crpix = [nxpix/2., nypix/2.]\\n w.wcs.ctype = ['RA---TAN', 'DEC--TAN']\\n\\n pixels = w.wcs_world2pix(xy,0)\\n ygrid, xgrid = np.meshgrid(np.arange(nypix),np.arange(nxpix))\\n\\n pixCens = w.wcs_pix2world(np.array([xgrid.flatten(), ygrid.flatten()]).T,0)\\n pixCens[:,0] += 1./2.*cd\\n pixCens[:,1] += 1./2.*cd\\n pflat = (pixels[:,1].astype(int) + (nypix)*pixels[:,0].astype(int)).astype(int)\\n\\n\\n pEdges = np.arange(nxpix*nypix+1)\\n m = np.histogram(pflat,pEdges, weights=d)[0]\\n h = np.histogram(pflat,pEdges)[0]\\n m = m/h\\n return m,pixCens,w\",\n \"def _generate_character_map(self):\\n self._ct = [-1] * 256\\n index = 0\\n for c_range in self._meta.character_ranges:\\n for c_pos in range(c_range['min'], c_range['max'] + 1):\\n self._ct[c_pos] = index\\n index += 1\",\n \"def cellannotation2ID(self, annotation_type):\\n annotations = list(self.adata.obs[annotation_type])\\n annotations_set = sorted(set(annotations))\\n \\n mapping = {a:idx for idx,a in enumerate(annotations_set)}\\n \\n truth_labels = [mapping[a] for a in annotations]\\n self.adata.obs['label'] = pd.Categorical(values=truth_labels)\\n #18m-unannotated\\n # \\n return mapping\",\n \"def test_create_new_mapper(self):\\n GraphMapper()\",\n \"def to_mapping(self, dim):\\n mim = cifti2.Cifti2MatrixIndicesMap([dim], 'CIFTI_INDEX_TYPE_SCALARS')\\n for name, meta in zip(self.name, self.meta):\\n named_map = cifti2.Cifti2NamedMap(name, cifti2.Cifti2MetaData(meta))\\n mim.append(named_map)\\n return mim\",\n \"def __init__(self, nx, dx, map=None, ny=None, dy=None):\\n super(rmap, self).__init__(nx, dx, ny=ny, dy=dy)\\n if map == None:\\n self.map = np.zeros((self.ny, self.nx))\\n else:\\n self.map = map\\n assert ((self.ny, self.nx) == self.map.shape)\",\n \"def assign(self, gateObj, mapping):\\n copied = deepcopy(gateObj)\\n if isinstance(mapping, dict):\\n for key, idx_tag in mapping.items():\\n qubitIdx = self.get_index(idx_tag[0])\\n blockNum = idx_tag[1] - len(self.diagram[0, :]) + 1\\n if blockNum > 0:\\n self.diagram = np.concatenate((\\n self.diagram, np.asarray(\\n [[np.nan] * blockNum] * len(self.diagram[:, 0])\\n )\\n ), axis=1)\\n self.diagram[qubitIdx, idx_tag[1]] = copied._qubitDict[key]\\n else:\\n qubitIdx = self.get_index(mapping[0])\\n blockNum = mapping[1] - len(self.diagram[0, :]) + 1\\n if blockNum > 0:\\n self.diagram = np.concatenate((\\n self.diagram, np.asarray(\\n [[np.nan] * blockNum] * len(self.diagram[:, 0])\\n )\\n ), axis=1)\\n self.diagram[qubitIdx, mapping[1]] = copied\",\n \"def constructCell():\\n\\t\\tself.weightGenerate()\",\n \"def physical_maps(x, y):\\n assert x.shape == (3,) and y.shape == (3,)\\n assert x.dtype == np.float64 and y.dtype == np.float64\\n\\n C = np.empty((21,21), dtype=np.float64)\\n B = np.empty((2,2), dtype=np.float64)\\n b = np.empty((2,), dtype=np.float64)\\n _ap.ap_physical_maps(x, y, C, B, b)\\n return (C, B, b)\",\n \"def generate_map(nrows, ncols, nrooms, max_col_size, max_row_size):\\n arr = np.zeros((nrows, ncols), dtype=np.int8)\\n\\n for i in range(nrooms):\\n rand_row_start = np.random.randint(nrows)\\n rand_col_start = np.random.randint(ncols)\\n\\n rand_row_size = np.random.randint(max_row_size / 2, max_row_size)\\n rand_col_size = np.random.randint(max_col_size / 2, max_col_size)\\n\\n arr[rand_row_start:rand_row_start + rand_row_size, rand_col_start:rand_col_start + rand_col_size] = 1\\n\\n labels = measure.label(arr)\\n regions = measure.regionprops(labels)\\n\\n centroids = list()\\n for region in regions:\\n centroids.append(region.centroid)\\n\\n num_centroids = len(centroids)\\n\\n # get distances between every pair of centroids\\n dists = scipy.spatial.distance.cdist(centroids, centroids)\\n\\n # get a distance that is greater than all current distances\\n max_dist = np.max(dists) + 1\\n\\n # make sure upper triangle is at least max_dist so that when picking closest\\n # pairs, we won't choose a diagonal element or a duplicate connection\\n dists = dists + np.triu(np.ones((num_centroids, num_centroids))) * max_dist\\n\\n for i in range(num_centroids - 1):\\n min_dist_idx = np.argmin(dists)\\n min_dist_idx = np.unravel_index(min_dist_idx, dists.shape)\\n\\n # create a hallway between regionprops\\n centroid1 = np.array(centroids[min_dist_idx[0]], dtype=np.int)\\n centroid2 = np.array(centroids[min_dist_idx[1]], dtype=np.int)\\n\\n [row_centroid_1, row_centroid_2] = sorted([centroid1, centroid2], key=lambda x: x[0])\\n [col_centroid_1, col_centroid_2] = sorted([centroid1, centroid2], key=lambda x: x[1])\\n\\n arr[row_centroid_1[0]:row_centroid_2[0] + 1, row_centroid_1[1]] = 1\\n arr[row_centroid_2[0], col_centroid_1[1]:col_centroid_2[1] + 1] = 1\\n\\n dists[:, min_dist_idx[1]] += max_dist\\n\\n return arr\",\n \"def __init__(self, *args):\\n _itkQuadEdgeCellTraitsInfoPython.itkMapContainerULLQEMPF2GQEULLULLBBT_swiginit(self, _itkQuadEdgeCellTraitsInfoPython.new_itkMapContainerULLQEMPF2GQEULLULLBBT(*args))\",\n \"def uvmap(self, p):\\n pass\",\n \"def _declare_auto_variable_mapping(self):\\n if self.name not in self.nlp.variable_mappings:\\n self.nlp.variable_mappings[self.name] = BiMapping(\\n range(len(self.name_elements)), range(len(self.name_elements))\\n )\",\n \"def __init__(self):\\n self.map = {}\\n self.vec = []\",\n \"def create_map(width, height, pixels):\\n\\n\\n\\n\\n def index_to_xy(i, width, height):\\n \\\"\\\"\\\" Takes 0 based index going line wise from top\\n left to bottom right, returns x, y coordinates so\\n that 0,0 is on bottom left corner\\n \\\"\\\"\\\"\\n x = i % width\\n y = i // width\\n y*= -1\\n y+= height - 1\\n return (x,y)\\n\\n def place_terrain(type, i):\\n \\\"\\\"\\\"This won't return anything, just do side effects\\n\\n The object \\\"gameLogic\\\" is used to place the object\\n initially. It doesn't matter where this object is,\\n as long as it exists. There must be an easier way,\\n but this works.\\n \\\"\\\"\\\"\\n x,y = index_to_xy(i, width, height)\\n\\n object_name = terrain_types.get(type, \\\"water\\\")\\n\\n if ob[\\\"fast_create\\\"] > 0 and not (x%ob[\\\"fast_create\\\"] == 0 and y%ob[\\\"fast_create\\\"] == 0):\\n return\\n\\n if object_name != \\\"water\\\":\\n object = scene.addObject(object_name, \\\"gameLogic\\\")\\n object.worldPosition = (x,y,0)\\n\\n\\n list(map( (lambda tup : place_terrain(tup[1], tup[0])), list(enumerate(pixels)) ))\",\n \"def prepare_map(self):\\n for y_coord, row in enumerate(self.contents):\\n for x_coord, tile in enumerate(row):\\n bit_map = self.get_tile_bitmap(tile)\\n self.image[y_coord * TILE_SIZE:(y_coord+1) * TILE_SIZE,\\n x_coord * TILE_SIZE:(x_coord+1) * TILE_SIZE] = bit_map\",\n \"def map_to_homo_nid(self, ids, ntype):\\n ...\",\n \"def map_to_homo_nid(self, ids, ntype):\\n ...\",\n \"def create_map(grid_size):\\n STATUS['game_grid'] = [] # Could be a tuple?\\n STATUS['grid_size'] = grid_size\\n x_coord = 1\\n y_coord = 1\\n grid_size_counter = grid_size * grid_size\\n while grid_size_counter:\\n STATUS['game_grid'].append([x_coord, y_coord])\\n x_coord += 1\\n if x_coord == grid_size + 1:\\n y_coord += 1\\n x_coord = 1\\n grid_size_counter -= 1\",\n \"def _cellTopology(self):\\n facesPerCell = self.mesh._facesPerCell\\n nodesPerFace = self.mesh._nodesPerFace\\n\\n def faceCountsMatch(targetCounts):\\n if len(targetCounts) > nodesPerFace.shape[0]:\\n # pad nodesPerFace with zeros\\n paddedNodesPerFace = numerix.zeros((len(targetCounts), nodesPerFace.shape[1]), dtype=numerix.INT_DTYPE)\\n paddedNodesPerFace[:nodesPerFace.shape[0],:] = nodesPerFace\\n\\n paddedTargetCounts = numerix.array(targetCounts)[..., numerix.newaxis]\\n else:\\n # pad target face node count with zeros\\n paddedTargetCounts = numerix.concatenate((targetCounts,\\n [0] * (self.mesh._maxFacesPerCell - len(targetCounts))))\\n paddedTargetCounts = paddedTargetCounts[..., numerix.newaxis]\\n\\n paddedNodesPerFace = nodesPerFace\\n\\n return ((facesPerCell == len(targetCounts))\\n & (paddedNodesPerFace == paddedTargetCounts).all(axis=0))\\n\\n cellTopology = numerix.empty((self.mesh.numberOfCells,), dtype=numerix.ubyte)\\n\\n t = self._elementTopology\\n\\n if self.mesh.dim == 1:\\n cellTopology[:] = t[\\\"line\\\"]\\n elif self.mesh.dim == 2:\\n cellTopology[:] = t[\\\"polygon\\\"]\\n cellTopology[faceCountsMatch([2, 2, 2])] = t[\\\"triangle\\\"]\\n cellTopology[faceCountsMatch([2, 2, 2, 2])] = t[\\\"quadrangle\\\"]\\n else:\\n cellTopology[:] = t[\\\"unknown\\\"]\\n cellTopology[faceCountsMatch([3, 3, 3, 3])] = t[\\\"tetrahedron\\\"]\\n cellTopology[faceCountsMatch([4, 4, 4, 4, 4, 4])] = t[\\\"hexahedron\\\"]\\n cellTopology[faceCountsMatch([4, 4, 4, 3, 3])] = t[\\\"prism\\\"]\\n cellTopology[faceCountsMatch([4, 3, 3, 3, 3])] = t[\\\"pyramid\\\"]\\n\\n return cellTopology\",\n \"def generate_bitmap_to_linear_index_map(bladeTupList, firstIdx):\\n bitmap_map = np.zeros(len(bladeTupList), dtype=int)\\n for ind, blade in enumerate(bladeTupList):\\n bitmap_map[compute_bitmap_representation(blade, firstIdx)] = ind\\n return bitmap_map\",\n \"def get_tm_mapping(mapping):\\n df = mapping[['slot', 'row', 'col', 'xpix', 'ypix']]\\n f_rowcol = lambda v: v.values[0] // 8\\n f = dict(row=f_rowcol, col=f_rowcol, xpix='mean', ypix='mean')\\n df = df.groupby('slot').agg(f, as_index=False).reset_index()\\n with warnings.catch_warnings():\\n warnings.simplefilter('ignore', UserWarning)\\n df.metadata = mapping.metadata\\n df.metadata['n_rows'] = df['row'].max() + 1\\n df.metadata['n_columns'] = df['col'].max() + 1\\n df.metadata['size'] *= 8\\n return df\",\n \"def _fill_maps(maps, map_counts, paths, pixels, mapper, ctx):\\n xy_ind = 0\\n with RestructuredTODStore(paths) as store:\\n for i, tod_pixel in enumerate(pixels):\\n re_data = store.get(tod_pixel)\\n values, counts = mapper.get_mapped_values(re_data, ctx)\\n maps[:, xy_ind, i] = values\\n map_counts[:, xy_ind, i] = counts\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.65705234","0.6363869","0.6342318","0.6297688","0.6223787","0.61639667","0.6126557","0.60234666","0.5995557","0.5985049","0.5910921","0.5882835","0.5853043","0.58468926","0.58390236","0.5810125","0.58074224","0.5806761","0.5770566","0.5770005","0.5767886","0.57576734","0.57509196","0.5729536","0.56797534","0.5676852","0.56733805","0.5668129","0.5657325","0.5646988","0.5646252","0.56053746","0.5604648","0.557692","0.5571769","0.5562079","0.5536634","0.55054843","0.5505451","0.55044204","0.5493885","0.5484571","0.5456098","0.54472077","0.543598","0.5428912","0.54287","0.5424336","0.541762","0.540808","0.54079765","0.5396903","0.5395123","0.5380762","0.53807247","0.53621286","0.5354411","0.5337675","0.5333538","0.53013724","0.53011984","0.5297483","0.5296455","0.5294254","0.5292906","0.528334","0.52817595","0.52814853","0.5276279","0.5262516","0.5257381","0.52457386","0.52407277","0.52356493","0.52351","0.52350396","0.522331","0.52157414","0.52135164","0.52059984","0.5203294","0.5198923","0.5177403","0.5173287","0.51690143","0.5165091","0.5164667","0.5162699","0.5154353","0.5137625","0.5136784","0.51358014","0.5132504","0.5131116","0.5131116","0.5130754","0.5124634","0.51242167","0.5118003","0.51097524"],"string":"[\n \"0.65705234\",\n \"0.6363869\",\n \"0.6342318\",\n \"0.6297688\",\n \"0.6223787\",\n \"0.61639667\",\n \"0.6126557\",\n \"0.60234666\",\n \"0.5995557\",\n \"0.5985049\",\n \"0.5910921\",\n \"0.5882835\",\n \"0.5853043\",\n \"0.58468926\",\n \"0.58390236\",\n \"0.5810125\",\n \"0.58074224\",\n \"0.5806761\",\n \"0.5770566\",\n \"0.5770005\",\n \"0.5767886\",\n \"0.57576734\",\n \"0.57509196\",\n \"0.5729536\",\n \"0.56797534\",\n \"0.5676852\",\n \"0.56733805\",\n \"0.5668129\",\n \"0.5657325\",\n \"0.5646988\",\n \"0.5646252\",\n \"0.56053746\",\n \"0.5604648\",\n \"0.557692\",\n \"0.5571769\",\n \"0.5562079\",\n \"0.5536634\",\n \"0.55054843\",\n \"0.5505451\",\n \"0.55044204\",\n \"0.5493885\",\n \"0.5484571\",\n \"0.5456098\",\n \"0.54472077\",\n \"0.543598\",\n \"0.5428912\",\n \"0.54287\",\n \"0.5424336\",\n \"0.541762\",\n \"0.540808\",\n \"0.54079765\",\n \"0.5396903\",\n \"0.5395123\",\n \"0.5380762\",\n \"0.53807247\",\n \"0.53621286\",\n \"0.5354411\",\n \"0.5337675\",\n \"0.5333538\",\n \"0.53013724\",\n \"0.53011984\",\n \"0.5297483\",\n \"0.5296455\",\n \"0.5294254\",\n \"0.5292906\",\n \"0.528334\",\n \"0.52817595\",\n \"0.52814853\",\n \"0.5276279\",\n \"0.5262516\",\n \"0.5257381\",\n \"0.52457386\",\n \"0.52407277\",\n \"0.52356493\",\n \"0.52351\",\n \"0.52350396\",\n \"0.522331\",\n \"0.52157414\",\n \"0.52135164\",\n \"0.52059984\",\n \"0.5203294\",\n \"0.5198923\",\n \"0.5177403\",\n \"0.5173287\",\n \"0.51690143\",\n \"0.5165091\",\n \"0.5164667\",\n \"0.5162699\",\n \"0.5154353\",\n \"0.5137625\",\n \"0.5136784\",\n \"0.51358014\",\n \"0.5132504\",\n \"0.5131116\",\n \"0.5131116\",\n \"0.5130754\",\n \"0.5124634\",\n \"0.51242167\",\n \"0.5118003\",\n \"0.51097524\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6313567"},"document_rank":{"kind":"string","value":"3"}}},{"rowIdx":14385,"cells":{"query":{"kind":"string","value":"Providing random mock values for resource capacity and usage."},"document":{"kind":"string","value":"def randomise(mock_info):\n mock_info[\"resource_info\"][\"usage\"][\"cpu\"] = round(random.uniform(0, 1), 2)\n mock_info[\"resource_info\"][\"usage\"][\"cpu_percentage\"] = round(random.uniform(0, 1), 2)\n mock_info[\"resource_info\"][\"usage\"][\"memory\"] = round(random.uniform(0, 1), 2)\n mock_info[\"resource_info\"][\"usage\"][\"memory_percentage\"] = round(random.uniform(0, 1), 2)\n return mock_info"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def test_glass_capacity__has_expected_default_value():\n glass = moet.create_glass(\"A\")\n assert glass.capacity == 250","def _get_random_returns(self): \n return self.asset_process.distrib.random()","def test_sdram(self):\n sdram = SDRAMResource(128 * (2**20))\n self.assertEqual(sdram.get_value(), 128 * (2**20))\n sdram = SDRAMResource(128 * (2**19))\n self.assertEqual(sdram.get_value(), 128 * (2**19))\n sdram = SDRAMResource(128 * (2**21))\n self.assertEqual(sdram.get_value(), 128 * (2**21))","def __init__(self, capacity, units=0):\n self.capacity = capacity\n self.units = units","def _get_random_value(self):\r\n return random.randint(1, 10)","def random(vms_cpu):\n return choice(vms_cpu.keys())","def randomize_value(self) -> None:","def test_rest_v20_dd_systems_systemid_stats_capacity_get(self):\n pass","def __init__(self):\n self.set_health(100)\n self.set_recharge(random.randint(100, 2000) / 10000)","def generate_products(self = random.sample, name = random.choice(result), price = random.randint(5, 100), weight = random.randint(5, 100), \nflammability= random.uniform(0, 2.5)):\n return sample","def test_set_glass_capacity__with_valid_numbers__returns_expected():\n glass = moet.create_glass(\"A\")\n numbers = [0, 1, 250, 0.0, 100.5]\n for number in numbers:\n glass.capacity = number\n assert glass.capacity == number","def __init__(self, capacity, initial):\n\t\tself.capacity = capacity\n\t\tself.amount = initial","def test_loc_techs_resource_capacity_constraint(self, override):\n\n if override is None:\n m = build_model(\n {}, \"simple_supply_and_supply_plus,two_hours,investment_costs\"\n )\n m.run(build_only=True)\n expr = m._backend_model.resource_cap[(\"b\", \"test_supply_plus\")]\n assert expr.lb == 0\n assert expr.ub == np.inf\n\n else:\n m = build_model(\n {\n \"techs.test_supply_plus.constraints.resource_cap_{}\".format(\n override\n ): 10\n },\n \"simple_supply_and_supply_plus,two_hours,investment_costs\",\n )\n m.run(build_only=True)\n expr = m._backend_model.resource_cap[(\"b\", \"test_supply_plus\")]\n if override == \"max\":\n assert expr.ub == 10\n assert expr.lb == 0\n elif override == \"equals\":\n assert expr.ub == 10\n assert expr.lb == 10\n if override == \"min\":\n assert expr.lb == 10\n assert expr.ub == np.inf","def randomize(self):\n self.size = randint(1,5)\n self.resource = randint(1,3)\n self.temperature = randint(20, 1000)\n self.gravity = randint(0, 10)\n for key in self.get_atmosphere().keys():\n setattr(self, key, randint(0, 5))\n for attribute_count in range(randint(0, 3)):\n pa = PlanetaryAttribute.objects.order_by('?')[0]\n self.attributes.add(pa)","def init_physical_resources():\n test_physical_resources = []\n\n # add info to list in memory, one by one, following signature values\n phys_resrc_ID = 1\n phys_resrc_name = \"small-cavium-1\"\n phys_resrc_info = \"Jump server in Arm pod, 48 cores, 64G RAM, 447G SSD, aarch64 Cavium ThunderX, Ubuntu OS\"\n phys_resrc_IPAddress = \"10.10.50.12\"\n phys_resrc_MACAddress = \"00-14-22-01-23-45\"\n\n test_physical_resources.append(PhysicalResource(phys_resrc_ID, phys_resrc_name,\n phys_resrc_info,\n phys_resrc_IPAddress,\n phys_resrc_MACAddress))\n\n phys_resrc_ID = 2\n phys_resrc_name = \"medium-cavium-1\"\n phys_resrc_info = \"Jump server in New York pod, 96 cores, 64G RAM, 447G SSD, aarch64 Cavium ThunderX, Ubuntu OS\"\n phys_resrc_IPAddress = \"30.31.32.33\"\n phys_resrc_MACAddress = \"0xb3:22:05:c1:aa:82\"\n\n test_physical_resources.append(PhysicalResource(phys_resrc_ID, phys_resrc_name,\n phys_resrc_info,\n phys_resrc_IPAddress,\n phys_resrc_MACAddress))\n\n phys_resrc_ID = 3\n phys_resrc_name = \"mega-cavium-666\"\n phys_resrc_info = \"Jump server in Las Vegas, 1024 cores, 1024G RAM, 6666G SSD, aarch64 Cavium ThunderX, Ubuntu OS\"\n phys_resrc_IPAddress = \"54.53.52.51\"\n phys_resrc_MACAddress = \"01-23-45-67-89-ab\"\n\n test_physical_resources.append(PhysicalResource(phys_resrc_ID, phys_resrc_name,\n phys_resrc_info,\n phys_resrc_IPAddress,\n phys_resrc_MACAddress))\n\n\n # write list to binary file\n write_list_bin(test_physical_resources, FILE_PHYSICAL_RESOURCES)\n\n return test_physical_resources","def test_cpu(self):\n cpu = CPUCyclesResource(128 * (2**20))\n self.assertEqual(cpu.get_value(), 128 * (2**20))\n cpu = CPUCyclesResource(128 * (2**19))\n self.assertEqual(cpu.get_value(), 128 * (2**19))\n cpu = CPUCyclesResource(128 * (2**21))\n self.assertEqual(cpu.get_value(), 128 * (2**21))","def test_set_glass_capacity__with_invalid_numbers__returns_expected():\n glass = moet.create_glass(\"A\")\n with pytest.raises(ValueError):\n glass.capacity = -100","def get_random(self):\n base_genom = \"1\" * sum(self._size_var)\n return utils.randomise_a_string(base_genom)","def Capacity(self) -> int:","def capacity_used(self):\n raise NotImplementedError()","def test_resource_container(self):\n sdram = SDRAMResource(128 * (2**20))\n dtcm = DTCMResource(128 * (2**20) + 1)\n cpu = CPUCyclesResource(128 * (2**20) + 2)\n\n container = ResourceContainer(dtcm, sdram, cpu)\n self.assertEqual(container.sdram.get_value(), 128 * (2**20))\n self.assertEqual(container.dtcm.get_value(), 128 * (2**20) + 1)\n self.assertEqual(container.cpu.get_value(), 128 * (2**20) + 2)\n\n sdram = SDRAMResource(128 * (2**19))\n dtcm = DTCMResource(128 * (2**19) + 1)\n cpu = CPUCyclesResource(128 * (2**19) + 2)\n\n container = ResourceContainer(dtcm, sdram, cpu)\n self.assertEqual(container.sdram.get_value(), 128 * (2**19))\n self.assertEqual(container.dtcm.get_value(), 128 * (2**19) + 1)\n self.assertEqual(container.cpu.get_value(), 128 * (2**19) + 2)\n\n sdram = SDRAMResource(128 * (2**21))\n dtcm = DTCMResource(128 * (2**21) + 1)\n cpu = CPUCyclesResource(128 * (2**21) + 2)\n\n container = ResourceContainer(dtcm, sdram, cpu)\n self.assertEqual(container.sdram.get_value(), 128 * (2**21))\n self.assertEqual(container.dtcm.get_value(), 128 * (2**21) + 1)\n self.assertEqual(container.cpu.get_value(), 128 * (2**21) + 2)","def get_capacity():\n fs.get_capacity()","def test_read_cluster_resource_quota(self):\n pass","def get_capacity_var(self):\n return self._capacity_var","def test_capacity_factor(pudl_out_eia):\n print(\"\\nCalculating generator capacity factors...\")\n cf = pudl_out_eia.capacity_factor()\n print(f\" capacity_factor: {len(cf)} records\")","def test_limit_cpu(self):\n url = '/api/apps'\n body = {'cluster': 'autotest'}\n response = self.client.post(url, json.dumps(body), content_type='application/json')\n self.assertEqual(response.status_code, 201)\n app_id = response.data['id']\n url = '/api/apps/{app_id}/limits'.format(**locals())\n # check default limit\n response = self.client.get(url, content_type='application/json')\n self.assertEqual(response.status_code, 200)\n self.assertIn('cpu', response.data)\n self.assertEqual(json.loads(response.data['cpu']), {})\n # regression test for https://github.com/deis/deis/issues/1563\n self.assertNotIn('\"', response.data['cpu'])\n # set an initial limit\n body = {'cpu': json.dumps({'web': '1024'})}\n response = self.client.post(url, json.dumps(body), content_type='application/json')\n self.assertEqual(response.status_code, 201)\n self.assertIn('x-deis-release', response._headers)\n limit1 = response.data\n # check memory limits\n response = self.client.get(url, content_type='application/json')\n self.assertEqual(response.status_code, 200)\n self.assertIn('cpu', response.data)\n cpu = json.loads(response.data['cpu'])\n self.assertIn('web', cpu)\n self.assertEqual(cpu['web'], '1024')\n # set an additional value\n body = {'cpu': json.dumps({'worker': '512'})}\n response = self.client.post(url, json.dumps(body), content_type='application/json')\n self.assertEqual(response.status_code, 201)\n limit2 = response.data\n self.assertNotEqual(limit1['uuid'], limit2['uuid'])\n cpu = json.loads(response.data['cpu'])\n self.assertIn('worker', cpu)\n self.assertEqual(cpu['worker'], '512')\n self.assertIn('web', cpu)\n self.assertEqual(cpu['web'], '1024')\n # read the limit again\n response = self.client.get(url)\n self.assertEqual(response.status_code, 200)\n limit3 = response.data\n self.assertEqual(limit2, limit3)\n cpu = json.loads(response.data['cpu'])\n self.assertIn('worker', cpu)\n self.assertEqual(cpu['worker'], '512')\n self.assertIn('web', cpu)\n self.assertEqual(cpu['web'], '1024')\n # unset a value\n body = {'memory': json.dumps({'worker': None})}\n response = self.client.post(url, json.dumps(body), content_type='application/json')\n self.assertEqual(response.status_code, 201)\n limit4 = response.data\n self.assertNotEqual(limit3['uuid'], limit4['uuid'])\n self.assertNotIn('worker', json.dumps(response.data['memory']))\n # disallow put/patch/delete\n self.assertEqual(self.client.put(url).status_code, 405)\n self.assertEqual(self.client.patch(url).status_code, 405)\n self.assertEqual(self.client.delete(url).status_code, 405)\n return limit4","def test_create_cluster_resource_quota(self):\n pass","def test_sample(system_generator):\n\n name, test = system_generator()\n print(name)\n\n w_F, w_R, N_k = test.sample([10, 8], mode=\"wFwR\")\n w_F, w_R, N_k = test.sample([1, 1], mode=\"wFwR\")\n w_F, w_R, N_k = test.sample([10, 0], mode=\"wFwR\")\n w_F, w_R, N_k = test.sample([0, 5], mode=\"wFwR\")","def get_capacity_param(self):\n intr = self.get_interaction()\n return intr.get_capacity(None, None, None, None, raw=True)","def test_default_num(self):\n products = generate_products()\n self.assertEqual(len(products), 30)","def random_test(self):\r\n return 1","def random_test(self):\r\n return 1","def rand(self):\n raise NotImplementedError","def test_collect_demands(self):\n pass","def test_qpu_0_shots():\n _aws_device(wires=2, shots=0)","def test_get_random_recipes(self):\n pass","def test_default_weight(self):\n prod = Product('Test Product')\n self.assertEqual(prod.weight, 20)","def __init__(self):\n self.data = dict()\n self.total = 0\n self.rand = random.Random()","async def test_sampling_size_reduced(hass: HomeAssistant) -> None:\n assert await async_setup_component(\n hass,\n \"sensor\",\n {\n \"sensor\": [\n {\n \"platform\": \"statistics\",\n \"name\": \"test\",\n \"entity_id\": \"sensor.test_monitored\",\n \"state_characteristic\": \"mean\",\n \"sampling_size\": 5,\n },\n ]\n },\n )\n await hass.async_block_till_done()\n\n for value in VALUES_NUMERIC:\n hass.states.async_set(\n \"sensor.test_monitored\",\n str(value),\n {ATTR_UNIT_OF_MEASUREMENT: UnitOfTemperature.CELSIUS},\n )\n await hass.async_block_till_done()\n\n state = hass.states.get(\"sensor.test\")\n new_mean = round(sum(VALUES_NUMERIC[-5:]) / len(VALUES_NUMERIC[-5:]), 2)\n assert state is not None\n assert state.state == str(new_mean)\n assert state.attributes.get(\"buffer_usage_ratio\") == round(5 / 5, 2)","def test_claim_resources_older_alloc_req(self):\n get_resp_mock = mock.Mock(status_code=200)\n get_resp_mock.json.return_value = {\n 'allocations': {}, # build instance, not move\n }\n self.ks_adap_mock.get.return_value = get_resp_mock\n resp_mock = mock.Mock(status_code=204)\n self.ks_adap_mock.put.return_value = resp_mock\n consumer_uuid = uuids.consumer_uuid\n alloc_req = {\n 'allocations': {\n uuids.cn1: {\n 'resources': {\n 'VCPU': 1,\n 'MEMORY_MB': 1024,\n }\n },\n },\n }\n\n project_id = uuids.project_id\n user_id = uuids.user_id\n res = self.client.claim_resources(self.context, consumer_uuid,\n alloc_req, project_id, user_id,\n allocation_request_version='1.12')\n\n expected_url = \"/allocations/%s\" % consumer_uuid\n expected_payload = {\n 'allocations': {\n rp_uuid: res\n for rp_uuid, res in alloc_req['allocations'].items()},\n # no consumer generation in the payload as the caller requested\n # older microversion to be used\n 'project_id': project_id,\n 'user_id': user_id}\n self.ks_adap_mock.put.assert_called_once_with(\n expected_url, microversion='1.12', json=expected_payload,\n global_request_id=self.context.global_id)\n self.assertTrue(res)","def rand(self):\n raise NotImplementedError(\"Not implemented yet.\")","def computer_random():\r\n ci = random.sample(range(1,43),5)\r\n return ci","def __init__(self): \n self.min = 1000\n self.max = 9999\n self.secret_code = random.randint(self.min, self.max)","def test_loc_techs_storage_capacity_constraint(self):\n m = build_model({}, \"simple_storage,two_hours,investment_costs\")\n m.run(build_only=True)\n assert hasattr(m._backend_model, \"storage_max_constraint\")\n\n m = build_model({}, \"simple_supply_and_supply_plus,two_hours,investment_costs\")\n m.run(build_only=True)\n assert hasattr(m._backend_model, \"storage_max_constraint\")\n\n m = build_model(\n {\"techs.test_storage.constraints.storage_cap_equals\": 20},\n \"simple_storage,two_hours,investment_costs\",\n )\n m.run(build_only=True)\n assert m._backend_model.storage_cap[\"a\", \"test_storage\"].ub == 20\n assert m._backend_model.storage_cap[\"a\", \"test_storage\"].lb == 20","def test_gcp_generate_daily_w_resource_data(self):\n usage = random.randint(1, 10)\n cost = random.randint(1, 10)\n data = [\n {\n \"billing_account_id\": \"fact\",\n \"service_id\": \"95FF-2EF5-5EA1\",\n \"service_description\": \"Cloud Storage\",\n \"sku_id\": \"E5F0-6A5D-7BAD\",\n \"sku_description\": \"Standard Storage US Regional\",\n \"usage_start_time\": datetime(2022, 1, 1, 13, 0, 0),\n \"usage_end_time\": datetime(2022, 1, 1, 14, 0, 0),\n \"project_id\": \"trouble-although-mind\",\n \"project_name\": \"trouble-although-mind\",\n \"labels\": '{\"key\": \"test_storage_key\", \"value\": \"test_storage_label\"}',\n \"system_labels\": \"{}\",\n \"cost_type\": \"regular\",\n \"credits\": \"{}\",\n \"location_region\": \"us-central1\",\n \"usage_pricing_unit\": \"byte-seconds\",\n \"usage_amount_in_pricing_units\": usage,\n \"currency\": \"USD\",\n \"cost\": cost,\n \"invoice_month\": \"202201\",\n \"resource_name\": None,\n \"resource_global_name\": None,\n },\n {\n \"billing_account_id\": \"fact\",\n \"service_id\": \"95FF-2EF5-5EA1\",\n \"service_description\": \"Cloud Storage\",\n \"sku_id\": \"E5F0-6A5D-7BAD\",\n \"sku_description\": \"Standard Storage US Regional\",\n \"usage_start_time\": datetime(2022, 1, 1, 14, 0, 0),\n \"usage_end_time\": datetime(2022, 1, 1, 15, 0, 0),\n \"project_id\": \"trouble-although-mind\",\n \"project_name\": \"trouble-although-mind\",\n \"labels\": '{\"key\": \"test_storage_key\", \"value\": \"test_storage_label\"}',\n \"system_labels\": \"{}\",\n \"cost_type\": \"regular\",\n \"credits\": \"{}\",\n \"location_region\": \"us-central1\",\n \"usage_pricing_unit\": \"byte-seconds\",\n \"usage_amount_in_pricing_units\": usage,\n \"currency\": \"USD\",\n \"cost\": cost,\n \"invoice_month\": \"202201\",\n \"resource_name\": None,\n \"resource_global_name\": None,\n },\n {\n \"billing_account_id\": \"fact\",\n \"service_id\": \"95FF-2EF5-5EA1\",\n \"service_description\": \"Cloud Storage\",\n \"sku_id\": \"E5F0-6A5D-7BAD\",\n \"sku_description\": \"Standard Storage US Regional\",\n \"usage_start_time\": datetime(2022, 1, 2, 4, 0, 0),\n \"usage_end_time\": datetime(2022, 1, 2, 5, 0, 0),\n \"project_id\": \"trouble-although-mind\",\n \"project_name\": \"trouble-although-mind\",\n \"labels\": '{\"key\": \"test_storage_key\", \"value\": \"test_storage_label\"}',\n \"system_labels\": \"{}\",\n \"cost_type\": \"regular\",\n \"credits\": \"{}\",\n \"location_region\": \"us-central1\",\n \"usage_pricing_unit\": \"byte-seconds\",\n \"usage_amount_in_pricing_units\": usage,\n \"currency\": \"USD\",\n \"cost\": cost,\n \"invoice_month\": \"202201\",\n \"resource_name\": None,\n \"resource_global_name\": None,\n },\n ]\n df = DataFrame(data)\n daily_df = self.post_processor._generate_daily_data(df)\n\n first_day = daily_df[daily_df[\"usage_start_time\"] == \"2022-01-01\"]\n second_day = daily_df[daily_df[\"usage_start_time\"] == \"2022-01-02\"]\n\n self.assertTrue((first_day[\"cost\"] == cost * 2).bool())\n self.assertTrue((second_day[\"cost\"] == cost).bool())\n self.assertTrue((first_day[\"usage_amount_in_pricing_units\"] == usage * 2).bool())\n self.assertTrue((second_day[\"usage_amount_in_pricing_units\"] == usage).bool())","def random():\n return constant(1)","def test_str_thermal_capacity(self):\n xknx = XKNX()\n sensor = Sensor(\n xknx,\n \"TestSensor\",\n group_address_state=\"1/2/3\",\n value_type=\"thermal_capacity\",\n )\n sensor.sensor_value.payload = DPTArray(\n (\n 0x45,\n 0x83,\n 0xEA,\n 0xB3,\n )\n )\n\n self.assertEqual(sensor.resolve_state(), 4221.33740234375)\n self.assertEqual(sensor.unit_of_measurement(), \"J/K\")\n self.assertEqual(sensor.ha_device_class(), None)","def test_default_num_products(self):\r\n prod = generate_products()\r\n self.assertEqual(len(prod), 30)","def mt_rand(min = 0, max = sys.maxint):\n return random.randint(min, max)","def random(self):\r\n return random.randint(1, 4)","def mock_bestbuy():\n bestbuy = BestBuy()\n bestbuy.potato1 = 'batata baroa'\n bestbuy.potato2 = 'batata inglesa'\n bestbuy.potato_number = 666\n return bestbuy","def __init__(self):\n self.set_recharge(random.randint(1000, 2000) / 10000)\n operator_count = random.randint(1, 3)\n self.operators = [Solder() for _ in range(0, operator_count)]\n list_operators = [i.get_health for i in self.operators]\n self.set_health(sum(list_operators) / len(list_operators))","def test_default_product_weight(self):\r\n prod = Product('Test Product')\r\n self.assertEqual(prod.weight, 20)","def test_capacity(self, space_each_type):\n tspace = build_required_space(space_each_type, type_requirement=\"real\")\n assert tspace.cardinality == numpy.inf\n\n space = Space()\n probs = (0.1, 0.2, 0.3, 0.4)\n categories = (\"asdfa\", 2, 3, 4)\n dim = Categorical(\"yolo\", OrderedDict(zip(categories, probs)), shape=2)\n space.register(dim)\n dim = Integer(\"yolo2\", \"uniform\", -3, 6)\n space.register(dim)\n tspace = build_required_space(space, type_requirement=\"integer\")\n assert tspace.cardinality == (4**2) * (6 + 1)\n\n dim = Integer(\"yolo3\", \"uniform\", -3, 6, shape=(2, 1))\n space.register(dim)\n tspace = build_required_space(space, type_requirement=\"integer\")\n assert tspace.cardinality == (4**2) * (6 + 1) * ((6 + 1) ** (2 * 1))\n\n tspace = build_required_space(\n space, type_requirement=\"integer\", shape_requirement=\"flattened\"\n )\n assert tspace.cardinality == (4**2) * (6 + 1) * ((6 + 1) ** (2 * 1))\n\n tspace = build_required_space(\n space, type_requirement=\"integer\", dist_requirement=\"linear\"\n )\n assert tspace.cardinality == (4**2) * (6 + 1) * ((6 + 1) ** (2 * 1))","def _create_resource_consumption_dict():\n\n returned_resource_dict = {}\n\n # things that are quantities should start at 0.0\n for resource in resource_constants.quantity_resources:\n returned_resource_dict[resource] = 0.0\n\n for resource in resource_constants.item_resources:\n # double check there is no overlap...\n if resource in resource_constants.quantity_resources:\n raise InternalRepyError(\"Resource '\"+resource+\"' cannot be both quantity and item based!\")\n\n returned_resource_dict[resource] = set()\n\n # I need locks to protect races in accesses to some items...\n returned_resource_dict['fungible_locks'] = {}\n for init_resource in resource_constants.fungible_item_resources:\n returned_resource_dict['fungible_locks'][init_resource] = threading.Lock()\n\n returned_resource_dict['renewable_locks'] = {}\n for init_resource in resource_constants.renewable_resources:\n returned_resource_dict['renewable_locks'][init_resource] = threading.Lock()\n\n\n # I also need to track when the last update of a renewable resource occurred\n returned_resource_dict['renewable_update_time'] = {}\n\n # (Aside) JAC: I've thought about this and looked through the commit history.\n # I don't see any reason to initialize the renewable resources with the\n # current time (as was done before).\n for init_resource in resource_constants.renewable_resources:\n returned_resource_dict['renewable_update_time'][init_resource] = 0.0\n\n\n return returned_resource_dict","def _random_weight(self):\n return random.uniform(MIN_WEIGHT, MAX_WEIGHT)","def capacity(self):\n raise NotImplementedError()","def __init__(__self__, *,\n capacity: Optional[int] = None,\n name: Optional[str] = None):\n if capacity is not None:\n pulumi.set(__self__, \"capacity\", capacity)\n if name is not None:\n pulumi.set(__self__, \"name\", name)","def random():\r\n return R.NextDouble()","def calculate_init_stock(self, good=None):\n # random quantity * systemsize * techlevel (hence, it can be zero)\n size = self.planet.system_size\n level = self.planet.tech_level\n stock = random.randrange(0, 25) * (size + 1) * level\n\n # SPECIALRESOURCES add 50% production\n if self.planet.special in [self.tradeitem.cr]:\n stock = stock + (stock * 0.5)\n\n # TODO enhancement: difficulty levels should affect fuel stocks\n if good in ['fuel']:\n stock = stock * 10\n\n return int(stock)","def test_default_product_weight(self):\n prod = Product('Test Product')\n self.assertEqual(prod.weight, 20)","def test_default_product_weight(self):\n prod = Product('Test Product')\n self.assertEqual(prod.weight, 20)","def test_default_product_weight(self):\n prod = Product('Test Product')\n self.assertEqual(prod.weight, 20)","def test_default_product_weight(self):\n prod = Product('Test Product')\n self.assertEqual(prod.weight, 20)","def test_default_product_weight(self):\n prod = Product('Test Product')\n self.assertEqual(prod.weight, 20)","def test_default_product_weight(self):\n prod = Product('Test Product')\n self.assertEqual(prod.weight, 20)","def get_num_slots(self):\n # Your code here\n return self.capacity","def get_capacity():\n\n try:\n raw_capacity = PLIST[\"TotalSize\"]\n raw_capacity = str(raw_capacity)\n\n except KeyError:\n return \"Unknown\", \"Unknown\"\n\n #Round the sizes to make them human-readable.\n unit_list = [None, \"B\", \"KB\", \"MB\", \"GB\", \"TB\", \"PB\", \"EB\"]\n unit = \"B\"\n human_readable_size = int(raw_capacity)\n\n try:\n while len(str(human_readable_size)) > 3:\n #Shift up one unit.\n unit = unit_list[unit_list.index(unit)+1]\n human_readable_size = human_readable_size//1000\n\n except IndexError:\n return \"Unknown\", \"Unknown\"\n\n #Include the unit in the result for both exact and human-readable sizes.\n return raw_capacity, str(human_readable_size)+\" \"+unit","def test_sample_quality(self):\r\n self.assertEqual(self.test_sample.quality, 'medium')","def test_operate_storage(self, param):\n param = \"energy_cap_per_storage_cap_max\"\n m = build_model(\n {f\"techs.test_supply_plus.constraints.{param}\": 0.1},\n \"simple_supply_and_supply_plus,operate,investment_costs\",\n )\n\n with pytest.warns(exceptions.ModelWarning) as warning:\n with pytest.raises(exceptions.ModelError) as error:\n m.run(build_only=True)\n\n assert check_error_or_warning(\n error,\n \"fixed storage capacity * {} is not larger than fixed energy \"\n \"capacity for loc, tech {}\".format(param, (\"a\", \"test_supply_plus\")),\n )\n assert check_error_or_warning(\n warning,\n [\n \"Initial stored energy not defined\",\n \"Resource capacity constraint defined and set to infinity\",\n \"Storage cannot be cyclic in operate run mode\",\n ],\n )","def get_capacity_var(self):\n return self.get_interaction().get_capacity_var()","def test_default_num_products(self):\n test_list = generate_products()\n self.assertEqual(len(test_list), 30, msg=\"Length is Bad\")","async def test_sampling_size_1(hass: HomeAssistant) -> None:\n assert await async_setup_component(\n hass,\n \"sensor\",\n {\n \"sensor\": [\n {\n \"platform\": \"statistics\",\n \"name\": \"test\",\n \"entity_id\": \"sensor.test_monitored\",\n \"state_characteristic\": \"mean\",\n \"sampling_size\": 1,\n },\n ]\n },\n )\n await hass.async_block_till_done()\n\n for value in VALUES_NUMERIC:\n hass.states.async_set(\n \"sensor.test_monitored\",\n str(value),\n {ATTR_UNIT_OF_MEASUREMENT: UnitOfTemperature.CELSIUS},\n )\n await hass.async_block_till_done()\n\n state = hass.states.get(\"sensor.test\")\n new_mean = float(VALUES_NUMERIC[-1])\n assert state is not None\n assert state.state == str(new_mean)\n assert state.attributes.get(\"buffer_usage_ratio\") == round(1 / 1, 2)","async def test_random_caching(players, strats, _):\n game = gamegen.samplegame(players, strats)\n rest1, rest2 = game.random_restrictions(2)\n sched = gamesched.samplegamesched(game)\n sgame = schedgame.schedgame(sched)\n assert str(sgame) == str(sched)\n\n rgame11 = await sgame.get_restricted_game(rest1)\n rgame21 = await sgame.get_restricted_game(rest2)\n devs11 = await sgame.get_deviation_game(rest1)\n devs21 = await sgame.get_deviation_game(rest2)\n\n rgame12 = await sgame.get_restricted_game(rest1)\n rgame22 = await sgame.get_restricted_game(rest2)\n assert rgame11 == rgame12\n assert rgame21 == rgame22\n\n devs12 = await sgame.get_deviation_game(rest1)\n devs22 = await sgame.get_deviation_game(rest2)\n assert devs11 == devs12\n assert devs21 == devs22","def _tattle_quantity(resource, quantity, resourcesalloweddict, resourcesuseddict):\n\n\n # I assume that the quantity will never be negative\n if quantity < 0:\n # This will cause the program to exit and log things if logging is\n # enabled. -Brent\n tracebackrepy.handle_internalerror(\"Resource '\" + resource + \n \"' has a negative quantity \" + str(quantity) + \"!\", 132)\n \n # get the lock for this resource\n resourcesuseddict['renewable_locks'][resource].acquire()\n \n # release the lock afterwards no matter what\n try: \n # update the resource counters based upon the current time.\n _update_resource_consumption_table(resource, resourcesalloweddict, resourcesuseddict)\n\n # It's renewable, so I can wait for it to clear\n if resource not in resource_constants.renewable_resources:\n # Should never have a quantity tattle for a non-renewable resource\n # This will cause the program to exit and log things if logging is\n # enabled. -Brent\n tracebackrepy.handle_internalerror(\"Resource '\" + resource + \n \"' is not renewable!\", 133)\n \n\n resourcesuseddict[resource] = resourcesuseddict[resource] + quantity\n # I'll block if I'm over...\n _sleep_until_resource_drains(resource, resourcesalloweddict, resourcesuseddict)\n \n finally:\n # release the lock for this resource\n resourcesuseddict['renewable_locks'][resource].release()","def _randomVelocity(self):\n\t\treturn random.choice([-1, 1]) * random.randint(10, 50)","def capacity(self) -> typing.Optional[str]:\n value = self._properties.get(\"capacity\")\n return f\"{value}\" if value is not None else None","def test_default_num_products(self):\n products = generate_products()\n self.assertEqual(len(products), 30)","def random_factory(time_step, migration_time, params):\n return lambda vms_cpu, vms_ram, state=None: ([random(vms_cpu)], {})","def __get_deal_price(self):\n return self.create_random_decimal(min=1, max=100000)","def set_generator(random, args):\n representation = args.get('representation')\n indices = list(range(len(representation)))\n max_size = args.get('max_size', 9)\n variable_size = args.get('variable_size', True)\n if variable_size and max_size > 1:\n size = random.randint(1, max_size)\n else:\n size = max_size\n candidate = random.sample(indices, size)\n return sorted(candidate)","def test_patch_cluster_resource_quota(self):\n pass","def test_operate_resource_cap_max(self, on):\n\n if on is False:\n override = {}\n else:\n override = {\"techs.test_supply_plus.constraints.resource_cap_max\": 1e6}\n m = build_model(\n override, \"simple_supply_and_supply_plus,operate,investment_costs\"\n )\n\n with pytest.warns(exceptions.ModelWarning) as warning:\n m.run(build_only=True)\n if on is False:\n assert check_error_or_warning(\n warning, \"Resource capacity constraint defined and set to infinity\"\n )\n assert np.isinf(\n m._model_data.resource_cap.loc[\"a\", \"test_supply_plus\"].item()\n )\n elif on is True:\n assert not check_error_or_warning(\n warning, \"Resource capacity constraint defined and set to infinity\"\n )\n assert m._model_data.resource_cap.loc[\"a\", \"test_supply_plus\"].item() == 1e6","def test_limit_memory(self):\n url = '/api/apps'\n body = {'cluster': 'autotest'}\n response = self.client.post(url, json.dumps(body), content_type='application/json')\n self.assertEqual(response.status_code, 201)\n app_id = response.data['id']\n url = '/api/apps/{app_id}/limits'.format(**locals())\n # check default limit\n response = self.client.get(url, content_type='application/json')\n self.assertEqual(response.status_code, 200)\n self.assertIn('memory', response.data)\n self.assertEqual(json.loads(response.data['memory']), {})\n # regression test for https://github.com/deis/deis/issues/1563\n self.assertNotIn('\"', response.data['memory'])\n # set an initial limit\n mem = {'web': '1G'}\n body = {'memory': json.dumps(mem)}\n response = self.client.post(url, json.dumps(body), content_type='application/json')\n self.assertEqual(response.status_code, 201)\n self.assertIn('x-deis-release', response._headers)\n limit1 = response.data\n # check memory limits\n response = self.client.get(url, content_type='application/json')\n self.assertEqual(response.status_code, 200)\n self.assertIn('memory', response.data)\n memory = json.loads(response.data['memory'])\n self.assertIn('web', memory)\n self.assertEqual(memory['web'], '1G')\n # set an additional value\n body = {'memory': json.dumps({'worker': '512M'})}\n response = self.client.post(url, json.dumps(body), content_type='application/json')\n self.assertEqual(response.status_code, 201)\n limit2 = response.data\n self.assertNotEqual(limit1['uuid'], limit2['uuid'])\n memory = json.loads(response.data['memory'])\n self.assertIn('worker', memory)\n self.assertEqual(memory['worker'], '512M')\n self.assertIn('web', memory)\n self.assertEqual(memory['web'], '1G')\n # read the limit again\n response = self.client.get(url)\n self.assertEqual(response.status_code, 200)\n limit3 = response.data\n self.assertEqual(limit2, limit3)\n memory = json.loads(response.data['memory'])\n self.assertIn('worker', memory)\n self.assertEqual(memory['worker'], '512M')\n self.assertIn('web', memory)\n self.assertEqual(memory['web'], '1G')\n # unset a value\n body = {'memory': json.dumps({'worker': None})}\n response = self.client.post(url, json.dumps(body), content_type='application/json')\n self.assertEqual(response.status_code, 201)\n limit4 = response.data\n self.assertNotEqual(limit3['uuid'], limit4['uuid'])\n self.assertNotIn('worker', json.dumps(response.data['memory']))\n # disallow put/patch/delete\n self.assertEqual(self.client.put(url).status_code, 405)\n self.assertEqual(self.client.patch(url).status_code, 405)\n self.assertEqual(self.client.delete(url).status_code, 405)\n return limit4","def test_gcp_generate_daily_wo_resource_data(self):\n usage = random.randint(1, 10)\n cost = random.randint(1, 10)\n data = [\n {\n \"billing_account_id\": \"fact\",\n \"service_id\": \"95FF-2EF5-5EA1\",\n \"service_description\": \"Cloud Storage\",\n \"sku_id\": \"E5F0-6A5D-7BAD\",\n \"sku_description\": \"Standard Storage US Regional\",\n \"usage_start_time\": datetime(2022, 1, 1, 13, 0, 0),\n \"usage_end_time\": datetime(2022, 1, 1, 14, 0, 0),\n \"project_id\": \"trouble-although-mind\",\n \"project_name\": \"trouble-although-mind\",\n \"labels\": '{\"key\": \"test_storage_key\", \"value\": \"test_storage_label\"}',\n \"system_labels\": \"{}\",\n \"cost_type\": \"regular\",\n \"credits\": \"{}\",\n \"location_region\": \"us-central1\",\n \"usage_pricing_unit\": \"byte-seconds\",\n \"usage_amount_in_pricing_units\": usage,\n \"currency\": \"USD\",\n \"cost\": cost,\n \"invoice_month\": \"202201\",\n },\n {\n \"billing_account_id\": \"fact\",\n \"service_id\": \"95FF-2EF5-5EA1\",\n \"service_description\": \"Cloud Storage\",\n \"sku_id\": \"E5F0-6A5D-7BAD\",\n \"sku_description\": \"Standard Storage US Regional\",\n \"usage_start_time\": datetime(2022, 1, 1, 14, 0, 0),\n \"usage_end_time\": datetime(2022, 1, 1, 15, 0, 0),\n \"project_id\": \"trouble-although-mind\",\n \"project_name\": \"trouble-although-mind\",\n \"labels\": '{\"key\": \"test_storage_key\", \"value\": \"test_storage_label\"}',\n \"system_labels\": \"{}\",\n \"cost_type\": \"regular\",\n \"credits\": \"{}\",\n \"location_region\": \"us-central1\",\n \"usage_pricing_unit\": \"byte-seconds\",\n \"usage_amount_in_pricing_units\": usage,\n \"currency\": \"USD\",\n \"cost\": cost,\n \"invoice_month\": \"202201\",\n },\n {\n \"billing_account_id\": \"fact\",\n \"service_id\": \"95FF-2EF5-5EA1\",\n \"service_description\": \"Cloud Storage\",\n \"sku_id\": \"E5F0-6A5D-7BAD\",\n \"sku_description\": \"Standard Storage US Regional\",\n \"usage_start_time\": datetime(2022, 1, 2, 4, 0, 0),\n \"usage_end_time\": datetime(2022, 1, 2, 5, 0, 0),\n \"project_id\": \"trouble-although-mind\",\n \"project_name\": \"trouble-although-mind\",\n \"labels\": '{\"key\": \"test_storage_key\", \"value\": \"test_storage_label\"}',\n \"system_labels\": \"{}\",\n \"cost_type\": \"regular\",\n \"credits\": \"{}\",\n \"location_region\": \"us-central1\",\n \"usage_pricing_unit\": \"byte-seconds\",\n \"usage_amount_in_pricing_units\": usage,\n \"currency\": \"USD\",\n \"cost\": cost,\n \"invoice_month\": \"202201\",\n },\n ]\n df = DataFrame(data)\n daily_df = self.post_processor._generate_daily_data(df)\n\n first_day = daily_df[daily_df[\"usage_start_time\"] == \"2022-01-01\"]\n second_day = daily_df[daily_df[\"usage_start_time\"] == \"2022-01-02\"]\n\n self.assertEqual(first_day.shape[0], 1)\n self.assertEqual(second_day.shape[0], 1)\n\n self.assertTrue((first_day[\"cost\"] == cost * 2).bool())\n self.assertTrue((second_day[\"cost\"] == cost).bool())\n self.assertTrue((first_day[\"usage_amount_in_pricing_units\"] == usage * 2).bool())\n self.assertTrue((second_day[\"usage_amount_in_pricing_units\"] == usage).bool())","def __init__(self, test_resources):\n self.info = None\n self.is_mock = False\n self.cluster = None\n self.bucket = None\n self.bucket_name = None\n self.cluster_version = None\n self.set_test_resources(test_resources)","def generator(self, random, args):\r\n if self.duplicates:\r\n max_count = [self.capacity // item[0] for item in self.items]\r\n return [random.randint(0, m) for m in max_count]\r\n else:\r\n return [random.choice([0, 1]) for _ in range(len(self.items))]","def random_temp():\n temp_min = 154\n temp_max = 500\n temp_interval = 1\n # `range`s are exclusive [min, max)\n return random.randrange(temp_min, temp_max + 1, temp_interval)","def sample_consumption():\n product = Product(\"NameA\", 15, 17.85, 0.07, \"oak\", 0.08, \"ENplusA1\",\n \"Pelletics.cz\", date(2020, 12, 20))\n delivery = Delivery(product, \"Pellets2Home\", 7350, 42500,\n date(2020, 12, 20))\n some_datetime = datetime(2020, 11, 20, 14, 22, 46, 0)\n consumption = Consumption(some_datetime, delivery, 30, \"30 kgs\")\n return consumption","def test_default_num_products(self):\n product_list = generate_products()\n self.assertEqual(len(product_list), 30)","def init_devices(self):\n self.hp_nb = int(self.rs_nb* self.hp_proportion/(1- self.hp_proportion))\n self.defense_cost = self.hp_nb * self.hp_unit_cost\n rs_devices = [True for i in range(self.rs_nb)] #rs --> True\n hp_devices = [False for i in range(self.hp_nb)] #hp --> False\n self.devices = rs_devices + hp_devices\n shuffle(self.devices)","def base_pick():\n\n rnd = generate_random(2, 15)\n return rnd","def get_additional_ball_capacity(cls):\n return 999","def test_list_cluster_resource_quota(self):\n pass","def capacity(self) -> pulumi.Input[int]:\n return pulumi.get(self, \"capacity\")","def test_default_num_products(self):\r\n lst = generate_products()\r\n self.assertEqual(len(lst), 30)","def system_quality_attribute(self) -> str:\n return self.random.choice(SYSTEM_QUALITY_ATTRIBUTES)","async def test_stretch_sensor_entities(\n hass: HomeAssistant, mock_stretch: MagicMock, init_integration: MockConfigEntry\n) -> None:\n state = hass.states.get(\"sensor.koelkast_92c4a_electricity_consumed\")\n assert state\n assert float(state.state) == 50.5\n\n state = hass.states.get(\"sensor.droger_52559_electricity_consumed_interval\")\n assert state\n assert float(state.state) == 0.0","def initialize_supply(self):\n unit_count = 0\n for i in range(self.start_allocation[0 ] -1, self.start_allocation[1]):\n for j in range(len(self.capacity_list[i][1])):\n self.capacity_list[i][1][j] = 1\n unit_count += 1\n self.total_supply -= unit_count","def rand_gen(below, baseline):\n\treturn secrets.randbelow(below)/ baseline"],"string":"[\n \"def test_glass_capacity__has_expected_default_value():\\n glass = moet.create_glass(\\\"A\\\")\\n assert glass.capacity == 250\",\n \"def _get_random_returns(self): \\n return self.asset_process.distrib.random()\",\n \"def test_sdram(self):\\n sdram = SDRAMResource(128 * (2**20))\\n self.assertEqual(sdram.get_value(), 128 * (2**20))\\n sdram = SDRAMResource(128 * (2**19))\\n self.assertEqual(sdram.get_value(), 128 * (2**19))\\n sdram = SDRAMResource(128 * (2**21))\\n self.assertEqual(sdram.get_value(), 128 * (2**21))\",\n \"def __init__(self, capacity, units=0):\\n self.capacity = capacity\\n self.units = units\",\n \"def _get_random_value(self):\\r\\n return random.randint(1, 10)\",\n \"def random(vms_cpu):\\n return choice(vms_cpu.keys())\",\n \"def randomize_value(self) -> None:\",\n \"def test_rest_v20_dd_systems_systemid_stats_capacity_get(self):\\n pass\",\n \"def __init__(self):\\n self.set_health(100)\\n self.set_recharge(random.randint(100, 2000) / 10000)\",\n \"def generate_products(self = random.sample, name = random.choice(result), price = random.randint(5, 100), weight = random.randint(5, 100), \\nflammability= random.uniform(0, 2.5)):\\n return sample\",\n \"def test_set_glass_capacity__with_valid_numbers__returns_expected():\\n glass = moet.create_glass(\\\"A\\\")\\n numbers = [0, 1, 250, 0.0, 100.5]\\n for number in numbers:\\n glass.capacity = number\\n assert glass.capacity == number\",\n \"def __init__(self, capacity, initial):\\n\\t\\tself.capacity = capacity\\n\\t\\tself.amount = initial\",\n \"def test_loc_techs_resource_capacity_constraint(self, override):\\n\\n if override is None:\\n m = build_model(\\n {}, \\\"simple_supply_and_supply_plus,two_hours,investment_costs\\\"\\n )\\n m.run(build_only=True)\\n expr = m._backend_model.resource_cap[(\\\"b\\\", \\\"test_supply_plus\\\")]\\n assert expr.lb == 0\\n assert expr.ub == np.inf\\n\\n else:\\n m = build_model(\\n {\\n \\\"techs.test_supply_plus.constraints.resource_cap_{}\\\".format(\\n override\\n ): 10\\n },\\n \\\"simple_supply_and_supply_plus,two_hours,investment_costs\\\",\\n )\\n m.run(build_only=True)\\n expr = m._backend_model.resource_cap[(\\\"b\\\", \\\"test_supply_plus\\\")]\\n if override == \\\"max\\\":\\n assert expr.ub == 10\\n assert expr.lb == 0\\n elif override == \\\"equals\\\":\\n assert expr.ub == 10\\n assert expr.lb == 10\\n if override == \\\"min\\\":\\n assert expr.lb == 10\\n assert expr.ub == np.inf\",\n \"def randomize(self):\\n self.size = randint(1,5)\\n self.resource = randint(1,3)\\n self.temperature = randint(20, 1000)\\n self.gravity = randint(0, 10)\\n for key in self.get_atmosphere().keys():\\n setattr(self, key, randint(0, 5))\\n for attribute_count in range(randint(0, 3)):\\n pa = PlanetaryAttribute.objects.order_by('?')[0]\\n self.attributes.add(pa)\",\n \"def init_physical_resources():\\n test_physical_resources = []\\n\\n # add info to list in memory, one by one, following signature values\\n phys_resrc_ID = 1\\n phys_resrc_name = \\\"small-cavium-1\\\"\\n phys_resrc_info = \\\"Jump server in Arm pod, 48 cores, 64G RAM, 447G SSD, aarch64 Cavium ThunderX, Ubuntu OS\\\"\\n phys_resrc_IPAddress = \\\"10.10.50.12\\\"\\n phys_resrc_MACAddress = \\\"00-14-22-01-23-45\\\"\\n\\n test_physical_resources.append(PhysicalResource(phys_resrc_ID, phys_resrc_name,\\n phys_resrc_info,\\n phys_resrc_IPAddress,\\n phys_resrc_MACAddress))\\n\\n phys_resrc_ID = 2\\n phys_resrc_name = \\\"medium-cavium-1\\\"\\n phys_resrc_info = \\\"Jump server in New York pod, 96 cores, 64G RAM, 447G SSD, aarch64 Cavium ThunderX, Ubuntu OS\\\"\\n phys_resrc_IPAddress = \\\"30.31.32.33\\\"\\n phys_resrc_MACAddress = \\\"0xb3:22:05:c1:aa:82\\\"\\n\\n test_physical_resources.append(PhysicalResource(phys_resrc_ID, phys_resrc_name,\\n phys_resrc_info,\\n phys_resrc_IPAddress,\\n phys_resrc_MACAddress))\\n\\n phys_resrc_ID = 3\\n phys_resrc_name = \\\"mega-cavium-666\\\"\\n phys_resrc_info = \\\"Jump server in Las Vegas, 1024 cores, 1024G RAM, 6666G SSD, aarch64 Cavium ThunderX, Ubuntu OS\\\"\\n phys_resrc_IPAddress = \\\"54.53.52.51\\\"\\n phys_resrc_MACAddress = \\\"01-23-45-67-89-ab\\\"\\n\\n test_physical_resources.append(PhysicalResource(phys_resrc_ID, phys_resrc_name,\\n phys_resrc_info,\\n phys_resrc_IPAddress,\\n phys_resrc_MACAddress))\\n\\n\\n # write list to binary file\\n write_list_bin(test_physical_resources, FILE_PHYSICAL_RESOURCES)\\n\\n return test_physical_resources\",\n \"def test_cpu(self):\\n cpu = CPUCyclesResource(128 * (2**20))\\n self.assertEqual(cpu.get_value(), 128 * (2**20))\\n cpu = CPUCyclesResource(128 * (2**19))\\n self.assertEqual(cpu.get_value(), 128 * (2**19))\\n cpu = CPUCyclesResource(128 * (2**21))\\n self.assertEqual(cpu.get_value(), 128 * (2**21))\",\n \"def test_set_glass_capacity__with_invalid_numbers__returns_expected():\\n glass = moet.create_glass(\\\"A\\\")\\n with pytest.raises(ValueError):\\n glass.capacity = -100\",\n \"def get_random(self):\\n base_genom = \\\"1\\\" * sum(self._size_var)\\n return utils.randomise_a_string(base_genom)\",\n \"def Capacity(self) -> int:\",\n \"def capacity_used(self):\\n raise NotImplementedError()\",\n \"def test_resource_container(self):\\n sdram = SDRAMResource(128 * (2**20))\\n dtcm = DTCMResource(128 * (2**20) + 1)\\n cpu = CPUCyclesResource(128 * (2**20) + 2)\\n\\n container = ResourceContainer(dtcm, sdram, cpu)\\n self.assertEqual(container.sdram.get_value(), 128 * (2**20))\\n self.assertEqual(container.dtcm.get_value(), 128 * (2**20) + 1)\\n self.assertEqual(container.cpu.get_value(), 128 * (2**20) + 2)\\n\\n sdram = SDRAMResource(128 * (2**19))\\n dtcm = DTCMResource(128 * (2**19) + 1)\\n cpu = CPUCyclesResource(128 * (2**19) + 2)\\n\\n container = ResourceContainer(dtcm, sdram, cpu)\\n self.assertEqual(container.sdram.get_value(), 128 * (2**19))\\n self.assertEqual(container.dtcm.get_value(), 128 * (2**19) + 1)\\n self.assertEqual(container.cpu.get_value(), 128 * (2**19) + 2)\\n\\n sdram = SDRAMResource(128 * (2**21))\\n dtcm = DTCMResource(128 * (2**21) + 1)\\n cpu = CPUCyclesResource(128 * (2**21) + 2)\\n\\n container = ResourceContainer(dtcm, sdram, cpu)\\n self.assertEqual(container.sdram.get_value(), 128 * (2**21))\\n self.assertEqual(container.dtcm.get_value(), 128 * (2**21) + 1)\\n self.assertEqual(container.cpu.get_value(), 128 * (2**21) + 2)\",\n \"def get_capacity():\\n fs.get_capacity()\",\n \"def test_read_cluster_resource_quota(self):\\n pass\",\n \"def get_capacity_var(self):\\n return self._capacity_var\",\n \"def test_capacity_factor(pudl_out_eia):\\n print(\\\"\\\\nCalculating generator capacity factors...\\\")\\n cf = pudl_out_eia.capacity_factor()\\n print(f\\\" capacity_factor: {len(cf)} records\\\")\",\n \"def test_limit_cpu(self):\\n url = '/api/apps'\\n body = {'cluster': 'autotest'}\\n response = self.client.post(url, json.dumps(body), content_type='application/json')\\n self.assertEqual(response.status_code, 201)\\n app_id = response.data['id']\\n url = '/api/apps/{app_id}/limits'.format(**locals())\\n # check default limit\\n response = self.client.get(url, content_type='application/json')\\n self.assertEqual(response.status_code, 200)\\n self.assertIn('cpu', response.data)\\n self.assertEqual(json.loads(response.data['cpu']), {})\\n # regression test for https://github.com/deis/deis/issues/1563\\n self.assertNotIn('\\\"', response.data['cpu'])\\n # set an initial limit\\n body = {'cpu': json.dumps({'web': '1024'})}\\n response = self.client.post(url, json.dumps(body), content_type='application/json')\\n self.assertEqual(response.status_code, 201)\\n self.assertIn('x-deis-release', response._headers)\\n limit1 = response.data\\n # check memory limits\\n response = self.client.get(url, content_type='application/json')\\n self.assertEqual(response.status_code, 200)\\n self.assertIn('cpu', response.data)\\n cpu = json.loads(response.data['cpu'])\\n self.assertIn('web', cpu)\\n self.assertEqual(cpu['web'], '1024')\\n # set an additional value\\n body = {'cpu': json.dumps({'worker': '512'})}\\n response = self.client.post(url, json.dumps(body), content_type='application/json')\\n self.assertEqual(response.status_code, 201)\\n limit2 = response.data\\n self.assertNotEqual(limit1['uuid'], limit2['uuid'])\\n cpu = json.loads(response.data['cpu'])\\n self.assertIn('worker', cpu)\\n self.assertEqual(cpu['worker'], '512')\\n self.assertIn('web', cpu)\\n self.assertEqual(cpu['web'], '1024')\\n # read the limit again\\n response = self.client.get(url)\\n self.assertEqual(response.status_code, 200)\\n limit3 = response.data\\n self.assertEqual(limit2, limit3)\\n cpu = json.loads(response.data['cpu'])\\n self.assertIn('worker', cpu)\\n self.assertEqual(cpu['worker'], '512')\\n self.assertIn('web', cpu)\\n self.assertEqual(cpu['web'], '1024')\\n # unset a value\\n body = {'memory': json.dumps({'worker': None})}\\n response = self.client.post(url, json.dumps(body), content_type='application/json')\\n self.assertEqual(response.status_code, 201)\\n limit4 = response.data\\n self.assertNotEqual(limit3['uuid'], limit4['uuid'])\\n self.assertNotIn('worker', json.dumps(response.data['memory']))\\n # disallow put/patch/delete\\n self.assertEqual(self.client.put(url).status_code, 405)\\n self.assertEqual(self.client.patch(url).status_code, 405)\\n self.assertEqual(self.client.delete(url).status_code, 405)\\n return limit4\",\n \"def test_create_cluster_resource_quota(self):\\n pass\",\n \"def test_sample(system_generator):\\n\\n name, test = system_generator()\\n print(name)\\n\\n w_F, w_R, N_k = test.sample([10, 8], mode=\\\"wFwR\\\")\\n w_F, w_R, N_k = test.sample([1, 1], mode=\\\"wFwR\\\")\\n w_F, w_R, N_k = test.sample([10, 0], mode=\\\"wFwR\\\")\\n w_F, w_R, N_k = test.sample([0, 5], mode=\\\"wFwR\\\")\",\n \"def get_capacity_param(self):\\n intr = self.get_interaction()\\n return intr.get_capacity(None, None, None, None, raw=True)\",\n \"def test_default_num(self):\\n products = generate_products()\\n self.assertEqual(len(products), 30)\",\n \"def random_test(self):\\r\\n return 1\",\n \"def random_test(self):\\r\\n return 1\",\n \"def rand(self):\\n raise NotImplementedError\",\n \"def test_collect_demands(self):\\n pass\",\n \"def test_qpu_0_shots():\\n _aws_device(wires=2, shots=0)\",\n \"def test_get_random_recipes(self):\\n pass\",\n \"def test_default_weight(self):\\n prod = Product('Test Product')\\n self.assertEqual(prod.weight, 20)\",\n \"def __init__(self):\\n self.data = dict()\\n self.total = 0\\n self.rand = random.Random()\",\n \"async def test_sampling_size_reduced(hass: HomeAssistant) -> None:\\n assert await async_setup_component(\\n hass,\\n \\\"sensor\\\",\\n {\\n \\\"sensor\\\": [\\n {\\n \\\"platform\\\": \\\"statistics\\\",\\n \\\"name\\\": \\\"test\\\",\\n \\\"entity_id\\\": \\\"sensor.test_monitored\\\",\\n \\\"state_characteristic\\\": \\\"mean\\\",\\n \\\"sampling_size\\\": 5,\\n },\\n ]\\n },\\n )\\n await hass.async_block_till_done()\\n\\n for value in VALUES_NUMERIC:\\n hass.states.async_set(\\n \\\"sensor.test_monitored\\\",\\n str(value),\\n {ATTR_UNIT_OF_MEASUREMENT: UnitOfTemperature.CELSIUS},\\n )\\n await hass.async_block_till_done()\\n\\n state = hass.states.get(\\\"sensor.test\\\")\\n new_mean = round(sum(VALUES_NUMERIC[-5:]) / len(VALUES_NUMERIC[-5:]), 2)\\n assert state is not None\\n assert state.state == str(new_mean)\\n assert state.attributes.get(\\\"buffer_usage_ratio\\\") == round(5 / 5, 2)\",\n \"def test_claim_resources_older_alloc_req(self):\\n get_resp_mock = mock.Mock(status_code=200)\\n get_resp_mock.json.return_value = {\\n 'allocations': {}, # build instance, not move\\n }\\n self.ks_adap_mock.get.return_value = get_resp_mock\\n resp_mock = mock.Mock(status_code=204)\\n self.ks_adap_mock.put.return_value = resp_mock\\n consumer_uuid = uuids.consumer_uuid\\n alloc_req = {\\n 'allocations': {\\n uuids.cn1: {\\n 'resources': {\\n 'VCPU': 1,\\n 'MEMORY_MB': 1024,\\n }\\n },\\n },\\n }\\n\\n project_id = uuids.project_id\\n user_id = uuids.user_id\\n res = self.client.claim_resources(self.context, consumer_uuid,\\n alloc_req, project_id, user_id,\\n allocation_request_version='1.12')\\n\\n expected_url = \\\"/allocations/%s\\\" % consumer_uuid\\n expected_payload = {\\n 'allocations': {\\n rp_uuid: res\\n for rp_uuid, res in alloc_req['allocations'].items()},\\n # no consumer generation in the payload as the caller requested\\n # older microversion to be used\\n 'project_id': project_id,\\n 'user_id': user_id}\\n self.ks_adap_mock.put.assert_called_once_with(\\n expected_url, microversion='1.12', json=expected_payload,\\n global_request_id=self.context.global_id)\\n self.assertTrue(res)\",\n \"def rand(self):\\n raise NotImplementedError(\\\"Not implemented yet.\\\")\",\n \"def computer_random():\\r\\n ci = random.sample(range(1,43),5)\\r\\n return ci\",\n \"def __init__(self): \\n self.min = 1000\\n self.max = 9999\\n self.secret_code = random.randint(self.min, self.max)\",\n \"def test_loc_techs_storage_capacity_constraint(self):\\n m = build_model({}, \\\"simple_storage,two_hours,investment_costs\\\")\\n m.run(build_only=True)\\n assert hasattr(m._backend_model, \\\"storage_max_constraint\\\")\\n\\n m = build_model({}, \\\"simple_supply_and_supply_plus,two_hours,investment_costs\\\")\\n m.run(build_only=True)\\n assert hasattr(m._backend_model, \\\"storage_max_constraint\\\")\\n\\n m = build_model(\\n {\\\"techs.test_storage.constraints.storage_cap_equals\\\": 20},\\n \\\"simple_storage,two_hours,investment_costs\\\",\\n )\\n m.run(build_only=True)\\n assert m._backend_model.storage_cap[\\\"a\\\", \\\"test_storage\\\"].ub == 20\\n assert m._backend_model.storage_cap[\\\"a\\\", \\\"test_storage\\\"].lb == 20\",\n \"def test_gcp_generate_daily_w_resource_data(self):\\n usage = random.randint(1, 10)\\n cost = random.randint(1, 10)\\n data = [\\n {\\n \\\"billing_account_id\\\": \\\"fact\\\",\\n \\\"service_id\\\": \\\"95FF-2EF5-5EA1\\\",\\n \\\"service_description\\\": \\\"Cloud Storage\\\",\\n \\\"sku_id\\\": \\\"E5F0-6A5D-7BAD\\\",\\n \\\"sku_description\\\": \\\"Standard Storage US Regional\\\",\\n \\\"usage_start_time\\\": datetime(2022, 1, 1, 13, 0, 0),\\n \\\"usage_end_time\\\": datetime(2022, 1, 1, 14, 0, 0),\\n \\\"project_id\\\": \\\"trouble-although-mind\\\",\\n \\\"project_name\\\": \\\"trouble-although-mind\\\",\\n \\\"labels\\\": '{\\\"key\\\": \\\"test_storage_key\\\", \\\"value\\\": \\\"test_storage_label\\\"}',\\n \\\"system_labels\\\": \\\"{}\\\",\\n \\\"cost_type\\\": \\\"regular\\\",\\n \\\"credits\\\": \\\"{}\\\",\\n \\\"location_region\\\": \\\"us-central1\\\",\\n \\\"usage_pricing_unit\\\": \\\"byte-seconds\\\",\\n \\\"usage_amount_in_pricing_units\\\": usage,\\n \\\"currency\\\": \\\"USD\\\",\\n \\\"cost\\\": cost,\\n \\\"invoice_month\\\": \\\"202201\\\",\\n \\\"resource_name\\\": None,\\n \\\"resource_global_name\\\": None,\\n },\\n {\\n \\\"billing_account_id\\\": \\\"fact\\\",\\n \\\"service_id\\\": \\\"95FF-2EF5-5EA1\\\",\\n \\\"service_description\\\": \\\"Cloud Storage\\\",\\n \\\"sku_id\\\": \\\"E5F0-6A5D-7BAD\\\",\\n \\\"sku_description\\\": \\\"Standard Storage US Regional\\\",\\n \\\"usage_start_time\\\": datetime(2022, 1, 1, 14, 0, 0),\\n \\\"usage_end_time\\\": datetime(2022, 1, 1, 15, 0, 0),\\n \\\"project_id\\\": \\\"trouble-although-mind\\\",\\n \\\"project_name\\\": \\\"trouble-although-mind\\\",\\n \\\"labels\\\": '{\\\"key\\\": \\\"test_storage_key\\\", \\\"value\\\": \\\"test_storage_label\\\"}',\\n \\\"system_labels\\\": \\\"{}\\\",\\n \\\"cost_type\\\": \\\"regular\\\",\\n \\\"credits\\\": \\\"{}\\\",\\n \\\"location_region\\\": \\\"us-central1\\\",\\n \\\"usage_pricing_unit\\\": \\\"byte-seconds\\\",\\n \\\"usage_amount_in_pricing_units\\\": usage,\\n \\\"currency\\\": \\\"USD\\\",\\n \\\"cost\\\": cost,\\n \\\"invoice_month\\\": \\\"202201\\\",\\n \\\"resource_name\\\": None,\\n \\\"resource_global_name\\\": None,\\n },\\n {\\n \\\"billing_account_id\\\": \\\"fact\\\",\\n \\\"service_id\\\": \\\"95FF-2EF5-5EA1\\\",\\n \\\"service_description\\\": \\\"Cloud Storage\\\",\\n \\\"sku_id\\\": \\\"E5F0-6A5D-7BAD\\\",\\n \\\"sku_description\\\": \\\"Standard Storage US Regional\\\",\\n \\\"usage_start_time\\\": datetime(2022, 1, 2, 4, 0, 0),\\n \\\"usage_end_time\\\": datetime(2022, 1, 2, 5, 0, 0),\\n \\\"project_id\\\": \\\"trouble-although-mind\\\",\\n \\\"project_name\\\": \\\"trouble-although-mind\\\",\\n \\\"labels\\\": '{\\\"key\\\": \\\"test_storage_key\\\", \\\"value\\\": \\\"test_storage_label\\\"}',\\n \\\"system_labels\\\": \\\"{}\\\",\\n \\\"cost_type\\\": \\\"regular\\\",\\n \\\"credits\\\": \\\"{}\\\",\\n \\\"location_region\\\": \\\"us-central1\\\",\\n \\\"usage_pricing_unit\\\": \\\"byte-seconds\\\",\\n \\\"usage_amount_in_pricing_units\\\": usage,\\n \\\"currency\\\": \\\"USD\\\",\\n \\\"cost\\\": cost,\\n \\\"invoice_month\\\": \\\"202201\\\",\\n \\\"resource_name\\\": None,\\n \\\"resource_global_name\\\": None,\\n },\\n ]\\n df = DataFrame(data)\\n daily_df = self.post_processor._generate_daily_data(df)\\n\\n first_day = daily_df[daily_df[\\\"usage_start_time\\\"] == \\\"2022-01-01\\\"]\\n second_day = daily_df[daily_df[\\\"usage_start_time\\\"] == \\\"2022-01-02\\\"]\\n\\n self.assertTrue((first_day[\\\"cost\\\"] == cost * 2).bool())\\n self.assertTrue((second_day[\\\"cost\\\"] == cost).bool())\\n self.assertTrue((first_day[\\\"usage_amount_in_pricing_units\\\"] == usage * 2).bool())\\n self.assertTrue((second_day[\\\"usage_amount_in_pricing_units\\\"] == usage).bool())\",\n \"def random():\\n return constant(1)\",\n \"def test_str_thermal_capacity(self):\\n xknx = XKNX()\\n sensor = Sensor(\\n xknx,\\n \\\"TestSensor\\\",\\n group_address_state=\\\"1/2/3\\\",\\n value_type=\\\"thermal_capacity\\\",\\n )\\n sensor.sensor_value.payload = DPTArray(\\n (\\n 0x45,\\n 0x83,\\n 0xEA,\\n 0xB3,\\n )\\n )\\n\\n self.assertEqual(sensor.resolve_state(), 4221.33740234375)\\n self.assertEqual(sensor.unit_of_measurement(), \\\"J/K\\\")\\n self.assertEqual(sensor.ha_device_class(), None)\",\n \"def test_default_num_products(self):\\r\\n prod = generate_products()\\r\\n self.assertEqual(len(prod), 30)\",\n \"def mt_rand(min = 0, max = sys.maxint):\\n return random.randint(min, max)\",\n \"def random(self):\\r\\n return random.randint(1, 4)\",\n \"def mock_bestbuy():\\n bestbuy = BestBuy()\\n bestbuy.potato1 = 'batata baroa'\\n bestbuy.potato2 = 'batata inglesa'\\n bestbuy.potato_number = 666\\n return bestbuy\",\n \"def __init__(self):\\n self.set_recharge(random.randint(1000, 2000) / 10000)\\n operator_count = random.randint(1, 3)\\n self.operators = [Solder() for _ in range(0, operator_count)]\\n list_operators = [i.get_health for i in self.operators]\\n self.set_health(sum(list_operators) / len(list_operators))\",\n \"def test_default_product_weight(self):\\r\\n prod = Product('Test Product')\\r\\n self.assertEqual(prod.weight, 20)\",\n \"def test_capacity(self, space_each_type):\\n tspace = build_required_space(space_each_type, type_requirement=\\\"real\\\")\\n assert tspace.cardinality == numpy.inf\\n\\n space = Space()\\n probs = (0.1, 0.2, 0.3, 0.4)\\n categories = (\\\"asdfa\\\", 2, 3, 4)\\n dim = Categorical(\\\"yolo\\\", OrderedDict(zip(categories, probs)), shape=2)\\n space.register(dim)\\n dim = Integer(\\\"yolo2\\\", \\\"uniform\\\", -3, 6)\\n space.register(dim)\\n tspace = build_required_space(space, type_requirement=\\\"integer\\\")\\n assert tspace.cardinality == (4**2) * (6 + 1)\\n\\n dim = Integer(\\\"yolo3\\\", \\\"uniform\\\", -3, 6, shape=(2, 1))\\n space.register(dim)\\n tspace = build_required_space(space, type_requirement=\\\"integer\\\")\\n assert tspace.cardinality == (4**2) * (6 + 1) * ((6 + 1) ** (2 * 1))\\n\\n tspace = build_required_space(\\n space, type_requirement=\\\"integer\\\", shape_requirement=\\\"flattened\\\"\\n )\\n assert tspace.cardinality == (4**2) * (6 + 1) * ((6 + 1) ** (2 * 1))\\n\\n tspace = build_required_space(\\n space, type_requirement=\\\"integer\\\", dist_requirement=\\\"linear\\\"\\n )\\n assert tspace.cardinality == (4**2) * (6 + 1) * ((6 + 1) ** (2 * 1))\",\n \"def _create_resource_consumption_dict():\\n\\n returned_resource_dict = {}\\n\\n # things that are quantities should start at 0.0\\n for resource in resource_constants.quantity_resources:\\n returned_resource_dict[resource] = 0.0\\n\\n for resource in resource_constants.item_resources:\\n # double check there is no overlap...\\n if resource in resource_constants.quantity_resources:\\n raise InternalRepyError(\\\"Resource '\\\"+resource+\\\"' cannot be both quantity and item based!\\\")\\n\\n returned_resource_dict[resource] = set()\\n\\n # I need locks to protect races in accesses to some items...\\n returned_resource_dict['fungible_locks'] = {}\\n for init_resource in resource_constants.fungible_item_resources:\\n returned_resource_dict['fungible_locks'][init_resource] = threading.Lock()\\n\\n returned_resource_dict['renewable_locks'] = {}\\n for init_resource in resource_constants.renewable_resources:\\n returned_resource_dict['renewable_locks'][init_resource] = threading.Lock()\\n\\n\\n # I also need to track when the last update of a renewable resource occurred\\n returned_resource_dict['renewable_update_time'] = {}\\n\\n # (Aside) JAC: I've thought about this and looked through the commit history.\\n # I don't see any reason to initialize the renewable resources with the\\n # current time (as was done before).\\n for init_resource in resource_constants.renewable_resources:\\n returned_resource_dict['renewable_update_time'][init_resource] = 0.0\\n\\n\\n return returned_resource_dict\",\n \"def _random_weight(self):\\n return random.uniform(MIN_WEIGHT, MAX_WEIGHT)\",\n \"def capacity(self):\\n raise NotImplementedError()\",\n \"def __init__(__self__, *,\\n capacity: Optional[int] = None,\\n name: Optional[str] = None):\\n if capacity is not None:\\n pulumi.set(__self__, \\\"capacity\\\", capacity)\\n if name is not None:\\n pulumi.set(__self__, \\\"name\\\", name)\",\n \"def random():\\r\\n return R.NextDouble()\",\n \"def calculate_init_stock(self, good=None):\\n # random quantity * systemsize * techlevel (hence, it can be zero)\\n size = self.planet.system_size\\n level = self.planet.tech_level\\n stock = random.randrange(0, 25) * (size + 1) * level\\n\\n # SPECIALRESOURCES add 50% production\\n if self.planet.special in [self.tradeitem.cr]:\\n stock = stock + (stock * 0.5)\\n\\n # TODO enhancement: difficulty levels should affect fuel stocks\\n if good in ['fuel']:\\n stock = stock * 10\\n\\n return int(stock)\",\n \"def test_default_product_weight(self):\\n prod = Product('Test Product')\\n self.assertEqual(prod.weight, 20)\",\n \"def test_default_product_weight(self):\\n prod = Product('Test Product')\\n self.assertEqual(prod.weight, 20)\",\n \"def test_default_product_weight(self):\\n prod = Product('Test Product')\\n self.assertEqual(prod.weight, 20)\",\n \"def test_default_product_weight(self):\\n prod = Product('Test Product')\\n self.assertEqual(prod.weight, 20)\",\n \"def test_default_product_weight(self):\\n prod = Product('Test Product')\\n self.assertEqual(prod.weight, 20)\",\n \"def test_default_product_weight(self):\\n prod = Product('Test Product')\\n self.assertEqual(prod.weight, 20)\",\n \"def get_num_slots(self):\\n # Your code here\\n return self.capacity\",\n \"def get_capacity():\\n\\n try:\\n raw_capacity = PLIST[\\\"TotalSize\\\"]\\n raw_capacity = str(raw_capacity)\\n\\n except KeyError:\\n return \\\"Unknown\\\", \\\"Unknown\\\"\\n\\n #Round the sizes to make them human-readable.\\n unit_list = [None, \\\"B\\\", \\\"KB\\\", \\\"MB\\\", \\\"GB\\\", \\\"TB\\\", \\\"PB\\\", \\\"EB\\\"]\\n unit = \\\"B\\\"\\n human_readable_size = int(raw_capacity)\\n\\n try:\\n while len(str(human_readable_size)) > 3:\\n #Shift up one unit.\\n unit = unit_list[unit_list.index(unit)+1]\\n human_readable_size = human_readable_size//1000\\n\\n except IndexError:\\n return \\\"Unknown\\\", \\\"Unknown\\\"\\n\\n #Include the unit in the result for both exact and human-readable sizes.\\n return raw_capacity, str(human_readable_size)+\\\" \\\"+unit\",\n \"def test_sample_quality(self):\\r\\n self.assertEqual(self.test_sample.quality, 'medium')\",\n \"def test_operate_storage(self, param):\\n param = \\\"energy_cap_per_storage_cap_max\\\"\\n m = build_model(\\n {f\\\"techs.test_supply_plus.constraints.{param}\\\": 0.1},\\n \\\"simple_supply_and_supply_plus,operate,investment_costs\\\",\\n )\\n\\n with pytest.warns(exceptions.ModelWarning) as warning:\\n with pytest.raises(exceptions.ModelError) as error:\\n m.run(build_only=True)\\n\\n assert check_error_or_warning(\\n error,\\n \\\"fixed storage capacity * {} is not larger than fixed energy \\\"\\n \\\"capacity for loc, tech {}\\\".format(param, (\\\"a\\\", \\\"test_supply_plus\\\")),\\n )\\n assert check_error_or_warning(\\n warning,\\n [\\n \\\"Initial stored energy not defined\\\",\\n \\\"Resource capacity constraint defined and set to infinity\\\",\\n \\\"Storage cannot be cyclic in operate run mode\\\",\\n ],\\n )\",\n \"def get_capacity_var(self):\\n return self.get_interaction().get_capacity_var()\",\n \"def test_default_num_products(self):\\n test_list = generate_products()\\n self.assertEqual(len(test_list), 30, msg=\\\"Length is Bad\\\")\",\n \"async def test_sampling_size_1(hass: HomeAssistant) -> None:\\n assert await async_setup_component(\\n hass,\\n \\\"sensor\\\",\\n {\\n \\\"sensor\\\": [\\n {\\n \\\"platform\\\": \\\"statistics\\\",\\n \\\"name\\\": \\\"test\\\",\\n \\\"entity_id\\\": \\\"sensor.test_monitored\\\",\\n \\\"state_characteristic\\\": \\\"mean\\\",\\n \\\"sampling_size\\\": 1,\\n },\\n ]\\n },\\n )\\n await hass.async_block_till_done()\\n\\n for value in VALUES_NUMERIC:\\n hass.states.async_set(\\n \\\"sensor.test_monitored\\\",\\n str(value),\\n {ATTR_UNIT_OF_MEASUREMENT: UnitOfTemperature.CELSIUS},\\n )\\n await hass.async_block_till_done()\\n\\n state = hass.states.get(\\\"sensor.test\\\")\\n new_mean = float(VALUES_NUMERIC[-1])\\n assert state is not None\\n assert state.state == str(new_mean)\\n assert state.attributes.get(\\\"buffer_usage_ratio\\\") == round(1 / 1, 2)\",\n \"async def test_random_caching(players, strats, _):\\n game = gamegen.samplegame(players, strats)\\n rest1, rest2 = game.random_restrictions(2)\\n sched = gamesched.samplegamesched(game)\\n sgame = schedgame.schedgame(sched)\\n assert str(sgame) == str(sched)\\n\\n rgame11 = await sgame.get_restricted_game(rest1)\\n rgame21 = await sgame.get_restricted_game(rest2)\\n devs11 = await sgame.get_deviation_game(rest1)\\n devs21 = await sgame.get_deviation_game(rest2)\\n\\n rgame12 = await sgame.get_restricted_game(rest1)\\n rgame22 = await sgame.get_restricted_game(rest2)\\n assert rgame11 == rgame12\\n assert rgame21 == rgame22\\n\\n devs12 = await sgame.get_deviation_game(rest1)\\n devs22 = await sgame.get_deviation_game(rest2)\\n assert devs11 == devs12\\n assert devs21 == devs22\",\n \"def _tattle_quantity(resource, quantity, resourcesalloweddict, resourcesuseddict):\\n\\n\\n # I assume that the quantity will never be negative\\n if quantity < 0:\\n # This will cause the program to exit and log things if logging is\\n # enabled. -Brent\\n tracebackrepy.handle_internalerror(\\\"Resource '\\\" + resource + \\n \\\"' has a negative quantity \\\" + str(quantity) + \\\"!\\\", 132)\\n \\n # get the lock for this resource\\n resourcesuseddict['renewable_locks'][resource].acquire()\\n \\n # release the lock afterwards no matter what\\n try: \\n # update the resource counters based upon the current time.\\n _update_resource_consumption_table(resource, resourcesalloweddict, resourcesuseddict)\\n\\n # It's renewable, so I can wait for it to clear\\n if resource not in resource_constants.renewable_resources:\\n # Should never have a quantity tattle for a non-renewable resource\\n # This will cause the program to exit and log things if logging is\\n # enabled. -Brent\\n tracebackrepy.handle_internalerror(\\\"Resource '\\\" + resource + \\n \\\"' is not renewable!\\\", 133)\\n \\n\\n resourcesuseddict[resource] = resourcesuseddict[resource] + quantity\\n # I'll block if I'm over...\\n _sleep_until_resource_drains(resource, resourcesalloweddict, resourcesuseddict)\\n \\n finally:\\n # release the lock for this resource\\n resourcesuseddict['renewable_locks'][resource].release()\",\n \"def _randomVelocity(self):\\n\\t\\treturn random.choice([-1, 1]) * random.randint(10, 50)\",\n \"def capacity(self) -> typing.Optional[str]:\\n value = self._properties.get(\\\"capacity\\\")\\n return f\\\"{value}\\\" if value is not None else None\",\n \"def test_default_num_products(self):\\n products = generate_products()\\n self.assertEqual(len(products), 30)\",\n \"def random_factory(time_step, migration_time, params):\\n return lambda vms_cpu, vms_ram, state=None: ([random(vms_cpu)], {})\",\n \"def __get_deal_price(self):\\n return self.create_random_decimal(min=1, max=100000)\",\n \"def set_generator(random, args):\\n representation = args.get('representation')\\n indices = list(range(len(representation)))\\n max_size = args.get('max_size', 9)\\n variable_size = args.get('variable_size', True)\\n if variable_size and max_size > 1:\\n size = random.randint(1, max_size)\\n else:\\n size = max_size\\n candidate = random.sample(indices, size)\\n return sorted(candidate)\",\n \"def test_patch_cluster_resource_quota(self):\\n pass\",\n \"def test_operate_resource_cap_max(self, on):\\n\\n if on is False:\\n override = {}\\n else:\\n override = {\\\"techs.test_supply_plus.constraints.resource_cap_max\\\": 1e6}\\n m = build_model(\\n override, \\\"simple_supply_and_supply_plus,operate,investment_costs\\\"\\n )\\n\\n with pytest.warns(exceptions.ModelWarning) as warning:\\n m.run(build_only=True)\\n if on is False:\\n assert check_error_or_warning(\\n warning, \\\"Resource capacity constraint defined and set to infinity\\\"\\n )\\n assert np.isinf(\\n m._model_data.resource_cap.loc[\\\"a\\\", \\\"test_supply_plus\\\"].item()\\n )\\n elif on is True:\\n assert not check_error_or_warning(\\n warning, \\\"Resource capacity constraint defined and set to infinity\\\"\\n )\\n assert m._model_data.resource_cap.loc[\\\"a\\\", \\\"test_supply_plus\\\"].item() == 1e6\",\n \"def test_limit_memory(self):\\n url = '/api/apps'\\n body = {'cluster': 'autotest'}\\n response = self.client.post(url, json.dumps(body), content_type='application/json')\\n self.assertEqual(response.status_code, 201)\\n app_id = response.data['id']\\n url = '/api/apps/{app_id}/limits'.format(**locals())\\n # check default limit\\n response = self.client.get(url, content_type='application/json')\\n self.assertEqual(response.status_code, 200)\\n self.assertIn('memory', response.data)\\n self.assertEqual(json.loads(response.data['memory']), {})\\n # regression test for https://github.com/deis/deis/issues/1563\\n self.assertNotIn('\\\"', response.data['memory'])\\n # set an initial limit\\n mem = {'web': '1G'}\\n body = {'memory': json.dumps(mem)}\\n response = self.client.post(url, json.dumps(body), content_type='application/json')\\n self.assertEqual(response.status_code, 201)\\n self.assertIn('x-deis-release', response._headers)\\n limit1 = response.data\\n # check memory limits\\n response = self.client.get(url, content_type='application/json')\\n self.assertEqual(response.status_code, 200)\\n self.assertIn('memory', response.data)\\n memory = json.loads(response.data['memory'])\\n self.assertIn('web', memory)\\n self.assertEqual(memory['web'], '1G')\\n # set an additional value\\n body = {'memory': json.dumps({'worker': '512M'})}\\n response = self.client.post(url, json.dumps(body), content_type='application/json')\\n self.assertEqual(response.status_code, 201)\\n limit2 = response.data\\n self.assertNotEqual(limit1['uuid'], limit2['uuid'])\\n memory = json.loads(response.data['memory'])\\n self.assertIn('worker', memory)\\n self.assertEqual(memory['worker'], '512M')\\n self.assertIn('web', memory)\\n self.assertEqual(memory['web'], '1G')\\n # read the limit again\\n response = self.client.get(url)\\n self.assertEqual(response.status_code, 200)\\n limit3 = response.data\\n self.assertEqual(limit2, limit3)\\n memory = json.loads(response.data['memory'])\\n self.assertIn('worker', memory)\\n self.assertEqual(memory['worker'], '512M')\\n self.assertIn('web', memory)\\n self.assertEqual(memory['web'], '1G')\\n # unset a value\\n body = {'memory': json.dumps({'worker': None})}\\n response = self.client.post(url, json.dumps(body), content_type='application/json')\\n self.assertEqual(response.status_code, 201)\\n limit4 = response.data\\n self.assertNotEqual(limit3['uuid'], limit4['uuid'])\\n self.assertNotIn('worker', json.dumps(response.data['memory']))\\n # disallow put/patch/delete\\n self.assertEqual(self.client.put(url).status_code, 405)\\n self.assertEqual(self.client.patch(url).status_code, 405)\\n self.assertEqual(self.client.delete(url).status_code, 405)\\n return limit4\",\n \"def test_gcp_generate_daily_wo_resource_data(self):\\n usage = random.randint(1, 10)\\n cost = random.randint(1, 10)\\n data = [\\n {\\n \\\"billing_account_id\\\": \\\"fact\\\",\\n \\\"service_id\\\": \\\"95FF-2EF5-5EA1\\\",\\n \\\"service_description\\\": \\\"Cloud Storage\\\",\\n \\\"sku_id\\\": \\\"E5F0-6A5D-7BAD\\\",\\n \\\"sku_description\\\": \\\"Standard Storage US Regional\\\",\\n \\\"usage_start_time\\\": datetime(2022, 1, 1, 13, 0, 0),\\n \\\"usage_end_time\\\": datetime(2022, 1, 1, 14, 0, 0),\\n \\\"project_id\\\": \\\"trouble-although-mind\\\",\\n \\\"project_name\\\": \\\"trouble-although-mind\\\",\\n \\\"labels\\\": '{\\\"key\\\": \\\"test_storage_key\\\", \\\"value\\\": \\\"test_storage_label\\\"}',\\n \\\"system_labels\\\": \\\"{}\\\",\\n \\\"cost_type\\\": \\\"regular\\\",\\n \\\"credits\\\": \\\"{}\\\",\\n \\\"location_region\\\": \\\"us-central1\\\",\\n \\\"usage_pricing_unit\\\": \\\"byte-seconds\\\",\\n \\\"usage_amount_in_pricing_units\\\": usage,\\n \\\"currency\\\": \\\"USD\\\",\\n \\\"cost\\\": cost,\\n \\\"invoice_month\\\": \\\"202201\\\",\\n },\\n {\\n \\\"billing_account_id\\\": \\\"fact\\\",\\n \\\"service_id\\\": \\\"95FF-2EF5-5EA1\\\",\\n \\\"service_description\\\": \\\"Cloud Storage\\\",\\n \\\"sku_id\\\": \\\"E5F0-6A5D-7BAD\\\",\\n \\\"sku_description\\\": \\\"Standard Storage US Regional\\\",\\n \\\"usage_start_time\\\": datetime(2022, 1, 1, 14, 0, 0),\\n \\\"usage_end_time\\\": datetime(2022, 1, 1, 15, 0, 0),\\n \\\"project_id\\\": \\\"trouble-although-mind\\\",\\n \\\"project_name\\\": \\\"trouble-although-mind\\\",\\n \\\"labels\\\": '{\\\"key\\\": \\\"test_storage_key\\\", \\\"value\\\": \\\"test_storage_label\\\"}',\\n \\\"system_labels\\\": \\\"{}\\\",\\n \\\"cost_type\\\": \\\"regular\\\",\\n \\\"credits\\\": \\\"{}\\\",\\n \\\"location_region\\\": \\\"us-central1\\\",\\n \\\"usage_pricing_unit\\\": \\\"byte-seconds\\\",\\n \\\"usage_amount_in_pricing_units\\\": usage,\\n \\\"currency\\\": \\\"USD\\\",\\n \\\"cost\\\": cost,\\n \\\"invoice_month\\\": \\\"202201\\\",\\n },\\n {\\n \\\"billing_account_id\\\": \\\"fact\\\",\\n \\\"service_id\\\": \\\"95FF-2EF5-5EA1\\\",\\n \\\"service_description\\\": \\\"Cloud Storage\\\",\\n \\\"sku_id\\\": \\\"E5F0-6A5D-7BAD\\\",\\n \\\"sku_description\\\": \\\"Standard Storage US Regional\\\",\\n \\\"usage_start_time\\\": datetime(2022, 1, 2, 4, 0, 0),\\n \\\"usage_end_time\\\": datetime(2022, 1, 2, 5, 0, 0),\\n \\\"project_id\\\": \\\"trouble-although-mind\\\",\\n \\\"project_name\\\": \\\"trouble-although-mind\\\",\\n \\\"labels\\\": '{\\\"key\\\": \\\"test_storage_key\\\", \\\"value\\\": \\\"test_storage_label\\\"}',\\n \\\"system_labels\\\": \\\"{}\\\",\\n \\\"cost_type\\\": \\\"regular\\\",\\n \\\"credits\\\": \\\"{}\\\",\\n \\\"location_region\\\": \\\"us-central1\\\",\\n \\\"usage_pricing_unit\\\": \\\"byte-seconds\\\",\\n \\\"usage_amount_in_pricing_units\\\": usage,\\n \\\"currency\\\": \\\"USD\\\",\\n \\\"cost\\\": cost,\\n \\\"invoice_month\\\": \\\"202201\\\",\\n },\\n ]\\n df = DataFrame(data)\\n daily_df = self.post_processor._generate_daily_data(df)\\n\\n first_day = daily_df[daily_df[\\\"usage_start_time\\\"] == \\\"2022-01-01\\\"]\\n second_day = daily_df[daily_df[\\\"usage_start_time\\\"] == \\\"2022-01-02\\\"]\\n\\n self.assertEqual(first_day.shape[0], 1)\\n self.assertEqual(second_day.shape[0], 1)\\n\\n self.assertTrue((first_day[\\\"cost\\\"] == cost * 2).bool())\\n self.assertTrue((second_day[\\\"cost\\\"] == cost).bool())\\n self.assertTrue((first_day[\\\"usage_amount_in_pricing_units\\\"] == usage * 2).bool())\\n self.assertTrue((second_day[\\\"usage_amount_in_pricing_units\\\"] == usage).bool())\",\n \"def __init__(self, test_resources):\\n self.info = None\\n self.is_mock = False\\n self.cluster = None\\n self.bucket = None\\n self.bucket_name = None\\n self.cluster_version = None\\n self.set_test_resources(test_resources)\",\n \"def generator(self, random, args):\\r\\n if self.duplicates:\\r\\n max_count = [self.capacity // item[0] for item in self.items]\\r\\n return [random.randint(0, m) for m in max_count]\\r\\n else:\\r\\n return [random.choice([0, 1]) for _ in range(len(self.items))]\",\n \"def random_temp():\\n temp_min = 154\\n temp_max = 500\\n temp_interval = 1\\n # `range`s are exclusive [min, max)\\n return random.randrange(temp_min, temp_max + 1, temp_interval)\",\n \"def sample_consumption():\\n product = Product(\\\"NameA\\\", 15, 17.85, 0.07, \\\"oak\\\", 0.08, \\\"ENplusA1\\\",\\n \\\"Pelletics.cz\\\", date(2020, 12, 20))\\n delivery = Delivery(product, \\\"Pellets2Home\\\", 7350, 42500,\\n date(2020, 12, 20))\\n some_datetime = datetime(2020, 11, 20, 14, 22, 46, 0)\\n consumption = Consumption(some_datetime, delivery, 30, \\\"30 kgs\\\")\\n return consumption\",\n \"def test_default_num_products(self):\\n product_list = generate_products()\\n self.assertEqual(len(product_list), 30)\",\n \"def init_devices(self):\\n self.hp_nb = int(self.rs_nb* self.hp_proportion/(1- self.hp_proportion))\\n self.defense_cost = self.hp_nb * self.hp_unit_cost\\n rs_devices = [True for i in range(self.rs_nb)] #rs --> True\\n hp_devices = [False for i in range(self.hp_nb)] #hp --> False\\n self.devices = rs_devices + hp_devices\\n shuffle(self.devices)\",\n \"def base_pick():\\n\\n rnd = generate_random(2, 15)\\n return rnd\",\n \"def get_additional_ball_capacity(cls):\\n return 999\",\n \"def test_list_cluster_resource_quota(self):\\n pass\",\n \"def capacity(self) -> pulumi.Input[int]:\\n return pulumi.get(self, \\\"capacity\\\")\",\n \"def test_default_num_products(self):\\r\\n lst = generate_products()\\r\\n self.assertEqual(len(lst), 30)\",\n \"def system_quality_attribute(self) -> str:\\n return self.random.choice(SYSTEM_QUALITY_ATTRIBUTES)\",\n \"async def test_stretch_sensor_entities(\\n hass: HomeAssistant, mock_stretch: MagicMock, init_integration: MockConfigEntry\\n) -> None:\\n state = hass.states.get(\\\"sensor.koelkast_92c4a_electricity_consumed\\\")\\n assert state\\n assert float(state.state) == 50.5\\n\\n state = hass.states.get(\\\"sensor.droger_52559_electricity_consumed_interval\\\")\\n assert state\\n assert float(state.state) == 0.0\",\n \"def initialize_supply(self):\\n unit_count = 0\\n for i in range(self.start_allocation[0 ] -1, self.start_allocation[1]):\\n for j in range(len(self.capacity_list[i][1])):\\n self.capacity_list[i][1][j] = 1\\n unit_count += 1\\n self.total_supply -= unit_count\",\n \"def rand_gen(below, baseline):\\n\\treturn secrets.randbelow(below)/ baseline\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.631372","0.61010695","0.60488284","0.6046735","0.5982311","0.5956989","0.59451956","0.5942304","0.59133613","0.589804","0.58927816","0.5846016","0.57881594","0.5747037","0.57254654","0.57179344","0.57009256","0.5674274","0.5642999","0.56372076","0.56222486","0.560608","0.55932564","0.55919045","0.55906326","0.55837125","0.55759865","0.5560307","0.55548155","0.55473036","0.5537524","0.5537524","0.54966694","0.5487925","0.54849434","0.54777247","0.5475818","0.54687154","0.54661155","0.5465262","0.5461903","0.545821","0.5453993","0.54501486","0.54470104","0.5437312","0.5433405","0.54329795","0.54254246","0.54235816","0.5407099","0.5397114","0.53878385","0.5385384","0.5379546","0.53756994","0.53732604","0.5371637","0.5369678","0.53680587","0.53618264","0.53618264","0.53618264","0.53618264","0.53618264","0.53618264","0.5360316","0.5347963","0.53411514","0.5340005","0.5337894","0.5337024","0.53327966","0.53237504","0.53214896","0.5320722","0.531904","0.53188837","0.53124285","0.53110975","0.53107643","0.53069603","0.5300834","0.53005105","0.52991533","0.5297277","0.5296238","0.5290959","0.52908534","0.52893037","0.5288445","0.52873576","0.5282052","0.52802724","0.5278147","0.52778286","0.5274152","0.5270018","0.52671057","0.5265709"],"string":"[\n \"0.631372\",\n \"0.61010695\",\n \"0.60488284\",\n \"0.6046735\",\n \"0.5982311\",\n \"0.5956989\",\n \"0.59451956\",\n \"0.5942304\",\n \"0.59133613\",\n \"0.589804\",\n \"0.58927816\",\n \"0.5846016\",\n \"0.57881594\",\n \"0.5747037\",\n \"0.57254654\",\n \"0.57179344\",\n \"0.57009256\",\n \"0.5674274\",\n \"0.5642999\",\n \"0.56372076\",\n \"0.56222486\",\n \"0.560608\",\n \"0.55932564\",\n \"0.55919045\",\n \"0.55906326\",\n \"0.55837125\",\n \"0.55759865\",\n \"0.5560307\",\n \"0.55548155\",\n \"0.55473036\",\n \"0.5537524\",\n \"0.5537524\",\n \"0.54966694\",\n \"0.5487925\",\n \"0.54849434\",\n \"0.54777247\",\n \"0.5475818\",\n \"0.54687154\",\n \"0.54661155\",\n \"0.5465262\",\n \"0.5461903\",\n \"0.545821\",\n \"0.5453993\",\n \"0.54501486\",\n \"0.54470104\",\n \"0.5437312\",\n \"0.5433405\",\n \"0.54329795\",\n \"0.54254246\",\n \"0.54235816\",\n \"0.5407099\",\n \"0.5397114\",\n \"0.53878385\",\n \"0.5385384\",\n \"0.5379546\",\n \"0.53756994\",\n \"0.53732604\",\n \"0.5371637\",\n \"0.5369678\",\n \"0.53680587\",\n \"0.53618264\",\n \"0.53618264\",\n \"0.53618264\",\n \"0.53618264\",\n \"0.53618264\",\n \"0.53618264\",\n \"0.5360316\",\n \"0.5347963\",\n \"0.53411514\",\n \"0.5340005\",\n \"0.5337894\",\n \"0.5337024\",\n \"0.53327966\",\n \"0.53237504\",\n \"0.53214896\",\n \"0.5320722\",\n \"0.531904\",\n \"0.53188837\",\n \"0.53124285\",\n \"0.53110975\",\n \"0.53107643\",\n \"0.53069603\",\n \"0.5300834\",\n \"0.53005105\",\n \"0.52991533\",\n \"0.5297277\",\n \"0.5296238\",\n \"0.5290959\",\n \"0.52908534\",\n \"0.52893037\",\n \"0.5288445\",\n \"0.52873576\",\n \"0.5282052\",\n \"0.52802724\",\n \"0.5278147\",\n \"0.52778286\",\n \"0.5274152\",\n \"0.5270018\",\n \"0.52671057\",\n \"0.5265709\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7475767"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":14386,"cells":{"query":{"kind":"string","value":"Abstract base class to define the interface for priors of GP hyperparameter."},"document":{"kind":"string","value":"def __init__(self, rng=None):\n if rng is None:\n self.rng = np.random.RandomState(np.random.randint(0, 10000))\n else:\n self.rng = rng"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def prim_method(self):","def prim_method(self):","def __init__(self, p1_proba=0.5):\n self.p1_proba = p1_proba","def get_hyperparams(self):","def Params(cls):\n return hyperparams.InstantiableParams(cls)","def __init__(self, prim):\n self.actual = prim","def gen_parameter(self, g, ng, p):\n pass","def __init__(self):\n super(PriProb, self).__init__()\n # initialize R: distribute R_TOTAL reward points in J_avi locations randomly\n # self.r preserved for debugging, no real use in the script\n self.r = np.array(ad.randint_upto_sum(R_TOTAL, J_avi)).astype(NP_DTYPE)\n\n # expand self.r from J_avi locations to J locations using is_avi\n self.r_exp = np.zeros((J), dtype=NP_DTYPE)\n self.r_exp[np.nonzero(is_avi.cpu().numpy())] = self.r\n\n #normalizedR = ad.normalize(self.r_exp, using_max=False)\n self.R = nn.Parameter(torch.from_numpy(self.r_exp))","def test_custom_hypergeometric():\n\n M = 958\n n = 458\n N = 332\n x = 2\n custom_prob = hypergeometric_pmf(2, 958, 458, 332)\n rv = hypergeom(M, n, N)\n pro_prob = rv.pmf(x)\n np.testing.assert_allclose(custom_prob, pro_prob, rtol=1e-5, atol=0)","def __init__(self, num_values, probability):\n self.num_values = num_values\n self.probability = probability\n self.register_bounds = GeometricDistribution._compute_register_bounds(num_values, probability)\n self._register_probs = GeometricDistribution._compute_register_probs(num_values, probability)","def p(self) -> Probability:\n ...","def __init__(self, p, *args, **kwargs):\n self.p = p\n super(BernoulliRV, self).__init__(*args, **kwargs)","def __init__(self):\n super().__init__()\n self.p = 0.0\n self.type = 'Bernoulli'\n self.distType = 'Discrete'\n self.lowerBound = 0.0\n self.upperBound = 1.0\n self.compatibleQuadrature.append('CDF')\n self.preferredQuadrature = 'CDF'\n self.preferredPolynomials = 'CDF'","def __init__(self, low=0.0, alpha=0.0, beta=1.0):\n super().__init__()\n self.low = low\n self.alpha = alpha\n self.beta = beta\n self.type = 'Gamma'\n self.distType = 'Continuous'\n self.hasInfiniteBound = True\n self.compatibleQuadrature.append('Laguerre')\n self.compatibleQuadrature.append('CDF')\n self.preferredQuadrature = 'Laguerre'\n self.preferredPolynomials = 'Laguerre'","def __init__(self):\n super().__init__()\n self.low = 0.0\n self.high = 1.0\n self.alpha = 0.0\n self.beta = 0.0\n self.type = 'Beta'\n self.distType = 'Continuous'\n self.hasInfiniteBound = True\n self.compatibleQuadrature.append('Jacobi')\n self.compatibleQuadrature.append('CDF')\n self.preferredQuadrature = 'Jacobi'\n self.preferredPolynomials = 'Jacobi'","def __init__(self, p, hyperpara, para, inst_name=\"\"):\n \n self.hyperpara = hyperpara\n self.para = para\n \n s = -np.log(-np.log(1.0 - p))\n \n k, cop = hyperpara\n \n # Sets colour\n colour = [\n None,\n None,\n {\n \"i\": 2,\n \"me\": 3},\n {\n \"i\": 0,\n \"me\": 1}][k][cop]\n \n if cop == \"i\":\n if k == 3:\n def f(X):\n q1, q2, q3 = X\n \n Y = np.array([q1, q2 - q1, q3 - q2])\n \n if np.any(Y <= 0.0):\n return None\n \n return -0.5 * np.sum(((Y - para[:, 0]) / para[:, 1]) ** 2)\n elif k == 2:\n def f(X):\n sigma, q1, q2 = X\n \n if sigma <= 0:\n return None\n \n Y = np.array([q1, q2 - q1])\n \n if np.any(Y <= 0.0):\n return None\n \n a = -0.5 * np.sum(((Y - para[:, 0]) / para[:, 1]) ** 2)\n \n return a - np.log(sigma)\n elif cop == \"me\":\n q_marg = [None for _ in range(k)]\n \n for i in range(k):\n dist = Normal(para[i, 0], para[i, 1])\n q_marg[i] = TruncatedDistribution(\n dist,\n 0.0,\n TruncatedDistribution.LOWER)\n \n ot = MaximumEntropyOrderStatisticsDistribution(q_marg)\n \n if k == 3:\n def f(X):\n q1, q2, q3 = X\n \n if np.any(np.array([q1, q2 - q1, q3 - q2]) <= 0.0):\n return None\n \n Y = ot.computePDF(X)\n \n if Y <= 0:\n return None\n \n return np.log(Y)\n elif k == 2:\n def f(X):\n sigma, q1, q2 = X\n \n if sigma <= 0 or q1 <= 0.0 or q2 <= q1:\n return None\n \n Y = ot.computePDF([q1, q2])\n \n if Y <= 0:\n return None\n \n return np.log(Y) - np.log(sigma)\n \n if k == 3:\n # Transformation (mu, theta, xi) -> (q1, q2, q3)\n def g(X):\n mu, sigma, xi = X\n \n if sigma <= 0:\n return None\n \n # When xi is close enough to 0, we consider it equal to 0\n if abs(xi) < 1e-300:\n q = mu + sigma * s\n else:\n q = mu + sigma * (np.exp(xi * s) - 1.0) / xi\n \n if q[0] < 0.0:\n return None\n return q\n \n \n # Log of determinant of g\n def g_det(X):\n mu, sigma, xi = X\n \n if abs(xi) < 1e-300:\n return np.log(sigma)\n \n e = np.exp(s * xi)\n \n sm = [\n s[i] * e[i] * (e[(i + 2) % 3] - e[(i + 1) % 3])\n for i in range(3)]\n \n return np.log(sigma) + np.log(sum(sm)) - np.log(xi ** 2.0)\n elif k == 2:\n # Transformation (mu, sigma, xi) -> (sigma, q1, q2)\n def g(X):\n mu, sigma, xi = X\n \n # When xi is close enough to 0, we consider it equal to 0\n if abs(xi) < 1e-300:\n q = mu + sigma * s\n else:\n q = mu + sigma * (np.exp(xi * s) - 1.0) / xi\n \n if q[0] < 0.0:\n return None\n \n return np.concatenate(([sigma], q))\n \n \n # Log of determinant of g\n def g_det(X):\n mu, sigma, xi = X\n \n if abs(xi) < 1e-300:\n return np.log(sigma)\n \n e = (s * xi - 1.0) * np.exp(s * xi)\n \n f = np.log(abs(e[0] - e[1]))\n \n return np.log(sigma) + f - np.log(xi ** 2.0)\n \n super().__init__(\n util.log_transform(f, g, g_det),\n colour=colour,\n inst_name=inst_name)\n \n if k == 2:\n self.prior[\"proper\"] = False","def __init__(self, epsilon=0.05, gamma=0.95, alpha=0.2, numTraining=0, **args):\n args['epsilon'] = epsilon\n args['gamma'] = gamma\n args['alpha'] = alpha\n args['numTraining'] = numTraining\n self.index = 0 # This is always Pacman\n PQLearningAgent.__init__(self, **args)","def define_interface(self):\n self._add_hyperparameter_signature(name=\"domain\", dtype=str,\n options=[\"uniform\", \"normal\", \"loguniform\", \"categorical\"])\n self._add_hyperparameter_signature(name=\"data\", dtype=list)\n self._add_hyperparameter_signature(name=\"frequency\", dtype=int)\n self._add_hyperparameter_signature(name=\"type\", dtype=type)","def __init__(self):\n super().__init__()\n self.n = 0.0\n self.p = 0.0\n self.type = 'Binomial'\n self.hasInfiniteBound = True\n self.distType = 'Discrete'\n self.compatibleQuadrature.append('CDF')\n self.preferredQuadrature = 'CDF'\n self.preferredPolynomials = 'CDF'","def __init__(self, p=1.5, eps=1e-8):\n assert 1 < p < 2, \"make sure 1 < p < 2\" \n self.p, self.eps = p, eps","def checkPriors(para):\n\t\n\t# extract parameters\n\tA = para[0]\n\tw = para[1]\n\tp = para[2]\n\t\n\t# check them\n\tif (A<0.01 or A>10.0): A = s.uniform.rvs(0.01,10.)\n\t\n\tif (w<0.01 or w>10.0): w = s.uniform.rvs(0.01,10.)\n\t\t\n\tif ( p<0. or p>2*np.pi): p = s.uniform.rvs(0.0,2*np.pi)\n\t\n\treturn np.array([A,w,p])","def potential_parameters(cls):\n raise NotImplementedError()","def __init__(self, p, multiplier=1e-1):\r\n super().__init__(multiplier=multiplier)\r\n self.p = p","def set_priors(parnames, limits, linenames, vsyst, nssps=1):\n priors = {}\n for parname in parnames:\n name = parname.split(\"_\")[0]\n if name in limits: #all the CvD ssp parameters\n vmin, vmax = limits[name]\n# print(parname,vmin,vmax)\n delta = vmax - vmin\n priors[parname] = stats.uniform(loc=vmin, scale=delta)\n elif parname in vsyst:\n priors[parname] = stats.norm(loc=vsyst[parname], scale=500)\n elif parname == \"eta\": #what does eta do?\n priors[\"eta\"] = stats.uniform(loc=1., scale=19)#uniform distribution in range [1,19]\n elif parname == \"nu\": #what does nu do?\n priors[\"nu\"] = stats.uniform(loc=2, scale=20)#uniform distribution in range [2,20]\n elif parname == \"sigma\":\n priors[\"sigma\"] = stats.uniform(loc=50, scale=300)#obtains the uniform distribution on [loc, loc + scale]. i.e. uniform in range [50,300]\n elif parname == \"sigma_gas\":\n priors[parname] = stats.uniform(loc=50, scale=100)#uniform between [50,100]km/s\n elif name == \"w\":\n priors[parname] = stats.uniform(loc=0, scale=1)#weights uniform between 0 and 1\n elif name in linenames:\n# priors[parname] = stats.expon(loc=0, scale=0.5)#favors low values>~0; make even stronger by decreasing scale. \n priors[parname] = stats.expon(loc=0, scale=0.2)#favors low values>~0; make even stronger by decreasing scale. \n elif name in [\"pred\", \"pblue\"]:\n porder = int(parname.split(\"_\")[1])\n if porder == 0:\n mu, sd = 1 / nssps, 1\n a, b = (0 - mu) / sd, (np.infty - mu) / sd\n priors[parname] = stats.truncnorm(a, b, mu, sd)\n else:\n priors[parname] = stats.norm(0, 0.05)\n else:\n print(f\"parameter without prior: {parname}\")\n return priors","def _get_guide_priors(self):\n if not self._alpha_guide_prior_params:\n # create initial parameters\n params = self._get_prior_params()\n # register all parameters in pyro\n for p, v in iteritems(params):\n pyro.param(p, v)\n self._alpha_guide_prior_params = dict(\n self._param_store.named_parameters()\n )\n else:\n # register all parameters in pyro\n for p, v in iteritems(self._alpha_guide_prior_params):\n pyro.param(p, v)\n return self._params2probs(self._alpha_guide_prior_params)","def getInputSpecification(cls):\n inputSpecification = super(Geometric, cls).getInputSpecification()\n inputSpecification.addSub(InputData.parameterInputFactory(\"p\", contentType=InputTypes.FloatType))\n\n return inputSpecification","def __init__(self, rp_dim:int,gamma,rp_method='Classical',rp_mode='Gaussian',solve_method='Exact'):\n self.rp_dim = rp_dim\n self.rp_method = rp_method\n self.rp_mode = rp_mode\n self.solve_method = solve_method\n if self.rp_method not in ['Classical', 'Hessian']:\n raise NotImplementedError('Only Classical and Hessian methods are supported.')\n if self.rp_mode not in ['Gaussian', 'SJLT']:\n raise NotImplementedError('Only Gaussian and SJLT modes are supported.')\n if self.solve_method not in ['Exact','ShiWoodbury']:\n raise NotImplementedError('Only Exact and ShiWoodbury methods are implemented')\n if not isinstance(gamma,list):\n self.gamma = [gamma]\n else:\n self.gamma = gamma","def phpcredits():\n raise NotImplementedError()","def fit_gp(self):\n raise NotImplementedError('Abstract Method')","def getInputSpecification(cls):\n inputSpecification = super(Bernoulli, cls).getInputSpecification()\n inputSpecification.addSub(InputData.parameterInputFactory(\"p\", contentType=InputTypes.FloatType))\n\n return inputSpecification","def __init__(self, p=0.5):\n self.p = p","def set_hyperparams(self, params):","def __init__(self):\n self.points = []\n names = ['x1']\n lows = [10**-5]\n highs = [10**5]\n # TODO 2: Use names, lows and highs defined above to code up decision\n # and objective metadata for POM3.\n decisions = [Decision(n, l, h) for n, l, h in zip(names, lows, highs)]\n objectives = [Objective(\"f1\", True), Objective(\"f2\", True)]\n Problem.__init__(self, decisions, objectives)","def Parameters():\n\n raise NotImplementedError()","def Geometric(name, p):\n return rv(name, GeometricDistribution, p)","def getInputSpecification(cls):\n inputSpecification = super(Binomial, cls).getInputSpecification()\n inputSpecification.addSub(InputData.parameterInputFactory(\"n\", contentType=InputTypes.IntegerType))\n inputSpecification.addSub(InputData.parameterInputFactory(\"p\", contentType=InputTypes.FloatType))\n\n return inputSpecification","def proximal(self):\n functional = self\n\n class EntRegOptTransProximal(Operator):\n\n \"\"\"Proximal operator of entropy regularized optimal transport.\n\n The prox is given by::\n\n prox_[gamma*T_eps](mu1) = arg min_x (T_epsilon(mu0, x) +\n 1/(2*gamma) ||x - mu1||^2_2)\n \"\"\"\n\n def __init__(self, sigma):\n \"\"\"Initialize a new instance.\n\n Parameters\n ----------\n sigma : positive float\n \"\"\"\n self.sigma = float(sigma)\n super().__init__(domain=functional.domain,\n range=functional.domain, linear=False)\n\n # Setting up parameters\n self.const = 1 / (functional.epsilon * sigma)\n\n def _call(self, x):\n \"\"\"Apply the operator to ``x``.\"\"\"\n u = functional.tmp_u_prox\n v = functional.tmp_v_prox\n\n # Running generalized Sinkhorn iterations\n for j in range(functional.niter):\n # Safe-guarded u-update, to avoid divide-by-zero error.\n u_old = u.copy()\n tmp1 = functional.K_op(v)\n if np.min(tmp1) < 1e-30 or np.max(tmp1) > 1e+50:\n print('Numerical instability, truncation in Transport prox (Kv)',\n str(np.min(tmp1)), str(np.max(tmp1)))\n\n tmp = np.fmax(tmp1, 1e-30)\n\n\n u = functional.mu0 / tmp\n if np.min(u) < 1e-30 or np.max(u) > 1e+50:\n print('u (min/max)', str(np.min(u)), str(np.max(u)))\n\n # Safe-guarded v-update, to avoid divide-by-zero error.\n v_old = v.copy()\n\n tmp3 = functional.K_op_adjoint(u)\n if np.min(tmp3) < 1e-30 or np.max(tmp3) > 1e+50:\n print('Truncation in Transport prox (KTu)',\n str(np.min(tmp3)), str(np.max(tmp3)))\n print('u (min/max)', str(np.min(u)), str(np.max(u)))\n\n tmp4 = (self.const * tmp3 * np.exp(self.const * x))\n\n if np.min(tmp4) < 1e-30 or np.max(tmp4) > 1e+200:\n print('Argument in lambdert omega (min/max)',\n str(np.min(tmp4)), str(np.max(tmp4)))\n\n v = np.exp(self.const * x - lambertw_fulfix(tmp4))\n\n v1 = np.exp(self.const * x - scipy.special.lambertw(\n tmp4))\n if (v-v1).norm() > 1e-10:\n print('diff pga ny lambderw omega funciton',\n str((v-v1).norm()))\n print('v (min/max)', str(np.min(v)), str(np.max(v)))\n print('Argument in lambdert omega (min/max)',\n str(np.min(tmp4)), str(np.max(tmp4)))\n\n # If the updates in both u and v are small, break the loop\n if ((np.log(v)-np.log(v_old)).norm() < 1e-8 and\n (np.log(u)-np.log(u_old)).norm() < 1e-8):\n break\n\n # Store the u and v in the internal temporary variables of the\n # functional\n functional.tmp_u_prox = u\n functional.tmp_v_prox = v\n\n return x - self.sigma * functional.epsilon * np.log(v)\n\n return EntRegOptTransProximal","def perplexity(self):\n raise NotImplementedError(\"To be implemented\")","def __init__(self, n_source, t_base):\n self.null = BetaBernouilli(1.0, 1.0) # p(NULL) ~ Beta(1, 1)\n self.a_table = AlignmentDistribution(GammaPrior(1.0, 1.0, 4.0))\n self.t_base = t_base\n self.t_table = [PYP(self.t_base, PYPPrior(1.0, 1.0, 1.0, 1.0, 0.1, 1.0))\n for _ in xrange(n_source)]","def __init__(self):\n super().__init__()\n self.p = 0.0\n self.type = 'Geometric'\n self.distType = 'Discrete'\n self.lowerBound = 0.0\n self.upperBound = 1.0\n self.compatibleQuadrature.append('CDF')\n self.preferredQuadrature = 'CDF'\n self.preferredPolynomials = 'CDF'","def set_priors(self,alpha):\n\n\t\tassert type(alpha) == float\n\t\tself.alpha = alpha","def obtain_parametric_priors(resolution, num_actions):\n # maximum prior magnitude for any discritized state\n max_prior = 10\n \n priors = []\n \n for p in range(resolution):\n for v in range(resolution):\n for a in range(num_actions):\n priors.append(set_parametric_prior(resolution, p, v, a, max_prior))\n \n priors = np.array(priors).reshape(resolution,resolution,num_actions)\n #print(\"priors\", priors[5,5,0])\n return priors","def set_priors(self,alpha,beta):\n\n\t\tassert type(alpha) == float and type(beta) == float\n\n\t\tself.alpha = alpha\n\t\tself.beta = beta","def __init__(self, dim, scaling=None, typical_x=None, bounds=None, fixed_values=None, tf=None):\r\n self.N = dim\r\n self.bounds = bounds\r\n self.fixed_values = fixed_values\r\n if tf is not None:\r\n self.tf_pheno = tf[0]\r\n self.tf_geno = tf[1] # TODO: should not necessarily be needed\r\n # r = np.random.randn(dim)\r\n # assert all(tf[0](tf[1](r)) - r < 1e-7)\r\n # r = np.random.randn(dim)\r\n # assert all(tf[0](tf[1](r)) - r > -1e-7)\r\n print(\"WARNING in class GenoPheno: user defined transformations have not been tested thoroughly\")\r\n else:\r\n self.tf_geno = None\r\n self.tf_pheno = None\r\n\r\n if fixed_values:\r\n if type(fixed_values) is not dict:\r\n raise _Error(\"fixed_values must be a dictionary {index:value,...}\")\r\n if max(fixed_values.keys()) >= dim:\r\n raise _Error(\"max(fixed_values.keys()) = \" + str(max(fixed_values.keys())) +\r\n \" >= dim=N=\" + str(dim) + \" is not a feasible index\")\r\n # convenience commenting functionality: drop negative keys\r\n for k in list(fixed_values.keys()):\r\n if k < 0:\r\n fixed_values.pop(k)\r\n if bounds:\r\n if len(bounds) != 2:\r\n raise _Error('len(bounds) must be 2 for lower and upper bounds')\r\n for i in (0,1):\r\n if bounds[i] is not None:\r\n bounds[i] = array(dim * [bounds[i]] if np.isscalar(bounds[i]) else\r\n [b for b in bounds[i]])\r\n\r\n def vec_is_default(vec, default_val=0):\r\n \"\"\"return True if `vec` has the value `default_val`,\r\n None or [None] are also recognized as default\"\"\"\r\n try:\r\n if len(vec) == 1:\r\n vec = vec[0] # [None] becomes None and is always default\r\n else:\r\n return False\r\n except TypeError:\r\n pass # vec is a scalar\r\n\r\n if vec is None or vec == array(None) or vec == default_val:\r\n return True\r\n return False\r\n\r\n self.scales = array(scaling)\r\n if vec_is_default(self.scales, 1):\r\n self.scales = 1 # CAVE: 1 is not array(1)\r\n elif self.scales.shape is not () and len(self.scales) != self.N:\r\n raise _Error('len(scales) == ' + str(len(self.scales)) +\r\n ' does not match dimension N == ' + str(self.N))\r\n\r\n self.typical_x = array(typical_x)\r\n if vec_is_default(self.typical_x, 0):\r\n self.typical_x = 0\r\n elif self.typical_x.shape is not () and len(self.typical_x) != self.N:\r\n raise _Error('len(typical_x) == ' + str(len(self.typical_x)) +\r\n ' does not match dimension N == ' + str(self.N))\r\n\r\n if (self.scales is 1 and\r\n self.typical_x is 0 and\r\n self.bounds in (None, [None, None]) and\r\n self.fixed_values is None and\r\n self.tf_pheno is None):\r\n self.isidentity = True\r\n else:\r\n self.isidentity = False","def __init__(self, n, g_bases, g_len, m_prob):\n self.n = n\n self.g_bases = g_bases\n self.g_len = g_len\n self.m_prob = m_prob","def __init__(self, probability: float):\n super().__init__()\n\n # store input parameters\n self.probability = probability","def __init__(self, r=1, p=3):\n self.p = p\n self.r = r","def __init__(self, population=25, initSampling='lhc', fracMutation=0.2, fracElite=0.2, fracLevy=1.0, alpha=0.5, gamma=1, n=1, scalingFactor=10.0, penalty=0.0, maxGens=20000, maxFevals=200000, convTol=1e-06, stallLimit=10000, optConvTol=0.01, **kwargs):\n ProblemParameters_multi.__init__(self, **kwargs)\n self.population = population\n self.initSampling = initSampling\n self.fracMutation = fracMutation\n assert self.fracMutation >= 0 and self.fracMutation <= 1, 'The probability of discovery must exist on (0,1]'\n self.fracElite = fracElite\n assert self.fracElite >= 0 and self.fracElite <= 1, 'The elitism fraction must exist on (0,1]'\n self.fracLevy = fracLevy\n assert self.fracLevy >= 0 and self.fracLevy <= 1, 'The probability that a Levy flight is performed must exist on (0,1]'\n self.alpha = alpha\n self.gamma = gamma\n self.n = n\n self.scalingFactor = scalingFactor\n self.penalty = penalty\n self.maxGens = maxGens\n self.maxFevals = maxFevals\n self.convTol = convTol\n self.stallLimit = stallLimit\n self.optConvTol = optConvTol","def __init__(self, \n p: int = 1,\n fit_intercept: bool = True,\n degree: int = 1,\n input_type: str = None):\n self.p = p \n self.fit_intercept = fit_intercept \n self.degree = degree\n self.input_type = input_type","def __init__(self,\n p_xi: float, p_yi: float, p_zi: float,\n p_xx: float, p_yx: float, p_zx: float,\n p_xy: float, p_yy: float, p_zy: float,\n p_xz: float, p_yz: float, p_zz: float,\n p_ix: float, p_iy: float, p_iz: float) -> None:\n self._p_xi = value.validate_probability(p_xi, 'p_xi')\n self._p_yi = value.validate_probability(p_yi, 'p_yi')\n self._p_zi = value.validate_probability(p_zi, 'p_zi')\n self._p_xx = value.validate_probability(p_xx, 'p_xx')\n self._p_yx = value.validate_probability(p_yx, 'p_yx')\n self._p_zx = value.validate_probability(p_zx, 'p_zx')\n self._p_xy = value.validate_probability(p_xy, 'p_xy')\n self._p_yy = value.validate_probability(p_yy, 'p_yy')\n self._p_zy = value.validate_probability(p_zy, 'p_zy')\n self._p_xz = value.validate_probability(p_xz, 'p_xz')\n self._p_yz = value.validate_probability(p_yz, 'p_yz')\n self._p_zz = value.validate_probability(p_zz, 'p_zz')\n self._p_ix = value.validate_probability(p_ix, 'p_ix')\n self._p_iy = value.validate_probability(p_iy, 'p_iy')\n self._p_iz = value.validate_probability(p_iz, 'p_iz')\n self._p_ii = 1 - value.validate_probability(p_xi + p_yi + p_zi +\n p_xx + p_yx + p_zx +\n p_xy + p_yy + p_zy +\n p_xz + p_yz + p_zz +\n p_ix + p_iy + p_iz, 'p_ii')","def __init__(self):\n self.points = []\n names = ['x1', 'x2', 'x3']\n lows = [-5, -5, -5]\n highs = [5, 5, 5]\n # TODO 2: Use names, lows and highs defined above to code up decision\n # and objective metadata for POM3.\n decisions = [Decision(n, l, h) for n, l, h in zip(names, lows, highs)]\n objectives = [Objective(\"f1\", True), Objective(\"f2\", True)]\n Problem.__init__(self, decisions, objectives)","def plot_priors(params):\n prior_dicts = {'ic' : params['ic_prior'], 'ii' : params['ii_prior']}\n pidxs = (pidx for pidx in onp.arange(1,12))\n f = plt.figure(figsize=(12,8))\n for k in prior_dicts:\n for j in prior_dicts[k]:\n plt.subplot(2,3,next(pidxs));\n data = prior_dicts[k][j]\n if \"log\" in j:\n data = onp.exp(data)\n j_title = j.strip('log')\n else:\n j_title = j\n plt.stem(data)\n plt.title(k + ' ' + j_title)\n return f","def __init__(self, classes, data_size):\r\n self.classes = classes\r\n self.data_size = data_size\r\n self.conditional_prob = {class_:{} for class_ in classes} # Conditional Probability Table for storing parameters useful to compute P(feat|class_)\r\n self.class_prob = {} # Stores the priors\r","def __init__(self):\n self.points = []\n names = ['x1', 'x2', 'x3', 'x4', 'x5', 'x6']\n lows = [0, 0, 1, 0, 1, 0]\n highs = [10, 10, 5, 6, 5, 10]\n # TODO 2: Use names, lows and highs defined above to code up decision\n # and objective metadata for POM3.\n decisions = [Decision(n, l, h) for n, l, h in zip(names, lows, highs)]\n objectives = [Objective(\"f1\", True), Objective(\"f2\", True)]\n Problem.__init__(self, decisions, objectives)","def __init__(self) :\n self.probabilities_ = None","def __init__(self) :\n self.probabilities_ = None","def __init__(self) :\n self.probabilities_ = None","def __init__(self):\n super().__init__()\n self.mu = 0.0\n self.type = 'Poisson'\n self.hasInfiniteBound = True\n self.distType = 'Discrete'\n self.compatibleQuadrature.append('CDF')\n self.preferredQuadrature = 'CDF'\n self.preferredPolynomials = 'CDF'","def __init__(self,func ,domain_space, max_evals = 10):\n self.func = func\n # optimizing for FLOAT values\n #self.space = hp.uniform('x', 36, 200)\n # optimizing for Integer values\n self.space = domain_space\n self.algorithm = tpe.suggest # creating algorithm\n self.trials = Trials() # to check records\n self.max_evals = max_evals","def __init__(self, num_hermgauss=20):\r\n super().__init__()\r\n\r\n gh_x, gh_w = np.polynomial.hermite.hermgauss(num_hermgauss)\r\n self.gh_x = torch.nn.Parameter(\r\n torch.from_numpy(gh_x[:, None, None].astype(NUMPY_DTYPE)),\r\n requires_grad=False)\r\n self.gh_w = torch.nn.Parameter(\r\n torch.from_numpy(gh_w[:, None, None].astype(NUMPY_DTYPE)),\r\n requires_grad=False)","def __init__(self, eps=None, *, min_samples=5, metric='euclidean',\n metric_params=None, algorithm='auto', leaf_size=30, p=None,\n n_jobs=None):\n self.eps = eps\n self.min_samples = min_samples\n self.metric = metric\n self.metric_params = metric_params\n self.algorithm = algorithm\n self.leaf_size = leaf_size\n self.p = p\n self.n_jobs = n_jobs","def prior_of_priors(self, tt):\n for i in xrange(self.n_params): \n try: \n p_theta *= self.param_obj.prior()[i].pdf(tt[i]) \n\n except UnboundLocalError: \n p_theta = self.param_obj.prior()[i].pdf(tt[i]) \n\n return p_theta","def __init__(self, dim, switch=False, **kwargs):\n # Define the hyperparameters\n super(PAE, self).__init__(dim, **kwargs)\n # Initialize the models\n self.encode_ = Encoder([self.h_dim, self.z_dim])\n self.decode_ = Decoder([self.h_dim, self.image_size])\n self.b = RealNVP(num_coupling_layers=6, z_dim=self.z_dim)\n self.scaler = MinMaxScaler()\n # Build the models\n self.encode_.build(input_shape=(None, self.image_size))\n self.decode_.build(input_shape=(None, self.z_dim))\n self.b.build(input_shape=(None, self.z_dim))\n # Use bijecter in incode or not\n self.switch = switch","def __init__(self,\n learning_rate=0.001,\n beta1=0.9,\n use_locking=False,\n name=\"GGT\",\n window=10,\n eps=1e-4,\n svd_eps=1e-6,\n sigma_eps=1e-2):\n super(GGTOptimizer, self).__init__(use_locking, name)\n self._set_hyper(\"lr\", learning_rate)\n self._set_hyper(\"beta1\", beta1)\n self._set_hyper(\"window\", window)\n self._set_hyper(\"eps\", eps)\n self._set_hyper(\"svd_eps\", svd_eps)\n self._set_hyper(\"sigma_eps\", sigma_eps)\n\n self.index_dict = {}\n self.shape_dict = {}","def __init__(self, \n param_epsilon, \n param_tau,\n param_u, \n param_gamma_left,\n param_gamma_right,\n param_beta):\n self.epsilon = param_epsilon\n self.tau = param_tau\n self.u = param_u\n self.gamma_left = param_gamma_left\n self.gamma_right = param_gamma_right\n \n self.sigma_retarded = 1j * (self.gamma_left + self.gamma_right) / 2.0\n self.sigma_advanced = - self.sigma_retarded;\n \n self.dim = len(self.u)\n self.rho = np.zeros((2**self.dim))\n \n self.beta = param_beta\n \n self.cutoff_chance = 0.0001\n self.external_distribution = False\n self.external_distribution_array = self.distribution()\n self.external_distribution = True","def __init__(self, n=5, p=0.05, p_real=0.1, p_neg=0.0):\n self.n = n\n self.p = p\n self.p_real = p_real\n self.p_neg = p_neg","def __init__(self, low, high, prior=\"uniform\", transform=None, name=None):\n if high <= low:\n raise ValueError(\n \"the lower bound {} has to be less than the\"\n \" upper bound {}\".format(low, high)\n )\n self.low = low\n self.high = high\n self.prior = prior\n self.name = name\n\n if transform is None:\n transform = \"identity\"\n\n self.transform_ = transform\n\n if self.transform_ not in [\"normalize\", \"identity\"]:\n raise ValueError(\n \"transform should be 'normalize' or 'identity'\"\n \" got {}\".format(self.transform_)\n )\n\n if self.transform_ == \"normalize\":\n # set upper bound to next float after 1. to make the numbers\n # inclusive of upper edge\n if self.prior == \"uniform\":\n self.transformer = Pipeline([Identity(), Normalize(low, high)])\n else:\n self.transformer = Pipeline(\n [Log10(), Normalize(np.log10(low), np.log10(high))]\n )\n else:\n if self.prior == \"uniform\":\n self.transformer = Identity()\n else:\n self.transformer = Log10()","def test_MPI_Parallel_Interface(comm):\n\n def printMPI(msg):\n for i in range(comm.Get_size()):\n comm.barrier()\n if comm.Get_rank() == i:\n print(\"Proc {}: {}\".format(i, msg))\n\n n = 10\n\n par = MPI_Objective_Interface(mp.Extended_Rosenbrock, nb_domain_grid_pts=n,\n comm=comm)\n\n printMPI(par.counts)\n\n # ref = mp.Extended_Rosenbrock\n\n np.testing.assert_array_equal(\n mp.Extended_Rosenbrock.startpoint(n)[par.subdomain_slices],\n par.startpoint())\n np.testing.assert_almost_equal(\n mp.Extended_Rosenbrock.f(mp.Extended_Rosenbrock.startpoint(n)),\n par.f(par.startpoint()),\n err_msg=\"Different Function Value at startpoint\")\n np.testing.assert_allclose(\n mp.Extended_Rosenbrock.grad(mp.Extended_Rosenbrock.startpoint(n))[\n par.subdomain_slices],\n par.grad(par.startpoint()),\n err_msg=\"Different Gradient Value at startpoint\")","def gen_parameter(self, g, ng, p):\n self.gen_apply(g, ng, p)","def __init__(self, v, p):\n self.val = v\n self.priority = p","def __init__(self, prime, server):\n self.N = prime\n self.g = 2\n self.k = 3\n self.server = server","def __init__(self, pmin=None, pmax=None, npl=1, nobs=1):\n\n if pmin is None:\n self._pmin = params.Parameters(npl=npl, nobs=nobs)\n self._pmin = 0.0*self._pmin\n self._pmin.V = float('-inf')\n else:\n self._pmin = pmin\n\n if pmax is None:\n self._pmax = params.Parameters(npl=npl, nobs=nobs)\n self._pmax = self._pmax + float('inf')\n self._pmax.chi = 1.0\n self._pmax.e = 1.0\n self._pmax.omega = 2.0*np.pi\n else:\n self._pmax = pmax\n\n self._npl = npl\n self._nobs = nobs","def _get_current_hyperparameters(self):","def _gen_policy_params(self, state: State) -> Tensor:\n ...","def _init_hyperparam(self, **p_par):\r\n \r\n try:\r\n p_input_size = self._input_space.get_num_dim()\r\n p_output_size = self._output_space.get_num_dim()\r\n except:\r\n raise ParamError('Input size and/or output size of the network are not defined.')\r\n \r\n if 'p_update_rate' not in p_par:\r\n p_par['p_update_rate'] = 1\r\n elif p_par.get('p_update_rate') < 1:\r\n raise ParamError(\"p_update_rate must be equal or higher than 1.\")\r\n \r\n if 'p_num_hidden_layers' not in p_par:\r\n raise ParamError(\"p_num_hidden_layers is not defined.\")\r\n \r\n if 'p_output_activation_fct' not in p_par:\r\n p_par['p_output_activation_fct'] = None\r\n \r\n if 'p_optimizer' not in p_par:\r\n raise ParamError(\"p_optimizer is not defined.\")\r\n \r\n if 'p_loss_fct' not in p_par:\r\n raise ParamError(\"p_loss_fct is not defined.\")\r\n\r\n if 'p_test_data' not in p_par:\r\n p_par['p_test_data'] = 0.3\r\n\r\n if 'p_batch_size' not in p_par:\r\n p_par['p_batch_size'] = 100\r\n\r\n if 'p_seed_buffer' not in p_par:\r\n p_par['p_seed_buffer'] = 1\r\n\r\n if 'p_learning_rate' not in p_par:\r\n p_par['p_learning_rate'] = 3e-4\r\n \r\n if 'p_hidden_size' not in p_par:\r\n raise ParamError(\"p_hidden_size is not defined.\")\r\n try:\r\n if len(p_par['p_hidden_size']) != p_par['p_num_hidden_layers']:\r\n raise ParamError(\"length of p_hidden_size list must be equal to p_num_hidden_layers or an integer.\")\r\n except:\r\n p_par['p_hidden_size'] = [int(p_par['p_hidden_size'])] * int(p_par['p_num_hidden_layers'])\r\n \r\n if 'p_activation_fct' not in p_par:\r\n raise ParamError(\"p_activation_fct is not defined.\")\r\n try:\r\n if len(p_par['p_activation_fct']) != p_par['p_num_hidden_layers']:\r\n raise ParamError(\"length of p_activation_fct list must be equal to p_num_hidden_layers or a single activation function.\")\r\n except:\r\n if isinstance(p_par['p_activation_fct'], list):\r\n raise ParamError(\"length of p_activation_fct list must be equal to p_num_hidden_layers or a single activation function.\")\r\n else:\r\n p_par['p_activation_fct'] = [p_par['p_activation_fct']] * int(p_par['p_num_hidden_layers'])\r\n \r\n if 'p_weight_bias_init' not in p_par:\r\n p_par['p_weight_bias_init'] = True\r\n \r\n if p_par['p_weight_bias_init']:\r\n if 'p_weight_init' not in p_par:\r\n p_par['p_weight_init'] = torch.nn.init.orthogonal_\r\n \r\n if 'p_bias_init' not in p_par:\r\n p_par['p_bias_init'] = lambda x: torch.nn.init.constant_(x, 0)\r\n \r\n if 'p_gain_init' not in p_par:\r\n p_par['p_gain_init'] = np.sqrt(2)\r\n \r\n self._hyperparam_space.add_dim(HyperParam('p_input_size','Z'))\r\n self._hyperparam_space.add_dim(HyperParam('p_output_size','Z'))\r\n self._hyperparam_space.add_dim(HyperParam('p_update_rate','Z'))\r\n self._hyperparam_space.add_dim(HyperParam('p_num_hidden_layers','Z'))\r\n self._hyperparam_space.add_dim(HyperParam('p_hidden_size','Z'))\r\n self._hyperparam_space.add_dim(HyperParam('p_activation_fct'))\r\n self._hyperparam_space.add_dim(HyperParam('p_output_activation_fct'))\r\n self._hyperparam_space.add_dim(HyperParam('p_optimizer'))\r\n self._hyperparam_space.add_dim(HyperParam('p_loss_fct'))\r\n self._hyperparam_space.add_dim(HyperParam('p_test_data'))\r\n self._hyperparam_space.add_dim(HyperParam('p_batch_size'))\r\n self._hyperparam_space.add_dim(HyperParam('p_seed_buffer'))\r\n self._hyperparam_space.add_dim(HyperParam('p_learning_rate'))\r\n self._hyperparam_space.add_dim(HyperParam('p_weight_bias_init'))\r\n self._hyperparam_space.add_dim(HyperParam('p_weight_init'))\r\n self._hyperparam_space.add_dim(HyperParam('p_bias_init'))\r\n self._hyperparam_space.add_dim(HyperParam('p_gain_init'))\r\n self._hyperparam_tuple = HyperParamTuple(self._hyperparam_space)\r\n \r\n ids_ = self.get_hyperparam().get_dim_ids()\r\n self.get_hyperparam().set_value(ids_[0], p_input_size)\r\n self.get_hyperparam().set_value(ids_[1], p_output_size)\r\n self.get_hyperparam().set_value(ids_[2], p_par['p_update_rate'])\r\n self.get_hyperparam().set_value(ids_[3], p_par['p_num_hidden_layers'])\r\n self.get_hyperparam().set_value(ids_[4], p_par['p_hidden_size'])\r\n self.get_hyperparam().set_value(ids_[5], p_par['p_activation_fct'])\r\n self.get_hyperparam().set_value(ids_[6], p_par['p_output_activation_fct'])\r\n self.get_hyperparam().set_value(ids_[7], p_par['p_optimizer'])\r\n self.get_hyperparam().set_value(ids_[8], p_par['p_loss_fct'])\r\n self.get_hyperparam().set_value(ids_[9], p_par['p_test_data'])\r\n self.get_hyperparam().set_value(ids_[10], p_par['p_batch_size'])\r\n self.get_hyperparam().set_value(ids_[11], p_par['p_seed_buffer'])\r\n self.get_hyperparam().set_value(ids_[12], p_par['p_learning_rate'])\r\n self.get_hyperparam().set_value(ids_[13], p_par['p_weight_bias_init'])\r\n self.get_hyperparam().set_value(ids_[14], p_par['p_weight_init'])\r\n self.get_hyperparam().set_value(ids_[15], p_par['p_bias_init'])\r\n self.get_hyperparam().set_value(ids_[16], p_par['p_gain_init'])","def Params(cls):\n p = hyperparams.InstantiableParams(cls)\n p.Define('task', None, 'Underlying task')\n p.Define('logdir', None, 'Log directory')\n p.Define('num_splits_per_client', None, '')\n p.Define('steps_per_loop', None, 'Number of steps to run.')\n p.Define('dataset_name', None,\n 'Dataset the program is operating on, eg: \"Test\"')\n p.Define('name', 'base_program', 'Program name.')\n p.Define('task_name', None,\n 'If multi-task, what the high-level task name is')\n p.Define('num_threads', 1, 'Number of threads in multiprocessing pool.')\n p.Define('spmd', False, 'Whether program is running under SPMD mode.')\n p.Define('write_train_input_stats', False,\n 'Whether to write input data stats during training.')\n p.Define('max_metrics', 256, 'Overrides TpuEvalMetrics.max_metrics')\n p.Define('ml_perf', None, 'MLPerf config')\n return p","def pred_prob(self, t, x, **kwargs):\n raise NotImplementedError","def __init__(self, bandits: List[Bandit]):\n self.bandits = bandits\n # mu_pri and var_pri are hypermaterers for prior distribution\n self.mu_pri = np.zeros(len(self.bandits)) # 5 for each\n self.var_pri = np.ones(len(self.bandits))*5\n # mu and var are hyperparameters for posterior distribution\n self.mu = self.mu_pri\n self.var = self.var_pri\n self.var0 = 1 # 1 is taken but any can be taken no prob upto a limit constant of inintial distribution\n self.logging = Logging()","def __init__(self, name, description, nbits, prime, generator):\n self.name = name\n self.description = description\n self.nbits = nbits\n self.prime = prime\n self.generator = generator","def __init__(self, lhs, rhs, **prob_kwarg):\n ImmutableProbabilisticMixIn.__init__(self, **prob_kwarg)\n CFGProduction.__init__(self, lhs, rhs)","def __init__(self, sigma):\n self.sigma = float(sigma)\n super().__init__(domain=functional.domain,\n range=functional.domain, linear=False)\n\n # Setting up parameters\n self.const = 1 / (functional.epsilon * sigma)","def __init__(self, nb):\n\n # Approximation parameters\n self.o_min = -.1\n self.o_max = .1\n self.o_nb = nb\n self.o_bw = (self.o_max - self.o_min) / self.o_nb\n self.o_e = np.linspace(self.o_min, self.o_max, self.o_nb+1)\n self.o = self.o_e[:-1] + (self.o_bw/2)\n self.r_e = [0, 1]\n self.r_nb = len(self.r_e)\n self.mu_min = 0\n self.mu_max = 1\n self.mu_nb = nb\n self.mu_bw = (self.mu_max - self.mu_min) / self.mu_nb\n self.mu_e = np.linspace(self.mu_min, self.mu_max, self.mu_nb+1)\n self.mu = self.mu_e[:-1] + (self.mu_bw/2)\n self.p_s_giv_o_hat = np.nan\n self.S = np.nan\n self.p_mu_giv_r_o_hat = np.nan","def _handleInput(self, paramInput):\n super()._handleInput(paramInput)\n pFind = paramInput.findFirst('p')\n if pFind != None:\n self.p = pFind.value\n else: self.raiseAnError(IOError,'p value needed for Geometric distribution')\n self.initializeDistribution()","def fitness(self):\n params = np.array([self['p{}'.format(i)] for i in range(n_pars)])\n \n return func(params)","def test_base_hyper_parameters_reg(self):\n hyper_parameter_set = modelgen.generate_base_hyper_parameter_set()\n assert 'regularization_rate' in hyper_parameter_set.keys()","def __init__(self, input_placeholder, server, trivial_prob):\n self.logits = self._get_null_logits(input_placeholder)\n self.precision = (1-trivial_prob)*server.adapter.frequencies[1]\n self.prior_threshold = 0.5","def __init__(self, multiplicity, in_features, cardinality, dropout=0.0):\n super(IndependentNormal, self).__init__(multiplicity, in_features, dropout)\n self.gauss = Normal(\n multiplicity=multiplicity, in_features=in_features, dropout=dropout\n )\n self.prod = Product(in_features=in_features, cardinality=cardinality)\n\n self.cardinality = cardinality","def _handleInput(self, paramInput):\n super()._handleInput(paramInput)\n pFind = paramInput.findFirst('p')\n if pFind != None:\n self.p = pFind.value\n else:\n self.raiseAnError(IOError,'p value needed for Bernoulli distribution')\n self.initializeDistribution()","def __init__(self, num_params):\r\n self.num_params = num_params","def __init__(self, num_params):\r\n self.num_params = num_params","def __init__(self, p=0.5):\n assert 0. <= p <= 1.\n self.p = p\n self.rng = T.shared_randomstreams.RandomStreams(seed=123456)\n self.params = []","def get_PI(self, alpha):\n raise NotImplementedError(\"This is an abstract method and needs to be implemented in derived classes.\")","def __init__(self, cost_fn, out_dim, popsize, num_elites, max_iters, \n upper_bound=None, lower_bound=None, epsilon=0.001):\n super().__init__()\n self.cost_fn = cost_fn\n self.out_dim = out_dim \n self.popsize = popsize\n self.num_elites = num_elites # redundant arguments for Random Policy\n self.max_iters = max_iters\n self.ub = upper_bound.reshape([1, out_dim])\n self.lb = lower_bound.reshape([1, out_dim])\n self.epsilon = epsilon\n\n # Sanity checks\n assert (num_elites <= popsize), \"Error: Number of elites must <= population size!\"","def parameters(self):\n raise NotImplementedError('Abstract method \"parameters\" must be '\n 'specialised!')","def __init__(self, prior: float, beta: float = 0., gamma: float = 1.0):\n super(NonNegativePULoss, self).__init__()\n\n self.beta = beta\n self.prior = prior\n self.gamma = gamma\n self.loss_fn = torch.nn.CrossEntropyLoss(reduction='none')","def __init__ (self, p, q):\n self.n = p * q\n self.n_sq = self.n * self.n\n self.g = self.n + 1","def __init__(self, D, NP, nFES, nRuns, useAlgorithms, useBenchmarks, **kwargs):\n\t\tself.D = D\n\t\tself.NP = NP\n\t\tself.nFES = nFES\n\t\tself.nRuns = nRuns\n\t\tself.useAlgorithms = useAlgorithms\n\t\tself.useBenchmarks = useBenchmarks\n\t\tself.A = kwargs.get('A', 0.5)\n\t\tself.r = kwargs.get('r', 0.5)\n\t\tself.Qmin = kwargs.get('Qmin', 0.0)\n\t\tself.Qmax = kwargs.get('Qmax', 2.0)\n\t\tself.Pa = kwargs.get('Pa', 0.25)\n\t\tself.F = kwargs.get('F', 0.5)\n\t\tself.CR = kwargs.get('CR', 0.9)\n\t\tself.alpha = kwargs.get('alpha', 0.5)\n\t\tself.betamin = kwargs.get('betamin', 0.2)\n\t\tself.gamma = kwargs.get('gamma', 1.0)\n\t\tself.p = kwargs.get('p', 0.5)\n\t\tself.Ts = kwargs.get('Ts', 4)\n\t\tself.Mr = kwargs.get('Mr', 0.05)\n\t\tself.C1 = kwargs.get('C1', 2.0)\n\t\tself.C2 = kwargs.get('C2', 2.0)\n\t\tself.w = kwargs.get('w', 0.7)\n\t\tself.vMin = kwargs.get('vMin', -4)\n\t\tself.vMax = kwargs.get('vMax', 4)\n\t\tself.Tao = kwargs.get('Tao', 0.1)\n\t\tself.n = kwargs.get('n', 10)\n\t\tself.omega = kwargs.get('omega', 0.25)\n\t\tself.mu = kwargs.get('mu', 0.5)\n\t\tself.E_init = kwargs.get('E_init', 10)\n\t\tself.S_init = kwargs.get('S_init', 10)\n\t\tself.T_min = kwargs.get('T_min', -10)\n\t\tself.T_max = kwargs.get('T_max', 10)\n\t\tself.C_a = kwargs.get('C_a', 2)\n\t\tself.C_r = kwargs.get('C_r', 0.5)\n\t\tself.results = {}","def PPO(parclass):\n class PPO(parclass):\n \"\"\"\n Proximal Policy Optimization algorithm (PPO).\n Requires parent class inherited from A2C.\n Based on: https://arxiv.org/abs/1707.06347\n \n Args:\n ppo_clip - clipping rate of pi_new / pi_old fraction\n epochs - number of epochs to run through rollout on each update\n batch_size - size of mini-batch to select without replacement on each gradient ascent step\n \"\"\"\n __doc__ += parclass.__doc__\n PARAMS = parclass.PARAMS | {\"ppo_clip\", \"epochs\", \"batch_size\"} \n \n def __init__(self, config):\n super().__init__(config)\n \n self.config.setdefault(\"ppo_clip\", 0.2)\n self.config.setdefault(\"epochs\", 3)\n self.config.setdefault(\"batch_size\", 32)\n \n assert self.env.num_envs * self.config.rollout >= self.config.batch_size, \"batch_size is bigger than rollout * number of threads!\"\n \n def optimized_function(self):\n # advantage is estimated using advantage function for previous policy\n # so we take advantage from original rollout\n advantages = (self.returns_b - self.old_values_b).detach() #self.advantages_b.detach() - КОСТЫЛЬ\n \n # importance sampling for making an update of current policy using samples from old policy\n # the gradients to policy will flow through the numerator.\n ratio = torch.exp(self.action_log_probs_b - self.old_action_log_probs_b.detach())\n \n # PPO clipping! Prevents from \"too high updates\".\n surr1 = ratio * advantages\n surr2 = torch.clamp(ratio, 1.0 - self.config.ppo_clip, 1.0 + self.config.ppo_clip) * advantages\n return -torch.min(surr1, surr2)\n \n def update(self):\n \"\"\"One step of optimization based on rollout memory\"\"\"\n with torch.no_grad():\n self.preprocess_rollout()\n \n # DEEP-RL TUTORIALS: КОСТЫЛЬ\n #self.advantages = self.returns[:-1] - self.values[:-1]\n #self.advantages = (self.advantages - self.advantages.mean()) / (self.advantages.std() + 1e-5)\n \n # going through rollout several (config.epochs) times:\n for epoch in range(self.config.epochs):\n # TODO: drop last = False? What if there is 1 sample?\n sampler = BatchSampler(SubsetRandomSampler(range(self.env.num_envs * self.config.rollout)), self.config.batch_size, drop_last=False)\n \n for indices in sampler:\n # retrieving new batch as part of rollout\n self.returns_b = self.returns.view(-1, *self.config.value_repr_shape)[indices]\n self.old_values_b = self.values.view(-1, *self.config.value_repr_shape)[indices]\n self.old_action_log_probs_b = self.action_log_probs.view(-1)[indices]\n #self.advantages_b = self.advantages.view(-1)[indices] # КОСТЫЛЬ\n \n # calculating current value, action_log_prob, entropy\n dist, self.values_b = self.policy(self.observations.view(-1, *self.config.observation_shape)[indices])\n self.values_b = self.values_b.squeeze() # IMPORTANT ([32] - [32, 1] problem)\n self.action_log_probs_b = dist.log_prob(self.actions.view(-1, *self.config.actions_shape)[indices])#.sum(dim=-1) \n self.entropy_b = dist.entropy()#.sum(dim=-1)\n \n # performing step\n self.gradient_ascent_step()\n return PPO","def Params(cls):\n p = hyperparams.InstantiableParams(cls)\n\n p.Define('task_dict', None, 'dataset_name -> task params')\n p.Define('task_name', None, 'High level task name')\n p.Define('logdir', None, 'Log directory')\n p.Define('train_program', None, 'Train program params')\n p.Define('train_executions_per_eval', 1, '')\n p.Define('dataset_names', [], 'List of all dataset names.')\n p.Define('num_splits_per_client', None, '')\n\n p.Define('ml_perf', hyperparams.Params(), 'MlPerf configuration.')\n\n mlp = p.ml_perf\n mlp.Define('benchmark_name', None, 'Benchmark name for compliance log.')\n mlp.Define('decoder_metric_name', None,\n 'Name of the decoder metric to report for compliance log.')\n mlp.Define('decoder_metric_success_threshold', None,\n 'Benchmark run must exceed this value to succeed.')\n mlp.Define('max_steps_to_train', None,\n 'Maximum number of steps to reach target accuracy')\n mlp.Define('steps_per_epoch', None, 'Number of training steps per epoch.')\n mlp.Define('global_batch_size', None, 'Global batch size.')\n mlp.Define('max_sequence_length', None, 'Maximum sequence length.')\n mlp.Define('optimizer_name', None, 'Optimizer used.')\n mlp.Define('base_learning_rate', None, 'Base learning rate.')\n mlp.Define('warmup_steps', None, 'Number of warm-up steps.')\n\n return p","def __init__(self):\n GinacFunction.__init__(self, \"binomial\", nargs=2, preserved_arg=1,\n conversions=dict(maxima='binomial',\n mathematica='Binomial',\n sympy='binomial'))","def _gen_prime(self, n_bits):\n n = gmpy2.mpz(prng.getrandbits(n_bits))\n return gmpy2.next_prime(n)"],"string":"[\n \"def prim_method(self):\",\n \"def prim_method(self):\",\n \"def __init__(self, p1_proba=0.5):\\n self.p1_proba = p1_proba\",\n \"def get_hyperparams(self):\",\n \"def Params(cls):\\n return hyperparams.InstantiableParams(cls)\",\n \"def __init__(self, prim):\\n self.actual = prim\",\n \"def gen_parameter(self, g, ng, p):\\n pass\",\n \"def __init__(self):\\n super(PriProb, self).__init__()\\n # initialize R: distribute R_TOTAL reward points in J_avi locations randomly\\n # self.r preserved for debugging, no real use in the script\\n self.r = np.array(ad.randint_upto_sum(R_TOTAL, J_avi)).astype(NP_DTYPE)\\n\\n # expand self.r from J_avi locations to J locations using is_avi\\n self.r_exp = np.zeros((J), dtype=NP_DTYPE)\\n self.r_exp[np.nonzero(is_avi.cpu().numpy())] = self.r\\n\\n #normalizedR = ad.normalize(self.r_exp, using_max=False)\\n self.R = nn.Parameter(torch.from_numpy(self.r_exp))\",\n \"def test_custom_hypergeometric():\\n\\n M = 958\\n n = 458\\n N = 332\\n x = 2\\n custom_prob = hypergeometric_pmf(2, 958, 458, 332)\\n rv = hypergeom(M, n, N)\\n pro_prob = rv.pmf(x)\\n np.testing.assert_allclose(custom_prob, pro_prob, rtol=1e-5, atol=0)\",\n \"def __init__(self, num_values, probability):\\n self.num_values = num_values\\n self.probability = probability\\n self.register_bounds = GeometricDistribution._compute_register_bounds(num_values, probability)\\n self._register_probs = GeometricDistribution._compute_register_probs(num_values, probability)\",\n \"def p(self) -> Probability:\\n ...\",\n \"def __init__(self, p, *args, **kwargs):\\n self.p = p\\n super(BernoulliRV, self).__init__(*args, **kwargs)\",\n \"def __init__(self):\\n super().__init__()\\n self.p = 0.0\\n self.type = 'Bernoulli'\\n self.distType = 'Discrete'\\n self.lowerBound = 0.0\\n self.upperBound = 1.0\\n self.compatibleQuadrature.append('CDF')\\n self.preferredQuadrature = 'CDF'\\n self.preferredPolynomials = 'CDF'\",\n \"def __init__(self, low=0.0, alpha=0.0, beta=1.0):\\n super().__init__()\\n self.low = low\\n self.alpha = alpha\\n self.beta = beta\\n self.type = 'Gamma'\\n self.distType = 'Continuous'\\n self.hasInfiniteBound = True\\n self.compatibleQuadrature.append('Laguerre')\\n self.compatibleQuadrature.append('CDF')\\n self.preferredQuadrature = 'Laguerre'\\n self.preferredPolynomials = 'Laguerre'\",\n \"def __init__(self):\\n super().__init__()\\n self.low = 0.0\\n self.high = 1.0\\n self.alpha = 0.0\\n self.beta = 0.0\\n self.type = 'Beta'\\n self.distType = 'Continuous'\\n self.hasInfiniteBound = True\\n self.compatibleQuadrature.append('Jacobi')\\n self.compatibleQuadrature.append('CDF')\\n self.preferredQuadrature = 'Jacobi'\\n self.preferredPolynomials = 'Jacobi'\",\n \"def __init__(self, p, hyperpara, para, inst_name=\\\"\\\"):\\n \\n self.hyperpara = hyperpara\\n self.para = para\\n \\n s = -np.log(-np.log(1.0 - p))\\n \\n k, cop = hyperpara\\n \\n # Sets colour\\n colour = [\\n None,\\n None,\\n {\\n \\\"i\\\": 2,\\n \\\"me\\\": 3},\\n {\\n \\\"i\\\": 0,\\n \\\"me\\\": 1}][k][cop]\\n \\n if cop == \\\"i\\\":\\n if k == 3:\\n def f(X):\\n q1, q2, q3 = X\\n \\n Y = np.array([q1, q2 - q1, q3 - q2])\\n \\n if np.any(Y <= 0.0):\\n return None\\n \\n return -0.5 * np.sum(((Y - para[:, 0]) / para[:, 1]) ** 2)\\n elif k == 2:\\n def f(X):\\n sigma, q1, q2 = X\\n \\n if sigma <= 0:\\n return None\\n \\n Y = np.array([q1, q2 - q1])\\n \\n if np.any(Y <= 0.0):\\n return None\\n \\n a = -0.5 * np.sum(((Y - para[:, 0]) / para[:, 1]) ** 2)\\n \\n return a - np.log(sigma)\\n elif cop == \\\"me\\\":\\n q_marg = [None for _ in range(k)]\\n \\n for i in range(k):\\n dist = Normal(para[i, 0], para[i, 1])\\n q_marg[i] = TruncatedDistribution(\\n dist,\\n 0.0,\\n TruncatedDistribution.LOWER)\\n \\n ot = MaximumEntropyOrderStatisticsDistribution(q_marg)\\n \\n if k == 3:\\n def f(X):\\n q1, q2, q3 = X\\n \\n if np.any(np.array([q1, q2 - q1, q3 - q2]) <= 0.0):\\n return None\\n \\n Y = ot.computePDF(X)\\n \\n if Y <= 0:\\n return None\\n \\n return np.log(Y)\\n elif k == 2:\\n def f(X):\\n sigma, q1, q2 = X\\n \\n if sigma <= 0 or q1 <= 0.0 or q2 <= q1:\\n return None\\n \\n Y = ot.computePDF([q1, q2])\\n \\n if Y <= 0:\\n return None\\n \\n return np.log(Y) - np.log(sigma)\\n \\n if k == 3:\\n # Transformation (mu, theta, xi) -> (q1, q2, q3)\\n def g(X):\\n mu, sigma, xi = X\\n \\n if sigma <= 0:\\n return None\\n \\n # When xi is close enough to 0, we consider it equal to 0\\n if abs(xi) < 1e-300:\\n q = mu + sigma * s\\n else:\\n q = mu + sigma * (np.exp(xi * s) - 1.0) / xi\\n \\n if q[0] < 0.0:\\n return None\\n return q\\n \\n \\n # Log of determinant of g\\n def g_det(X):\\n mu, sigma, xi = X\\n \\n if abs(xi) < 1e-300:\\n return np.log(sigma)\\n \\n e = np.exp(s * xi)\\n \\n sm = [\\n s[i] * e[i] * (e[(i + 2) % 3] - e[(i + 1) % 3])\\n for i in range(3)]\\n \\n return np.log(sigma) + np.log(sum(sm)) - np.log(xi ** 2.0)\\n elif k == 2:\\n # Transformation (mu, sigma, xi) -> (sigma, q1, q2)\\n def g(X):\\n mu, sigma, xi = X\\n \\n # When xi is close enough to 0, we consider it equal to 0\\n if abs(xi) < 1e-300:\\n q = mu + sigma * s\\n else:\\n q = mu + sigma * (np.exp(xi * s) - 1.0) / xi\\n \\n if q[0] < 0.0:\\n return None\\n \\n return np.concatenate(([sigma], q))\\n \\n \\n # Log of determinant of g\\n def g_det(X):\\n mu, sigma, xi = X\\n \\n if abs(xi) < 1e-300:\\n return np.log(sigma)\\n \\n e = (s * xi - 1.0) * np.exp(s * xi)\\n \\n f = np.log(abs(e[0] - e[1]))\\n \\n return np.log(sigma) + f - np.log(xi ** 2.0)\\n \\n super().__init__(\\n util.log_transform(f, g, g_det),\\n colour=colour,\\n inst_name=inst_name)\\n \\n if k == 2:\\n self.prior[\\\"proper\\\"] = False\",\n \"def __init__(self, epsilon=0.05, gamma=0.95, alpha=0.2, numTraining=0, **args):\\n args['epsilon'] = epsilon\\n args['gamma'] = gamma\\n args['alpha'] = alpha\\n args['numTraining'] = numTraining\\n self.index = 0 # This is always Pacman\\n PQLearningAgent.__init__(self, **args)\",\n \"def define_interface(self):\\n self._add_hyperparameter_signature(name=\\\"domain\\\", dtype=str,\\n options=[\\\"uniform\\\", \\\"normal\\\", \\\"loguniform\\\", \\\"categorical\\\"])\\n self._add_hyperparameter_signature(name=\\\"data\\\", dtype=list)\\n self._add_hyperparameter_signature(name=\\\"frequency\\\", dtype=int)\\n self._add_hyperparameter_signature(name=\\\"type\\\", dtype=type)\",\n \"def __init__(self):\\n super().__init__()\\n self.n = 0.0\\n self.p = 0.0\\n self.type = 'Binomial'\\n self.hasInfiniteBound = True\\n self.distType = 'Discrete'\\n self.compatibleQuadrature.append('CDF')\\n self.preferredQuadrature = 'CDF'\\n self.preferredPolynomials = 'CDF'\",\n \"def __init__(self, p=1.5, eps=1e-8):\\n assert 1 < p < 2, \\\"make sure 1 < p < 2\\\" \\n self.p, self.eps = p, eps\",\n \"def checkPriors(para):\\n\\t\\n\\t# extract parameters\\n\\tA = para[0]\\n\\tw = para[1]\\n\\tp = para[2]\\n\\t\\n\\t# check them\\n\\tif (A<0.01 or A>10.0): A = s.uniform.rvs(0.01,10.)\\n\\t\\n\\tif (w<0.01 or w>10.0): w = s.uniform.rvs(0.01,10.)\\n\\t\\t\\n\\tif ( p<0. or p>2*np.pi): p = s.uniform.rvs(0.0,2*np.pi)\\n\\t\\n\\treturn np.array([A,w,p])\",\n \"def potential_parameters(cls):\\n raise NotImplementedError()\",\n \"def __init__(self, p, multiplier=1e-1):\\r\\n super().__init__(multiplier=multiplier)\\r\\n self.p = p\",\n \"def set_priors(parnames, limits, linenames, vsyst, nssps=1):\\n priors = {}\\n for parname in parnames:\\n name = parname.split(\\\"_\\\")[0]\\n if name in limits: #all the CvD ssp parameters\\n vmin, vmax = limits[name]\\n# print(parname,vmin,vmax)\\n delta = vmax - vmin\\n priors[parname] = stats.uniform(loc=vmin, scale=delta)\\n elif parname in vsyst:\\n priors[parname] = stats.norm(loc=vsyst[parname], scale=500)\\n elif parname == \\\"eta\\\": #what does eta do?\\n priors[\\\"eta\\\"] = stats.uniform(loc=1., scale=19)#uniform distribution in range [1,19]\\n elif parname == \\\"nu\\\": #what does nu do?\\n priors[\\\"nu\\\"] = stats.uniform(loc=2, scale=20)#uniform distribution in range [2,20]\\n elif parname == \\\"sigma\\\":\\n priors[\\\"sigma\\\"] = stats.uniform(loc=50, scale=300)#obtains the uniform distribution on [loc, loc + scale]. i.e. uniform in range [50,300]\\n elif parname == \\\"sigma_gas\\\":\\n priors[parname] = stats.uniform(loc=50, scale=100)#uniform between [50,100]km/s\\n elif name == \\\"w\\\":\\n priors[parname] = stats.uniform(loc=0, scale=1)#weights uniform between 0 and 1\\n elif name in linenames:\\n# priors[parname] = stats.expon(loc=0, scale=0.5)#favors low values>~0; make even stronger by decreasing scale. \\n priors[parname] = stats.expon(loc=0, scale=0.2)#favors low values>~0; make even stronger by decreasing scale. \\n elif name in [\\\"pred\\\", \\\"pblue\\\"]:\\n porder = int(parname.split(\\\"_\\\")[1])\\n if porder == 0:\\n mu, sd = 1 / nssps, 1\\n a, b = (0 - mu) / sd, (np.infty - mu) / sd\\n priors[parname] = stats.truncnorm(a, b, mu, sd)\\n else:\\n priors[parname] = stats.norm(0, 0.05)\\n else:\\n print(f\\\"parameter without prior: {parname}\\\")\\n return priors\",\n \"def _get_guide_priors(self):\\n if not self._alpha_guide_prior_params:\\n # create initial parameters\\n params = self._get_prior_params()\\n # register all parameters in pyro\\n for p, v in iteritems(params):\\n pyro.param(p, v)\\n self._alpha_guide_prior_params = dict(\\n self._param_store.named_parameters()\\n )\\n else:\\n # register all parameters in pyro\\n for p, v in iteritems(self._alpha_guide_prior_params):\\n pyro.param(p, v)\\n return self._params2probs(self._alpha_guide_prior_params)\",\n \"def getInputSpecification(cls):\\n inputSpecification = super(Geometric, cls).getInputSpecification()\\n inputSpecification.addSub(InputData.parameterInputFactory(\\\"p\\\", contentType=InputTypes.FloatType))\\n\\n return inputSpecification\",\n \"def __init__(self, rp_dim:int,gamma,rp_method='Classical',rp_mode='Gaussian',solve_method='Exact'):\\n self.rp_dim = rp_dim\\n self.rp_method = rp_method\\n self.rp_mode = rp_mode\\n self.solve_method = solve_method\\n if self.rp_method not in ['Classical', 'Hessian']:\\n raise NotImplementedError('Only Classical and Hessian methods are supported.')\\n if self.rp_mode not in ['Gaussian', 'SJLT']:\\n raise NotImplementedError('Only Gaussian and SJLT modes are supported.')\\n if self.solve_method not in ['Exact','ShiWoodbury']:\\n raise NotImplementedError('Only Exact and ShiWoodbury methods are implemented')\\n if not isinstance(gamma,list):\\n self.gamma = [gamma]\\n else:\\n self.gamma = gamma\",\n \"def phpcredits():\\n raise NotImplementedError()\",\n \"def fit_gp(self):\\n raise NotImplementedError('Abstract Method')\",\n \"def getInputSpecification(cls):\\n inputSpecification = super(Bernoulli, cls).getInputSpecification()\\n inputSpecification.addSub(InputData.parameterInputFactory(\\\"p\\\", contentType=InputTypes.FloatType))\\n\\n return inputSpecification\",\n \"def __init__(self, p=0.5):\\n self.p = p\",\n \"def set_hyperparams(self, params):\",\n \"def __init__(self):\\n self.points = []\\n names = ['x1']\\n lows = [10**-5]\\n highs = [10**5]\\n # TODO 2: Use names, lows and highs defined above to code up decision\\n # and objective metadata for POM3.\\n decisions = [Decision(n, l, h) for n, l, h in zip(names, lows, highs)]\\n objectives = [Objective(\\\"f1\\\", True), Objective(\\\"f2\\\", True)]\\n Problem.__init__(self, decisions, objectives)\",\n \"def Parameters():\\n\\n raise NotImplementedError()\",\n \"def Geometric(name, p):\\n return rv(name, GeometricDistribution, p)\",\n \"def getInputSpecification(cls):\\n inputSpecification = super(Binomial, cls).getInputSpecification()\\n inputSpecification.addSub(InputData.parameterInputFactory(\\\"n\\\", contentType=InputTypes.IntegerType))\\n inputSpecification.addSub(InputData.parameterInputFactory(\\\"p\\\", contentType=InputTypes.FloatType))\\n\\n return inputSpecification\",\n \"def proximal(self):\\n functional = self\\n\\n class EntRegOptTransProximal(Operator):\\n\\n \\\"\\\"\\\"Proximal operator of entropy regularized optimal transport.\\n\\n The prox is given by::\\n\\n prox_[gamma*T_eps](mu1) = arg min_x (T_epsilon(mu0, x) +\\n 1/(2*gamma) ||x - mu1||^2_2)\\n \\\"\\\"\\\"\\n\\n def __init__(self, sigma):\\n \\\"\\\"\\\"Initialize a new instance.\\n\\n Parameters\\n ----------\\n sigma : positive float\\n \\\"\\\"\\\"\\n self.sigma = float(sigma)\\n super().__init__(domain=functional.domain,\\n range=functional.domain, linear=False)\\n\\n # Setting up parameters\\n self.const = 1 / (functional.epsilon * sigma)\\n\\n def _call(self, x):\\n \\\"\\\"\\\"Apply the operator to ``x``.\\\"\\\"\\\"\\n u = functional.tmp_u_prox\\n v = functional.tmp_v_prox\\n\\n # Running generalized Sinkhorn iterations\\n for j in range(functional.niter):\\n # Safe-guarded u-update, to avoid divide-by-zero error.\\n u_old = u.copy()\\n tmp1 = functional.K_op(v)\\n if np.min(tmp1) < 1e-30 or np.max(tmp1) > 1e+50:\\n print('Numerical instability, truncation in Transport prox (Kv)',\\n str(np.min(tmp1)), str(np.max(tmp1)))\\n\\n tmp = np.fmax(tmp1, 1e-30)\\n\\n\\n u = functional.mu0 / tmp\\n if np.min(u) < 1e-30 or np.max(u) > 1e+50:\\n print('u (min/max)', str(np.min(u)), str(np.max(u)))\\n\\n # Safe-guarded v-update, to avoid divide-by-zero error.\\n v_old = v.copy()\\n\\n tmp3 = functional.K_op_adjoint(u)\\n if np.min(tmp3) < 1e-30 or np.max(tmp3) > 1e+50:\\n print('Truncation in Transport prox (KTu)',\\n str(np.min(tmp3)), str(np.max(tmp3)))\\n print('u (min/max)', str(np.min(u)), str(np.max(u)))\\n\\n tmp4 = (self.const * tmp3 * np.exp(self.const * x))\\n\\n if np.min(tmp4) < 1e-30 or np.max(tmp4) > 1e+200:\\n print('Argument in lambdert omega (min/max)',\\n str(np.min(tmp4)), str(np.max(tmp4)))\\n\\n v = np.exp(self.const * x - lambertw_fulfix(tmp4))\\n\\n v1 = np.exp(self.const * x - scipy.special.lambertw(\\n tmp4))\\n if (v-v1).norm() > 1e-10:\\n print('diff pga ny lambderw omega funciton',\\n str((v-v1).norm()))\\n print('v (min/max)', str(np.min(v)), str(np.max(v)))\\n print('Argument in lambdert omega (min/max)',\\n str(np.min(tmp4)), str(np.max(tmp4)))\\n\\n # If the updates in both u and v are small, break the loop\\n if ((np.log(v)-np.log(v_old)).norm() < 1e-8 and\\n (np.log(u)-np.log(u_old)).norm() < 1e-8):\\n break\\n\\n # Store the u and v in the internal temporary variables of the\\n # functional\\n functional.tmp_u_prox = u\\n functional.tmp_v_prox = v\\n\\n return x - self.sigma * functional.epsilon * np.log(v)\\n\\n return EntRegOptTransProximal\",\n \"def perplexity(self):\\n raise NotImplementedError(\\\"To be implemented\\\")\",\n \"def __init__(self, n_source, t_base):\\n self.null = BetaBernouilli(1.0, 1.0) # p(NULL) ~ Beta(1, 1)\\n self.a_table = AlignmentDistribution(GammaPrior(1.0, 1.0, 4.0))\\n self.t_base = t_base\\n self.t_table = [PYP(self.t_base, PYPPrior(1.0, 1.0, 1.0, 1.0, 0.1, 1.0))\\n for _ in xrange(n_source)]\",\n \"def __init__(self):\\n super().__init__()\\n self.p = 0.0\\n self.type = 'Geometric'\\n self.distType = 'Discrete'\\n self.lowerBound = 0.0\\n self.upperBound = 1.0\\n self.compatibleQuadrature.append('CDF')\\n self.preferredQuadrature = 'CDF'\\n self.preferredPolynomials = 'CDF'\",\n \"def set_priors(self,alpha):\\n\\n\\t\\tassert type(alpha) == float\\n\\t\\tself.alpha = alpha\",\n \"def obtain_parametric_priors(resolution, num_actions):\\n # maximum prior magnitude for any discritized state\\n max_prior = 10\\n \\n priors = []\\n \\n for p in range(resolution):\\n for v in range(resolution):\\n for a in range(num_actions):\\n priors.append(set_parametric_prior(resolution, p, v, a, max_prior))\\n \\n priors = np.array(priors).reshape(resolution,resolution,num_actions)\\n #print(\\\"priors\\\", priors[5,5,0])\\n return priors\",\n \"def set_priors(self,alpha,beta):\\n\\n\\t\\tassert type(alpha) == float and type(beta) == float\\n\\n\\t\\tself.alpha = alpha\\n\\t\\tself.beta = beta\",\n \"def __init__(self, dim, scaling=None, typical_x=None, bounds=None, fixed_values=None, tf=None):\\r\\n self.N = dim\\r\\n self.bounds = bounds\\r\\n self.fixed_values = fixed_values\\r\\n if tf is not None:\\r\\n self.tf_pheno = tf[0]\\r\\n self.tf_geno = tf[1] # TODO: should not necessarily be needed\\r\\n # r = np.random.randn(dim)\\r\\n # assert all(tf[0](tf[1](r)) - r < 1e-7)\\r\\n # r = np.random.randn(dim)\\r\\n # assert all(tf[0](tf[1](r)) - r > -1e-7)\\r\\n print(\\\"WARNING in class GenoPheno: user defined transformations have not been tested thoroughly\\\")\\r\\n else:\\r\\n self.tf_geno = None\\r\\n self.tf_pheno = None\\r\\n\\r\\n if fixed_values:\\r\\n if type(fixed_values) is not dict:\\r\\n raise _Error(\\\"fixed_values must be a dictionary {index:value,...}\\\")\\r\\n if max(fixed_values.keys()) >= dim:\\r\\n raise _Error(\\\"max(fixed_values.keys()) = \\\" + str(max(fixed_values.keys())) +\\r\\n \\\" >= dim=N=\\\" + str(dim) + \\\" is not a feasible index\\\")\\r\\n # convenience commenting functionality: drop negative keys\\r\\n for k in list(fixed_values.keys()):\\r\\n if k < 0:\\r\\n fixed_values.pop(k)\\r\\n if bounds:\\r\\n if len(bounds) != 2:\\r\\n raise _Error('len(bounds) must be 2 for lower and upper bounds')\\r\\n for i in (0,1):\\r\\n if bounds[i] is not None:\\r\\n bounds[i] = array(dim * [bounds[i]] if np.isscalar(bounds[i]) else\\r\\n [b for b in bounds[i]])\\r\\n\\r\\n def vec_is_default(vec, default_val=0):\\r\\n \\\"\\\"\\\"return True if `vec` has the value `default_val`,\\r\\n None or [None] are also recognized as default\\\"\\\"\\\"\\r\\n try:\\r\\n if len(vec) == 1:\\r\\n vec = vec[0] # [None] becomes None and is always default\\r\\n else:\\r\\n return False\\r\\n except TypeError:\\r\\n pass # vec is a scalar\\r\\n\\r\\n if vec is None or vec == array(None) or vec == default_val:\\r\\n return True\\r\\n return False\\r\\n\\r\\n self.scales = array(scaling)\\r\\n if vec_is_default(self.scales, 1):\\r\\n self.scales = 1 # CAVE: 1 is not array(1)\\r\\n elif self.scales.shape is not () and len(self.scales) != self.N:\\r\\n raise _Error('len(scales) == ' + str(len(self.scales)) +\\r\\n ' does not match dimension N == ' + str(self.N))\\r\\n\\r\\n self.typical_x = array(typical_x)\\r\\n if vec_is_default(self.typical_x, 0):\\r\\n self.typical_x = 0\\r\\n elif self.typical_x.shape is not () and len(self.typical_x) != self.N:\\r\\n raise _Error('len(typical_x) == ' + str(len(self.typical_x)) +\\r\\n ' does not match dimension N == ' + str(self.N))\\r\\n\\r\\n if (self.scales is 1 and\\r\\n self.typical_x is 0 and\\r\\n self.bounds in (None, [None, None]) and\\r\\n self.fixed_values is None and\\r\\n self.tf_pheno is None):\\r\\n self.isidentity = True\\r\\n else:\\r\\n self.isidentity = False\",\n \"def __init__(self, n, g_bases, g_len, m_prob):\\n self.n = n\\n self.g_bases = g_bases\\n self.g_len = g_len\\n self.m_prob = m_prob\",\n \"def __init__(self, probability: float):\\n super().__init__()\\n\\n # store input parameters\\n self.probability = probability\",\n \"def __init__(self, r=1, p=3):\\n self.p = p\\n self.r = r\",\n \"def __init__(self, population=25, initSampling='lhc', fracMutation=0.2, fracElite=0.2, fracLevy=1.0, alpha=0.5, gamma=1, n=1, scalingFactor=10.0, penalty=0.0, maxGens=20000, maxFevals=200000, convTol=1e-06, stallLimit=10000, optConvTol=0.01, **kwargs):\\n ProblemParameters_multi.__init__(self, **kwargs)\\n self.population = population\\n self.initSampling = initSampling\\n self.fracMutation = fracMutation\\n assert self.fracMutation >= 0 and self.fracMutation <= 1, 'The probability of discovery must exist on (0,1]'\\n self.fracElite = fracElite\\n assert self.fracElite >= 0 and self.fracElite <= 1, 'The elitism fraction must exist on (0,1]'\\n self.fracLevy = fracLevy\\n assert self.fracLevy >= 0 and self.fracLevy <= 1, 'The probability that a Levy flight is performed must exist on (0,1]'\\n self.alpha = alpha\\n self.gamma = gamma\\n self.n = n\\n self.scalingFactor = scalingFactor\\n self.penalty = penalty\\n self.maxGens = maxGens\\n self.maxFevals = maxFevals\\n self.convTol = convTol\\n self.stallLimit = stallLimit\\n self.optConvTol = optConvTol\",\n \"def __init__(self, \\n p: int = 1,\\n fit_intercept: bool = True,\\n degree: int = 1,\\n input_type: str = None):\\n self.p = p \\n self.fit_intercept = fit_intercept \\n self.degree = degree\\n self.input_type = input_type\",\n \"def __init__(self,\\n p_xi: float, p_yi: float, p_zi: float,\\n p_xx: float, p_yx: float, p_zx: float,\\n p_xy: float, p_yy: float, p_zy: float,\\n p_xz: float, p_yz: float, p_zz: float,\\n p_ix: float, p_iy: float, p_iz: float) -> None:\\n self._p_xi = value.validate_probability(p_xi, 'p_xi')\\n self._p_yi = value.validate_probability(p_yi, 'p_yi')\\n self._p_zi = value.validate_probability(p_zi, 'p_zi')\\n self._p_xx = value.validate_probability(p_xx, 'p_xx')\\n self._p_yx = value.validate_probability(p_yx, 'p_yx')\\n self._p_zx = value.validate_probability(p_zx, 'p_zx')\\n self._p_xy = value.validate_probability(p_xy, 'p_xy')\\n self._p_yy = value.validate_probability(p_yy, 'p_yy')\\n self._p_zy = value.validate_probability(p_zy, 'p_zy')\\n self._p_xz = value.validate_probability(p_xz, 'p_xz')\\n self._p_yz = value.validate_probability(p_yz, 'p_yz')\\n self._p_zz = value.validate_probability(p_zz, 'p_zz')\\n self._p_ix = value.validate_probability(p_ix, 'p_ix')\\n self._p_iy = value.validate_probability(p_iy, 'p_iy')\\n self._p_iz = value.validate_probability(p_iz, 'p_iz')\\n self._p_ii = 1 - value.validate_probability(p_xi + p_yi + p_zi +\\n p_xx + p_yx + p_zx +\\n p_xy + p_yy + p_zy +\\n p_xz + p_yz + p_zz +\\n p_ix + p_iy + p_iz, 'p_ii')\",\n \"def __init__(self):\\n self.points = []\\n names = ['x1', 'x2', 'x3']\\n lows = [-5, -5, -5]\\n highs = [5, 5, 5]\\n # TODO 2: Use names, lows and highs defined above to code up decision\\n # and objective metadata for POM3.\\n decisions = [Decision(n, l, h) for n, l, h in zip(names, lows, highs)]\\n objectives = [Objective(\\\"f1\\\", True), Objective(\\\"f2\\\", True)]\\n Problem.__init__(self, decisions, objectives)\",\n \"def plot_priors(params):\\n prior_dicts = {'ic' : params['ic_prior'], 'ii' : params['ii_prior']}\\n pidxs = (pidx for pidx in onp.arange(1,12))\\n f = plt.figure(figsize=(12,8))\\n for k in prior_dicts:\\n for j in prior_dicts[k]:\\n plt.subplot(2,3,next(pidxs));\\n data = prior_dicts[k][j]\\n if \\\"log\\\" in j:\\n data = onp.exp(data)\\n j_title = j.strip('log')\\n else:\\n j_title = j\\n plt.stem(data)\\n plt.title(k + ' ' + j_title)\\n return f\",\n \"def __init__(self, classes, data_size):\\r\\n self.classes = classes\\r\\n self.data_size = data_size\\r\\n self.conditional_prob = {class_:{} for class_ in classes} # Conditional Probability Table for storing parameters useful to compute P(feat|class_)\\r\\n self.class_prob = {} # Stores the priors\\r\",\n \"def __init__(self):\\n self.points = []\\n names = ['x1', 'x2', 'x3', 'x4', 'x5', 'x6']\\n lows = [0, 0, 1, 0, 1, 0]\\n highs = [10, 10, 5, 6, 5, 10]\\n # TODO 2: Use names, lows and highs defined above to code up decision\\n # and objective metadata for POM3.\\n decisions = [Decision(n, l, h) for n, l, h in zip(names, lows, highs)]\\n objectives = [Objective(\\\"f1\\\", True), Objective(\\\"f2\\\", True)]\\n Problem.__init__(self, decisions, objectives)\",\n \"def __init__(self) :\\n self.probabilities_ = None\",\n \"def __init__(self) :\\n self.probabilities_ = None\",\n \"def __init__(self) :\\n self.probabilities_ = None\",\n \"def __init__(self):\\n super().__init__()\\n self.mu = 0.0\\n self.type = 'Poisson'\\n self.hasInfiniteBound = True\\n self.distType = 'Discrete'\\n self.compatibleQuadrature.append('CDF')\\n self.preferredQuadrature = 'CDF'\\n self.preferredPolynomials = 'CDF'\",\n \"def __init__(self,func ,domain_space, max_evals = 10):\\n self.func = func\\n # optimizing for FLOAT values\\n #self.space = hp.uniform('x', 36, 200)\\n # optimizing for Integer values\\n self.space = domain_space\\n self.algorithm = tpe.suggest # creating algorithm\\n self.trials = Trials() # to check records\\n self.max_evals = max_evals\",\n \"def __init__(self, num_hermgauss=20):\\r\\n super().__init__()\\r\\n\\r\\n gh_x, gh_w = np.polynomial.hermite.hermgauss(num_hermgauss)\\r\\n self.gh_x = torch.nn.Parameter(\\r\\n torch.from_numpy(gh_x[:, None, None].astype(NUMPY_DTYPE)),\\r\\n requires_grad=False)\\r\\n self.gh_w = torch.nn.Parameter(\\r\\n torch.from_numpy(gh_w[:, None, None].astype(NUMPY_DTYPE)),\\r\\n requires_grad=False)\",\n \"def __init__(self, eps=None, *, min_samples=5, metric='euclidean',\\n metric_params=None, algorithm='auto', leaf_size=30, p=None,\\n n_jobs=None):\\n self.eps = eps\\n self.min_samples = min_samples\\n self.metric = metric\\n self.metric_params = metric_params\\n self.algorithm = algorithm\\n self.leaf_size = leaf_size\\n self.p = p\\n self.n_jobs = n_jobs\",\n \"def prior_of_priors(self, tt):\\n for i in xrange(self.n_params): \\n try: \\n p_theta *= self.param_obj.prior()[i].pdf(tt[i]) \\n\\n except UnboundLocalError: \\n p_theta = self.param_obj.prior()[i].pdf(tt[i]) \\n\\n return p_theta\",\n \"def __init__(self, dim, switch=False, **kwargs):\\n # Define the hyperparameters\\n super(PAE, self).__init__(dim, **kwargs)\\n # Initialize the models\\n self.encode_ = Encoder([self.h_dim, self.z_dim])\\n self.decode_ = Decoder([self.h_dim, self.image_size])\\n self.b = RealNVP(num_coupling_layers=6, z_dim=self.z_dim)\\n self.scaler = MinMaxScaler()\\n # Build the models\\n self.encode_.build(input_shape=(None, self.image_size))\\n self.decode_.build(input_shape=(None, self.z_dim))\\n self.b.build(input_shape=(None, self.z_dim))\\n # Use bijecter in incode or not\\n self.switch = switch\",\n \"def __init__(self,\\n learning_rate=0.001,\\n beta1=0.9,\\n use_locking=False,\\n name=\\\"GGT\\\",\\n window=10,\\n eps=1e-4,\\n svd_eps=1e-6,\\n sigma_eps=1e-2):\\n super(GGTOptimizer, self).__init__(use_locking, name)\\n self._set_hyper(\\\"lr\\\", learning_rate)\\n self._set_hyper(\\\"beta1\\\", beta1)\\n self._set_hyper(\\\"window\\\", window)\\n self._set_hyper(\\\"eps\\\", eps)\\n self._set_hyper(\\\"svd_eps\\\", svd_eps)\\n self._set_hyper(\\\"sigma_eps\\\", sigma_eps)\\n\\n self.index_dict = {}\\n self.shape_dict = {}\",\n \"def __init__(self, \\n param_epsilon, \\n param_tau,\\n param_u, \\n param_gamma_left,\\n param_gamma_right,\\n param_beta):\\n self.epsilon = param_epsilon\\n self.tau = param_tau\\n self.u = param_u\\n self.gamma_left = param_gamma_left\\n self.gamma_right = param_gamma_right\\n \\n self.sigma_retarded = 1j * (self.gamma_left + self.gamma_right) / 2.0\\n self.sigma_advanced = - self.sigma_retarded;\\n \\n self.dim = len(self.u)\\n self.rho = np.zeros((2**self.dim))\\n \\n self.beta = param_beta\\n \\n self.cutoff_chance = 0.0001\\n self.external_distribution = False\\n self.external_distribution_array = self.distribution()\\n self.external_distribution = True\",\n \"def __init__(self, n=5, p=0.05, p_real=0.1, p_neg=0.0):\\n self.n = n\\n self.p = p\\n self.p_real = p_real\\n self.p_neg = p_neg\",\n \"def __init__(self, low, high, prior=\\\"uniform\\\", transform=None, name=None):\\n if high <= low:\\n raise ValueError(\\n \\\"the lower bound {} has to be less than the\\\"\\n \\\" upper bound {}\\\".format(low, high)\\n )\\n self.low = low\\n self.high = high\\n self.prior = prior\\n self.name = name\\n\\n if transform is None:\\n transform = \\\"identity\\\"\\n\\n self.transform_ = transform\\n\\n if self.transform_ not in [\\\"normalize\\\", \\\"identity\\\"]:\\n raise ValueError(\\n \\\"transform should be 'normalize' or 'identity'\\\"\\n \\\" got {}\\\".format(self.transform_)\\n )\\n\\n if self.transform_ == \\\"normalize\\\":\\n # set upper bound to next float after 1. to make the numbers\\n # inclusive of upper edge\\n if self.prior == \\\"uniform\\\":\\n self.transformer = Pipeline([Identity(), Normalize(low, high)])\\n else:\\n self.transformer = Pipeline(\\n [Log10(), Normalize(np.log10(low), np.log10(high))]\\n )\\n else:\\n if self.prior == \\\"uniform\\\":\\n self.transformer = Identity()\\n else:\\n self.transformer = Log10()\",\n \"def test_MPI_Parallel_Interface(comm):\\n\\n def printMPI(msg):\\n for i in range(comm.Get_size()):\\n comm.barrier()\\n if comm.Get_rank() == i:\\n print(\\\"Proc {}: {}\\\".format(i, msg))\\n\\n n = 10\\n\\n par = MPI_Objective_Interface(mp.Extended_Rosenbrock, nb_domain_grid_pts=n,\\n comm=comm)\\n\\n printMPI(par.counts)\\n\\n # ref = mp.Extended_Rosenbrock\\n\\n np.testing.assert_array_equal(\\n mp.Extended_Rosenbrock.startpoint(n)[par.subdomain_slices],\\n par.startpoint())\\n np.testing.assert_almost_equal(\\n mp.Extended_Rosenbrock.f(mp.Extended_Rosenbrock.startpoint(n)),\\n par.f(par.startpoint()),\\n err_msg=\\\"Different Function Value at startpoint\\\")\\n np.testing.assert_allclose(\\n mp.Extended_Rosenbrock.grad(mp.Extended_Rosenbrock.startpoint(n))[\\n par.subdomain_slices],\\n par.grad(par.startpoint()),\\n err_msg=\\\"Different Gradient Value at startpoint\\\")\",\n \"def gen_parameter(self, g, ng, p):\\n self.gen_apply(g, ng, p)\",\n \"def __init__(self, v, p):\\n self.val = v\\n self.priority = p\",\n \"def __init__(self, prime, server):\\n self.N = prime\\n self.g = 2\\n self.k = 3\\n self.server = server\",\n \"def __init__(self, pmin=None, pmax=None, npl=1, nobs=1):\\n\\n if pmin is None:\\n self._pmin = params.Parameters(npl=npl, nobs=nobs)\\n self._pmin = 0.0*self._pmin\\n self._pmin.V = float('-inf')\\n else:\\n self._pmin = pmin\\n\\n if pmax is None:\\n self._pmax = params.Parameters(npl=npl, nobs=nobs)\\n self._pmax = self._pmax + float('inf')\\n self._pmax.chi = 1.0\\n self._pmax.e = 1.0\\n self._pmax.omega = 2.0*np.pi\\n else:\\n self._pmax = pmax\\n\\n self._npl = npl\\n self._nobs = nobs\",\n \"def _get_current_hyperparameters(self):\",\n \"def _gen_policy_params(self, state: State) -> Tensor:\\n ...\",\n \"def _init_hyperparam(self, **p_par):\\r\\n \\r\\n try:\\r\\n p_input_size = self._input_space.get_num_dim()\\r\\n p_output_size = self._output_space.get_num_dim()\\r\\n except:\\r\\n raise ParamError('Input size and/or output size of the network are not defined.')\\r\\n \\r\\n if 'p_update_rate' not in p_par:\\r\\n p_par['p_update_rate'] = 1\\r\\n elif p_par.get('p_update_rate') < 1:\\r\\n raise ParamError(\\\"p_update_rate must be equal or higher than 1.\\\")\\r\\n \\r\\n if 'p_num_hidden_layers' not in p_par:\\r\\n raise ParamError(\\\"p_num_hidden_layers is not defined.\\\")\\r\\n \\r\\n if 'p_output_activation_fct' not in p_par:\\r\\n p_par['p_output_activation_fct'] = None\\r\\n \\r\\n if 'p_optimizer' not in p_par:\\r\\n raise ParamError(\\\"p_optimizer is not defined.\\\")\\r\\n \\r\\n if 'p_loss_fct' not in p_par:\\r\\n raise ParamError(\\\"p_loss_fct is not defined.\\\")\\r\\n\\r\\n if 'p_test_data' not in p_par:\\r\\n p_par['p_test_data'] = 0.3\\r\\n\\r\\n if 'p_batch_size' not in p_par:\\r\\n p_par['p_batch_size'] = 100\\r\\n\\r\\n if 'p_seed_buffer' not in p_par:\\r\\n p_par['p_seed_buffer'] = 1\\r\\n\\r\\n if 'p_learning_rate' not in p_par:\\r\\n p_par['p_learning_rate'] = 3e-4\\r\\n \\r\\n if 'p_hidden_size' not in p_par:\\r\\n raise ParamError(\\\"p_hidden_size is not defined.\\\")\\r\\n try:\\r\\n if len(p_par['p_hidden_size']) != p_par['p_num_hidden_layers']:\\r\\n raise ParamError(\\\"length of p_hidden_size list must be equal to p_num_hidden_layers or an integer.\\\")\\r\\n except:\\r\\n p_par['p_hidden_size'] = [int(p_par['p_hidden_size'])] * int(p_par['p_num_hidden_layers'])\\r\\n \\r\\n if 'p_activation_fct' not in p_par:\\r\\n raise ParamError(\\\"p_activation_fct is not defined.\\\")\\r\\n try:\\r\\n if len(p_par['p_activation_fct']) != p_par['p_num_hidden_layers']:\\r\\n raise ParamError(\\\"length of p_activation_fct list must be equal to p_num_hidden_layers or a single activation function.\\\")\\r\\n except:\\r\\n if isinstance(p_par['p_activation_fct'], list):\\r\\n raise ParamError(\\\"length of p_activation_fct list must be equal to p_num_hidden_layers or a single activation function.\\\")\\r\\n else:\\r\\n p_par['p_activation_fct'] = [p_par['p_activation_fct']] * int(p_par['p_num_hidden_layers'])\\r\\n \\r\\n if 'p_weight_bias_init' not in p_par:\\r\\n p_par['p_weight_bias_init'] = True\\r\\n \\r\\n if p_par['p_weight_bias_init']:\\r\\n if 'p_weight_init' not in p_par:\\r\\n p_par['p_weight_init'] = torch.nn.init.orthogonal_\\r\\n \\r\\n if 'p_bias_init' not in p_par:\\r\\n p_par['p_bias_init'] = lambda x: torch.nn.init.constant_(x, 0)\\r\\n \\r\\n if 'p_gain_init' not in p_par:\\r\\n p_par['p_gain_init'] = np.sqrt(2)\\r\\n \\r\\n self._hyperparam_space.add_dim(HyperParam('p_input_size','Z'))\\r\\n self._hyperparam_space.add_dim(HyperParam('p_output_size','Z'))\\r\\n self._hyperparam_space.add_dim(HyperParam('p_update_rate','Z'))\\r\\n self._hyperparam_space.add_dim(HyperParam('p_num_hidden_layers','Z'))\\r\\n self._hyperparam_space.add_dim(HyperParam('p_hidden_size','Z'))\\r\\n self._hyperparam_space.add_dim(HyperParam('p_activation_fct'))\\r\\n self._hyperparam_space.add_dim(HyperParam('p_output_activation_fct'))\\r\\n self._hyperparam_space.add_dim(HyperParam('p_optimizer'))\\r\\n self._hyperparam_space.add_dim(HyperParam('p_loss_fct'))\\r\\n self._hyperparam_space.add_dim(HyperParam('p_test_data'))\\r\\n self._hyperparam_space.add_dim(HyperParam('p_batch_size'))\\r\\n self._hyperparam_space.add_dim(HyperParam('p_seed_buffer'))\\r\\n self._hyperparam_space.add_dim(HyperParam('p_learning_rate'))\\r\\n self._hyperparam_space.add_dim(HyperParam('p_weight_bias_init'))\\r\\n self._hyperparam_space.add_dim(HyperParam('p_weight_init'))\\r\\n self._hyperparam_space.add_dim(HyperParam('p_bias_init'))\\r\\n self._hyperparam_space.add_dim(HyperParam('p_gain_init'))\\r\\n self._hyperparam_tuple = HyperParamTuple(self._hyperparam_space)\\r\\n \\r\\n ids_ = self.get_hyperparam().get_dim_ids()\\r\\n self.get_hyperparam().set_value(ids_[0], p_input_size)\\r\\n self.get_hyperparam().set_value(ids_[1], p_output_size)\\r\\n self.get_hyperparam().set_value(ids_[2], p_par['p_update_rate'])\\r\\n self.get_hyperparam().set_value(ids_[3], p_par['p_num_hidden_layers'])\\r\\n self.get_hyperparam().set_value(ids_[4], p_par['p_hidden_size'])\\r\\n self.get_hyperparam().set_value(ids_[5], p_par['p_activation_fct'])\\r\\n self.get_hyperparam().set_value(ids_[6], p_par['p_output_activation_fct'])\\r\\n self.get_hyperparam().set_value(ids_[7], p_par['p_optimizer'])\\r\\n self.get_hyperparam().set_value(ids_[8], p_par['p_loss_fct'])\\r\\n self.get_hyperparam().set_value(ids_[9], p_par['p_test_data'])\\r\\n self.get_hyperparam().set_value(ids_[10], p_par['p_batch_size'])\\r\\n self.get_hyperparam().set_value(ids_[11], p_par['p_seed_buffer'])\\r\\n self.get_hyperparam().set_value(ids_[12], p_par['p_learning_rate'])\\r\\n self.get_hyperparam().set_value(ids_[13], p_par['p_weight_bias_init'])\\r\\n self.get_hyperparam().set_value(ids_[14], p_par['p_weight_init'])\\r\\n self.get_hyperparam().set_value(ids_[15], p_par['p_bias_init'])\\r\\n self.get_hyperparam().set_value(ids_[16], p_par['p_gain_init'])\",\n \"def Params(cls):\\n p = hyperparams.InstantiableParams(cls)\\n p.Define('task', None, 'Underlying task')\\n p.Define('logdir', None, 'Log directory')\\n p.Define('num_splits_per_client', None, '')\\n p.Define('steps_per_loop', None, 'Number of steps to run.')\\n p.Define('dataset_name', None,\\n 'Dataset the program is operating on, eg: \\\"Test\\\"')\\n p.Define('name', 'base_program', 'Program name.')\\n p.Define('task_name', None,\\n 'If multi-task, what the high-level task name is')\\n p.Define('num_threads', 1, 'Number of threads in multiprocessing pool.')\\n p.Define('spmd', False, 'Whether program is running under SPMD mode.')\\n p.Define('write_train_input_stats', False,\\n 'Whether to write input data stats during training.')\\n p.Define('max_metrics', 256, 'Overrides TpuEvalMetrics.max_metrics')\\n p.Define('ml_perf', None, 'MLPerf config')\\n return p\",\n \"def pred_prob(self, t, x, **kwargs):\\n raise NotImplementedError\",\n \"def __init__(self, bandits: List[Bandit]):\\n self.bandits = bandits\\n # mu_pri and var_pri are hypermaterers for prior distribution\\n self.mu_pri = np.zeros(len(self.bandits)) # 5 for each\\n self.var_pri = np.ones(len(self.bandits))*5\\n # mu and var are hyperparameters for posterior distribution\\n self.mu = self.mu_pri\\n self.var = self.var_pri\\n self.var0 = 1 # 1 is taken but any can be taken no prob upto a limit constant of inintial distribution\\n self.logging = Logging()\",\n \"def __init__(self, name, description, nbits, prime, generator):\\n self.name = name\\n self.description = description\\n self.nbits = nbits\\n self.prime = prime\\n self.generator = generator\",\n \"def __init__(self, lhs, rhs, **prob_kwarg):\\n ImmutableProbabilisticMixIn.__init__(self, **prob_kwarg)\\n CFGProduction.__init__(self, lhs, rhs)\",\n \"def __init__(self, sigma):\\n self.sigma = float(sigma)\\n super().__init__(domain=functional.domain,\\n range=functional.domain, linear=False)\\n\\n # Setting up parameters\\n self.const = 1 / (functional.epsilon * sigma)\",\n \"def __init__(self, nb):\\n\\n # Approximation parameters\\n self.o_min = -.1\\n self.o_max = .1\\n self.o_nb = nb\\n self.o_bw = (self.o_max - self.o_min) / self.o_nb\\n self.o_e = np.linspace(self.o_min, self.o_max, self.o_nb+1)\\n self.o = self.o_e[:-1] + (self.o_bw/2)\\n self.r_e = [0, 1]\\n self.r_nb = len(self.r_e)\\n self.mu_min = 0\\n self.mu_max = 1\\n self.mu_nb = nb\\n self.mu_bw = (self.mu_max - self.mu_min) / self.mu_nb\\n self.mu_e = np.linspace(self.mu_min, self.mu_max, self.mu_nb+1)\\n self.mu = self.mu_e[:-1] + (self.mu_bw/2)\\n self.p_s_giv_o_hat = np.nan\\n self.S = np.nan\\n self.p_mu_giv_r_o_hat = np.nan\",\n \"def _handleInput(self, paramInput):\\n super()._handleInput(paramInput)\\n pFind = paramInput.findFirst('p')\\n if pFind != None:\\n self.p = pFind.value\\n else: self.raiseAnError(IOError,'p value needed for Geometric distribution')\\n self.initializeDistribution()\",\n \"def fitness(self):\\n params = np.array([self['p{}'.format(i)] for i in range(n_pars)])\\n \\n return func(params)\",\n \"def test_base_hyper_parameters_reg(self):\\n hyper_parameter_set = modelgen.generate_base_hyper_parameter_set()\\n assert 'regularization_rate' in hyper_parameter_set.keys()\",\n \"def __init__(self, input_placeholder, server, trivial_prob):\\n self.logits = self._get_null_logits(input_placeholder)\\n self.precision = (1-trivial_prob)*server.adapter.frequencies[1]\\n self.prior_threshold = 0.5\",\n \"def __init__(self, multiplicity, in_features, cardinality, dropout=0.0):\\n super(IndependentNormal, self).__init__(multiplicity, in_features, dropout)\\n self.gauss = Normal(\\n multiplicity=multiplicity, in_features=in_features, dropout=dropout\\n )\\n self.prod = Product(in_features=in_features, cardinality=cardinality)\\n\\n self.cardinality = cardinality\",\n \"def _handleInput(self, paramInput):\\n super()._handleInput(paramInput)\\n pFind = paramInput.findFirst('p')\\n if pFind != None:\\n self.p = pFind.value\\n else:\\n self.raiseAnError(IOError,'p value needed for Bernoulli distribution')\\n self.initializeDistribution()\",\n \"def __init__(self, num_params):\\r\\n self.num_params = num_params\",\n \"def __init__(self, num_params):\\r\\n self.num_params = num_params\",\n \"def __init__(self, p=0.5):\\n assert 0. <= p <= 1.\\n self.p = p\\n self.rng = T.shared_randomstreams.RandomStreams(seed=123456)\\n self.params = []\",\n \"def get_PI(self, alpha):\\n raise NotImplementedError(\\\"This is an abstract method and needs to be implemented in derived classes.\\\")\",\n \"def __init__(self, cost_fn, out_dim, popsize, num_elites, max_iters, \\n upper_bound=None, lower_bound=None, epsilon=0.001):\\n super().__init__()\\n self.cost_fn = cost_fn\\n self.out_dim = out_dim \\n self.popsize = popsize\\n self.num_elites = num_elites # redundant arguments for Random Policy\\n self.max_iters = max_iters\\n self.ub = upper_bound.reshape([1, out_dim])\\n self.lb = lower_bound.reshape([1, out_dim])\\n self.epsilon = epsilon\\n\\n # Sanity checks\\n assert (num_elites <= popsize), \\\"Error: Number of elites must <= population size!\\\"\",\n \"def parameters(self):\\n raise NotImplementedError('Abstract method \\\"parameters\\\" must be '\\n 'specialised!')\",\n \"def __init__(self, prior: float, beta: float = 0., gamma: float = 1.0):\\n super(NonNegativePULoss, self).__init__()\\n\\n self.beta = beta\\n self.prior = prior\\n self.gamma = gamma\\n self.loss_fn = torch.nn.CrossEntropyLoss(reduction='none')\",\n \"def __init__ (self, p, q):\\n self.n = p * q\\n self.n_sq = self.n * self.n\\n self.g = self.n + 1\",\n \"def __init__(self, D, NP, nFES, nRuns, useAlgorithms, useBenchmarks, **kwargs):\\n\\t\\tself.D = D\\n\\t\\tself.NP = NP\\n\\t\\tself.nFES = nFES\\n\\t\\tself.nRuns = nRuns\\n\\t\\tself.useAlgorithms = useAlgorithms\\n\\t\\tself.useBenchmarks = useBenchmarks\\n\\t\\tself.A = kwargs.get('A', 0.5)\\n\\t\\tself.r = kwargs.get('r', 0.5)\\n\\t\\tself.Qmin = kwargs.get('Qmin', 0.0)\\n\\t\\tself.Qmax = kwargs.get('Qmax', 2.0)\\n\\t\\tself.Pa = kwargs.get('Pa', 0.25)\\n\\t\\tself.F = kwargs.get('F', 0.5)\\n\\t\\tself.CR = kwargs.get('CR', 0.9)\\n\\t\\tself.alpha = kwargs.get('alpha', 0.5)\\n\\t\\tself.betamin = kwargs.get('betamin', 0.2)\\n\\t\\tself.gamma = kwargs.get('gamma', 1.0)\\n\\t\\tself.p = kwargs.get('p', 0.5)\\n\\t\\tself.Ts = kwargs.get('Ts', 4)\\n\\t\\tself.Mr = kwargs.get('Mr', 0.05)\\n\\t\\tself.C1 = kwargs.get('C1', 2.0)\\n\\t\\tself.C2 = kwargs.get('C2', 2.0)\\n\\t\\tself.w = kwargs.get('w', 0.7)\\n\\t\\tself.vMin = kwargs.get('vMin', -4)\\n\\t\\tself.vMax = kwargs.get('vMax', 4)\\n\\t\\tself.Tao = kwargs.get('Tao', 0.1)\\n\\t\\tself.n = kwargs.get('n', 10)\\n\\t\\tself.omega = kwargs.get('omega', 0.25)\\n\\t\\tself.mu = kwargs.get('mu', 0.5)\\n\\t\\tself.E_init = kwargs.get('E_init', 10)\\n\\t\\tself.S_init = kwargs.get('S_init', 10)\\n\\t\\tself.T_min = kwargs.get('T_min', -10)\\n\\t\\tself.T_max = kwargs.get('T_max', 10)\\n\\t\\tself.C_a = kwargs.get('C_a', 2)\\n\\t\\tself.C_r = kwargs.get('C_r', 0.5)\\n\\t\\tself.results = {}\",\n \"def PPO(parclass):\\n class PPO(parclass):\\n \\\"\\\"\\\"\\n Proximal Policy Optimization algorithm (PPO).\\n Requires parent class inherited from A2C.\\n Based on: https://arxiv.org/abs/1707.06347\\n \\n Args:\\n ppo_clip - clipping rate of pi_new / pi_old fraction\\n epochs - number of epochs to run through rollout on each update\\n batch_size - size of mini-batch to select without replacement on each gradient ascent step\\n \\\"\\\"\\\"\\n __doc__ += parclass.__doc__\\n PARAMS = parclass.PARAMS | {\\\"ppo_clip\\\", \\\"epochs\\\", \\\"batch_size\\\"} \\n \\n def __init__(self, config):\\n super().__init__(config)\\n \\n self.config.setdefault(\\\"ppo_clip\\\", 0.2)\\n self.config.setdefault(\\\"epochs\\\", 3)\\n self.config.setdefault(\\\"batch_size\\\", 32)\\n \\n assert self.env.num_envs * self.config.rollout >= self.config.batch_size, \\\"batch_size is bigger than rollout * number of threads!\\\"\\n \\n def optimized_function(self):\\n # advantage is estimated using advantage function for previous policy\\n # so we take advantage from original rollout\\n advantages = (self.returns_b - self.old_values_b).detach() #self.advantages_b.detach() - КОСТЫЛЬ\\n \\n # importance sampling for making an update of current policy using samples from old policy\\n # the gradients to policy will flow through the numerator.\\n ratio = torch.exp(self.action_log_probs_b - self.old_action_log_probs_b.detach())\\n \\n # PPO clipping! Prevents from \\\"too high updates\\\".\\n surr1 = ratio * advantages\\n surr2 = torch.clamp(ratio, 1.0 - self.config.ppo_clip, 1.0 + self.config.ppo_clip) * advantages\\n return -torch.min(surr1, surr2)\\n \\n def update(self):\\n \\\"\\\"\\\"One step of optimization based on rollout memory\\\"\\\"\\\"\\n with torch.no_grad():\\n self.preprocess_rollout()\\n \\n # DEEP-RL TUTORIALS: КОСТЫЛЬ\\n #self.advantages = self.returns[:-1] - self.values[:-1]\\n #self.advantages = (self.advantages - self.advantages.mean()) / (self.advantages.std() + 1e-5)\\n \\n # going through rollout several (config.epochs) times:\\n for epoch in range(self.config.epochs):\\n # TODO: drop last = False? What if there is 1 sample?\\n sampler = BatchSampler(SubsetRandomSampler(range(self.env.num_envs * self.config.rollout)), self.config.batch_size, drop_last=False)\\n \\n for indices in sampler:\\n # retrieving new batch as part of rollout\\n self.returns_b = self.returns.view(-1, *self.config.value_repr_shape)[indices]\\n self.old_values_b = self.values.view(-1, *self.config.value_repr_shape)[indices]\\n self.old_action_log_probs_b = self.action_log_probs.view(-1)[indices]\\n #self.advantages_b = self.advantages.view(-1)[indices] # КОСТЫЛЬ\\n \\n # calculating current value, action_log_prob, entropy\\n dist, self.values_b = self.policy(self.observations.view(-1, *self.config.observation_shape)[indices])\\n self.values_b = self.values_b.squeeze() # IMPORTANT ([32] - [32, 1] problem)\\n self.action_log_probs_b = dist.log_prob(self.actions.view(-1, *self.config.actions_shape)[indices])#.sum(dim=-1) \\n self.entropy_b = dist.entropy()#.sum(dim=-1)\\n \\n # performing step\\n self.gradient_ascent_step()\\n return PPO\",\n \"def Params(cls):\\n p = hyperparams.InstantiableParams(cls)\\n\\n p.Define('task_dict', None, 'dataset_name -> task params')\\n p.Define('task_name', None, 'High level task name')\\n p.Define('logdir', None, 'Log directory')\\n p.Define('train_program', None, 'Train program params')\\n p.Define('train_executions_per_eval', 1, '')\\n p.Define('dataset_names', [], 'List of all dataset names.')\\n p.Define('num_splits_per_client', None, '')\\n\\n p.Define('ml_perf', hyperparams.Params(), 'MlPerf configuration.')\\n\\n mlp = p.ml_perf\\n mlp.Define('benchmark_name', None, 'Benchmark name for compliance log.')\\n mlp.Define('decoder_metric_name', None,\\n 'Name of the decoder metric to report for compliance log.')\\n mlp.Define('decoder_metric_success_threshold', None,\\n 'Benchmark run must exceed this value to succeed.')\\n mlp.Define('max_steps_to_train', None,\\n 'Maximum number of steps to reach target accuracy')\\n mlp.Define('steps_per_epoch', None, 'Number of training steps per epoch.')\\n mlp.Define('global_batch_size', None, 'Global batch size.')\\n mlp.Define('max_sequence_length', None, 'Maximum sequence length.')\\n mlp.Define('optimizer_name', None, 'Optimizer used.')\\n mlp.Define('base_learning_rate', None, 'Base learning rate.')\\n mlp.Define('warmup_steps', None, 'Number of warm-up steps.')\\n\\n return p\",\n \"def __init__(self):\\n GinacFunction.__init__(self, \\\"binomial\\\", nargs=2, preserved_arg=1,\\n conversions=dict(maxima='binomial',\\n mathematica='Binomial',\\n sympy='binomial'))\",\n \"def _gen_prime(self, n_bits):\\n n = gmpy2.mpz(prng.getrandbits(n_bits))\\n return gmpy2.next_prime(n)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6436486","0.6436486","0.6161557","0.60954475","0.6026573","0.5960026","0.59499425","0.59067774","0.5904224","0.5884801","0.58277154","0.5816997","0.56939894","0.56818277","0.5675402","0.56377643","0.5627205","0.56166977","0.56117594","0.5608884","0.5592191","0.55660427","0.55576944","0.5532769","0.5526844","0.55254155","0.5515894","0.5511884","0.54974306","0.54970574","0.5493664","0.5492856","0.54838735","0.5477617","0.5473819","0.5464491","0.5457051","0.54563904","0.54527545","0.54430234","0.5431099","0.54050887","0.540262","0.5395228","0.5381725","0.53732353","0.5372421","0.53589606","0.5345009","0.5332488","0.53268945","0.532488","0.5324585","0.53213376","0.5312239","0.5312239","0.5312239","0.5305064","0.53021204","0.52983934","0.5272979","0.52706075","0.526896","0.52676564","0.5258226","0.52573454","0.52552855","0.5253541","0.52473885","0.52406394","0.52395016","0.52170724","0.52120197","0.52072585","0.52070993","0.5203115","0.5200379","0.5194642","0.5194359","0.5181397","0.5174687","0.5171383","0.5169916","0.516969","0.516741","0.51670825","0.51669604","0.5158716","0.5154669","0.5154669","0.515293","0.5151969","0.5149995","0.51467425","0.5144955","0.51405627","0.51325256","0.51323175","0.5127079","0.5126447","0.5124586"],"string":"[\n \"0.6436486\",\n \"0.6436486\",\n \"0.6161557\",\n \"0.60954475\",\n \"0.6026573\",\n \"0.5960026\",\n \"0.59499425\",\n \"0.59067774\",\n \"0.5904224\",\n \"0.5884801\",\n \"0.58277154\",\n \"0.5816997\",\n \"0.56939894\",\n \"0.56818277\",\n \"0.5675402\",\n \"0.56377643\",\n \"0.5627205\",\n \"0.56166977\",\n \"0.56117594\",\n \"0.5608884\",\n \"0.5592191\",\n \"0.55660427\",\n \"0.55576944\",\n \"0.5532769\",\n \"0.5526844\",\n \"0.55254155\",\n \"0.5515894\",\n \"0.5511884\",\n \"0.54974306\",\n \"0.54970574\",\n \"0.5493664\",\n \"0.5492856\",\n \"0.54838735\",\n \"0.5477617\",\n \"0.5473819\",\n \"0.5464491\",\n \"0.5457051\",\n \"0.54563904\",\n \"0.54527545\",\n \"0.54430234\",\n \"0.5431099\",\n \"0.54050887\",\n \"0.540262\",\n \"0.5395228\",\n \"0.5381725\",\n \"0.53732353\",\n \"0.5372421\",\n \"0.53589606\",\n \"0.5345009\",\n \"0.5332488\",\n \"0.53268945\",\n \"0.532488\",\n \"0.5324585\",\n \"0.53213376\",\n \"0.5312239\",\n \"0.5312239\",\n \"0.5312239\",\n \"0.5305064\",\n \"0.53021204\",\n \"0.52983934\",\n \"0.5272979\",\n \"0.52706075\",\n \"0.526896\",\n \"0.52676564\",\n \"0.5258226\",\n \"0.52573454\",\n \"0.52552855\",\n \"0.5253541\",\n \"0.52473885\",\n \"0.52406394\",\n \"0.52395016\",\n \"0.52170724\",\n \"0.52120197\",\n \"0.52072585\",\n \"0.52070993\",\n \"0.5203115\",\n \"0.5200379\",\n \"0.5194642\",\n \"0.5194359\",\n \"0.5181397\",\n \"0.5174687\",\n \"0.5171383\",\n \"0.5169916\",\n \"0.516969\",\n \"0.516741\",\n \"0.51670825\",\n \"0.51669604\",\n \"0.5158716\",\n \"0.5154669\",\n \"0.5154669\",\n \"0.515293\",\n \"0.5151969\",\n \"0.5149995\",\n \"0.51467425\",\n \"0.5144955\",\n \"0.51405627\",\n \"0.51325256\",\n \"0.51323175\",\n \"0.5127079\",\n \"0.5126447\",\n \"0.5124586\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":14387,"cells":{"query":{"kind":"string","value":"Returns N samples from the prior."},"document":{"kind":"string","value":"def sample_from_prior(self, n_samples):\n pass"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def sample_from_prior(self, n_samples):\n\n p0 = self.min + self.rng.rand(n_samples) * (self.max - self.min)\n return p0[:, np.newaxis]","def sample_from_prior(self, n_samples):\n\n p0 = self.rng.normal(loc=self.mean, scale=self.sigma, size=n_samples)\n return p0[:, np.newaxis]","def sample_from_prior(self, n_samples):\n\n p0 = self.rng.lognormal(mean=self.mean, sigma=self.sigma, size=n_samples)\n return p0[:, np.newaxis]","def sample_from_prior(self, n_samples):\n\n lamda = np.abs(self.rng.standard_cauchy(size=n_samples))\n\n p0 = np.log(np.abs(self.rng.randn() * lamda * self.scale))\n return p0[:, np.newaxis]","def sample_prior(size):\n return torch.randn(size)","def sample_from_prior(self):\n raise NotImplementedError","def sample_parameters_prior(self, n_samples, random_seed=None):\n\n if random_seed is not None:\n np.random.seed(random_seed)\n samples = []\n samples.append(self.var_noise.sample_from_prior(n_samples))\n samples.append(self.mean.sample_from_prior(n_samples))\n samples.append(self.kernel.sample_parameters(n_samples))\n\n return np.concatenate(samples, 1)","def sample(self, n_samples=1):\n\n\t\tif isinstance(n_samples, int):\n\t\t\tn_samples = (n_samples,)\n\t\tbeta_sample = self.beta_prior.sample(n_samples)\n\t\tgamma_sample = self.gamma_prior.sample(n_samples)\n\t\tp = torch.stack((beta_sample, gamma_sample)).T\n\t\tif n_samples == (1,):\n\t\t\tp = p[0]\n\t\tp = p.numpy()\n\t\treturn p","def sample_from_prior(self, *args, **kwargs):\n pass","def forward_sample(self, n):\n return self.flow.sample(n)","def prior_sample(self):\n pass","def generate_samples(self, n_samples):","def generate_samples(self, n_samples):","def sample(self, n):\n # Est probs from priority weights\n summed = sum(self.weight) + self.eps\n self.probs = [w / summed for w in self.priority]\n\n # Wieghted sample\n return np.random.choice(self.memory, size=n, p=self.probs).tolist()","def prior_samples(self, nsamples, coords=None):\n S = self.eval_S(self.kappa, self.sigma_f)\n if coords is None:\n coords = slice(self.mesh.num_vertices()) # take all coords\n weights = np.random.normal(scale=np.sqrt(S),\n size=(nsamples,) + S.shape) # shape (s, l)\n prior = np.einsum('sl,nl->sn', weights, self.eigenfunctions[coords])\n\n return prior","def samples(self):\n return self._values[:self.nsamples]","def generate_samples(self, no=10):\n observations = []\n state_sequence = []\n initial_state = np.random.choice(\n self.latent_variable_markov_chain.states,\n p=self.prior_probabilities)\n state_sequence.append(initial_state)\n observations.append(self.observation_from_state(initial_state))\n current_state = initial_state\n for i in range(2, no):\n next_state = self.latent_variable_markov_chain.next_state(current_state)\n state_sequence.append(next_state)\n observations.append(self.observation_from_state(next_state))\n current_state = next_state\n return observations, state_sequence","def sample(self, n, include=True):\n return [self(t / n) for t in range(n + int(include))]","def prior(n=10):\r\n p = []\r\n trials = 0\r\n acc = 0\r\n while acc < n:\r\n trials += 1\r\n r = np.random.rand(2) * np.array([4, 2]) + np.array([-2, -1])\r\n # print(\"r: \", r)\r\n if r[1] + r[0] >= -1 and r[1] - r[0] >= -1:\r\n p.append(r)\r\n acc += 1\r\n # print(\"trials: \", trials, \", acc: \", acc)\r\n return p","def pull_reads(self, n, prng): \n if not self.sampling:\n self.convert_to_array()\n index = prng.randint(0, self.total, size=n)\n index = np.sort(index)\n return self.reads[index,:]","def sample(self, n=1):\n raise NotImplementedError","def get_training_n(self, n: Optional[int] = None, random: bool = True) -> Tuple[torch.Tensor, torch.Tensor]:\n return self.__sample(self.train_data, self.train_los, n, random)","def sample(self, n):\n raise NotImplementedError","def _sample(self, n=1):\n return [self[i] for i in np.random.choice(self.length, n, replace=False)]","def get_n_samples(self):\n return {op.rx.get_n_samples() for op in self.ops}","def sample(self, n_samples):\n\n z = sample_prior((n_samples,) + self.flow.z_shape)\n ldj = torch.zeros(z.size(0))\n\n z, ldj = self.flow (z, ldj, reverse=True)\n z, ldj = self.logit_normalize(z, ldj, reverse=True)\n\n return z","def Sample(n=6):\n t = [random.normalvariate(0.0, 1.0) for i in range(n)]\n t.sort()\n return t","def posterior_sampler(self, nsamples, seed=0, verbose=True):\n\n import random\n\n random.seed(seed)\n sample = self.get_chain()[-self.get_tune:]\n sample = sample.reshape(-1, sample.shape[-1])\n sample = random.choices(sample, k=nsamples)\n\n return sample","def generate_samples(self, n_samples=100):\n \t\t\n\t\t#how many times should ancestral sampling be run\n\t\t#n_samples\n prior_samples=[]\n for i in range(0,n_samples):\n prior_sample = self.prior.get_samples(\n n_latent_nodes=self.n_latent_nodes,\n n_gibbs_sampling_steps=100, \n sampling_mode=\"gibbs_ancestral\")\n prior_sample = torch.cat(prior_sample)\n prior_samples.append(prior_sample)\n prior_samples=torch.stack(prior_samples)\n # prior_samples = tf.slice(prior_samples, [0, 0], [num_samples, -1])\n output_activations = self.decoder.decode(prior_samples)\n output_activations = output_activations+self._train_bias\n output_distribution = Bernoulli(logit=output_activations)\n output=torch.sigmoid(output_distribution.logits)\n # output_activations[0] = output_activations[0] + self.train_bias\n # output_dist = FactorialBernoulliUtil(output_activations)\n # output_samples = tf.nn.sigmoid(output_dist.logit_mu)\n # print(\"--- \",\"end VAE::generate_samples()\")\n return output","def get_random_samplers(self, n):\n if not self.has_samplers():\n self.draw_samplers()\n \n def get_shuffledcopy(x):\n x_ = x.copy()\n np.random.shuffle(x_)\n return x_\n \n return get_shuffledcopy(self.samplers)[:n]","def sample_gp_prior(self, x_list, n_samp, full_cov=True):\n mu, cov = self.get_gp_prior_mu_cov(x_list, full_cov)\n return self.get_normal_samples(mu, cov, n_samp, full_cov)","def get_posterior_sample(self):\n total_tries = self.prior_success + self.prior_failure\n prob_success = self.prior_success / total_tries\n # np.random.binomial采样出来的是二项分布的均值, 即正面朝上的次数,所以要除以N\n boot_sample = np.random.binomial(total_tries, prob_success) / total_tries\n return boot_sample","def sample(self, n_samples=1):\n\n\t\tsamples = np.random.random((n_samples, self.__ndims))*self.__range + self.__low\n\t\treturn samples","def get_samples(self, nsamples):\n sample_ids = np.random.choice(self.embed.shape[0], nsamples, replace=False)\n return self.embed[sample_ids]","def sample_predict(self, X, n_samples):\n W_samples = self.W_prior.sample(n_samples) ##size: [n_samples, n_in, n_out]\n if self.use_bias:\n bs = W_samples[:, :1, :]\n Ws = W_samples[:, 1:, :]\n if self.scaled_variance:\n Ws = Ws / np.sqrt(self.n_in)\n return torch.matmul(X, Ws) + bs\n else:\n if self.scaled_variance:\n W_samples = W_samples / np.sqrt(self.n_in)\n return torch.matmul(X, W_samples)","def pickTopicFromPrior(self):\r\n return self.priorTopicDistr.sampleIndex()","def fetch_samples(self):\n return torch.cat(self.samples,dim=0).reshape(-1,self.parameters.numel())","def _uniform_number(self, N=None):\n num_frames = self.data.shape[0]\n if not N:\n N = int(np.floor(\n Constants.DEFAULT_SAMPLE_PERCENTAGE * num_frames\n ))\n k = int(np.floor(num_frames / N))\n if k == 0:\n k = 1\n elif N > num_frames:\n k = 1\n else:\n assert type(N) is IntType, 'N is not an integer: {}'.format(N)\n k = int(np.floor(num_frames / N))\n\n # ensure that at most N samples are taken if N does not divide nicely\n return self.data[::k][:N], self.labels[::k][:N]","def sample(self, n_samples, sample_seed):\n self.seed_samples(sample_seed)\n eps = torch.randn(self.batch_size, n_samples, self.Y_dim)\n samples = eps*torch.exp(0.5*self.logvar.unsqueeze(1)) + self.mu.unsqueeze(1)\n samples = self.unwhiten_back(samples)\n samples = samples.data.cpu().numpy()\n return samples","def prior_sample(self, bn):\n x = np.zeros(3)\n\n # first joint prob\n random_choice = np.random.choice(bn[0], 1, bn[0].all(), bn[0])\n x[0] = random_choice[0]\n\n # Second Joint Prob\n if x[0] == 0.1:\n random_choice = np.random.choice(bn[1][0], 1, bn[1][0].all(), bn[1][0])\n x[1] = random_choice\n elif x[0] == 0.9:\n random_choice = np.random.choice(bn[1][1], 1, bn[1][1].all(), bn[1][1])\n x[1] = random_choice\n\n # Third Joint Prob\n if random_choice[0] == 0.8 or random_choice == 0.1:\n random_choice = np.random.choice(bn[2][0], 1, bn[2][0].all(), bn[2][0])\n x[2] = random_choice\n else:\n random_choice = np.random.choice(bn[2][1], 1, bn[2][1].all(), bn[2][1])\n x[2] = random_choice\n return x","def prob1(n):\n#raise NotImplementedError(\"Problem 1 Incomplete\")\n if n == 0 :\n raise ValueError(\"Sampling 0 points is not defined.\")\n total = 0\n for i in xrange(n) :\n if np.random.normal() > 3 :\n total += 1\n return float(total)/n","def sample (self, n):\n y = self.bins\n x = np.r_[0, self.values.cumsum ()] / self.sum\n # interpolate inverse CDF\n out = np.interp (np.random.random (n), x, y)\n if n == 1:\n return out[0]\n else:\n return out.reshape ((n,))","def get_n(self, n):\n \n return [self.get_values() for _ in range(n)]","def getNrSamples(self): \r\n return self.numSamples","def sample(self, n):\n idx = np.random.randint(0, len(self.memory), size=n)\n return [self.memory[i] for i in idx]","def pull_n_samples(dset, n):\n return list(dset[i] for i in random.sample(range(len(dset)), n))","def lnprior(self):\n \n return","def samples(self) -> int:\n return self._samples","def _construct_sample_from_prior(self):\n z_sym = T.matrix()\n x_sym = T.matrix()\n irs = self.ir_steps\n oputs = [self.obs_transform(self.s0)]\n oputs.extend([self.obs_transform(self.si[i]) for i in range(irs)])\n _, hi_zmuv = self._construct_zmuv_samples(x_sym, 1)\n sample_func = theano.function(inputs=[z_sym, x_sym], outputs=oputs, \\\n givens={ self.z: z_sym, \\\n self.x_in: T.zeros_like(x_sym), \\\n self.x_out: T.zeros_like(x_sym), \\\n self.hi_zmuv: hi_zmuv }, \\\n updates=self.scan_updates)\n def prior_sampler(samp_count):\n x_samps = to_fX( np.zeros((samp_count, self.obs_dim)) )\n old_switch = self.train_switch.get_value(borrow=False)\n # set model to generation mode\n self.set_train_switch(switch_val=0.0)\n z_samps = to_fX( npr.randn(samp_count, self.z_dim) )\n model_samps = sample_func(z_samps, x_samps)\n # set model back to either training or generation mode\n self.set_train_switch(switch_val=old_switch)\n return model_samps\n return prior_sampler","def generate_samples(self):\n self.analytic_probability()","def prior_distribution(self):\n out = self.model.forward(self.inducing_points)\n return MultivariateNormal(out.mean, out.lazy_covariance_matrix.evaluate_kernel())","def sample(self, size=1):\n samples = scipy.stats.bernoulli.rvs(self.p, size=size)\n if size == 1:\n return samples[0]\n return samples","def n_remaining_samples(self):\n return -1","def n_remaining_samples(self):\n return -1","def n_remaining_samples(self):\n return -1","def sample(self, nsamples):\n return self.dist.sample(nsamples)","def __resample(self):\n p_resample = []\n w_max = max(self.weights)\n index = int(round(random.uniform(0, self.n - 1)))\n beta = 0\n for i in range(self.n):\n beta += random.uniform(0, 2 * w_max)\n while self.weights[index] < beta:\n beta -= self.weights[index]\n index = (index + 1) % self.n\n p_resample.append(self.particles[index, :])\n return np.array(p_resample)","def burnin_by_first_n(samples, N, step_number=False, logger_level=\"debug\"):\n _samples = copy.deepcopy(samples)\n n_samples = {key: N for key in _samples.keys()}\n if step_number:\n n_samples = {\n key: item + N if item is not None else N for key, item in\n _number_of_negative_steps(_samples, logger_level=logger_level).items()\n }\n getattr(logger, logger_level)(\n \"Removing the first {} as burnin\".format(\n \", \".join(\n [\"{} samples from {}\".format(val, key) for key, val in n_samples.items()]\n )\n )\n )\n return _samples.discard_samples(n_samples)","def take_samples(self, num_samples: int) -> List:\n if num_samples > len(self.samples):\n return random.sample(self.samples, len(self.samples))\n return random.sample(self.samples, num_samples)","def n_samples(self) -> int: # pragma: no cover\n return self.samples.shape[0]","def sample_count(self):","def sample_users(self, n):\n return torch.rand((n, self.num_topics), generator=self.generator) * 2 - 1","def sample_size_with_prior(self, prior=\"small\"):\n if prior == \"small\":\n prior_sizes = list(range(self._min_size, self._max_size))\n elif prior == \"big\":\n prior_sizes = list(range(self._min_size+1, self._max_size+1))\n return random.choice(prior_sizes)","def sample(self,\n n_samples: Optional[int],\n temperatures: Optional[List[float]] = None) -> T.Tensor:\n samples = self.levels[-1].sample(\n self.top_prior_shape, n_samples=n_samples, temperatures=temperatures)\n for level in self.levels[-2::-1]:\n samples = level.sample(context=samples)\n return samples","def prior_predictive(self):\n cfg = self.config\n n = cfg['batch_size'] * cfg['q/n_samples']\n n_samples = cfg['q/n_samples']\n with util.get_or_create_scope('model', reuse=True):\n h_prior = tf.cast(self.p_h_L.sample(n), cfg['dtype'])\n h_prior = tf.reshape(\n h_prior, [cfg['q/n_samples'], cfg['batch_size'], -1])\n h = [None] * cfg['p/n_layers']\n h[cfg['p/n_layers'] - 1] = h_prior\n for n in range(cfg['p/n_layers'] - 1, 0, -1):\n p_h_n = self.build_stochastic_layer(n, h_above=h[n])\n h[n - 1] = tf.cast(p_h_n.sample(), cfg['dtype'])\n return self.likelihood(h[0])","def sample(self, num_samples = 1):\n\n X = np.empty(num_samples)\n for k in range(len(X)):\n\n # starting\n n = 1\n a = 1\n Un = self.UG.sample()\n a = a * Un\n\n # iterate over\n while a >= np.exp(-self.rate):\n n = n + 1\n Un = self.UG.sample()\n a = a * Un\n\n X[k] = n - 1\n\n return X","def sample_pagerank(corpus, damping_factor, n):\n probabilities = dict()\n samples = []\n\n # Random first sample\n page = random.choice(list(corpus.keys()))\n samples.append(page)\n \n # Remaining samples after first\n for i in range(n-1):\n p = transition_model(corpus, page, damping_factor)\n page = random.choices(list(p.keys()), weights=list(p.values()), k=1)[0]\n samples.append(page)\n\n # Count\n for p in corpus.keys():\n probabilities[p] = samples.count(p) / n\n\n return probabilities","def get_prev_samples(self):\n return self.y_p","def main(n_samples):\n uso = usolib.uso.uar(N)\n lst = [usolib.randomfacet.randomfacet_sample(uso, N) for i in range(n_samples)]\n return sum(lst) / float(n_samples)","def sample_data(self, N, pmin=8., pmax=12.):\n ps = [matrix(ra.uniform(pmin, pmax, (self.n, 1))) for k in range(N)]\n xs = [self.compute_demand(p) for p in ps]\n return zip(xs,ps)","def simplex_sampler(self, n):\n\n # edge case: n = 1\n if n == 1:\n return np.array([self.initial_potential])\n\n values = [np.random.uniform() for i in range(n-1)]\n values.extend([0, 1])\n values.sort()\n values_arr = np.array(values)\n\n xs = values_arr[1:] - values_arr[:-1]\n\n # return in decresing order of magnitude, to use for higher levels\n xs = self.initial_potential*np.sort(xs)\n xs = xs[::-1]\n return xs","def _number_of_samples(self):\n return len(self._raw_data.samples)","def get_next_sample(self):","def get_number_samples(self):\n return self.samples.shape[0]","def sample_low_rank(self, n_samples, mu, logvar, F):\n #F = torch.unsqueeze(F, dim=1).repeat(1, n_samples, 1, 1) # [self.batch_size, n_samples, self.Y_dim, self.rank]\n F = F.repeat(n_samples, 1, 1) # [self.batch_size*n_samples, self.Y_dim, self.rank]\n mu = mu.repeat(n_samples, 1) # [self.batch_size*n_samples, self.Y_dim]\n logvar = logvar.repeat(n_samples, 1) # [self.batch_size*n_samples, self.Y_dim]\n eps_low_rank = torch.randn(self.batch_size*n_samples, self.rank, 1)\n eps_diag = torch.randn(self.batch_size*n_samples, self.Y_dim)\n half_var = torch.exp(0.5*logvar) # [self.batch_size*n_samples, self.Y_dim]\n samples = torch.bmm(F, eps_low_rank).squeeze() + mu + half_var*eps_diag\n samples = samples.reshape(n_samples, self.batch_size, self.Y_dim)\n samples = samples.transpose(0, 1)\n samples = self.unwhiten_back(samples)\n samples = samples.data.cpu().numpy()\n return samples","def get_log_prior(samples):\n prior_distribution = tfp.distributions.Normal(loc=0., scale=1.)\n log_prior = tf.reduce_sum(prior_distribution.log_prob(samples), axis=1)\n return log_prior","def sample_from_log_prob(A, n):\n A_cum = log_cum_sum(A)\n C_pos = [ -c for c in reversed(A_cum)]\n sel = np.log(np.random.random(n))\n pos = [len(A) - np.searchsorted(C_pos,-r) for r in sel]\n return pos","def priors_sample(self):\n \n theta_star = np.zeros(self.n_params)\n\n for i in xrange(self.n_params): \n np.random.seed() \n theta_star[i] = self.param_obj.prior()[i].rvs(size=1)[0]\n\n return theta_star","def sample_pagerank(corpus, damping_factor, n):\n all_pages = []\n first_sample_prob = random.randint(0, len(corpus) - 1)\n distribution_count = dict()\n\n for u in corpus:\n distribution_count[u] = 0\n all_pages.append(u)\n\n sample = all_pages[first_sample_prob]\n for i in range(n - 1): # n - 1 because first sample was already calculated\n selection_bucket = dict()\n selection_start = 0.0\n sample_distribution = transition_model(corpus, sample, damping_factor)\n sample_prob = random.random()\n for u in sample_distribution:\n floor = selection_start\n ceiling = selection_start + sample_distribution[u]\n selection_start = ceiling\n selection_bucket[u] = [floor, ceiling]\n for u in selection_bucket:\n v = selection_bucket[u]\n if v[0] < sample_prob < v[1]:\n sample = u\n distribution_count[u] += 1\n distribution = dict()\n for u in distribution_count:\n distribution[u] = float(distribution_count[u]) / n\n\n return distribution","def get_num_samples(self):\n return self._num_samples","def sample(self, n):\n unparameterized = [\n v for v in self._weights.keys() if v not in self._weights_p.keys()\n ]\n if len(unparameterized) > 0:\n raise ValueError(\n \"The following variables must be numerically parameterized before sampling: {0}\".format(\n unparameterized\n )\n )\n mu, cov = self.parameterized_mean_cov()\n cov = np.asarray(cov).astype(np.float64)\n mu = np.asarray(mu).astype(np.float64)\n return np.random.multivariate_normal(mu.ravel(), cov, size=n)","def generate_samples(self,n_samples=100):\n rnd_input=torch.randn((n_samples,self._reparam_nodes[1]))\n zeta=rnd_input \n # rnd_input=torch.where((rnd_input>0.5),torch.ones(rnd_input.size()),torch.zeros(rnd_input.size()))\n # print(rnd_input) \n # output, mu, logvar, zeta=self.forward(rnd_input)\n # mu = self._reparam_layers['mu'](rnd_input)\n # logvar = self._reparam_layers['var'](rnd_input)\n # zeta = self.reparameterize(mu, logvar)\n output = self.decoder.decode(zeta)\n return output","def process_custom_prior(prior) -> Tuple[Distribution, int, bool]:\n\n check_prior_methods(prior)\n\n check_prior_batch_behavior(prior)\n\n prior, is_prior_numpy = maybe_wrap_prior_to_pytorch(prior)\n\n parameter_dim = prior.sample().numel()\n\n return prior, parameter_dim, is_prior_numpy","def __call__(self, *args):\n r = np.random.rand(*args)\n if type(r) is float:\n samples = self.values[(r < self.p).nonzero()[0][0]]\n elif type(r) is np.ndarray:\n samples = np.array(\n [self.values[np.nonzero(x < self.p)[0][0]] \n for x in r.flat]).reshape(r.shape)\n return samples","def get_num_samples(self) -> int:\n # must be implemented in subclass\n raise NotImplementedError","def sample(self, n_samples: int) -> torch.Tensor:\n return self.dist.sample((n_samples,))","def ts_rewards_sample(self):\n exp_rewards_list = [np.random.beta(a, b) for a, b in zip(self.alpha_params, self.beta_params)]\n return np.array(exp_rewards_list)","def prediction_samples(self) -> np.ndarray:\n return self.prediction_samples","def sample(self, n_samples: int, random_state: Optional[int] = None) -> np.ndarray:\n\n if not isinstance(self.prob, np.ndarray):\n samples = stats.bernoulli.rvs(\n p=self.prob, size=n_samples, random_state=random_state\n )\n else:\n n_classes = len(self.prob) # type: ignore\n samples = np.zeros((n_samples, n_classes))\n\n for cls in range(n_classes):\n samples[:, cls] = stats.bernoulli.rvs(p=self.prob[cls], size=n_samples, random_state=random_state) # type: ignore\n\n return samples","def generate_synth_data(n):","def prior_sample_parameter(self, parameter):\n pass","def get_samples_per_signal(self):\n return np.array([self.samples_in_file(chn) for chn in range(self.signals_in_file)])","def sample(self, num):\n state, action, reward, next_state, not_terminal = [], [], [], [], []\n for exp in self.experiences.sample(num):\n state.append(exp.state)\n action.append(exp.action)\n reward.append(exp.reward)\n next_state.append(exp.next_state)\n not_terminal.append(exp.not_terminal)\n\n return np.asarray(state), np.asarray(action), np.asarray(reward), \\\n np.asarray(next_state), np.asarray(not_terminal)","def samples(self):\n pass","def sample_observations(self, n_samples, z):\n lat_dim = self.transition.dim_x\n x = []\n for i in range(self.time_steps):\n z_t = tf.slice(z, [0, i * lat_dim], [-1, lat_dim]) \n x.append(self.emmision.sample(z_t))\n return tf.concat(x, axis=1)","def sample(self, n_samples):\n with torch.no_grad():\n z = torch.randn((n_samples, self.z_dim))\n samples = self.decoder(z)\n im_size = int(np.sqrt(self.input_dim))\n samples = samples.view(-1, 1, im_size, im_size)\n\n return samples","def sample_pagerank(corpus, damping_factor, n):\n # All pages in entire corpus\n all_pages = [key for key in corpus.keys()]\n\n # Dictionary which will map each page to the number of generated samples that were the page\n counts = dict()\n for page in all_pages:\n counts[page] = 0\n\n # Choosing first sample randomly and updating counts dict\n first_sample = random.choice(all_pages)\n counts[first_sample] += 1\n\n for i in range(1, n):\n # If we are on the second sample, do the calculation based on the first\n if i == 1:\n prev_sample = first_sample\n\n distributions = transition_model(corpus, prev_sample, damping_factor)\n\n keys = [key for key in distributions.keys()]\n values = [distributions[key] for key in distributions.keys()]\n\n # Using random.choices to select an option given a list of weights (distribution)\n current_sample = random.choices(keys, weights=values, k=1)[0]\n\n # Updating counts dict given the current sample\n counts[current_sample] += 1\n\n prev_sample = current_sample\n\n # Modifying counts dict to represent the proportion of samples that corresponded to that page instead of the raw amount\n for page in all_pages:\n counts[page] /= n\n\n return counts","def take(n, seq):\n return itertools.islice(seq, n)","def get_samples(self) -> McmcPtResult:","def deterministic_sample(choices, n_to_sample, p): # pylint: disable=invalid-name\n\n sample_counts = np.ceil(n_to_sample * p).astype(int)\n\n n_to_remove = np.sum(sample_counts) - n_to_sample\n\n if n_to_remove == 0:\n return choices[counts_to_vector(sample_counts)]\n\n non_zero_mask = sample_counts > 0\n\n removal_indices = np.floor(np.linspace(0.0,\n np.sum(non_zero_mask),\n n_to_remove,\n endpoint=False)).astype(int)\n\n tmp = sample_counts[non_zero_mask]\n tmp[removal_indices] = tmp[removal_indices] - 1\n\n sample_counts[non_zero_mask] = tmp\n\n assert np.sum(sample_counts) == n_to_sample\n\n samples = choices[counts_to_vector(sample_counts)]\n\n return samples"],"string":"[\n \"def sample_from_prior(self, n_samples):\\n\\n p0 = self.min + self.rng.rand(n_samples) * (self.max - self.min)\\n return p0[:, np.newaxis]\",\n \"def sample_from_prior(self, n_samples):\\n\\n p0 = self.rng.normal(loc=self.mean, scale=self.sigma, size=n_samples)\\n return p0[:, np.newaxis]\",\n \"def sample_from_prior(self, n_samples):\\n\\n p0 = self.rng.lognormal(mean=self.mean, sigma=self.sigma, size=n_samples)\\n return p0[:, np.newaxis]\",\n \"def sample_from_prior(self, n_samples):\\n\\n lamda = np.abs(self.rng.standard_cauchy(size=n_samples))\\n\\n p0 = np.log(np.abs(self.rng.randn() * lamda * self.scale))\\n return p0[:, np.newaxis]\",\n \"def sample_prior(size):\\n return torch.randn(size)\",\n \"def sample_from_prior(self):\\n raise NotImplementedError\",\n \"def sample_parameters_prior(self, n_samples, random_seed=None):\\n\\n if random_seed is not None:\\n np.random.seed(random_seed)\\n samples = []\\n samples.append(self.var_noise.sample_from_prior(n_samples))\\n samples.append(self.mean.sample_from_prior(n_samples))\\n samples.append(self.kernel.sample_parameters(n_samples))\\n\\n return np.concatenate(samples, 1)\",\n \"def sample(self, n_samples=1):\\n\\n\\t\\tif isinstance(n_samples, int):\\n\\t\\t\\tn_samples = (n_samples,)\\n\\t\\tbeta_sample = self.beta_prior.sample(n_samples)\\n\\t\\tgamma_sample = self.gamma_prior.sample(n_samples)\\n\\t\\tp = torch.stack((beta_sample, gamma_sample)).T\\n\\t\\tif n_samples == (1,):\\n\\t\\t\\tp = p[0]\\n\\t\\tp = p.numpy()\\n\\t\\treturn p\",\n \"def sample_from_prior(self, *args, **kwargs):\\n pass\",\n \"def forward_sample(self, n):\\n return self.flow.sample(n)\",\n \"def prior_sample(self):\\n pass\",\n \"def generate_samples(self, n_samples):\",\n \"def generate_samples(self, n_samples):\",\n \"def sample(self, n):\\n # Est probs from priority weights\\n summed = sum(self.weight) + self.eps\\n self.probs = [w / summed for w in self.priority]\\n\\n # Wieghted sample\\n return np.random.choice(self.memory, size=n, p=self.probs).tolist()\",\n \"def prior_samples(self, nsamples, coords=None):\\n S = self.eval_S(self.kappa, self.sigma_f)\\n if coords is None:\\n coords = slice(self.mesh.num_vertices()) # take all coords\\n weights = np.random.normal(scale=np.sqrt(S),\\n size=(nsamples,) + S.shape) # shape (s, l)\\n prior = np.einsum('sl,nl->sn', weights, self.eigenfunctions[coords])\\n\\n return prior\",\n \"def samples(self):\\n return self._values[:self.nsamples]\",\n \"def generate_samples(self, no=10):\\n observations = []\\n state_sequence = []\\n initial_state = np.random.choice(\\n self.latent_variable_markov_chain.states,\\n p=self.prior_probabilities)\\n state_sequence.append(initial_state)\\n observations.append(self.observation_from_state(initial_state))\\n current_state = initial_state\\n for i in range(2, no):\\n next_state = self.latent_variable_markov_chain.next_state(current_state)\\n state_sequence.append(next_state)\\n observations.append(self.observation_from_state(next_state))\\n current_state = next_state\\n return observations, state_sequence\",\n \"def sample(self, n, include=True):\\n return [self(t / n) for t in range(n + int(include))]\",\n \"def prior(n=10):\\r\\n p = []\\r\\n trials = 0\\r\\n acc = 0\\r\\n while acc < n:\\r\\n trials += 1\\r\\n r = np.random.rand(2) * np.array([4, 2]) + np.array([-2, -1])\\r\\n # print(\\\"r: \\\", r)\\r\\n if r[1] + r[0] >= -1 and r[1] - r[0] >= -1:\\r\\n p.append(r)\\r\\n acc += 1\\r\\n # print(\\\"trials: \\\", trials, \\\", acc: \\\", acc)\\r\\n return p\",\n \"def pull_reads(self, n, prng): \\n if not self.sampling:\\n self.convert_to_array()\\n index = prng.randint(0, self.total, size=n)\\n index = np.sort(index)\\n return self.reads[index,:]\",\n \"def sample(self, n=1):\\n raise NotImplementedError\",\n \"def get_training_n(self, n: Optional[int] = None, random: bool = True) -> Tuple[torch.Tensor, torch.Tensor]:\\n return self.__sample(self.train_data, self.train_los, n, random)\",\n \"def sample(self, n):\\n raise NotImplementedError\",\n \"def _sample(self, n=1):\\n return [self[i] for i in np.random.choice(self.length, n, replace=False)]\",\n \"def get_n_samples(self):\\n return {op.rx.get_n_samples() for op in self.ops}\",\n \"def sample(self, n_samples):\\n\\n z = sample_prior((n_samples,) + self.flow.z_shape)\\n ldj = torch.zeros(z.size(0))\\n\\n z, ldj = self.flow (z, ldj, reverse=True)\\n z, ldj = self.logit_normalize(z, ldj, reverse=True)\\n\\n return z\",\n \"def Sample(n=6):\\n t = [random.normalvariate(0.0, 1.0) for i in range(n)]\\n t.sort()\\n return t\",\n \"def posterior_sampler(self, nsamples, seed=0, verbose=True):\\n\\n import random\\n\\n random.seed(seed)\\n sample = self.get_chain()[-self.get_tune:]\\n sample = sample.reshape(-1, sample.shape[-1])\\n sample = random.choices(sample, k=nsamples)\\n\\n return sample\",\n \"def generate_samples(self, n_samples=100):\\n \\t\\t\\n\\t\\t#how many times should ancestral sampling be run\\n\\t\\t#n_samples\\n prior_samples=[]\\n for i in range(0,n_samples):\\n prior_sample = self.prior.get_samples(\\n n_latent_nodes=self.n_latent_nodes,\\n n_gibbs_sampling_steps=100, \\n sampling_mode=\\\"gibbs_ancestral\\\")\\n prior_sample = torch.cat(prior_sample)\\n prior_samples.append(prior_sample)\\n prior_samples=torch.stack(prior_samples)\\n # prior_samples = tf.slice(prior_samples, [0, 0], [num_samples, -1])\\n output_activations = self.decoder.decode(prior_samples)\\n output_activations = output_activations+self._train_bias\\n output_distribution = Bernoulli(logit=output_activations)\\n output=torch.sigmoid(output_distribution.logits)\\n # output_activations[0] = output_activations[0] + self.train_bias\\n # output_dist = FactorialBernoulliUtil(output_activations)\\n # output_samples = tf.nn.sigmoid(output_dist.logit_mu)\\n # print(\\\"--- \\\",\\\"end VAE::generate_samples()\\\")\\n return output\",\n \"def get_random_samplers(self, n):\\n if not self.has_samplers():\\n self.draw_samplers()\\n \\n def get_shuffledcopy(x):\\n x_ = x.copy()\\n np.random.shuffle(x_)\\n return x_\\n \\n return get_shuffledcopy(self.samplers)[:n]\",\n \"def sample_gp_prior(self, x_list, n_samp, full_cov=True):\\n mu, cov = self.get_gp_prior_mu_cov(x_list, full_cov)\\n return self.get_normal_samples(mu, cov, n_samp, full_cov)\",\n \"def get_posterior_sample(self):\\n total_tries = self.prior_success + self.prior_failure\\n prob_success = self.prior_success / total_tries\\n # np.random.binomial采样出来的是二项分布的均值, 即正面朝上的次数,所以要除以N\\n boot_sample = np.random.binomial(total_tries, prob_success) / total_tries\\n return boot_sample\",\n \"def sample(self, n_samples=1):\\n\\n\\t\\tsamples = np.random.random((n_samples, self.__ndims))*self.__range + self.__low\\n\\t\\treturn samples\",\n \"def get_samples(self, nsamples):\\n sample_ids = np.random.choice(self.embed.shape[0], nsamples, replace=False)\\n return self.embed[sample_ids]\",\n \"def sample_predict(self, X, n_samples):\\n W_samples = self.W_prior.sample(n_samples) ##size: [n_samples, n_in, n_out]\\n if self.use_bias:\\n bs = W_samples[:, :1, :]\\n Ws = W_samples[:, 1:, :]\\n if self.scaled_variance:\\n Ws = Ws / np.sqrt(self.n_in)\\n return torch.matmul(X, Ws) + bs\\n else:\\n if self.scaled_variance:\\n W_samples = W_samples / np.sqrt(self.n_in)\\n return torch.matmul(X, W_samples)\",\n \"def pickTopicFromPrior(self):\\r\\n return self.priorTopicDistr.sampleIndex()\",\n \"def fetch_samples(self):\\n return torch.cat(self.samples,dim=0).reshape(-1,self.parameters.numel())\",\n \"def _uniform_number(self, N=None):\\n num_frames = self.data.shape[0]\\n if not N:\\n N = int(np.floor(\\n Constants.DEFAULT_SAMPLE_PERCENTAGE * num_frames\\n ))\\n k = int(np.floor(num_frames / N))\\n if k == 0:\\n k = 1\\n elif N > num_frames:\\n k = 1\\n else:\\n assert type(N) is IntType, 'N is not an integer: {}'.format(N)\\n k = int(np.floor(num_frames / N))\\n\\n # ensure that at most N samples are taken if N does not divide nicely\\n return self.data[::k][:N], self.labels[::k][:N]\",\n \"def sample(self, n_samples, sample_seed):\\n self.seed_samples(sample_seed)\\n eps = torch.randn(self.batch_size, n_samples, self.Y_dim)\\n samples = eps*torch.exp(0.5*self.logvar.unsqueeze(1)) + self.mu.unsqueeze(1)\\n samples = self.unwhiten_back(samples)\\n samples = samples.data.cpu().numpy()\\n return samples\",\n \"def prior_sample(self, bn):\\n x = np.zeros(3)\\n\\n # first joint prob\\n random_choice = np.random.choice(bn[0], 1, bn[0].all(), bn[0])\\n x[0] = random_choice[0]\\n\\n # Second Joint Prob\\n if x[0] == 0.1:\\n random_choice = np.random.choice(bn[1][0], 1, bn[1][0].all(), bn[1][0])\\n x[1] = random_choice\\n elif x[0] == 0.9:\\n random_choice = np.random.choice(bn[1][1], 1, bn[1][1].all(), bn[1][1])\\n x[1] = random_choice\\n\\n # Third Joint Prob\\n if random_choice[0] == 0.8 or random_choice == 0.1:\\n random_choice = np.random.choice(bn[2][0], 1, bn[2][0].all(), bn[2][0])\\n x[2] = random_choice\\n else:\\n random_choice = np.random.choice(bn[2][1], 1, bn[2][1].all(), bn[2][1])\\n x[2] = random_choice\\n return x\",\n \"def prob1(n):\\n#raise NotImplementedError(\\\"Problem 1 Incomplete\\\")\\n if n == 0 :\\n raise ValueError(\\\"Sampling 0 points is not defined.\\\")\\n total = 0\\n for i in xrange(n) :\\n if np.random.normal() > 3 :\\n total += 1\\n return float(total)/n\",\n \"def sample (self, n):\\n y = self.bins\\n x = np.r_[0, self.values.cumsum ()] / self.sum\\n # interpolate inverse CDF\\n out = np.interp (np.random.random (n), x, y)\\n if n == 1:\\n return out[0]\\n else:\\n return out.reshape ((n,))\",\n \"def get_n(self, n):\\n \\n return [self.get_values() for _ in range(n)]\",\n \"def getNrSamples(self): \\r\\n return self.numSamples\",\n \"def sample(self, n):\\n idx = np.random.randint(0, len(self.memory), size=n)\\n return [self.memory[i] for i in idx]\",\n \"def pull_n_samples(dset, n):\\n return list(dset[i] for i in random.sample(range(len(dset)), n))\",\n \"def lnprior(self):\\n \\n return\",\n \"def samples(self) -> int:\\n return self._samples\",\n \"def _construct_sample_from_prior(self):\\n z_sym = T.matrix()\\n x_sym = T.matrix()\\n irs = self.ir_steps\\n oputs = [self.obs_transform(self.s0)]\\n oputs.extend([self.obs_transform(self.si[i]) for i in range(irs)])\\n _, hi_zmuv = self._construct_zmuv_samples(x_sym, 1)\\n sample_func = theano.function(inputs=[z_sym, x_sym], outputs=oputs, \\\\\\n givens={ self.z: z_sym, \\\\\\n self.x_in: T.zeros_like(x_sym), \\\\\\n self.x_out: T.zeros_like(x_sym), \\\\\\n self.hi_zmuv: hi_zmuv }, \\\\\\n updates=self.scan_updates)\\n def prior_sampler(samp_count):\\n x_samps = to_fX( np.zeros((samp_count, self.obs_dim)) )\\n old_switch = self.train_switch.get_value(borrow=False)\\n # set model to generation mode\\n self.set_train_switch(switch_val=0.0)\\n z_samps = to_fX( npr.randn(samp_count, self.z_dim) )\\n model_samps = sample_func(z_samps, x_samps)\\n # set model back to either training or generation mode\\n self.set_train_switch(switch_val=old_switch)\\n return model_samps\\n return prior_sampler\",\n \"def generate_samples(self):\\n self.analytic_probability()\",\n \"def prior_distribution(self):\\n out = self.model.forward(self.inducing_points)\\n return MultivariateNormal(out.mean, out.lazy_covariance_matrix.evaluate_kernel())\",\n \"def sample(self, size=1):\\n samples = scipy.stats.bernoulli.rvs(self.p, size=size)\\n if size == 1:\\n return samples[0]\\n return samples\",\n \"def n_remaining_samples(self):\\n return -1\",\n \"def n_remaining_samples(self):\\n return -1\",\n \"def n_remaining_samples(self):\\n return -1\",\n \"def sample(self, nsamples):\\n return self.dist.sample(nsamples)\",\n \"def __resample(self):\\n p_resample = []\\n w_max = max(self.weights)\\n index = int(round(random.uniform(0, self.n - 1)))\\n beta = 0\\n for i in range(self.n):\\n beta += random.uniform(0, 2 * w_max)\\n while self.weights[index] < beta:\\n beta -= self.weights[index]\\n index = (index + 1) % self.n\\n p_resample.append(self.particles[index, :])\\n return np.array(p_resample)\",\n \"def burnin_by_first_n(samples, N, step_number=False, logger_level=\\\"debug\\\"):\\n _samples = copy.deepcopy(samples)\\n n_samples = {key: N for key in _samples.keys()}\\n if step_number:\\n n_samples = {\\n key: item + N if item is not None else N for key, item in\\n _number_of_negative_steps(_samples, logger_level=logger_level).items()\\n }\\n getattr(logger, logger_level)(\\n \\\"Removing the first {} as burnin\\\".format(\\n \\\", \\\".join(\\n [\\\"{} samples from {}\\\".format(val, key) for key, val in n_samples.items()]\\n )\\n )\\n )\\n return _samples.discard_samples(n_samples)\",\n \"def take_samples(self, num_samples: int) -> List:\\n if num_samples > len(self.samples):\\n return random.sample(self.samples, len(self.samples))\\n return random.sample(self.samples, num_samples)\",\n \"def n_samples(self) -> int: # pragma: no cover\\n return self.samples.shape[0]\",\n \"def sample_count(self):\",\n \"def sample_users(self, n):\\n return torch.rand((n, self.num_topics), generator=self.generator) * 2 - 1\",\n \"def sample_size_with_prior(self, prior=\\\"small\\\"):\\n if prior == \\\"small\\\":\\n prior_sizes = list(range(self._min_size, self._max_size))\\n elif prior == \\\"big\\\":\\n prior_sizes = list(range(self._min_size+1, self._max_size+1))\\n return random.choice(prior_sizes)\",\n \"def sample(self,\\n n_samples: Optional[int],\\n temperatures: Optional[List[float]] = None) -> T.Tensor:\\n samples = self.levels[-1].sample(\\n self.top_prior_shape, n_samples=n_samples, temperatures=temperatures)\\n for level in self.levels[-2::-1]:\\n samples = level.sample(context=samples)\\n return samples\",\n \"def prior_predictive(self):\\n cfg = self.config\\n n = cfg['batch_size'] * cfg['q/n_samples']\\n n_samples = cfg['q/n_samples']\\n with util.get_or_create_scope('model', reuse=True):\\n h_prior = tf.cast(self.p_h_L.sample(n), cfg['dtype'])\\n h_prior = tf.reshape(\\n h_prior, [cfg['q/n_samples'], cfg['batch_size'], -1])\\n h = [None] * cfg['p/n_layers']\\n h[cfg['p/n_layers'] - 1] = h_prior\\n for n in range(cfg['p/n_layers'] - 1, 0, -1):\\n p_h_n = self.build_stochastic_layer(n, h_above=h[n])\\n h[n - 1] = tf.cast(p_h_n.sample(), cfg['dtype'])\\n return self.likelihood(h[0])\",\n \"def sample(self, num_samples = 1):\\n\\n X = np.empty(num_samples)\\n for k in range(len(X)):\\n\\n # starting\\n n = 1\\n a = 1\\n Un = self.UG.sample()\\n a = a * Un\\n\\n # iterate over\\n while a >= np.exp(-self.rate):\\n n = n + 1\\n Un = self.UG.sample()\\n a = a * Un\\n\\n X[k] = n - 1\\n\\n return X\",\n \"def sample_pagerank(corpus, damping_factor, n):\\n probabilities = dict()\\n samples = []\\n\\n # Random first sample\\n page = random.choice(list(corpus.keys()))\\n samples.append(page)\\n \\n # Remaining samples after first\\n for i in range(n-1):\\n p = transition_model(corpus, page, damping_factor)\\n page = random.choices(list(p.keys()), weights=list(p.values()), k=1)[0]\\n samples.append(page)\\n\\n # Count\\n for p in corpus.keys():\\n probabilities[p] = samples.count(p) / n\\n\\n return probabilities\",\n \"def get_prev_samples(self):\\n return self.y_p\",\n \"def main(n_samples):\\n uso = usolib.uso.uar(N)\\n lst = [usolib.randomfacet.randomfacet_sample(uso, N) for i in range(n_samples)]\\n return sum(lst) / float(n_samples)\",\n \"def sample_data(self, N, pmin=8., pmax=12.):\\n ps = [matrix(ra.uniform(pmin, pmax, (self.n, 1))) for k in range(N)]\\n xs = [self.compute_demand(p) for p in ps]\\n return zip(xs,ps)\",\n \"def simplex_sampler(self, n):\\n\\n # edge case: n = 1\\n if n == 1:\\n return np.array([self.initial_potential])\\n\\n values = [np.random.uniform() for i in range(n-1)]\\n values.extend([0, 1])\\n values.sort()\\n values_arr = np.array(values)\\n\\n xs = values_arr[1:] - values_arr[:-1]\\n\\n # return in decresing order of magnitude, to use for higher levels\\n xs = self.initial_potential*np.sort(xs)\\n xs = xs[::-1]\\n return xs\",\n \"def _number_of_samples(self):\\n return len(self._raw_data.samples)\",\n \"def get_next_sample(self):\",\n \"def get_number_samples(self):\\n return self.samples.shape[0]\",\n \"def sample_low_rank(self, n_samples, mu, logvar, F):\\n #F = torch.unsqueeze(F, dim=1).repeat(1, n_samples, 1, 1) # [self.batch_size, n_samples, self.Y_dim, self.rank]\\n F = F.repeat(n_samples, 1, 1) # [self.batch_size*n_samples, self.Y_dim, self.rank]\\n mu = mu.repeat(n_samples, 1) # [self.batch_size*n_samples, self.Y_dim]\\n logvar = logvar.repeat(n_samples, 1) # [self.batch_size*n_samples, self.Y_dim]\\n eps_low_rank = torch.randn(self.batch_size*n_samples, self.rank, 1)\\n eps_diag = torch.randn(self.batch_size*n_samples, self.Y_dim)\\n half_var = torch.exp(0.5*logvar) # [self.batch_size*n_samples, self.Y_dim]\\n samples = torch.bmm(F, eps_low_rank).squeeze() + mu + half_var*eps_diag\\n samples = samples.reshape(n_samples, self.batch_size, self.Y_dim)\\n samples = samples.transpose(0, 1)\\n samples = self.unwhiten_back(samples)\\n samples = samples.data.cpu().numpy()\\n return samples\",\n \"def get_log_prior(samples):\\n prior_distribution = tfp.distributions.Normal(loc=0., scale=1.)\\n log_prior = tf.reduce_sum(prior_distribution.log_prob(samples), axis=1)\\n return log_prior\",\n \"def sample_from_log_prob(A, n):\\n A_cum = log_cum_sum(A)\\n C_pos = [ -c for c in reversed(A_cum)]\\n sel = np.log(np.random.random(n))\\n pos = [len(A) - np.searchsorted(C_pos,-r) for r in sel]\\n return pos\",\n \"def priors_sample(self):\\n \\n theta_star = np.zeros(self.n_params)\\n\\n for i in xrange(self.n_params): \\n np.random.seed() \\n theta_star[i] = self.param_obj.prior()[i].rvs(size=1)[0]\\n\\n return theta_star\",\n \"def sample_pagerank(corpus, damping_factor, n):\\n all_pages = []\\n first_sample_prob = random.randint(0, len(corpus) - 1)\\n distribution_count = dict()\\n\\n for u in corpus:\\n distribution_count[u] = 0\\n all_pages.append(u)\\n\\n sample = all_pages[first_sample_prob]\\n for i in range(n - 1): # n - 1 because first sample was already calculated\\n selection_bucket = dict()\\n selection_start = 0.0\\n sample_distribution = transition_model(corpus, sample, damping_factor)\\n sample_prob = random.random()\\n for u in sample_distribution:\\n floor = selection_start\\n ceiling = selection_start + sample_distribution[u]\\n selection_start = ceiling\\n selection_bucket[u] = [floor, ceiling]\\n for u in selection_bucket:\\n v = selection_bucket[u]\\n if v[0] < sample_prob < v[1]:\\n sample = u\\n distribution_count[u] += 1\\n distribution = dict()\\n for u in distribution_count:\\n distribution[u] = float(distribution_count[u]) / n\\n\\n return distribution\",\n \"def get_num_samples(self):\\n return self._num_samples\",\n \"def sample(self, n):\\n unparameterized = [\\n v for v in self._weights.keys() if v not in self._weights_p.keys()\\n ]\\n if len(unparameterized) > 0:\\n raise ValueError(\\n \\\"The following variables must be numerically parameterized before sampling: {0}\\\".format(\\n unparameterized\\n )\\n )\\n mu, cov = self.parameterized_mean_cov()\\n cov = np.asarray(cov).astype(np.float64)\\n mu = np.asarray(mu).astype(np.float64)\\n return np.random.multivariate_normal(mu.ravel(), cov, size=n)\",\n \"def generate_samples(self,n_samples=100):\\n rnd_input=torch.randn((n_samples,self._reparam_nodes[1]))\\n zeta=rnd_input \\n # rnd_input=torch.where((rnd_input>0.5),torch.ones(rnd_input.size()),torch.zeros(rnd_input.size()))\\n # print(rnd_input) \\n # output, mu, logvar, zeta=self.forward(rnd_input)\\n # mu = self._reparam_layers['mu'](rnd_input)\\n # logvar = self._reparam_layers['var'](rnd_input)\\n # zeta = self.reparameterize(mu, logvar)\\n output = self.decoder.decode(zeta)\\n return output\",\n \"def process_custom_prior(prior) -> Tuple[Distribution, int, bool]:\\n\\n check_prior_methods(prior)\\n\\n check_prior_batch_behavior(prior)\\n\\n prior, is_prior_numpy = maybe_wrap_prior_to_pytorch(prior)\\n\\n parameter_dim = prior.sample().numel()\\n\\n return prior, parameter_dim, is_prior_numpy\",\n \"def __call__(self, *args):\\n r = np.random.rand(*args)\\n if type(r) is float:\\n samples = self.values[(r < self.p).nonzero()[0][0]]\\n elif type(r) is np.ndarray:\\n samples = np.array(\\n [self.values[np.nonzero(x < self.p)[0][0]] \\n for x in r.flat]).reshape(r.shape)\\n return samples\",\n \"def get_num_samples(self) -> int:\\n # must be implemented in subclass\\n raise NotImplementedError\",\n \"def sample(self, n_samples: int) -> torch.Tensor:\\n return self.dist.sample((n_samples,))\",\n \"def ts_rewards_sample(self):\\n exp_rewards_list = [np.random.beta(a, b) for a, b in zip(self.alpha_params, self.beta_params)]\\n return np.array(exp_rewards_list)\",\n \"def prediction_samples(self) -> np.ndarray:\\n return self.prediction_samples\",\n \"def sample(self, n_samples: int, random_state: Optional[int] = None) -> np.ndarray:\\n\\n if not isinstance(self.prob, np.ndarray):\\n samples = stats.bernoulli.rvs(\\n p=self.prob, size=n_samples, random_state=random_state\\n )\\n else:\\n n_classes = len(self.prob) # type: ignore\\n samples = np.zeros((n_samples, n_classes))\\n\\n for cls in range(n_classes):\\n samples[:, cls] = stats.bernoulli.rvs(p=self.prob[cls], size=n_samples, random_state=random_state) # type: ignore\\n\\n return samples\",\n \"def generate_synth_data(n):\",\n \"def prior_sample_parameter(self, parameter):\\n pass\",\n \"def get_samples_per_signal(self):\\n return np.array([self.samples_in_file(chn) for chn in range(self.signals_in_file)])\",\n \"def sample(self, num):\\n state, action, reward, next_state, not_terminal = [], [], [], [], []\\n for exp in self.experiences.sample(num):\\n state.append(exp.state)\\n action.append(exp.action)\\n reward.append(exp.reward)\\n next_state.append(exp.next_state)\\n not_terminal.append(exp.not_terminal)\\n\\n return np.asarray(state), np.asarray(action), np.asarray(reward), \\\\\\n np.asarray(next_state), np.asarray(not_terminal)\",\n \"def samples(self):\\n pass\",\n \"def sample_observations(self, n_samples, z):\\n lat_dim = self.transition.dim_x\\n x = []\\n for i in range(self.time_steps):\\n z_t = tf.slice(z, [0, i * lat_dim], [-1, lat_dim]) \\n x.append(self.emmision.sample(z_t))\\n return tf.concat(x, axis=1)\",\n \"def sample(self, n_samples):\\n with torch.no_grad():\\n z = torch.randn((n_samples, self.z_dim))\\n samples = self.decoder(z)\\n im_size = int(np.sqrt(self.input_dim))\\n samples = samples.view(-1, 1, im_size, im_size)\\n\\n return samples\",\n \"def sample_pagerank(corpus, damping_factor, n):\\n # All pages in entire corpus\\n all_pages = [key for key in corpus.keys()]\\n\\n # Dictionary which will map each page to the number of generated samples that were the page\\n counts = dict()\\n for page in all_pages:\\n counts[page] = 0\\n\\n # Choosing first sample randomly and updating counts dict\\n first_sample = random.choice(all_pages)\\n counts[first_sample] += 1\\n\\n for i in range(1, n):\\n # If we are on the second sample, do the calculation based on the first\\n if i == 1:\\n prev_sample = first_sample\\n\\n distributions = transition_model(corpus, prev_sample, damping_factor)\\n\\n keys = [key for key in distributions.keys()]\\n values = [distributions[key] for key in distributions.keys()]\\n\\n # Using random.choices to select an option given a list of weights (distribution)\\n current_sample = random.choices(keys, weights=values, k=1)[0]\\n\\n # Updating counts dict given the current sample\\n counts[current_sample] += 1\\n\\n prev_sample = current_sample\\n\\n # Modifying counts dict to represent the proportion of samples that corresponded to that page instead of the raw amount\\n for page in all_pages:\\n counts[page] /= n\\n\\n return counts\",\n \"def take(n, seq):\\n return itertools.islice(seq, n)\",\n \"def get_samples(self) -> McmcPtResult:\",\n \"def deterministic_sample(choices, n_to_sample, p): # pylint: disable=invalid-name\\n\\n sample_counts = np.ceil(n_to_sample * p).astype(int)\\n\\n n_to_remove = np.sum(sample_counts) - n_to_sample\\n\\n if n_to_remove == 0:\\n return choices[counts_to_vector(sample_counts)]\\n\\n non_zero_mask = sample_counts > 0\\n\\n removal_indices = np.floor(np.linspace(0.0,\\n np.sum(non_zero_mask),\\n n_to_remove,\\n endpoint=False)).astype(int)\\n\\n tmp = sample_counts[non_zero_mask]\\n tmp[removal_indices] = tmp[removal_indices] - 1\\n\\n sample_counts[non_zero_mask] = tmp\\n\\n assert np.sum(sample_counts) == n_to_sample\\n\\n samples = choices[counts_to_vector(sample_counts)]\\n\\n return samples\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7308122","0.7254096","0.71898454","0.7128596","0.6979248","0.6961805","0.67606914","0.6745526","0.6690407","0.6622515","0.6562736","0.65446556","0.65446556","0.6425273","0.6419413","0.6395372","0.63613814","0.6323354","0.63091654","0.6305061","0.6287302","0.6239015","0.6221694","0.6207399","0.6197996","0.6184932","0.6165855","0.6153619","0.6153358","0.6140597","0.61257386","0.6109061","0.6078866","0.60286486","0.60100406","0.6004092","0.59905887","0.5970731","0.59330416","0.5920617","0.59058857","0.58966595","0.589106","0.5890634","0.58760834","0.58759904","0.5870663","0.5850432","0.58440155","0.58292997","0.58253235","0.5818603","0.5816317","0.5816317","0.5816317","0.58079565","0.58007497","0.5788163","0.57879496","0.57798713","0.5776126","0.5774034","0.577337","0.5767064","0.5765437","0.5762816","0.5749947","0.5746394","0.5736112","0.5728723","0.57278186","0.5722879","0.5718899","0.5717263","0.5683421","0.56821394","0.56575644","0.5654737","0.5651699","0.5648227","0.56469744","0.5641609","0.5639136","0.5636752","0.56361675","0.56329775","0.56267655","0.5622955","0.5622405","0.5618009","0.5616757","0.5616295","0.5613543","0.56135225","0.5599461","0.55893856","0.55878794","0.55790335","0.5576369","0.5575169"],"string":"[\n \"0.7308122\",\n \"0.7254096\",\n \"0.71898454\",\n \"0.7128596\",\n \"0.6979248\",\n \"0.6961805\",\n \"0.67606914\",\n \"0.6745526\",\n \"0.6690407\",\n \"0.6622515\",\n \"0.6562736\",\n \"0.65446556\",\n \"0.65446556\",\n \"0.6425273\",\n \"0.6419413\",\n \"0.6395372\",\n \"0.63613814\",\n \"0.6323354\",\n \"0.63091654\",\n \"0.6305061\",\n \"0.6287302\",\n \"0.6239015\",\n \"0.6221694\",\n \"0.6207399\",\n \"0.6197996\",\n \"0.6184932\",\n \"0.6165855\",\n \"0.6153619\",\n \"0.6153358\",\n \"0.6140597\",\n \"0.61257386\",\n \"0.6109061\",\n \"0.6078866\",\n \"0.60286486\",\n \"0.60100406\",\n \"0.6004092\",\n \"0.59905887\",\n \"0.5970731\",\n \"0.59330416\",\n \"0.5920617\",\n \"0.59058857\",\n \"0.58966595\",\n \"0.589106\",\n \"0.5890634\",\n \"0.58760834\",\n \"0.58759904\",\n \"0.5870663\",\n \"0.5850432\",\n \"0.58440155\",\n \"0.58292997\",\n \"0.58253235\",\n \"0.5818603\",\n \"0.5816317\",\n \"0.5816317\",\n \"0.5816317\",\n \"0.58079565\",\n \"0.58007497\",\n \"0.5788163\",\n \"0.57879496\",\n \"0.57798713\",\n \"0.5776126\",\n \"0.5774034\",\n \"0.577337\",\n \"0.5767064\",\n \"0.5765437\",\n \"0.5762816\",\n \"0.5749947\",\n \"0.5746394\",\n \"0.5736112\",\n \"0.5728723\",\n \"0.57278186\",\n \"0.5722879\",\n \"0.5718899\",\n \"0.5717263\",\n \"0.5683421\",\n \"0.56821394\",\n \"0.56575644\",\n \"0.5654737\",\n \"0.5651699\",\n \"0.5648227\",\n \"0.56469744\",\n \"0.5641609\",\n \"0.5639136\",\n \"0.5636752\",\n \"0.56361675\",\n \"0.56329775\",\n \"0.56267655\",\n \"0.5622955\",\n \"0.5622405\",\n \"0.5618009\",\n \"0.5616757\",\n \"0.5616295\",\n \"0.5613543\",\n \"0.56135225\",\n \"0.5599461\",\n \"0.55893856\",\n \"0.55878794\",\n \"0.55790335\",\n \"0.5576369\",\n \"0.5575169\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7954478"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":14388,"cells":{"query":{"kind":"string","value":"Computes the gradient of the prior with respect to theta."},"document":{"kind":"string","value":"def gradient(self, theta):\n pass"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def gradient(self, theta):\n return (1 / (self.sigma * np.sqrt(2 * np.pi))) * (\n -theta / (self.sigma ** 2) * np.exp(-(theta ** 2) / (2 * self.sigma ** 2))\n )","def gradient(self, theta):\n a = -(6 * self.scale ** 2)\n b = 3 * self.scale ** 2 + np.exp(2 * theta)\n b *= np.log(3 * self.scale ** 2 * np.exp(-2 * theta) + 1)\n return a / b","def gradient(theta, X, y, learning_rate):\n m = len(y)\n\n theta = theta.reshape((-1,1))\n grad = np.zeros(theta.shape)\n h = sigmoid(np.dot(X, theta)) \n \n grad = np.dot((h-y).T, X)/m\n grad = grad.T\n grad[1:] += (learning_rate/m)*theta[1:]\n return grad","def gradient_function(theta, X, y):\n\n grad = None\n #######################################################################\n # TODO: #\n # Compute the gradient for a particular choice of theta. #\n # Compute the partial derivatives and set grad to the partial #\n # derivatives of the cost w.r.t. each parameter in theta #\n # #\n #######################################################################\n \n theta = theta[:, np.newaxis]\n \n thetatrans = theta.T\n Xtrans = X.T\n \n MulThetaX = np.dot(thetatrans, Xtrans)\n \n h = sigmoid(MulThetaX)\n \n grad = (y - h) * Xtrans\n\n #######################################################################\n # END OF YOUR CODE #\n #######################################################################\n return grad","def compute_gradient(theta, X, y):\n m = X.shape[0]\n grad_theta = np.dot(X.transpose(), (np.dot(X, theta) - y)) / m\n #print theta, grad_theta, objective_function(theta, X, y)\n return grad_theta","def gradient(self, theta):\n return np.zeros([theta.shape[0]])","def gradientFunction(theta, X, y):\n y = y[:, 0]\n m = y.shape # number of training samples\n grad = X.T.dot(sigmoid(theta.dot(X.T))-1*y)\n grad /= m\n return grad","def gradient(theta, x, y):\n m = len(y)\n n = len(theta)\n z = theta.dot(x.T)\n grad = np.zeros(n)\n for i in xrange(m):\n grad += (g(z[i]) - y[i]) * x[i]\n return 1. / m * grad","def gradient(theta, X, y, Lambda=0.0):\n m = X.shape[0] # number of samples\n\n h = hypothesis(theta, X)\n\n if Lambda:\n g_0 = (1/m)*(X.T@(h - y))[0]\n g_1 = (1/m)*(X.T@(h - y))[1:] + (Lambda/m)*theta[1:] # skip theta-0\n \n return np.append(g_0, g_1)\n else:\n return (1/m)*(X.T@(h - y))","def gradient(x, y, theta):\n if x.ndim == 1:\n x = x[:, np.newaxis]\n if y.ndim == 2 and y.shape[1] == 1:\n y = y.flatten()\n if theta.ndim == 2 and theta.shape[1] == 1:\n theta = theta.flatten()\n\n if (x.size == 0 or y.size == 0 or theta.size == 0\n or x.ndim != 2 or y.ndim != 1 or theta.ndim != 1\n or x.shape[0] != y.shape[0] or x.shape[1] + 1 != theta.shape[0]):\n return None\n\n x_padded = np.c_[np.ones(x.shape[0]), x]\n\n return x_padded.T.dot(x_padded.dot(theta) - y) / y.shape[0]","def calc_grad(X, Y, theta):\n m, n = X.shape\n\n margins = Y * X.dot(theta)\n probs = 1. / (1 + np.exp(margins))\n grad = -(1./m) * (X.T.dot(probs * Y))\n\n return grad","def gradient_descent(X, y, theta, alpha, total_iterations, hypothesis):\n len_theta = len(theta)\n m = len(y)\n one_over_m = (1.0 / float(m))\n\n for _ in range(0, total_iterations):\n temp_theta = numpy.zeros(len_theta)\n\n X_by_theta_minus_y = numpy.subtract(hypothesis(numpy.matrix(theta), X), y)\n\n for j in range(0, len_theta):\n jth_column_of_X = X[:,j]\n derivative_j = one_over_m * numpy.multiply(X_by_theta_minus_y, jth_column_of_X).sum()\n temp_theta[j] = theta[j] - alpha*derivative_j\n\n theta = temp_theta\n\n return numpy.matrix(theta)","def gradient_descent(self, X, theta, Y, m):\n\n Z = X.dot(theta)\n H = Predict.g(Z)\n gradient = np.dot(X.T, (H - Y)) / m\n return self.alpha * gradient","def gradientFunctionReg(theta, X, y, Lambda): \n y = np.squeeze(y)\n m = y.shape # number of training samples\n grad = X.T.dot(sigmoid(theta.dot(X.T))-1*y)\n grad[1:] = grad[1:] + Lambda*theta[1:]\n grad /= m\n\n return grad","def gradientFunctionReg(theta, X, y, Lambda):\n m = len(y) # number of training examples\n grad = np.zeros(theta.shape[0])\n theta = np.transpose(theta)\n sum_1 = 0\n X = X.values\n y = y.values\n #calcuate the theta_0 \n# ====================== YOUR CODE HERE ======================\n# Instructions: Compute the gradient of a particular choice of theta.\n# Compute the partial derivatives and set grad to the partial\n# derivatives of the cost w.r.t. each parameter in theta\n for i in range(theta.shape[0]):\n if i == 0:\n for j in range(m):\n sum_1 += (sigmoid(np.dot(X[j],theta)) - y[j]) * X[j,i]\n else:\n for j in range(m):\n sum_1 += (sigmoid(np.dot(X[j],theta)) - y[j]) * X[j,i] + Lambda*theta[i]\n grad[i] = sum_1/m\n sum_1 = 0\n\n# =============================================================\n\n return grad","def logit_cost_grad(self, theta, X, y):\n\n grad = np.zeros(len(theta))\n\n ### YOUR CODE HERE\n sig = utils.sigmoid(theta)\n # sig = np.subtract(sig, y)\n sig = sig - y\n grad = np.dot(X.T, sig) + 2 * self.params['lamb'] * self.regularizer[1](self.weights)\n ### END YOUR CODE\n\n return grad","def compute_loss_gradient(theta_vector, *args):\n\n psi = args[0] # feed psi as a parameter\n circ_depth = args[1]\n num_qbits = args[2]\n theta = np.reshape(theta_vector, (circ_depth, num_qbits)) # reshapes the flat theta vector\n fidelity = get_fidelity(theta, psi)\n\n # the derivative of the loss wrt fidelity\n dl_df = -0.5 * fidelity ** (-0.5)\n\n df_dtheta = [] # a list of partial derivatives of the fidelity wrt the theta parameters\n\n for index in range(len(theta_vector)):\n layer_index = index // num_qbits\n qbit_index = index % num_qbits\n\n theta_plus = np.copy(theta)\n theta_plus[layer_index][qbit_index] += np.pi / 2 # added pi/2 to the ith theta parameter\n\n theta_minus = np.copy(theta)\n theta_minus[layer_index][qbit_index] -= np.pi / 2 # subtracted pi/2 to the ith theta parameter\n\n df_dtheta_i = 0.5 * (get_fidelity(theta_plus, psi) - get_fidelity(theta_minus, psi)) # ith derivative\n df_dtheta.append(df_dtheta_i)\n\n df_dtheta = np.array(df_dtheta)\n dl_dtheta = dl_df * df_dtheta # chain rule to get partial derivative of loss wrt theta parameters\n\n return dl_dtheta","def run_gradient_descent_iteration(X, Y, theta, alpha, lambda_factor, temp_parameter):\n delta = sparse.coo_matrix(theta.shape).toarray()\n\n h = compute_probabilities(X, theta, temp_parameter)\n\n for j in range(delta.shape[0]):\n y = Y\n y = np.where(y != j, 0, 1)\n p = y - h[j]\n\n x = X.T * p\n x = x.T\n x = x.sum(axis=0)\n\n grad = -x / (temp_parameter * X.shape[0]) + lambda_factor * theta[j]\n\n delta[j] += grad\n\n theta -= alpha * delta\n\n return theta","def gradient_descent(features, values, theta, alpha, num_iterations):\n \n # number of points\n npoints = len(values)\n \n # intialize cost history\n cost_history = []\n \n # num_interations iterations\n for iiter in range(num_iterations):\n \n # compute and store cost\n cost = compute_cost(features, values, theta)\n cost_history.append(cost)\n \n # update values of theta\n values_predicted = np.dot(features, theta)\n theta = theta + (alpha/npoints)*(np.dot(values - values_predicted,features))\n \n return theta, pandas.Series(cost_history)","def gradient_descent(x, y, theta=[[0], [0]]):\n m = y.size\n j_history = []\n for i in range(ITERATIONS):\n h = x.dot(theta)\n theta = theta - (ALPHA / m) * (x.T.dot(h - y))\n j_history.append(compute_cost(x, y, theta))\n return theta, j_history","def approx_grad(theta, X, y):\n grad_a = np.array([(cost(theta + e, X, y) - cost(theta - e, X, y)) / (2 * 1e-5)\n for e in np.identity(len(theta)) * 1e-5])\n return grad_a","def gradient_descent(features, values, theta, alpha, num_iterations):\r\n\r\n m = len(values)\r\n cost_history = []\r\n\r\n for i in range (num_iterations):\r\n \r\n h = numpy.dot(features, theta)\r\n \r\n theta = theta - alpha / m * numpy.dot((h-values),features)\r\n \r\n cost = compute_cost(features, values, theta)\r\n \r\n cost_history.append(cost)\r\n\r\n return theta, pandas.Series(cost_history) # leave this line for the grader\r","def calculate_gradient(y, tx, w): \n return tx.T@(sigmoid(tx@w)-y)","def gradient_descent(features, values, theta, alpha, num_iterations):\n m = len(values)\n cost_history = []\n\n for i in range(num_iterations):\n predicted_values = np.dot(features, theta)\n delta = alpha / m * np.dot((predicted_values - values), features)\n theta = theta - delta\n cost = compute_cost(features, values, theta)\n cost_history.append(cost)\n return theta, pandas.Series(cost_history)","def lr_cost_function_grad(theta: np.ndarray, X: np.ndarray, y: np.ndarray, l: float) -> np.ndarray:\n # Initialize some useful values\n m = len(y) # number of training examples\n\n # You need to return the following variable correctly.\n grad = np.zeros(theta.shape)\n\n # ====================== YOUR CODE HERE ======================\n # Instructions: Compute the partial derivatives and set grad to the partial\n # derivatives of the cost w.r.t. each parameter in theta.\n\n # =============================================================\n return grad","def gradient_ascent(self, w, X, y, lr):\r\n # INSERT YOUR CODE HERE\r\n #raise Exception('Function not yet implemented!')\r\n # gradient = x_j*(y-σ(wTX))\r\n return np.dot(X.T, y-self.sigmoid(np.dot(X, w)))","def computeNumericalGradient(J, theta):\n numgrad = np.zeros(theta.size)\n perturb = np.zeros(theta.size)\n e = 1e-4\n for p in range(theta.size):\n # Set perturbation vector\n perturb[p] = e\n loss1 = J(theta - perturb)[0]\n loss2 = J(theta + perturb)[0]\n # Compute Numerical Gradient\n numgrad[p] = (loss2 - loss1) / (2*e)\n perturb[p] = 0\n return numgrad","def computeNumericalGradient(J, theta):\n numgrad = np.zeros_like(theta).reshape(-1)\n perturb = np.zeros_like(theta).reshape(-1)\n e = 1e-4\n for p in range(theta.size):\n # Set perturbation vector\n perturb[p] = e\n loss1, _ = J(theta - perturb.reshape(theta.shape))\n loss2, _ = J(theta + perturb.reshape(theta.shape))\n # Compute Numerical Gradient\n numgrad[p] = (loss2 - loss1) / (2 * e)\n perturb[p] = 0\n\n return numgrad.reshape(theta.shape)","def gradientdescent(cost_func, theta, args=(), delta_func = 0):\n step = 1\n old_cost = 0\n while True:\n theta_old = theta.copy()\n cost = cost_func(theta, *args)\n delta = delta_func(theta, *args)\n theta = theta - step * delta\n if cost > old_cost and old_cost != 0:\n step = step*0.7\n if np.allclose(theta_old, theta):\n break\n old_cost = cost\n return theta","def gradient(cls, x):\n y = Sigmoid.apply(x)\n return np.multiply(y, 1 - y)","def gradient_descent(features, values, theta, alpha, num_iterations):\r\n \r\n m = len(values)\r\n cost_history = []\r\n\r\n for i in range(num_iterations):\r\n # your code here\r\n cost = compute_cost(features, values, theta)/(2.0*m)\r\n cost_history.append([cost])\r\n \r\n error = features.dot(theta) - values\r\n error = np.reshape(error,(error.shape[0], 1))\r\n errorWeighted = features*error\r\n errorSum = (np.sum(errorWeighted,0))/(m*1.0)\r\n theta = theta - alpha*errorSum \r\n \r\n return theta, pandas.Series(cost_history)","def compute_gradient(y, tx, w):\n\tN = y.shape[0]\n\te = y - np.dot(tx, w)\n\n\tgradLw = -1/N * np.dot(tx.T, e)\n\treturn gradLw","def compute_square_loss_gradient(X, y, theta):\n #TODO\n P = (np.dot(X, theta)-y)\n m = X.shape[0]\n\n return (2/m)*np.dot(X.T, P)","def learning_by_gradient_descent(y, tx, w, gamma):\n\tgrad = calculate_gradient(y, tx, w)\n\n\tw = w - gamma * grad\n\treturn w","def calculate_gradient(y, tx, w):\n return tx.T.dot(sigmoid(tx.dot(w))-np.reshape(y,(len(y),1)))","def gradientDescent(self,X, y, theta): \n # number of instances\n m = len(y)\n J_history = np.zeros((self.NUM_ITERS,1))\n for i in range(self.NUM_ITERS):\n h = self.sigmoid(X@theta)\n grad = 1 / m * X.T @ (h - y)\n theta = theta - self.ALPHA * grad \n J_history[i] = self.costFunction(theta, X, y)\n \n \n return theta, J_history","def compute_square_loss_gradient(X, y, theta):\n #TODO\n (N,p) = np.shape(X)\n grad = -(1/np.float(N))*np.array([(y - X.dot(theta))*X[:,i] for i in range(p)])\n return np.sum(grad,axis=1)","def gradient_descent(X, y, theta, learning_rate, num_iters, lambda_reg):\n thetas = [theta]\n cost = np.zeros(num_iters)\n\n J = mean_cross_entropy_costs(X, y, lambda_reg)\n cost[0] = J(thetas[0])\n for i in range(1, num_iters):\n thetas.append(compute_new_theta(X, y, thetas[i - 1], learning_rate, lambda_reg))\n cost[i] = J(thetas[i])\n return cost, thetas","def gradient(img):\n G = np.zeros(img.shape)\n theta = np.zeros(img.shape)\n\n #####################################\n # START YOUR CODE HERE #\n #####################################\n Gx = partial_x(img)\n Gy = partial_y(img)\n G = np.sqrt(np.square(Gx) + np.square(Gy))\n theta = np.degrees(np.arctan2(Gy, Gx)) % 360\n ######################################\n # END OF YOUR CODE #\n ######################################\n\n return G, theta","def gradient_descent(initial_theta, X, y, niter, alpha, Lambda=0.0):\n theta_list = []\n cost_list = []\n\n theta = initial_theta\n for i in range(0, niter):\n theta -= alpha*gradient(theta, X, y, Lambda)\n theta_list.append(theta)\n cost_list.append(cost(theta, X, y, Lambda))\n\n return theta_list, cost_list","def linreg_stochastic_grad(X, y, alpha=.01):\n m = X.shape[0]\n n = X.shape[1]\n theta = np.zeros(n)\n for i in range(m):\n delta = alpha * (np.dot(theta.transpose(), X[i,:]) -y[i]) * X[i,:]\n theta = theta - delta\n return theta","def log_likelihood_gradient(y, tx, w):\n return tx.T.dot(sigmoid(tx.dot(w))-y)","def gradient(cls, x):\n return 1 - TanH.apply(x) ** 2","def learning_by_gradient_descent(y, tx, w, gamma):\n loss = calculate_loss(y,tx,w)\n grad = calculate_gradient(y,tx,w)\n w_new = w - gamma*grad\n #grad is for debugging purpose\n return loss, w_new,grad","def simple_gradient(x, y, theta):\n if x.shape[0] * y.shape[0] * theta.shape[0] == 0:\n return None\n if x.shape[0] != y.shape[0] or theta.shape[0] != 2:\n return None\n\n x = add_intercept(x)\n\n result = [\n forumla(x, y, theta, 0),\n forumla(x, y, theta, 1)\n ]\n return result","def calculate_gradient(y, tx, w):\n\n\tret = tx.T.dot(sigmoid(np.dot(tx, w)) - y)\n\treturn ret","def learning_by_gradient_descent(y, tx, w, gamma):\n loss = calculate_loss(y,tx,w)\n grad = calculate_gradient(y,tx,w)\n w = w-gamma*grad\n return w, loss","def train_fn_grad(self, theta_value, theta):\n theta.set_value(theta_value, borrow=True)\n\n train_grads = [self.batch_grad(i, self.n_train)\n for i in xrange(self.n_train_batches)]\n train_batch_sizes = [self.get_batch_size(i, self.n_train)\n for i in xrange(self.n_train_batches)]\n\n return np.average(train_grads, weights=train_batch_sizes,\n axis=0)","def gradient(self, x):\n pass","def gradient(img):\n G = np.zeros(img.shape)\n theta = np.zeros(img.shape)\n\n ### YOUR CODE HERE\n Gx = partial_x(img)\n Gy = partial_y(img)\n G = np.sqrt(np.power(Gx,2)+np.power(Gy,2))\n theta = (np.arctan2(Gy, Gx) * 180 / np.pi) % 360\n ### END YOUR CODE\n\n return G, theta","def approx_grad_reg(theta, X, y, _lambda):\n grad_a = np.array([(cost_reg(theta + e, X, y, _lambda) - cost_reg(theta - e, X, y, _lambda)) / (2 * 1e-5)\n for e in np.identity(len(theta)) * 1e-5])\n return grad_a","def get_gradient(phi, pred, t, dot_product, weight, reg= 1, regression= \"logistic\"):\n if regression == \"logistic\":\n gradient = np.matmul(phi.T, pred - t)\n elif regression == \"probit\":\n R = np.eye(pred.shape[0])\n for i in range(pred.shape[0]):\n y_n = pred[i,0]\n dotp = dot_product[i, 0]\n pdf = norm.pdf(dotp)\n R[i,i] = pdf/(y_n*(1-y_n) + TOLERANCE)\n gradient = np.matmul(np.matmul(phi.T, R), pred-t)\n elif regression == \"multiclass\":\n gradient = np.matmul(phi.T, pred - t)\n\n # Add regularization\n gradient += weight/ reg\n return gradient","def gradient(self, x):\n u = np.asarray([x[0]])\n C = self.C_func(u)\n dC = self.dC_func(u, order=1)\n P = self.P\n numerator = np.sum((C - P) * dC, axis=0)\n denominator = np.sum(np.sum((C - P) ** 2, axis=0) ** (1 / 2))\n if np.abs(denominator) > 0:\n gradient = numerator/denominator\n else:\n gradient = np.asarray(0)[np.newaxis]\n return gradient","def apply_gradient(params: torch.Tensor, grads: torch.Tensor, lr: float) -> torch.Tensor:\n params_prime = params + lr * grads\n return params_prime","def run_steep_gradient_descent(data_x, data_y, len_data, alpha, theta):\n n = len_data\n # WE NEED TO transpose data_x into (p+1) *n ,theta is 1*(p+1)\n prod = np.dot(theta, data_x.transpose())\n\n prod -= data_y\n print(\"pro: data_x\", prod.shape, data_x.shape)\n #prod represent the loss of the hypothesis and true label\n sum_grad = np.dot(prod, data_x)\n print(\"总梯度的值：\",sum_grad.shape)\n\n # batch-gradient descent\n theta = theta -(alpha / n) * sum_grad\n return theta","def GDS(x, theta, y, lamb, alpha):\n\tm = y.shape[0]\n\tdif = (1/m) * x.T @ (sigmoid(x,theta) - y) #classical differentiation term, output is (n,1)\n\tdif_reg = (lamb/m) * theta #regularization term\n\treturn (dif + dif_reg) * alpha","def gradientDescent(X, y, theta, alpha, num_iters):\n\n # Initialize some useful values\n J_history = []\n m = y.size # number of training examples\n\n for i in range(num_iters):\n # ====================== YOUR CODE HERE ======================\n # Instructions: Perform a single gradient step on the parameter vector\n # theta.\n #\n # Hint: While debugging, it can be useful to print out the values\n # of the cost function (computeCost) and gradient here.\n #\n # Calculate the gradient step according to the equation for theta1:\n g_step1 = (alpha / m * np.sum( (np.dot(X,theta) - y) * X[:,1]) )\n # Gradient step for theta knot:\n g_step0 = (alpha / m * np.sum( (np.dot(X,theta) - y) ) )\n \n #update theta\n theta[0] = (theta[0] - g_step0)\n theta[1] = (theta[1] - g_step1)\n \n #print([theta , g_step1, g_step0])\n\n # ============================================================\n\n # Save the cost J in every iteration\n J_history.append(computeCost(X, y, theta))\n\n return theta, J_history","def gradient(w, x, t):\n return 2 * np.dot(x.T, (nn(x, w) - t))","def costFunction(theta,X,y):\n m = X.shape[0]\n J = 0\n h = sigmoid (np.dot(X,theta))\n \n J = (1/m)* ((-np.dot(y.T,(np.log(h)))) - np.dot((1 - y).T,(np.log(1-h))))\n \n #grad = (1/m) * np.dot(X.T,(h-y))\n grad = (1/m) * np.dot((h.T - y), X).T\n \n return J, grad","def stochasticGradientDescent(x,y,theta,alpha):\n m,n = np.shape(x)\n convergence = 0.000000001\n lastCost = 0\n cost = -1 \n recurseCount = 0\n while abs(lastCost - cost) > convergence: # rcurse until converge\n lastCost = cost\n hypothesis = np.dot(x,theta) \n for i in range(m):\n # alpha = 4.0 / (1.0 + i) + 0.01 \n loss = hypothesis[i] - y[i]\n # gradient = np.dot(x[i],loss)\n gradient = x[i,:].transpose() * loss \n theta = theta - alpha * gradient\n cost = np.sum((hypothesis-y)**2)/(2*m)\n recurseCount += 1\n return recurseCount,theta","def gradient(self):\n result = np.zeros(len(self.variables))\n result[self.bivariateGradInd] = (self.shape-1)/self.variable - self.rate\n return result","def gradient(self):\n functional = self\n\n class KLGradient(Operator):\n\n \"\"\"The gradient operator of this functional.\"\"\"\n\n def __init__(self):\n \"\"\"Initialize a new instance.\"\"\"\n super().__init__(functional.domain, functional.domain,\n linear=False)\n\n def _call(self, x):\n \"\"\"Apply the gradient operator to the given point.\n The gradient is not defined in points where one or more\n components are non-positive.\n \"\"\"\n if functional.prior is None:\n return (-1.0) / x + 1\n else:\n return (-functional.prior) / x + 1\n\n return KLGradient()","def batchGradientDescent(x,y,theta,alpha):\n m,n = np.shape(x)\n xTran = x.transpose()\n convergence = 0.000000001\n lastCost = 0\n cost = -1 \n recurseCount = 0\n while abs(lastCost - cost) > convergence: # rcurse until converge\n lastCost = cost\n hypothesis = np.dot(x,theta)\n loss = hypothesis - y\n cost = np.sum(loss**2)/(2*m)\n gradient = np.dot(xTran,loss)/m\n theta = theta - alpha*gradient\n recurseCount += 1\n return recurseCount,theta","def learning_by_penalized_gradient(y, tx, w, gamma, lambda_):\n\tgrad, H = penalized_logistic_regression(y, tx, w, lambda_)\n\n\thgrad = np.linalg.inv(H).dot(grad)\n\n\tw = w - gamma * hgrad\n\n\treturn w","def grad(self, w):\n l1_grad = self.r * np.sign(w)\n l2_grad = np.asarray(1 - self.r) * w \n\n gradient_penalty = self.alpha * (l1_grad + l2_grad)\n\n # Insert 0 for bias term.\n return np.insert(gradient_penalty, 0, 0, axis=0)","def compute_gradient(self):\n A = np.dot(self.X, self.w)\n m = self.t.shape[0]\n C = -1 * self.t * (1 / (1 + np.exp(A * self.t)))\n return (1 / m) * np.dot(self.X.T, C)","def gradient(self, x):\n g = self._grad(\n time_series=self.observed_data,\n a=x[0],\n b=x[1],\n c=x[2],\n sigma=self.sigma\n )\n return g","def reg_autograd(alpha,X,theta,y):\n g=grad(func)\n for i in range(0,20):\n theta=theta- alpha * g(theta)\n print(\"cost\", func(theta))\n print(\"final theta\",theta)","def grad(self, x):\n norm_beta = np.linalg.norm(x)\n if norm_beta > utils.TOLERANCE:\n return (self.l / norm_beta) * x\n else:\n D = x.shape[0]\n u = (self.rng(D, 1) * 2.0) - 1.0 # [-1, 1]^D\n norm_u = np.linalg.norm(u)\n a = self.rng() # [0, 1]\n\n return (self.l * (a / norm_u)) * u","def gradcovfunc(self, theta, d):\n sigmaf, l = theta[:2] \n xxl = np.sum((d/l)**2, axis=1)\n dk_dsigmaf = 2 * sigmaf * np.exp(-xxl/2.)\n dk_dl = sigmaf**2/l * xxl * np.exp(-xxl/2.)\n grad = np.array([dk_dsigmaf, dk_dl])\n return grad","def log_prior_grad(self, inputs):","def gradient(series, **options):\n x = series.index\n y = series.values\n\n a = np.gradient(y, x, **options)\n return series.__class__(a, series.index)","def derivative(self, theta):\n diag_gamma = np.dot(theta.T, self.X.T)\n logistic_term = self.logistic_fn(diag_gamma)\n diag_gamma = logistic_term * (1.0 - logistic_term)\n gamma = np.diag(diag_gamma)\n\n # v computation\n diags_v = 1.0 - 2*self.logistic_fn(np.dot(theta.T, self.X.T))\n diags_v = diags_v.reshape((-1, 1))\n diags_v = diags_v*self.X\n assert diags_v.shape == self.X.shape #N*d shape\n\n XtGamma = np.dot(self.X.T, gamma) # d*N shape\n\n # TODO: Verifier car pas sur de mon coup ... et surtout plus long...\n # id = np.eye(self.n_examples).reshape((self.n_examples, self.n_examples, 1))\n # diags_v = diags_v.reshape((self.n_examples, 1, self.dim))\n # v = id*diags_v # n*n*d tensor\n # left = np.tensordot(XtGamma, v, axes=(1, 0)) # shape d*N*d\n # assert left.shape == (self.dim, self.n_examples, self.dim)\n # dg = np.tensordot(left, self.X, axes=(1, 0))\n # dg = np.swapaxes(dg, axis1=-2, axis2=-1)\n\n dg = np.zeros((self.dim, self.dim, self.dim))\n for idx, v_i_diag in enumerate(diags_v.T):\n v_i = np.diag(v_i_diag)\n dg_di = np.dot(np.dot(XtGamma, v_i), self.X)\n dg[:, :, idx] = dg_di\n return dg","def compute_logistic_gradient(y, tx, w):\n return tx.T.dot(sigmoid(tx.dot(w)) - y) / len(y)","def run_gradient_descent(data,theta,alpha,num_iters):\n population = data[:,0]\n prices = data[:,1]\n x = ones(shape=(len(population),2)) #add ones for theta0 \n x[:,1] = population\n x = transpose(x)\n error_history = zeros(shape=(num_iters,1))\n \n for i in range(num_iters):\n predictions = theta.dot(x)\n errors_x1 = (predictions - prices) * x[0,:]\n errors_x2 = (predictions - prices) * x[1,:]\n theta[0][0] = theta[0][0] - alpha*(1.0/len(population))*errors_x1.sum()\n theta[0][1] = theta[0][1] - alpha*(1.0/len(population))*errors_x2.sum()\n error_history[i,0] = calculate_cost(theta,data)\n \n return theta, error_history","def gradient(self, x):\n return 0.0","def costFunction(self,theta, X, y): \n m = len(y)\n h = self.sigmoid(X@theta)\n J = 1 / m * (- y.T @ self.log(h) - (1-y).T @ self.log(1-h)) \n # grad = 1/ m * X.T @ (h - y)\n return J","def gradient(poly):\n return differential(\n poly, chaospy.poly.collection.basis(1, 1, poly.dim, sort=\"GR\"))","def curves_gradient(theta, m_, LAMBDA=0):\n # X0: column of ones\n theta0_gradient = (1 / m_) * sum((curves_hypothesis(theta, m_) - y[:m_].ravel()) * np.ones(m_))\n # X1: data given as X\n theta1_gradient = ((1 / m_) * sum((curves_hypothesis(theta, m_) - y[:m_].ravel()) * X[:m_].ravel())) \\\n + (LAMBDA / m_) * theta[1]\n return np.array([theta0_gradient, theta1_gradient])","def gradient(self, x_in):\r\n\r\n return 1 - torch.pow(self.forward(x_in), 2)","def compute_gradient(self): # TODO: try to change to square loss since it's hessian is easier to obtain\n A = np.dot(self.X, self.w)\n m = self.t.shape[0]\n C = -1 * self.t * (1 / (1 + np.exp(A * self.t)))\n return (1 / m) * np.dot(self.X.T, C)","def vb_grad_natgrad(self):\r\n\r\n # Gradient for eta\r\n dL_dmmT_S = -0.5*self.Lambda/self.data_prop + 0.5*self.q_u_prec\r\n Kmmipsi1V,_ = dpotrs(self.Lm,self.psi1V,lower=1)\r\n dL_dm = (Kmmipsi1V - np.dot(self.Lambda,self.q_u_mean))/self.data_prop\r\n\r\n # Gradients for theta\r\n S = self.q_u_cov\r\n Si = self.q_u_prec\r\n m = self.q_u_mean\r\n dL_dSi = -mdot(S,dL_dmmT_S, S)\r\n\r\n dL_dmhSi = -2*dL_dSi\r\n dL_dSim = np.dot(dL_dSi,m) + np.dot(Si, dL_dm)\r\n\r\n return np.hstack((dL_dm.flatten(),dL_dmmT_S.flatten())) , np.hstack((dL_dSim.flatten(), dL_dmhSi.flatten()))","def grad(self, w):\n gradient_penalty = np.asarray(self.alpha) * w\n # Insert 0 for bias term.\n return np.insert(gradient_penalty, 0, 0, axis=0)","def grad(self, w):\n gradient_penalty = np.asarray(self.alpha) * w\n # Insert 0 for bias term.\n return np.insert(gradient_penalty, 0, 0, axis=0)","def gradient(self):\n functional = self\n\n class KLCCGradient(Operator):\n\n \"\"\"The gradient operator of this functional.\"\"\"\n\n def __init__(self):\n \"\"\"Initialize a new instance.\"\"\"\n super().__init__(functional.domain, functional.domain,\n linear=False)\n\n def _call(self, x):\n \"\"\"Apply the gradient operator to the given point.\n\n The gradient is not defined in points where one or more\n components are larger than or equal to one.\n \"\"\"\n if functional.prior is None:\n return 1.0 / (1 - x)\n else:\n return functional.prior / (1 - x)\n\n return KLCCGradient()","def learning_by_penalized_gradient(y, tx, w, gamma, lambda_):\n\n #on test avec Newton\n\n loss,gradient,_ = penalized_logistic_regression(y,tx,w,lambda_)\n\n w = w - gamma*gradient\n return loss, w,gradient","def gradient_ascent(f, df, theta_init, step_size, max_iter):\n\n fs = []\n xs = []\n thetas = theta_init\n for i in range(max_iter): #for each data example\n fs.append(f(thetas))\n\n temp = step_size*df(thetas)\n thetas = step_size*df(thetas) #modify that feature by using the derivative of log likelihood\n xs.append(thetas.flatten())\n if i % 10 == 0:\n print(i, thetas)\n\n return thetas, fs, xs","def calculate_maxL_gradient(y, tx, w):\n sig=sigmoid(tx.dot(w))\n grad=tx.T.dot(sig-y)\n #divides with number of samples so that learning rate is not dependant on number of samples\n grad=grad/len(y) \n return grad","def _log_prior_gradients(self):\n if self.priors.size == 0:\n return 0.\n x = self.param_array\n ret = np.zeros(x.size)\n #compute derivate of prior density\n [np.put(ret, ind, p.lnpdf_grad(x[ind])) for p, ind in self.priors.items()]\n #add in jacobian derivatives if transformed\n priored_indexes = np.hstack([i for p, i in self.priors.items()])\n for c,j in self.constraints.items():\n if not isinstance(c, Transformation):continue\n for jj in j:\n if jj in priored_indexes:\n ret[jj] += c.log_jacobian_grad(x[jj])\n return ret","def grad(self, w):\n gradient_penalty = np.asarray(self.alpha) * w\n\n # Insert 0 for bias term.\n return np.insert(gradient_penalty, 0, 0, axis=0)","def grad(theta, X, y, lambda_):\n # ... dopolnite (naloga 1, naloga 2)\n\n l = []\n for i, e in enumerate(theta):\n l.append(1 / len(y) * sum([(h(x, theta) - yi) * x[i] for x, yi in zip(X, y)]) + 2 * lambda_ * e)\n\n return np.array(l)","def lr_cost_function(theta, X, y, learning_rate):\n m = len(y) # number of training examples\n # You need to return the following variables correctly \n J = 0\n grad = np.zeros(theta.shape)\n h = sigmoid(np.dot(X, theta))\n\n #Compute cost:\n first = (1/m)*np.sum(np.dot(-y.T, np.log(h)) - np.dot((1-y).T, np.log(1-h)), axis=0)\n second = (learning_rate/(2*m))*np.sum(theta[1:]**2)\n J = first + second\n \n #Compute gradient\n grad = np.dot((h-y).T, X)/m\n grad = grad.T\n grad[1:] += (learning_rate/m)*theta[1:]\n return float(J), grad","def gradient(self, x, i=None):\n self.set_parameters(x=x)\n gradient = self.gradient_loss()\n if self.add_bias:\n gradient[1:] += self.alpha * self.gradient_regularizer()\n else:\n gradient += self.alpha * self.gradient_regularizer()\n if i is not None:\n return gradient.ravel()[i]\n return gradient.ravel()","def compute_gradient_lasso(y, tx, w, lambda_):\n e = y - tx.dot(w)\n subgrad = lambda_ * np.sign(w)\n\n return -tx.T.dot(e)/len(e) + subgrad","def compute_gradient(self, X, y, weights):\n sigmoid = self.sigmoid(np.dot(X, weights))\n return np.dot(X.T, y - sigmoid)","def batch_grad_descent(X, y, alpha=0.1, num_iter=1000, check_gradient=False):\n num_instances, num_features = X.shape[0], X.shape[1]\n theta_hist = np.zeros((num_iter+1, num_features)) #Initialize theta_hist\n loss_hist = np.zeros(num_iter+1) #initialize loss_hist\n theta = np.ones(num_features) #initialize theta\n\n count = 0\n while count < num_iter+1:\n if check_gradient:\n assert grad_checker(X,y,theta)\n\n grad = compute_square_loss_gradient(X,y,theta)\n theta -= alpha*grad\n theta_hist[count] = theta\n loss_hist[count] = compute_square_loss(X,y,theta)\n count += 1\n \n return theta_hist, loss_hist","def check_gradient(self, x, y):\n x = x.transpose()\n y = y.transpose()\n layers_copy = deepcopy(self.layers)\n epsilon = 10 ** -4\n a, layer = self.forward_propagation(x)\n delta = self.calculate_delta(a, y, layer)\n self.backpropagation(delta=delta, theta=layer.theta)\n previous_layer_output = x\n for layer in self.layers:\n theta_copy = deepcopy(layer.theta)\n real_theta_size = theta_copy.shape\n delta = layer.delta\n dc_dtheta = np.outer(previous_layer_output, delta).transpose()\n previous_layer_output = layer.a\n R, C = theta_copy.shape\n for i in range(R):\n for j in range(C):\n theta_plus = deepcopy(theta_copy)\n theta_plus[i, j] += epsilon\n layer.theta = theta_plus\n a_plus, l_plus = self.forward_propagation(x)\n err_plus = self.calculate_loss(a_plus, y)\n theta_minus = deepcopy(theta_copy)\n theta_minus[i, j] -= epsilon\n layer.theta = theta_minus\n a_minus, l_minus = self.forward_propagation(x)\n err_minus = self.calculate_loss(a_minus, y)\n limit = (err_plus - err_minus)/(2*epsilon)\n grad_diff = abs(dc_dtheta[i,j] - limit)\n assert grad_diff < 10 ** -6, f\"Diff {grad_diff} is too big.\"\n layer.theta = theta_copy","def _compute_func_grad(self, w):\n W = w.reshape((self.X.shape[1], self.Y.shape[1]))\n self.nll_, self.grad_ = calculate_gradient(self.X, self.Y, W, self.prior, self.weighted,0)","def gradient(data_x, data_y, parameters):\n return data_x.T @ (data_x @ parameters - data_y) / data_x.shape[0]"],"string":"[\n \"def gradient(self, theta):\\n return (1 / (self.sigma * np.sqrt(2 * np.pi))) * (\\n -theta / (self.sigma ** 2) * np.exp(-(theta ** 2) / (2 * self.sigma ** 2))\\n )\",\n \"def gradient(self, theta):\\n a = -(6 * self.scale ** 2)\\n b = 3 * self.scale ** 2 + np.exp(2 * theta)\\n b *= np.log(3 * self.scale ** 2 * np.exp(-2 * theta) + 1)\\n return a / b\",\n \"def gradient(theta, X, y, learning_rate):\\n m = len(y)\\n\\n theta = theta.reshape((-1,1))\\n grad = np.zeros(theta.shape)\\n h = sigmoid(np.dot(X, theta)) \\n \\n grad = np.dot((h-y).T, X)/m\\n grad = grad.T\\n grad[1:] += (learning_rate/m)*theta[1:]\\n return grad\",\n \"def gradient_function(theta, X, y):\\n\\n grad = None\\n #######################################################################\\n # TODO: #\\n # Compute the gradient for a particular choice of theta. #\\n # Compute the partial derivatives and set grad to the partial #\\n # derivatives of the cost w.r.t. each parameter in theta #\\n # #\\n #######################################################################\\n \\n theta = theta[:, np.newaxis]\\n \\n thetatrans = theta.T\\n Xtrans = X.T\\n \\n MulThetaX = np.dot(thetatrans, Xtrans)\\n \\n h = sigmoid(MulThetaX)\\n \\n grad = (y - h) * Xtrans\\n\\n #######################################################################\\n # END OF YOUR CODE #\\n #######################################################################\\n return grad\",\n \"def compute_gradient(theta, X, y):\\n m = X.shape[0]\\n grad_theta = np.dot(X.transpose(), (np.dot(X, theta) - y)) / m\\n #print theta, grad_theta, objective_function(theta, X, y)\\n return grad_theta\",\n \"def gradient(self, theta):\\n return np.zeros([theta.shape[0]])\",\n \"def gradientFunction(theta, X, y):\\n y = y[:, 0]\\n m = y.shape # number of training samples\\n grad = X.T.dot(sigmoid(theta.dot(X.T))-1*y)\\n grad /= m\\n return grad\",\n \"def gradient(theta, x, y):\\n m = len(y)\\n n = len(theta)\\n z = theta.dot(x.T)\\n grad = np.zeros(n)\\n for i in xrange(m):\\n grad += (g(z[i]) - y[i]) * x[i]\\n return 1. / m * grad\",\n \"def gradient(theta, X, y, Lambda=0.0):\\n m = X.shape[0] # number of samples\\n\\n h = hypothesis(theta, X)\\n\\n if Lambda:\\n g_0 = (1/m)*(X.T@(h - y))[0]\\n g_1 = (1/m)*(X.T@(h - y))[1:] + (Lambda/m)*theta[1:] # skip theta-0\\n \\n return np.append(g_0, g_1)\\n else:\\n return (1/m)*(X.T@(h - y))\",\n \"def gradient(x, y, theta):\\n if x.ndim == 1:\\n x = x[:, np.newaxis]\\n if y.ndim == 2 and y.shape[1] == 1:\\n y = y.flatten()\\n if theta.ndim == 2 and theta.shape[1] == 1:\\n theta = theta.flatten()\\n\\n if (x.size == 0 or y.size == 0 or theta.size == 0\\n or x.ndim != 2 or y.ndim != 1 or theta.ndim != 1\\n or x.shape[0] != y.shape[0] or x.shape[1] + 1 != theta.shape[0]):\\n return None\\n\\n x_padded = np.c_[np.ones(x.shape[0]), x]\\n\\n return x_padded.T.dot(x_padded.dot(theta) - y) / y.shape[0]\",\n \"def calc_grad(X, Y, theta):\\n m, n = X.shape\\n\\n margins = Y * X.dot(theta)\\n probs = 1. / (1 + np.exp(margins))\\n grad = -(1./m) * (X.T.dot(probs * Y))\\n\\n return grad\",\n \"def gradient_descent(X, y, theta, alpha, total_iterations, hypothesis):\\n len_theta = len(theta)\\n m = len(y)\\n one_over_m = (1.0 / float(m))\\n\\n for _ in range(0, total_iterations):\\n temp_theta = numpy.zeros(len_theta)\\n\\n X_by_theta_minus_y = numpy.subtract(hypothesis(numpy.matrix(theta), X), y)\\n\\n for j in range(0, len_theta):\\n jth_column_of_X = X[:,j]\\n derivative_j = one_over_m * numpy.multiply(X_by_theta_minus_y, jth_column_of_X).sum()\\n temp_theta[j] = theta[j] - alpha*derivative_j\\n\\n theta = temp_theta\\n\\n return numpy.matrix(theta)\",\n \"def gradient_descent(self, X, theta, Y, m):\\n\\n Z = X.dot(theta)\\n H = Predict.g(Z)\\n gradient = np.dot(X.T, (H - Y)) / m\\n return self.alpha * gradient\",\n \"def gradientFunctionReg(theta, X, y, Lambda): \\n y = np.squeeze(y)\\n m = y.shape # number of training samples\\n grad = X.T.dot(sigmoid(theta.dot(X.T))-1*y)\\n grad[1:] = grad[1:] + Lambda*theta[1:]\\n grad /= m\\n\\n return grad\",\n \"def gradientFunctionReg(theta, X, y, Lambda):\\n m = len(y) # number of training examples\\n grad = np.zeros(theta.shape[0])\\n theta = np.transpose(theta)\\n sum_1 = 0\\n X = X.values\\n y = y.values\\n #calcuate the theta_0 \\n# ====================== YOUR CODE HERE ======================\\n# Instructions: Compute the gradient of a particular choice of theta.\\n# Compute the partial derivatives and set grad to the partial\\n# derivatives of the cost w.r.t. each parameter in theta\\n for i in range(theta.shape[0]):\\n if i == 0:\\n for j in range(m):\\n sum_1 += (sigmoid(np.dot(X[j],theta)) - y[j]) * X[j,i]\\n else:\\n for j in range(m):\\n sum_1 += (sigmoid(np.dot(X[j],theta)) - y[j]) * X[j,i] + Lambda*theta[i]\\n grad[i] = sum_1/m\\n sum_1 = 0\\n\\n# =============================================================\\n\\n return grad\",\n \"def logit_cost_grad(self, theta, X, y):\\n\\n grad = np.zeros(len(theta))\\n\\n ### YOUR CODE HERE\\n sig = utils.sigmoid(theta)\\n # sig = np.subtract(sig, y)\\n sig = sig - y\\n grad = np.dot(X.T, sig) + 2 * self.params['lamb'] * self.regularizer[1](self.weights)\\n ### END YOUR CODE\\n\\n return grad\",\n \"def compute_loss_gradient(theta_vector, *args):\\n\\n psi = args[0] # feed psi as a parameter\\n circ_depth = args[1]\\n num_qbits = args[2]\\n theta = np.reshape(theta_vector, (circ_depth, num_qbits)) # reshapes the flat theta vector\\n fidelity = get_fidelity(theta, psi)\\n\\n # the derivative of the loss wrt fidelity\\n dl_df = -0.5 * fidelity ** (-0.5)\\n\\n df_dtheta = [] # a list of partial derivatives of the fidelity wrt the theta parameters\\n\\n for index in range(len(theta_vector)):\\n layer_index = index // num_qbits\\n qbit_index = index % num_qbits\\n\\n theta_plus = np.copy(theta)\\n theta_plus[layer_index][qbit_index] += np.pi / 2 # added pi/2 to the ith theta parameter\\n\\n theta_minus = np.copy(theta)\\n theta_minus[layer_index][qbit_index] -= np.pi / 2 # subtracted pi/2 to the ith theta parameter\\n\\n df_dtheta_i = 0.5 * (get_fidelity(theta_plus, psi) - get_fidelity(theta_minus, psi)) # ith derivative\\n df_dtheta.append(df_dtheta_i)\\n\\n df_dtheta = np.array(df_dtheta)\\n dl_dtheta = dl_df * df_dtheta # chain rule to get partial derivative of loss wrt theta parameters\\n\\n return dl_dtheta\",\n \"def run_gradient_descent_iteration(X, Y, theta, alpha, lambda_factor, temp_parameter):\\n delta = sparse.coo_matrix(theta.shape).toarray()\\n\\n h = compute_probabilities(X, theta, temp_parameter)\\n\\n for j in range(delta.shape[0]):\\n y = Y\\n y = np.where(y != j, 0, 1)\\n p = y - h[j]\\n\\n x = X.T * p\\n x = x.T\\n x = x.sum(axis=0)\\n\\n grad = -x / (temp_parameter * X.shape[0]) + lambda_factor * theta[j]\\n\\n delta[j] += grad\\n\\n theta -= alpha * delta\\n\\n return theta\",\n \"def gradient_descent(features, values, theta, alpha, num_iterations):\\n \\n # number of points\\n npoints = len(values)\\n \\n # intialize cost history\\n cost_history = []\\n \\n # num_interations iterations\\n for iiter in range(num_iterations):\\n \\n # compute and store cost\\n cost = compute_cost(features, values, theta)\\n cost_history.append(cost)\\n \\n # update values of theta\\n values_predicted = np.dot(features, theta)\\n theta = theta + (alpha/npoints)*(np.dot(values - values_predicted,features))\\n \\n return theta, pandas.Series(cost_history)\",\n \"def gradient_descent(x, y, theta=[[0], [0]]):\\n m = y.size\\n j_history = []\\n for i in range(ITERATIONS):\\n h = x.dot(theta)\\n theta = theta - (ALPHA / m) * (x.T.dot(h - y))\\n j_history.append(compute_cost(x, y, theta))\\n return theta, j_history\",\n \"def approx_grad(theta, X, y):\\n grad_a = np.array([(cost(theta + e, X, y) - cost(theta - e, X, y)) / (2 * 1e-5)\\n for e in np.identity(len(theta)) * 1e-5])\\n return grad_a\",\n \"def gradient_descent(features, values, theta, alpha, num_iterations):\\r\\n\\r\\n m = len(values)\\r\\n cost_history = []\\r\\n\\r\\n for i in range (num_iterations):\\r\\n \\r\\n h = numpy.dot(features, theta)\\r\\n \\r\\n theta = theta - alpha / m * numpy.dot((h-values),features)\\r\\n \\r\\n cost = compute_cost(features, values, theta)\\r\\n \\r\\n cost_history.append(cost)\\r\\n\\r\\n return theta, pandas.Series(cost_history) # leave this line for the grader\\r\",\n \"def calculate_gradient(y, tx, w): \\n return tx.T@(sigmoid(tx@w)-y)\",\n \"def gradient_descent(features, values, theta, alpha, num_iterations):\\n m = len(values)\\n cost_history = []\\n\\n for i in range(num_iterations):\\n predicted_values = np.dot(features, theta)\\n delta = alpha / m * np.dot((predicted_values - values), features)\\n theta = theta - delta\\n cost = compute_cost(features, values, theta)\\n cost_history.append(cost)\\n return theta, pandas.Series(cost_history)\",\n \"def lr_cost_function_grad(theta: np.ndarray, X: np.ndarray, y: np.ndarray, l: float) -> np.ndarray:\\n # Initialize some useful values\\n m = len(y) # number of training examples\\n\\n # You need to return the following variable correctly.\\n grad = np.zeros(theta.shape)\\n\\n # ====================== YOUR CODE HERE ======================\\n # Instructions: Compute the partial derivatives and set grad to the partial\\n # derivatives of the cost w.r.t. each parameter in theta.\\n\\n # =============================================================\\n return grad\",\n \"def gradient_ascent(self, w, X, y, lr):\\r\\n # INSERT YOUR CODE HERE\\r\\n #raise Exception('Function not yet implemented!')\\r\\n # gradient = x_j*(y-σ(wTX))\\r\\n return np.dot(X.T, y-self.sigmoid(np.dot(X, w)))\",\n \"def computeNumericalGradient(J, theta):\\n numgrad = np.zeros(theta.size)\\n perturb = np.zeros(theta.size)\\n e = 1e-4\\n for p in range(theta.size):\\n # Set perturbation vector\\n perturb[p] = e\\n loss1 = J(theta - perturb)[0]\\n loss2 = J(theta + perturb)[0]\\n # Compute Numerical Gradient\\n numgrad[p] = (loss2 - loss1) / (2*e)\\n perturb[p] = 0\\n return numgrad\",\n \"def computeNumericalGradient(J, theta):\\n numgrad = np.zeros_like(theta).reshape(-1)\\n perturb = np.zeros_like(theta).reshape(-1)\\n e = 1e-4\\n for p in range(theta.size):\\n # Set perturbation vector\\n perturb[p] = e\\n loss1, _ = J(theta - perturb.reshape(theta.shape))\\n loss2, _ = J(theta + perturb.reshape(theta.shape))\\n # Compute Numerical Gradient\\n numgrad[p] = (loss2 - loss1) / (2 * e)\\n perturb[p] = 0\\n\\n return numgrad.reshape(theta.shape)\",\n \"def gradientdescent(cost_func, theta, args=(), delta_func = 0):\\n step = 1\\n old_cost = 0\\n while True:\\n theta_old = theta.copy()\\n cost = cost_func(theta, *args)\\n delta = delta_func(theta, *args)\\n theta = theta - step * delta\\n if cost > old_cost and old_cost != 0:\\n step = step*0.7\\n if np.allclose(theta_old, theta):\\n break\\n old_cost = cost\\n return theta\",\n \"def gradient(cls, x):\\n y = Sigmoid.apply(x)\\n return np.multiply(y, 1 - y)\",\n \"def gradient_descent(features, values, theta, alpha, num_iterations):\\r\\n \\r\\n m = len(values)\\r\\n cost_history = []\\r\\n\\r\\n for i in range(num_iterations):\\r\\n # your code here\\r\\n cost = compute_cost(features, values, theta)/(2.0*m)\\r\\n cost_history.append([cost])\\r\\n \\r\\n error = features.dot(theta) - values\\r\\n error = np.reshape(error,(error.shape[0], 1))\\r\\n errorWeighted = features*error\\r\\n errorSum = (np.sum(errorWeighted,0))/(m*1.0)\\r\\n theta = theta - alpha*errorSum \\r\\n \\r\\n return theta, pandas.Series(cost_history)\",\n \"def compute_gradient(y, tx, w):\\n\\tN = y.shape[0]\\n\\te = y - np.dot(tx, w)\\n\\n\\tgradLw = -1/N * np.dot(tx.T, e)\\n\\treturn gradLw\",\n \"def compute_square_loss_gradient(X, y, theta):\\n #TODO\\n P = (np.dot(X, theta)-y)\\n m = X.shape[0]\\n\\n return (2/m)*np.dot(X.T, P)\",\n \"def learning_by_gradient_descent(y, tx, w, gamma):\\n\\tgrad = calculate_gradient(y, tx, w)\\n\\n\\tw = w - gamma * grad\\n\\treturn w\",\n \"def calculate_gradient(y, tx, w):\\n return tx.T.dot(sigmoid(tx.dot(w))-np.reshape(y,(len(y),1)))\",\n \"def gradientDescent(self,X, y, theta): \\n # number of instances\\n m = len(y)\\n J_history = np.zeros((self.NUM_ITERS,1))\\n for i in range(self.NUM_ITERS):\\n h = self.sigmoid(X@theta)\\n grad = 1 / m * X.T @ (h - y)\\n theta = theta - self.ALPHA * grad \\n J_history[i] = self.costFunction(theta, X, y)\\n \\n \\n return theta, J_history\",\n \"def compute_square_loss_gradient(X, y, theta):\\n #TODO\\n (N,p) = np.shape(X)\\n grad = -(1/np.float(N))*np.array([(y - X.dot(theta))*X[:,i] for i in range(p)])\\n return np.sum(grad,axis=1)\",\n \"def gradient_descent(X, y, theta, learning_rate, num_iters, lambda_reg):\\n thetas = [theta]\\n cost = np.zeros(num_iters)\\n\\n J = mean_cross_entropy_costs(X, y, lambda_reg)\\n cost[0] = J(thetas[0])\\n for i in range(1, num_iters):\\n thetas.append(compute_new_theta(X, y, thetas[i - 1], learning_rate, lambda_reg))\\n cost[i] = J(thetas[i])\\n return cost, thetas\",\n \"def gradient(img):\\n G = np.zeros(img.shape)\\n theta = np.zeros(img.shape)\\n\\n #####################################\\n # START YOUR CODE HERE #\\n #####################################\\n Gx = partial_x(img)\\n Gy = partial_y(img)\\n G = np.sqrt(np.square(Gx) + np.square(Gy))\\n theta = np.degrees(np.arctan2(Gy, Gx)) % 360\\n ######################################\\n # END OF YOUR CODE #\\n ######################################\\n\\n return G, theta\",\n \"def gradient_descent(initial_theta, X, y, niter, alpha, Lambda=0.0):\\n theta_list = []\\n cost_list = []\\n\\n theta = initial_theta\\n for i in range(0, niter):\\n theta -= alpha*gradient(theta, X, y, Lambda)\\n theta_list.append(theta)\\n cost_list.append(cost(theta, X, y, Lambda))\\n\\n return theta_list, cost_list\",\n \"def linreg_stochastic_grad(X, y, alpha=.01):\\n m = X.shape[0]\\n n = X.shape[1]\\n theta = np.zeros(n)\\n for i in range(m):\\n delta = alpha * (np.dot(theta.transpose(), X[i,:]) -y[i]) * X[i,:]\\n theta = theta - delta\\n return theta\",\n \"def log_likelihood_gradient(y, tx, w):\\n return tx.T.dot(sigmoid(tx.dot(w))-y)\",\n \"def gradient(cls, x):\\n return 1 - TanH.apply(x) ** 2\",\n \"def learning_by_gradient_descent(y, tx, w, gamma):\\n loss = calculate_loss(y,tx,w)\\n grad = calculate_gradient(y,tx,w)\\n w_new = w - gamma*grad\\n #grad is for debugging purpose\\n return loss, w_new,grad\",\n \"def simple_gradient(x, y, theta):\\n if x.shape[0] * y.shape[0] * theta.shape[0] == 0:\\n return None\\n if x.shape[0] != y.shape[0] or theta.shape[0] != 2:\\n return None\\n\\n x = add_intercept(x)\\n\\n result = [\\n forumla(x, y, theta, 0),\\n forumla(x, y, theta, 1)\\n ]\\n return result\",\n \"def calculate_gradient(y, tx, w):\\n\\n\\tret = tx.T.dot(sigmoid(np.dot(tx, w)) - y)\\n\\treturn ret\",\n \"def learning_by_gradient_descent(y, tx, w, gamma):\\n loss = calculate_loss(y,tx,w)\\n grad = calculate_gradient(y,tx,w)\\n w = w-gamma*grad\\n return w, loss\",\n \"def train_fn_grad(self, theta_value, theta):\\n theta.set_value(theta_value, borrow=True)\\n\\n train_grads = [self.batch_grad(i, self.n_train)\\n for i in xrange(self.n_train_batches)]\\n train_batch_sizes = [self.get_batch_size(i, self.n_train)\\n for i in xrange(self.n_train_batches)]\\n\\n return np.average(train_grads, weights=train_batch_sizes,\\n axis=0)\",\n \"def gradient(self, x):\\n pass\",\n \"def gradient(img):\\n G = np.zeros(img.shape)\\n theta = np.zeros(img.shape)\\n\\n ### YOUR CODE HERE\\n Gx = partial_x(img)\\n Gy = partial_y(img)\\n G = np.sqrt(np.power(Gx,2)+np.power(Gy,2))\\n theta = (np.arctan2(Gy, Gx) * 180 / np.pi) % 360\\n ### END YOUR CODE\\n\\n return G, theta\",\n \"def approx_grad_reg(theta, X, y, _lambda):\\n grad_a = np.array([(cost_reg(theta + e, X, y, _lambda) - cost_reg(theta - e, X, y, _lambda)) / (2 * 1e-5)\\n for e in np.identity(len(theta)) * 1e-5])\\n return grad_a\",\n \"def get_gradient(phi, pred, t, dot_product, weight, reg= 1, regression= \\\"logistic\\\"):\\n if regression == \\\"logistic\\\":\\n gradient = np.matmul(phi.T, pred - t)\\n elif regression == \\\"probit\\\":\\n R = np.eye(pred.shape[0])\\n for i in range(pred.shape[0]):\\n y_n = pred[i,0]\\n dotp = dot_product[i, 0]\\n pdf = norm.pdf(dotp)\\n R[i,i] = pdf/(y_n*(1-y_n) + TOLERANCE)\\n gradient = np.matmul(np.matmul(phi.T, R), pred-t)\\n elif regression == \\\"multiclass\\\":\\n gradient = np.matmul(phi.T, pred - t)\\n\\n # Add regularization\\n gradient += weight/ reg\\n return gradient\",\n \"def gradient(self, x):\\n u = np.asarray([x[0]])\\n C = self.C_func(u)\\n dC = self.dC_func(u, order=1)\\n P = self.P\\n numerator = np.sum((C - P) * dC, axis=0)\\n denominator = np.sum(np.sum((C - P) ** 2, axis=0) ** (1 / 2))\\n if np.abs(denominator) > 0:\\n gradient = numerator/denominator\\n else:\\n gradient = np.asarray(0)[np.newaxis]\\n return gradient\",\n \"def apply_gradient(params: torch.Tensor, grads: torch.Tensor, lr: float) -> torch.Tensor:\\n params_prime = params + lr * grads\\n return params_prime\",\n \"def run_steep_gradient_descent(data_x, data_y, len_data, alpha, theta):\\n n = len_data\\n # WE NEED TO transpose data_x into (p+1) *n ,theta is 1*(p+1)\\n prod = np.dot(theta, data_x.transpose())\\n\\n prod -= data_y\\n print(\\\"pro: data_x\\\", prod.shape, data_x.shape)\\n #prod represent the loss of the hypothesis and true label\\n sum_grad = np.dot(prod, data_x)\\n print(\\\"总梯度的值：\\\",sum_grad.shape)\\n\\n # batch-gradient descent\\n theta = theta -(alpha / n) * sum_grad\\n return theta\",\n \"def GDS(x, theta, y, lamb, alpha):\\n\\tm = y.shape[0]\\n\\tdif = (1/m) * x.T @ (sigmoid(x,theta) - y) #classical differentiation term, output is (n,1)\\n\\tdif_reg = (lamb/m) * theta #regularization term\\n\\treturn (dif + dif_reg) * alpha\",\n \"def gradientDescent(X, y, theta, alpha, num_iters):\\n\\n # Initialize some useful values\\n J_history = []\\n m = y.size # number of training examples\\n\\n for i in range(num_iters):\\n # ====================== YOUR CODE HERE ======================\\n # Instructions: Perform a single gradient step on the parameter vector\\n # theta.\\n #\\n # Hint: While debugging, it can be useful to print out the values\\n # of the cost function (computeCost) and gradient here.\\n #\\n # Calculate the gradient step according to the equation for theta1:\\n g_step1 = (alpha / m * np.sum( (np.dot(X,theta) - y) * X[:,1]) )\\n # Gradient step for theta knot:\\n g_step0 = (alpha / m * np.sum( (np.dot(X,theta) - y) ) )\\n \\n #update theta\\n theta[0] = (theta[0] - g_step0)\\n theta[1] = (theta[1] - g_step1)\\n \\n #print([theta , g_step1, g_step0])\\n\\n # ============================================================\\n\\n # Save the cost J in every iteration\\n J_history.append(computeCost(X, y, theta))\\n\\n return theta, J_history\",\n \"def gradient(w, x, t):\\n return 2 * np.dot(x.T, (nn(x, w) - t))\",\n \"def costFunction(theta,X,y):\\n m = X.shape[0]\\n J = 0\\n h = sigmoid (np.dot(X,theta))\\n \\n J = (1/m)* ((-np.dot(y.T,(np.log(h)))) - np.dot((1 - y).T,(np.log(1-h))))\\n \\n #grad = (1/m) * np.dot(X.T,(h-y))\\n grad = (1/m) * np.dot((h.T - y), X).T\\n \\n return J, grad\",\n \"def stochasticGradientDescent(x,y,theta,alpha):\\n m,n = np.shape(x)\\n convergence = 0.000000001\\n lastCost = 0\\n cost = -1 \\n recurseCount = 0\\n while abs(lastCost - cost) > convergence: # rcurse until converge\\n lastCost = cost\\n hypothesis = np.dot(x,theta) \\n for i in range(m):\\n # alpha = 4.0 / (1.0 + i) + 0.01 \\n loss = hypothesis[i] - y[i]\\n # gradient = np.dot(x[i],loss)\\n gradient = x[i,:].transpose() * loss \\n theta = theta - alpha * gradient\\n cost = np.sum((hypothesis-y)**2)/(2*m)\\n recurseCount += 1\\n return recurseCount,theta\",\n \"def gradient(self):\\n result = np.zeros(len(self.variables))\\n result[self.bivariateGradInd] = (self.shape-1)/self.variable - self.rate\\n return result\",\n \"def gradient(self):\\n functional = self\\n\\n class KLGradient(Operator):\\n\\n \\\"\\\"\\\"The gradient operator of this functional.\\\"\\\"\\\"\\n\\n def __init__(self):\\n \\\"\\\"\\\"Initialize a new instance.\\\"\\\"\\\"\\n super().__init__(functional.domain, functional.domain,\\n linear=False)\\n\\n def _call(self, x):\\n \\\"\\\"\\\"Apply the gradient operator to the given point.\\n The gradient is not defined in points where one or more\\n components are non-positive.\\n \\\"\\\"\\\"\\n if functional.prior is None:\\n return (-1.0) / x + 1\\n else:\\n return (-functional.prior) / x + 1\\n\\n return KLGradient()\",\n \"def batchGradientDescent(x,y,theta,alpha):\\n m,n = np.shape(x)\\n xTran = x.transpose()\\n convergence = 0.000000001\\n lastCost = 0\\n cost = -1 \\n recurseCount = 0\\n while abs(lastCost - cost) > convergence: # rcurse until converge\\n lastCost = cost\\n hypothesis = np.dot(x,theta)\\n loss = hypothesis - y\\n cost = np.sum(loss**2)/(2*m)\\n gradient = np.dot(xTran,loss)/m\\n theta = theta - alpha*gradient\\n recurseCount += 1\\n return recurseCount,theta\",\n \"def learning_by_penalized_gradient(y, tx, w, gamma, lambda_):\\n\\tgrad, H = penalized_logistic_regression(y, tx, w, lambda_)\\n\\n\\thgrad = np.linalg.inv(H).dot(grad)\\n\\n\\tw = w - gamma * hgrad\\n\\n\\treturn w\",\n \"def grad(self, w):\\n l1_grad = self.r * np.sign(w)\\n l2_grad = np.asarray(1 - self.r) * w \\n\\n gradient_penalty = self.alpha * (l1_grad + l2_grad)\\n\\n # Insert 0 for bias term.\\n return np.insert(gradient_penalty, 0, 0, axis=0)\",\n \"def compute_gradient(self):\\n A = np.dot(self.X, self.w)\\n m = self.t.shape[0]\\n C = -1 * self.t * (1 / (1 + np.exp(A * self.t)))\\n return (1 / m) * np.dot(self.X.T, C)\",\n \"def gradient(self, x):\\n g = self._grad(\\n time_series=self.observed_data,\\n a=x[0],\\n b=x[1],\\n c=x[2],\\n sigma=self.sigma\\n )\\n return g\",\n \"def reg_autograd(alpha,X,theta,y):\\n g=grad(func)\\n for i in range(0,20):\\n theta=theta- alpha * g(theta)\\n print(\\\"cost\\\", func(theta))\\n print(\\\"final theta\\\",theta)\",\n \"def grad(self, x):\\n norm_beta = np.linalg.norm(x)\\n if norm_beta > utils.TOLERANCE:\\n return (self.l / norm_beta) * x\\n else:\\n D = x.shape[0]\\n u = (self.rng(D, 1) * 2.0) - 1.0 # [-1, 1]^D\\n norm_u = np.linalg.norm(u)\\n a = self.rng() # [0, 1]\\n\\n return (self.l * (a / norm_u)) * u\",\n \"def gradcovfunc(self, theta, d):\\n sigmaf, l = theta[:2] \\n xxl = np.sum((d/l)**2, axis=1)\\n dk_dsigmaf = 2 * sigmaf * np.exp(-xxl/2.)\\n dk_dl = sigmaf**2/l * xxl * np.exp(-xxl/2.)\\n grad = np.array([dk_dsigmaf, dk_dl])\\n return grad\",\n \"def log_prior_grad(self, inputs):\",\n \"def gradient(series, **options):\\n x = series.index\\n y = series.values\\n\\n a = np.gradient(y, x, **options)\\n return series.__class__(a, series.index)\",\n \"def derivative(self, theta):\\n diag_gamma = np.dot(theta.T, self.X.T)\\n logistic_term = self.logistic_fn(diag_gamma)\\n diag_gamma = logistic_term * (1.0 - logistic_term)\\n gamma = np.diag(diag_gamma)\\n\\n # v computation\\n diags_v = 1.0 - 2*self.logistic_fn(np.dot(theta.T, self.X.T))\\n diags_v = diags_v.reshape((-1, 1))\\n diags_v = diags_v*self.X\\n assert diags_v.shape == self.X.shape #N*d shape\\n\\n XtGamma = np.dot(self.X.T, gamma) # d*N shape\\n\\n # TODO: Verifier car pas sur de mon coup ... et surtout plus long...\\n # id = np.eye(self.n_examples).reshape((self.n_examples, self.n_examples, 1))\\n # diags_v = diags_v.reshape((self.n_examples, 1, self.dim))\\n # v = id*diags_v # n*n*d tensor\\n # left = np.tensordot(XtGamma, v, axes=(1, 0)) # shape d*N*d\\n # assert left.shape == (self.dim, self.n_examples, self.dim)\\n # dg = np.tensordot(left, self.X, axes=(1, 0))\\n # dg = np.swapaxes(dg, axis1=-2, axis2=-1)\\n\\n dg = np.zeros((self.dim, self.dim, self.dim))\\n for idx, v_i_diag in enumerate(diags_v.T):\\n v_i = np.diag(v_i_diag)\\n dg_di = np.dot(np.dot(XtGamma, v_i), self.X)\\n dg[:, :, idx] = dg_di\\n return dg\",\n \"def compute_logistic_gradient(y, tx, w):\\n return tx.T.dot(sigmoid(tx.dot(w)) - y) / len(y)\",\n \"def run_gradient_descent(data,theta,alpha,num_iters):\\n population = data[:,0]\\n prices = data[:,1]\\n x = ones(shape=(len(population),2)) #add ones for theta0 \\n x[:,1] = population\\n x = transpose(x)\\n error_history = zeros(shape=(num_iters,1))\\n \\n for i in range(num_iters):\\n predictions = theta.dot(x)\\n errors_x1 = (predictions - prices) * x[0,:]\\n errors_x2 = (predictions - prices) * x[1,:]\\n theta[0][0] = theta[0][0] - alpha*(1.0/len(population))*errors_x1.sum()\\n theta[0][1] = theta[0][1] - alpha*(1.0/len(population))*errors_x2.sum()\\n error_history[i,0] = calculate_cost(theta,data)\\n \\n return theta, error_history\",\n \"def gradient(self, x):\\n return 0.0\",\n \"def costFunction(self,theta, X, y): \\n m = len(y)\\n h = self.sigmoid(X@theta)\\n J = 1 / m * (- y.T @ self.log(h) - (1-y).T @ self.log(1-h)) \\n # grad = 1/ m * X.T @ (h - y)\\n return J\",\n \"def gradient(poly):\\n return differential(\\n poly, chaospy.poly.collection.basis(1, 1, poly.dim, sort=\\\"GR\\\"))\",\n \"def curves_gradient(theta, m_, LAMBDA=0):\\n # X0: column of ones\\n theta0_gradient = (1 / m_) * sum((curves_hypothesis(theta, m_) - y[:m_].ravel()) * np.ones(m_))\\n # X1: data given as X\\n theta1_gradient = ((1 / m_) * sum((curves_hypothesis(theta, m_) - y[:m_].ravel()) * X[:m_].ravel())) \\\\\\n + (LAMBDA / m_) * theta[1]\\n return np.array([theta0_gradient, theta1_gradient])\",\n \"def gradient(self, x_in):\\r\\n\\r\\n return 1 - torch.pow(self.forward(x_in), 2)\",\n \"def compute_gradient(self): # TODO: try to change to square loss since it's hessian is easier to obtain\\n A = np.dot(self.X, self.w)\\n m = self.t.shape[0]\\n C = -1 * self.t * (1 / (1 + np.exp(A * self.t)))\\n return (1 / m) * np.dot(self.X.T, C)\",\n \"def vb_grad_natgrad(self):\\r\\n\\r\\n # Gradient for eta\\r\\n dL_dmmT_S = -0.5*self.Lambda/self.data_prop + 0.5*self.q_u_prec\\r\\n Kmmipsi1V,_ = dpotrs(self.Lm,self.psi1V,lower=1)\\r\\n dL_dm = (Kmmipsi1V - np.dot(self.Lambda,self.q_u_mean))/self.data_prop\\r\\n\\r\\n # Gradients for theta\\r\\n S = self.q_u_cov\\r\\n Si = self.q_u_prec\\r\\n m = self.q_u_mean\\r\\n dL_dSi = -mdot(S,dL_dmmT_S, S)\\r\\n\\r\\n dL_dmhSi = -2*dL_dSi\\r\\n dL_dSim = np.dot(dL_dSi,m) + np.dot(Si, dL_dm)\\r\\n\\r\\n return np.hstack((dL_dm.flatten(),dL_dmmT_S.flatten())) , np.hstack((dL_dSim.flatten(), dL_dmhSi.flatten()))\",\n \"def grad(self, w):\\n gradient_penalty = np.asarray(self.alpha) * w\\n # Insert 0 for bias term.\\n return np.insert(gradient_penalty, 0, 0, axis=0)\",\n \"def grad(self, w):\\n gradient_penalty = np.asarray(self.alpha) * w\\n # Insert 0 for bias term.\\n return np.insert(gradient_penalty, 0, 0, axis=0)\",\n \"def gradient(self):\\n functional = self\\n\\n class KLCCGradient(Operator):\\n\\n \\\"\\\"\\\"The gradient operator of this functional.\\\"\\\"\\\"\\n\\n def __init__(self):\\n \\\"\\\"\\\"Initialize a new instance.\\\"\\\"\\\"\\n super().__init__(functional.domain, functional.domain,\\n linear=False)\\n\\n def _call(self, x):\\n \\\"\\\"\\\"Apply the gradient operator to the given point.\\n\\n The gradient is not defined in points where one or more\\n components are larger than or equal to one.\\n \\\"\\\"\\\"\\n if functional.prior is None:\\n return 1.0 / (1 - x)\\n else:\\n return functional.prior / (1 - x)\\n\\n return KLCCGradient()\",\n \"def learning_by_penalized_gradient(y, tx, w, gamma, lambda_):\\n\\n #on test avec Newton\\n\\n loss,gradient,_ = penalized_logistic_regression(y,tx,w,lambda_)\\n\\n w = w - gamma*gradient\\n return loss, w,gradient\",\n \"def gradient_ascent(f, df, theta_init, step_size, max_iter):\\n\\n fs = []\\n xs = []\\n thetas = theta_init\\n for i in range(max_iter): #for each data example\\n fs.append(f(thetas))\\n\\n temp = step_size*df(thetas)\\n thetas = step_size*df(thetas) #modify that feature by using the derivative of log likelihood\\n xs.append(thetas.flatten())\\n if i % 10 == 0:\\n print(i, thetas)\\n\\n return thetas, fs, xs\",\n \"def calculate_maxL_gradient(y, tx, w):\\n sig=sigmoid(tx.dot(w))\\n grad=tx.T.dot(sig-y)\\n #divides with number of samples so that learning rate is not dependant on number of samples\\n grad=grad/len(y) \\n return grad\",\n \"def _log_prior_gradients(self):\\n if self.priors.size == 0:\\n return 0.\\n x = self.param_array\\n ret = np.zeros(x.size)\\n #compute derivate of prior density\\n [np.put(ret, ind, p.lnpdf_grad(x[ind])) for p, ind in self.priors.items()]\\n #add in jacobian derivatives if transformed\\n priored_indexes = np.hstack([i for p, i in self.priors.items()])\\n for c,j in self.constraints.items():\\n if not isinstance(c, Transformation):continue\\n for jj in j:\\n if jj in priored_indexes:\\n ret[jj] += c.log_jacobian_grad(x[jj])\\n return ret\",\n \"def grad(self, w):\\n gradient_penalty = np.asarray(self.alpha) * w\\n\\n # Insert 0 for bias term.\\n return np.insert(gradient_penalty, 0, 0, axis=0)\",\n \"def grad(theta, X, y, lambda_):\\n # ... dopolnite (naloga 1, naloga 2)\\n\\n l = []\\n for i, e in enumerate(theta):\\n l.append(1 / len(y) * sum([(h(x, theta) - yi) * x[i] for x, yi in zip(X, y)]) + 2 * lambda_ * e)\\n\\n return np.array(l)\",\n \"def lr_cost_function(theta, X, y, learning_rate):\\n m = len(y) # number of training examples\\n # You need to return the following variables correctly \\n J = 0\\n grad = np.zeros(theta.shape)\\n h = sigmoid(np.dot(X, theta))\\n\\n #Compute cost:\\n first = (1/m)*np.sum(np.dot(-y.T, np.log(h)) - np.dot((1-y).T, np.log(1-h)), axis=0)\\n second = (learning_rate/(2*m))*np.sum(theta[1:]**2)\\n J = first + second\\n \\n #Compute gradient\\n grad = np.dot((h-y).T, X)/m\\n grad = grad.T\\n grad[1:] += (learning_rate/m)*theta[1:]\\n return float(J), grad\",\n \"def gradient(self, x, i=None):\\n self.set_parameters(x=x)\\n gradient = self.gradient_loss()\\n if self.add_bias:\\n gradient[1:] += self.alpha * self.gradient_regularizer()\\n else:\\n gradient += self.alpha * self.gradient_regularizer()\\n if i is not None:\\n return gradient.ravel()[i]\\n return gradient.ravel()\",\n \"def compute_gradient_lasso(y, tx, w, lambda_):\\n e = y - tx.dot(w)\\n subgrad = lambda_ * np.sign(w)\\n\\n return -tx.T.dot(e)/len(e) + subgrad\",\n \"def compute_gradient(self, X, y, weights):\\n sigmoid = self.sigmoid(np.dot(X, weights))\\n return np.dot(X.T, y - sigmoid)\",\n \"def batch_grad_descent(X, y, alpha=0.1, num_iter=1000, check_gradient=False):\\n num_instances, num_features = X.shape[0], X.shape[1]\\n theta_hist = np.zeros((num_iter+1, num_features)) #Initialize theta_hist\\n loss_hist = np.zeros(num_iter+1) #initialize loss_hist\\n theta = np.ones(num_features) #initialize theta\\n\\n count = 0\\n while count < num_iter+1:\\n if check_gradient:\\n assert grad_checker(X,y,theta)\\n\\n grad = compute_square_loss_gradient(X,y,theta)\\n theta -= alpha*grad\\n theta_hist[count] = theta\\n loss_hist[count] = compute_square_loss(X,y,theta)\\n count += 1\\n \\n return theta_hist, loss_hist\",\n \"def check_gradient(self, x, y):\\n x = x.transpose()\\n y = y.transpose()\\n layers_copy = deepcopy(self.layers)\\n epsilon = 10 ** -4\\n a, layer = self.forward_propagation(x)\\n delta = self.calculate_delta(a, y, layer)\\n self.backpropagation(delta=delta, theta=layer.theta)\\n previous_layer_output = x\\n for layer in self.layers:\\n theta_copy = deepcopy(layer.theta)\\n real_theta_size = theta_copy.shape\\n delta = layer.delta\\n dc_dtheta = np.outer(previous_layer_output, delta).transpose()\\n previous_layer_output = layer.a\\n R, C = theta_copy.shape\\n for i in range(R):\\n for j in range(C):\\n theta_plus = deepcopy(theta_copy)\\n theta_plus[i, j] += epsilon\\n layer.theta = theta_plus\\n a_plus, l_plus = self.forward_propagation(x)\\n err_plus = self.calculate_loss(a_plus, y)\\n theta_minus = deepcopy(theta_copy)\\n theta_minus[i, j] -= epsilon\\n layer.theta = theta_minus\\n a_minus, l_minus = self.forward_propagation(x)\\n err_minus = self.calculate_loss(a_minus, y)\\n limit = (err_plus - err_minus)/(2*epsilon)\\n grad_diff = abs(dc_dtheta[i,j] - limit)\\n assert grad_diff < 10 ** -6, f\\\"Diff {grad_diff} is too big.\\\"\\n layer.theta = theta_copy\",\n \"def _compute_func_grad(self, w):\\n W = w.reshape((self.X.shape[1], self.Y.shape[1]))\\n self.nll_, self.grad_ = calculate_gradient(self.X, self.Y, W, self.prior, self.weighted,0)\",\n \"def gradient(data_x, data_y, parameters):\\n return data_x.T @ (data_x @ parameters - data_y) / data_x.shape[0]\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8256445","0.8055304","0.7947039","0.7867961","0.78287417","0.78280646","0.7741965","0.77182055","0.77098596","0.76828635","0.76208895","0.75614756","0.72576725","0.72168595","0.7216574","0.7211889","0.70136374","0.70051277","0.6994997","0.69862175","0.69123644","0.6910823","0.68812525","0.6881161","0.6863728","0.6838628","0.6829457","0.68013173","0.67898685","0.67635065","0.6753912","0.6751456","0.67212635","0.67147833","0.6712214","0.67026615","0.6683733","0.6665269","0.6664574","0.66554016","0.6632431","0.66249985","0.6618367","0.66131556","0.6605929","0.66040915","0.658713","0.6579545","0.65779126","0.65694577","0.65687287","0.65535915","0.65233195","0.6507123","0.64980435","0.6492682","0.64849585","0.64616925","0.6455795","0.64541864","0.64431417","0.64234275","0.64233476","0.64115876","0.6388624","0.6375172","0.63749987","0.63732344","0.63698816","0.6358501","0.6354826","0.6352325","0.63463557","0.6346066","0.63423747","0.6325621","0.6305958","0.63044894","0.6303761","0.6282822","0.6278642","0.62777394","0.62690866","0.62690866","0.6268236","0.6257674","0.6254688","0.62541944","0.625029","0.624863","0.6246089","0.62321514","0.6226891","0.6226241","0.6183781","0.61833704","0.6182383","0.61771554","0.6168317"],"string":"[\n \"0.8256445\",\n \"0.8055304\",\n \"0.7947039\",\n \"0.7867961\",\n \"0.78287417\",\n \"0.78280646\",\n \"0.7741965\",\n \"0.77182055\",\n \"0.77098596\",\n \"0.76828635\",\n \"0.76208895\",\n \"0.75614756\",\n \"0.72576725\",\n \"0.72168595\",\n \"0.7216574\",\n \"0.7211889\",\n \"0.70136374\",\n \"0.70051277\",\n \"0.6994997\",\n \"0.69862175\",\n \"0.69123644\",\n \"0.6910823\",\n \"0.68812525\",\n \"0.6881161\",\n \"0.6863728\",\n \"0.6838628\",\n \"0.6829457\",\n \"0.68013173\",\n \"0.67898685\",\n \"0.67635065\",\n \"0.6753912\",\n \"0.6751456\",\n \"0.67212635\",\n \"0.67147833\",\n \"0.6712214\",\n \"0.67026615\",\n \"0.6683733\",\n \"0.6665269\",\n \"0.6664574\",\n \"0.66554016\",\n \"0.6632431\",\n \"0.66249985\",\n \"0.6618367\",\n \"0.66131556\",\n \"0.6605929\",\n \"0.66040915\",\n \"0.658713\",\n \"0.6579545\",\n \"0.65779126\",\n \"0.65694577\",\n \"0.65687287\",\n \"0.65535915\",\n \"0.65233195\",\n \"0.6507123\",\n \"0.64980435\",\n \"0.6492682\",\n \"0.64849585\",\n \"0.64616925\",\n \"0.6455795\",\n \"0.64541864\",\n \"0.64431417\",\n \"0.64234275\",\n \"0.64233476\",\n \"0.64115876\",\n \"0.6388624\",\n \"0.6375172\",\n \"0.63749987\",\n \"0.63732344\",\n \"0.63698816\",\n \"0.6358501\",\n \"0.6354826\",\n \"0.6352325\",\n \"0.63463557\",\n \"0.6346066\",\n \"0.63423747\",\n \"0.6325621\",\n \"0.6305958\",\n \"0.63044894\",\n \"0.6303761\",\n \"0.6282822\",\n \"0.6278642\",\n \"0.62777394\",\n \"0.62690866\",\n \"0.62690866\",\n \"0.6268236\",\n \"0.6257674\",\n \"0.6254688\",\n \"0.62541944\",\n \"0.625029\",\n \"0.624863\",\n \"0.6246089\",\n \"0.62321514\",\n \"0.6226891\",\n \"0.6226241\",\n \"0.6183781\",\n \"0.61833704\",\n \"0.6182383\",\n \"0.61771554\",\n \"0.6168317\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.83195007"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":14389,"cells":{"query":{"kind":"string","value":"Tophat prior as it used in the original spearmint code."},"document":{"kind":"string","value":"def __init__(self, l_bound, u_bound, rng=None):\n if rng is None:\n self.rng = np.random.RandomState(np.random.randint(0, 10000))\n else:\n self.rng = rng\n self.min = l_bound\n self.max = u_bound\n if not (self.max > self.min):\n raise Exception(\n \"Upper bound of Tophat prior must be greater \\\n than the lower bound!\"\n )"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def lnprior(self):\n \n return","def prior(self):\n return self.__prior","def prior(self):\n return self.__prior","def prior(self):\n return self.__prior","def prior(self):\n return self.__prior","def get_prior(self):\n return self.prior","def prior_predictive(self):\n cfg = self.config\n n = cfg['batch_size'] * cfg['q/n_samples']\n n_samples = cfg['q/n_samples']\n with util.get_or_create_scope('model', reuse=True):\n h_prior = tf.cast(self.p_h_L.sample(n), cfg['dtype'])\n h_prior = tf.reshape(\n h_prior, [cfg['q/n_samples'], cfg['batch_size'], -1])\n h = [None] * cfg['p/n_layers']\n h[cfg['p/n_layers'] - 1] = h_prior\n for n in range(cfg['p/n_layers'] - 1, 0, -1):\n p_h_n = self.build_stochastic_layer(n, h_above=h[n])\n h[n - 1] = tf.cast(p_h_n.sample(), cfg['dtype'])\n return self.likelihood(h[0])","def log_prior(self):\n raise NotImplementedError(\"the log_prior property should \"\n \"be defined in the Estimator sub-class\")","def log_prior(self, params):","def get_prior(self):\n return self._prior","def prior(\n self,\n next_state: np.ndarray,\n state: np.ndarray,\n control_x: Optional[np.ndarray] = None\n ) -> np.ndarray:\n pass","def bias_prior(self):","def prior_sample(self):\n pass","def testHrtPrior(self):\n attr = self.session.create_visit_attr()\n\n self.util.boolTypeTest(self, attr, \"prior\")\n\n self.util.boolPropertyTest(self, attr, \"prior\")","def _prior_teach(self):\n pass","def _compute_mix_prior(self):\n if np.all(self.mix_prior == 1):\n return 0\n return np.dot(np.log(self.mix_weight).T, (self.mix_prior - 1))","def buildWPriorTerm(self):\r\n\r\n # self.w_prior.shape == (minibatch size,)\r\n self.w_prior = 0.5*T.sum(1 + T.log(self.qwgy_var) - self.qwgy_mu**2-self.qwgy_var, axis=1)\r\n\r\n self.w_prior_modif = - T.maximum(self.hyper['treshold_w_prior'], -self.w_prior)","def buildConditionalPriorTerm(self):\r\n\r\n # shape is (batch size,)\r\n self.conditional_prior = - T.mean(T.sum(T.exp(self.log_pzgxw)*(self.log_qxgy.dimshuffle(0,'x',1,'x') - self.log_pxgzw), axis=3), axis=[1,2])","def buildZPriorTerm(self):\r\n\r\n # shape is (batch size,)\r\n self.z_prior = - T.mean(T.sum(T.exp(self.log_pzgxw)*(self.log_pzgxw + T.log(self.hyper['num_clust'])), axis=3), axis=[1,2])\r\n\r\n self.z_prior_modif = - T.maximum(self.hyper['treshold_z_prior'], - self.z_prior)","def P_prior(self):\n return dot(self.U_prior, dot(diag(self.D_prior), self.U_prior.T))","def ComputeLogPosePriorTerm(self, *args):\n return _ITKCostFunctionsPython.itkShapePriorMAPCostFunctionBaseIF2F_ComputeLogPosePriorTerm(self, *args)","def ComputeLogPosePriorTerm(self, *args):\n return _ITKCostFunctionsPython.itkShapePriorMAPCostFunctionBaseIF3F_ComputeLogPosePriorTerm(self, *args)","def get_prior(self):\n assert self._prior in self._priors, 'Unsupported prior! Check the _priors attribute for a list of priors.'\n if self._prior == 'Gaussian':\n prior = 0.5 * torch.sum(self.parameters ** 2)/self.prior_var\n elif self._prior == 'Cauchy':\n dimconst = (self.parameters.shape[0] + 1)/2.\n prior = dimconst*torch.log(self.prior_var + torch.sum(self.parameters ** 2))\n elif self._prior == 'Sparse':\n n = self.dataset.shape[1]\n gauss_prior = 0.5 * torch.sum(torch.exp(self.parameters[-1] * torch.exp(self.parameters[n:2*n]) * self.parameters[:n] ** 2))\n gamma_density = torch.distributions.Gamma(1.5,0.5)\n# gamma_prior = -gamma_density.log_prob(torch.exp(self.parameters[n:])).sum()\n# lambda_density = torch.distributions.Gamma(1.5,0.5)\n lambda_prior = -gamma_density.log_prob(torch.exp(self.parameters[n:])).sum()\n prior = gauss_prior + lambda_prior\n return prior","def lnprior(self, theta):\n return_lp = 0.0\n # ensure parameter limits are kept and frozen parameters are frozen.\n for t in np.arange(self.ig.size):\n if self.prior_string[t] == 't': ## for tophat\n if self.pars_frozen[t] == 1 and theta[t] != self.ig[t]: ## if the parameter is frozen and the trial value is not the starting value\n print('ERROR: theta[',t,'] is frozen and the trial value is not the starting value')\n return_lp = return_lp -np.inf\n elif theta[t] < self.parmins[t] or theta[t] > self.parmaxs[t]: ## if the parameter isn't frozen, but the trial value is outside limits\n return_lp = return_lp -np.inf\n else:\n return_lp = return_lp + 0.0 # 0, because log of 1 is 0.\n if self.prior_string[t] == 'g': ## for gaussian -- get informative prior value\n if (self.pars_frozen[t] == 1 and theta[t] != self.ig[t]) or (theta[t] < self.parmins[t] or theta[t] > self.parmaxs[t]):\n return_lp = return_lp -np.inf\n elif self.pars_frozen[t] == 1 and theta[t] == self.ig[t]:\n return_lp = return_lp + 0.0\n else:\n #gaussian prior on a\n mu = self.ig[t]\n sigma = self.priorsigmas[t]\n return_lp = return_lp + (np.log(1.0/( sigma * np.sqrt(2*np.pi) ) ) - 0.5*(theta[t] - mu)**2/sigma**2)\n return return_lp","def prior(store):\n mu = zeros(store['beta'].shape[0])\n Prec = diag(0.005 * ones(store['beta'].shape[0]))\n return -0.5 * dot(store['beta'].transpose(), dot(Prec, store['beta']))","def eval_prior(self, state, action):\n\n return np.dot(state, self.a.T) + np.dot(action, self.b.T)","def prior(cube, ndim, nparams):\n # construct prior from recovery file\n counter = 0\n if params2 is None:\n return\n for key in params2.keys():\n nparams_tmp = int(params2[key]['nparams'])\n for ii in range(nparams_tmp):\n # sp = [name, prior type, x1, x2]\n sp =\\\n params2[key]['param'+str(ii+1)].split(',')\n if sp[1][0] == 'U' and sp[2][:5]=='param' and sp[3][:5]=='param':\n subtract1 = int(key[-1]) - int(sp[2][-1])\n subtract2 = int(key[-1]) - int(sp[3][-1])\n cube[counter] = GeneralPrior(cube[counter], 'U',\n cube[counter-subtract1], cube[counter-subtract2])\n elif sp[1][0] == 'U' and sp[2][:5]=='param':\n subtract = int(key[-1]) - int(sp[2][-1])\n cube[counter] = GeneralPrior(cube[counter], 'U',\n cube[counter-subtract], float(sp[3]))\n elif sp[1][0] == 'U' and sp[3][:5]=='param':\n subtract = int(key[-1]) - int(sp[2][-1])\n cube[counter] = GeneralPrior(cube[counter], 'U',\n float(sp[2]), cube[counter - subtract])\n else:\n cube[counter] = GeneralPrior(cube[counter], sp[1], float(sp[2]),\n float(sp[3]))\n counter += 1","def ComputeLogPosePriorTerm(self, *args):\n return _ITKCostFunctionsPython.itkShapePriorMAPCostFunctionBaseID2D_ComputeLogPosePriorTerm(self, *args)","def lnprior(theta):\n\n\t# keys = theta.keys()\n\ttheta_keys = [key for key in ap.init.keys()]\n\tif type(theta) == np.ndarray:\n\t\ttheta = dict(zip(theta_keys, theta))\n\tkeys = theta.keys()\n\n\tfor k in keys:\n\t\tif (ap.prior[k][0] < theta[k] < ap.prior[k][1]):\n\t\t\tpass\n\t\telse:\n\t\t\treturn -np.inf\n\t\t\tbreak\n\treturn 0.0","def _one_step_ahead_prediction(self, X, y):\n lagged_data = self.build_matrix(X, y)\n\n if self.basis_function.__class__.__name__ == \"Polynomial\":\n X_base = self.basis_function.transform(\n lagged_data,\n self.max_lag,\n predefined_regressors=self.pivv[: len(self.final_model)],\n )\n else:\n X_base, _ = self.basis_function.transform(\n lagged_data,\n self.max_lag,\n predefined_regressors=self.pivv[: len(self.final_model)],\n )\n\n yhat = super()._one_step_ahead_prediction(X_base)\n return yhat.reshape(-1, 1)","def ComputeLogPosePriorTerm(self, *args):\n return _ITKCostFunctionsPython.itkShapePriorMAPCostFunctionBaseID3D_ComputeLogPosePriorTerm(self, *args)","def sample_from_prior(self, *args, **kwargs):\n pass","def prior_z(self) -> distributions.Distribution:\n return distributions.Categorical(self.pi)","def _n_step_ahead_prediction(self, X, y, steps_ahead):\n yhat = super()._n_step_ahead_prediction(X, y, steps_ahead)\n return yhat","def set_prior_priorunc_synthetic(self):\n\n lai_coeff_absunc = None\n statevec_absunc = None\n\n #-- \n if self.prior_inifile!=None:\n lai_coeff_absunc, statevec_absunc = self._setprior_from_inifile()\n elif self.use_generic_prior:\n self._setprior_generic_agriculture()\n statevec_absunc = self.generic_prior_unc\n else:\n #-- overall number of time-points in schedule\n npts = self.get_npts()\n\n #-- default prior file\n prior_file = os.path.join(ipt_dir_path, 'mni_stat_jules_2017.csv')\n\n #-- get signature simulator default state\n msg = \"START reading state variables from file ***{}***...\".format(prior_file)\n FileLogger.info(msg)\n state_inst = sv.get_state_csv(fname=prior_file, fmt='%Y-%m-%d %H:%M:%S' )\n msg = \"...reading DONE\"\n FileLogger.info(msg)\n\n #-- LAI,Canopy-Height,Soil-Moisture\n self.prstate = np.empty((3,npts), dtype=np.float64)\n\n for i,date_utc in enumerate(self.schedule_dct['date_utc']):\n idx, timedelt = sv.find_nearest_date_idx(state_inst.date_utc, date_utc)\n # print \"MVMV::nearest={} idx={} timedelt={}\".format(\n # state_inst.date_utc[idx], idx, timedelt)\n #-- LAI\n self.prstate[0,i] = state_inst.lai[idx]\n #-- canopy-height\n self.prstate[1,i] = state_inst.can_height[idx]\n #-- SM\n self.prstate[2,i] = state_inst.soil_moisture[idx]\n\n #-- set uncertainty values\n self._set_priorunc(statevec_absunc=statevec_absunc, lai_coeff_absunc=lai_coeff_absunc)","def prior(self, state, action, rnn_hidden, rnn_hidden_c=None):\n state_action = self.state_action_layer(torch.cat([state, action], dim=-1)) # [batch, hidden]\n if self.mode == 'LSTM':\n rnn_hidden, rnn_hidden_c = self.transition_RNN(state_action, (rnn_hidden, rnn_hidden_c))\n else:\n rnn_hidden = self.transition_RNN(state_action, rnn_hidden) # [batch, hidden]\n # hidden_prior = self.hidden_prior(rnn_hidden) #[batch, hidden]\n hidden_prior = rnn_hidden\n prior_mean = self.prior_mean(hidden_prior) # [batch, state]\n # prior_sigma = F.softplus(self.prior_sigma(hidden_prior)) + self._min_stddev #[batch, state]\n prior_sigma = torch.exp(self.prior_sigma(hidden_prior))\n if self.mode == 'LSTM':\n return prior_mean, prior_sigma, rnn_hidden, rnn_hidden_c\n else:\n return prior_mean, prior_sigma, rnn_hidden","def set_prior(self,which,what):\n\n which = self.grep_param_names(which)\n\n #check tied situation\n tie_partial_matches = [tie for tie in self.tied_indices if (not set(tie).isdisjoint(set(which))) & (not set(tie)==set(which))]\n if len(tie_partial_matches):\n raise ValueError, \"cannot place prior across partial ties\"\n tie_matches = [tie for tie in self.tied_indices if set(which)==set(tie) ]\n if len(tie_matches)>1:\n raise ValueError, \"cannot place prior across multiple ties\"\n elif len(tie_matches)==1:\n which = which[:1]# just place a prior object on the first parameter\n\n\n #check constraints are okay\n if isinstance(what, (priors.gamma, priors.log_Gaussian)):\n assert not np.any(which[:,None]==self.constrained_negative_indices), \"constraint and prior incompatible\"\n assert not np.any(which[:,None]==self.constrained_bounded_indices), \"constraint and prior incompatible\"\n unconst = np.setdiff1d(which, self.constrained_positive_indices)\n if len(unconst):\n print \"Warning: constraining parameters to be positive:\"\n print '\\n'.join([n for i,n in enumerate(self._get_param_names()) if i in unconst])\n print '\\n'\n self.constrain_positive(unconst)\n elif isinstance(what,priors.Gaussian):\n assert not np.any(which[:,None]==self.all_constrained_indices()), \"constraint and prior incompatible\"\n else:\n raise ValueError, \"prior not recognised\"\n\n\n #store the prior in a local list\n for w in which:\n self.priors[w] = what","def set_prior_priorunc_general(self):\n\n #-- some configurations apply absolute uncertainties\n lai_coeff_absunc = None\n statevec_absunc = None\n is_generic_prior = False\n\n #--\n if self.prior_states_file!=None:\n states_file = self.prior_states_file\n basename = os.path.basename(states_file)\n if os.path.splitext(basename)[1]=='.nc':\n msg = \"Prior state information will be read from ***{}***\".format(states_file)\n FileLogger.info(msg)\n self._setprior_jules(states_file)\n msg = \"...reading prior DONE\"\n FileLogger.info(msg)\n elif os.path.splitext(basename)[1]=='.csv':\n msg = \"Prior state information will be read from ***{}***\".format(states_file)\n FileLogger.info(msg)\n self._setprior_csv(states_file)\n msg = \"...reading prior DONE\"\n FileLogger.info(msg)\n else:\n msg = \"Unrecognised format of states file ***{}***. Cannot continue!\".format(\n states_file)\n FileLogger.fatal(msg)\n raise RuntimeError(msg)\n return\n elif self.prior_inifile!=None:\n lai_coeff_absunc, statevec_absunc = self._setprior_from_inifile()\n else:\n self._setprior_generic_agriculture()\n is_generic_prior = True\n statevec_absunc = self.generic_prior_unc\n\n #-- set uncertainty values\n self._set_priorunc( lai_coeff_absunc=lai_coeff_absunc,\n statevec_absunc=statevec_absunc,\n is_generic_prior=is_generic_prior )","def lnprior_abso(theta,fixed,fix,log,priorinf,priorsup):\n for i in range(len(theta)):\n if not (priorinf[i] < theta[i] < priorsup[i]):\n return -np.inf\n return 0.0","def prior_model(self) -> Collection:\n return Collection(self.prior)","def prior_sample_parameter(self, parameter):\n pass","def add_prior(self, prior):\n if self.rate_variation:\n # Gamma prior with mean 1 over all mutation rates\n sub_prior = ET.SubElement(prior, \"prior\", {\"id\":\"featureClockRatePrior.s:%s\" % self.name, \"name\":\"distribution\"})\n compound = ET.SubElement(sub_prior, \"input\", {\"id\":\"featureClockRateCompound:%s\" % self.name, \"spec\":\"beast.core.parameter.CompoundValuable\", \"name\":\"x\"})\n plate = ET.SubElement(compound, \"plate\", {\n \"var\":\"feature\",\n \"range\":\",\".join(self.features)})\n ET.SubElement(plate, \"var\", {\n \"idref\":\"featureClockRate:%s:$(feature)\" % self.name})\n gamma = ET.SubElement(sub_prior, \"input\", {\"id\":\"featureClockRatePriorGamma:%s\" % self.name, \"spec\":\"beast.math.distributions.Gamma\", \"name\":\"distr\", \"alpha\":\"@featureClockRateGammaShape:%s\" % self.name, \"beta\":\"@featureClockRateGammaScale:%s\" % self.name})\n # Exponential hyperprior on scale of Gamma prior\n # Exponential prior favours small scales over large scales, i.e. less rate variation\n # Mean scale 0.23 chosen for general sensibility, e.g.:\n # - Prior distribution is roughly 50/50 that ratio of fastest\n # to slowest feature rate in a dataset of size 200 is below\n # or above 10.\n # - Prior probability of roughly 0.90 that this ratio is below\n # 100.\n sub_prior = ET.SubElement(prior, \"prior\", {\"id\":\"featureClockRateGammaScalePrior.s:%s\" % self.name, \"name\":\"distribution\", \"x\":\"@featureClockRateGammaScale:%s\" % self.name})\n ET.SubElement(sub_prior, \"Exponential\", {\"id\":\"featureClockRateGammaShapePriorExponential.s:%s\" % self.name, \"mean\":\"0.23\", \"name\":\"distr\"})","def lower_cb(self,p):\n avg_elo=self.elo0+self.elo1\n delta=self.elo1-self.elo0\n N=30\n# Various error conditions must be handled better here!\n while True:\n Elo0=avg_elo-N*delta\n Elo1=avg_elo+N*delta\n sol=brentq(lambda elo:self.outcome_prob(elo)-(1-p),Elo0,Elo1)\n if sol['msg']=='no bracket':\n N*=2\n continue\n break\n return sol['x0']","def analysis(self) -> \"PriorFactor\":\n return self","def lnprior(self, params):\n self.debug.start_function('lnprior')\n lower_bounds = self.mcmc_version.prior_bounds[:, 0]\n upper_bounds = self.mcmc_version.prior_bounds[:, 1]\n inside_bounds = np.logical_and(params > lower_bounds,\n params < upper_bounds)\n\n if False in inside_bounds:\n self.debug.end_function()\n return self.zero_lhood\n\n if self.has_logz:\n z_input = params[self.param_idxs['logz']]\n else:\n z = params[self.param_idxs['z']]\n z_input = np.log10(z / z_sun)\n\n prior_lhood = np.log(self.z_prior(z_input))\n\n # ===== anisotropy/inclination priors =====\n if self.has_two_f:\n xi_ratio = params[self.param_idxs['f_p']] / params[self.param_idxs['f_b']]\n prior_lhood += np.log(self.xi_ratio_prior(xi_ratio))\n elif self.has_xi_ratio:\n xi_ratio = params[self.param_idxs['xi_ratio']]\n d_b = params[self.param_idxs['d_b']]\n prior_lhood += np.log(self.xi_ratio_prior(xi_ratio))\n prior_lhood += np.log(self.d_b_prior(d_b))\n\n self.debug.variable('prior_lhood', prior_lhood, formatter='f')\n self.debug.end_function()\n return prior_lhood","def get_prior(self, x):\n\n K_xx = self.kernel.eval(x, x)\n prior_mean = self.mean.eval(x)\n return prior_mean, K_xx","def set_prior(self, regexp, what):\r\n if self.priors is None:\r\n self.priors = [None for i in range(self._get_params().size)]\r\n\r\n which = self.grep_param_names(regexp)\r\n\r\n # check tied situation\r\n tie_partial_matches = [tie for tie in self.tied_indices if (not set(tie).isdisjoint(set(which))) & (not set(tie) == set(which))]\r\n if len(tie_partial_matches):\r\n raise ValueError, \"cannot place prior across partial ties\"\r\n tie_matches = [tie for tie in self.tied_indices if set(which) == set(tie) ]\r\n if len(tie_matches) > 1:\r\n raise ValueError, \"cannot place prior across multiple ties\"\r\n elif len(tie_matches) == 1:\r\n which = which[:1] # just place a prior object on the first parameter\r\n\r\n\r\n # check constraints are okay\r\n\r\n if what.domain is POSITIVE:\r\n constrained_positive_indices = [i for i, t in zip(self.constrained_indices, self.constraints) if t.domain is POSITIVE]\r\n if len(constrained_positive_indices):\r\n constrained_positive_indices = np.hstack(constrained_positive_indices)\r\n else:\r\n constrained_positive_indices = np.zeros(shape=(0,))\r\n bad_constraints = np.setdiff1d(self.all_constrained_indices(), constrained_positive_indices)\r\n assert not np.any(which[:, None] == bad_constraints), \"constraint and prior incompatible\"\r\n unconst = np.setdiff1d(which, constrained_positive_indices)\r\n if len(unconst):\r\n print \"Warning: constraining parameters to be positive:\"\r\n print '\\n'.join([n for i, n in enumerate(self._get_param_names()) if i in unconst])\r\n print '\\n'\r\n self.constrain_positive(unconst)\r\n elif what.domain is REAL:\r\n assert not np.any(which[:, None] == self.all_constrained_indices()), \"constraint and prior incompatible\"\r\n else:\r\n raise ValueError, \"prior not recognised\"\r\n\r\n # store the prior in a local list\r\n for w in which:\r\n self.priors[w] = what","def test_3_prior(self):\n print(\"test 3: prior probabilities\")\n\n for i, x in enumerate(self.X):\n print(i+1, prior_probability(\n x, self.means, self.dispersions, self.cluster_probabilities\n ), sep=' : ')","def lnprior(theta):\n\ta, b = theta\n\tif -5.0 < a < 5.0 and -10.0 < b < 10.0:\n\t\treturn 0.0\n\treturn -np.inf","def prior(old_params,params):\n \n for s in range(len(params)):\n if params[s] < 0.0 or params[s] > 2:\n return 0\n return 1","def priorProb(self, state):\n actions = []\n for i in range(0, 10):\n actions.append(((i, i+1), random.uniform(0, 1))) \n \n return actions","def pickTopicFromPrior(self):\r\n return self.priorTopicDistr.sampleIndex()","def prior_distribution(self):\n out = self.model.forward(self.inducing_points)\n return MultivariateNormal(out.mean, out.lazy_covariance_matrix.evaluate_kernel())","def get_action(self, terminal=None, prior=None):\n\t\tself.extend_plan(self.H)\n\n\t\t# Log initial trajectory\n\t\tenv_state = self.env.sim.get_state() if self.mujoco else None\n\t\tterminal = terminal if self.use_terminal else None\n\t\tinit_states, _, init_rews, cum_rew, emp_rew = \\\n\t\t\ttraj.eval_traj(\n\t\t\t\tcopy.deepcopy(self.env),\n\t\t\t\tenv_state, self.prev_obs,\n\t\t\t\tmujoco=self.mujoco, perturb=self.perturb,\n\t\t\t\tH=self.H, gamma=self.gamma, act_mode='fixed',\n\t\t\t\tpt=(np.array(self.planned_actions), 0),\n\t\t\t\tterminal=terminal, tvel=self.tvel\n\t\t\t)\n\n\t\tself.hist['init_plan'][self.time] = [cum_rew, emp_rew]\n\n\t\tself.init_cache = (init_states, init_rews, emp_rew)\n\n\t\t# Use the prior as the starting trajectory, if better\n\t\tif prior is not None:\n\t\t\tpol_val = self.hist['pols'][self.time][0]\n\t\t\tratio = (cum_rew-pol_val) / abs(pol_val)\n\t\t\tif pol_val > cum_rew:\n\t\t\t\tself.hist['prior_des'].append('pol')\n\t\t\t\tself.planned_actions = []\n\t\t\t\tfor i in range(prior.shape[0]):\n\t\t\t\t\tself.planned_actions.append(prior[i])\n\n\t\t\t\t# Store for calculations later\n\t\t\t\tinit_states = self.pol_cache[0]\n\t\t\t\tinit_rews = self.pol_cache[1]\n\t\t\telse:\n\t\t\t\tself.hist['prior_des'].append('plan')\n\t\telse:\n\t\t\tratio = 1\n\n\t\t# Determine if we should use a lower planning horizon\n\t\tH = self.H\n\t\tif self.has_aop:\n\t\t\t# First, check std threshold\n\t\t\tstd = self.hist['vals'][self.time][2]\n\t\t\tforce_full_H = std > self.params['aop']['std_thres']\n\n\t\t\t# Pick Bellman error based on future values\n\t\t\trews = init_rews\n\t\t\tstates_t = torch.tensor(init_states, dtype=self.dtype)\n\t\t\tstates_t = states_t.to(device=self.device)\n\t\t\tvals = self.val_ens.forward(states_t)\n\t\t\tvals = torch.squeeze(vals, dim=-1)\n\t\t\tvals = vals.detach().cpu().numpy()\n\t\t\tcval = self.hist['vals'][self.time][0]\n\n\t\t\t# Calculate the Bellman error from each state\n\t\t\tbelerrs, run_rew = np.zeros(rews.shape[0]), 0\n\t\t\tfor k in reversed(range(self.H)):\n\t\t\t\trun_rew = rews[k] + self.gamma * run_rew\n\t\t\t\tcv = vals[k-1] if k > 0 else cval\n\t\t\t\tbelerrs[k] = abs(run_rew + vals[-1] - cv)\n\n\t\t\tif force_full_H:\n\t\t\t\tH = self.H\n\t\t\telse:\n\t\t\t\t# Use the first H where the Bellman error is too high\n\t\t\t\tH = 1\n\t\t\t\tBE_thres = self.params['aop']['bellman_thres']\n\t\t\t\tfor k in reversed(range(self.H)):\n\t\t\t\t\tif belerrs[k] > BE_thres:\n\t\t\t\t\t\tH += k\n\t\t\t\t\t\tbreak\n\n\t\t\t\t# Get the value of the plan w.r.t. new H\n\t\t\t\tcum_rew = (self.gamma ** H) * vals[H-1]\n\t\t\t\tfor t in range(H):\n\t\t\t\t\tcum_rew += rews[t]\n\n\t\t# Logging for AOP\n\t\tself.hist['H_des'][self.time] = H\n\t\tif self.has_aop:\n\t\t\tself.hist['plan_belerr'][self.time].append(belerrs)\n\n\t\t# Store best plan found by MPC\n\t\tbest_plan_val = cum_rew\n\t\tbest_plan = copy.deepcopy(self.planned_actions)\n\t\told_plan_val = cum_rew\n\n\t\t# Run multiple iterations of trajectory optimization\n\t\tfor i in range(self.params['mpc']['num_iter']):\n\t\t\t# Continue if the ratio threshold is met\n\t\t\t_continue = True\n\t\t\tif self.has_aop:\n\t\t\t\tthres = self.params['aop']['init_thres'] if i == 0 else \\\n\t\t\t\t\t\tself.params['aop']['ratio_thres']\n\t\t\t\trprob = random.random() < self.params['aop']['eps_plan']\n\t\t\t\t_continue = (ratio > thres) or rprob\n\t\t\tif not _continue:\n\t\t\t\tbreak\n\n\t\t\tnum_rollouts = self.params['mpc']['num_rollouts']\n\n\t\t\tself.planned_actions = copy.deepcopy(best_plan)\n\t\t\tself.extend_plan(H)\n\n\t\t\t# Run MPC optimization\n\t\t\tself.update_plan(terminal, num_rollouts)\n\n\t\t\tself.finished_plan_update(i)\n\n\t\t\t# Update best plan found by MPC\n\t\t\tnew_plan_val = self.hist['plan'][self.time][i][2]\n\t\t\tratio = (new_plan_val - old_plan_val) / abs(old_plan_val)\n\t\t\tif new_plan_val > best_plan_val:\n\t\t\t\tbest_plan_val = new_plan_val\n\t\t\t\tbest_plan = copy.deepcopy(self.planned_actions)\n\n\n\t\t\told_plan_val = new_plan_val\n\n\t\tself.planned_actions = copy.deepcopy(best_plan)\n\n\t\t# Measure final planning information metrics\n\t\tenv_state = self.env.sim.get_state() if self.mujoco else None\n\t\tfin_states, _, fin_rews, fin_cum_rew, fin_emp_rew = \\\n\t\t\ttraj.eval_traj(\n\t\t\t\tcopy.deepcopy(self.env),\n\t\t\t\tenv_state, self.prev_obs,\n\t\t\t\tmujoco=self.mujoco, perturb=self.perturb,\n\t\t\t\tH=len(self.planned_actions),\n\t\t\t\tgamma=self.gamma, act_mode='fixed',\n\t\t\t\tpt=(np.array(self.planned_actions), 0),\n\t\t\t\tterminal=terminal, tvel=self.tvel\n\t\t\t)\n\n\t\tif len(self.cache) == 0:\n\t\t\tself.cache = ([], [])\n\t\tself.cache = (\n\t\t\tself.cache[0], self.cache[1], \n\t\t\tfin_states, fin_rews\n\t\t)\n\n\t\t# Increment the trajectory by a timestep\n\t\taction = self.advance_plan()\n\n\t\treturn action","def prior(mu):\n p = np.ones(len(mu))/(mu.max()-mu.min())\n return p","def low(self):","def ComputeLogShapePriorTerm(self, *args):\n return _ITKCostFunctionsPython.itkShapePriorMAPCostFunctionBaseIF3F_ComputeLogShapePriorTerm(self, *args)","def ComputeLogShapePriorTerm(self, *args):\n return _ITKCostFunctionsPython.itkShapePriorMAPCostFunctionBaseIF2F_ComputeLogShapePriorTerm(self, *args)","def smaller(self):\n return [x for x in TransitiveIdeal(attrcall('pred'), [self])]","def lnprior(params):\n a, b, f = params\n if -10.0 < b < 0. and 0. < a < 10 and 0. < f:\n return 0.0\n\n return -np.inf","def get_log_prior(samples):\n prior_distribution = tfp.distributions.Normal(loc=0., scale=1.)\n log_prior = tf.reduce_sum(prior_distribution.log_prob(samples), axis=1)\n return log_prior","def previous(self) -> Optional[Chainable]:\n return None","def sample_from_prior(self):\n raise NotImplementedError","def log_prior(x):\n logp = (-0.5 * x.pow(2) - torch.tensor(2 * math.pi).sqrt().log()).sum(dim=1)\n return logp","def __init__(self, prior: Prior):\n # TODO: Consider analytical solution rather than implementing optimisation\n super().__init__(prior.factor, x=prior, name=namer(self.__class__.__name__))\n self.prior = prior\n self.label = f\"PriorFactor({prior.label})\"","def previous(self):\n\n pass","def lnprior(self,theta):\n kwargs = dict(zip(self.params,theta))\n err = np.seterr(invalid='raise')\n try:\n lnprior = np.sum(np.log([self.priors[k](v) for k,v in kwargs.items()]))\n except FloatingPointError,ValueError:\n lnprior = -np.inf\n np.seterr(**err)\n return lnprior","def propose(self):\n\n p = type(self)(self.n, alpha=self.alpha)\n\n return p, p.compute_prior() - self.compute_prior()","def priorProbabilities():\r\n\ttotal = 0.0\r\n\tpos = 0.0\r\n\tneg = 0.0\r\n\r\n\t# Count the amount of positives and negatives in the training data\r\n\tfor item in trainingData:\r\n\t\ttotal += 1\r\n\t\tif item[1] == '0':\r\n\t\t\tpos +=1\r\n\t\tif item[1] == '1':\r\n\t\t\tneg +=1\r\n\t\t\t\r\n\t\t\t\r\n\t# Return the positive and negative probabilities \r\n\tposProb = float(pos / total * 100)\r\n\tnegProb = float(neg / total * 100)\r\n\r\n\t\r\n\t\r\n\treturn posProb, negProb","def _prey_step(self):\n raise NotImplementedError()","def prior_of_priors(self, tt):\n for i in xrange(self.n_params): \n try: \n p_theta *= self.param_obj.prior()[i].pdf(tt[i]) \n\n except UnboundLocalError: \n p_theta = self.param_obj.prior()[i].pdf(tt[i]) \n\n return p_theta","def prediccion(self):\n # Project the state ahead\n self.X = self.F @ self.X + self.B @ self.M\n self.P = self.F @ self.P @ self.F.T + self.Q\n\n return self.X","def _call(self, x):\n if functional.prior is None:\n return (-1.0) / x + 1\n else:\n return (-functional.prior) / x + 1","def _log_prior(self, theta, bounds):\n if not ((bounds[0] < theta).all() and (theta < bounds[1]).all()):\n return -np.inf\n else:\n return 0.0","def bmshj2018_hyperprior(\n quality, metric=\"mse\", pretrained=False, progress=True, **kwargs\n):\n if metric not in (\"mse\", \"ms-ssim\"):\n raise ValueError(f'Invalid metric \"{metric}\"')\n\n if quality < 1 or quality > 8:\n raise ValueError(f'Invalid quality \"{quality}\", should be between (1, 8)')\n\n return _load_model(\n \"bmshj2018-hyperprior\", metric, quality, pretrained, progress, **kwargs\n )","def _set_priorunc(self, lai_coeff_absunc=None, statevec_absunc=None, is_generic_prior=False):\n if not self.__dict__.has_key('prstate') or self.prstate is None:\n msg = \"internal error, prior state does not yet exist!\"\n FileLogger.fatal(msg)\n raise RuntimeError(msg)\n if lai_coeff_absunc!=None:\n self.lai_coeff_unc = np.maximum( lai_coeff_absunc, self.lai_coeff_uncfloor )\n msg = \"applied absolute uncertainty on lai coefficient, \"\n msg += \"lai_coeff_absunc={}\".format(lai_coeff_absunc)\n FileLogger.info(msg)\n else:\n self.lai_coeff_unc = np.maximum( self.lai_coeff*self.lai_coeff_relunc,\n self.lai_coeff_uncfloor )\n msg = \"applied relative uncertainty {} on lai coefficient.\".format(\n self.lai_coeff_relunc)\n FileLogger.info(msg)\n\n #-- allocate space for uncertainty array\n self.prstate_unc = np.empty(self.prstate.shape, dtype=np.float64)\n\n #-- generic prior, user-supplied relative uncertainties\n if is_generic_prior and self.relunc_by_user:\n self.prstate_unc[0,:] = np.maximum( self.prstate[0,:]*self.lai_relunc,\n self.lai_uncfloor )\n self.prstate_unc[1,:] = np.maximum( self.prstate[1,:]*self.canht_relunc,\n self.canht_uncfloor )\n self.prstate_unc[2,:] = np.maximum( self.prstate[2,:]*self.sm_relunc,\n self.sm_uncfloor )\n msg = \"applied relative uncertainty on state vector components, \"\n msg += \"lai_relunc={} canht_relunc={} sm_relunc={}\".format(\n self.lai_relunc, self.canht_relunc, self.sm_relunc)\n FileLogger.info(msg)\n #-- either: prior information from ini-file or generic prior\n elif statevec_absunc!=None:\n lai_absunc,canht_absunc,sm_absunc = statevec_absunc\n self.prstate_unc[0,:] = np.maximum( lai_absunc, self.lai_uncfloor )\n self.prstate_unc[1,:] = np.maximum( canht_absunc, self.canht_uncfloor )\n self.prstate_unc[2,:] = np.maximum( sm_absunc, self.sm_uncfloor )\n msg = \"applied absolute uncertainty on state vector components, \"\n msg += \"lai_absunc={} canht_absunc={} sm_absunc={}\".format(\n lai_absunc, canht_absunc, sm_absunc)\n FileLogger.info(msg)\n #-- relative uncertainty (default or user-supplied)\n else:\n self.prstate_unc[0,:] = np.maximum( self.prstate[0,:]*self.lai_relunc,\n self.lai_uncfloor )\n self.prstate_unc[1,:] = np.maximum( self.prstate[1,:]*self.canht_relunc,\n self.canht_uncfloor )\n self.prstate_unc[2,:] = np.maximum( self.prstate[2,:]*self.sm_relunc,\n self.sm_uncfloor )\n msg = \"applied relative uncertainty on state vector components, \"\n msg += \"lai_relunc={} canht_relunc={} sm_relunc={}\".format(\n self.lai_relunc, self.canht_relunc, self.sm_relunc)\n FileLogger.info(msg)\n #-- logging\n msg = \"uncertainty floor values were applied as follows: \"\n msg += \"lai_uncfloor={} canht_uncfloor={} sm_uncfloor={}\".format(\n self.lai_uncfloor, self.canht_uncfloor, self.sm_uncfloor)\n FileLogger.info(msg)","def compute_log_prior(self,params: ndarray) -> float:\n ln_tE = params[0]\n ln_A0 = params[1]\n ln_deltaT = params[2]\n fbl = params[3]\n mb = params[4]\n\n # Equation (16,15,17) (note that Albrow uses \"log\" for log10)\n log10e = np.log10(np.exp(1))\n ln_pr_ln_tE = np.log(0.476) - ((log10e*ln_tE - 1.333)**2 / 0.330) + np.log(log10e)\n ln_pr_ln_A0 = np.log(0.660) - (1.289*log10e*ln_A0) + np.log(log10e)\n ln_pr_ln_deltaT = np.log(0.156) - ((log10e*ln_deltaT - 1.432)**2 / 0.458) +\\\n np.log(log10e)\n \n # Paper doesnt mention the prior used, but I assume it to be uniform\n ln_pr_fbl = uniform.logpdf(fbl,0.0,1.0)\n\n # Paper doesnr mention the prior used but I will asuumed it to be uniform\n ln_pr_mb = uniform.logpdf(mb,self.mag_min - 1.0, self.mag_max + 1.0)\n \n \n return ln_pr_fbl + ln_pr_ln_A0 + ln_pr_ln_deltaT + ln_pr_ln_tE + ln_pr_mb","def get_prey(self, t):\n i_dead = self.output[self.output['time'] <= t]['index']\n i_alive = np.setdiff1d(self.prey_original['index'], i_dead,\n assume_unique=True)\n return self.prey_original[i_alive]","def getPrev(self):\n\t\t\treturn self.prev","def _construct_sample_from_prior(self):\n z_sym = T.matrix()\n x_sym = T.matrix()\n irs = self.ir_steps\n oputs = [self.obs_transform(self.s0)]\n oputs.extend([self.obs_transform(self.si[i]) for i in range(irs)])\n _, hi_zmuv = self._construct_zmuv_samples(x_sym, 1)\n sample_func = theano.function(inputs=[z_sym, x_sym], outputs=oputs, \\\n givens={ self.z: z_sym, \\\n self.x_in: T.zeros_like(x_sym), \\\n self.x_out: T.zeros_like(x_sym), \\\n self.hi_zmuv: hi_zmuv }, \\\n updates=self.scan_updates)\n def prior_sampler(samp_count):\n x_samps = to_fX( np.zeros((samp_count, self.obs_dim)) )\n old_switch = self.train_switch.get_value(borrow=False)\n # set model to generation mode\n self.set_train_switch(switch_val=0.0)\n z_samps = to_fX( npr.randn(samp_count, self.z_dim) )\n model_samps = sample_func(z_samps, x_samps)\n # set model back to either training or generation mode\n self.set_train_switch(switch_val=old_switch)\n return model_samps\n return prior_sampler","def set_prior(self,field):\n self.observation_thresholds = [i/self.observations for i in range(0,self.observations)]\n self.observation_samples = 1\n # TODO: For use after integrating image processing with MCESP for Game-Delayed Reinforcements\n # self.norm = field.max()","def pred(self, index, prev):\n return self.tour[index - 1] if prev else self.tour[index + 1]","def _calc_pareto_front(self, *args, **kwargs):\n fname = f\"{self.fct.name}_PF.dat\"\n F = load_pareto_front_from_file(os.path.join(\"modact\", fname))\n if F is not None:\n return F*self.weights*-1","def log_prior(self, i):\n mu = self.prior.m_0\n var = (self.prior.k_0 + 1.) / (self.prior.k_0*self.prior.v_0) * self.prior.S_0\n log_prod_var = np.log(var).sum()\n inv_var = 1./var\n v = self.prior.v_0\n return self._log_prod_students_t(i, mu, log_prod_var, inv_var, v)","def _get_initial_hypos(self):\n return [CombiStatePartialHypo(self.get_predictor_states())]","def _call(self, x):\n if functional.prior is None:\n return np.exp(x)\n else:\n return functional.prior * np.exp(x)","def ComputeLogShapePriorTerm(self, *args):\n return _ITKCostFunctionsPython.itkShapePriorMAPCostFunctionBaseID3D_ComputeLogShapePriorTerm(self, *args)","def get_initial_priority(self,state, action, rewards, next_state, done):\n ## obtain the discounted sum of rewards from reward list\n ## also obtain final gamma multiplier\n reduced_reward, gamma_multiplier = self.reduce_rewards([rewards])\n \n ## Convert to tensors\n state = np_to_tensor(state).unsqueeze(0)\n action = np_to_tensor(action).unsqueeze(0)\n reduced_reward = np_to_tensor(reduced_reward)\n gamma_multiplier = np_to_tensor(gamma_multiplier)\n next_state = np_to_tensor(next_state).unsqueeze(0)\n done = np_to_tensor(np.array(done).astype(np.uint8))\n \n ## Obtain the action from actor target network\n greedy_action = self.actor_target(next_state)\n ## Compute the temporal difference\n target = reduced_reward + torch.mul( torch.mul(gamma_multiplier , self.QNetwork_target(next_state, greedy_action)) , (1-done))\n with torch.no_grad():\n Q_sa = self.QNetwork_local(state, action)\n td = Q_sa-target\n ## return absolute value of temporal difference as priority \n return tensor_to_np(td.abs().squeeze())","def transformPriorErrorCovariance(self):\n U_a, w_a, V_aT = np.linalg.linalg.svd(self.model.priorCovariance, \n full_matrices=False)\n V_a = V_aT.T\n self.priorSinvh = V_a * np.matrix(np.diag(np.sqrt(1.0/w_a))) * U_a.T\n self.priorSh = U_a * np.matrix(np.diag(np.sqrt(w_a))) * V_aT\n self.priorSinv = V_a * np.matrix(np.diag(1.0/w_a)) * U_a.T","def ComputeLogShapePriorTerm(self, *args):\n return _ITKCostFunctionsPython.itkShapePriorMAPCostFunctionBaseID2D_ComputeLogShapePriorTerm(self, *args)","def get_prev(self):\n return self.prev","def gaussian_prior(self):\n self.prior = sps.multivariate_normal(self.m0,self.S0)","def _call(self, x):\n if functional.prior is None:\n return 1.0 / (1 - x)\n else:\n return functional.prior / (1 - x)","def prior_param(self, param_dict={}): \n self.param_obj = Params(param_dict) # parameter object \n self.param_names = param_dict.keys() \n self.n_params = len(param_dict.keys()) # number of parameters in theta ","def pre_weight(self, y):\n\n if not isinstance(self._kernel, (TransformedDistribution, Independent)):\n return self._model.log_prob(y, self._kernel.loc)\n elif isinstance(self._kernel, Independent):\n return self._model.log_prob(y, self._kernel.base_dist.loc)\n\n # TODO: Not entirely sure about this, but think this is the case\n at = next(k for k in self._kernel.transforms if isinstance(k, AffineTransform))\n\n return self._model.log_prob(y, at.loc)","def RankineHugoniot(P):\n # prevP = P+1\n # while(abs(P-prevP)>tol):\n # prevP = P\n Pr = P/rbc[0]\n temp = 1/beta*(cR/cL)*(Pr-1)\n temp = temp/np.sqrt(1+(gamma+1)/(2*gamma)*(Pr-1))\n temp = (1-temp)**beta\n # return P\n return temp*lbc[0]-P","def prior(self, c, labeled):\n return log(len(labeled[c])/self.N_features)","def log_prior(self, x_scaled):\n x = scale_inverse_transform(self.parameters_scaler, x_scaled)\n ln_prior = {}\n for key in self.param_names:\n try:\n ln_prior[key] = self.priors[key].log_prob(x[:, self.param_names.index(key)])\n except TypeError:\n x = tf.cast(x, tf.float32)\n ln_prior[key] = self.priors[key].log_prob(x[:, self.param_names.index(key)])\n ln_prior_sum = tf.math.reduce_sum(tf.convert_to_tensor(list(ln_prior.values()), dtype=float_type),\n axis=0)\n # If prior is finite, replace with zero\n zero_prior = tf.zeros_like(ln_prior_sum)\n constraints = tf.math.is_inf(ln_prior_sum)\n tensor_log_prior = tf.where(constraints, ln_prior_sum, zero_prior)\n return tensor_log_prior","def _call(self, x):\n if self.prior is None:\n tmp = ((x - 1 - np.log(x)).inner(self.domain.one()))\n else:\n # This is the old line from odl version 0.6.0.\n # tmp = ((x - self.prior + self.prior * np.log(self.prior / x))\n tmp = ((x - self.prior + self.prior * np.log((self.prior + 1e-12) / x))\n .inner(self.domain.one()))\n if np.isnan(tmp):\n # In this case, some element was less than or equal to zero\n return np.inf\n else:\n return tmp","def add_prior(self, beastxml):\n coalescent = xml.distribution(\n beastxml.prior, id=\"Coalescent.t:beastlingTree\", spec=\"Coalescent\")\n popmod = xml.populationModel(\n coalescent, id=\"ConstantPopulation:beastlingTree\", spec=\"ConstantPopulation\")\n xml.parameter(popmod, idref=\"popSize.t:beastlingTree\", name=\"popSize\")\n xml.treeIntervals(\n coalescent, id=\"TreeIntervals\", spec=\"TreeIntervals\", tree=\"@Tree.t:beastlingTree\")","def prev(self):\n return self.__prev"],"string":"[\n \"def lnprior(self):\\n \\n return\",\n \"def prior(self):\\n return self.__prior\",\n \"def prior(self):\\n return self.__prior\",\n \"def prior(self):\\n return self.__prior\",\n \"def prior(self):\\n return self.__prior\",\n \"def get_prior(self):\\n return self.prior\",\n \"def prior_predictive(self):\\n cfg = self.config\\n n = cfg['batch_size'] * cfg['q/n_samples']\\n n_samples = cfg['q/n_samples']\\n with util.get_or_create_scope('model', reuse=True):\\n h_prior = tf.cast(self.p_h_L.sample(n), cfg['dtype'])\\n h_prior = tf.reshape(\\n h_prior, [cfg['q/n_samples'], cfg['batch_size'], -1])\\n h = [None] * cfg['p/n_layers']\\n h[cfg['p/n_layers'] - 1] = h_prior\\n for n in range(cfg['p/n_layers'] - 1, 0, -1):\\n p_h_n = self.build_stochastic_layer(n, h_above=h[n])\\n h[n - 1] = tf.cast(p_h_n.sample(), cfg['dtype'])\\n return self.likelihood(h[0])\",\n \"def log_prior(self):\\n raise NotImplementedError(\\\"the log_prior property should \\\"\\n \\\"be defined in the Estimator sub-class\\\")\",\n \"def log_prior(self, params):\",\n \"def get_prior(self):\\n return self._prior\",\n \"def prior(\\n self,\\n next_state: np.ndarray,\\n state: np.ndarray,\\n control_x: Optional[np.ndarray] = None\\n ) -> np.ndarray:\\n pass\",\n \"def bias_prior(self):\",\n \"def prior_sample(self):\\n pass\",\n \"def testHrtPrior(self):\\n attr = self.session.create_visit_attr()\\n\\n self.util.boolTypeTest(self, attr, \\\"prior\\\")\\n\\n self.util.boolPropertyTest(self, attr, \\\"prior\\\")\",\n \"def _prior_teach(self):\\n pass\",\n \"def _compute_mix_prior(self):\\n if np.all(self.mix_prior == 1):\\n return 0\\n return np.dot(np.log(self.mix_weight).T, (self.mix_prior - 1))\",\n \"def buildWPriorTerm(self):\\r\\n\\r\\n # self.w_prior.shape == (minibatch size,)\\r\\n self.w_prior = 0.5*T.sum(1 + T.log(self.qwgy_var) - self.qwgy_mu**2-self.qwgy_var, axis=1)\\r\\n\\r\\n self.w_prior_modif = - T.maximum(self.hyper['treshold_w_prior'], -self.w_prior)\",\n \"def buildConditionalPriorTerm(self):\\r\\n\\r\\n # shape is (batch size,)\\r\\n self.conditional_prior = - T.mean(T.sum(T.exp(self.log_pzgxw)*(self.log_qxgy.dimshuffle(0,'x',1,'x') - self.log_pxgzw), axis=3), axis=[1,2])\",\n \"def buildZPriorTerm(self):\\r\\n\\r\\n # shape is (batch size,)\\r\\n self.z_prior = - T.mean(T.sum(T.exp(self.log_pzgxw)*(self.log_pzgxw + T.log(self.hyper['num_clust'])), axis=3), axis=[1,2])\\r\\n\\r\\n self.z_prior_modif = - T.maximum(self.hyper['treshold_z_prior'], - self.z_prior)\",\n \"def P_prior(self):\\n return dot(self.U_prior, dot(diag(self.D_prior), self.U_prior.T))\",\n \"def ComputeLogPosePriorTerm(self, *args):\\n return _ITKCostFunctionsPython.itkShapePriorMAPCostFunctionBaseIF2F_ComputeLogPosePriorTerm(self, *args)\",\n \"def ComputeLogPosePriorTerm(self, *args):\\n return _ITKCostFunctionsPython.itkShapePriorMAPCostFunctionBaseIF3F_ComputeLogPosePriorTerm(self, *args)\",\n \"def get_prior(self):\\n assert self._prior in self._priors, 'Unsupported prior! Check the _priors attribute for a list of priors.'\\n if self._prior == 'Gaussian':\\n prior = 0.5 * torch.sum(self.parameters ** 2)/self.prior_var\\n elif self._prior == 'Cauchy':\\n dimconst = (self.parameters.shape[0] + 1)/2.\\n prior = dimconst*torch.log(self.prior_var + torch.sum(self.parameters ** 2))\\n elif self._prior == 'Sparse':\\n n = self.dataset.shape[1]\\n gauss_prior = 0.5 * torch.sum(torch.exp(self.parameters[-1] * torch.exp(self.parameters[n:2*n]) * self.parameters[:n] ** 2))\\n gamma_density = torch.distributions.Gamma(1.5,0.5)\\n# gamma_prior = -gamma_density.log_prob(torch.exp(self.parameters[n:])).sum()\\n# lambda_density = torch.distributions.Gamma(1.5,0.5)\\n lambda_prior = -gamma_density.log_prob(torch.exp(self.parameters[n:])).sum()\\n prior = gauss_prior + lambda_prior\\n return prior\",\n \"def lnprior(self, theta):\\n return_lp = 0.0\\n # ensure parameter limits are kept and frozen parameters are frozen.\\n for t in np.arange(self.ig.size):\\n if self.prior_string[t] == 't': ## for tophat\\n if self.pars_frozen[t] == 1 and theta[t] != self.ig[t]: ## if the parameter is frozen and the trial value is not the starting value\\n print('ERROR: theta[',t,'] is frozen and the trial value is not the starting value')\\n return_lp = return_lp -np.inf\\n elif theta[t] < self.parmins[t] or theta[t] > self.parmaxs[t]: ## if the parameter isn't frozen, but the trial value is outside limits\\n return_lp = return_lp -np.inf\\n else:\\n return_lp = return_lp + 0.0 # 0, because log of 1 is 0.\\n if self.prior_string[t] == 'g': ## for gaussian -- get informative prior value\\n if (self.pars_frozen[t] == 1 and theta[t] != self.ig[t]) or (theta[t] < self.parmins[t] or theta[t] > self.parmaxs[t]):\\n return_lp = return_lp -np.inf\\n elif self.pars_frozen[t] == 1 and theta[t] == self.ig[t]:\\n return_lp = return_lp + 0.0\\n else:\\n #gaussian prior on a\\n mu = self.ig[t]\\n sigma = self.priorsigmas[t]\\n return_lp = return_lp + (np.log(1.0/( sigma * np.sqrt(2*np.pi) ) ) - 0.5*(theta[t] - mu)**2/sigma**2)\\n return return_lp\",\n \"def prior(store):\\n mu = zeros(store['beta'].shape[0])\\n Prec = diag(0.005 * ones(store['beta'].shape[0]))\\n return -0.5 * dot(store['beta'].transpose(), dot(Prec, store['beta']))\",\n \"def eval_prior(self, state, action):\\n\\n return np.dot(state, self.a.T) + np.dot(action, self.b.T)\",\n \"def prior(cube, ndim, nparams):\\n # construct prior from recovery file\\n counter = 0\\n if params2 is None:\\n return\\n for key in params2.keys():\\n nparams_tmp = int(params2[key]['nparams'])\\n for ii in range(nparams_tmp):\\n # sp = [name, prior type, x1, x2]\\n sp =\\\\\\n params2[key]['param'+str(ii+1)].split(',')\\n if sp[1][0] == 'U' and sp[2][:5]=='param' and sp[3][:5]=='param':\\n subtract1 = int(key[-1]) - int(sp[2][-1])\\n subtract2 = int(key[-1]) - int(sp[3][-1])\\n cube[counter] = GeneralPrior(cube[counter], 'U',\\n cube[counter-subtract1], cube[counter-subtract2])\\n elif sp[1][0] == 'U' and sp[2][:5]=='param':\\n subtract = int(key[-1]) - int(sp[2][-1])\\n cube[counter] = GeneralPrior(cube[counter], 'U',\\n cube[counter-subtract], float(sp[3]))\\n elif sp[1][0] == 'U' and sp[3][:5]=='param':\\n subtract = int(key[-1]) - int(sp[2][-1])\\n cube[counter] = GeneralPrior(cube[counter], 'U',\\n float(sp[2]), cube[counter - subtract])\\n else:\\n cube[counter] = GeneralPrior(cube[counter], sp[1], float(sp[2]),\\n float(sp[3]))\\n counter += 1\",\n \"def ComputeLogPosePriorTerm(self, *args):\\n return _ITKCostFunctionsPython.itkShapePriorMAPCostFunctionBaseID2D_ComputeLogPosePriorTerm(self, *args)\",\n \"def lnprior(theta):\\n\\n\\t# keys = theta.keys()\\n\\ttheta_keys = [key for key in ap.init.keys()]\\n\\tif type(theta) == np.ndarray:\\n\\t\\ttheta = dict(zip(theta_keys, theta))\\n\\tkeys = theta.keys()\\n\\n\\tfor k in keys:\\n\\t\\tif (ap.prior[k][0] < theta[k] < ap.prior[k][1]):\\n\\t\\t\\tpass\\n\\t\\telse:\\n\\t\\t\\treturn -np.inf\\n\\t\\t\\tbreak\\n\\treturn 0.0\",\n \"def _one_step_ahead_prediction(self, X, y):\\n lagged_data = self.build_matrix(X, y)\\n\\n if self.basis_function.__class__.__name__ == \\\"Polynomial\\\":\\n X_base = self.basis_function.transform(\\n lagged_data,\\n self.max_lag,\\n predefined_regressors=self.pivv[: len(self.final_model)],\\n )\\n else:\\n X_base, _ = self.basis_function.transform(\\n lagged_data,\\n self.max_lag,\\n predefined_regressors=self.pivv[: len(self.final_model)],\\n )\\n\\n yhat = super()._one_step_ahead_prediction(X_base)\\n return yhat.reshape(-1, 1)\",\n \"def ComputeLogPosePriorTerm(self, *args):\\n return _ITKCostFunctionsPython.itkShapePriorMAPCostFunctionBaseID3D_ComputeLogPosePriorTerm(self, *args)\",\n \"def sample_from_prior(self, *args, **kwargs):\\n pass\",\n \"def prior_z(self) -> distributions.Distribution:\\n return distributions.Categorical(self.pi)\",\n \"def _n_step_ahead_prediction(self, X, y, steps_ahead):\\n yhat = super()._n_step_ahead_prediction(X, y, steps_ahead)\\n return yhat\",\n \"def set_prior_priorunc_synthetic(self):\\n\\n lai_coeff_absunc = None\\n statevec_absunc = None\\n\\n #-- \\n if self.prior_inifile!=None:\\n lai_coeff_absunc, statevec_absunc = self._setprior_from_inifile()\\n elif self.use_generic_prior:\\n self._setprior_generic_agriculture()\\n statevec_absunc = self.generic_prior_unc\\n else:\\n #-- overall number of time-points in schedule\\n npts = self.get_npts()\\n\\n #-- default prior file\\n prior_file = os.path.join(ipt_dir_path, 'mni_stat_jules_2017.csv')\\n\\n #-- get signature simulator default state\\n msg = \\\"START reading state variables from file ***{}***...\\\".format(prior_file)\\n FileLogger.info(msg)\\n state_inst = sv.get_state_csv(fname=prior_file, fmt='%Y-%m-%d %H:%M:%S' )\\n msg = \\\"...reading DONE\\\"\\n FileLogger.info(msg)\\n\\n #-- LAI,Canopy-Height,Soil-Moisture\\n self.prstate = np.empty((3,npts), dtype=np.float64)\\n\\n for i,date_utc in enumerate(self.schedule_dct['date_utc']):\\n idx, timedelt = sv.find_nearest_date_idx(state_inst.date_utc, date_utc)\\n # print \\\"MVMV::nearest={} idx={} timedelt={}\\\".format(\\n # state_inst.date_utc[idx], idx, timedelt)\\n #-- LAI\\n self.prstate[0,i] = state_inst.lai[idx]\\n #-- canopy-height\\n self.prstate[1,i] = state_inst.can_height[idx]\\n #-- SM\\n self.prstate[2,i] = state_inst.soil_moisture[idx]\\n\\n #-- set uncertainty values\\n self._set_priorunc(statevec_absunc=statevec_absunc, lai_coeff_absunc=lai_coeff_absunc)\",\n \"def prior(self, state, action, rnn_hidden, rnn_hidden_c=None):\\n state_action = self.state_action_layer(torch.cat([state, action], dim=-1)) # [batch, hidden]\\n if self.mode == 'LSTM':\\n rnn_hidden, rnn_hidden_c = self.transition_RNN(state_action, (rnn_hidden, rnn_hidden_c))\\n else:\\n rnn_hidden = self.transition_RNN(state_action, rnn_hidden) # [batch, hidden]\\n # hidden_prior = self.hidden_prior(rnn_hidden) #[batch, hidden]\\n hidden_prior = rnn_hidden\\n prior_mean = self.prior_mean(hidden_prior) # [batch, state]\\n # prior_sigma = F.softplus(self.prior_sigma(hidden_prior)) + self._min_stddev #[batch, state]\\n prior_sigma = torch.exp(self.prior_sigma(hidden_prior))\\n if self.mode == 'LSTM':\\n return prior_mean, prior_sigma, rnn_hidden, rnn_hidden_c\\n else:\\n return prior_mean, prior_sigma, rnn_hidden\",\n \"def set_prior(self,which,what):\\n\\n which = self.grep_param_names(which)\\n\\n #check tied situation\\n tie_partial_matches = [tie for tie in self.tied_indices if (not set(tie).isdisjoint(set(which))) & (not set(tie)==set(which))]\\n if len(tie_partial_matches):\\n raise ValueError, \\\"cannot place prior across partial ties\\\"\\n tie_matches = [tie for tie in self.tied_indices if set(which)==set(tie) ]\\n if len(tie_matches)>1:\\n raise ValueError, \\\"cannot place prior across multiple ties\\\"\\n elif len(tie_matches)==1:\\n which = which[:1]# just place a prior object on the first parameter\\n\\n\\n #check constraints are okay\\n if isinstance(what, (priors.gamma, priors.log_Gaussian)):\\n assert not np.any(which[:,None]==self.constrained_negative_indices), \\\"constraint and prior incompatible\\\"\\n assert not np.any(which[:,None]==self.constrained_bounded_indices), \\\"constraint and prior incompatible\\\"\\n unconst = np.setdiff1d(which, self.constrained_positive_indices)\\n if len(unconst):\\n print \\\"Warning: constraining parameters to be positive:\\\"\\n print '\\\\n'.join([n for i,n in enumerate(self._get_param_names()) if i in unconst])\\n print '\\\\n'\\n self.constrain_positive(unconst)\\n elif isinstance(what,priors.Gaussian):\\n assert not np.any(which[:,None]==self.all_constrained_indices()), \\\"constraint and prior incompatible\\\"\\n else:\\n raise ValueError, \\\"prior not recognised\\\"\\n\\n\\n #store the prior in a local list\\n for w in which:\\n self.priors[w] = what\",\n \"def set_prior_priorunc_general(self):\\n\\n #-- some configurations apply absolute uncertainties\\n lai_coeff_absunc = None\\n statevec_absunc = None\\n is_generic_prior = False\\n\\n #--\\n if self.prior_states_file!=None:\\n states_file = self.prior_states_file\\n basename = os.path.basename(states_file)\\n if os.path.splitext(basename)[1]=='.nc':\\n msg = \\\"Prior state information will be read from ***{}***\\\".format(states_file)\\n FileLogger.info(msg)\\n self._setprior_jules(states_file)\\n msg = \\\"...reading prior DONE\\\"\\n FileLogger.info(msg)\\n elif os.path.splitext(basename)[1]=='.csv':\\n msg = \\\"Prior state information will be read from ***{}***\\\".format(states_file)\\n FileLogger.info(msg)\\n self._setprior_csv(states_file)\\n msg = \\\"...reading prior DONE\\\"\\n FileLogger.info(msg)\\n else:\\n msg = \\\"Unrecognised format of states file ***{}***. Cannot continue!\\\".format(\\n states_file)\\n FileLogger.fatal(msg)\\n raise RuntimeError(msg)\\n return\\n elif self.prior_inifile!=None:\\n lai_coeff_absunc, statevec_absunc = self._setprior_from_inifile()\\n else:\\n self._setprior_generic_agriculture()\\n is_generic_prior = True\\n statevec_absunc = self.generic_prior_unc\\n\\n #-- set uncertainty values\\n self._set_priorunc( lai_coeff_absunc=lai_coeff_absunc,\\n statevec_absunc=statevec_absunc,\\n is_generic_prior=is_generic_prior )\",\n \"def lnprior_abso(theta,fixed,fix,log,priorinf,priorsup):\\n for i in range(len(theta)):\\n if not (priorinf[i] < theta[i] < priorsup[i]):\\n return -np.inf\\n return 0.0\",\n \"def prior_model(self) -> Collection:\\n return Collection(self.prior)\",\n \"def prior_sample_parameter(self, parameter):\\n pass\",\n \"def add_prior(self, prior):\\n if self.rate_variation:\\n # Gamma prior with mean 1 over all mutation rates\\n sub_prior = ET.SubElement(prior, \\\"prior\\\", {\\\"id\\\":\\\"featureClockRatePrior.s:%s\\\" % self.name, \\\"name\\\":\\\"distribution\\\"})\\n compound = ET.SubElement(sub_prior, \\\"input\\\", {\\\"id\\\":\\\"featureClockRateCompound:%s\\\" % self.name, \\\"spec\\\":\\\"beast.core.parameter.CompoundValuable\\\", \\\"name\\\":\\\"x\\\"})\\n plate = ET.SubElement(compound, \\\"plate\\\", {\\n \\\"var\\\":\\\"feature\\\",\\n \\\"range\\\":\\\",\\\".join(self.features)})\\n ET.SubElement(plate, \\\"var\\\", {\\n \\\"idref\\\":\\\"featureClockRate:%s:$(feature)\\\" % self.name})\\n gamma = ET.SubElement(sub_prior, \\\"input\\\", {\\\"id\\\":\\\"featureClockRatePriorGamma:%s\\\" % self.name, \\\"spec\\\":\\\"beast.math.distributions.Gamma\\\", \\\"name\\\":\\\"distr\\\", \\\"alpha\\\":\\\"@featureClockRateGammaShape:%s\\\" % self.name, \\\"beta\\\":\\\"@featureClockRateGammaScale:%s\\\" % self.name})\\n # Exponential hyperprior on scale of Gamma prior\\n # Exponential prior favours small scales over large scales, i.e. less rate variation\\n # Mean scale 0.23 chosen for general sensibility, e.g.:\\n # - Prior distribution is roughly 50/50 that ratio of fastest\\n # to slowest feature rate in a dataset of size 200 is below\\n # or above 10.\\n # - Prior probability of roughly 0.90 that this ratio is below\\n # 100.\\n sub_prior = ET.SubElement(prior, \\\"prior\\\", {\\\"id\\\":\\\"featureClockRateGammaScalePrior.s:%s\\\" % self.name, \\\"name\\\":\\\"distribution\\\", \\\"x\\\":\\\"@featureClockRateGammaScale:%s\\\" % self.name})\\n ET.SubElement(sub_prior, \\\"Exponential\\\", {\\\"id\\\":\\\"featureClockRateGammaShapePriorExponential.s:%s\\\" % self.name, \\\"mean\\\":\\\"0.23\\\", \\\"name\\\":\\\"distr\\\"})\",\n \"def lower_cb(self,p):\\n avg_elo=self.elo0+self.elo1\\n delta=self.elo1-self.elo0\\n N=30\\n# Various error conditions must be handled better here!\\n while True:\\n Elo0=avg_elo-N*delta\\n Elo1=avg_elo+N*delta\\n sol=brentq(lambda elo:self.outcome_prob(elo)-(1-p),Elo0,Elo1)\\n if sol['msg']=='no bracket':\\n N*=2\\n continue\\n break\\n return sol['x0']\",\n \"def analysis(self) -> \\\"PriorFactor\\\":\\n return self\",\n \"def lnprior(self, params):\\n self.debug.start_function('lnprior')\\n lower_bounds = self.mcmc_version.prior_bounds[:, 0]\\n upper_bounds = self.mcmc_version.prior_bounds[:, 1]\\n inside_bounds = np.logical_and(params > lower_bounds,\\n params < upper_bounds)\\n\\n if False in inside_bounds:\\n self.debug.end_function()\\n return self.zero_lhood\\n\\n if self.has_logz:\\n z_input = params[self.param_idxs['logz']]\\n else:\\n z = params[self.param_idxs['z']]\\n z_input = np.log10(z / z_sun)\\n\\n prior_lhood = np.log(self.z_prior(z_input))\\n\\n # ===== anisotropy/inclination priors =====\\n if self.has_two_f:\\n xi_ratio = params[self.param_idxs['f_p']] / params[self.param_idxs['f_b']]\\n prior_lhood += np.log(self.xi_ratio_prior(xi_ratio))\\n elif self.has_xi_ratio:\\n xi_ratio = params[self.param_idxs['xi_ratio']]\\n d_b = params[self.param_idxs['d_b']]\\n prior_lhood += np.log(self.xi_ratio_prior(xi_ratio))\\n prior_lhood += np.log(self.d_b_prior(d_b))\\n\\n self.debug.variable('prior_lhood', prior_lhood, formatter='f')\\n self.debug.end_function()\\n return prior_lhood\",\n \"def get_prior(self, x):\\n\\n K_xx = self.kernel.eval(x, x)\\n prior_mean = self.mean.eval(x)\\n return prior_mean, K_xx\",\n \"def set_prior(self, regexp, what):\\r\\n if self.priors is None:\\r\\n self.priors = [None for i in range(self._get_params().size)]\\r\\n\\r\\n which = self.grep_param_names(regexp)\\r\\n\\r\\n # check tied situation\\r\\n tie_partial_matches = [tie for tie in self.tied_indices if (not set(tie).isdisjoint(set(which))) & (not set(tie) == set(which))]\\r\\n if len(tie_partial_matches):\\r\\n raise ValueError, \\\"cannot place prior across partial ties\\\"\\r\\n tie_matches = [tie for tie in self.tied_indices if set(which) == set(tie) ]\\r\\n if len(tie_matches) > 1:\\r\\n raise ValueError, \\\"cannot place prior across multiple ties\\\"\\r\\n elif len(tie_matches) == 1:\\r\\n which = which[:1] # just place a prior object on the first parameter\\r\\n\\r\\n\\r\\n # check constraints are okay\\r\\n\\r\\n if what.domain is POSITIVE:\\r\\n constrained_positive_indices = [i for i, t in zip(self.constrained_indices, self.constraints) if t.domain is POSITIVE]\\r\\n if len(constrained_positive_indices):\\r\\n constrained_positive_indices = np.hstack(constrained_positive_indices)\\r\\n else:\\r\\n constrained_positive_indices = np.zeros(shape=(0,))\\r\\n bad_constraints = np.setdiff1d(self.all_constrained_indices(), constrained_positive_indices)\\r\\n assert not np.any(which[:, None] == bad_constraints), \\\"constraint and prior incompatible\\\"\\r\\n unconst = np.setdiff1d(which, constrained_positive_indices)\\r\\n if len(unconst):\\r\\n print \\\"Warning: constraining parameters to be positive:\\\"\\r\\n print '\\\\n'.join([n for i, n in enumerate(self._get_param_names()) if i in unconst])\\r\\n print '\\\\n'\\r\\n self.constrain_positive(unconst)\\r\\n elif what.domain is REAL:\\r\\n assert not np.any(which[:, None] == self.all_constrained_indices()), \\\"constraint and prior incompatible\\\"\\r\\n else:\\r\\n raise ValueError, \\\"prior not recognised\\\"\\r\\n\\r\\n # store the prior in a local list\\r\\n for w in which:\\r\\n self.priors[w] = what\",\n \"def test_3_prior(self):\\n print(\\\"test 3: prior probabilities\\\")\\n\\n for i, x in enumerate(self.X):\\n print(i+1, prior_probability(\\n x, self.means, self.dispersions, self.cluster_probabilities\\n ), sep=' : ')\",\n \"def lnprior(theta):\\n\\ta, b = theta\\n\\tif -5.0 < a < 5.0 and -10.0 < b < 10.0:\\n\\t\\treturn 0.0\\n\\treturn -np.inf\",\n \"def prior(old_params,params):\\n \\n for s in range(len(params)):\\n if params[s] < 0.0 or params[s] > 2:\\n return 0\\n return 1\",\n \"def priorProb(self, state):\\n actions = []\\n for i in range(0, 10):\\n actions.append(((i, i+1), random.uniform(0, 1))) \\n \\n return actions\",\n \"def pickTopicFromPrior(self):\\r\\n return self.priorTopicDistr.sampleIndex()\",\n \"def prior_distribution(self):\\n out = self.model.forward(self.inducing_points)\\n return MultivariateNormal(out.mean, out.lazy_covariance_matrix.evaluate_kernel())\",\n \"def get_action(self, terminal=None, prior=None):\\n\\t\\tself.extend_plan(self.H)\\n\\n\\t\\t# Log initial trajectory\\n\\t\\tenv_state = self.env.sim.get_state() if self.mujoco else None\\n\\t\\tterminal = terminal if self.use_terminal else None\\n\\t\\tinit_states, _, init_rews, cum_rew, emp_rew = \\\\\\n\\t\\t\\ttraj.eval_traj(\\n\\t\\t\\t\\tcopy.deepcopy(self.env),\\n\\t\\t\\t\\tenv_state, self.prev_obs,\\n\\t\\t\\t\\tmujoco=self.mujoco, perturb=self.perturb,\\n\\t\\t\\t\\tH=self.H, gamma=self.gamma, act_mode='fixed',\\n\\t\\t\\t\\tpt=(np.array(self.planned_actions), 0),\\n\\t\\t\\t\\tterminal=terminal, tvel=self.tvel\\n\\t\\t\\t)\\n\\n\\t\\tself.hist['init_plan'][self.time] = [cum_rew, emp_rew]\\n\\n\\t\\tself.init_cache = (init_states, init_rews, emp_rew)\\n\\n\\t\\t# Use the prior as the starting trajectory, if better\\n\\t\\tif prior is not None:\\n\\t\\t\\tpol_val = self.hist['pols'][self.time][0]\\n\\t\\t\\tratio = (cum_rew-pol_val) / abs(pol_val)\\n\\t\\t\\tif pol_val > cum_rew:\\n\\t\\t\\t\\tself.hist['prior_des'].append('pol')\\n\\t\\t\\t\\tself.planned_actions = []\\n\\t\\t\\t\\tfor i in range(prior.shape[0]):\\n\\t\\t\\t\\t\\tself.planned_actions.append(prior[i])\\n\\n\\t\\t\\t\\t# Store for calculations later\\n\\t\\t\\t\\tinit_states = self.pol_cache[0]\\n\\t\\t\\t\\tinit_rews = self.pol_cache[1]\\n\\t\\t\\telse:\\n\\t\\t\\t\\tself.hist['prior_des'].append('plan')\\n\\t\\telse:\\n\\t\\t\\tratio = 1\\n\\n\\t\\t# Determine if we should use a lower planning horizon\\n\\t\\tH = self.H\\n\\t\\tif self.has_aop:\\n\\t\\t\\t# First, check std threshold\\n\\t\\t\\tstd = self.hist['vals'][self.time][2]\\n\\t\\t\\tforce_full_H = std > self.params['aop']['std_thres']\\n\\n\\t\\t\\t# Pick Bellman error based on future values\\n\\t\\t\\trews = init_rews\\n\\t\\t\\tstates_t = torch.tensor(init_states, dtype=self.dtype)\\n\\t\\t\\tstates_t = states_t.to(device=self.device)\\n\\t\\t\\tvals = self.val_ens.forward(states_t)\\n\\t\\t\\tvals = torch.squeeze(vals, dim=-1)\\n\\t\\t\\tvals = vals.detach().cpu().numpy()\\n\\t\\t\\tcval = self.hist['vals'][self.time][0]\\n\\n\\t\\t\\t# Calculate the Bellman error from each state\\n\\t\\t\\tbelerrs, run_rew = np.zeros(rews.shape[0]), 0\\n\\t\\t\\tfor k in reversed(range(self.H)):\\n\\t\\t\\t\\trun_rew = rews[k] + self.gamma * run_rew\\n\\t\\t\\t\\tcv = vals[k-1] if k > 0 else cval\\n\\t\\t\\t\\tbelerrs[k] = abs(run_rew + vals[-1] - cv)\\n\\n\\t\\t\\tif force_full_H:\\n\\t\\t\\t\\tH = self.H\\n\\t\\t\\telse:\\n\\t\\t\\t\\t# Use the first H where the Bellman error is too high\\n\\t\\t\\t\\tH = 1\\n\\t\\t\\t\\tBE_thres = self.params['aop']['bellman_thres']\\n\\t\\t\\t\\tfor k in reversed(range(self.H)):\\n\\t\\t\\t\\t\\tif belerrs[k] > BE_thres:\\n\\t\\t\\t\\t\\t\\tH += k\\n\\t\\t\\t\\t\\t\\tbreak\\n\\n\\t\\t\\t\\t# Get the value of the plan w.r.t. new H\\n\\t\\t\\t\\tcum_rew = (self.gamma ** H) * vals[H-1]\\n\\t\\t\\t\\tfor t in range(H):\\n\\t\\t\\t\\t\\tcum_rew += rews[t]\\n\\n\\t\\t# Logging for AOP\\n\\t\\tself.hist['H_des'][self.time] = H\\n\\t\\tif self.has_aop:\\n\\t\\t\\tself.hist['plan_belerr'][self.time].append(belerrs)\\n\\n\\t\\t# Store best plan found by MPC\\n\\t\\tbest_plan_val = cum_rew\\n\\t\\tbest_plan = copy.deepcopy(self.planned_actions)\\n\\t\\told_plan_val = cum_rew\\n\\n\\t\\t# Run multiple iterations of trajectory optimization\\n\\t\\tfor i in range(self.params['mpc']['num_iter']):\\n\\t\\t\\t# Continue if the ratio threshold is met\\n\\t\\t\\t_continue = True\\n\\t\\t\\tif self.has_aop:\\n\\t\\t\\t\\tthres = self.params['aop']['init_thres'] if i == 0 else \\\\\\n\\t\\t\\t\\t\\t\\tself.params['aop']['ratio_thres']\\n\\t\\t\\t\\trprob = random.random() < self.params['aop']['eps_plan']\\n\\t\\t\\t\\t_continue = (ratio > thres) or rprob\\n\\t\\t\\tif not _continue:\\n\\t\\t\\t\\tbreak\\n\\n\\t\\t\\tnum_rollouts = self.params['mpc']['num_rollouts']\\n\\n\\t\\t\\tself.planned_actions = copy.deepcopy(best_plan)\\n\\t\\t\\tself.extend_plan(H)\\n\\n\\t\\t\\t# Run MPC optimization\\n\\t\\t\\tself.update_plan(terminal, num_rollouts)\\n\\n\\t\\t\\tself.finished_plan_update(i)\\n\\n\\t\\t\\t# Update best plan found by MPC\\n\\t\\t\\tnew_plan_val = self.hist['plan'][self.time][i][2]\\n\\t\\t\\tratio = (new_plan_val - old_plan_val) / abs(old_plan_val)\\n\\t\\t\\tif new_plan_val > best_plan_val:\\n\\t\\t\\t\\tbest_plan_val = new_plan_val\\n\\t\\t\\t\\tbest_plan = copy.deepcopy(self.planned_actions)\\n\\n\\n\\t\\t\\told_plan_val = new_plan_val\\n\\n\\t\\tself.planned_actions = copy.deepcopy(best_plan)\\n\\n\\t\\t# Measure final planning information metrics\\n\\t\\tenv_state = self.env.sim.get_state() if self.mujoco else None\\n\\t\\tfin_states, _, fin_rews, fin_cum_rew, fin_emp_rew = \\\\\\n\\t\\t\\ttraj.eval_traj(\\n\\t\\t\\t\\tcopy.deepcopy(self.env),\\n\\t\\t\\t\\tenv_state, self.prev_obs,\\n\\t\\t\\t\\tmujoco=self.mujoco, perturb=self.perturb,\\n\\t\\t\\t\\tH=len(self.planned_actions),\\n\\t\\t\\t\\tgamma=self.gamma, act_mode='fixed',\\n\\t\\t\\t\\tpt=(np.array(self.planned_actions), 0),\\n\\t\\t\\t\\tterminal=terminal, tvel=self.tvel\\n\\t\\t\\t)\\n\\n\\t\\tif len(self.cache) == 0:\\n\\t\\t\\tself.cache = ([], [])\\n\\t\\tself.cache = (\\n\\t\\t\\tself.cache[0], self.cache[1], \\n\\t\\t\\tfin_states, fin_rews\\n\\t\\t)\\n\\n\\t\\t# Increment the trajectory by a timestep\\n\\t\\taction = self.advance_plan()\\n\\n\\t\\treturn action\",\n \"def prior(mu):\\n p = np.ones(len(mu))/(mu.max()-mu.min())\\n return p\",\n \"def low(self):\",\n \"def ComputeLogShapePriorTerm(self, *args):\\n return _ITKCostFunctionsPython.itkShapePriorMAPCostFunctionBaseIF3F_ComputeLogShapePriorTerm(self, *args)\",\n \"def ComputeLogShapePriorTerm(self, *args):\\n return _ITKCostFunctionsPython.itkShapePriorMAPCostFunctionBaseIF2F_ComputeLogShapePriorTerm(self, *args)\",\n \"def smaller(self):\\n return [x for x in TransitiveIdeal(attrcall('pred'), [self])]\",\n \"def lnprior(params):\\n a, b, f = params\\n if -10.0 < b < 0. and 0. < a < 10 and 0. < f:\\n return 0.0\\n\\n return -np.inf\",\n \"def get_log_prior(samples):\\n prior_distribution = tfp.distributions.Normal(loc=0., scale=1.)\\n log_prior = tf.reduce_sum(prior_distribution.log_prob(samples), axis=1)\\n return log_prior\",\n \"def previous(self) -> Optional[Chainable]:\\n return None\",\n \"def sample_from_prior(self):\\n raise NotImplementedError\",\n \"def log_prior(x):\\n logp = (-0.5 * x.pow(2) - torch.tensor(2 * math.pi).sqrt().log()).sum(dim=1)\\n return logp\",\n \"def __init__(self, prior: Prior):\\n # TODO: Consider analytical solution rather than implementing optimisation\\n super().__init__(prior.factor, x=prior, name=namer(self.__class__.__name__))\\n self.prior = prior\\n self.label = f\\\"PriorFactor({prior.label})\\\"\",\n \"def previous(self):\\n\\n pass\",\n \"def lnprior(self,theta):\\n kwargs = dict(zip(self.params,theta))\\n err = np.seterr(invalid='raise')\\n try:\\n lnprior = np.sum(np.log([self.priors[k](v) for k,v in kwargs.items()]))\\n except FloatingPointError,ValueError:\\n lnprior = -np.inf\\n np.seterr(**err)\\n return lnprior\",\n \"def propose(self):\\n\\n p = type(self)(self.n, alpha=self.alpha)\\n\\n return p, p.compute_prior() - self.compute_prior()\",\n \"def priorProbabilities():\\r\\n\\ttotal = 0.0\\r\\n\\tpos = 0.0\\r\\n\\tneg = 0.0\\r\\n\\r\\n\\t# Count the amount of positives and negatives in the training data\\r\\n\\tfor item in trainingData:\\r\\n\\t\\ttotal += 1\\r\\n\\t\\tif item[1] == '0':\\r\\n\\t\\t\\tpos +=1\\r\\n\\t\\tif item[1] == '1':\\r\\n\\t\\t\\tneg +=1\\r\\n\\t\\t\\t\\r\\n\\t\\t\\t\\r\\n\\t# Return the positive and negative probabilities \\r\\n\\tposProb = float(pos / total * 100)\\r\\n\\tnegProb = float(neg / total * 100)\\r\\n\\r\\n\\t\\r\\n\\t\\r\\n\\treturn posProb, negProb\",\n \"def _prey_step(self):\\n raise NotImplementedError()\",\n \"def prior_of_priors(self, tt):\\n for i in xrange(self.n_params): \\n try: \\n p_theta *= self.param_obj.prior()[i].pdf(tt[i]) \\n\\n except UnboundLocalError: \\n p_theta = self.param_obj.prior()[i].pdf(tt[i]) \\n\\n return p_theta\",\n \"def prediccion(self):\\n # Project the state ahead\\n self.X = self.F @ self.X + self.B @ self.M\\n self.P = self.F @ self.P @ self.F.T + self.Q\\n\\n return self.X\",\n \"def _call(self, x):\\n if functional.prior is None:\\n return (-1.0) / x + 1\\n else:\\n return (-functional.prior) / x + 1\",\n \"def _log_prior(self, theta, bounds):\\n if not ((bounds[0] < theta).all() and (theta < bounds[1]).all()):\\n return -np.inf\\n else:\\n return 0.0\",\n \"def bmshj2018_hyperprior(\\n quality, metric=\\\"mse\\\", pretrained=False, progress=True, **kwargs\\n):\\n if metric not in (\\\"mse\\\", \\\"ms-ssim\\\"):\\n raise ValueError(f'Invalid metric \\\"{metric}\\\"')\\n\\n if quality < 1 or quality > 8:\\n raise ValueError(f'Invalid quality \\\"{quality}\\\", should be between (1, 8)')\\n\\n return _load_model(\\n \\\"bmshj2018-hyperprior\\\", metric, quality, pretrained, progress, **kwargs\\n )\",\n \"def _set_priorunc(self, lai_coeff_absunc=None, statevec_absunc=None, is_generic_prior=False):\\n if not self.__dict__.has_key('prstate') or self.prstate is None:\\n msg = \\\"internal error, prior state does not yet exist!\\\"\\n FileLogger.fatal(msg)\\n raise RuntimeError(msg)\\n if lai_coeff_absunc!=None:\\n self.lai_coeff_unc = np.maximum( lai_coeff_absunc, self.lai_coeff_uncfloor )\\n msg = \\\"applied absolute uncertainty on lai coefficient, \\\"\\n msg += \\\"lai_coeff_absunc={}\\\".format(lai_coeff_absunc)\\n FileLogger.info(msg)\\n else:\\n self.lai_coeff_unc = np.maximum( self.lai_coeff*self.lai_coeff_relunc,\\n self.lai_coeff_uncfloor )\\n msg = \\\"applied relative uncertainty {} on lai coefficient.\\\".format(\\n self.lai_coeff_relunc)\\n FileLogger.info(msg)\\n\\n #-- allocate space for uncertainty array\\n self.prstate_unc = np.empty(self.prstate.shape, dtype=np.float64)\\n\\n #-- generic prior, user-supplied relative uncertainties\\n if is_generic_prior and self.relunc_by_user:\\n self.prstate_unc[0,:] = np.maximum( self.prstate[0,:]*self.lai_relunc,\\n self.lai_uncfloor )\\n self.prstate_unc[1,:] = np.maximum( self.prstate[1,:]*self.canht_relunc,\\n self.canht_uncfloor )\\n self.prstate_unc[2,:] = np.maximum( self.prstate[2,:]*self.sm_relunc,\\n self.sm_uncfloor )\\n msg = \\\"applied relative uncertainty on state vector components, \\\"\\n msg += \\\"lai_relunc={} canht_relunc={} sm_relunc={}\\\".format(\\n self.lai_relunc, self.canht_relunc, self.sm_relunc)\\n FileLogger.info(msg)\\n #-- either: prior information from ini-file or generic prior\\n elif statevec_absunc!=None:\\n lai_absunc,canht_absunc,sm_absunc = statevec_absunc\\n self.prstate_unc[0,:] = np.maximum( lai_absunc, self.lai_uncfloor )\\n self.prstate_unc[1,:] = np.maximum( canht_absunc, self.canht_uncfloor )\\n self.prstate_unc[2,:] = np.maximum( sm_absunc, self.sm_uncfloor )\\n msg = \\\"applied absolute uncertainty on state vector components, \\\"\\n msg += \\\"lai_absunc={} canht_absunc={} sm_absunc={}\\\".format(\\n lai_absunc, canht_absunc, sm_absunc)\\n FileLogger.info(msg)\\n #-- relative uncertainty (default or user-supplied)\\n else:\\n self.prstate_unc[0,:] = np.maximum( self.prstate[0,:]*self.lai_relunc,\\n self.lai_uncfloor )\\n self.prstate_unc[1,:] = np.maximum( self.prstate[1,:]*self.canht_relunc,\\n self.canht_uncfloor )\\n self.prstate_unc[2,:] = np.maximum( self.prstate[2,:]*self.sm_relunc,\\n self.sm_uncfloor )\\n msg = \\\"applied relative uncertainty on state vector components, \\\"\\n msg += \\\"lai_relunc={} canht_relunc={} sm_relunc={}\\\".format(\\n self.lai_relunc, self.canht_relunc, self.sm_relunc)\\n FileLogger.info(msg)\\n #-- logging\\n msg = \\\"uncertainty floor values were applied as follows: \\\"\\n msg += \\\"lai_uncfloor={} canht_uncfloor={} sm_uncfloor={}\\\".format(\\n self.lai_uncfloor, self.canht_uncfloor, self.sm_uncfloor)\\n FileLogger.info(msg)\",\n \"def compute_log_prior(self,params: ndarray) -> float:\\n ln_tE = params[0]\\n ln_A0 = params[1]\\n ln_deltaT = params[2]\\n fbl = params[3]\\n mb = params[4]\\n\\n # Equation (16,15,17) (note that Albrow uses \\\"log\\\" for log10)\\n log10e = np.log10(np.exp(1))\\n ln_pr_ln_tE = np.log(0.476) - ((log10e*ln_tE - 1.333)**2 / 0.330) + np.log(log10e)\\n ln_pr_ln_A0 = np.log(0.660) - (1.289*log10e*ln_A0) + np.log(log10e)\\n ln_pr_ln_deltaT = np.log(0.156) - ((log10e*ln_deltaT - 1.432)**2 / 0.458) +\\\\\\n np.log(log10e)\\n \\n # Paper doesnt mention the prior used, but I assume it to be uniform\\n ln_pr_fbl = uniform.logpdf(fbl,0.0,1.0)\\n\\n # Paper doesnr mention the prior used but I will asuumed it to be uniform\\n ln_pr_mb = uniform.logpdf(mb,self.mag_min - 1.0, self.mag_max + 1.0)\\n \\n \\n return ln_pr_fbl + ln_pr_ln_A0 + ln_pr_ln_deltaT + ln_pr_ln_tE + ln_pr_mb\",\n \"def get_prey(self, t):\\n i_dead = self.output[self.output['time'] <= t]['index']\\n i_alive = np.setdiff1d(self.prey_original['index'], i_dead,\\n assume_unique=True)\\n return self.prey_original[i_alive]\",\n \"def getPrev(self):\\n\\t\\t\\treturn self.prev\",\n \"def _construct_sample_from_prior(self):\\n z_sym = T.matrix()\\n x_sym = T.matrix()\\n irs = self.ir_steps\\n oputs = [self.obs_transform(self.s0)]\\n oputs.extend([self.obs_transform(self.si[i]) for i in range(irs)])\\n _, hi_zmuv = self._construct_zmuv_samples(x_sym, 1)\\n sample_func = theano.function(inputs=[z_sym, x_sym], outputs=oputs, \\\\\\n givens={ self.z: z_sym, \\\\\\n self.x_in: T.zeros_like(x_sym), \\\\\\n self.x_out: T.zeros_like(x_sym), \\\\\\n self.hi_zmuv: hi_zmuv }, \\\\\\n updates=self.scan_updates)\\n def prior_sampler(samp_count):\\n x_samps = to_fX( np.zeros((samp_count, self.obs_dim)) )\\n old_switch = self.train_switch.get_value(borrow=False)\\n # set model to generation mode\\n self.set_train_switch(switch_val=0.0)\\n z_samps = to_fX( npr.randn(samp_count, self.z_dim) )\\n model_samps = sample_func(z_samps, x_samps)\\n # set model back to either training or generation mode\\n self.set_train_switch(switch_val=old_switch)\\n return model_samps\\n return prior_sampler\",\n \"def set_prior(self,field):\\n self.observation_thresholds = [i/self.observations for i in range(0,self.observations)]\\n self.observation_samples = 1\\n # TODO: For use after integrating image processing with MCESP for Game-Delayed Reinforcements\\n # self.norm = field.max()\",\n \"def pred(self, index, prev):\\n return self.tour[index - 1] if prev else self.tour[index + 1]\",\n \"def _calc_pareto_front(self, *args, **kwargs):\\n fname = f\\\"{self.fct.name}_PF.dat\\\"\\n F = load_pareto_front_from_file(os.path.join(\\\"modact\\\", fname))\\n if F is not None:\\n return F*self.weights*-1\",\n \"def log_prior(self, i):\\n mu = self.prior.m_0\\n var = (self.prior.k_0 + 1.) / (self.prior.k_0*self.prior.v_0) * self.prior.S_0\\n log_prod_var = np.log(var).sum()\\n inv_var = 1./var\\n v = self.prior.v_0\\n return self._log_prod_students_t(i, mu, log_prod_var, inv_var, v)\",\n \"def _get_initial_hypos(self):\\n return [CombiStatePartialHypo(self.get_predictor_states())]\",\n \"def _call(self, x):\\n if functional.prior is None:\\n return np.exp(x)\\n else:\\n return functional.prior * np.exp(x)\",\n \"def ComputeLogShapePriorTerm(self, *args):\\n return _ITKCostFunctionsPython.itkShapePriorMAPCostFunctionBaseID3D_ComputeLogShapePriorTerm(self, *args)\",\n \"def get_initial_priority(self,state, action, rewards, next_state, done):\\n ## obtain the discounted sum of rewards from reward list\\n ## also obtain final gamma multiplier\\n reduced_reward, gamma_multiplier = self.reduce_rewards([rewards])\\n \\n ## Convert to tensors\\n state = np_to_tensor(state).unsqueeze(0)\\n action = np_to_tensor(action).unsqueeze(0)\\n reduced_reward = np_to_tensor(reduced_reward)\\n gamma_multiplier = np_to_tensor(gamma_multiplier)\\n next_state = np_to_tensor(next_state).unsqueeze(0)\\n done = np_to_tensor(np.array(done).astype(np.uint8))\\n \\n ## Obtain the action from actor target network\\n greedy_action = self.actor_target(next_state)\\n ## Compute the temporal difference\\n target = reduced_reward + torch.mul( torch.mul(gamma_multiplier , self.QNetwork_target(next_state, greedy_action)) , (1-done))\\n with torch.no_grad():\\n Q_sa = self.QNetwork_local(state, action)\\n td = Q_sa-target\\n ## return absolute value of temporal difference as priority \\n return tensor_to_np(td.abs().squeeze())\",\n \"def transformPriorErrorCovariance(self):\\n U_a, w_a, V_aT = np.linalg.linalg.svd(self.model.priorCovariance, \\n full_matrices=False)\\n V_a = V_aT.T\\n self.priorSinvh = V_a * np.matrix(np.diag(np.sqrt(1.0/w_a))) * U_a.T\\n self.priorSh = U_a * np.matrix(np.diag(np.sqrt(w_a))) * V_aT\\n self.priorSinv = V_a * np.matrix(np.diag(1.0/w_a)) * U_a.T\",\n \"def ComputeLogShapePriorTerm(self, *args):\\n return _ITKCostFunctionsPython.itkShapePriorMAPCostFunctionBaseID2D_ComputeLogShapePriorTerm(self, *args)\",\n \"def get_prev(self):\\n return self.prev\",\n \"def gaussian_prior(self):\\n self.prior = sps.multivariate_normal(self.m0,self.S0)\",\n \"def _call(self, x):\\n if functional.prior is None:\\n return 1.0 / (1 - x)\\n else:\\n return functional.prior / (1 - x)\",\n \"def prior_param(self, param_dict={}): \\n self.param_obj = Params(param_dict) # parameter object \\n self.param_names = param_dict.keys() \\n self.n_params = len(param_dict.keys()) # number of parameters in theta \",\n \"def pre_weight(self, y):\\n\\n if not isinstance(self._kernel, (TransformedDistribution, Independent)):\\n return self._model.log_prob(y, self._kernel.loc)\\n elif isinstance(self._kernel, Independent):\\n return self._model.log_prob(y, self._kernel.base_dist.loc)\\n\\n # TODO: Not entirely sure about this, but think this is the case\\n at = next(k for k in self._kernel.transforms if isinstance(k, AffineTransform))\\n\\n return self._model.log_prob(y, at.loc)\",\n \"def RankineHugoniot(P):\\n # prevP = P+1\\n # while(abs(P-prevP)>tol):\\n # prevP = P\\n Pr = P/rbc[0]\\n temp = 1/beta*(cR/cL)*(Pr-1)\\n temp = temp/np.sqrt(1+(gamma+1)/(2*gamma)*(Pr-1))\\n temp = (1-temp)**beta\\n # return P\\n return temp*lbc[0]-P\",\n \"def prior(self, c, labeled):\\n return log(len(labeled[c])/self.N_features)\",\n \"def log_prior(self, x_scaled):\\n x = scale_inverse_transform(self.parameters_scaler, x_scaled)\\n ln_prior = {}\\n for key in self.param_names:\\n try:\\n ln_prior[key] = self.priors[key].log_prob(x[:, self.param_names.index(key)])\\n except TypeError:\\n x = tf.cast(x, tf.float32)\\n ln_prior[key] = self.priors[key].log_prob(x[:, self.param_names.index(key)])\\n ln_prior_sum = tf.math.reduce_sum(tf.convert_to_tensor(list(ln_prior.values()), dtype=float_type),\\n axis=0)\\n # If prior is finite, replace with zero\\n zero_prior = tf.zeros_like(ln_prior_sum)\\n constraints = tf.math.is_inf(ln_prior_sum)\\n tensor_log_prior = tf.where(constraints, ln_prior_sum, zero_prior)\\n return tensor_log_prior\",\n \"def _call(self, x):\\n if self.prior is None:\\n tmp = ((x - 1 - np.log(x)).inner(self.domain.one()))\\n else:\\n # This is the old line from odl version 0.6.0.\\n # tmp = ((x - self.prior + self.prior * np.log(self.prior / x))\\n tmp = ((x - self.prior + self.prior * np.log((self.prior + 1e-12) / x))\\n .inner(self.domain.one()))\\n if np.isnan(tmp):\\n # In this case, some element was less than or equal to zero\\n return np.inf\\n else:\\n return tmp\",\n \"def add_prior(self, beastxml):\\n coalescent = xml.distribution(\\n beastxml.prior, id=\\\"Coalescent.t:beastlingTree\\\", spec=\\\"Coalescent\\\")\\n popmod = xml.populationModel(\\n coalescent, id=\\\"ConstantPopulation:beastlingTree\\\", spec=\\\"ConstantPopulation\\\")\\n xml.parameter(popmod, idref=\\\"popSize.t:beastlingTree\\\", name=\\\"popSize\\\")\\n xml.treeIntervals(\\n coalescent, id=\\\"TreeIntervals\\\", spec=\\\"TreeIntervals\\\", tree=\\\"@Tree.t:beastlingTree\\\")\",\n \"def prev(self):\\n return self.__prev\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.74466044","0.68139035","0.68139035","0.68139035","0.68139035","0.6795314","0.678799","0.660923","0.66089433","0.65597916","0.6525363","0.6508171","0.6482998","0.64318466","0.6383781","0.6362514","0.62887466","0.6265354","0.6228101","0.61609983","0.6075698","0.60189307","0.60142726","0.59640867","0.59429353","0.59119356","0.5910019","0.5894401","0.5879844","0.5870879","0.5859968","0.58402187","0.5824264","0.5807515","0.5803167","0.58028686","0.5798655","0.57858545","0.5775339","0.5753801","0.57519436","0.573279","0.5728825","0.57160825","0.5712878","0.5711678","0.57018936","0.56913435","0.5684122","0.56827325","0.5677063","0.56432784","0.5630409","0.5625431","0.56139916","0.56139004","0.5612373","0.56102645","0.560236","0.56006503","0.5579596","0.55635595","0.55286956","0.5528512","0.55282193","0.5525621","0.5510931","0.54941446","0.5488524","0.5487495","0.54794055","0.54789466","0.5454416","0.5451486","0.5440352","0.543641","0.54357904","0.5426042","0.5412664","0.5410727","0.54083955","0.54022473","0.5402113","0.5398928","0.53801715","0.5373213","0.53715575","0.53518885","0.53489333","0.53481513","0.53433156","0.53421086","0.5341543","0.53414243","0.5327208","0.53238297","0.5316665","0.5302505","0.5294053","0.52595365","0.52560055"],"string":"[\n \"0.74466044\",\n \"0.68139035\",\n \"0.68139035\",\n \"0.68139035\",\n \"0.68139035\",\n \"0.6795314\",\n \"0.678799\",\n \"0.660923\",\n \"0.66089433\",\n \"0.65597916\",\n \"0.6525363\",\n \"0.6508171\",\n \"0.6482998\",\n \"0.64318466\",\n \"0.6383781\",\n \"0.6362514\",\n \"0.62887466\",\n \"0.6265354\",\n \"0.6228101\",\n \"0.61609983\",\n \"0.6075698\",\n \"0.60189307\",\n \"0.60142726\",\n \"0.59640867\",\n \"0.59429353\",\n \"0.59119356\",\n \"0.5910019\",\n \"0.5894401\",\n \"0.5879844\",\n \"0.5870879\",\n \"0.5859968\",\n \"0.58402187\",\n \"0.5824264\",\n \"0.5807515\",\n \"0.5803167\",\n \"0.58028686\",\n \"0.5798655\",\n \"0.57858545\",\n \"0.5775339\",\n \"0.5753801\",\n \"0.57519436\",\n \"0.573279\",\n \"0.5728825\",\n \"0.57160825\",\n \"0.5712878\",\n \"0.5711678\",\n \"0.57018936\",\n \"0.56913435\",\n \"0.5684122\",\n \"0.56827325\",\n \"0.5677063\",\n \"0.56432784\",\n \"0.5630409\",\n \"0.5625431\",\n \"0.56139916\",\n \"0.56139004\",\n \"0.5612373\",\n \"0.56102645\",\n \"0.560236\",\n \"0.56006503\",\n \"0.5579596\",\n \"0.55635595\",\n \"0.55286956\",\n \"0.5528512\",\n \"0.55282193\",\n \"0.5525621\",\n \"0.5510931\",\n \"0.54941446\",\n \"0.5488524\",\n \"0.5487495\",\n \"0.54794055\",\n \"0.54789466\",\n \"0.5454416\",\n \"0.5451486\",\n \"0.5440352\",\n \"0.543641\",\n \"0.54357904\",\n \"0.5426042\",\n \"0.5412664\",\n \"0.5410727\",\n \"0.54083955\",\n \"0.54022473\",\n \"0.5402113\",\n \"0.5398928\",\n \"0.53801715\",\n \"0.5373213\",\n \"0.53715575\",\n \"0.53518885\",\n \"0.53489333\",\n \"0.53481513\",\n \"0.53433156\",\n \"0.53421086\",\n \"0.5341543\",\n \"0.53414243\",\n \"0.5327208\",\n \"0.53238297\",\n \"0.5316665\",\n \"0.5302505\",\n \"0.5294053\",\n \"0.52595365\",\n \"0.52560055\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":14390,"cells":{"query":{"kind":"string","value":"Returns N samples from the prior."},"document":{"kind":"string","value":"def sample_from_prior(self, n_samples):\n\n p0 = self.min + self.rng.rand(n_samples) * (self.max - self.min)\n return p0[:, np.newaxis]"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def sample_from_prior(self, n_samples):\n pass","def sample_from_prior(self, n_samples):\n\n p0 = self.rng.normal(loc=self.mean, scale=self.sigma, size=n_samples)\n return p0[:, np.newaxis]","def sample_from_prior(self, n_samples):\n\n p0 = self.rng.lognormal(mean=self.mean, sigma=self.sigma, size=n_samples)\n return p0[:, np.newaxis]","def sample_from_prior(self, n_samples):\n\n lamda = np.abs(self.rng.standard_cauchy(size=n_samples))\n\n p0 = np.log(np.abs(self.rng.randn() * lamda * self.scale))\n return p0[:, np.newaxis]","def sample_prior(size):\n return torch.randn(size)","def sample_from_prior(self):\n raise NotImplementedError","def sample_parameters_prior(self, n_samples, random_seed=None):\n\n if random_seed is not None:\n np.random.seed(random_seed)\n samples = []\n samples.append(self.var_noise.sample_from_prior(n_samples))\n samples.append(self.mean.sample_from_prior(n_samples))\n samples.append(self.kernel.sample_parameters(n_samples))\n\n return np.concatenate(samples, 1)","def sample(self, n_samples=1):\n\n\t\tif isinstance(n_samples, int):\n\t\t\tn_samples = (n_samples,)\n\t\tbeta_sample = self.beta_prior.sample(n_samples)\n\t\tgamma_sample = self.gamma_prior.sample(n_samples)\n\t\tp = torch.stack((beta_sample, gamma_sample)).T\n\t\tif n_samples == (1,):\n\t\t\tp = p[0]\n\t\tp = p.numpy()\n\t\treturn p","def sample_from_prior(self, *args, **kwargs):\n pass","def forward_sample(self, n):\n return self.flow.sample(n)","def prior_sample(self):\n pass","def generate_samples(self, n_samples):","def generate_samples(self, n_samples):","def sample(self, n):\n # Est probs from priority weights\n summed = sum(self.weight) + self.eps\n self.probs = [w / summed for w in self.priority]\n\n # Wieghted sample\n return np.random.choice(self.memory, size=n, p=self.probs).tolist()","def prior_samples(self, nsamples, coords=None):\n S = self.eval_S(self.kappa, self.sigma_f)\n if coords is None:\n coords = slice(self.mesh.num_vertices()) # take all coords\n weights = np.random.normal(scale=np.sqrt(S),\n size=(nsamples,) + S.shape) # shape (s, l)\n prior = np.einsum('sl,nl->sn', weights, self.eigenfunctions[coords])\n\n return prior","def samples(self):\n return self._values[:self.nsamples]","def generate_samples(self, no=10):\n observations = []\n state_sequence = []\n initial_state = np.random.choice(\n self.latent_variable_markov_chain.states,\n p=self.prior_probabilities)\n state_sequence.append(initial_state)\n observations.append(self.observation_from_state(initial_state))\n current_state = initial_state\n for i in range(2, no):\n next_state = self.latent_variable_markov_chain.next_state(current_state)\n state_sequence.append(next_state)\n observations.append(self.observation_from_state(next_state))\n current_state = next_state\n return observations, state_sequence","def sample(self, n, include=True):\n return [self(t / n) for t in range(n + int(include))]","def prior(n=10):\r\n p = []\r\n trials = 0\r\n acc = 0\r\n while acc < n:\r\n trials += 1\r\n r = np.random.rand(2) * np.array([4, 2]) + np.array([-2, -1])\r\n # print(\"r: \", r)\r\n if r[1] + r[0] >= -1 and r[1] - r[0] >= -1:\r\n p.append(r)\r\n acc += 1\r\n # print(\"trials: \", trials, \", acc: \", acc)\r\n return p","def pull_reads(self, n, prng): \n if not self.sampling:\n self.convert_to_array()\n index = prng.randint(0, self.total, size=n)\n index = np.sort(index)\n return self.reads[index,:]","def sample(self, n=1):\n raise NotImplementedError","def get_training_n(self, n: Optional[int] = None, random: bool = True) -> Tuple[torch.Tensor, torch.Tensor]:\n return self.__sample(self.train_data, self.train_los, n, random)","def sample(self, n):\n raise NotImplementedError","def _sample(self, n=1):\n return [self[i] for i in np.random.choice(self.length, n, replace=False)]","def get_n_samples(self):\n return {op.rx.get_n_samples() for op in self.ops}","def sample(self, n_samples):\n\n z = sample_prior((n_samples,) + self.flow.z_shape)\n ldj = torch.zeros(z.size(0))\n\n z, ldj = self.flow (z, ldj, reverse=True)\n z, ldj = self.logit_normalize(z, ldj, reverse=True)\n\n return z","def Sample(n=6):\n t = [random.normalvariate(0.0, 1.0) for i in range(n)]\n t.sort()\n return t","def generate_samples(self, n_samples=100):\n \t\t\n\t\t#how many times should ancestral sampling be run\n\t\t#n_samples\n prior_samples=[]\n for i in range(0,n_samples):\n prior_sample = self.prior.get_samples(\n n_latent_nodes=self.n_latent_nodes,\n n_gibbs_sampling_steps=100, \n sampling_mode=\"gibbs_ancestral\")\n prior_sample = torch.cat(prior_sample)\n prior_samples.append(prior_sample)\n prior_samples=torch.stack(prior_samples)\n # prior_samples = tf.slice(prior_samples, [0, 0], [num_samples, -1])\n output_activations = self.decoder.decode(prior_samples)\n output_activations = output_activations+self._train_bias\n output_distribution = Bernoulli(logit=output_activations)\n output=torch.sigmoid(output_distribution.logits)\n # output_activations[0] = output_activations[0] + self.train_bias\n # output_dist = FactorialBernoulliUtil(output_activations)\n # output_samples = tf.nn.sigmoid(output_dist.logit_mu)\n # print(\"--- \",\"end VAE::generate_samples()\")\n return output","def posterior_sampler(self, nsamples, seed=0, verbose=True):\n\n import random\n\n random.seed(seed)\n sample = self.get_chain()[-self.get_tune:]\n sample = sample.reshape(-1, sample.shape[-1])\n sample = random.choices(sample, k=nsamples)\n\n return sample","def get_random_samplers(self, n):\n if not self.has_samplers():\n self.draw_samplers()\n \n def get_shuffledcopy(x):\n x_ = x.copy()\n np.random.shuffle(x_)\n return x_\n \n return get_shuffledcopy(self.samplers)[:n]","def sample_gp_prior(self, x_list, n_samp, full_cov=True):\n mu, cov = self.get_gp_prior_mu_cov(x_list, full_cov)\n return self.get_normal_samples(mu, cov, n_samp, full_cov)","def get_posterior_sample(self):\n total_tries = self.prior_success + self.prior_failure\n prob_success = self.prior_success / total_tries\n # np.random.binomial采样出来的是二项分布的均值, 即正面朝上的次数,所以要除以N\n boot_sample = np.random.binomial(total_tries, prob_success) / total_tries\n return boot_sample","def sample(self, n_samples=1):\n\n\t\tsamples = np.random.random((n_samples, self.__ndims))*self.__range + self.__low\n\t\treturn samples","def get_samples(self, nsamples):\n sample_ids = np.random.choice(self.embed.shape[0], nsamples, replace=False)\n return self.embed[sample_ids]","def sample_predict(self, X, n_samples):\n W_samples = self.W_prior.sample(n_samples) ##size: [n_samples, n_in, n_out]\n if self.use_bias:\n bs = W_samples[:, :1, :]\n Ws = W_samples[:, 1:, :]\n if self.scaled_variance:\n Ws = Ws / np.sqrt(self.n_in)\n return torch.matmul(X, Ws) + bs\n else:\n if self.scaled_variance:\n W_samples = W_samples / np.sqrt(self.n_in)\n return torch.matmul(X, W_samples)","def pickTopicFromPrior(self):\r\n return self.priorTopicDistr.sampleIndex()","def fetch_samples(self):\n return torch.cat(self.samples,dim=0).reshape(-1,self.parameters.numel())","def _uniform_number(self, N=None):\n num_frames = self.data.shape[0]\n if not N:\n N = int(np.floor(\n Constants.DEFAULT_SAMPLE_PERCENTAGE * num_frames\n ))\n k = int(np.floor(num_frames / N))\n if k == 0:\n k = 1\n elif N > num_frames:\n k = 1\n else:\n assert type(N) is IntType, 'N is not an integer: {}'.format(N)\n k = int(np.floor(num_frames / N))\n\n # ensure that at most N samples are taken if N does not divide nicely\n return self.data[::k][:N], self.labels[::k][:N]","def sample(self, n_samples, sample_seed):\n self.seed_samples(sample_seed)\n eps = torch.randn(self.batch_size, n_samples, self.Y_dim)\n samples = eps*torch.exp(0.5*self.logvar.unsqueeze(1)) + self.mu.unsqueeze(1)\n samples = self.unwhiten_back(samples)\n samples = samples.data.cpu().numpy()\n return samples","def prior_sample(self, bn):\n x = np.zeros(3)\n\n # first joint prob\n random_choice = np.random.choice(bn[0], 1, bn[0].all(), bn[0])\n x[0] = random_choice[0]\n\n # Second Joint Prob\n if x[0] == 0.1:\n random_choice = np.random.choice(bn[1][0], 1, bn[1][0].all(), bn[1][0])\n x[1] = random_choice\n elif x[0] == 0.9:\n random_choice = np.random.choice(bn[1][1], 1, bn[1][1].all(), bn[1][1])\n x[1] = random_choice\n\n # Third Joint Prob\n if random_choice[0] == 0.8 or random_choice == 0.1:\n random_choice = np.random.choice(bn[2][0], 1, bn[2][0].all(), bn[2][0])\n x[2] = random_choice\n else:\n random_choice = np.random.choice(bn[2][1], 1, bn[2][1].all(), bn[2][1])\n x[2] = random_choice\n return x","def prob1(n):\n#raise NotImplementedError(\"Problem 1 Incomplete\")\n if n == 0 :\n raise ValueError(\"Sampling 0 points is not defined.\")\n total = 0\n for i in xrange(n) :\n if np.random.normal() > 3 :\n total += 1\n return float(total)/n","def sample (self, n):\n y = self.bins\n x = np.r_[0, self.values.cumsum ()] / self.sum\n # interpolate inverse CDF\n out = np.interp (np.random.random (n), x, y)\n if n == 1:\n return out[0]\n else:\n return out.reshape ((n,))","def get_n(self, n):\n \n return [self.get_values() for _ in range(n)]","def getNrSamples(self): \r\n return self.numSamples","def pull_n_samples(dset, n):\n return list(dset[i] for i in random.sample(range(len(dset)), n))","def sample(self, n):\n idx = np.random.randint(0, len(self.memory), size=n)\n return [self.memory[i] for i in idx]","def lnprior(self):\n \n return","def samples(self) -> int:\n return self._samples","def _construct_sample_from_prior(self):\n z_sym = T.matrix()\n x_sym = T.matrix()\n irs = self.ir_steps\n oputs = [self.obs_transform(self.s0)]\n oputs.extend([self.obs_transform(self.si[i]) for i in range(irs)])\n _, hi_zmuv = self._construct_zmuv_samples(x_sym, 1)\n sample_func = theano.function(inputs=[z_sym, x_sym], outputs=oputs, \\\n givens={ self.z: z_sym, \\\n self.x_in: T.zeros_like(x_sym), \\\n self.x_out: T.zeros_like(x_sym), \\\n self.hi_zmuv: hi_zmuv }, \\\n updates=self.scan_updates)\n def prior_sampler(samp_count):\n x_samps = to_fX( np.zeros((samp_count, self.obs_dim)) )\n old_switch = self.train_switch.get_value(borrow=False)\n # set model to generation mode\n self.set_train_switch(switch_val=0.0)\n z_samps = to_fX( npr.randn(samp_count, self.z_dim) )\n model_samps = sample_func(z_samps, x_samps)\n # set model back to either training or generation mode\n self.set_train_switch(switch_val=old_switch)\n return model_samps\n return prior_sampler","def generate_samples(self):\n self.analytic_probability()","def prior_distribution(self):\n out = self.model.forward(self.inducing_points)\n return MultivariateNormal(out.mean, out.lazy_covariance_matrix.evaluate_kernel())","def sample(self, size=1):\n samples = scipy.stats.bernoulli.rvs(self.p, size=size)\n if size == 1:\n return samples[0]\n return samples","def n_remaining_samples(self):\n return -1","def n_remaining_samples(self):\n return -1","def n_remaining_samples(self):\n return -1","def sample(self, nsamples):\n return self.dist.sample(nsamples)","def __resample(self):\n p_resample = []\n w_max = max(self.weights)\n index = int(round(random.uniform(0, self.n - 1)))\n beta = 0\n for i in range(self.n):\n beta += random.uniform(0, 2 * w_max)\n while self.weights[index] < beta:\n beta -= self.weights[index]\n index = (index + 1) % self.n\n p_resample.append(self.particles[index, :])\n return np.array(p_resample)","def take_samples(self, num_samples: int) -> List:\n if num_samples > len(self.samples):\n return random.sample(self.samples, len(self.samples))\n return random.sample(self.samples, num_samples)","def burnin_by_first_n(samples, N, step_number=False, logger_level=\"debug\"):\n _samples = copy.deepcopy(samples)\n n_samples = {key: N for key in _samples.keys()}\n if step_number:\n n_samples = {\n key: item + N if item is not None else N for key, item in\n _number_of_negative_steps(_samples, logger_level=logger_level).items()\n }\n getattr(logger, logger_level)(\n \"Removing the first {} as burnin\".format(\n \", \".join(\n [\"{} samples from {}\".format(val, key) for key, val in n_samples.items()]\n )\n )\n )\n return _samples.discard_samples(n_samples)","def n_samples(self) -> int: # pragma: no cover\n return self.samples.shape[0]","def sample_count(self):","def sample_size_with_prior(self, prior=\"small\"):\n if prior == \"small\":\n prior_sizes = list(range(self._min_size, self._max_size))\n elif prior == \"big\":\n prior_sizes = list(range(self._min_size+1, self._max_size+1))\n return random.choice(prior_sizes)","def sample_users(self, n):\n return torch.rand((n, self.num_topics), generator=self.generator) * 2 - 1","def sample(self,\n n_samples: Optional[int],\n temperatures: Optional[List[float]] = None) -> T.Tensor:\n samples = self.levels[-1].sample(\n self.top_prior_shape, n_samples=n_samples, temperatures=temperatures)\n for level in self.levels[-2::-1]:\n samples = level.sample(context=samples)\n return samples","def sample(self, num_samples = 1):\n\n X = np.empty(num_samples)\n for k in range(len(X)):\n\n # starting\n n = 1\n a = 1\n Un = self.UG.sample()\n a = a * Un\n\n # iterate over\n while a >= np.exp(-self.rate):\n n = n + 1\n Un = self.UG.sample()\n a = a * Un\n\n X[k] = n - 1\n\n return X","def prior_predictive(self):\n cfg = self.config\n n = cfg['batch_size'] * cfg['q/n_samples']\n n_samples = cfg['q/n_samples']\n with util.get_or_create_scope('model', reuse=True):\n h_prior = tf.cast(self.p_h_L.sample(n), cfg['dtype'])\n h_prior = tf.reshape(\n h_prior, [cfg['q/n_samples'], cfg['batch_size'], -1])\n h = [None] * cfg['p/n_layers']\n h[cfg['p/n_layers'] - 1] = h_prior\n for n in range(cfg['p/n_layers'] - 1, 0, -1):\n p_h_n = self.build_stochastic_layer(n, h_above=h[n])\n h[n - 1] = tf.cast(p_h_n.sample(), cfg['dtype'])\n return self.likelihood(h[0])","def sample_pagerank(corpus, damping_factor, n):\n probabilities = dict()\n samples = []\n\n # Random first sample\n page = random.choice(list(corpus.keys()))\n samples.append(page)\n \n # Remaining samples after first\n for i in range(n-1):\n p = transition_model(corpus, page, damping_factor)\n page = random.choices(list(p.keys()), weights=list(p.values()), k=1)[0]\n samples.append(page)\n\n # Count\n for p in corpus.keys():\n probabilities[p] = samples.count(p) / n\n\n return probabilities","def get_prev_samples(self):\n return self.y_p","def main(n_samples):\n uso = usolib.uso.uar(N)\n lst = [usolib.randomfacet.randomfacet_sample(uso, N) for i in range(n_samples)]\n return sum(lst) / float(n_samples)","def sample_data(self, N, pmin=8., pmax=12.):\n ps = [matrix(ra.uniform(pmin, pmax, (self.n, 1))) for k in range(N)]\n xs = [self.compute_demand(p) for p in ps]\n return zip(xs,ps)","def simplex_sampler(self, n):\n\n # edge case: n = 1\n if n == 1:\n return np.array([self.initial_potential])\n\n values = [np.random.uniform() for i in range(n-1)]\n values.extend([0, 1])\n values.sort()\n values_arr = np.array(values)\n\n xs = values_arr[1:] - values_arr[:-1]\n\n # return in decresing order of magnitude, to use for higher levels\n xs = self.initial_potential*np.sort(xs)\n xs = xs[::-1]\n return xs","def _number_of_samples(self):\n return len(self._raw_data.samples)","def get_next_sample(self):","def get_number_samples(self):\n return self.samples.shape[0]","def sample_low_rank(self, n_samples, mu, logvar, F):\n #F = torch.unsqueeze(F, dim=1).repeat(1, n_samples, 1, 1) # [self.batch_size, n_samples, self.Y_dim, self.rank]\n F = F.repeat(n_samples, 1, 1) # [self.batch_size*n_samples, self.Y_dim, self.rank]\n mu = mu.repeat(n_samples, 1) # [self.batch_size*n_samples, self.Y_dim]\n logvar = logvar.repeat(n_samples, 1) # [self.batch_size*n_samples, self.Y_dim]\n eps_low_rank = torch.randn(self.batch_size*n_samples, self.rank, 1)\n eps_diag = torch.randn(self.batch_size*n_samples, self.Y_dim)\n half_var = torch.exp(0.5*logvar) # [self.batch_size*n_samples, self.Y_dim]\n samples = torch.bmm(F, eps_low_rank).squeeze() + mu + half_var*eps_diag\n samples = samples.reshape(n_samples, self.batch_size, self.Y_dim)\n samples = samples.transpose(0, 1)\n samples = self.unwhiten_back(samples)\n samples = samples.data.cpu().numpy()\n return samples","def get_log_prior(samples):\n prior_distribution = tfp.distributions.Normal(loc=0., scale=1.)\n log_prior = tf.reduce_sum(prior_distribution.log_prob(samples), axis=1)\n return log_prior","def sample_from_log_prob(A, n):\n A_cum = log_cum_sum(A)\n C_pos = [ -c for c in reversed(A_cum)]\n sel = np.log(np.random.random(n))\n pos = [len(A) - np.searchsorted(C_pos,-r) for r in sel]\n return pos","def priors_sample(self):\n \n theta_star = np.zeros(self.n_params)\n\n for i in xrange(self.n_params): \n np.random.seed() \n theta_star[i] = self.param_obj.prior()[i].rvs(size=1)[0]\n\n return theta_star","def sample_pagerank(corpus, damping_factor, n):\n all_pages = []\n first_sample_prob = random.randint(0, len(corpus) - 1)\n distribution_count = dict()\n\n for u in corpus:\n distribution_count[u] = 0\n all_pages.append(u)\n\n sample = all_pages[first_sample_prob]\n for i in range(n - 1): # n - 1 because first sample was already calculated\n selection_bucket = dict()\n selection_start = 0.0\n sample_distribution = transition_model(corpus, sample, damping_factor)\n sample_prob = random.random()\n for u in sample_distribution:\n floor = selection_start\n ceiling = selection_start + sample_distribution[u]\n selection_start = ceiling\n selection_bucket[u] = [floor, ceiling]\n for u in selection_bucket:\n v = selection_bucket[u]\n if v[0] < sample_prob < v[1]:\n sample = u\n distribution_count[u] += 1\n distribution = dict()\n for u in distribution_count:\n distribution[u] = float(distribution_count[u]) / n\n\n return distribution","def get_num_samples(self):\n return self._num_samples","def sample(self, n):\n unparameterized = [\n v for v in self._weights.keys() if v not in self._weights_p.keys()\n ]\n if len(unparameterized) > 0:\n raise ValueError(\n \"The following variables must be numerically parameterized before sampling: {0}\".format(\n unparameterized\n )\n )\n mu, cov = self.parameterized_mean_cov()\n cov = np.asarray(cov).astype(np.float64)\n mu = np.asarray(mu).astype(np.float64)\n return np.random.multivariate_normal(mu.ravel(), cov, size=n)","def generate_samples(self,n_samples=100):\n rnd_input=torch.randn((n_samples,self._reparam_nodes[1]))\n zeta=rnd_input \n # rnd_input=torch.where((rnd_input>0.5),torch.ones(rnd_input.size()),torch.zeros(rnd_input.size()))\n # print(rnd_input) \n # output, mu, logvar, zeta=self.forward(rnd_input)\n # mu = self._reparam_layers['mu'](rnd_input)\n # logvar = self._reparam_layers['var'](rnd_input)\n # zeta = self.reparameterize(mu, logvar)\n output = self.decoder.decode(zeta)\n return output","def process_custom_prior(prior) -> Tuple[Distribution, int, bool]:\n\n check_prior_methods(prior)\n\n check_prior_batch_behavior(prior)\n\n prior, is_prior_numpy = maybe_wrap_prior_to_pytorch(prior)\n\n parameter_dim = prior.sample().numel()\n\n return prior, parameter_dim, is_prior_numpy","def __call__(self, *args):\n r = np.random.rand(*args)\n if type(r) is float:\n samples = self.values[(r < self.p).nonzero()[0][0]]\n elif type(r) is np.ndarray:\n samples = np.array(\n [self.values[np.nonzero(x < self.p)[0][0]] \n for x in r.flat]).reshape(r.shape)\n return samples","def get_num_samples(self) -> int:\n # must be implemented in subclass\n raise NotImplementedError","def sample(self, n_samples: int) -> torch.Tensor:\n return self.dist.sample((n_samples,))","def ts_rewards_sample(self):\n exp_rewards_list = [np.random.beta(a, b) for a, b in zip(self.alpha_params, self.beta_params)]\n return np.array(exp_rewards_list)","def prediction_samples(self) -> np.ndarray:\n return self.prediction_samples","def sample(self, n_samples: int, random_state: Optional[int] = None) -> np.ndarray:\n\n if not isinstance(self.prob, np.ndarray):\n samples = stats.bernoulli.rvs(\n p=self.prob, size=n_samples, random_state=random_state\n )\n else:\n n_classes = len(self.prob) # type: ignore\n samples = np.zeros((n_samples, n_classes))\n\n for cls in range(n_classes):\n samples[:, cls] = stats.bernoulli.rvs(p=self.prob[cls], size=n_samples, random_state=random_state) # type: ignore\n\n return samples","def generate_synth_data(n):","def get_samples_per_signal(self):\n return np.array([self.samples_in_file(chn) for chn in range(self.signals_in_file)])","def samples(self):\n pass","def prior_sample_parameter(self, parameter):\n pass","def sample(self, num):\n state, action, reward, next_state, not_terminal = [], [], [], [], []\n for exp in self.experiences.sample(num):\n state.append(exp.state)\n action.append(exp.action)\n reward.append(exp.reward)\n next_state.append(exp.next_state)\n not_terminal.append(exp.not_terminal)\n\n return np.asarray(state), np.asarray(action), np.asarray(reward), \\\n np.asarray(next_state), np.asarray(not_terminal)","def sample_observations(self, n_samples, z):\n lat_dim = self.transition.dim_x\n x = []\n for i in range(self.time_steps):\n z_t = tf.slice(z, [0, i * lat_dim], [-1, lat_dim]) \n x.append(self.emmision.sample(z_t))\n return tf.concat(x, axis=1)","def sample(self, n_samples):\n with torch.no_grad():\n z = torch.randn((n_samples, self.z_dim))\n samples = self.decoder(z)\n im_size = int(np.sqrt(self.input_dim))\n samples = samples.view(-1, 1, im_size, im_size)\n\n return samples","def sample_pagerank(corpus, damping_factor, n):\n # All pages in entire corpus\n all_pages = [key for key in corpus.keys()]\n\n # Dictionary which will map each page to the number of generated samples that were the page\n counts = dict()\n for page in all_pages:\n counts[page] = 0\n\n # Choosing first sample randomly and updating counts dict\n first_sample = random.choice(all_pages)\n counts[first_sample] += 1\n\n for i in range(1, n):\n # If we are on the second sample, do the calculation based on the first\n if i == 1:\n prev_sample = first_sample\n\n distributions = transition_model(corpus, prev_sample, damping_factor)\n\n keys = [key for key in distributions.keys()]\n values = [distributions[key] for key in distributions.keys()]\n\n # Using random.choices to select an option given a list of weights (distribution)\n current_sample = random.choices(keys, weights=values, k=1)[0]\n\n # Updating counts dict given the current sample\n counts[current_sample] += 1\n\n prev_sample = current_sample\n\n # Modifying counts dict to represent the proportion of samples that corresponded to that page instead of the raw amount\n for page in all_pages:\n counts[page] /= n\n\n return counts","def take(n, seq):\n return itertools.islice(seq, n)","def get_samples(self) -> McmcPtResult:","def lhs(\n self,\n n: int,\n seed: Union[int, float, np.random.RandomState, np.random.Generator, None] = 42,\n ):\n rng = get_random_generator(seed)\n samples = []\n for i in range(self.n_dims):\n lhs_perm = []\n # Get evenly distributed samples from one dimension\n sample_indices = (np.arange(n) + 0.5) / n\n lhs_aranged = self.dimensions[i].sample(sample_indices)\n perm = rng.permutation(n)\n for p in perm: # Random permutate the order of the samples\n lhs_perm.append(lhs_aranged[p])\n samples.append(lhs_perm)\n # Now we have a list of lists with samples for each dimension.\n # We need to transpose this so that we get a list of lists with\n # samples for all the dimensions\n transposed_samples = []\n for i in range(n):\n row = []\n for j in range(self.n_dims):\n row.append(samples[j][i])\n transposed_samples.append(row)\n return transposed_samples"],"string":"[\n \"def sample_from_prior(self, n_samples):\\n pass\",\n \"def sample_from_prior(self, n_samples):\\n\\n p0 = self.rng.normal(loc=self.mean, scale=self.sigma, size=n_samples)\\n return p0[:, np.newaxis]\",\n \"def sample_from_prior(self, n_samples):\\n\\n p0 = self.rng.lognormal(mean=self.mean, sigma=self.sigma, size=n_samples)\\n return p0[:, np.newaxis]\",\n \"def sample_from_prior(self, n_samples):\\n\\n lamda = np.abs(self.rng.standard_cauchy(size=n_samples))\\n\\n p0 = np.log(np.abs(self.rng.randn() * lamda * self.scale))\\n return p0[:, np.newaxis]\",\n \"def sample_prior(size):\\n return torch.randn(size)\",\n \"def sample_from_prior(self):\\n raise NotImplementedError\",\n \"def sample_parameters_prior(self, n_samples, random_seed=None):\\n\\n if random_seed is not None:\\n np.random.seed(random_seed)\\n samples = []\\n samples.append(self.var_noise.sample_from_prior(n_samples))\\n samples.append(self.mean.sample_from_prior(n_samples))\\n samples.append(self.kernel.sample_parameters(n_samples))\\n\\n return np.concatenate(samples, 1)\",\n \"def sample(self, n_samples=1):\\n\\n\\t\\tif isinstance(n_samples, int):\\n\\t\\t\\tn_samples = (n_samples,)\\n\\t\\tbeta_sample = self.beta_prior.sample(n_samples)\\n\\t\\tgamma_sample = self.gamma_prior.sample(n_samples)\\n\\t\\tp = torch.stack((beta_sample, gamma_sample)).T\\n\\t\\tif n_samples == (1,):\\n\\t\\t\\tp = p[0]\\n\\t\\tp = p.numpy()\\n\\t\\treturn p\",\n \"def sample_from_prior(self, *args, **kwargs):\\n pass\",\n \"def forward_sample(self, n):\\n return self.flow.sample(n)\",\n \"def prior_sample(self):\\n pass\",\n \"def generate_samples(self, n_samples):\",\n \"def generate_samples(self, n_samples):\",\n \"def sample(self, n):\\n # Est probs from priority weights\\n summed = sum(self.weight) + self.eps\\n self.probs = [w / summed for w in self.priority]\\n\\n # Wieghted sample\\n return np.random.choice(self.memory, size=n, p=self.probs).tolist()\",\n \"def prior_samples(self, nsamples, coords=None):\\n S = self.eval_S(self.kappa, self.sigma_f)\\n if coords is None:\\n coords = slice(self.mesh.num_vertices()) # take all coords\\n weights = np.random.normal(scale=np.sqrt(S),\\n size=(nsamples,) + S.shape) # shape (s, l)\\n prior = np.einsum('sl,nl->sn', weights, self.eigenfunctions[coords])\\n\\n return prior\",\n \"def samples(self):\\n return self._values[:self.nsamples]\",\n \"def generate_samples(self, no=10):\\n observations = []\\n state_sequence = []\\n initial_state = np.random.choice(\\n self.latent_variable_markov_chain.states,\\n p=self.prior_probabilities)\\n state_sequence.append(initial_state)\\n observations.append(self.observation_from_state(initial_state))\\n current_state = initial_state\\n for i in range(2, no):\\n next_state = self.latent_variable_markov_chain.next_state(current_state)\\n state_sequence.append(next_state)\\n observations.append(self.observation_from_state(next_state))\\n current_state = next_state\\n return observations, state_sequence\",\n \"def sample(self, n, include=True):\\n return [self(t / n) for t in range(n + int(include))]\",\n \"def prior(n=10):\\r\\n p = []\\r\\n trials = 0\\r\\n acc = 0\\r\\n while acc < n:\\r\\n trials += 1\\r\\n r = np.random.rand(2) * np.array([4, 2]) + np.array([-2, -1])\\r\\n # print(\\\"r: \\\", r)\\r\\n if r[1] + r[0] >= -1 and r[1] - r[0] >= -1:\\r\\n p.append(r)\\r\\n acc += 1\\r\\n # print(\\\"trials: \\\", trials, \\\", acc: \\\", acc)\\r\\n return p\",\n \"def pull_reads(self, n, prng): \\n if not self.sampling:\\n self.convert_to_array()\\n index = prng.randint(0, self.total, size=n)\\n index = np.sort(index)\\n return self.reads[index,:]\",\n \"def sample(self, n=1):\\n raise NotImplementedError\",\n \"def get_training_n(self, n: Optional[int] = None, random: bool = True) -> Tuple[torch.Tensor, torch.Tensor]:\\n return self.__sample(self.train_data, self.train_los, n, random)\",\n \"def sample(self, n):\\n raise NotImplementedError\",\n \"def _sample(self, n=1):\\n return [self[i] for i in np.random.choice(self.length, n, replace=False)]\",\n \"def get_n_samples(self):\\n return {op.rx.get_n_samples() for op in self.ops}\",\n \"def sample(self, n_samples):\\n\\n z = sample_prior((n_samples,) + self.flow.z_shape)\\n ldj = torch.zeros(z.size(0))\\n\\n z, ldj = self.flow (z, ldj, reverse=True)\\n z, ldj = self.logit_normalize(z, ldj, reverse=True)\\n\\n return z\",\n \"def Sample(n=6):\\n t = [random.normalvariate(0.0, 1.0) for i in range(n)]\\n t.sort()\\n return t\",\n \"def generate_samples(self, n_samples=100):\\n \\t\\t\\n\\t\\t#how many times should ancestral sampling be run\\n\\t\\t#n_samples\\n prior_samples=[]\\n for i in range(0,n_samples):\\n prior_sample = self.prior.get_samples(\\n n_latent_nodes=self.n_latent_nodes,\\n n_gibbs_sampling_steps=100, \\n sampling_mode=\\\"gibbs_ancestral\\\")\\n prior_sample = torch.cat(prior_sample)\\n prior_samples.append(prior_sample)\\n prior_samples=torch.stack(prior_samples)\\n # prior_samples = tf.slice(prior_samples, [0, 0], [num_samples, -1])\\n output_activations = self.decoder.decode(prior_samples)\\n output_activations = output_activations+self._train_bias\\n output_distribution = Bernoulli(logit=output_activations)\\n output=torch.sigmoid(output_distribution.logits)\\n # output_activations[0] = output_activations[0] + self.train_bias\\n # output_dist = FactorialBernoulliUtil(output_activations)\\n # output_samples = tf.nn.sigmoid(output_dist.logit_mu)\\n # print(\\\"--- \\\",\\\"end VAE::generate_samples()\\\")\\n return output\",\n \"def posterior_sampler(self, nsamples, seed=0, verbose=True):\\n\\n import random\\n\\n random.seed(seed)\\n sample = self.get_chain()[-self.get_tune:]\\n sample = sample.reshape(-1, sample.shape[-1])\\n sample = random.choices(sample, k=nsamples)\\n\\n return sample\",\n \"def get_random_samplers(self, n):\\n if not self.has_samplers():\\n self.draw_samplers()\\n \\n def get_shuffledcopy(x):\\n x_ = x.copy()\\n np.random.shuffle(x_)\\n return x_\\n \\n return get_shuffledcopy(self.samplers)[:n]\",\n \"def sample_gp_prior(self, x_list, n_samp, full_cov=True):\\n mu, cov = self.get_gp_prior_mu_cov(x_list, full_cov)\\n return self.get_normal_samples(mu, cov, n_samp, full_cov)\",\n \"def get_posterior_sample(self):\\n total_tries = self.prior_success + self.prior_failure\\n prob_success = self.prior_success / total_tries\\n # np.random.binomial采样出来的是二项分布的均值, 即正面朝上的次数,所以要除以N\\n boot_sample = np.random.binomial(total_tries, prob_success) / total_tries\\n return boot_sample\",\n \"def sample(self, n_samples=1):\\n\\n\\t\\tsamples = np.random.random((n_samples, self.__ndims))*self.__range + self.__low\\n\\t\\treturn samples\",\n \"def get_samples(self, nsamples):\\n sample_ids = np.random.choice(self.embed.shape[0], nsamples, replace=False)\\n return self.embed[sample_ids]\",\n \"def sample_predict(self, X, n_samples):\\n W_samples = self.W_prior.sample(n_samples) ##size: [n_samples, n_in, n_out]\\n if self.use_bias:\\n bs = W_samples[:, :1, :]\\n Ws = W_samples[:, 1:, :]\\n if self.scaled_variance:\\n Ws = Ws / np.sqrt(self.n_in)\\n return torch.matmul(X, Ws) + bs\\n else:\\n if self.scaled_variance:\\n W_samples = W_samples / np.sqrt(self.n_in)\\n return torch.matmul(X, W_samples)\",\n \"def pickTopicFromPrior(self):\\r\\n return self.priorTopicDistr.sampleIndex()\",\n \"def fetch_samples(self):\\n return torch.cat(self.samples,dim=0).reshape(-1,self.parameters.numel())\",\n \"def _uniform_number(self, N=None):\\n num_frames = self.data.shape[0]\\n if not N:\\n N = int(np.floor(\\n Constants.DEFAULT_SAMPLE_PERCENTAGE * num_frames\\n ))\\n k = int(np.floor(num_frames / N))\\n if k == 0:\\n k = 1\\n elif N > num_frames:\\n k = 1\\n else:\\n assert type(N) is IntType, 'N is not an integer: {}'.format(N)\\n k = int(np.floor(num_frames / N))\\n\\n # ensure that at most N samples are taken if N does not divide nicely\\n return self.data[::k][:N], self.labels[::k][:N]\",\n \"def sample(self, n_samples, sample_seed):\\n self.seed_samples(sample_seed)\\n eps = torch.randn(self.batch_size, n_samples, self.Y_dim)\\n samples = eps*torch.exp(0.5*self.logvar.unsqueeze(1)) + self.mu.unsqueeze(1)\\n samples = self.unwhiten_back(samples)\\n samples = samples.data.cpu().numpy()\\n return samples\",\n \"def prior_sample(self, bn):\\n x = np.zeros(3)\\n\\n # first joint prob\\n random_choice = np.random.choice(bn[0], 1, bn[0].all(), bn[0])\\n x[0] = random_choice[0]\\n\\n # Second Joint Prob\\n if x[0] == 0.1:\\n random_choice = np.random.choice(bn[1][0], 1, bn[1][0].all(), bn[1][0])\\n x[1] = random_choice\\n elif x[0] == 0.9:\\n random_choice = np.random.choice(bn[1][1], 1, bn[1][1].all(), bn[1][1])\\n x[1] = random_choice\\n\\n # Third Joint Prob\\n if random_choice[0] == 0.8 or random_choice == 0.1:\\n random_choice = np.random.choice(bn[2][0], 1, bn[2][0].all(), bn[2][0])\\n x[2] = random_choice\\n else:\\n random_choice = np.random.choice(bn[2][1], 1, bn[2][1].all(), bn[2][1])\\n x[2] = random_choice\\n return x\",\n \"def prob1(n):\\n#raise NotImplementedError(\\\"Problem 1 Incomplete\\\")\\n if n == 0 :\\n raise ValueError(\\\"Sampling 0 points is not defined.\\\")\\n total = 0\\n for i in xrange(n) :\\n if np.random.normal() > 3 :\\n total += 1\\n return float(total)/n\",\n \"def sample (self, n):\\n y = self.bins\\n x = np.r_[0, self.values.cumsum ()] / self.sum\\n # interpolate inverse CDF\\n out = np.interp (np.random.random (n), x, y)\\n if n == 1:\\n return out[0]\\n else:\\n return out.reshape ((n,))\",\n \"def get_n(self, n):\\n \\n return [self.get_values() for _ in range(n)]\",\n \"def getNrSamples(self): \\r\\n return self.numSamples\",\n \"def pull_n_samples(dset, n):\\n return list(dset[i] for i in random.sample(range(len(dset)), n))\",\n \"def sample(self, n):\\n idx = np.random.randint(0, len(self.memory), size=n)\\n return [self.memory[i] for i in idx]\",\n \"def lnprior(self):\\n \\n return\",\n \"def samples(self) -> int:\\n return self._samples\",\n \"def _construct_sample_from_prior(self):\\n z_sym = T.matrix()\\n x_sym = T.matrix()\\n irs = self.ir_steps\\n oputs = [self.obs_transform(self.s0)]\\n oputs.extend([self.obs_transform(self.si[i]) for i in range(irs)])\\n _, hi_zmuv = self._construct_zmuv_samples(x_sym, 1)\\n sample_func = theano.function(inputs=[z_sym, x_sym], outputs=oputs, \\\\\\n givens={ self.z: z_sym, \\\\\\n self.x_in: T.zeros_like(x_sym), \\\\\\n self.x_out: T.zeros_like(x_sym), \\\\\\n self.hi_zmuv: hi_zmuv }, \\\\\\n updates=self.scan_updates)\\n def prior_sampler(samp_count):\\n x_samps = to_fX( np.zeros((samp_count, self.obs_dim)) )\\n old_switch = self.train_switch.get_value(borrow=False)\\n # set model to generation mode\\n self.set_train_switch(switch_val=0.0)\\n z_samps = to_fX( npr.randn(samp_count, self.z_dim) )\\n model_samps = sample_func(z_samps, x_samps)\\n # set model back to either training or generation mode\\n self.set_train_switch(switch_val=old_switch)\\n return model_samps\\n return prior_sampler\",\n \"def generate_samples(self):\\n self.analytic_probability()\",\n \"def prior_distribution(self):\\n out = self.model.forward(self.inducing_points)\\n return MultivariateNormal(out.mean, out.lazy_covariance_matrix.evaluate_kernel())\",\n \"def sample(self, size=1):\\n samples = scipy.stats.bernoulli.rvs(self.p, size=size)\\n if size == 1:\\n return samples[0]\\n return samples\",\n \"def n_remaining_samples(self):\\n return -1\",\n \"def n_remaining_samples(self):\\n return -1\",\n \"def n_remaining_samples(self):\\n return -1\",\n \"def sample(self, nsamples):\\n return self.dist.sample(nsamples)\",\n \"def __resample(self):\\n p_resample = []\\n w_max = max(self.weights)\\n index = int(round(random.uniform(0, self.n - 1)))\\n beta = 0\\n for i in range(self.n):\\n beta += random.uniform(0, 2 * w_max)\\n while self.weights[index] < beta:\\n beta -= self.weights[index]\\n index = (index + 1) % self.n\\n p_resample.append(self.particles[index, :])\\n return np.array(p_resample)\",\n \"def take_samples(self, num_samples: int) -> List:\\n if num_samples > len(self.samples):\\n return random.sample(self.samples, len(self.samples))\\n return random.sample(self.samples, num_samples)\",\n \"def burnin_by_first_n(samples, N, step_number=False, logger_level=\\\"debug\\\"):\\n _samples = copy.deepcopy(samples)\\n n_samples = {key: N for key in _samples.keys()}\\n if step_number:\\n n_samples = {\\n key: item + N if item is not None else N for key, item in\\n _number_of_negative_steps(_samples, logger_level=logger_level).items()\\n }\\n getattr(logger, logger_level)(\\n \\\"Removing the first {} as burnin\\\".format(\\n \\\", \\\".join(\\n [\\\"{} samples from {}\\\".format(val, key) for key, val in n_samples.items()]\\n )\\n )\\n )\\n return _samples.discard_samples(n_samples)\",\n \"def n_samples(self) -> int: # pragma: no cover\\n return self.samples.shape[0]\",\n \"def sample_count(self):\",\n \"def sample_size_with_prior(self, prior=\\\"small\\\"):\\n if prior == \\\"small\\\":\\n prior_sizes = list(range(self._min_size, self._max_size))\\n elif prior == \\\"big\\\":\\n prior_sizes = list(range(self._min_size+1, self._max_size+1))\\n return random.choice(prior_sizes)\",\n \"def sample_users(self, n):\\n return torch.rand((n, self.num_topics), generator=self.generator) * 2 - 1\",\n \"def sample(self,\\n n_samples: Optional[int],\\n temperatures: Optional[List[float]] = None) -> T.Tensor:\\n samples = self.levels[-1].sample(\\n self.top_prior_shape, n_samples=n_samples, temperatures=temperatures)\\n for level in self.levels[-2::-1]:\\n samples = level.sample(context=samples)\\n return samples\",\n \"def sample(self, num_samples = 1):\\n\\n X = np.empty(num_samples)\\n for k in range(len(X)):\\n\\n # starting\\n n = 1\\n a = 1\\n Un = self.UG.sample()\\n a = a * Un\\n\\n # iterate over\\n while a >= np.exp(-self.rate):\\n n = n + 1\\n Un = self.UG.sample()\\n a = a * Un\\n\\n X[k] = n - 1\\n\\n return X\",\n \"def prior_predictive(self):\\n cfg = self.config\\n n = cfg['batch_size'] * cfg['q/n_samples']\\n n_samples = cfg['q/n_samples']\\n with util.get_or_create_scope('model', reuse=True):\\n h_prior = tf.cast(self.p_h_L.sample(n), cfg['dtype'])\\n h_prior = tf.reshape(\\n h_prior, [cfg['q/n_samples'], cfg['batch_size'], -1])\\n h = [None] * cfg['p/n_layers']\\n h[cfg['p/n_layers'] - 1] = h_prior\\n for n in range(cfg['p/n_layers'] - 1, 0, -1):\\n p_h_n = self.build_stochastic_layer(n, h_above=h[n])\\n h[n - 1] = tf.cast(p_h_n.sample(), cfg['dtype'])\\n return self.likelihood(h[0])\",\n \"def sample_pagerank(corpus, damping_factor, n):\\n probabilities = dict()\\n samples = []\\n\\n # Random first sample\\n page = random.choice(list(corpus.keys()))\\n samples.append(page)\\n \\n # Remaining samples after first\\n for i in range(n-1):\\n p = transition_model(corpus, page, damping_factor)\\n page = random.choices(list(p.keys()), weights=list(p.values()), k=1)[0]\\n samples.append(page)\\n\\n # Count\\n for p in corpus.keys():\\n probabilities[p] = samples.count(p) / n\\n\\n return probabilities\",\n \"def get_prev_samples(self):\\n return self.y_p\",\n \"def main(n_samples):\\n uso = usolib.uso.uar(N)\\n lst = [usolib.randomfacet.randomfacet_sample(uso, N) for i in range(n_samples)]\\n return sum(lst) / float(n_samples)\",\n \"def sample_data(self, N, pmin=8., pmax=12.):\\n ps = [matrix(ra.uniform(pmin, pmax, (self.n, 1))) for k in range(N)]\\n xs = [self.compute_demand(p) for p in ps]\\n return zip(xs,ps)\",\n \"def simplex_sampler(self, n):\\n\\n # edge case: n = 1\\n if n == 1:\\n return np.array([self.initial_potential])\\n\\n values = [np.random.uniform() for i in range(n-1)]\\n values.extend([0, 1])\\n values.sort()\\n values_arr = np.array(values)\\n\\n xs = values_arr[1:] - values_arr[:-1]\\n\\n # return in decresing order of magnitude, to use for higher levels\\n xs = self.initial_potential*np.sort(xs)\\n xs = xs[::-1]\\n return xs\",\n \"def _number_of_samples(self):\\n return len(self._raw_data.samples)\",\n \"def get_next_sample(self):\",\n \"def get_number_samples(self):\\n return self.samples.shape[0]\",\n \"def sample_low_rank(self, n_samples, mu, logvar, F):\\n #F = torch.unsqueeze(F, dim=1).repeat(1, n_samples, 1, 1) # [self.batch_size, n_samples, self.Y_dim, self.rank]\\n F = F.repeat(n_samples, 1, 1) # [self.batch_size*n_samples, self.Y_dim, self.rank]\\n mu = mu.repeat(n_samples, 1) # [self.batch_size*n_samples, self.Y_dim]\\n logvar = logvar.repeat(n_samples, 1) # [self.batch_size*n_samples, self.Y_dim]\\n eps_low_rank = torch.randn(self.batch_size*n_samples, self.rank, 1)\\n eps_diag = torch.randn(self.batch_size*n_samples, self.Y_dim)\\n half_var = torch.exp(0.5*logvar) # [self.batch_size*n_samples, self.Y_dim]\\n samples = torch.bmm(F, eps_low_rank).squeeze() + mu + half_var*eps_diag\\n samples = samples.reshape(n_samples, self.batch_size, self.Y_dim)\\n samples = samples.transpose(0, 1)\\n samples = self.unwhiten_back(samples)\\n samples = samples.data.cpu().numpy()\\n return samples\",\n \"def get_log_prior(samples):\\n prior_distribution = tfp.distributions.Normal(loc=0., scale=1.)\\n log_prior = tf.reduce_sum(prior_distribution.log_prob(samples), axis=1)\\n return log_prior\",\n \"def sample_from_log_prob(A, n):\\n A_cum = log_cum_sum(A)\\n C_pos = [ -c for c in reversed(A_cum)]\\n sel = np.log(np.random.random(n))\\n pos = [len(A) - np.searchsorted(C_pos,-r) for r in sel]\\n return pos\",\n \"def priors_sample(self):\\n \\n theta_star = np.zeros(self.n_params)\\n\\n for i in xrange(self.n_params): \\n np.random.seed() \\n theta_star[i] = self.param_obj.prior()[i].rvs(size=1)[0]\\n\\n return theta_star\",\n \"def sample_pagerank(corpus, damping_factor, n):\\n all_pages = []\\n first_sample_prob = random.randint(0, len(corpus) - 1)\\n distribution_count = dict()\\n\\n for u in corpus:\\n distribution_count[u] = 0\\n all_pages.append(u)\\n\\n sample = all_pages[first_sample_prob]\\n for i in range(n - 1): # n - 1 because first sample was already calculated\\n selection_bucket = dict()\\n selection_start = 0.0\\n sample_distribution = transition_model(corpus, sample, damping_factor)\\n sample_prob = random.random()\\n for u in sample_distribution:\\n floor = selection_start\\n ceiling = selection_start + sample_distribution[u]\\n selection_start = ceiling\\n selection_bucket[u] = [floor, ceiling]\\n for u in selection_bucket:\\n v = selection_bucket[u]\\n if v[0] < sample_prob < v[1]:\\n sample = u\\n distribution_count[u] += 1\\n distribution = dict()\\n for u in distribution_count:\\n distribution[u] = float(distribution_count[u]) / n\\n\\n return distribution\",\n \"def get_num_samples(self):\\n return self._num_samples\",\n \"def sample(self, n):\\n unparameterized = [\\n v for v in self._weights.keys() if v not in self._weights_p.keys()\\n ]\\n if len(unparameterized) > 0:\\n raise ValueError(\\n \\\"The following variables must be numerically parameterized before sampling: {0}\\\".format(\\n unparameterized\\n )\\n )\\n mu, cov = self.parameterized_mean_cov()\\n cov = np.asarray(cov).astype(np.float64)\\n mu = np.asarray(mu).astype(np.float64)\\n return np.random.multivariate_normal(mu.ravel(), cov, size=n)\",\n \"def generate_samples(self,n_samples=100):\\n rnd_input=torch.randn((n_samples,self._reparam_nodes[1]))\\n zeta=rnd_input \\n # rnd_input=torch.where((rnd_input>0.5),torch.ones(rnd_input.size()),torch.zeros(rnd_input.size()))\\n # print(rnd_input) \\n # output, mu, logvar, zeta=self.forward(rnd_input)\\n # mu = self._reparam_layers['mu'](rnd_input)\\n # logvar = self._reparam_layers['var'](rnd_input)\\n # zeta = self.reparameterize(mu, logvar)\\n output = self.decoder.decode(zeta)\\n return output\",\n \"def process_custom_prior(prior) -> Tuple[Distribution, int, bool]:\\n\\n check_prior_methods(prior)\\n\\n check_prior_batch_behavior(prior)\\n\\n prior, is_prior_numpy = maybe_wrap_prior_to_pytorch(prior)\\n\\n parameter_dim = prior.sample().numel()\\n\\n return prior, parameter_dim, is_prior_numpy\",\n \"def __call__(self, *args):\\n r = np.random.rand(*args)\\n if type(r) is float:\\n samples = self.values[(r < self.p).nonzero()[0][0]]\\n elif type(r) is np.ndarray:\\n samples = np.array(\\n [self.values[np.nonzero(x < self.p)[0][0]] \\n for x in r.flat]).reshape(r.shape)\\n return samples\",\n \"def get_num_samples(self) -> int:\\n # must be implemented in subclass\\n raise NotImplementedError\",\n \"def sample(self, n_samples: int) -> torch.Tensor:\\n return self.dist.sample((n_samples,))\",\n \"def ts_rewards_sample(self):\\n exp_rewards_list = [np.random.beta(a, b) for a, b in zip(self.alpha_params, self.beta_params)]\\n return np.array(exp_rewards_list)\",\n \"def prediction_samples(self) -> np.ndarray:\\n return self.prediction_samples\",\n \"def sample(self, n_samples: int, random_state: Optional[int] = None) -> np.ndarray:\\n\\n if not isinstance(self.prob, np.ndarray):\\n samples = stats.bernoulli.rvs(\\n p=self.prob, size=n_samples, random_state=random_state\\n )\\n else:\\n n_classes = len(self.prob) # type: ignore\\n samples = np.zeros((n_samples, n_classes))\\n\\n for cls in range(n_classes):\\n samples[:, cls] = stats.bernoulli.rvs(p=self.prob[cls], size=n_samples, random_state=random_state) # type: ignore\\n\\n return samples\",\n \"def generate_synth_data(n):\",\n \"def get_samples_per_signal(self):\\n return np.array([self.samples_in_file(chn) for chn in range(self.signals_in_file)])\",\n \"def samples(self):\\n pass\",\n \"def prior_sample_parameter(self, parameter):\\n pass\",\n \"def sample(self, num):\\n state, action, reward, next_state, not_terminal = [], [], [], [], []\\n for exp in self.experiences.sample(num):\\n state.append(exp.state)\\n action.append(exp.action)\\n reward.append(exp.reward)\\n next_state.append(exp.next_state)\\n not_terminal.append(exp.not_terminal)\\n\\n return np.asarray(state), np.asarray(action), np.asarray(reward), \\\\\\n np.asarray(next_state), np.asarray(not_terminal)\",\n \"def sample_observations(self, n_samples, z):\\n lat_dim = self.transition.dim_x\\n x = []\\n for i in range(self.time_steps):\\n z_t = tf.slice(z, [0, i * lat_dim], [-1, lat_dim]) \\n x.append(self.emmision.sample(z_t))\\n return tf.concat(x, axis=1)\",\n \"def sample(self, n_samples):\\n with torch.no_grad():\\n z = torch.randn((n_samples, self.z_dim))\\n samples = self.decoder(z)\\n im_size = int(np.sqrt(self.input_dim))\\n samples = samples.view(-1, 1, im_size, im_size)\\n\\n return samples\",\n \"def sample_pagerank(corpus, damping_factor, n):\\n # All pages in entire corpus\\n all_pages = [key for key in corpus.keys()]\\n\\n # Dictionary which will map each page to the number of generated samples that were the page\\n counts = dict()\\n for page in all_pages:\\n counts[page] = 0\\n\\n # Choosing first sample randomly and updating counts dict\\n first_sample = random.choice(all_pages)\\n counts[first_sample] += 1\\n\\n for i in range(1, n):\\n # If we are on the second sample, do the calculation based on the first\\n if i == 1:\\n prev_sample = first_sample\\n\\n distributions = transition_model(corpus, prev_sample, damping_factor)\\n\\n keys = [key for key in distributions.keys()]\\n values = [distributions[key] for key in distributions.keys()]\\n\\n # Using random.choices to select an option given a list of weights (distribution)\\n current_sample = random.choices(keys, weights=values, k=1)[0]\\n\\n # Updating counts dict given the current sample\\n counts[current_sample] += 1\\n\\n prev_sample = current_sample\\n\\n # Modifying counts dict to represent the proportion of samples that corresponded to that page instead of the raw amount\\n for page in all_pages:\\n counts[page] /= n\\n\\n return counts\",\n \"def take(n, seq):\\n return itertools.islice(seq, n)\",\n \"def get_samples(self) -> McmcPtResult:\",\n \"def lhs(\\n self,\\n n: int,\\n seed: Union[int, float, np.random.RandomState, np.random.Generator, None] = 42,\\n ):\\n rng = get_random_generator(seed)\\n samples = []\\n for i in range(self.n_dims):\\n lhs_perm = []\\n # Get evenly distributed samples from one dimension\\n sample_indices = (np.arange(n) + 0.5) / n\\n lhs_aranged = self.dimensions[i].sample(sample_indices)\\n perm = rng.permutation(n)\\n for p in perm: # Random permutate the order of the samples\\n lhs_perm.append(lhs_aranged[p])\\n samples.append(lhs_perm)\\n # Now we have a list of lists with samples for each dimension.\\n # We need to transpose this so that we get a list of lists with\\n # samples for all the dimensions\\n transposed_samples = []\\n for i in range(n):\\n row = []\\n for j in range(self.n_dims):\\n row.append(samples[j][i])\\n transposed_samples.append(row)\\n return transposed_samples\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7953035","0.72522676","0.718797","0.71269745","0.6977341","0.6960459","0.6759681","0.6744323","0.66894495","0.6622551","0.6560197","0.65461904","0.65461904","0.64246196","0.641773","0.63970256","0.6361261","0.6323558","0.6306985","0.63066787","0.62868726","0.6237001","0.6221359","0.62077487","0.620033","0.61837375","0.61646336","0.6154083","0.615204","0.6140827","0.6124439","0.61080766","0.60789466","0.6029138","0.60081846","0.6002796","0.5992458","0.5969409","0.5932291","0.59192795","0.59044236","0.58961016","0.58915186","0.58912045","0.5877209","0.5876434","0.5867642","0.58516467","0.5844115","0.5829508","0.5823226","0.58192647","0.5815583","0.5815583","0.5815583","0.58080715","0.5802151","0.5788934","0.57885796","0.57805705","0.5776559","0.5773315","0.5773198","0.5766512","0.57632893","0.5762385","0.5749447","0.5744704","0.5736448","0.57299155","0.5727703","0.5723194","0.5719757","0.571796","0.56824356","0.56801414","0.56564116","0.5653986","0.5651856","0.5648522","0.5645757","0.56413674","0.5639064","0.56379294","0.56364584","0.5632592","0.5625722","0.56228036","0.5622342","0.56192654","0.5618719","0.5615743","0.5614022","0.5613133","0.55997115","0.5589782","0.55881435","0.5580497","0.55784404","0.55753255"],"string":"[\n \"0.7953035\",\n \"0.72522676\",\n \"0.718797\",\n \"0.71269745\",\n \"0.6977341\",\n \"0.6960459\",\n \"0.6759681\",\n \"0.6744323\",\n \"0.66894495\",\n \"0.6622551\",\n \"0.6560197\",\n \"0.65461904\",\n \"0.65461904\",\n \"0.64246196\",\n \"0.641773\",\n \"0.63970256\",\n \"0.6361261\",\n \"0.6323558\",\n \"0.6306985\",\n \"0.63066787\",\n \"0.62868726\",\n \"0.6237001\",\n \"0.6221359\",\n \"0.62077487\",\n \"0.620033\",\n \"0.61837375\",\n \"0.61646336\",\n \"0.6154083\",\n \"0.615204\",\n \"0.6140827\",\n \"0.6124439\",\n \"0.61080766\",\n \"0.60789466\",\n \"0.6029138\",\n \"0.60081846\",\n \"0.6002796\",\n \"0.5992458\",\n \"0.5969409\",\n \"0.5932291\",\n \"0.59192795\",\n \"0.59044236\",\n \"0.58961016\",\n \"0.58915186\",\n \"0.58912045\",\n \"0.5877209\",\n \"0.5876434\",\n \"0.5867642\",\n \"0.58516467\",\n \"0.5844115\",\n \"0.5829508\",\n \"0.5823226\",\n \"0.58192647\",\n \"0.5815583\",\n \"0.5815583\",\n \"0.5815583\",\n \"0.58080715\",\n \"0.5802151\",\n \"0.5788934\",\n \"0.57885796\",\n \"0.57805705\",\n \"0.5776559\",\n \"0.5773315\",\n \"0.5773198\",\n \"0.5766512\",\n \"0.57632893\",\n \"0.5762385\",\n \"0.5749447\",\n \"0.5744704\",\n \"0.5736448\",\n \"0.57299155\",\n \"0.5727703\",\n \"0.5723194\",\n \"0.5719757\",\n \"0.571796\",\n \"0.56824356\",\n \"0.56801414\",\n \"0.56564116\",\n \"0.5653986\",\n \"0.5651856\",\n \"0.5648522\",\n \"0.5645757\",\n \"0.56413674\",\n \"0.5639064\",\n \"0.56379294\",\n \"0.56364584\",\n \"0.5632592\",\n \"0.5625722\",\n \"0.56228036\",\n \"0.5622342\",\n \"0.56192654\",\n \"0.5618719\",\n \"0.5615743\",\n \"0.5614022\",\n \"0.5613133\",\n \"0.55997115\",\n \"0.5589782\",\n \"0.55881435\",\n \"0.5580497\",\n \"0.55784404\",\n \"0.55753255\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.73065966"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":14391,"cells":{"query":{"kind":"string","value":"Computes the gradient of the prior with respect to theta."},"document":{"kind":"string","value":"def gradient(self, theta):\n return np.zeros([theta.shape[0]])"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def gradient(self, theta):\n pass","def gradient(self, theta):\n pass","def gradient(self, theta):\n return (1 / (self.sigma * np.sqrt(2 * np.pi))) * (\n -theta / (self.sigma ** 2) * np.exp(-(theta ** 2) / (2 * self.sigma ** 2))\n )","def gradient(self, theta):\n a = -(6 * self.scale ** 2)\n b = 3 * self.scale ** 2 + np.exp(2 * theta)\n b *= np.log(3 * self.scale ** 2 * np.exp(-2 * theta) + 1)\n return a / b","def gradient(theta, X, y, learning_rate):\n m = len(y)\n\n theta = theta.reshape((-1,1))\n grad = np.zeros(theta.shape)\n h = sigmoid(np.dot(X, theta)) \n \n grad = np.dot((h-y).T, X)/m\n grad = grad.T\n grad[1:] += (learning_rate/m)*theta[1:]\n return grad","def gradient_function(theta, X, y):\n\n grad = None\n #######################################################################\n # TODO: #\n # Compute the gradient for a particular choice of theta. #\n # Compute the partial derivatives and set grad to the partial #\n # derivatives of the cost w.r.t. each parameter in theta #\n # #\n #######################################################################\n \n theta = theta[:, np.newaxis]\n \n thetatrans = theta.T\n Xtrans = X.T\n \n MulThetaX = np.dot(thetatrans, Xtrans)\n \n h = sigmoid(MulThetaX)\n \n grad = (y - h) * Xtrans\n\n #######################################################################\n # END OF YOUR CODE #\n #######################################################################\n return grad","def compute_gradient(theta, X, y):\n m = X.shape[0]\n grad_theta = np.dot(X.transpose(), (np.dot(X, theta) - y)) / m\n #print theta, grad_theta, objective_function(theta, X, y)\n return grad_theta","def gradientFunction(theta, X, y):\n y = y[:, 0]\n m = y.shape # number of training samples\n grad = X.T.dot(sigmoid(theta.dot(X.T))-1*y)\n grad /= m\n return grad","def gradient(theta, x, y):\n m = len(y)\n n = len(theta)\n z = theta.dot(x.T)\n grad = np.zeros(n)\n for i in xrange(m):\n grad += (g(z[i]) - y[i]) * x[i]\n return 1. / m * grad","def gradient(theta, X, y, Lambda=0.0):\n m = X.shape[0] # number of samples\n\n h = hypothesis(theta, X)\n\n if Lambda:\n g_0 = (1/m)*(X.T@(h - y))[0]\n g_1 = (1/m)*(X.T@(h - y))[1:] + (Lambda/m)*theta[1:] # skip theta-0\n \n return np.append(g_0, g_1)\n else:\n return (1/m)*(X.T@(h - y))","def gradient(x, y, theta):\n if x.ndim == 1:\n x = x[:, np.newaxis]\n if y.ndim == 2 and y.shape[1] == 1:\n y = y.flatten()\n if theta.ndim == 2 and theta.shape[1] == 1:\n theta = theta.flatten()\n\n if (x.size == 0 or y.size == 0 or theta.size == 0\n or x.ndim != 2 or y.ndim != 1 or theta.ndim != 1\n or x.shape[0] != y.shape[0] or x.shape[1] + 1 != theta.shape[0]):\n return None\n\n x_padded = np.c_[np.ones(x.shape[0]), x]\n\n return x_padded.T.dot(x_padded.dot(theta) - y) / y.shape[0]","def calc_grad(X, Y, theta):\n m, n = X.shape\n\n margins = Y * X.dot(theta)\n probs = 1. / (1 + np.exp(margins))\n grad = -(1./m) * (X.T.dot(probs * Y))\n\n return grad","def gradient_descent(X, y, theta, alpha, total_iterations, hypothesis):\n len_theta = len(theta)\n m = len(y)\n one_over_m = (1.0 / float(m))\n\n for _ in range(0, total_iterations):\n temp_theta = numpy.zeros(len_theta)\n\n X_by_theta_minus_y = numpy.subtract(hypothesis(numpy.matrix(theta), X), y)\n\n for j in range(0, len_theta):\n jth_column_of_X = X[:,j]\n derivative_j = one_over_m * numpy.multiply(X_by_theta_minus_y, jth_column_of_X).sum()\n temp_theta[j] = theta[j] - alpha*derivative_j\n\n theta = temp_theta\n\n return numpy.matrix(theta)","def gradient_descent(self, X, theta, Y, m):\n\n Z = X.dot(theta)\n H = Predict.g(Z)\n gradient = np.dot(X.T, (H - Y)) / m\n return self.alpha * gradient","def gradientFunctionReg(theta, X, y, Lambda): \n y = np.squeeze(y)\n m = y.shape # number of training samples\n grad = X.T.dot(sigmoid(theta.dot(X.T))-1*y)\n grad[1:] = grad[1:] + Lambda*theta[1:]\n grad /= m\n\n return grad","def gradientFunctionReg(theta, X, y, Lambda):\n m = len(y) # number of training examples\n grad = np.zeros(theta.shape[0])\n theta = np.transpose(theta)\n sum_1 = 0\n X = X.values\n y = y.values\n #calcuate the theta_0 \n# ====================== YOUR CODE HERE ======================\n# Instructions: Compute the gradient of a particular choice of theta.\n# Compute the partial derivatives and set grad to the partial\n# derivatives of the cost w.r.t. each parameter in theta\n for i in range(theta.shape[0]):\n if i == 0:\n for j in range(m):\n sum_1 += (sigmoid(np.dot(X[j],theta)) - y[j]) * X[j,i]\n else:\n for j in range(m):\n sum_1 += (sigmoid(np.dot(X[j],theta)) - y[j]) * X[j,i] + Lambda*theta[i]\n grad[i] = sum_1/m\n sum_1 = 0\n\n# =============================================================\n\n return grad","def logit_cost_grad(self, theta, X, y):\n\n grad = np.zeros(len(theta))\n\n ### YOUR CODE HERE\n sig = utils.sigmoid(theta)\n # sig = np.subtract(sig, y)\n sig = sig - y\n grad = np.dot(X.T, sig) + 2 * self.params['lamb'] * self.regularizer[1](self.weights)\n ### END YOUR CODE\n\n return grad","def compute_loss_gradient(theta_vector, *args):\n\n psi = args[0] # feed psi as a parameter\n circ_depth = args[1]\n num_qbits = args[2]\n theta = np.reshape(theta_vector, (circ_depth, num_qbits)) # reshapes the flat theta vector\n fidelity = get_fidelity(theta, psi)\n\n # the derivative of the loss wrt fidelity\n dl_df = -0.5 * fidelity ** (-0.5)\n\n df_dtheta = [] # a list of partial derivatives of the fidelity wrt the theta parameters\n\n for index in range(len(theta_vector)):\n layer_index = index // num_qbits\n qbit_index = index % num_qbits\n\n theta_plus = np.copy(theta)\n theta_plus[layer_index][qbit_index] += np.pi / 2 # added pi/2 to the ith theta parameter\n\n theta_minus = np.copy(theta)\n theta_minus[layer_index][qbit_index] -= np.pi / 2 # subtracted pi/2 to the ith theta parameter\n\n df_dtheta_i = 0.5 * (get_fidelity(theta_plus, psi) - get_fidelity(theta_minus, psi)) # ith derivative\n df_dtheta.append(df_dtheta_i)\n\n df_dtheta = np.array(df_dtheta)\n dl_dtheta = dl_df * df_dtheta # chain rule to get partial derivative of loss wrt theta parameters\n\n return dl_dtheta","def run_gradient_descent_iteration(X, Y, theta, alpha, lambda_factor, temp_parameter):\n delta = sparse.coo_matrix(theta.shape).toarray()\n\n h = compute_probabilities(X, theta, temp_parameter)\n\n for j in range(delta.shape[0]):\n y = Y\n y = np.where(y != j, 0, 1)\n p = y - h[j]\n\n x = X.T * p\n x = x.T\n x = x.sum(axis=0)\n\n grad = -x / (temp_parameter * X.shape[0]) + lambda_factor * theta[j]\n\n delta[j] += grad\n\n theta -= alpha * delta\n\n return theta","def gradient_descent(features, values, theta, alpha, num_iterations):\n \n # number of points\n npoints = len(values)\n \n # intialize cost history\n cost_history = []\n \n # num_interations iterations\n for iiter in range(num_iterations):\n \n # compute and store cost\n cost = compute_cost(features, values, theta)\n cost_history.append(cost)\n \n # update values of theta\n values_predicted = np.dot(features, theta)\n theta = theta + (alpha/npoints)*(np.dot(values - values_predicted,features))\n \n return theta, pandas.Series(cost_history)","def gradient_descent(x, y, theta=[[0], [0]]):\n m = y.size\n j_history = []\n for i in range(ITERATIONS):\n h = x.dot(theta)\n theta = theta - (ALPHA / m) * (x.T.dot(h - y))\n j_history.append(compute_cost(x, y, theta))\n return theta, j_history","def approx_grad(theta, X, y):\n grad_a = np.array([(cost(theta + e, X, y) - cost(theta - e, X, y)) / (2 * 1e-5)\n for e in np.identity(len(theta)) * 1e-5])\n return grad_a","def gradient_descent(features, values, theta, alpha, num_iterations):\r\n\r\n m = len(values)\r\n cost_history = []\r\n\r\n for i in range (num_iterations):\r\n \r\n h = numpy.dot(features, theta)\r\n \r\n theta = theta - alpha / m * numpy.dot((h-values),features)\r\n \r\n cost = compute_cost(features, values, theta)\r\n \r\n cost_history.append(cost)\r\n\r\n return theta, pandas.Series(cost_history) # leave this line for the grader\r","def calculate_gradient(y, tx, w): \n return tx.T@(sigmoid(tx@w)-y)","def gradient_descent(features, values, theta, alpha, num_iterations):\n m = len(values)\n cost_history = []\n\n for i in range(num_iterations):\n predicted_values = np.dot(features, theta)\n delta = alpha / m * np.dot((predicted_values - values), features)\n theta = theta - delta\n cost = compute_cost(features, values, theta)\n cost_history.append(cost)\n return theta, pandas.Series(cost_history)","def lr_cost_function_grad(theta: np.ndarray, X: np.ndarray, y: np.ndarray, l: float) -> np.ndarray:\n # Initialize some useful values\n m = len(y) # number of training examples\n\n # You need to return the following variable correctly.\n grad = np.zeros(theta.shape)\n\n # ====================== YOUR CODE HERE ======================\n # Instructions: Compute the partial derivatives and set grad to the partial\n # derivatives of the cost w.r.t. each parameter in theta.\n\n # =============================================================\n return grad","def gradient_ascent(self, w, X, y, lr):\r\n # INSERT YOUR CODE HERE\r\n #raise Exception('Function not yet implemented!')\r\n # gradient = x_j*(y-σ(wTX))\r\n return np.dot(X.T, y-self.sigmoid(np.dot(X, w)))","def computeNumericalGradient(J, theta):\n numgrad = np.zeros(theta.size)\n perturb = np.zeros(theta.size)\n e = 1e-4\n for p in range(theta.size):\n # Set perturbation vector\n perturb[p] = e\n loss1 = J(theta - perturb)[0]\n loss2 = J(theta + perturb)[0]\n # Compute Numerical Gradient\n numgrad[p] = (loss2 - loss1) / (2*e)\n perturb[p] = 0\n return numgrad","def computeNumericalGradient(J, theta):\n numgrad = np.zeros_like(theta).reshape(-1)\n perturb = np.zeros_like(theta).reshape(-1)\n e = 1e-4\n for p in range(theta.size):\n # Set perturbation vector\n perturb[p] = e\n loss1, _ = J(theta - perturb.reshape(theta.shape))\n loss2, _ = J(theta + perturb.reshape(theta.shape))\n # Compute Numerical Gradient\n numgrad[p] = (loss2 - loss1) / (2 * e)\n perturb[p] = 0\n\n return numgrad.reshape(theta.shape)","def gradientdescent(cost_func, theta, args=(), delta_func = 0):\n step = 1\n old_cost = 0\n while True:\n theta_old = theta.copy()\n cost = cost_func(theta, *args)\n delta = delta_func(theta, *args)\n theta = theta - step * delta\n if cost > old_cost and old_cost != 0:\n step = step*0.7\n if np.allclose(theta_old, theta):\n break\n old_cost = cost\n return theta","def gradient(cls, x):\n y = Sigmoid.apply(x)\n return np.multiply(y, 1 - y)","def gradient_descent(features, values, theta, alpha, num_iterations):\r\n \r\n m = len(values)\r\n cost_history = []\r\n\r\n for i in range(num_iterations):\r\n # your code here\r\n cost = compute_cost(features, values, theta)/(2.0*m)\r\n cost_history.append([cost])\r\n \r\n error = features.dot(theta) - values\r\n error = np.reshape(error,(error.shape[0], 1))\r\n errorWeighted = features*error\r\n errorSum = (np.sum(errorWeighted,0))/(m*1.0)\r\n theta = theta - alpha*errorSum \r\n \r\n return theta, pandas.Series(cost_history)","def compute_gradient(y, tx, w):\n\tN = y.shape[0]\n\te = y - np.dot(tx, w)\n\n\tgradLw = -1/N * np.dot(tx.T, e)\n\treturn gradLw","def compute_square_loss_gradient(X, y, theta):\n #TODO\n P = (np.dot(X, theta)-y)\n m = X.shape[0]\n\n return (2/m)*np.dot(X.T, P)","def learning_by_gradient_descent(y, tx, w, gamma):\n\tgrad = calculate_gradient(y, tx, w)\n\n\tw = w - gamma * grad\n\treturn w","def calculate_gradient(y, tx, w):\n return tx.T.dot(sigmoid(tx.dot(w))-np.reshape(y,(len(y),1)))","def gradientDescent(self,X, y, theta): \n # number of instances\n m = len(y)\n J_history = np.zeros((self.NUM_ITERS,1))\n for i in range(self.NUM_ITERS):\n h = self.sigmoid(X@theta)\n grad = 1 / m * X.T @ (h - y)\n theta = theta - self.ALPHA * grad \n J_history[i] = self.costFunction(theta, X, y)\n \n \n return theta, J_history","def compute_square_loss_gradient(X, y, theta):\n #TODO\n (N,p) = np.shape(X)\n grad = -(1/np.float(N))*np.array([(y - X.dot(theta))*X[:,i] for i in range(p)])\n return np.sum(grad,axis=1)","def gradient_descent(X, y, theta, learning_rate, num_iters, lambda_reg):\n thetas = [theta]\n cost = np.zeros(num_iters)\n\n J = mean_cross_entropy_costs(X, y, lambda_reg)\n cost[0] = J(thetas[0])\n for i in range(1, num_iters):\n thetas.append(compute_new_theta(X, y, thetas[i - 1], learning_rate, lambda_reg))\n cost[i] = J(thetas[i])\n return cost, thetas","def gradient(img):\n G = np.zeros(img.shape)\n theta = np.zeros(img.shape)\n\n #####################################\n # START YOUR CODE HERE #\n #####################################\n Gx = partial_x(img)\n Gy = partial_y(img)\n G = np.sqrt(np.square(Gx) + np.square(Gy))\n theta = np.degrees(np.arctan2(Gy, Gx)) % 360\n ######################################\n # END OF YOUR CODE #\n ######################################\n\n return G, theta","def gradient_descent(initial_theta, X, y, niter, alpha, Lambda=0.0):\n theta_list = []\n cost_list = []\n\n theta = initial_theta\n for i in range(0, niter):\n theta -= alpha*gradient(theta, X, y, Lambda)\n theta_list.append(theta)\n cost_list.append(cost(theta, X, y, Lambda))\n\n return theta_list, cost_list","def linreg_stochastic_grad(X, y, alpha=.01):\n m = X.shape[0]\n n = X.shape[1]\n theta = np.zeros(n)\n for i in range(m):\n delta = alpha * (np.dot(theta.transpose(), X[i,:]) -y[i]) * X[i,:]\n theta = theta - delta\n return theta","def log_likelihood_gradient(y, tx, w):\n return tx.T.dot(sigmoid(tx.dot(w))-y)","def gradient(cls, x):\n return 1 - TanH.apply(x) ** 2","def learning_by_gradient_descent(y, tx, w, gamma):\n loss = calculate_loss(y,tx,w)\n grad = calculate_gradient(y,tx,w)\n w_new = w - gamma*grad\n #grad is for debugging purpose\n return loss, w_new,grad","def simple_gradient(x, y, theta):\n if x.shape[0] * y.shape[0] * theta.shape[0] == 0:\n return None\n if x.shape[0] != y.shape[0] or theta.shape[0] != 2:\n return None\n\n x = add_intercept(x)\n\n result = [\n forumla(x, y, theta, 0),\n forumla(x, y, theta, 1)\n ]\n return result","def calculate_gradient(y, tx, w):\n\n\tret = tx.T.dot(sigmoid(np.dot(tx, w)) - y)\n\treturn ret","def learning_by_gradient_descent(y, tx, w, gamma):\n loss = calculate_loss(y,tx,w)\n grad = calculate_gradient(y,tx,w)\n w = w-gamma*grad\n return w, loss","def train_fn_grad(self, theta_value, theta):\n theta.set_value(theta_value, borrow=True)\n\n train_grads = [self.batch_grad(i, self.n_train)\n for i in xrange(self.n_train_batches)]\n train_batch_sizes = [self.get_batch_size(i, self.n_train)\n for i in xrange(self.n_train_batches)]\n\n return np.average(train_grads, weights=train_batch_sizes,\n axis=0)","def gradient(self, x):\n pass","def gradient(img):\n G = np.zeros(img.shape)\n theta = np.zeros(img.shape)\n\n ### YOUR CODE HERE\n Gx = partial_x(img)\n Gy = partial_y(img)\n G = np.sqrt(np.power(Gx,2)+np.power(Gy,2))\n theta = (np.arctan2(Gy, Gx) * 180 / np.pi) % 360\n ### END YOUR CODE\n\n return G, theta","def approx_grad_reg(theta, X, y, _lambda):\n grad_a = np.array([(cost_reg(theta + e, X, y, _lambda) - cost_reg(theta - e, X, y, _lambda)) / (2 * 1e-5)\n for e in np.identity(len(theta)) * 1e-5])\n return grad_a","def get_gradient(phi, pred, t, dot_product, weight, reg= 1, regression= \"logistic\"):\n if regression == \"logistic\":\n gradient = np.matmul(phi.T, pred - t)\n elif regression == \"probit\":\n R = np.eye(pred.shape[0])\n for i in range(pred.shape[0]):\n y_n = pred[i,0]\n dotp = dot_product[i, 0]\n pdf = norm.pdf(dotp)\n R[i,i] = pdf/(y_n*(1-y_n) + TOLERANCE)\n gradient = np.matmul(np.matmul(phi.T, R), pred-t)\n elif regression == \"multiclass\":\n gradient = np.matmul(phi.T, pred - t)\n\n # Add regularization\n gradient += weight/ reg\n return gradient","def gradient(self, x):\n u = np.asarray([x[0]])\n C = self.C_func(u)\n dC = self.dC_func(u, order=1)\n P = self.P\n numerator = np.sum((C - P) * dC, axis=0)\n denominator = np.sum(np.sum((C - P) ** 2, axis=0) ** (1 / 2))\n if np.abs(denominator) > 0:\n gradient = numerator/denominator\n else:\n gradient = np.asarray(0)[np.newaxis]\n return gradient","def apply_gradient(params: torch.Tensor, grads: torch.Tensor, lr: float) -> torch.Tensor:\n params_prime = params + lr * grads\n return params_prime","def run_steep_gradient_descent(data_x, data_y, len_data, alpha, theta):\n n = len_data\n # WE NEED TO transpose data_x into (p+1) *n ,theta is 1*(p+1)\n prod = np.dot(theta, data_x.transpose())\n\n prod -= data_y\n print(\"pro: data_x\", prod.shape, data_x.shape)\n #prod represent the loss of the hypothesis and true label\n sum_grad = np.dot(prod, data_x)\n print(\"总梯度的值：\",sum_grad.shape)\n\n # batch-gradient descent\n theta = theta -(alpha / n) * sum_grad\n return theta","def GDS(x, theta, y, lamb, alpha):\n\tm = y.shape[0]\n\tdif = (1/m) * x.T @ (sigmoid(x,theta) - y) #classical differentiation term, output is (n,1)\n\tdif_reg = (lamb/m) * theta #regularization term\n\treturn (dif + dif_reg) * alpha","def gradientDescent(X, y, theta, alpha, num_iters):\n\n # Initialize some useful values\n J_history = []\n m = y.size # number of training examples\n\n for i in range(num_iters):\n # ====================== YOUR CODE HERE ======================\n # Instructions: Perform a single gradient step on the parameter vector\n # theta.\n #\n # Hint: While debugging, it can be useful to print out the values\n # of the cost function (computeCost) and gradient here.\n #\n # Calculate the gradient step according to the equation for theta1:\n g_step1 = (alpha / m * np.sum( (np.dot(X,theta) - y) * X[:,1]) )\n # Gradient step for theta knot:\n g_step0 = (alpha / m * np.sum( (np.dot(X,theta) - y) ) )\n \n #update theta\n theta[0] = (theta[0] - g_step0)\n theta[1] = (theta[1] - g_step1)\n \n #print([theta , g_step1, g_step0])\n\n # ============================================================\n\n # Save the cost J in every iteration\n J_history.append(computeCost(X, y, theta))\n\n return theta, J_history","def gradient(w, x, t):\n return 2 * np.dot(x.T, (nn(x, w) - t))","def costFunction(theta,X,y):\n m = X.shape[0]\n J = 0\n h = sigmoid (np.dot(X,theta))\n \n J = (1/m)* ((-np.dot(y.T,(np.log(h)))) - np.dot((1 - y).T,(np.log(1-h))))\n \n #grad = (1/m) * np.dot(X.T,(h-y))\n grad = (1/m) * np.dot((h.T - y), X).T\n \n return J, grad","def stochasticGradientDescent(x,y,theta,alpha):\n m,n = np.shape(x)\n convergence = 0.000000001\n lastCost = 0\n cost = -1 \n recurseCount = 0\n while abs(lastCost - cost) > convergence: # rcurse until converge\n lastCost = cost\n hypothesis = np.dot(x,theta) \n for i in range(m):\n # alpha = 4.0 / (1.0 + i) + 0.01 \n loss = hypothesis[i] - y[i]\n # gradient = np.dot(x[i],loss)\n gradient = x[i,:].transpose() * loss \n theta = theta - alpha * gradient\n cost = np.sum((hypothesis-y)**2)/(2*m)\n recurseCount += 1\n return recurseCount,theta","def gradient(self):\n result = np.zeros(len(self.variables))\n result[self.bivariateGradInd] = (self.shape-1)/self.variable - self.rate\n return result","def gradient(self):\n functional = self\n\n class KLGradient(Operator):\n\n \"\"\"The gradient operator of this functional.\"\"\"\n\n def __init__(self):\n \"\"\"Initialize a new instance.\"\"\"\n super().__init__(functional.domain, functional.domain,\n linear=False)\n\n def _call(self, x):\n \"\"\"Apply the gradient operator to the given point.\n The gradient is not defined in points where one or more\n components are non-positive.\n \"\"\"\n if functional.prior is None:\n return (-1.0) / x + 1\n else:\n return (-functional.prior) / x + 1\n\n return KLGradient()","def batchGradientDescent(x,y,theta,alpha):\n m,n = np.shape(x)\n xTran = x.transpose()\n convergence = 0.000000001\n lastCost = 0\n cost = -1 \n recurseCount = 0\n while abs(lastCost - cost) > convergence: # rcurse until converge\n lastCost = cost\n hypothesis = np.dot(x,theta)\n loss = hypothesis - y\n cost = np.sum(loss**2)/(2*m)\n gradient = np.dot(xTran,loss)/m\n theta = theta - alpha*gradient\n recurseCount += 1\n return recurseCount,theta","def learning_by_penalized_gradient(y, tx, w, gamma, lambda_):\n\tgrad, H = penalized_logistic_regression(y, tx, w, lambda_)\n\n\thgrad = np.linalg.inv(H).dot(grad)\n\n\tw = w - gamma * hgrad\n\n\treturn w","def grad(self, w):\n l1_grad = self.r * np.sign(w)\n l2_grad = np.asarray(1 - self.r) * w \n\n gradient_penalty = self.alpha * (l1_grad + l2_grad)\n\n # Insert 0 for bias term.\n return np.insert(gradient_penalty, 0, 0, axis=0)","def compute_gradient(self):\n A = np.dot(self.X, self.w)\n m = self.t.shape[0]\n C = -1 * self.t * (1 / (1 + np.exp(A * self.t)))\n return (1 / m) * np.dot(self.X.T, C)","def gradient(self, x):\n g = self._grad(\n time_series=self.observed_data,\n a=x[0],\n b=x[1],\n c=x[2],\n sigma=self.sigma\n )\n return g","def reg_autograd(alpha,X,theta,y):\n g=grad(func)\n for i in range(0,20):\n theta=theta- alpha * g(theta)\n print(\"cost\", func(theta))\n print(\"final theta\",theta)","def grad(self, x):\n norm_beta = np.linalg.norm(x)\n if norm_beta > utils.TOLERANCE:\n return (self.l / norm_beta) * x\n else:\n D = x.shape[0]\n u = (self.rng(D, 1) * 2.0) - 1.0 # [-1, 1]^D\n norm_u = np.linalg.norm(u)\n a = self.rng() # [0, 1]\n\n return (self.l * (a / norm_u)) * u","def gradcovfunc(self, theta, d):\n sigmaf, l = theta[:2] \n xxl = np.sum((d/l)**2, axis=1)\n dk_dsigmaf = 2 * sigmaf * np.exp(-xxl/2.)\n dk_dl = sigmaf**2/l * xxl * np.exp(-xxl/2.)\n grad = np.array([dk_dsigmaf, dk_dl])\n return grad","def log_prior_grad(self, inputs):","def gradient(series, **options):\n x = series.index\n y = series.values\n\n a = np.gradient(y, x, **options)\n return series.__class__(a, series.index)","def derivative(self, theta):\n diag_gamma = np.dot(theta.T, self.X.T)\n logistic_term = self.logistic_fn(diag_gamma)\n diag_gamma = logistic_term * (1.0 - logistic_term)\n gamma = np.diag(diag_gamma)\n\n # v computation\n diags_v = 1.0 - 2*self.logistic_fn(np.dot(theta.T, self.X.T))\n diags_v = diags_v.reshape((-1, 1))\n diags_v = diags_v*self.X\n assert diags_v.shape == self.X.shape #N*d shape\n\n XtGamma = np.dot(self.X.T, gamma) # d*N shape\n\n # TODO: Verifier car pas sur de mon coup ... et surtout plus long...\n # id = np.eye(self.n_examples).reshape((self.n_examples, self.n_examples, 1))\n # diags_v = diags_v.reshape((self.n_examples, 1, self.dim))\n # v = id*diags_v # n*n*d tensor\n # left = np.tensordot(XtGamma, v, axes=(1, 0)) # shape d*N*d\n # assert left.shape == (self.dim, self.n_examples, self.dim)\n # dg = np.tensordot(left, self.X, axes=(1, 0))\n # dg = np.swapaxes(dg, axis1=-2, axis2=-1)\n\n dg = np.zeros((self.dim, self.dim, self.dim))\n for idx, v_i_diag in enumerate(diags_v.T):\n v_i = np.diag(v_i_diag)\n dg_di = np.dot(np.dot(XtGamma, v_i), self.X)\n dg[:, :, idx] = dg_di\n return dg","def compute_logistic_gradient(y, tx, w):\n return tx.T.dot(sigmoid(tx.dot(w)) - y) / len(y)","def run_gradient_descent(data,theta,alpha,num_iters):\n population = data[:,0]\n prices = data[:,1]\n x = ones(shape=(len(population),2)) #add ones for theta0 \n x[:,1] = population\n x = transpose(x)\n error_history = zeros(shape=(num_iters,1))\n \n for i in range(num_iters):\n predictions = theta.dot(x)\n errors_x1 = (predictions - prices) * x[0,:]\n errors_x2 = (predictions - prices) * x[1,:]\n theta[0][0] = theta[0][0] - alpha*(1.0/len(population))*errors_x1.sum()\n theta[0][1] = theta[0][1] - alpha*(1.0/len(population))*errors_x2.sum()\n error_history[i,0] = calculate_cost(theta,data)\n \n return theta, error_history","def gradient(self, x):\n return 0.0","def costFunction(self,theta, X, y): \n m = len(y)\n h = self.sigmoid(X@theta)\n J = 1 / m * (- y.T @ self.log(h) - (1-y).T @ self.log(1-h)) \n # grad = 1/ m * X.T @ (h - y)\n return J","def gradient(poly):\n return differential(\n poly, chaospy.poly.collection.basis(1, 1, poly.dim, sort=\"GR\"))","def curves_gradient(theta, m_, LAMBDA=0):\n # X0: column of ones\n theta0_gradient = (1 / m_) * sum((curves_hypothesis(theta, m_) - y[:m_].ravel()) * np.ones(m_))\n # X1: data given as X\n theta1_gradient = ((1 / m_) * sum((curves_hypothesis(theta, m_) - y[:m_].ravel()) * X[:m_].ravel())) \\\n + (LAMBDA / m_) * theta[1]\n return np.array([theta0_gradient, theta1_gradient])","def gradient(self, x_in):\r\n\r\n return 1 - torch.pow(self.forward(x_in), 2)","def compute_gradient(self): # TODO: try to change to square loss since it's hessian is easier to obtain\n A = np.dot(self.X, self.w)\n m = self.t.shape[0]\n C = -1 * self.t * (1 / (1 + np.exp(A * self.t)))\n return (1 / m) * np.dot(self.X.T, C)","def vb_grad_natgrad(self):\r\n\r\n # Gradient for eta\r\n dL_dmmT_S = -0.5*self.Lambda/self.data_prop + 0.5*self.q_u_prec\r\n Kmmipsi1V,_ = dpotrs(self.Lm,self.psi1V,lower=1)\r\n dL_dm = (Kmmipsi1V - np.dot(self.Lambda,self.q_u_mean))/self.data_prop\r\n\r\n # Gradients for theta\r\n S = self.q_u_cov\r\n Si = self.q_u_prec\r\n m = self.q_u_mean\r\n dL_dSi = -mdot(S,dL_dmmT_S, S)\r\n\r\n dL_dmhSi = -2*dL_dSi\r\n dL_dSim = np.dot(dL_dSi,m) + np.dot(Si, dL_dm)\r\n\r\n return np.hstack((dL_dm.flatten(),dL_dmmT_S.flatten())) , np.hstack((dL_dSim.flatten(), dL_dmhSi.flatten()))","def grad(self, w):\n gradient_penalty = np.asarray(self.alpha) * w\n # Insert 0 for bias term.\n return np.insert(gradient_penalty, 0, 0, axis=0)","def grad(self, w):\n gradient_penalty = np.asarray(self.alpha) * w\n # Insert 0 for bias term.\n return np.insert(gradient_penalty, 0, 0, axis=0)","def gradient(self):\n functional = self\n\n class KLCCGradient(Operator):\n\n \"\"\"The gradient operator of this functional.\"\"\"\n\n def __init__(self):\n \"\"\"Initialize a new instance.\"\"\"\n super().__init__(functional.domain, functional.domain,\n linear=False)\n\n def _call(self, x):\n \"\"\"Apply the gradient operator to the given point.\n\n The gradient is not defined in points where one or more\n components are larger than or equal to one.\n \"\"\"\n if functional.prior is None:\n return 1.0 / (1 - x)\n else:\n return functional.prior / (1 - x)\n\n return KLCCGradient()","def learning_by_penalized_gradient(y, tx, w, gamma, lambda_):\n\n #on test avec Newton\n\n loss,gradient,_ = penalized_logistic_regression(y,tx,w,lambda_)\n\n w = w - gamma*gradient\n return loss, w,gradient","def gradient_ascent(f, df, theta_init, step_size, max_iter):\n\n fs = []\n xs = []\n thetas = theta_init\n for i in range(max_iter): #for each data example\n fs.append(f(thetas))\n\n temp = step_size*df(thetas)\n thetas = step_size*df(thetas) #modify that feature by using the derivative of log likelihood\n xs.append(thetas.flatten())\n if i % 10 == 0:\n print(i, thetas)\n\n return thetas, fs, xs","def calculate_maxL_gradient(y, tx, w):\n sig=sigmoid(tx.dot(w))\n grad=tx.T.dot(sig-y)\n #divides with number of samples so that learning rate is not dependant on number of samples\n grad=grad/len(y) \n return grad","def _log_prior_gradients(self):\n if self.priors.size == 0:\n return 0.\n x = self.param_array\n ret = np.zeros(x.size)\n #compute derivate of prior density\n [np.put(ret, ind, p.lnpdf_grad(x[ind])) for p, ind in self.priors.items()]\n #add in jacobian derivatives if transformed\n priored_indexes = np.hstack([i for p, i in self.priors.items()])\n for c,j in self.constraints.items():\n if not isinstance(c, Transformation):continue\n for jj in j:\n if jj in priored_indexes:\n ret[jj] += c.log_jacobian_grad(x[jj])\n return ret","def grad(self, w):\n gradient_penalty = np.asarray(self.alpha) * w\n\n # Insert 0 for bias term.\n return np.insert(gradient_penalty, 0, 0, axis=0)","def grad(theta, X, y, lambda_):\n # ... dopolnite (naloga 1, naloga 2)\n\n l = []\n for i, e in enumerate(theta):\n l.append(1 / len(y) * sum([(h(x, theta) - yi) * x[i] for x, yi in zip(X, y)]) + 2 * lambda_ * e)\n\n return np.array(l)","def lr_cost_function(theta, X, y, learning_rate):\n m = len(y) # number of training examples\n # You need to return the following variables correctly \n J = 0\n grad = np.zeros(theta.shape)\n h = sigmoid(np.dot(X, theta))\n\n #Compute cost:\n first = (1/m)*np.sum(np.dot(-y.T, np.log(h)) - np.dot((1-y).T, np.log(1-h)), axis=0)\n second = (learning_rate/(2*m))*np.sum(theta[1:]**2)\n J = first + second\n \n #Compute gradient\n grad = np.dot((h-y).T, X)/m\n grad = grad.T\n grad[1:] += (learning_rate/m)*theta[1:]\n return float(J), grad","def gradient(self, x, i=None):\n self.set_parameters(x=x)\n gradient = self.gradient_loss()\n if self.add_bias:\n gradient[1:] += self.alpha * self.gradient_regularizer()\n else:\n gradient += self.alpha * self.gradient_regularizer()\n if i is not None:\n return gradient.ravel()[i]\n return gradient.ravel()","def compute_gradient_lasso(y, tx, w, lambda_):\n e = y - tx.dot(w)\n subgrad = lambda_ * np.sign(w)\n\n return -tx.T.dot(e)/len(e) + subgrad","def compute_gradient(self, X, y, weights):\n sigmoid = self.sigmoid(np.dot(X, weights))\n return np.dot(X.T, y - sigmoid)","def batch_grad_descent(X, y, alpha=0.1, num_iter=1000, check_gradient=False):\n num_instances, num_features = X.shape[0], X.shape[1]\n theta_hist = np.zeros((num_iter+1, num_features)) #Initialize theta_hist\n loss_hist = np.zeros(num_iter+1) #initialize loss_hist\n theta = np.ones(num_features) #initialize theta\n\n count = 0\n while count < num_iter+1:\n if check_gradient:\n assert grad_checker(X,y,theta)\n\n grad = compute_square_loss_gradient(X,y,theta)\n theta -= alpha*grad\n theta_hist[count] = theta\n loss_hist[count] = compute_square_loss(X,y,theta)\n count += 1\n \n return theta_hist, loss_hist","def check_gradient(self, x, y):\n x = x.transpose()\n y = y.transpose()\n layers_copy = deepcopy(self.layers)\n epsilon = 10 ** -4\n a, layer = self.forward_propagation(x)\n delta = self.calculate_delta(a, y, layer)\n self.backpropagation(delta=delta, theta=layer.theta)\n previous_layer_output = x\n for layer in self.layers:\n theta_copy = deepcopy(layer.theta)\n real_theta_size = theta_copy.shape\n delta = layer.delta\n dc_dtheta = np.outer(previous_layer_output, delta).transpose()\n previous_layer_output = layer.a\n R, C = theta_copy.shape\n for i in range(R):\n for j in range(C):\n theta_plus = deepcopy(theta_copy)\n theta_plus[i, j] += epsilon\n layer.theta = theta_plus\n a_plus, l_plus = self.forward_propagation(x)\n err_plus = self.calculate_loss(a_plus, y)\n theta_minus = deepcopy(theta_copy)\n theta_minus[i, j] -= epsilon\n layer.theta = theta_minus\n a_minus, l_minus = self.forward_propagation(x)\n err_minus = self.calculate_loss(a_minus, y)\n limit = (err_plus - err_minus)/(2*epsilon)\n grad_diff = abs(dc_dtheta[i,j] - limit)\n assert grad_diff < 10 ** -6, f\"Diff {grad_diff} is too big.\"\n layer.theta = theta_copy","def _compute_func_grad(self, w):\n W = w.reshape((self.X.shape[1], self.Y.shape[1]))\n self.nll_, self.grad_ = calculate_gradient(self.X, self.Y, W, self.prior, self.weighted,0)","def gradient(data_x, data_y, parameters):\n return data_x.T @ (data_x @ parameters - data_y) / data_x.shape[0]"],"string":"[\n \"def gradient(self, theta):\\n pass\",\n \"def gradient(self, theta):\\n pass\",\n \"def gradient(self, theta):\\n return (1 / (self.sigma * np.sqrt(2 * np.pi))) * (\\n -theta / (self.sigma ** 2) * np.exp(-(theta ** 2) / (2 * self.sigma ** 2))\\n )\",\n \"def gradient(self, theta):\\n a = -(6 * self.scale ** 2)\\n b = 3 * self.scale ** 2 + np.exp(2 * theta)\\n b *= np.log(3 * self.scale ** 2 * np.exp(-2 * theta) + 1)\\n return a / b\",\n \"def gradient(theta, X, y, learning_rate):\\n m = len(y)\\n\\n theta = theta.reshape((-1,1))\\n grad = np.zeros(theta.shape)\\n h = sigmoid(np.dot(X, theta)) \\n \\n grad = np.dot((h-y).T, X)/m\\n grad = grad.T\\n grad[1:] += (learning_rate/m)*theta[1:]\\n return grad\",\n \"def gradient_function(theta, X, y):\\n\\n grad = None\\n #######################################################################\\n # TODO: #\\n # Compute the gradient for a particular choice of theta. #\\n # Compute the partial derivatives and set grad to the partial #\\n # derivatives of the cost w.r.t. each parameter in theta #\\n # #\\n #######################################################################\\n \\n theta = theta[:, np.newaxis]\\n \\n thetatrans = theta.T\\n Xtrans = X.T\\n \\n MulThetaX = np.dot(thetatrans, Xtrans)\\n \\n h = sigmoid(MulThetaX)\\n \\n grad = (y - h) * Xtrans\\n\\n #######################################################################\\n # END OF YOUR CODE #\\n #######################################################################\\n return grad\",\n \"def compute_gradient(theta, X, y):\\n m = X.shape[0]\\n grad_theta = np.dot(X.transpose(), (np.dot(X, theta) - y)) / m\\n #print theta, grad_theta, objective_function(theta, X, y)\\n return grad_theta\",\n \"def gradientFunction(theta, X, y):\\n y = y[:, 0]\\n m = y.shape # number of training samples\\n grad = X.T.dot(sigmoid(theta.dot(X.T))-1*y)\\n grad /= m\\n return grad\",\n \"def gradient(theta, x, y):\\n m = len(y)\\n n = len(theta)\\n z = theta.dot(x.T)\\n grad = np.zeros(n)\\n for i in xrange(m):\\n grad += (g(z[i]) - y[i]) * x[i]\\n return 1. / m * grad\",\n \"def gradient(theta, X, y, Lambda=0.0):\\n m = X.shape[0] # number of samples\\n\\n h = hypothesis(theta, X)\\n\\n if Lambda:\\n g_0 = (1/m)*(X.T@(h - y))[0]\\n g_1 = (1/m)*(X.T@(h - y))[1:] + (Lambda/m)*theta[1:] # skip theta-0\\n \\n return np.append(g_0, g_1)\\n else:\\n return (1/m)*(X.T@(h - y))\",\n \"def gradient(x, y, theta):\\n if x.ndim == 1:\\n x = x[:, np.newaxis]\\n if y.ndim == 2 and y.shape[1] == 1:\\n y = y.flatten()\\n if theta.ndim == 2 and theta.shape[1] == 1:\\n theta = theta.flatten()\\n\\n if (x.size == 0 or y.size == 0 or theta.size == 0\\n or x.ndim != 2 or y.ndim != 1 or theta.ndim != 1\\n or x.shape[0] != y.shape[0] or x.shape[1] + 1 != theta.shape[0]):\\n return None\\n\\n x_padded = np.c_[np.ones(x.shape[0]), x]\\n\\n return x_padded.T.dot(x_padded.dot(theta) - y) / y.shape[0]\",\n \"def calc_grad(X, Y, theta):\\n m, n = X.shape\\n\\n margins = Y * X.dot(theta)\\n probs = 1. / (1 + np.exp(margins))\\n grad = -(1./m) * (X.T.dot(probs * Y))\\n\\n return grad\",\n \"def gradient_descent(X, y, theta, alpha, total_iterations, hypothesis):\\n len_theta = len(theta)\\n m = len(y)\\n one_over_m = (1.0 / float(m))\\n\\n for _ in range(0, total_iterations):\\n temp_theta = numpy.zeros(len_theta)\\n\\n X_by_theta_minus_y = numpy.subtract(hypothesis(numpy.matrix(theta), X), y)\\n\\n for j in range(0, len_theta):\\n jth_column_of_X = X[:,j]\\n derivative_j = one_over_m * numpy.multiply(X_by_theta_minus_y, jth_column_of_X).sum()\\n temp_theta[j] = theta[j] - alpha*derivative_j\\n\\n theta = temp_theta\\n\\n return numpy.matrix(theta)\",\n \"def gradient_descent(self, X, theta, Y, m):\\n\\n Z = X.dot(theta)\\n H = Predict.g(Z)\\n gradient = np.dot(X.T, (H - Y)) / m\\n return self.alpha * gradient\",\n \"def gradientFunctionReg(theta, X, y, Lambda): \\n y = np.squeeze(y)\\n m = y.shape # number of training samples\\n grad = X.T.dot(sigmoid(theta.dot(X.T))-1*y)\\n grad[1:] = grad[1:] + Lambda*theta[1:]\\n grad /= m\\n\\n return grad\",\n \"def gradientFunctionReg(theta, X, y, Lambda):\\n m = len(y) # number of training examples\\n grad = np.zeros(theta.shape[0])\\n theta = np.transpose(theta)\\n sum_1 = 0\\n X = X.values\\n y = y.values\\n #calcuate the theta_0 \\n# ====================== YOUR CODE HERE ======================\\n# Instructions: Compute the gradient of a particular choice of theta.\\n# Compute the partial derivatives and set grad to the partial\\n# derivatives of the cost w.r.t. each parameter in theta\\n for i in range(theta.shape[0]):\\n if i == 0:\\n for j in range(m):\\n sum_1 += (sigmoid(np.dot(X[j],theta)) - y[j]) * X[j,i]\\n else:\\n for j in range(m):\\n sum_1 += (sigmoid(np.dot(X[j],theta)) - y[j]) * X[j,i] + Lambda*theta[i]\\n grad[i] = sum_1/m\\n sum_1 = 0\\n\\n# =============================================================\\n\\n return grad\",\n \"def logit_cost_grad(self, theta, X, y):\\n\\n grad = np.zeros(len(theta))\\n\\n ### YOUR CODE HERE\\n sig = utils.sigmoid(theta)\\n # sig = np.subtract(sig, y)\\n sig = sig - y\\n grad = np.dot(X.T, sig) + 2 * self.params['lamb'] * self.regularizer[1](self.weights)\\n ### END YOUR CODE\\n\\n return grad\",\n \"def compute_loss_gradient(theta_vector, *args):\\n\\n psi = args[0] # feed psi as a parameter\\n circ_depth = args[1]\\n num_qbits = args[2]\\n theta = np.reshape(theta_vector, (circ_depth, num_qbits)) # reshapes the flat theta vector\\n fidelity = get_fidelity(theta, psi)\\n\\n # the derivative of the loss wrt fidelity\\n dl_df = -0.5 * fidelity ** (-0.5)\\n\\n df_dtheta = [] # a list of partial derivatives of the fidelity wrt the theta parameters\\n\\n for index in range(len(theta_vector)):\\n layer_index = index // num_qbits\\n qbit_index = index % num_qbits\\n\\n theta_plus = np.copy(theta)\\n theta_plus[layer_index][qbit_index] += np.pi / 2 # added pi/2 to the ith theta parameter\\n\\n theta_minus = np.copy(theta)\\n theta_minus[layer_index][qbit_index] -= np.pi / 2 # subtracted pi/2 to the ith theta parameter\\n\\n df_dtheta_i = 0.5 * (get_fidelity(theta_plus, psi) - get_fidelity(theta_minus, psi)) # ith derivative\\n df_dtheta.append(df_dtheta_i)\\n\\n df_dtheta = np.array(df_dtheta)\\n dl_dtheta = dl_df * df_dtheta # chain rule to get partial derivative of loss wrt theta parameters\\n\\n return dl_dtheta\",\n \"def run_gradient_descent_iteration(X, Y, theta, alpha, lambda_factor, temp_parameter):\\n delta = sparse.coo_matrix(theta.shape).toarray()\\n\\n h = compute_probabilities(X, theta, temp_parameter)\\n\\n for j in range(delta.shape[0]):\\n y = Y\\n y = np.where(y != j, 0, 1)\\n p = y - h[j]\\n\\n x = X.T * p\\n x = x.T\\n x = x.sum(axis=0)\\n\\n grad = -x / (temp_parameter * X.shape[0]) + lambda_factor * theta[j]\\n\\n delta[j] += grad\\n\\n theta -= alpha * delta\\n\\n return theta\",\n \"def gradient_descent(features, values, theta, alpha, num_iterations):\\n \\n # number of points\\n npoints = len(values)\\n \\n # intialize cost history\\n cost_history = []\\n \\n # num_interations iterations\\n for iiter in range(num_iterations):\\n \\n # compute and store cost\\n cost = compute_cost(features, values, theta)\\n cost_history.append(cost)\\n \\n # update values of theta\\n values_predicted = np.dot(features, theta)\\n theta = theta + (alpha/npoints)*(np.dot(values - values_predicted,features))\\n \\n return theta, pandas.Series(cost_history)\",\n \"def gradient_descent(x, y, theta=[[0], [0]]):\\n m = y.size\\n j_history = []\\n for i in range(ITERATIONS):\\n h = x.dot(theta)\\n theta = theta - (ALPHA / m) * (x.T.dot(h - y))\\n j_history.append(compute_cost(x, y, theta))\\n return theta, j_history\",\n \"def approx_grad(theta, X, y):\\n grad_a = np.array([(cost(theta + e, X, y) - cost(theta - e, X, y)) / (2 * 1e-5)\\n for e in np.identity(len(theta)) * 1e-5])\\n return grad_a\",\n \"def gradient_descent(features, values, theta, alpha, num_iterations):\\r\\n\\r\\n m = len(values)\\r\\n cost_history = []\\r\\n\\r\\n for i in range (num_iterations):\\r\\n \\r\\n h = numpy.dot(features, theta)\\r\\n \\r\\n theta = theta - alpha / m * numpy.dot((h-values),features)\\r\\n \\r\\n cost = compute_cost(features, values, theta)\\r\\n \\r\\n cost_history.append(cost)\\r\\n\\r\\n return theta, pandas.Series(cost_history) # leave this line for the grader\\r\",\n \"def calculate_gradient(y, tx, w): \\n return tx.T@(sigmoid(tx@w)-y)\",\n \"def gradient_descent(features, values, theta, alpha, num_iterations):\\n m = len(values)\\n cost_history = []\\n\\n for i in range(num_iterations):\\n predicted_values = np.dot(features, theta)\\n delta = alpha / m * np.dot((predicted_values - values), features)\\n theta = theta - delta\\n cost = compute_cost(features, values, theta)\\n cost_history.append(cost)\\n return theta, pandas.Series(cost_history)\",\n \"def lr_cost_function_grad(theta: np.ndarray, X: np.ndarray, y: np.ndarray, l: float) -> np.ndarray:\\n # Initialize some useful values\\n m = len(y) # number of training examples\\n\\n # You need to return the following variable correctly.\\n grad = np.zeros(theta.shape)\\n\\n # ====================== YOUR CODE HERE ======================\\n # Instructions: Compute the partial derivatives and set grad to the partial\\n # derivatives of the cost w.r.t. each parameter in theta.\\n\\n # =============================================================\\n return grad\",\n \"def gradient_ascent(self, w, X, y, lr):\\r\\n # INSERT YOUR CODE HERE\\r\\n #raise Exception('Function not yet implemented!')\\r\\n # gradient = x_j*(y-σ(wTX))\\r\\n return np.dot(X.T, y-self.sigmoid(np.dot(X, w)))\",\n \"def computeNumericalGradient(J, theta):\\n numgrad = np.zeros(theta.size)\\n perturb = np.zeros(theta.size)\\n e = 1e-4\\n for p in range(theta.size):\\n # Set perturbation vector\\n perturb[p] = e\\n loss1 = J(theta - perturb)[0]\\n loss2 = J(theta + perturb)[0]\\n # Compute Numerical Gradient\\n numgrad[p] = (loss2 - loss1) / (2*e)\\n perturb[p] = 0\\n return numgrad\",\n \"def computeNumericalGradient(J, theta):\\n numgrad = np.zeros_like(theta).reshape(-1)\\n perturb = np.zeros_like(theta).reshape(-1)\\n e = 1e-4\\n for p in range(theta.size):\\n # Set perturbation vector\\n perturb[p] = e\\n loss1, _ = J(theta - perturb.reshape(theta.shape))\\n loss2, _ = J(theta + perturb.reshape(theta.shape))\\n # Compute Numerical Gradient\\n numgrad[p] = (loss2 - loss1) / (2 * e)\\n perturb[p] = 0\\n\\n return numgrad.reshape(theta.shape)\",\n \"def gradientdescent(cost_func, theta, args=(), delta_func = 0):\\n step = 1\\n old_cost = 0\\n while True:\\n theta_old = theta.copy()\\n cost = cost_func(theta, *args)\\n delta = delta_func(theta, *args)\\n theta = theta - step * delta\\n if cost > old_cost and old_cost != 0:\\n step = step*0.7\\n if np.allclose(theta_old, theta):\\n break\\n old_cost = cost\\n return theta\",\n \"def gradient(cls, x):\\n y = Sigmoid.apply(x)\\n return np.multiply(y, 1 - y)\",\n \"def gradient_descent(features, values, theta, alpha, num_iterations):\\r\\n \\r\\n m = len(values)\\r\\n cost_history = []\\r\\n\\r\\n for i in range(num_iterations):\\r\\n # your code here\\r\\n cost = compute_cost(features, values, theta)/(2.0*m)\\r\\n cost_history.append([cost])\\r\\n \\r\\n error = features.dot(theta) - values\\r\\n error = np.reshape(error,(error.shape[0], 1))\\r\\n errorWeighted = features*error\\r\\n errorSum = (np.sum(errorWeighted,0))/(m*1.0)\\r\\n theta = theta - alpha*errorSum \\r\\n \\r\\n return theta, pandas.Series(cost_history)\",\n \"def compute_gradient(y, tx, w):\\n\\tN = y.shape[0]\\n\\te = y - np.dot(tx, w)\\n\\n\\tgradLw = -1/N * np.dot(tx.T, e)\\n\\treturn gradLw\",\n \"def compute_square_loss_gradient(X, y, theta):\\n #TODO\\n P = (np.dot(X, theta)-y)\\n m = X.shape[0]\\n\\n return (2/m)*np.dot(X.T, P)\",\n \"def learning_by_gradient_descent(y, tx, w, gamma):\\n\\tgrad = calculate_gradient(y, tx, w)\\n\\n\\tw = w - gamma * grad\\n\\treturn w\",\n \"def calculate_gradient(y, tx, w):\\n return tx.T.dot(sigmoid(tx.dot(w))-np.reshape(y,(len(y),1)))\",\n \"def gradientDescent(self,X, y, theta): \\n # number of instances\\n m = len(y)\\n J_history = np.zeros((self.NUM_ITERS,1))\\n for i in range(self.NUM_ITERS):\\n h = self.sigmoid(X@theta)\\n grad = 1 / m * X.T @ (h - y)\\n theta = theta - self.ALPHA * grad \\n J_history[i] = self.costFunction(theta, X, y)\\n \\n \\n return theta, J_history\",\n \"def compute_square_loss_gradient(X, y, theta):\\n #TODO\\n (N,p) = np.shape(X)\\n grad = -(1/np.float(N))*np.array([(y - X.dot(theta))*X[:,i] for i in range(p)])\\n return np.sum(grad,axis=1)\",\n \"def gradient_descent(X, y, theta, learning_rate, num_iters, lambda_reg):\\n thetas = [theta]\\n cost = np.zeros(num_iters)\\n\\n J = mean_cross_entropy_costs(X, y, lambda_reg)\\n cost[0] = J(thetas[0])\\n for i in range(1, num_iters):\\n thetas.append(compute_new_theta(X, y, thetas[i - 1], learning_rate, lambda_reg))\\n cost[i] = J(thetas[i])\\n return cost, thetas\",\n \"def gradient(img):\\n G = np.zeros(img.shape)\\n theta = np.zeros(img.shape)\\n\\n #####################################\\n # START YOUR CODE HERE #\\n #####################################\\n Gx = partial_x(img)\\n Gy = partial_y(img)\\n G = np.sqrt(np.square(Gx) + np.square(Gy))\\n theta = np.degrees(np.arctan2(Gy, Gx)) % 360\\n ######################################\\n # END OF YOUR CODE #\\n ######################################\\n\\n return G, theta\",\n \"def gradient_descent(initial_theta, X, y, niter, alpha, Lambda=0.0):\\n theta_list = []\\n cost_list = []\\n\\n theta = initial_theta\\n for i in range(0, niter):\\n theta -= alpha*gradient(theta, X, y, Lambda)\\n theta_list.append(theta)\\n cost_list.append(cost(theta, X, y, Lambda))\\n\\n return theta_list, cost_list\",\n \"def linreg_stochastic_grad(X, y, alpha=.01):\\n m = X.shape[0]\\n n = X.shape[1]\\n theta = np.zeros(n)\\n for i in range(m):\\n delta = alpha * (np.dot(theta.transpose(), X[i,:]) -y[i]) * X[i,:]\\n theta = theta - delta\\n return theta\",\n \"def log_likelihood_gradient(y, tx, w):\\n return tx.T.dot(sigmoid(tx.dot(w))-y)\",\n \"def gradient(cls, x):\\n return 1 - TanH.apply(x) ** 2\",\n \"def learning_by_gradient_descent(y, tx, w, gamma):\\n loss = calculate_loss(y,tx,w)\\n grad = calculate_gradient(y,tx,w)\\n w_new = w - gamma*grad\\n #grad is for debugging purpose\\n return loss, w_new,grad\",\n \"def simple_gradient(x, y, theta):\\n if x.shape[0] * y.shape[0] * theta.shape[0] == 0:\\n return None\\n if x.shape[0] != y.shape[0] or theta.shape[0] != 2:\\n return None\\n\\n x = add_intercept(x)\\n\\n result = [\\n forumla(x, y, theta, 0),\\n forumla(x, y, theta, 1)\\n ]\\n return result\",\n \"def calculate_gradient(y, tx, w):\\n\\n\\tret = tx.T.dot(sigmoid(np.dot(tx, w)) - y)\\n\\treturn ret\",\n \"def learning_by_gradient_descent(y, tx, w, gamma):\\n loss = calculate_loss(y,tx,w)\\n grad = calculate_gradient(y,tx,w)\\n w = w-gamma*grad\\n return w, loss\",\n \"def train_fn_grad(self, theta_value, theta):\\n theta.set_value(theta_value, borrow=True)\\n\\n train_grads = [self.batch_grad(i, self.n_train)\\n for i in xrange(self.n_train_batches)]\\n train_batch_sizes = [self.get_batch_size(i, self.n_train)\\n for i in xrange(self.n_train_batches)]\\n\\n return np.average(train_grads, weights=train_batch_sizes,\\n axis=0)\",\n \"def gradient(self, x):\\n pass\",\n \"def gradient(img):\\n G = np.zeros(img.shape)\\n theta = np.zeros(img.shape)\\n\\n ### YOUR CODE HERE\\n Gx = partial_x(img)\\n Gy = partial_y(img)\\n G = np.sqrt(np.power(Gx,2)+np.power(Gy,2))\\n theta = (np.arctan2(Gy, Gx) * 180 / np.pi) % 360\\n ### END YOUR CODE\\n\\n return G, theta\",\n \"def approx_grad_reg(theta, X, y, _lambda):\\n grad_a = np.array([(cost_reg(theta + e, X, y, _lambda) - cost_reg(theta - e, X, y, _lambda)) / (2 * 1e-5)\\n for e in np.identity(len(theta)) * 1e-5])\\n return grad_a\",\n \"def get_gradient(phi, pred, t, dot_product, weight, reg= 1, regression= \\\"logistic\\\"):\\n if regression == \\\"logistic\\\":\\n gradient = np.matmul(phi.T, pred - t)\\n elif regression == \\\"probit\\\":\\n R = np.eye(pred.shape[0])\\n for i in range(pred.shape[0]):\\n y_n = pred[i,0]\\n dotp = dot_product[i, 0]\\n pdf = norm.pdf(dotp)\\n R[i,i] = pdf/(y_n*(1-y_n) + TOLERANCE)\\n gradient = np.matmul(np.matmul(phi.T, R), pred-t)\\n elif regression == \\\"multiclass\\\":\\n gradient = np.matmul(phi.T, pred - t)\\n\\n # Add regularization\\n gradient += weight/ reg\\n return gradient\",\n \"def gradient(self, x):\\n u = np.asarray([x[0]])\\n C = self.C_func(u)\\n dC = self.dC_func(u, order=1)\\n P = self.P\\n numerator = np.sum((C - P) * dC, axis=0)\\n denominator = np.sum(np.sum((C - P) ** 2, axis=0) ** (1 / 2))\\n if np.abs(denominator) > 0:\\n gradient = numerator/denominator\\n else:\\n gradient = np.asarray(0)[np.newaxis]\\n return gradient\",\n \"def apply_gradient(params: torch.Tensor, grads: torch.Tensor, lr: float) -> torch.Tensor:\\n params_prime = params + lr * grads\\n return params_prime\",\n \"def run_steep_gradient_descent(data_x, data_y, len_data, alpha, theta):\\n n = len_data\\n # WE NEED TO transpose data_x into (p+1) *n ,theta is 1*(p+1)\\n prod = np.dot(theta, data_x.transpose())\\n\\n prod -= data_y\\n print(\\\"pro: data_x\\\", prod.shape, data_x.shape)\\n #prod represent the loss of the hypothesis and true label\\n sum_grad = np.dot(prod, data_x)\\n print(\\\"总梯度的值：\\\",sum_grad.shape)\\n\\n # batch-gradient descent\\n theta = theta -(alpha / n) * sum_grad\\n return theta\",\n \"def GDS(x, theta, y, lamb, alpha):\\n\\tm = y.shape[0]\\n\\tdif = (1/m) * x.T @ (sigmoid(x,theta) - y) #classical differentiation term, output is (n,1)\\n\\tdif_reg = (lamb/m) * theta #regularization term\\n\\treturn (dif + dif_reg) * alpha\",\n \"def gradientDescent(X, y, theta, alpha, num_iters):\\n\\n # Initialize some useful values\\n J_history = []\\n m = y.size # number of training examples\\n\\n for i in range(num_iters):\\n # ====================== YOUR CODE HERE ======================\\n # Instructions: Perform a single gradient step on the parameter vector\\n # theta.\\n #\\n # Hint: While debugging, it can be useful to print out the values\\n # of the cost function (computeCost) and gradient here.\\n #\\n # Calculate the gradient step according to the equation for theta1:\\n g_step1 = (alpha / m * np.sum( (np.dot(X,theta) - y) * X[:,1]) )\\n # Gradient step for theta knot:\\n g_step0 = (alpha / m * np.sum( (np.dot(X,theta) - y) ) )\\n \\n #update theta\\n theta[0] = (theta[0] - g_step0)\\n theta[1] = (theta[1] - g_step1)\\n \\n #print([theta , g_step1, g_step0])\\n\\n # ============================================================\\n\\n # Save the cost J in every iteration\\n J_history.append(computeCost(X, y, theta))\\n\\n return theta, J_history\",\n \"def gradient(w, x, t):\\n return 2 * np.dot(x.T, (nn(x, w) - t))\",\n \"def costFunction(theta,X,y):\\n m = X.shape[0]\\n J = 0\\n h = sigmoid (np.dot(X,theta))\\n \\n J = (1/m)* ((-np.dot(y.T,(np.log(h)))) - np.dot((1 - y).T,(np.log(1-h))))\\n \\n #grad = (1/m) * np.dot(X.T,(h-y))\\n grad = (1/m) * np.dot((h.T - y), X).T\\n \\n return J, grad\",\n \"def stochasticGradientDescent(x,y,theta,alpha):\\n m,n = np.shape(x)\\n convergence = 0.000000001\\n lastCost = 0\\n cost = -1 \\n recurseCount = 0\\n while abs(lastCost - cost) > convergence: # rcurse until converge\\n lastCost = cost\\n hypothesis = np.dot(x,theta) \\n for i in range(m):\\n # alpha = 4.0 / (1.0 + i) + 0.01 \\n loss = hypothesis[i] - y[i]\\n # gradient = np.dot(x[i],loss)\\n gradient = x[i,:].transpose() * loss \\n theta = theta - alpha * gradient\\n cost = np.sum((hypothesis-y)**2)/(2*m)\\n recurseCount += 1\\n return recurseCount,theta\",\n \"def gradient(self):\\n result = np.zeros(len(self.variables))\\n result[self.bivariateGradInd] = (self.shape-1)/self.variable - self.rate\\n return result\",\n \"def gradient(self):\\n functional = self\\n\\n class KLGradient(Operator):\\n\\n \\\"\\\"\\\"The gradient operator of this functional.\\\"\\\"\\\"\\n\\n def __init__(self):\\n \\\"\\\"\\\"Initialize a new instance.\\\"\\\"\\\"\\n super().__init__(functional.domain, functional.domain,\\n linear=False)\\n\\n def _call(self, x):\\n \\\"\\\"\\\"Apply the gradient operator to the given point.\\n The gradient is not defined in points where one or more\\n components are non-positive.\\n \\\"\\\"\\\"\\n if functional.prior is None:\\n return (-1.0) / x + 1\\n else:\\n return (-functional.prior) / x + 1\\n\\n return KLGradient()\",\n \"def batchGradientDescent(x,y,theta,alpha):\\n m,n = np.shape(x)\\n xTran = x.transpose()\\n convergence = 0.000000001\\n lastCost = 0\\n cost = -1 \\n recurseCount = 0\\n while abs(lastCost - cost) > convergence: # rcurse until converge\\n lastCost = cost\\n hypothesis = np.dot(x,theta)\\n loss = hypothesis - y\\n cost = np.sum(loss**2)/(2*m)\\n gradient = np.dot(xTran,loss)/m\\n theta = theta - alpha*gradient\\n recurseCount += 1\\n return recurseCount,theta\",\n \"def learning_by_penalized_gradient(y, tx, w, gamma, lambda_):\\n\\tgrad, H = penalized_logistic_regression(y, tx, w, lambda_)\\n\\n\\thgrad = np.linalg.inv(H).dot(grad)\\n\\n\\tw = w - gamma * hgrad\\n\\n\\treturn w\",\n \"def grad(self, w):\\n l1_grad = self.r * np.sign(w)\\n l2_grad = np.asarray(1 - self.r) * w \\n\\n gradient_penalty = self.alpha * (l1_grad + l2_grad)\\n\\n # Insert 0 for bias term.\\n return np.insert(gradient_penalty, 0, 0, axis=0)\",\n \"def compute_gradient(self):\\n A = np.dot(self.X, self.w)\\n m = self.t.shape[0]\\n C = -1 * self.t * (1 / (1 + np.exp(A * self.t)))\\n return (1 / m) * np.dot(self.X.T, C)\",\n \"def gradient(self, x):\\n g = self._grad(\\n time_series=self.observed_data,\\n a=x[0],\\n b=x[1],\\n c=x[2],\\n sigma=self.sigma\\n )\\n return g\",\n \"def reg_autograd(alpha,X,theta,y):\\n g=grad(func)\\n for i in range(0,20):\\n theta=theta- alpha * g(theta)\\n print(\\\"cost\\\", func(theta))\\n print(\\\"final theta\\\",theta)\",\n \"def grad(self, x):\\n norm_beta = np.linalg.norm(x)\\n if norm_beta > utils.TOLERANCE:\\n return (self.l / norm_beta) * x\\n else:\\n D = x.shape[0]\\n u = (self.rng(D, 1) * 2.0) - 1.0 # [-1, 1]^D\\n norm_u = np.linalg.norm(u)\\n a = self.rng() # [0, 1]\\n\\n return (self.l * (a / norm_u)) * u\",\n \"def gradcovfunc(self, theta, d):\\n sigmaf, l = theta[:2] \\n xxl = np.sum((d/l)**2, axis=1)\\n dk_dsigmaf = 2 * sigmaf * np.exp(-xxl/2.)\\n dk_dl = sigmaf**2/l * xxl * np.exp(-xxl/2.)\\n grad = np.array([dk_dsigmaf, dk_dl])\\n return grad\",\n \"def log_prior_grad(self, inputs):\",\n \"def gradient(series, **options):\\n x = series.index\\n y = series.values\\n\\n a = np.gradient(y, x, **options)\\n return series.__class__(a, series.index)\",\n \"def derivative(self, theta):\\n diag_gamma = np.dot(theta.T, self.X.T)\\n logistic_term = self.logistic_fn(diag_gamma)\\n diag_gamma = logistic_term * (1.0 - logistic_term)\\n gamma = np.diag(diag_gamma)\\n\\n # v computation\\n diags_v = 1.0 - 2*self.logistic_fn(np.dot(theta.T, self.X.T))\\n diags_v = diags_v.reshape((-1, 1))\\n diags_v = diags_v*self.X\\n assert diags_v.shape == self.X.shape #N*d shape\\n\\n XtGamma = np.dot(self.X.T, gamma) # d*N shape\\n\\n # TODO: Verifier car pas sur de mon coup ... et surtout plus long...\\n # id = np.eye(self.n_examples).reshape((self.n_examples, self.n_examples, 1))\\n # diags_v = diags_v.reshape((self.n_examples, 1, self.dim))\\n # v = id*diags_v # n*n*d tensor\\n # left = np.tensordot(XtGamma, v, axes=(1, 0)) # shape d*N*d\\n # assert left.shape == (self.dim, self.n_examples, self.dim)\\n # dg = np.tensordot(left, self.X, axes=(1, 0))\\n # dg = np.swapaxes(dg, axis1=-2, axis2=-1)\\n\\n dg = np.zeros((self.dim, self.dim, self.dim))\\n for idx, v_i_diag in enumerate(diags_v.T):\\n v_i = np.diag(v_i_diag)\\n dg_di = np.dot(np.dot(XtGamma, v_i), self.X)\\n dg[:, :, idx] = dg_di\\n return dg\",\n \"def compute_logistic_gradient(y, tx, w):\\n return tx.T.dot(sigmoid(tx.dot(w)) - y) / len(y)\",\n \"def run_gradient_descent(data,theta,alpha,num_iters):\\n population = data[:,0]\\n prices = data[:,1]\\n x = ones(shape=(len(population),2)) #add ones for theta0 \\n x[:,1] = population\\n x = transpose(x)\\n error_history = zeros(shape=(num_iters,1))\\n \\n for i in range(num_iters):\\n predictions = theta.dot(x)\\n errors_x1 = (predictions - prices) * x[0,:]\\n errors_x2 = (predictions - prices) * x[1,:]\\n theta[0][0] = theta[0][0] - alpha*(1.0/len(population))*errors_x1.sum()\\n theta[0][1] = theta[0][1] - alpha*(1.0/len(population))*errors_x2.sum()\\n error_history[i,0] = calculate_cost(theta,data)\\n \\n return theta, error_history\",\n \"def gradient(self, x):\\n return 0.0\",\n \"def costFunction(self,theta, X, y): \\n m = len(y)\\n h = self.sigmoid(X@theta)\\n J = 1 / m * (- y.T @ self.log(h) - (1-y).T @ self.log(1-h)) \\n # grad = 1/ m * X.T @ (h - y)\\n return J\",\n \"def gradient(poly):\\n return differential(\\n poly, chaospy.poly.collection.basis(1, 1, poly.dim, sort=\\\"GR\\\"))\",\n \"def curves_gradient(theta, m_, LAMBDA=0):\\n # X0: column of ones\\n theta0_gradient = (1 / m_) * sum((curves_hypothesis(theta, m_) - y[:m_].ravel()) * np.ones(m_))\\n # X1: data given as X\\n theta1_gradient = ((1 / m_) * sum((curves_hypothesis(theta, m_) - y[:m_].ravel()) * X[:m_].ravel())) \\\\\\n + (LAMBDA / m_) * theta[1]\\n return np.array([theta0_gradient, theta1_gradient])\",\n \"def gradient(self, x_in):\\r\\n\\r\\n return 1 - torch.pow(self.forward(x_in), 2)\",\n \"def compute_gradient(self): # TODO: try to change to square loss since it's hessian is easier to obtain\\n A = np.dot(self.X, self.w)\\n m = self.t.shape[0]\\n C = -1 * self.t * (1 / (1 + np.exp(A * self.t)))\\n return (1 / m) * np.dot(self.X.T, C)\",\n \"def vb_grad_natgrad(self):\\r\\n\\r\\n # Gradient for eta\\r\\n dL_dmmT_S = -0.5*self.Lambda/self.data_prop + 0.5*self.q_u_prec\\r\\n Kmmipsi1V,_ = dpotrs(self.Lm,self.psi1V,lower=1)\\r\\n dL_dm = (Kmmipsi1V - np.dot(self.Lambda,self.q_u_mean))/self.data_prop\\r\\n\\r\\n # Gradients for theta\\r\\n S = self.q_u_cov\\r\\n Si = self.q_u_prec\\r\\n m = self.q_u_mean\\r\\n dL_dSi = -mdot(S,dL_dmmT_S, S)\\r\\n\\r\\n dL_dmhSi = -2*dL_dSi\\r\\n dL_dSim = np.dot(dL_dSi,m) + np.dot(Si, dL_dm)\\r\\n\\r\\n return np.hstack((dL_dm.flatten(),dL_dmmT_S.flatten())) , np.hstack((dL_dSim.flatten(), dL_dmhSi.flatten()))\",\n \"def grad(self, w):\\n gradient_penalty = np.asarray(self.alpha) * w\\n # Insert 0 for bias term.\\n return np.insert(gradient_penalty, 0, 0, axis=0)\",\n \"def grad(self, w):\\n gradient_penalty = np.asarray(self.alpha) * w\\n # Insert 0 for bias term.\\n return np.insert(gradient_penalty, 0, 0, axis=0)\",\n \"def gradient(self):\\n functional = self\\n\\n class KLCCGradient(Operator):\\n\\n \\\"\\\"\\\"The gradient operator of this functional.\\\"\\\"\\\"\\n\\n def __init__(self):\\n \\\"\\\"\\\"Initialize a new instance.\\\"\\\"\\\"\\n super().__init__(functional.domain, functional.domain,\\n linear=False)\\n\\n def _call(self, x):\\n \\\"\\\"\\\"Apply the gradient operator to the given point.\\n\\n The gradient is not defined in points where one or more\\n components are larger than or equal to one.\\n \\\"\\\"\\\"\\n if functional.prior is None:\\n return 1.0 / (1 - x)\\n else:\\n return functional.prior / (1 - x)\\n\\n return KLCCGradient()\",\n \"def learning_by_penalized_gradient(y, tx, w, gamma, lambda_):\\n\\n #on test avec Newton\\n\\n loss,gradient,_ = penalized_logistic_regression(y,tx,w,lambda_)\\n\\n w = w - gamma*gradient\\n return loss, w,gradient\",\n \"def gradient_ascent(f, df, theta_init, step_size, max_iter):\\n\\n fs = []\\n xs = []\\n thetas = theta_init\\n for i in range(max_iter): #for each data example\\n fs.append(f(thetas))\\n\\n temp = step_size*df(thetas)\\n thetas = step_size*df(thetas) #modify that feature by using the derivative of log likelihood\\n xs.append(thetas.flatten())\\n if i % 10 == 0:\\n print(i, thetas)\\n\\n return thetas, fs, xs\",\n \"def calculate_maxL_gradient(y, tx, w):\\n sig=sigmoid(tx.dot(w))\\n grad=tx.T.dot(sig-y)\\n #divides with number of samples so that learning rate is not dependant on number of samples\\n grad=grad/len(y) \\n return grad\",\n \"def _log_prior_gradients(self):\\n if self.priors.size == 0:\\n return 0.\\n x = self.param_array\\n ret = np.zeros(x.size)\\n #compute derivate of prior density\\n [np.put(ret, ind, p.lnpdf_grad(x[ind])) for p, ind in self.priors.items()]\\n #add in jacobian derivatives if transformed\\n priored_indexes = np.hstack([i for p, i in self.priors.items()])\\n for c,j in self.constraints.items():\\n if not isinstance(c, Transformation):continue\\n for jj in j:\\n if jj in priored_indexes:\\n ret[jj] += c.log_jacobian_grad(x[jj])\\n return ret\",\n \"def grad(self, w):\\n gradient_penalty = np.asarray(self.alpha) * w\\n\\n # Insert 0 for bias term.\\n return np.insert(gradient_penalty, 0, 0, axis=0)\",\n \"def grad(theta, X, y, lambda_):\\n # ... dopolnite (naloga 1, naloga 2)\\n\\n l = []\\n for i, e in enumerate(theta):\\n l.append(1 / len(y) * sum([(h(x, theta) - yi) * x[i] for x, yi in zip(X, y)]) + 2 * lambda_ * e)\\n\\n return np.array(l)\",\n \"def lr_cost_function(theta, X, y, learning_rate):\\n m = len(y) # number of training examples\\n # You need to return the following variables correctly \\n J = 0\\n grad = np.zeros(theta.shape)\\n h = sigmoid(np.dot(X, theta))\\n\\n #Compute cost:\\n first = (1/m)*np.sum(np.dot(-y.T, np.log(h)) - np.dot((1-y).T, np.log(1-h)), axis=0)\\n second = (learning_rate/(2*m))*np.sum(theta[1:]**2)\\n J = first + second\\n \\n #Compute gradient\\n grad = np.dot((h-y).T, X)/m\\n grad = grad.T\\n grad[1:] += (learning_rate/m)*theta[1:]\\n return float(J), grad\",\n \"def gradient(self, x, i=None):\\n self.set_parameters(x=x)\\n gradient = self.gradient_loss()\\n if self.add_bias:\\n gradient[1:] += self.alpha * self.gradient_regularizer()\\n else:\\n gradient += self.alpha * self.gradient_regularizer()\\n if i is not None:\\n return gradient.ravel()[i]\\n return gradient.ravel()\",\n \"def compute_gradient_lasso(y, tx, w, lambda_):\\n e = y - tx.dot(w)\\n subgrad = lambda_ * np.sign(w)\\n\\n return -tx.T.dot(e)/len(e) + subgrad\",\n \"def compute_gradient(self, X, y, weights):\\n sigmoid = self.sigmoid(np.dot(X, weights))\\n return np.dot(X.T, y - sigmoid)\",\n \"def batch_grad_descent(X, y, alpha=0.1, num_iter=1000, check_gradient=False):\\n num_instances, num_features = X.shape[0], X.shape[1]\\n theta_hist = np.zeros((num_iter+1, num_features)) #Initialize theta_hist\\n loss_hist = np.zeros(num_iter+1) #initialize loss_hist\\n theta = np.ones(num_features) #initialize theta\\n\\n count = 0\\n while count < num_iter+1:\\n if check_gradient:\\n assert grad_checker(X,y,theta)\\n\\n grad = compute_square_loss_gradient(X,y,theta)\\n theta -= alpha*grad\\n theta_hist[count] = theta\\n loss_hist[count] = compute_square_loss(X,y,theta)\\n count += 1\\n \\n return theta_hist, loss_hist\",\n \"def check_gradient(self, x, y):\\n x = x.transpose()\\n y = y.transpose()\\n layers_copy = deepcopy(self.layers)\\n epsilon = 10 ** -4\\n a, layer = self.forward_propagation(x)\\n delta = self.calculate_delta(a, y, layer)\\n self.backpropagation(delta=delta, theta=layer.theta)\\n previous_layer_output = x\\n for layer in self.layers:\\n theta_copy = deepcopy(layer.theta)\\n real_theta_size = theta_copy.shape\\n delta = layer.delta\\n dc_dtheta = np.outer(previous_layer_output, delta).transpose()\\n previous_layer_output = layer.a\\n R, C = theta_copy.shape\\n for i in range(R):\\n for j in range(C):\\n theta_plus = deepcopy(theta_copy)\\n theta_plus[i, j] += epsilon\\n layer.theta = theta_plus\\n a_plus, l_plus = self.forward_propagation(x)\\n err_plus = self.calculate_loss(a_plus, y)\\n theta_minus = deepcopy(theta_copy)\\n theta_minus[i, j] -= epsilon\\n layer.theta = theta_minus\\n a_minus, l_minus = self.forward_propagation(x)\\n err_minus = self.calculate_loss(a_minus, y)\\n limit = (err_plus - err_minus)/(2*epsilon)\\n grad_diff = abs(dc_dtheta[i,j] - limit)\\n assert grad_diff < 10 ** -6, f\\\"Diff {grad_diff} is too big.\\\"\\n layer.theta = theta_copy\",\n \"def _compute_func_grad(self, w):\\n W = w.reshape((self.X.shape[1], self.Y.shape[1]))\\n self.nll_, self.grad_ = calculate_gradient(self.X, self.Y, W, self.prior, self.weighted,0)\",\n \"def gradient(data_x, data_y, parameters):\\n return data_x.T @ (data_x @ parameters - data_y) / data_x.shape[0]\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.83195007","0.83195007","0.8256445","0.8055304","0.7947039","0.7867961","0.78287417","0.7741965","0.77182055","0.77098596","0.76828635","0.76208895","0.75614756","0.72576725","0.72168595","0.7216574","0.7211889","0.70136374","0.70051277","0.6994997","0.69862175","0.69123644","0.6910823","0.68812525","0.6881161","0.6863728","0.6838628","0.6829457","0.68013173","0.67898685","0.67635065","0.6753912","0.6751456","0.67212635","0.67147833","0.6712214","0.67026615","0.6683733","0.6665269","0.6664574","0.66554016","0.6632431","0.66249985","0.6618367","0.66131556","0.6605929","0.66040915","0.658713","0.6579545","0.65779126","0.65694577","0.65687287","0.65535915","0.65233195","0.6507123","0.64980435","0.6492682","0.64849585","0.64616925","0.6455795","0.64541864","0.64431417","0.64234275","0.64233476","0.64115876","0.6388624","0.6375172","0.63749987","0.63732344","0.63698816","0.6358501","0.6354826","0.6352325","0.63463557","0.6346066","0.63423747","0.6325621","0.6305958","0.63044894","0.6303761","0.6282822","0.6278642","0.62777394","0.62690866","0.62690866","0.6268236","0.6257674","0.6254688","0.62541944","0.625029","0.624863","0.6246089","0.62321514","0.6226891","0.6226241","0.6183781","0.61833704","0.6182383","0.61771554","0.6168317"],"string":"[\n \"0.83195007\",\n \"0.83195007\",\n \"0.8256445\",\n \"0.8055304\",\n \"0.7947039\",\n \"0.7867961\",\n \"0.78287417\",\n \"0.7741965\",\n \"0.77182055\",\n \"0.77098596\",\n \"0.76828635\",\n \"0.76208895\",\n \"0.75614756\",\n \"0.72576725\",\n \"0.72168595\",\n \"0.7216574\",\n \"0.7211889\",\n \"0.70136374\",\n \"0.70051277\",\n \"0.6994997\",\n \"0.69862175\",\n \"0.69123644\",\n \"0.6910823\",\n \"0.68812525\",\n \"0.6881161\",\n \"0.6863728\",\n \"0.6838628\",\n \"0.6829457\",\n \"0.68013173\",\n \"0.67898685\",\n \"0.67635065\",\n \"0.6753912\",\n \"0.6751456\",\n \"0.67212635\",\n \"0.67147833\",\n \"0.6712214\",\n \"0.67026615\",\n \"0.6683733\",\n \"0.6665269\",\n \"0.6664574\",\n \"0.66554016\",\n \"0.6632431\",\n \"0.66249985\",\n \"0.6618367\",\n \"0.66131556\",\n \"0.6605929\",\n \"0.66040915\",\n \"0.658713\",\n \"0.6579545\",\n \"0.65779126\",\n \"0.65694577\",\n \"0.65687287\",\n \"0.65535915\",\n \"0.65233195\",\n \"0.6507123\",\n \"0.64980435\",\n \"0.6492682\",\n \"0.64849585\",\n \"0.64616925\",\n \"0.6455795\",\n \"0.64541864\",\n \"0.64431417\",\n \"0.64234275\",\n \"0.64233476\",\n \"0.64115876\",\n \"0.6388624\",\n \"0.6375172\",\n \"0.63749987\",\n \"0.63732344\",\n \"0.63698816\",\n \"0.6358501\",\n \"0.6354826\",\n \"0.6352325\",\n \"0.63463557\",\n \"0.6346066\",\n \"0.63423747\",\n \"0.6325621\",\n \"0.6305958\",\n \"0.63044894\",\n \"0.6303761\",\n \"0.6282822\",\n \"0.6278642\",\n \"0.62777394\",\n \"0.62690866\",\n \"0.62690866\",\n \"0.6268236\",\n \"0.6257674\",\n \"0.6254688\",\n \"0.62541944\",\n \"0.625029\",\n \"0.624863\",\n \"0.6246089\",\n \"0.62321514\",\n \"0.6226891\",\n \"0.6226241\",\n \"0.6183781\",\n \"0.61833704\",\n \"0.6182383\",\n \"0.61771554\",\n \"0.6168317\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.78280646"},"document_rank":{"kind":"string","value":"7"}}},{"rowIdx":14392,"cells":{"query":{"kind":"string","value":"Horseshoe Prior as it is used in spearmint"},"document":{"kind":"string","value":"def __init__(self, scale=0.1, rng=None):\n if rng is None:\n self.rng = np.random.RandomState(np.random.randint(0, 10000))\n else:\n self.rng = rng\n self.scale = scale"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def prior_sample(self):\n pass","def horde_step(self, observation):","def lnprior(self):\n \n return","def brain_weight_oz(self):\r\n return Heart.heart_weight_oz(self) # Used method from Heart Class\r","def RankineHugoniot(P):\n # prevP = P+1\n # while(abs(P-prevP)>tol):\n # prevP = P\n Pr = P/rbc[0]\n temp = 1/beta*(cR/cL)*(Pr-1)\n temp = temp/np.sqrt(1+(gamma+1)/(2*gamma)*(Pr-1))\n temp = (1-temp)**beta\n # return P\n return temp*lbc[0]-P","def low(self):","def prior(self):\n return self.__prior","def prior(self):\n return self.__prior","def prior(self):\n return self.__prior","def prior(self):\n return self.__prior","def head_hearteyes():\n print (hair_longer())\n print (eye_heart())\n print (nose_rightwards())\n print (mouth_smile())\n print (chin_curvy())","def wind_shear(self):\n return self.flow_field.wind_shear","def _prey_step(self):\n raise NotImplementedError()","def lower_cb(self,p):\n avg_elo=self.elo0+self.elo1\n delta=self.elo1-self.elo0\n N=30\n# Various error conditions must be handled better here!\n while True:\n Elo0=avg_elo-N*delta\n Elo1=avg_elo+N*delta\n sol=brentq(lambda elo:self.outcome_prob(elo)-(1-p),Elo0,Elo1)\n if sol['msg']=='no bracket':\n N*=2\n continue\n break\n return sol['x0']","def H(self, dstrct):\n new_pop = self.population + dstrct.current_pop\n score = (self.x - new_pop)/self.x\n # Here the absolute value tries to prevent a district that only\n # needs a minor increase in population taking the most populous pt.\n score = 1-abs(score)\n self.H_ = self.w_H * score\n return self.H_","def get_prior(self):\n return self.prior","def _compute_mix_prior(self):\n if np.all(self.mix_prior == 1):\n return 0\n return np.dot(np.log(self.mix_weight).T, (self.mix_prior - 1))","def mort(self, h):\n return(self.mu_bg +\\\n (1.0 - self.mu_bg) * self.pr_P * self.p_att *\\\n (1 - h**self.ap))","def _prior_teach(self):\n pass","def handDecision(handIn):","def get_guess(self):\n guess = self.player.higher_or_lower","def testHrtPrior(self):\n attr = self.session.create_visit_attr()\n\n self.util.boolTypeTest(self, attr, \"prior\")\n\n self.util.boolPropertyTest(self, attr, \"prior\")","def enthalpy(temp,pres):\n g = liq_g(0,0,temp,pres)\n g_t = liq_g(1,0,temp,pres)\n h = g - temp*g_t\n return h","def _calculate_strehl(self):\n\n self.strehl = np.exp(-1*((2*np.pi/self.science_wavelength)*self.high_order_wfe)**2)","def horde_start(self, observation):","def head_surprised():\n print (hair_spiky())\n print (eye_wide())\n print (nose_leftwards())\n print (mouth_open())\n print (chin_combo())","def high(self):","def o_wo_per_head(self):\n assert self.ff % self.heads == 0\n # fuse ff->e and projection layer of self-attention\n return (self.ff // (self.heads-self.padded_heads)) + self.qkv","def a_realization(self):\n if self.t==1:\n return self.kmonomial()\n else:\n return self.kHallLittlewoodP()","def prior_predictive(self):\n cfg = self.config\n n = cfg['batch_size'] * cfg['q/n_samples']\n n_samples = cfg['q/n_samples']\n with util.get_or_create_scope('model', reuse=True):\n h_prior = tf.cast(self.p_h_L.sample(n), cfg['dtype'])\n h_prior = tf.reshape(\n h_prior, [cfg['q/n_samples'], cfg['batch_size'], -1])\n h = [None] * cfg['p/n_layers']\n h[cfg['p/n_layers'] - 1] = h_prior\n for n in range(cfg['p/n_layers'] - 1, 0, -1):\n p_h_n = self.build_stochastic_layer(n, h_above=h[n])\n h[n - 1] = tf.cast(p_h_n.sample(), cfg['dtype'])\n return self.likelihood(h[0])","def _get_initial_hypos(self):\n return [CombiStatePartialHypo(self.get_predictor_states())]","def sound_horizon_EH(self):\n om_m = self.omega_cb\n om_b = self.omega_b\n om_n = np.sum(self.omega_nu)\n h = self.h \n if self.M_nu_tot == 0.: rs = 44.5*np.log(9.83/om_m)/np.sqrt(1+10*om_b**0.75)*h\n else: rs = 55.154*np.exp(-72.3*(om_n+0.0006)**2.)/(om_m**0.25351*om_b**0.12807)*h\n return rs","def hapax(self):\n return self.nlegomena(1)","def curve_no_hillcoef(ph, pka):\n# return ph - pka\n return 1/(10**(pka-ph)+1)","def generate_heuristic(self):\r\n\t\tif self.hType == 0:\r\n\t\t\tself.use_displaced_heur()\r\n\r\n\t\telif self.hType == 1:\r\n\t\t\tself.use_manhatten_heur()","def vanilaScore(self,attended,state,W):","def a_realization(self):\n return self.UHP()","def get_prior(self):\n assert self._prior in self._priors, 'Unsupported prior! Check the _priors attribute for a list of priors.'\n if self._prior == 'Gaussian':\n prior = 0.5 * torch.sum(self.parameters ** 2)/self.prior_var\n elif self._prior == 'Cauchy':\n dimconst = (self.parameters.shape[0] + 1)/2.\n prior = dimconst*torch.log(self.prior_var + torch.sum(self.parameters ** 2))\n elif self._prior == 'Sparse':\n n = self.dataset.shape[1]\n gauss_prior = 0.5 * torch.sum(torch.exp(self.parameters[-1] * torch.exp(self.parameters[n:2*n]) * self.parameters[:n] ** 2))\n gamma_density = torch.distributions.Gamma(1.5,0.5)\n# gamma_prior = -gamma_density.log_prob(torch.exp(self.parameters[n:])).sum()\n# lambda_density = torch.distributions.Gamma(1.5,0.5)\n lambda_prior = -gamma_density.log_prob(torch.exp(self.parameters[n:])).sum()\n prior = gauss_prior + lambda_prior\n return prior","def prior_rho(store):\n if store['rho'] > 0. and store['rho'] < 1.0:\n alpha = 1.0\n beta = 1.0\n return (alpha - 1.) * log(store['rho']) + (beta - 1.) * \\\n log(1.-store['rho'])\n else:\n return -1E256","def win_probs(*, home_elo, road_elo, hca_elo):\n h = math.pow(10, home_elo/400)\n r = math.pow(10, road_elo/400)\n a = math.pow(10, hca_elo/400)\n denom = r + a*h\n home_prob = a*h / denom\n road_prob = r / denom\n return home_prob, road_prob","def buildWPriorTerm(self):\r\n\r\n # self.w_prior.shape == (minibatch size,)\r\n self.w_prior = 0.5*T.sum(1 + T.log(self.qwgy_var) - self.qwgy_mu**2-self.qwgy_var, axis=1)\r\n\r\n self.w_prior_modif = - T.maximum(self.hyper['treshold_w_prior'], -self.w_prior)","def rh(self, h):\n sez=self.getSect(h)\n area=self.area(sez)\n wetborder = self.wetBorder(sez)\n return area/wetborder","def homozygotie(self):\n if self.allele[1] == 0.0:\n self.homozygote = True","def test_set_hs(self):\n s = State(substance=\"water\")\n s.hs = Q_(1061602.391543017, \"J/kg\"), Q_(3028.9867985920914, \"J/(kg*K)\")\n # Pylance does not support NumPy ufuncs\n assert np.isclose(s.T, Q_(373.1242958476843, \"K\")) # type: ignore\n assert np.isclose(s.p, Q_(101325.0, \"Pa\")) # type: ignore\n assert np.isclose(s.hs[0], Q_(1061602.391543017, \"J/kg\")) # type: ignore\n assert np.isclose(s.hs[1], Q_(3028.9867985920914, \"J/(kg*K)\")) # type: ignore\n assert np.isclose(s.u, Q_(1013250, \"J/kg\")) # type: ignore\n assert np.isclose(s.s, Q_(3028.9867985920914, \"J/(kg*K)\")) # type: ignore\n assert np.isclose(s.v, Q_(0.4772010021515822, \"m**3/kg\")) # type: ignore\n assert np.isclose(s.h, Q_(1061602.391543017, \"J/kg\")) # type: ignore\n assert np.isclose(s.x, Q_(0.28475636946248034, \"dimensionless\")) # type: ignore","def pre_flop_strength(hand):\n highs = {}\n highs[4] = [\n \"AA\", \"AKs\", \"AQs\", \"AJs\", \"ATs\", \"AKo\", \"KK\", \"KQs\", \"KJs\", \"AQo\",\n \"QQ\", \"QJs\", \"JJ\", \"TT\"\n ]\n highs[3] = [\n \"A5s\", \"A4s\", \"A3s\", \"KTs\", \"KQo\", \"QTs\", \"AJo\", \"JTs\", \"T9s\", \"99\",\n \"98s\", \"88\", \"87s\", \"77\", \"66\"\n ]\n highs[2] = [\n \"A9s\", \"A8s\", \"A7s\", \"A6s\", \"A2s\", \"K9s\", \"K8s\", \"Q9s\", \"KJo\", \"QJo\",\n \"J9s\", \"ATo\", \"KTo\", \"QTo\", \"JTo\", \"T8s\", \"A9o\", \"J9o\", \"T9o\", \"97s\",\n \"98o\", \"86s\", \"76s\", \"75s\", \"65s\", \"55\", \"44\", \"33\", \"22\"\n ]\n highs[1] = [\n \"K7s\", \"K6s\", \"K5s\", \"K4s\", \"K3s\", \"Q8s\", \"Q7s\", \"Q6s\", \"Q5s\", \"Q4s\",\n \"J8s\", \"J7s\", \"J6s\", \"J5s\", \"T7s\", \"T6s\", \"K9o\", \"Q9o\", \"96s\", \"A8o\",\n \"K8o\", \"Q8o\", \"J8o\", \"T8o\", \"85s\", \"A7o\", \"K7o\", \"Q7o\", \"T7o\", \"97o\",\n \"87o\", \"74s\", \"A6o\", \"K6o\", \"86o\", \"76o\", \"64s\", \"63s\", \"A5o\", \"75o\",\n \"65o\", \"54s\", \"53s\", \"A4o\", \"43s\", \"A3o\"\n ]\n card0, card1 = hand\n if card0[0] == card1[0]:\n pair = \"\".join([card0[0], card1[0]])\n elif card0[1] == card1[1]:\n pair = \"\".join([card0[0], card1[0], \"s\"])\n else:\n pair = \"\".join([card0[0], card1[0], \"o\"])\n for strenght in highs:\n if pair in highs[strenght]:\n return strenght\n return 0","def getHoracekFromEQ(self, ep):\n #check if power is in MW or W (assumes Pinj > 500W)\n if self.Pinj < 500:\n Pinj = self.Pinj * 1e6\n else:\n Pinj = self.Pinj\n\n #find the plasma volume from the equilibrium\n Rlim = ep.g['lcfs'][:,0]\n Zlim = ep.g['lcfs'][:,1]\n\n #calculate cross sectional area using shoelace formula\n i=np.arange(len(Rlim))\n crossSecArea=np.abs(np.sum(Rlim[i-1]*Zlim[i]-Rlim[i]*Zlim[i-1])*0.5)\n\n #calculate (approximate) volume inside separatrix [m^3]\n #assumes RmAxis is close to R of center of mass of crossSecArea\n vol = crossSecArea * 2 * np.pi * ep.g['RmAxis']\n\n #assuming plasma is centered in machine here\n zMin = ep.g['ZmAxis'] - 0.25\n zMax = ep.g['ZmAxis'] + 0.25\n zLCFS = ep.g['lcfs'][:,1]\n rLCFS = ep.g['lcfs'][:,0]\n #this prevents us from getting locations not at midplane\n idx = np.where(np.logical_and(zLCFS>zMin,zLCFS<zMax))\n Rmax = ep.g['lcfs'][:,0][idx].max()\n Rmin = ep.g['lcfs'][:,0][idx].min()\n # geometric quantities\n Rgeo = (Rmax + Rmin) / 2.0\n a = (Rmax - Rmin) / 2.0\n aspect = a/Rgeo\n\n #maximum z point and elongation\n idx2 = np.where(np.logical_and(rLCFS>Rmin,rLCFS<Rmax))\n b = ep.g['lcfs'][:,1][idx].max() #assumes equatorial plane is z=0\n k = b / a\n\n #lambda q from Horacek engineering scaling figure 6a\n self.lqCF = 10 * (Pinj / vol)**(-0.38) * aspect**(1.3) * k**(-1.3) * 1e3 #in mm\n return","def get_prior(self):\n return self._prior","def getHighestRank_LowerWilson(self, higherBound = True):\n \n if len(self.Predictors) == 1:\n # there is only one predictor. choose that immediately\n predictor = self.Predictors[0]\n return (predictor, predictor.confidence)\n \n # grab the top 3 wins, top 3 wins-lost, top 3 confidences\n# maxWins = sorted(self.Predictors, key=lambda i: i.scoreWins)\n# maxDiff = sorted(self.Predictors, key=lambda i: i.scoreWins - i.scoreLosts)\n# maxConfidence = sorted(self.Predictors, key=lambda i: i.confidence)\n \n # grab the top predictors by wins, diffs and confidence.\n # on test, this has worse effect on ranking. (need more testing for confirmation)\n filteredPredictors = self.Predictors # no union\n\n # warning: set is non-deterministic\n #filteredPredictors = set(maxWins[:3]) | set(maxDiff[:3]) | set(maxConfidence[:3]) # union\n #filteredPredictors = set(maxWins[:5]) | set(maxDiff[:5]) | set(maxConfidence[:5]) # union\n #filteredPredictors = list(filteredPredictors)\n \n##############\n##todo: add treshold instead?\n#########\n \n predictorScores = []\n for i, predictor in enumerate(filteredPredictors):\n\n if useScoreBuffer == False:\n positiveRatings = predictor.scoreWins\n negativeRatings = predictor.scoreLosts\n totalRatings = predictor.totalTurns\n totalRatings = positiveRatings + negativeRatings\n else:\n positiveRatings = predictor.scoreBuffer.count(scoreBufferWin)\n negativeRatings = predictor.scoreBuffer.count(scoreBufferLost)\n totalRatings = len(predictor.scoreBuffer)\n totalRatings = positiveRatings + negativeRatings\n \n confidence = predictor.confidence\n \n # experiment: what happens if we use our score as confidence in self?\n \n# if confidence >= 1: # possible DNA\n# predictorScores.append((1.0, predictor))\n# continue\n \n if positiveRatings <= 0 or totalRatings <= 0:\n continue\n \n if 1:\n #confidence = 1 - confidence\n maxPredictionRating = 0.99 # possible DNA\n #maxPredictionRating = 1 # possible DNA\n \n if confidence > maxPredictionRating: confidence = maxPredictionRating\n if confidence < 0.0: confidence = 0.0\n\n ratings = rps.binconf(positiveRatings, negativeRatings, confidence)\n #ratings = binconf(positiveRatings, negativeRatings, confidence)\n \n if higherBound:\n rating = float(ratings[1])\n else:\n rating = float(ratings[0])\n \n #rating += (ratings[1] - ratings[0]) / 2\n \n if math.isnan(rating): rating = 0\n \n rating = round(rating,3) # fix for conversion from C float to Python float \n else:\n maxPredictionRating = 0.99 # possible DNA\n #maxPredictionRating = 1 # possible DNA\n if confidence > maxPredictionRating: confidence = maxPredictionRating\n if confidence < 0.0: confidence = 0.0\n \n #z = 1.96 # hardcorded for confidence=95%\n #z = 1.0 # 1.44=85% 1.96=95%\n p = 1 - 0.5 * (1 - confidence)\n z = cached_normcdfi(p)\n #z = rps.normcdfi(p)\n \n phat = float(positiveRatings) / totalRatings\n n = totalRatings\n \n rating = (phat + z*z/(2*n) - z * math.sqrt((phat*(1-phat)+z*z/(4*n))/n))/(1+z*z/n)\n \n #rating = round(rating, 3) # round to the nearest 3 decimals. experiment\n \n predictor.rankingConfidence = rating\n predictorScores.append((rating, predictor))\n\n if len(predictorScores) > 1:\n # filter out predictors that does not tie with the maximum rating, for optimization purposes\n maxRating = max(predictorScores, key=lambda i: i[0])[0]\n p = [p for p in predictorScores if p[0] == maxRating]\n\n if predictorScores[0] != maxRating:\n assert(\"Something is wrong. We filtered out predictions that is not the maximum but we got some here\") \n \n predictorScores = p\n elif len(predictorScores) == 1:\n rating, chosenPredictor = predictorScores[0]\n return chosenPredictor, rating\n else:\n random = rps.random() % len(filteredPredictors)\n chosenPredictor = filteredPredictors[random]\n rating = 0\n return chosenPredictor, rating\n \n # there are multiple predictors with the same rating.\n # let's choose the one with the biggest score (positive - negative)\n if useScoreBuffer == False:\n highestScorers = max(predictorScores, key=lambda i: i[1].scoreWins)\n else:\n highestScorers = max(predictorScores, key=lambda i: i[1].scoreBuffer.count(scoreBufferWin))\n predictorScores = [p for p in predictorScores if p[0] == highestScorers[0]]\n\n # tally the moves and choose the move with the most tally\n \n tally = [0, 0, 0]\n for p in predictorScores:\n # tally[p[1].moveLastTurn] += 1\n if p[1].moveLastTurn == 0: tally[0] += 1\n if p[1].moveLastTurn == 1: tally[1] += 1\n if p[1].moveLastTurn == 2: tally[2] += 1\n \n # let's choose a move at random between them \n # Filter predictorScores to only include the predictors with the maximum tally.\n maxTally = max(tally)\n talliedScorers = []\n if tally[0] == maxTally: \n rocks = [talliedScorers.append(p) for p in predictorScores if p[1].moveLastTurn == 0]\n if tally[1] == maxTally: \n papers = [talliedScorers.append(p) for p in predictorScores if p[1].moveLastTurn == 1]\n if tally[2] == maxTally: \n scissors = [talliedScorers.append(p) for p in predictorScores if p[1].moveLastTurn == 2] \n \n if len(talliedScorers) == 1:\n # in practice, this doesn't happen, but we put in this option to try to minimize bugs\n rating, chosenPredictor = talliedScorers[0]\n else:\n # play the move with the highest score\n finalChoice = None\n \n if tally[0] and tally[0] > tally[1] and tally[0] > tally[2]:\n Rmoves = [p for p in talliedScorers if p[1].moveLastTurn == 0]\n finalChoice = Rmoves[0]\n elif tally[1] and tally[1] > tally[0] and tally[1] > tally[2]:\n Pmoves = [p for p in talliedScorers if p[1].moveLastTurn == 1]\n finalChoice = Pmoves[0]\n elif tally[2] and tally[2] > tally[0] and tally[2] > tally[1]:\n Smoves = [p for p in talliedScorers if p[1].moveLastTurn == 2]\n finalChoice = Smoves[0]\n else: \n # there are still ties so we choose at random\n random = rps.random() % len(talliedScorers)\n finalChoice = talliedScorers[random]\n \n chosenPredictor = finalChoice[1]\n rating = finalChoice[0] \n \n if Debug:\n currentTurn = rps.getTurn()\n print(\"currentTurn\", currentTurn)\n for p in talliedScorers:\n print (\"%s (%i) Wilson Rating: %.3f. Confidence: %.3f Score +%i/-%i\" % (p[1].name, p[1].moveLastTurn, p[0], p[1].confidence, p[1].scoreWins, p[1].scoreLosts))\n \n input() \n\n return chosenPredictor, rating","def pre_phasing_r1(self, pre_phasing_r1):\n\n self._pre_phasing_r1 = pre_phasing_r1","def preamp(self):\n return self.preamp_index","def print_prevalence(self):\n\t\ts = len(self.state['s'])\n\t\tp = len(self.state['p'])\n\t\tc = len(self.state['c'])\n\t\tprint self.time, float(p + c) / (s + p + c)","def straight(handIn):","def prior_z(self) -> distributions.Distribution:\n return distributions.Categorical(self.pi)","def _thruster_hfp(motions: Dict[str, float]) -> int:\n surge, yaw, sway = motions[\"surge\"], motions[\"yaw\"], motions[\"sway\"]\n\n if surge and yaw:\n\n # If backwards, else forwards\n if surge < CONTROL_NORM_IDLE:\n value = -surge\n else:\n value = -yaw\n\n elif surge:\n value = -surge\n\n elif sway:\n value = sway\n\n elif yaw:\n value = -yaw\n\n else:\n value = CONTROL_NORM_IDLE\n\n return Converter._to_thruster_value(value)","def hp(self):\n return gon2hp(self.gon_angle)","def fredkin(ha, hb, hc):\n\n return controlled_U(ha, swap(hb, hc))","def multiple_premia_heston(nobs=2000):\r\n lm = 1.5768\r\n mu = .12**2\r\n eta = .5751\r\n rho = -.0\r\n sigma = .12**2\r\n\r\n price = 1\r\n strike = np.exp(np.linspace(-.1, .1, nobs))\r\n maturity = 30/365\r\n riskfree = .01 * np.ones(nobs)\r\n moneyness = np.log(strike/price) - riskfree * maturity\r\n call = np.ones_like(moneyness).astype(bool)\r\n call[moneyness < 0] = False\r\n\r\n param = HestonParam(lm=lm, mu=mu, eta=eta, rho=rho, sigma=sigma)\r\n model = Heston(param, riskfree, maturity)\r\n premium = cosmethod(model, moneyness=moneyness, call=call)\r\n plt.plot(strike, premium)\r\n plt.show()","def test_is_57o_preHandSimple_correct(self):\n self.assertEqual(self.hand.getPreHandSimple(), '75o')","def evaulate_monster(generikmon):\r\n score = generikmon['headA']\r\n #score = generikmon['chinA']\r\n return score","def set_prior(self,which,what):\n\n which = self.grep_param_names(which)\n\n #check tied situation\n tie_partial_matches = [tie for tie in self.tied_indices if (not set(tie).isdisjoint(set(which))) & (not set(tie)==set(which))]\n if len(tie_partial_matches):\n raise ValueError, \"cannot place prior across partial ties\"\n tie_matches = [tie for tie in self.tied_indices if set(which)==set(tie) ]\n if len(tie_matches)>1:\n raise ValueError, \"cannot place prior across multiple ties\"\n elif len(tie_matches)==1:\n which = which[:1]# just place a prior object on the first parameter\n\n\n #check constraints are okay\n if isinstance(what, (priors.gamma, priors.log_Gaussian)):\n assert not np.any(which[:,None]==self.constrained_negative_indices), \"constraint and prior incompatible\"\n assert not np.any(which[:,None]==self.constrained_bounded_indices), \"constraint and prior incompatible\"\n unconst = np.setdiff1d(which, self.constrained_positive_indices)\n if len(unconst):\n print \"Warning: constraining parameters to be positive:\"\n print '\\n'.join([n for i,n in enumerate(self._get_param_names()) if i in unconst])\n print '\\n'\n self.constrain_positive(unconst)\n elif isinstance(what,priors.Gaussian):\n assert not np.any(which[:,None]==self.all_constrained_indices()), \"constraint and prior incompatible\"\n else:\n raise ValueError, \"prior not recognised\"\n\n\n #store the prior in a local list\n for w in which:\n self.priors[w] = what","def etap2rhog ( self ) :\n from GaudiConfUtils.ConfigurableGenerators import DaVinci__N3BodyDecays \n pre_etap = self.make_selection (\n ## the unique tag \n 'PreEtapRhoG' ,\n ## algorithm type to be used\n DaVinci__N3BodyDecays ,\n ## input selections \n [ self.pions () , self.gamma () ] ,\n ##\n DecayDescriptor = \" eta_prime -> pi+ pi- gamma\" ,\n ##\n DaughtersCuts = { 'gamma' : self['GammaCut'] } ,\n ## \n Combination12Cut = \"\"\" ( AM < 950 * MeV ) &\n ( ACHI2DOCA(1,2) < 12 ) \n \"\"\" , \n CombinationCut = \"\"\"\n ( APT > %s ) & \n in_range ( 500 * MeV , AM12 , 950 * MeV ) & \n ( ADAMASS ( 'eta_prime' ) < 100 * MeV ) \n \"\"\" % ( 0.9 * self['ETAP_PT'] ),\n ##\n MotherCut = \"\"\"\n ( PT > %s ) &\n ( chi2vx < 9 )\n \"\"\" % self['ETAP_PT'] \n )\n ## \n from GaudiConfUtils.ConfigurableGenerators import Pi0Veto__Tagger\n ## \n return self.make_selection (\n 'Etap2rhogamma' ,\n Pi0Veto__Tagger ,\n [ pre_etap ] ,\n MassWindow = 25 * MeV ,\n MassChi2 = -1 ,\n ExtraInfoIndex = 25017 ## unique ! \n )","def front_column_model_p_gain():","def who_won(self):\n if self.scoreB >= WP: return Stone.BLACK\n if self.scoreW >= WP: return Stone.WHITE\n return self.last","def rough_outcome(self) -> float:\n # HUYNH YOU PRICK WHY THE FUCK DO YOU MAKE US WRITE THIS SHIT EVEN IT'S NOT USED ANYWHERE\n # pick move based on this may not be optimal but better than random\n # return 1 if win immediately\n # return -1 if all states reachable will result the other player win\n # return 0 if otherwise ??? what the fuck does this mean\n # look two states forward\n pass","def Shifted_Uion_H(E):\n\treturn 0.5 + (9.0/4.0)*E**2 + 55.55*E**4 + 4908.0*E**6 + 794200*E**8","def get_desired_heading(self, h, p):\n heading_vector = [self.alpha*h[0] + self.beta*p[0],\n self.alpha*h[1] + self.beta*p[1]]\n a = [(heading_vector[0]/linalg.norm(heading_vector)),\n (heading_vector[1]/linalg.norm(heading_vector))]\n return a","def hit_stand_ev_diff(hand, shoe, dealer_hand, dealer_probabilities):\n dealer_end_probs = dealer_probabilities[dealer_hand]\n # maps a player's hand to his or her (hit_ev, stand_ev, max_ev)\n player_payoffs = get_player_payoff(dealer_end_probs)\n\n ev = 0 # contains weighted ev\n total = 0 # contains total weights, to normalized at the end\n val, hard = hand\n for card in shoe:\n weight = shoe[card] # number of a card in the shoe\n total += weight\n if hard and 11 <= val <= 21:\n new_hand = (val + card, hard)\n if new_hand[0] > 21:\n ev -= weight # default loss\n else:\n ev += weight * player_payoffs[new_hand][-1]\n elif not hard and 12 <= val <= 21:\n new_val = val + card\n new_hard = False\n if new_val > 21: # go back to hard value, take A = 1\n new_val -= 10\n new_hard = True\n ev += weight * player_payoffs[(new_val, new_hard)][-1]\n elif hard and 4 <= val <= 10:\n new_val = val + card\n new_hard = True\n if card == 1: # go to soft value, take A = 11\n new_val += 10\n new_hard = False\n ev += weight * player_payoffs[(new_val, new_hard)][-1]\n else:\n raise RuntimeError(\"Should not get here: \" + str(hand))\n return (1.0 * ev / total) - player_payoffs[hand][1] # hit ev - stand ev","def bias_prior(self):","def khorne_slide(obs, berzerker_x, berzerker_y):\n def environment_fits(obs, berzerker_x, berzerker_y):\n \"\"\" environment fits constraints \"\"\"\n # if prey has the ball\n if obs[\"ball_owned_team\"] == 1:\n prey_x = obs[\"right_team\"][obs[\"ball_owned_player\"]][0]\n prey_y = obs[\"right_team\"][obs[\"ball_owned_player\"]][1]\n # by x position, amount of berzerker's team players between prey and goal of berzerker's team\n players_amount = 0\n for i in range(1, len(obs[\"left_team\"])):\n if obs[\"left_team\"][i][0] < prey_x:\n players_amount += 1\n prey_x_direction = obs[\"right_team_direction\"][obs[\"ball_owned_player\"]][0]\n future_prey_x = prey_x + obs[\"right_team_direction\"][obs[\"ball_owned_player\"]][0]\n future_prey_y = prey_y + obs[\"right_team_direction\"][obs[\"ball_owned_player\"]][1]\n future_berzerker_x = berzerker_x + obs[\"left_team_direction\"][obs[\"active\"]][0]\n future_berzerker_y = berzerker_y + obs[\"left_team_direction\"][obs[\"active\"]][1]\n distance_to_prey = get_distance(berzerker_x, berzerker_y, prey_x, prey_y)\n future_distance_to_prey = get_distance(future_berzerker_x, future_berzerker_y, future_prey_x, future_prey_y)\n # if berzerker is not close to his own penalty zone\n # and prey is beyond x position of too many players of berzerker's team\n # and berzerker is close enough to prey\n # and berzerker is running in direction of prey\n if ((berzerker_x > -0.65 or abs(berzerker_y) > 0.3) and\n players_amount <= 7 and\n future_distance_to_prey < 0.015 and\n distance_to_prey > future_distance_to_prey):\n return True\n return False\n \n def get_action(obs, berzerker_x, berzerker_y):\n \"\"\" get action of this memory pattern \"\"\"\n return Action.Slide\n \n return {\"environment_fits\": environment_fits, \"get_action\": get_action}","def racetrack_filter(reversals, h):\n\n y = reversals\n yprev = None\n ix = []\n for n, yn in enumerate(y):\n if (n == 0) or (n == y.size-1):\n yprev = yn\n ix.append(n)\n continue\n dy = yn - yprev\n if fabs(dy) > h / 2.:\n yprev = yn - dy/fabs(dy) * h/2.\n ix.append(n)\n ix = np.array(ix, dtype=int)\n return y[ix], ix","def custom_score_7(game, player):\n \"\"\"custom_score_7 heuristic function also aims towards weighted chances heuristics\n \"\"\"\n if game.is_loser(player):\n return float(\"-inf\")\n\n if game.is_winner(player):\n return float(\"inf\")\n\n length_my_player_moves = len(game.get_legal_moves(player)) #Calculate length of available moves for myPlayer\n length_opp_payer_moves = len(game.get_legal_moves(game.get_opponent(player)))#Calculate length of available moves for the oppositePlayer\n return float(1.5*length_my_player_moves*length_my_player_moves - length_opp_payer_moves*length_opp_payer_moves)","def stokes_horizontal():\n return np.array([1, 1, 0, 0])","def shanten(self):\n if not self.is_concealed:\n return self.shanten_std\n\n return min(self.shanten_std, self.shanten_7, self.shanten_13)","def determine_winner1(self): \r\n sorted_player_rank = self._rank()\r\n print(f\"sorted player rank: {sorted_player_rank}\")\r\n print(f\"winner is player {sorted_player_rank[0]}: with points {sorted_player_rank[0][1]}\")","def p1(self):\n return self.lerp(1)","def custom_score(game, player):\n # return penalize_corners_heuristic(game, player)\n # return favor_run_away_heuristic(game, player)\n return look_ahead_heuristic(game, player)","def pre_phasing_r2(self, pre_phasing_r2):\n\n self._pre_phasing_r2 = pre_phasing_r2","def prior(cube, ndim, nparams):\n # construct prior from recovery file\n counter = 0\n if params2 is None:\n return\n for key in params2.keys():\n nparams_tmp = int(params2[key]['nparams'])\n for ii in range(nparams_tmp):\n # sp = [name, prior type, x1, x2]\n sp =\\\n params2[key]['param'+str(ii+1)].split(',')\n if sp[1][0] == 'U' and sp[2][:5]=='param' and sp[3][:5]=='param':\n subtract1 = int(key[-1]) - int(sp[2][-1])\n subtract2 = int(key[-1]) - int(sp[3][-1])\n cube[counter] = GeneralPrior(cube[counter], 'U',\n cube[counter-subtract1], cube[counter-subtract2])\n elif sp[1][0] == 'U' and sp[2][:5]=='param':\n subtract = int(key[-1]) - int(sp[2][-1])\n cube[counter] = GeneralPrior(cube[counter], 'U',\n cube[counter-subtract], float(sp[3]))\n elif sp[1][0] == 'U' and sp[3][:5]=='param':\n subtract = int(key[-1]) - int(sp[2][-1])\n cube[counter] = GeneralPrior(cube[counter], 'U',\n float(sp[2]), cube[counter - subtract])\n else:\n cube[counter] = GeneralPrior(cube[counter], sp[1], float(sp[2]),\n float(sp[3]))\n counter += 1","def getPrevManha(self):\n return self.getPrevision('Manhã')","def predict_winner(self):\n\t\tif len(self.players) > 1:\n\t\t\t# TODO: convert to using of max() function\n\t\t\twinner = self.players[0]\n\t\t\tfor player in self.players:\n\t\t\t\tif player.wr > winner.wr:\n\t\t\t\t\twinner = player\n\t\t\treturn winner\n\t\telse:\n\t\t\treturn None","def get_h_score(self):\n if self._h_score is None:\n self._h_score = self._heuristic.compute(self)\n return self._h_score","def bmshj2018_hyperprior(\n quality, metric=\"mse\", pretrained=False, progress=True, **kwargs\n):\n if metric not in (\"mse\", \"ms-ssim\"):\n raise ValueError(f'Invalid metric \"{metric}\"')\n\n if quality < 1 or quality > 8:\n raise ValueError(f'Invalid quality \"{quality}\", should be between (1, 8)')\n\n return _load_model(\n \"bmshj2018-hyperprior\", metric, quality, pretrained, progress, **kwargs\n )","def test_set_hp(self):\n s = State(substance=\"water\")\n s.hp = Q_(1061602.391543017, \"J/kg\"), Q_(101325.0, \"Pa\")\n # Pylance does not support NumPy ufuncs\n assert np.isclose(s.T, Q_(373.1242958476843, \"K\")) # type: ignore\n assert np.isclose(s.p, Q_(101325.0, \"Pa\")) # type: ignore\n assert np.isclose(s.hp[0], Q_(1061602.391543017, \"J/kg\")) # type: ignore\n assert np.isclose(s.hp[1], Q_(101325.0, \"Pa\")) # type: ignore\n assert np.isclose(s.u, Q_(1013250, \"J/kg\")) # type: ignore\n assert np.isclose(s.s, Q_(3028.9867985920914, \"J/(kg*K)\")) # type: ignore\n assert np.isclose(s.v, Q_(0.4772010021515822, \"m**3/kg\")) # type: ignore\n assert np.isclose(s.h, Q_(1061602.391543017, \"J/kg\")) # type: ignore\n assert np.isclose(s.x, Q_(0.28475636946248034, \"dimensionless\")) # type: ignore\n s.hp = Q_(3336406.139862406, \"J/kg\"), Q_(101325.0, \"Pa\")\n assert np.isclose(s.T, Q_(700.9882316847855, \"K\")) # type: ignore\n assert np.isclose(s.p, Q_(101325.0, \"Pa\")) # type: ignore\n assert np.isclose(s.hp[0], Q_(3336406.139862406, \"J/kg\")) # type: ignore\n assert np.isclose(s.hp[1], Q_(101325.0, \"Pa\")) # type: ignore\n assert np.isclose(s.u, Q_(3013250, \"J/kg\")) # type: ignore\n assert np.isclose(s.s, Q_(8623.283568815832, \"J/(kg*K)\")) # type: ignore\n assert np.isclose(s.v, Q_(3.189303132125469, \"m**3/kg\")) # type: ignore\n assert np.isclose(s.h, Q_(3336406.139862406, \"J/kg\")) # type: ignore\n assert s.x is None","def get_priority(self) -> str:\n if self.health >= 75 and self.food >= 75 and self.water >= 75:\n if min(self.food, self.water) == self.food:\n return 'food'\n else:\n return 'water'\n else:\n if self.food >= 75 and self.water >= 75:\n return 'monster'\n else:\n return 'food'","def GetHelix(helix):\r\n pass","def _thruster_hap(motions: Dict[str, float]) -> int:\n surge, yaw, sway = motions[\"surge\"], motions[\"yaw\"], motions[\"sway\"]\n\n if surge and yaw:\n\n # If backwards, else forwards\n if surge < CONTROL_NORM_IDLE:\n value = -yaw\n else:\n value = surge\n\n elif surge:\n value = surge\n\n elif sway:\n value = sway\n\n elif yaw:\n value = yaw\n\n else:\n value = CONTROL_NORM_IDLE\n\n return Converter._to_thruster_value(value)","def calc_winner(self):\n pass","def kHallLittlewoodP(self):\n return kbounded_HallLittlewoodP(self)","def model_prem(r):\n\n\t#- normalised radius\n\tx = r / 6371000.0\n\n\t#- march through the various depth levels -----------------------------------------------------\n\n\t#- upper crust\n\tif (r >= 6356000.0):\n\t\trho = 2.6\n\t\tvpv = 5.8\n\t\tvph = vpv\n\t\tvsv = 3.2\n\t\tvsh = vsv\n\t\teta = 1.0\n\n\t#- lower crust\n\telif (r >= 6346000.6) & (r < 6356000.0):\n\t\trho = 2.9\n\t\tvpv = 6.8\n\t\tvph = vpv\n\t\tvsv = 3.9\n\t\tvsh = vsv\n\t\teta = 1.0\n\n\t#- LID\n\telif (r >= 6291000.0) & (r < 6346000.6):\n\t\trho = 2.6910 + 0.6924 * x\n\t\tvpv = 0.8317 + 7.2180 * x\n\t\tvph = 3.5908 + 4.6172 * x\n\t\tvsv = 5.8582 - 1.4678 * x\n\t\tvsh = -1.0839 + 5.7176 * x\n\t\teta = 3.3687 - 2.4778 * x\n\n\t#- LVZ\n\telif (r >= 6151000.0) & (r < 6291000.0):\n\t\trho = 2.6910 + 0.6924 * x\n\t\tvpv = 0.8317 + 7.2180 * x\n\t\tvph = 3.5908 + 4.6172 * x\n\t\tvsv = 5.8582 - 1.4678 * x\n\t\tvsh = -1.0839 + 5.7176 * x\n\t\teta = 3.3687 - 2.4778 * x\n\n\t#- Transition zone 1\n\telif (r >= 5971000.0) & (r < 6151000.0):\n\t\trho = 7.1089 - 3.8045 * x\n\t\tvpv = 20.3926 - 12.2569 * x\n\t\tvph = vpv\n\t\tvsv = 8.9496 - 4.4597 * x\n\t\tvsh = vsv\n\t\teta = 1.0\n\n\t#- Transition zone 2\n\telif (r >= 5771000.0) & (r < 5971000.0):\n\t\trho = 11.2494 - 8.0298 * x\n\t\tvpv = 39.7027 - 32.6166 * x\n\t\tvph = vpv\n\t\tvsv = 22.3512 - 18.5856 * x\n\t\tvsh = vsv\n\t\teta = 1.0\n\n\t#- Transition zone 3\n\telif (r >= 5701000.0) & (r < 5771000.0):\n\t\trho = 5.3197 - 1.4836 * x\n\t\tvpv = 19.0957 - 9.8672 * x\n\t\tvph = vpv\n\t\tvsv = 9.9839 - 4.9324 * x\n\t\tvsh = vsv\n\t\teta = 1.0\n\n\t#- Lower mantle 1\n\telif (r >= 5600000.0) & (r < 5701000.0):\n\t\trho = 7.9565 - 6.4761 * x + 5.5283 * x**2 - 3.0807 * x**3\n\t\tvpv = 29.2766 - 23.6027 * x + 5.5242 * x**2 - 2.5514 * x**3\n\t\tvph = vpv\n\t\tvsv = 22.3459 - 17.2473 * x - 2.0834 * x**2 + 0.9783 * x**3\n\t\tvsh = vsv\n\t\teta = 1.0 \n\n\t#- Lower mantle 2\n\telif (r >= 3630000.0) & (r < 5600000.0):\n\t\trho = 7.9565 - 6.4761 * x + 5.5283 * x**2 - 3.0807 * x**3\n\t\tvpv = 24.9520 - 40.4673 * x + 51.4832 * x**2 - 26.6419 * x**3\n\t\tvph = vpv\n\t\tvsv = 11.1671 - 13.7818 * x + 17.4575 * x**2 - 9.2777 * x**3\n\t\tvsh = vsv\n\t\teta = 1.0\n\n\t#- Lower mantle 3\n\telif (r >= 3480000.0) & (r < 3630000.0):\n\t\trho = 7.9565 - 6.4761 * x + 5.5283 * x**2 - 3.0807 * x**3\n\t\tvpv = 15.3891 - 5.3181 * x + 5.5242 * x**2 - 2.5514 * x**3\n\t\tvph = vpv\n\t\tvsv = 6.9254 + 1.4672 * x - 2.0834 * x**2 + 0.9783 * x**3\n\t\tvsh = vsv\n\t\teta = 1.0\n\n\t#- Outer core\n\telif (r >= 1221000.5) & (r < 3480000.0):\n\t\trho = 12.5815 - 1.2638 * x - 3.6426 * x**2 - 5.5281 * x**3\n\t\tvpv = 11.0487 - 4.0362 * x + 4.8023 * x**2 - 13.5732 * x**3\n\t\tvph = vpv\n\t\tvsv = 0.0\n\t\tvsh = 0.0\n\t\teta = 1.0\n\n\t#- Inner Core\n\telif (r >= 0.0) & (r < 1221000.5):\n\t\trho = 13.0885 - 8.8381 * x**2\n\t\tvpv = 11.2622 - 6.3640 * x**2\n\t\tvph = vpv\n\t\tvsv = 3.6678 - 4.4475 * x**2\n\t\tvsh = vsv\n\t\teta = 1.0 \n\n\t#- convert to elastic parameters --------------------------------------------------------------\n\n\trho = 1000.0 * rho\n\tvpv = 1000.0 * vpv\n\tvph = 1000.0 * vph\n\tvsv = 1000.0 * vsv\n\tvsh = 1000.0 * vsh\n\n\tA = rho * vph**2\n\tC = rho * vpv**2\n\tN = rho * vsh**2\n\tL = rho * vsv**2\n\tF = eta * (A - 2 * L)\n\n\treturn rho, A, C, F, L, N","def __call__(self, u, t):\n return self.computerhs(u, self.inletf, t, self.a, self.alpha, self.msh, self.Minv, self.S)","def hss(self):\n return 2 * (self.table[0, 0] * self.table[1, 1] - self.table[0, 1] * self.table[1, 0]) / (\n (self.table[0, 0] + self.table[0, 1]) * (self.table[0, 1] + self.table[1, 1]) +\n (self.table[0, 0] + self.table[1, 0]) * (self.table[1, 0] + self.table[1, 1]))","def get_fwhm_prior(self,freq,feed):\n self.fitted_fwhm = {feed:np.poly1d(fit) for feed,fit in Data.average_beam_widths.items()}\n\n\n if (self.fwhm_prior == 'ModelFWHMPrior'):\n P0_priors={'sigx':{'mean':self.xfwhm(freq)/60./2.355,\n 'width':self.xfwhm(freq)/60./2.355/1e2}}\n elif (self.fwhm_prior == 'DataFWHMPrior'):\n P0_priors={'sigx':{'mean':self.fitted_fwhm[feed](1./freq)/60./2.355,\n 'width':self.fitted_fwhm[feed](1./freq)/60./2.355/1e2}}\n else:\n P0_priors = {}\n\n return P0_priors","def other(player):\n return 1 - player","def other(player):\n return 1 - player","def other(player):\n return 1 - player","def hpa(self):\n return HPAngle(gon2hp(self.gon_angle))","def _get_minth(self):\n return self.__minth","def _get_minth(self):\n return self.__minth","def _get_minth(self):\n return self.__minth","def _get_minth(self):\n return self.__minth","def _get_minth(self):\n return self.__minth"],"string":"[\n \"def prior_sample(self):\\n pass\",\n \"def horde_step(self, observation):\",\n \"def lnprior(self):\\n \\n return\",\n \"def brain_weight_oz(self):\\r\\n return Heart.heart_weight_oz(self) # Used method from Heart Class\\r\",\n \"def RankineHugoniot(P):\\n # prevP = P+1\\n # while(abs(P-prevP)>tol):\\n # prevP = P\\n Pr = P/rbc[0]\\n temp = 1/beta*(cR/cL)*(Pr-1)\\n temp = temp/np.sqrt(1+(gamma+1)/(2*gamma)*(Pr-1))\\n temp = (1-temp)**beta\\n # return P\\n return temp*lbc[0]-P\",\n \"def low(self):\",\n \"def prior(self):\\n return self.__prior\",\n \"def prior(self):\\n return self.__prior\",\n \"def prior(self):\\n return self.__prior\",\n \"def prior(self):\\n return self.__prior\",\n \"def head_hearteyes():\\n print (hair_longer())\\n print (eye_heart())\\n print (nose_rightwards())\\n print (mouth_smile())\\n print (chin_curvy())\",\n \"def wind_shear(self):\\n return self.flow_field.wind_shear\",\n \"def _prey_step(self):\\n raise NotImplementedError()\",\n \"def lower_cb(self,p):\\n avg_elo=self.elo0+self.elo1\\n delta=self.elo1-self.elo0\\n N=30\\n# Various error conditions must be handled better here!\\n while True:\\n Elo0=avg_elo-N*delta\\n Elo1=avg_elo+N*delta\\n sol=brentq(lambda elo:self.outcome_prob(elo)-(1-p),Elo0,Elo1)\\n if sol['msg']=='no bracket':\\n N*=2\\n continue\\n break\\n return sol['x0']\",\n \"def H(self, dstrct):\\n new_pop = self.population + dstrct.current_pop\\n score = (self.x - new_pop)/self.x\\n # Here the absolute value tries to prevent a district that only\\n # needs a minor increase in population taking the most populous pt.\\n score = 1-abs(score)\\n self.H_ = self.w_H * score\\n return self.H_\",\n \"def get_prior(self):\\n return self.prior\",\n \"def _compute_mix_prior(self):\\n if np.all(self.mix_prior == 1):\\n return 0\\n return np.dot(np.log(self.mix_weight).T, (self.mix_prior - 1))\",\n \"def mort(self, h):\\n return(self.mu_bg +\\\\\\n (1.0 - self.mu_bg) * self.pr_P * self.p_att *\\\\\\n (1 - h**self.ap))\",\n \"def _prior_teach(self):\\n pass\",\n \"def handDecision(handIn):\",\n \"def get_guess(self):\\n guess = self.player.higher_or_lower\",\n \"def testHrtPrior(self):\\n attr = self.session.create_visit_attr()\\n\\n self.util.boolTypeTest(self, attr, \\\"prior\\\")\\n\\n self.util.boolPropertyTest(self, attr, \\\"prior\\\")\",\n \"def enthalpy(temp,pres):\\n g = liq_g(0,0,temp,pres)\\n g_t = liq_g(1,0,temp,pres)\\n h = g - temp*g_t\\n return h\",\n \"def _calculate_strehl(self):\\n\\n self.strehl = np.exp(-1*((2*np.pi/self.science_wavelength)*self.high_order_wfe)**2)\",\n \"def horde_start(self, observation):\",\n \"def head_surprised():\\n print (hair_spiky())\\n print (eye_wide())\\n print (nose_leftwards())\\n print (mouth_open())\\n print (chin_combo())\",\n \"def high(self):\",\n \"def o_wo_per_head(self):\\n assert self.ff % self.heads == 0\\n # fuse ff->e and projection layer of self-attention\\n return (self.ff // (self.heads-self.padded_heads)) + self.qkv\",\n \"def a_realization(self):\\n if self.t==1:\\n return self.kmonomial()\\n else:\\n return self.kHallLittlewoodP()\",\n \"def prior_predictive(self):\\n cfg = self.config\\n n = cfg['batch_size'] * cfg['q/n_samples']\\n n_samples = cfg['q/n_samples']\\n with util.get_or_create_scope('model', reuse=True):\\n h_prior = tf.cast(self.p_h_L.sample(n), cfg['dtype'])\\n h_prior = tf.reshape(\\n h_prior, [cfg['q/n_samples'], cfg['batch_size'], -1])\\n h = [None] * cfg['p/n_layers']\\n h[cfg['p/n_layers'] - 1] = h_prior\\n for n in range(cfg['p/n_layers'] - 1, 0, -1):\\n p_h_n = self.build_stochastic_layer(n, h_above=h[n])\\n h[n - 1] = tf.cast(p_h_n.sample(), cfg['dtype'])\\n return self.likelihood(h[0])\",\n \"def _get_initial_hypos(self):\\n return [CombiStatePartialHypo(self.get_predictor_states())]\",\n \"def sound_horizon_EH(self):\\n om_m = self.omega_cb\\n om_b = self.omega_b\\n om_n = np.sum(self.omega_nu)\\n h = self.h \\n if self.M_nu_tot == 0.: rs = 44.5*np.log(9.83/om_m)/np.sqrt(1+10*om_b**0.75)*h\\n else: rs = 55.154*np.exp(-72.3*(om_n+0.0006)**2.)/(om_m**0.25351*om_b**0.12807)*h\\n return rs\",\n \"def hapax(self):\\n return self.nlegomena(1)\",\n \"def curve_no_hillcoef(ph, pka):\\n# return ph - pka\\n return 1/(10**(pka-ph)+1)\",\n \"def generate_heuristic(self):\\r\\n\\t\\tif self.hType == 0:\\r\\n\\t\\t\\tself.use_displaced_heur()\\r\\n\\r\\n\\t\\telif self.hType == 1:\\r\\n\\t\\t\\tself.use_manhatten_heur()\",\n \"def vanilaScore(self,attended,state,W):\",\n \"def a_realization(self):\\n return self.UHP()\",\n \"def get_prior(self):\\n assert self._prior in self._priors, 'Unsupported prior! Check the _priors attribute for a list of priors.'\\n if self._prior == 'Gaussian':\\n prior = 0.5 * torch.sum(self.parameters ** 2)/self.prior_var\\n elif self._prior == 'Cauchy':\\n dimconst = (self.parameters.shape[0] + 1)/2.\\n prior = dimconst*torch.log(self.prior_var + torch.sum(self.parameters ** 2))\\n elif self._prior == 'Sparse':\\n n = self.dataset.shape[1]\\n gauss_prior = 0.5 * torch.sum(torch.exp(self.parameters[-1] * torch.exp(self.parameters[n:2*n]) * self.parameters[:n] ** 2))\\n gamma_density = torch.distributions.Gamma(1.5,0.5)\\n# gamma_prior = -gamma_density.log_prob(torch.exp(self.parameters[n:])).sum()\\n# lambda_density = torch.distributions.Gamma(1.5,0.5)\\n lambda_prior = -gamma_density.log_prob(torch.exp(self.parameters[n:])).sum()\\n prior = gauss_prior + lambda_prior\\n return prior\",\n \"def prior_rho(store):\\n if store['rho'] > 0. and store['rho'] < 1.0:\\n alpha = 1.0\\n beta = 1.0\\n return (alpha - 1.) * log(store['rho']) + (beta - 1.) * \\\\\\n log(1.-store['rho'])\\n else:\\n return -1E256\",\n \"def win_probs(*, home_elo, road_elo, hca_elo):\\n h = math.pow(10, home_elo/400)\\n r = math.pow(10, road_elo/400)\\n a = math.pow(10, hca_elo/400)\\n denom = r + a*h\\n home_prob = a*h / denom\\n road_prob = r / denom\\n return home_prob, road_prob\",\n \"def buildWPriorTerm(self):\\r\\n\\r\\n # self.w_prior.shape == (minibatch size,)\\r\\n self.w_prior = 0.5*T.sum(1 + T.log(self.qwgy_var) - self.qwgy_mu**2-self.qwgy_var, axis=1)\\r\\n\\r\\n self.w_prior_modif = - T.maximum(self.hyper['treshold_w_prior'], -self.w_prior)\",\n \"def rh(self, h):\\n sez=self.getSect(h)\\n area=self.area(sez)\\n wetborder = self.wetBorder(sez)\\n return area/wetborder\",\n \"def homozygotie(self):\\n if self.allele[1] == 0.0:\\n self.homozygote = True\",\n \"def test_set_hs(self):\\n s = State(substance=\\\"water\\\")\\n s.hs = Q_(1061602.391543017, \\\"J/kg\\\"), Q_(3028.9867985920914, \\\"J/(kg*K)\\\")\\n # Pylance does not support NumPy ufuncs\\n assert np.isclose(s.T, Q_(373.1242958476843, \\\"K\\\")) # type: ignore\\n assert np.isclose(s.p, Q_(101325.0, \\\"Pa\\\")) # type: ignore\\n assert np.isclose(s.hs[0], Q_(1061602.391543017, \\\"J/kg\\\")) # type: ignore\\n assert np.isclose(s.hs[1], Q_(3028.9867985920914, \\\"J/(kg*K)\\\")) # type: ignore\\n assert np.isclose(s.u, Q_(1013250, \\\"J/kg\\\")) # type: ignore\\n assert np.isclose(s.s, Q_(3028.9867985920914, \\\"J/(kg*K)\\\")) # type: ignore\\n assert np.isclose(s.v, Q_(0.4772010021515822, \\\"m**3/kg\\\")) # type: ignore\\n assert np.isclose(s.h, Q_(1061602.391543017, \\\"J/kg\\\")) # type: ignore\\n assert np.isclose(s.x, Q_(0.28475636946248034, \\\"dimensionless\\\")) # type: ignore\",\n \"def pre_flop_strength(hand):\\n highs = {}\\n highs[4] = [\\n \\\"AA\\\", \\\"AKs\\\", \\\"AQs\\\", \\\"AJs\\\", \\\"ATs\\\", \\\"AKo\\\", \\\"KK\\\", \\\"KQs\\\", \\\"KJs\\\", \\\"AQo\\\",\\n \\\"QQ\\\", \\\"QJs\\\", \\\"JJ\\\", \\\"TT\\\"\\n ]\\n highs[3] = [\\n \\\"A5s\\\", \\\"A4s\\\", \\\"A3s\\\", \\\"KTs\\\", \\\"KQo\\\", \\\"QTs\\\", \\\"AJo\\\", \\\"JTs\\\", \\\"T9s\\\", \\\"99\\\",\\n \\\"98s\\\", \\\"88\\\", \\\"87s\\\", \\\"77\\\", \\\"66\\\"\\n ]\\n highs[2] = [\\n \\\"A9s\\\", \\\"A8s\\\", \\\"A7s\\\", \\\"A6s\\\", \\\"A2s\\\", \\\"K9s\\\", \\\"K8s\\\", \\\"Q9s\\\", \\\"KJo\\\", \\\"QJo\\\",\\n \\\"J9s\\\", \\\"ATo\\\", \\\"KTo\\\", \\\"QTo\\\", \\\"JTo\\\", \\\"T8s\\\", \\\"A9o\\\", \\\"J9o\\\", \\\"T9o\\\", \\\"97s\\\",\\n \\\"98o\\\", \\\"86s\\\", \\\"76s\\\", \\\"75s\\\", \\\"65s\\\", \\\"55\\\", \\\"44\\\", \\\"33\\\", \\\"22\\\"\\n ]\\n highs[1] = [\\n \\\"K7s\\\", \\\"K6s\\\", \\\"K5s\\\", \\\"K4s\\\", \\\"K3s\\\", \\\"Q8s\\\", \\\"Q7s\\\", \\\"Q6s\\\", \\\"Q5s\\\", \\\"Q4s\\\",\\n \\\"J8s\\\", \\\"J7s\\\", \\\"J6s\\\", \\\"J5s\\\", \\\"T7s\\\", \\\"T6s\\\", \\\"K9o\\\", \\\"Q9o\\\", \\\"96s\\\", \\\"A8o\\\",\\n \\\"K8o\\\", \\\"Q8o\\\", \\\"J8o\\\", \\\"T8o\\\", \\\"85s\\\", \\\"A7o\\\", \\\"K7o\\\", \\\"Q7o\\\", \\\"T7o\\\", \\\"97o\\\",\\n \\\"87o\\\", \\\"74s\\\", \\\"A6o\\\", \\\"K6o\\\", \\\"86o\\\", \\\"76o\\\", \\\"64s\\\", \\\"63s\\\", \\\"A5o\\\", \\\"75o\\\",\\n \\\"65o\\\", \\\"54s\\\", \\\"53s\\\", \\\"A4o\\\", \\\"43s\\\", \\\"A3o\\\"\\n ]\\n card0, card1 = hand\\n if card0[0] == card1[0]:\\n pair = \\\"\\\".join([card0[0], card1[0]])\\n elif card0[1] == card1[1]:\\n pair = \\\"\\\".join([card0[0], card1[0], \\\"s\\\"])\\n else:\\n pair = \\\"\\\".join([card0[0], card1[0], \\\"o\\\"])\\n for strenght in highs:\\n if pair in highs[strenght]:\\n return strenght\\n return 0\",\n \"def getHoracekFromEQ(self, ep):\\n #check if power is in MW or W (assumes Pinj > 500W)\\n if self.Pinj < 500:\\n Pinj = self.Pinj * 1e6\\n else:\\n Pinj = self.Pinj\\n\\n #find the plasma volume from the equilibrium\\n Rlim = ep.g['lcfs'][:,0]\\n Zlim = ep.g['lcfs'][:,1]\\n\\n #calculate cross sectional area using shoelace formula\\n i=np.arange(len(Rlim))\\n crossSecArea=np.abs(np.sum(Rlim[i-1]*Zlim[i]-Rlim[i]*Zlim[i-1])*0.5)\\n\\n #calculate (approximate) volume inside separatrix [m^3]\\n #assumes RmAxis is close to R of center of mass of crossSecArea\\n vol = crossSecArea * 2 * np.pi * ep.g['RmAxis']\\n\\n #assuming plasma is centered in machine here\\n zMin = ep.g['ZmAxis'] - 0.25\\n zMax = ep.g['ZmAxis'] + 0.25\\n zLCFS = ep.g['lcfs'][:,1]\\n rLCFS = ep.g['lcfs'][:,0]\\n #this prevents us from getting locations not at midplane\\n idx = np.where(np.logical_and(zLCFS>zMin,zLCFS<zMax))\\n Rmax = ep.g['lcfs'][:,0][idx].max()\\n Rmin = ep.g['lcfs'][:,0][idx].min()\\n # geometric quantities\\n Rgeo = (Rmax + Rmin) / 2.0\\n a = (Rmax - Rmin) / 2.0\\n aspect = a/Rgeo\\n\\n #maximum z point and elongation\\n idx2 = np.where(np.logical_and(rLCFS>Rmin,rLCFS<Rmax))\\n b = ep.g['lcfs'][:,1][idx].max() #assumes equatorial plane is z=0\\n k = b / a\\n\\n #lambda q from Horacek engineering scaling figure 6a\\n self.lqCF = 10 * (Pinj / vol)**(-0.38) * aspect**(1.3) * k**(-1.3) * 1e3 #in mm\\n return\",\n \"def get_prior(self):\\n return self._prior\",\n \"def getHighestRank_LowerWilson(self, higherBound = True):\\n \\n if len(self.Predictors) == 1:\\n # there is only one predictor. choose that immediately\\n predictor = self.Predictors[0]\\n return (predictor, predictor.confidence)\\n \\n # grab the top 3 wins, top 3 wins-lost, top 3 confidences\\n# maxWins = sorted(self.Predictors, key=lambda i: i.scoreWins)\\n# maxDiff = sorted(self.Predictors, key=lambda i: i.scoreWins - i.scoreLosts)\\n# maxConfidence = sorted(self.Predictors, key=lambda i: i.confidence)\\n \\n # grab the top predictors by wins, diffs and confidence.\\n # on test, this has worse effect on ranking. (need more testing for confirmation)\\n filteredPredictors = self.Predictors # no union\\n\\n # warning: set is non-deterministic\\n #filteredPredictors = set(maxWins[:3]) | set(maxDiff[:3]) | set(maxConfidence[:3]) # union\\n #filteredPredictors = set(maxWins[:5]) | set(maxDiff[:5]) | set(maxConfidence[:5]) # union\\n #filteredPredictors = list(filteredPredictors)\\n \\n##############\\n##todo: add treshold instead?\\n#########\\n \\n predictorScores = []\\n for i, predictor in enumerate(filteredPredictors):\\n\\n if useScoreBuffer == False:\\n positiveRatings = predictor.scoreWins\\n negativeRatings = predictor.scoreLosts\\n totalRatings = predictor.totalTurns\\n totalRatings = positiveRatings + negativeRatings\\n else:\\n positiveRatings = predictor.scoreBuffer.count(scoreBufferWin)\\n negativeRatings = predictor.scoreBuffer.count(scoreBufferLost)\\n totalRatings = len(predictor.scoreBuffer)\\n totalRatings = positiveRatings + negativeRatings\\n \\n confidence = predictor.confidence\\n \\n # experiment: what happens if we use our score as confidence in self?\\n \\n# if confidence >= 1: # possible DNA\\n# predictorScores.append((1.0, predictor))\\n# continue\\n \\n if positiveRatings <= 0 or totalRatings <= 0:\\n continue\\n \\n if 1:\\n #confidence = 1 - confidence\\n maxPredictionRating = 0.99 # possible DNA\\n #maxPredictionRating = 1 # possible DNA\\n \\n if confidence > maxPredictionRating: confidence = maxPredictionRating\\n if confidence < 0.0: confidence = 0.0\\n\\n ratings = rps.binconf(positiveRatings, negativeRatings, confidence)\\n #ratings = binconf(positiveRatings, negativeRatings, confidence)\\n \\n if higherBound:\\n rating = float(ratings[1])\\n else:\\n rating = float(ratings[0])\\n \\n #rating += (ratings[1] - ratings[0]) / 2\\n \\n if math.isnan(rating): rating = 0\\n \\n rating = round(rating,3) # fix for conversion from C float to Python float \\n else:\\n maxPredictionRating = 0.99 # possible DNA\\n #maxPredictionRating = 1 # possible DNA\\n if confidence > maxPredictionRating: confidence = maxPredictionRating\\n if confidence < 0.0: confidence = 0.0\\n \\n #z = 1.96 # hardcorded for confidence=95%\\n #z = 1.0 # 1.44=85% 1.96=95%\\n p = 1 - 0.5 * (1 - confidence)\\n z = cached_normcdfi(p)\\n #z = rps.normcdfi(p)\\n \\n phat = float(positiveRatings) / totalRatings\\n n = totalRatings\\n \\n rating = (phat + z*z/(2*n) - z * math.sqrt((phat*(1-phat)+z*z/(4*n))/n))/(1+z*z/n)\\n \\n #rating = round(rating, 3) # round to the nearest 3 decimals. experiment\\n \\n predictor.rankingConfidence = rating\\n predictorScores.append((rating, predictor))\\n\\n if len(predictorScores) > 1:\\n # filter out predictors that does not tie with the maximum rating, for optimization purposes\\n maxRating = max(predictorScores, key=lambda i: i[0])[0]\\n p = [p for p in predictorScores if p[0] == maxRating]\\n\\n if predictorScores[0] != maxRating:\\n assert(\\\"Something is wrong. We filtered out predictions that is not the maximum but we got some here\\\") \\n \\n predictorScores = p\\n elif len(predictorScores) == 1:\\n rating, chosenPredictor = predictorScores[0]\\n return chosenPredictor, rating\\n else:\\n random = rps.random() % len(filteredPredictors)\\n chosenPredictor = filteredPredictors[random]\\n rating = 0\\n return chosenPredictor, rating\\n \\n # there are multiple predictors with the same rating.\\n # let's choose the one with the biggest score (positive - negative)\\n if useScoreBuffer == False:\\n highestScorers = max(predictorScores, key=lambda i: i[1].scoreWins)\\n else:\\n highestScorers = max(predictorScores, key=lambda i: i[1].scoreBuffer.count(scoreBufferWin))\\n predictorScores = [p for p in predictorScores if p[0] == highestScorers[0]]\\n\\n # tally the moves and choose the move with the most tally\\n \\n tally = [0, 0, 0]\\n for p in predictorScores:\\n # tally[p[1].moveLastTurn] += 1\\n if p[1].moveLastTurn == 0: tally[0] += 1\\n if p[1].moveLastTurn == 1: tally[1] += 1\\n if p[1].moveLastTurn == 2: tally[2] += 1\\n \\n # let's choose a move at random between them \\n # Filter predictorScores to only include the predictors with the maximum tally.\\n maxTally = max(tally)\\n talliedScorers = []\\n if tally[0] == maxTally: \\n rocks = [talliedScorers.append(p) for p in predictorScores if p[1].moveLastTurn == 0]\\n if tally[1] == maxTally: \\n papers = [talliedScorers.append(p) for p in predictorScores if p[1].moveLastTurn == 1]\\n if tally[2] == maxTally: \\n scissors = [talliedScorers.append(p) for p in predictorScores if p[1].moveLastTurn == 2] \\n \\n if len(talliedScorers) == 1:\\n # in practice, this doesn't happen, but we put in this option to try to minimize bugs\\n rating, chosenPredictor = talliedScorers[0]\\n else:\\n # play the move with the highest score\\n finalChoice = None\\n \\n if tally[0] and tally[0] > tally[1] and tally[0] > tally[2]:\\n Rmoves = [p for p in talliedScorers if p[1].moveLastTurn == 0]\\n finalChoice = Rmoves[0]\\n elif tally[1] and tally[1] > tally[0] and tally[1] > tally[2]:\\n Pmoves = [p for p in talliedScorers if p[1].moveLastTurn == 1]\\n finalChoice = Pmoves[0]\\n elif tally[2] and tally[2] > tally[0] and tally[2] > tally[1]:\\n Smoves = [p for p in talliedScorers if p[1].moveLastTurn == 2]\\n finalChoice = Smoves[0]\\n else: \\n # there are still ties so we choose at random\\n random = rps.random() % len(talliedScorers)\\n finalChoice = talliedScorers[random]\\n \\n chosenPredictor = finalChoice[1]\\n rating = finalChoice[0] \\n \\n if Debug:\\n currentTurn = rps.getTurn()\\n print(\\\"currentTurn\\\", currentTurn)\\n for p in talliedScorers:\\n print (\\\"%s (%i) Wilson Rating: %.3f. Confidence: %.3f Score +%i/-%i\\\" % (p[1].name, p[1].moveLastTurn, p[0], p[1].confidence, p[1].scoreWins, p[1].scoreLosts))\\n \\n input() \\n\\n return chosenPredictor, rating\",\n \"def pre_phasing_r1(self, pre_phasing_r1):\\n\\n self._pre_phasing_r1 = pre_phasing_r1\",\n \"def preamp(self):\\n return self.preamp_index\",\n \"def print_prevalence(self):\\n\\t\\ts = len(self.state['s'])\\n\\t\\tp = len(self.state['p'])\\n\\t\\tc = len(self.state['c'])\\n\\t\\tprint self.time, float(p + c) / (s + p + c)\",\n \"def straight(handIn):\",\n \"def prior_z(self) -> distributions.Distribution:\\n return distributions.Categorical(self.pi)\",\n \"def _thruster_hfp(motions: Dict[str, float]) -> int:\\n surge, yaw, sway = motions[\\\"surge\\\"], motions[\\\"yaw\\\"], motions[\\\"sway\\\"]\\n\\n if surge and yaw:\\n\\n # If backwards, else forwards\\n if surge < CONTROL_NORM_IDLE:\\n value = -surge\\n else:\\n value = -yaw\\n\\n elif surge:\\n value = -surge\\n\\n elif sway:\\n value = sway\\n\\n elif yaw:\\n value = -yaw\\n\\n else:\\n value = CONTROL_NORM_IDLE\\n\\n return Converter._to_thruster_value(value)\",\n \"def hp(self):\\n return gon2hp(self.gon_angle)\",\n \"def fredkin(ha, hb, hc):\\n\\n return controlled_U(ha, swap(hb, hc))\",\n \"def multiple_premia_heston(nobs=2000):\\r\\n lm = 1.5768\\r\\n mu = .12**2\\r\\n eta = .5751\\r\\n rho = -.0\\r\\n sigma = .12**2\\r\\n\\r\\n price = 1\\r\\n strike = np.exp(np.linspace(-.1, .1, nobs))\\r\\n maturity = 30/365\\r\\n riskfree = .01 * np.ones(nobs)\\r\\n moneyness = np.log(strike/price) - riskfree * maturity\\r\\n call = np.ones_like(moneyness).astype(bool)\\r\\n call[moneyness < 0] = False\\r\\n\\r\\n param = HestonParam(lm=lm, mu=mu, eta=eta, rho=rho, sigma=sigma)\\r\\n model = Heston(param, riskfree, maturity)\\r\\n premium = cosmethod(model, moneyness=moneyness, call=call)\\r\\n plt.plot(strike, premium)\\r\\n plt.show()\",\n \"def test_is_57o_preHandSimple_correct(self):\\n self.assertEqual(self.hand.getPreHandSimple(), '75o')\",\n \"def evaulate_monster(generikmon):\\r\\n score = generikmon['headA']\\r\\n #score = generikmon['chinA']\\r\\n return score\",\n \"def set_prior(self,which,what):\\n\\n which = self.grep_param_names(which)\\n\\n #check tied situation\\n tie_partial_matches = [tie for tie in self.tied_indices if (not set(tie).isdisjoint(set(which))) & (not set(tie)==set(which))]\\n if len(tie_partial_matches):\\n raise ValueError, \\\"cannot place prior across partial ties\\\"\\n tie_matches = [tie for tie in self.tied_indices if set(which)==set(tie) ]\\n if len(tie_matches)>1:\\n raise ValueError, \\\"cannot place prior across multiple ties\\\"\\n elif len(tie_matches)==1:\\n which = which[:1]# just place a prior object on the first parameter\\n\\n\\n #check constraints are okay\\n if isinstance(what, (priors.gamma, priors.log_Gaussian)):\\n assert not np.any(which[:,None]==self.constrained_negative_indices), \\\"constraint and prior incompatible\\\"\\n assert not np.any(which[:,None]==self.constrained_bounded_indices), \\\"constraint and prior incompatible\\\"\\n unconst = np.setdiff1d(which, self.constrained_positive_indices)\\n if len(unconst):\\n print \\\"Warning: constraining parameters to be positive:\\\"\\n print '\\\\n'.join([n for i,n in enumerate(self._get_param_names()) if i in unconst])\\n print '\\\\n'\\n self.constrain_positive(unconst)\\n elif isinstance(what,priors.Gaussian):\\n assert not np.any(which[:,None]==self.all_constrained_indices()), \\\"constraint and prior incompatible\\\"\\n else:\\n raise ValueError, \\\"prior not recognised\\\"\\n\\n\\n #store the prior in a local list\\n for w in which:\\n self.priors[w] = what\",\n \"def etap2rhog ( self ) :\\n from GaudiConfUtils.ConfigurableGenerators import DaVinci__N3BodyDecays \\n pre_etap = self.make_selection (\\n ## the unique tag \\n 'PreEtapRhoG' ,\\n ## algorithm type to be used\\n DaVinci__N3BodyDecays ,\\n ## input selections \\n [ self.pions () , self.gamma () ] ,\\n ##\\n DecayDescriptor = \\\" eta_prime -> pi+ pi- gamma\\\" ,\\n ##\\n DaughtersCuts = { 'gamma' : self['GammaCut'] } ,\\n ## \\n Combination12Cut = \\\"\\\"\\\" ( AM < 950 * MeV ) &\\n ( ACHI2DOCA(1,2) < 12 ) \\n \\\"\\\"\\\" , \\n CombinationCut = \\\"\\\"\\\"\\n ( APT > %s ) & \\n in_range ( 500 * MeV , AM12 , 950 * MeV ) & \\n ( ADAMASS ( 'eta_prime' ) < 100 * MeV ) \\n \\\"\\\"\\\" % ( 0.9 * self['ETAP_PT'] ),\\n ##\\n MotherCut = \\\"\\\"\\\"\\n ( PT > %s ) &\\n ( chi2vx < 9 )\\n \\\"\\\"\\\" % self['ETAP_PT'] \\n )\\n ## \\n from GaudiConfUtils.ConfigurableGenerators import Pi0Veto__Tagger\\n ## \\n return self.make_selection (\\n 'Etap2rhogamma' ,\\n Pi0Veto__Tagger ,\\n [ pre_etap ] ,\\n MassWindow = 25 * MeV ,\\n MassChi2 = -1 ,\\n ExtraInfoIndex = 25017 ## unique ! \\n )\",\n \"def front_column_model_p_gain():\",\n \"def who_won(self):\\n if self.scoreB >= WP: return Stone.BLACK\\n if self.scoreW >= WP: return Stone.WHITE\\n return self.last\",\n \"def rough_outcome(self) -> float:\\n # HUYNH YOU PRICK WHY THE FUCK DO YOU MAKE US WRITE THIS SHIT EVEN IT'S NOT USED ANYWHERE\\n # pick move based on this may not be optimal but better than random\\n # return 1 if win immediately\\n # return -1 if all states reachable will result the other player win\\n # return 0 if otherwise ??? what the fuck does this mean\\n # look two states forward\\n pass\",\n \"def Shifted_Uion_H(E):\\n\\treturn 0.5 + (9.0/4.0)*E**2 + 55.55*E**4 + 4908.0*E**6 + 794200*E**8\",\n \"def get_desired_heading(self, h, p):\\n heading_vector = [self.alpha*h[0] + self.beta*p[0],\\n self.alpha*h[1] + self.beta*p[1]]\\n a = [(heading_vector[0]/linalg.norm(heading_vector)),\\n (heading_vector[1]/linalg.norm(heading_vector))]\\n return a\",\n \"def hit_stand_ev_diff(hand, shoe, dealer_hand, dealer_probabilities):\\n dealer_end_probs = dealer_probabilities[dealer_hand]\\n # maps a player's hand to his or her (hit_ev, stand_ev, max_ev)\\n player_payoffs = get_player_payoff(dealer_end_probs)\\n\\n ev = 0 # contains weighted ev\\n total = 0 # contains total weights, to normalized at the end\\n val, hard = hand\\n for card in shoe:\\n weight = shoe[card] # number of a card in the shoe\\n total += weight\\n if hard and 11 <= val <= 21:\\n new_hand = (val + card, hard)\\n if new_hand[0] > 21:\\n ev -= weight # default loss\\n else:\\n ev += weight * player_payoffs[new_hand][-1]\\n elif not hard and 12 <= val <= 21:\\n new_val = val + card\\n new_hard = False\\n if new_val > 21: # go back to hard value, take A = 1\\n new_val -= 10\\n new_hard = True\\n ev += weight * player_payoffs[(new_val, new_hard)][-1]\\n elif hard and 4 <= val <= 10:\\n new_val = val + card\\n new_hard = True\\n if card == 1: # go to soft value, take A = 11\\n new_val += 10\\n new_hard = False\\n ev += weight * player_payoffs[(new_val, new_hard)][-1]\\n else:\\n raise RuntimeError(\\\"Should not get here: \\\" + str(hand))\\n return (1.0 * ev / total) - player_payoffs[hand][1] # hit ev - stand ev\",\n \"def bias_prior(self):\",\n \"def khorne_slide(obs, berzerker_x, berzerker_y):\\n def environment_fits(obs, berzerker_x, berzerker_y):\\n \\\"\\\"\\\" environment fits constraints \\\"\\\"\\\"\\n # if prey has the ball\\n if obs[\\\"ball_owned_team\\\"] == 1:\\n prey_x = obs[\\\"right_team\\\"][obs[\\\"ball_owned_player\\\"]][0]\\n prey_y = obs[\\\"right_team\\\"][obs[\\\"ball_owned_player\\\"]][1]\\n # by x position, amount of berzerker's team players between prey and goal of berzerker's team\\n players_amount = 0\\n for i in range(1, len(obs[\\\"left_team\\\"])):\\n if obs[\\\"left_team\\\"][i][0] < prey_x:\\n players_amount += 1\\n prey_x_direction = obs[\\\"right_team_direction\\\"][obs[\\\"ball_owned_player\\\"]][0]\\n future_prey_x = prey_x + obs[\\\"right_team_direction\\\"][obs[\\\"ball_owned_player\\\"]][0]\\n future_prey_y = prey_y + obs[\\\"right_team_direction\\\"][obs[\\\"ball_owned_player\\\"]][1]\\n future_berzerker_x = berzerker_x + obs[\\\"left_team_direction\\\"][obs[\\\"active\\\"]][0]\\n future_berzerker_y = berzerker_y + obs[\\\"left_team_direction\\\"][obs[\\\"active\\\"]][1]\\n distance_to_prey = get_distance(berzerker_x, berzerker_y, prey_x, prey_y)\\n future_distance_to_prey = get_distance(future_berzerker_x, future_berzerker_y, future_prey_x, future_prey_y)\\n # if berzerker is not close to his own penalty zone\\n # and prey is beyond x position of too many players of berzerker's team\\n # and berzerker is close enough to prey\\n # and berzerker is running in direction of prey\\n if ((berzerker_x > -0.65 or abs(berzerker_y) > 0.3) and\\n players_amount <= 7 and\\n future_distance_to_prey < 0.015 and\\n distance_to_prey > future_distance_to_prey):\\n return True\\n return False\\n \\n def get_action(obs, berzerker_x, berzerker_y):\\n \\\"\\\"\\\" get action of this memory pattern \\\"\\\"\\\"\\n return Action.Slide\\n \\n return {\\\"environment_fits\\\": environment_fits, \\\"get_action\\\": get_action}\",\n \"def racetrack_filter(reversals, h):\\n\\n y = reversals\\n yprev = None\\n ix = []\\n for n, yn in enumerate(y):\\n if (n == 0) or (n == y.size-1):\\n yprev = yn\\n ix.append(n)\\n continue\\n dy = yn - yprev\\n if fabs(dy) > h / 2.:\\n yprev = yn - dy/fabs(dy) * h/2.\\n ix.append(n)\\n ix = np.array(ix, dtype=int)\\n return y[ix], ix\",\n \"def custom_score_7(game, player):\\n \\\"\\\"\\\"custom_score_7 heuristic function also aims towards weighted chances heuristics\\n \\\"\\\"\\\"\\n if game.is_loser(player):\\n return float(\\\"-inf\\\")\\n\\n if game.is_winner(player):\\n return float(\\\"inf\\\")\\n\\n length_my_player_moves = len(game.get_legal_moves(player)) #Calculate length of available moves for myPlayer\\n length_opp_payer_moves = len(game.get_legal_moves(game.get_opponent(player)))#Calculate length of available moves for the oppositePlayer\\n return float(1.5*length_my_player_moves*length_my_player_moves - length_opp_payer_moves*length_opp_payer_moves)\",\n \"def stokes_horizontal():\\n return np.array([1, 1, 0, 0])\",\n \"def shanten(self):\\n if not self.is_concealed:\\n return self.shanten_std\\n\\n return min(self.shanten_std, self.shanten_7, self.shanten_13)\",\n \"def determine_winner1(self): \\r\\n sorted_player_rank = self._rank()\\r\\n print(f\\\"sorted player rank: {sorted_player_rank}\\\")\\r\\n print(f\\\"winner is player {sorted_player_rank[0]}: with points {sorted_player_rank[0][1]}\\\")\",\n \"def p1(self):\\n return self.lerp(1)\",\n \"def custom_score(game, player):\\n # return penalize_corners_heuristic(game, player)\\n # return favor_run_away_heuristic(game, player)\\n return look_ahead_heuristic(game, player)\",\n \"def pre_phasing_r2(self, pre_phasing_r2):\\n\\n self._pre_phasing_r2 = pre_phasing_r2\",\n \"def prior(cube, ndim, nparams):\\n # construct prior from recovery file\\n counter = 0\\n if params2 is None:\\n return\\n for key in params2.keys():\\n nparams_tmp = int(params2[key]['nparams'])\\n for ii in range(nparams_tmp):\\n # sp = [name, prior type, x1, x2]\\n sp =\\\\\\n params2[key]['param'+str(ii+1)].split(',')\\n if sp[1][0] == 'U' and sp[2][:5]=='param' and sp[3][:5]=='param':\\n subtract1 = int(key[-1]) - int(sp[2][-1])\\n subtract2 = int(key[-1]) - int(sp[3][-1])\\n cube[counter] = GeneralPrior(cube[counter], 'U',\\n cube[counter-subtract1], cube[counter-subtract2])\\n elif sp[1][0] == 'U' and sp[2][:5]=='param':\\n subtract = int(key[-1]) - int(sp[2][-1])\\n cube[counter] = GeneralPrior(cube[counter], 'U',\\n cube[counter-subtract], float(sp[3]))\\n elif sp[1][0] == 'U' and sp[3][:5]=='param':\\n subtract = int(key[-1]) - int(sp[2][-1])\\n cube[counter] = GeneralPrior(cube[counter], 'U',\\n float(sp[2]), cube[counter - subtract])\\n else:\\n cube[counter] = GeneralPrior(cube[counter], sp[1], float(sp[2]),\\n float(sp[3]))\\n counter += 1\",\n \"def getPrevManha(self):\\n return self.getPrevision('Manhã')\",\n \"def predict_winner(self):\\n\\t\\tif len(self.players) > 1:\\n\\t\\t\\t# TODO: convert to using of max() function\\n\\t\\t\\twinner = self.players[0]\\n\\t\\t\\tfor player in self.players:\\n\\t\\t\\t\\tif player.wr > winner.wr:\\n\\t\\t\\t\\t\\twinner = player\\n\\t\\t\\treturn winner\\n\\t\\telse:\\n\\t\\t\\treturn None\",\n \"def get_h_score(self):\\n if self._h_score is None:\\n self._h_score = self._heuristic.compute(self)\\n return self._h_score\",\n \"def bmshj2018_hyperprior(\\n quality, metric=\\\"mse\\\", pretrained=False, progress=True, **kwargs\\n):\\n if metric not in (\\\"mse\\\", \\\"ms-ssim\\\"):\\n raise ValueError(f'Invalid metric \\\"{metric}\\\"')\\n\\n if quality < 1 or quality > 8:\\n raise ValueError(f'Invalid quality \\\"{quality}\\\", should be between (1, 8)')\\n\\n return _load_model(\\n \\\"bmshj2018-hyperprior\\\", metric, quality, pretrained, progress, **kwargs\\n )\",\n \"def test_set_hp(self):\\n s = State(substance=\\\"water\\\")\\n s.hp = Q_(1061602.391543017, \\\"J/kg\\\"), Q_(101325.0, \\\"Pa\\\")\\n # Pylance does not support NumPy ufuncs\\n assert np.isclose(s.T, Q_(373.1242958476843, \\\"K\\\")) # type: ignore\\n assert np.isclose(s.p, Q_(101325.0, \\\"Pa\\\")) # type: ignore\\n assert np.isclose(s.hp[0], Q_(1061602.391543017, \\\"J/kg\\\")) # type: ignore\\n assert np.isclose(s.hp[1], Q_(101325.0, \\\"Pa\\\")) # type: ignore\\n assert np.isclose(s.u, Q_(1013250, \\\"J/kg\\\")) # type: ignore\\n assert np.isclose(s.s, Q_(3028.9867985920914, \\\"J/(kg*K)\\\")) # type: ignore\\n assert np.isclose(s.v, Q_(0.4772010021515822, \\\"m**3/kg\\\")) # type: ignore\\n assert np.isclose(s.h, Q_(1061602.391543017, \\\"J/kg\\\")) # type: ignore\\n assert np.isclose(s.x, Q_(0.28475636946248034, \\\"dimensionless\\\")) # type: ignore\\n s.hp = Q_(3336406.139862406, \\\"J/kg\\\"), Q_(101325.0, \\\"Pa\\\")\\n assert np.isclose(s.T, Q_(700.9882316847855, \\\"K\\\")) # type: ignore\\n assert np.isclose(s.p, Q_(101325.0, \\\"Pa\\\")) # type: ignore\\n assert np.isclose(s.hp[0], Q_(3336406.139862406, \\\"J/kg\\\")) # type: ignore\\n assert np.isclose(s.hp[1], Q_(101325.0, \\\"Pa\\\")) # type: ignore\\n assert np.isclose(s.u, Q_(3013250, \\\"J/kg\\\")) # type: ignore\\n assert np.isclose(s.s, Q_(8623.283568815832, \\\"J/(kg*K)\\\")) # type: ignore\\n assert np.isclose(s.v, Q_(3.189303132125469, \\\"m**3/kg\\\")) # type: ignore\\n assert np.isclose(s.h, Q_(3336406.139862406, \\\"J/kg\\\")) # type: ignore\\n assert s.x is None\",\n \"def get_priority(self) -> str:\\n if self.health >= 75 and self.food >= 75 and self.water >= 75:\\n if min(self.food, self.water) == self.food:\\n return 'food'\\n else:\\n return 'water'\\n else:\\n if self.food >= 75 and self.water >= 75:\\n return 'monster'\\n else:\\n return 'food'\",\n \"def GetHelix(helix):\\r\\n pass\",\n \"def _thruster_hap(motions: Dict[str, float]) -> int:\\n surge, yaw, sway = motions[\\\"surge\\\"], motions[\\\"yaw\\\"], motions[\\\"sway\\\"]\\n\\n if surge and yaw:\\n\\n # If backwards, else forwards\\n if surge < CONTROL_NORM_IDLE:\\n value = -yaw\\n else:\\n value = surge\\n\\n elif surge:\\n value = surge\\n\\n elif sway:\\n value = sway\\n\\n elif yaw:\\n value = yaw\\n\\n else:\\n value = CONTROL_NORM_IDLE\\n\\n return Converter._to_thruster_value(value)\",\n \"def calc_winner(self):\\n pass\",\n \"def kHallLittlewoodP(self):\\n return kbounded_HallLittlewoodP(self)\",\n \"def model_prem(r):\\n\\n\\t#- normalised radius\\n\\tx = r / 6371000.0\\n\\n\\t#- march through the various depth levels -----------------------------------------------------\\n\\n\\t#- upper crust\\n\\tif (r >= 6356000.0):\\n\\t\\trho = 2.6\\n\\t\\tvpv = 5.8\\n\\t\\tvph = vpv\\n\\t\\tvsv = 3.2\\n\\t\\tvsh = vsv\\n\\t\\teta = 1.0\\n\\n\\t#- lower crust\\n\\telif (r >= 6346000.6) & (r < 6356000.0):\\n\\t\\trho = 2.9\\n\\t\\tvpv = 6.8\\n\\t\\tvph = vpv\\n\\t\\tvsv = 3.9\\n\\t\\tvsh = vsv\\n\\t\\teta = 1.0\\n\\n\\t#- LID\\n\\telif (r >= 6291000.0) & (r < 6346000.6):\\n\\t\\trho = 2.6910 + 0.6924 * x\\n\\t\\tvpv = 0.8317 + 7.2180 * x\\n\\t\\tvph = 3.5908 + 4.6172 * x\\n\\t\\tvsv = 5.8582 - 1.4678 * x\\n\\t\\tvsh = -1.0839 + 5.7176 * x\\n\\t\\teta = 3.3687 - 2.4778 * x\\n\\n\\t#- LVZ\\n\\telif (r >= 6151000.0) & (r < 6291000.0):\\n\\t\\trho = 2.6910 + 0.6924 * x\\n\\t\\tvpv = 0.8317 + 7.2180 * x\\n\\t\\tvph = 3.5908 + 4.6172 * x\\n\\t\\tvsv = 5.8582 - 1.4678 * x\\n\\t\\tvsh = -1.0839 + 5.7176 * x\\n\\t\\teta = 3.3687 - 2.4778 * x\\n\\n\\t#- Transition zone 1\\n\\telif (r >= 5971000.0) & (r < 6151000.0):\\n\\t\\trho = 7.1089 - 3.8045 * x\\n\\t\\tvpv = 20.3926 - 12.2569 * x\\n\\t\\tvph = vpv\\n\\t\\tvsv = 8.9496 - 4.4597 * x\\n\\t\\tvsh = vsv\\n\\t\\teta = 1.0\\n\\n\\t#- Transition zone 2\\n\\telif (r >= 5771000.0) & (r < 5971000.0):\\n\\t\\trho = 11.2494 - 8.0298 * x\\n\\t\\tvpv = 39.7027 - 32.6166 * x\\n\\t\\tvph = vpv\\n\\t\\tvsv = 22.3512 - 18.5856 * x\\n\\t\\tvsh = vsv\\n\\t\\teta = 1.0\\n\\n\\t#- Transition zone 3\\n\\telif (r >= 5701000.0) & (r < 5771000.0):\\n\\t\\trho = 5.3197 - 1.4836 * x\\n\\t\\tvpv = 19.0957 - 9.8672 * x\\n\\t\\tvph = vpv\\n\\t\\tvsv = 9.9839 - 4.9324 * x\\n\\t\\tvsh = vsv\\n\\t\\teta = 1.0\\n\\n\\t#- Lower mantle 1\\n\\telif (r >= 5600000.0) & (r < 5701000.0):\\n\\t\\trho = 7.9565 - 6.4761 * x + 5.5283 * x**2 - 3.0807 * x**3\\n\\t\\tvpv = 29.2766 - 23.6027 * x + 5.5242 * x**2 - 2.5514 * x**3\\n\\t\\tvph = vpv\\n\\t\\tvsv = 22.3459 - 17.2473 * x - 2.0834 * x**2 + 0.9783 * x**3\\n\\t\\tvsh = vsv\\n\\t\\teta = 1.0 \\n\\n\\t#- Lower mantle 2\\n\\telif (r >= 3630000.0) & (r < 5600000.0):\\n\\t\\trho = 7.9565 - 6.4761 * x + 5.5283 * x**2 - 3.0807 * x**3\\n\\t\\tvpv = 24.9520 - 40.4673 * x + 51.4832 * x**2 - 26.6419 * x**3\\n\\t\\tvph = vpv\\n\\t\\tvsv = 11.1671 - 13.7818 * x + 17.4575 * x**2 - 9.2777 * x**3\\n\\t\\tvsh = vsv\\n\\t\\teta = 1.0\\n\\n\\t#- Lower mantle 3\\n\\telif (r >= 3480000.0) & (r < 3630000.0):\\n\\t\\trho = 7.9565 - 6.4761 * x + 5.5283 * x**2 - 3.0807 * x**3\\n\\t\\tvpv = 15.3891 - 5.3181 * x + 5.5242 * x**2 - 2.5514 * x**3\\n\\t\\tvph = vpv\\n\\t\\tvsv = 6.9254 + 1.4672 * x - 2.0834 * x**2 + 0.9783 * x**3\\n\\t\\tvsh = vsv\\n\\t\\teta = 1.0\\n\\n\\t#- Outer core\\n\\telif (r >= 1221000.5) & (r < 3480000.0):\\n\\t\\trho = 12.5815 - 1.2638 * x - 3.6426 * x**2 - 5.5281 * x**3\\n\\t\\tvpv = 11.0487 - 4.0362 * x + 4.8023 * x**2 - 13.5732 * x**3\\n\\t\\tvph = vpv\\n\\t\\tvsv = 0.0\\n\\t\\tvsh = 0.0\\n\\t\\teta = 1.0\\n\\n\\t#- Inner Core\\n\\telif (r >= 0.0) & (r < 1221000.5):\\n\\t\\trho = 13.0885 - 8.8381 * x**2\\n\\t\\tvpv = 11.2622 - 6.3640 * x**2\\n\\t\\tvph = vpv\\n\\t\\tvsv = 3.6678 - 4.4475 * x**2\\n\\t\\tvsh = vsv\\n\\t\\teta = 1.0 \\n\\n\\t#- convert to elastic parameters --------------------------------------------------------------\\n\\n\\trho = 1000.0 * rho\\n\\tvpv = 1000.0 * vpv\\n\\tvph = 1000.0 * vph\\n\\tvsv = 1000.0 * vsv\\n\\tvsh = 1000.0 * vsh\\n\\n\\tA = rho * vph**2\\n\\tC = rho * vpv**2\\n\\tN = rho * vsh**2\\n\\tL = rho * vsv**2\\n\\tF = eta * (A - 2 * L)\\n\\n\\treturn rho, A, C, F, L, N\",\n \"def __call__(self, u, t):\\n return self.computerhs(u, self.inletf, t, self.a, self.alpha, self.msh, self.Minv, self.S)\",\n \"def hss(self):\\n return 2 * (self.table[0, 0] * self.table[1, 1] - self.table[0, 1] * self.table[1, 0]) / (\\n (self.table[0, 0] + self.table[0, 1]) * (self.table[0, 1] + self.table[1, 1]) +\\n (self.table[0, 0] + self.table[1, 0]) * (self.table[1, 0] + self.table[1, 1]))\",\n \"def get_fwhm_prior(self,freq,feed):\\n self.fitted_fwhm = {feed:np.poly1d(fit) for feed,fit in Data.average_beam_widths.items()}\\n\\n\\n if (self.fwhm_prior == 'ModelFWHMPrior'):\\n P0_priors={'sigx':{'mean':self.xfwhm(freq)/60./2.355,\\n 'width':self.xfwhm(freq)/60./2.355/1e2}}\\n elif (self.fwhm_prior == 'DataFWHMPrior'):\\n P0_priors={'sigx':{'mean':self.fitted_fwhm[feed](1./freq)/60./2.355,\\n 'width':self.fitted_fwhm[feed](1./freq)/60./2.355/1e2}}\\n else:\\n P0_priors = {}\\n\\n return P0_priors\",\n \"def other(player):\\n return 1 - player\",\n \"def other(player):\\n return 1 - player\",\n \"def other(player):\\n return 1 - player\",\n \"def hpa(self):\\n return HPAngle(gon2hp(self.gon_angle))\",\n \"def _get_minth(self):\\n return self.__minth\",\n \"def _get_minth(self):\\n return self.__minth\",\n \"def _get_minth(self):\\n return self.__minth\",\n \"def _get_minth(self):\\n return self.__minth\",\n \"def _get_minth(self):\\n return self.__minth\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.58937436","0.5873536","0.5848326","0.58077574","0.579661","0.570863","0.5638334","0.5638334","0.5638334","0.5638334","0.56263655","0.559631","0.55625165","0.55474657","0.55131316","0.5500774","0.54708487","0.54665077","0.54455525","0.5444476","0.5435669","0.5431408","0.54166716","0.5416457","0.5409957","0.5402142","0.53937525","0.5389804","0.5382655","0.5371392","0.53628856","0.5354957","0.5340758","0.533394","0.53257596","0.5323346","0.5310768","0.530845","0.52968484","0.5296391","0.5295901","0.5291023","0.52902913","0.5285416","0.52715194","0.5259223","0.5223905","0.5217739","0.52150947","0.5212068","0.5206148","0.5191842","0.5189338","0.51879185","0.5185643","0.5184003","0.51798975","0.51767766","0.5170359","0.51582587","0.5152168","0.51379794","0.5131235","0.5129917","0.5125868","0.5123272","0.5119525","0.5117297","0.5114103","0.51135826","0.5104114","0.5100372","0.5097135","0.50958955","0.5088927","0.5084417","0.5084121","0.50790054","0.5078008","0.50756407","0.5075496","0.50715566","0.50708723","0.5061252","0.5059788","0.5059506","0.5058137","0.5052964","0.50529456","0.50490445","0.50484264","0.50462675","0.504408","0.504408","0.504408","0.50427365","0.50427216","0.50427216","0.50427216","0.50427216","0.50427216"],"string":"[\n \"0.58937436\",\n \"0.5873536\",\n \"0.5848326\",\n \"0.58077574\",\n \"0.579661\",\n \"0.570863\",\n \"0.5638334\",\n \"0.5638334\",\n \"0.5638334\",\n \"0.5638334\",\n \"0.56263655\",\n \"0.559631\",\n \"0.55625165\",\n \"0.55474657\",\n \"0.55131316\",\n \"0.5500774\",\n \"0.54708487\",\n \"0.54665077\",\n \"0.54455525\",\n \"0.5444476\",\n \"0.5435669\",\n \"0.5431408\",\n \"0.54166716\",\n \"0.5416457\",\n \"0.5409957\",\n \"0.5402142\",\n \"0.53937525\",\n \"0.5389804\",\n \"0.5382655\",\n \"0.5371392\",\n \"0.53628856\",\n \"0.5354957\",\n \"0.5340758\",\n \"0.533394\",\n \"0.53257596\",\n \"0.5323346\",\n \"0.5310768\",\n \"0.530845\",\n \"0.52968484\",\n \"0.5296391\",\n \"0.5295901\",\n \"0.5291023\",\n \"0.52902913\",\n \"0.5285416\",\n \"0.52715194\",\n \"0.5259223\",\n \"0.5223905\",\n \"0.5217739\",\n \"0.52150947\",\n \"0.5212068\",\n \"0.5206148\",\n \"0.5191842\",\n \"0.5189338\",\n \"0.51879185\",\n \"0.5185643\",\n \"0.5184003\",\n \"0.51798975\",\n \"0.51767766\",\n \"0.5170359\",\n \"0.51582587\",\n \"0.5152168\",\n \"0.51379794\",\n \"0.5131235\",\n \"0.5129917\",\n \"0.5125868\",\n \"0.5123272\",\n \"0.5119525\",\n \"0.5117297\",\n \"0.5114103\",\n \"0.51135826\",\n \"0.5104114\",\n \"0.5100372\",\n \"0.5097135\",\n \"0.50958955\",\n \"0.5088927\",\n \"0.5084417\",\n \"0.5084121\",\n \"0.50790054\",\n \"0.5078008\",\n \"0.50756407\",\n \"0.5075496\",\n \"0.50715566\",\n \"0.50708723\",\n \"0.5061252\",\n \"0.5059788\",\n \"0.5059506\",\n \"0.5058137\",\n \"0.5052964\",\n \"0.50529456\",\n \"0.50490445\",\n \"0.50484264\",\n \"0.50462675\",\n \"0.504408\",\n \"0.504408\",\n \"0.504408\",\n \"0.50427365\",\n \"0.50427216\",\n \"0.50427216\",\n \"0.50427216\",\n \"0.50427216\",\n \"0.50427216\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":14393,"cells":{"query":{"kind":"string","value":"Returns N samples from the prior."},"document":{"kind":"string","value":"def sample_from_prior(self, n_samples):\n\n lamda = np.abs(self.rng.standard_cauchy(size=n_samples))\n\n p0 = np.log(np.abs(self.rng.randn() * lamda * self.scale))\n return p0[:, np.newaxis]"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def sample_from_prior(self, n_samples):\n pass","def sample_from_prior(self, n_samples):\n\n p0 = self.min + self.rng.rand(n_samples) * (self.max - self.min)\n return p0[:, np.newaxis]","def sample_from_prior(self, n_samples):\n\n p0 = self.rng.normal(loc=self.mean, scale=self.sigma, size=n_samples)\n return p0[:, np.newaxis]","def sample_from_prior(self, n_samples):\n\n p0 = self.rng.lognormal(mean=self.mean, sigma=self.sigma, size=n_samples)\n return p0[:, np.newaxis]","def sample_prior(size):\n return torch.randn(size)","def sample_from_prior(self):\n raise NotImplementedError","def sample_parameters_prior(self, n_samples, random_seed=None):\n\n if random_seed is not None:\n np.random.seed(random_seed)\n samples = []\n samples.append(self.var_noise.sample_from_prior(n_samples))\n samples.append(self.mean.sample_from_prior(n_samples))\n samples.append(self.kernel.sample_parameters(n_samples))\n\n return np.concatenate(samples, 1)","def sample(self, n_samples=1):\n\n\t\tif isinstance(n_samples, int):\n\t\t\tn_samples = (n_samples,)\n\t\tbeta_sample = self.beta_prior.sample(n_samples)\n\t\tgamma_sample = self.gamma_prior.sample(n_samples)\n\t\tp = torch.stack((beta_sample, gamma_sample)).T\n\t\tif n_samples == (1,):\n\t\t\tp = p[0]\n\t\tp = p.numpy()\n\t\treturn p","def sample_from_prior(self, *args, **kwargs):\n pass","def forward_sample(self, n):\n return self.flow.sample(n)","def prior_sample(self):\n pass","def generate_samples(self, n_samples):","def generate_samples(self, n_samples):","def sample(self, n):\n # Est probs from priority weights\n summed = sum(self.weight) + self.eps\n self.probs = [w / summed for w in self.priority]\n\n # Wieghted sample\n return np.random.choice(self.memory, size=n, p=self.probs).tolist()","def prior_samples(self, nsamples, coords=None):\n S = self.eval_S(self.kappa, self.sigma_f)\n if coords is None:\n coords = slice(self.mesh.num_vertices()) # take all coords\n weights = np.random.normal(scale=np.sqrt(S),\n size=(nsamples,) + S.shape) # shape (s, l)\n prior = np.einsum('sl,nl->sn', weights, self.eigenfunctions[coords])\n\n return prior","def samples(self):\n return self._values[:self.nsamples]","def generate_samples(self, no=10):\n observations = []\n state_sequence = []\n initial_state = np.random.choice(\n self.latent_variable_markov_chain.states,\n p=self.prior_probabilities)\n state_sequence.append(initial_state)\n observations.append(self.observation_from_state(initial_state))\n current_state = initial_state\n for i in range(2, no):\n next_state = self.latent_variable_markov_chain.next_state(current_state)\n state_sequence.append(next_state)\n observations.append(self.observation_from_state(next_state))\n current_state = next_state\n return observations, state_sequence","def sample(self, n, include=True):\n return [self(t / n) for t in range(n + int(include))]","def prior(n=10):\r\n p = []\r\n trials = 0\r\n acc = 0\r\n while acc < n:\r\n trials += 1\r\n r = np.random.rand(2) * np.array([4, 2]) + np.array([-2, -1])\r\n # print(\"r: \", r)\r\n if r[1] + r[0] >= -1 and r[1] - r[0] >= -1:\r\n p.append(r)\r\n acc += 1\r\n # print(\"trials: \", trials, \", acc: \", acc)\r\n return p","def pull_reads(self, n, prng): \n if not self.sampling:\n self.convert_to_array()\n index = prng.randint(0, self.total, size=n)\n index = np.sort(index)\n return self.reads[index,:]","def sample(self, n=1):\n raise NotImplementedError","def get_training_n(self, n: Optional[int] = None, random: bool = True) -> Tuple[torch.Tensor, torch.Tensor]:\n return self.__sample(self.train_data, self.train_los, n, random)","def sample(self, n):\n raise NotImplementedError","def _sample(self, n=1):\n return [self[i] for i in np.random.choice(self.length, n, replace=False)]","def get_n_samples(self):\n return {op.rx.get_n_samples() for op in self.ops}","def sample(self, n_samples):\n\n z = sample_prior((n_samples,) + self.flow.z_shape)\n ldj = torch.zeros(z.size(0))\n\n z, ldj = self.flow (z, ldj, reverse=True)\n z, ldj = self.logit_normalize(z, ldj, reverse=True)\n\n return z","def Sample(n=6):\n t = [random.normalvariate(0.0, 1.0) for i in range(n)]\n t.sort()\n return t","def posterior_sampler(self, nsamples, seed=0, verbose=True):\n\n import random\n\n random.seed(seed)\n sample = self.get_chain()[-self.get_tune:]\n sample = sample.reshape(-1, sample.shape[-1])\n sample = random.choices(sample, k=nsamples)\n\n return sample","def generate_samples(self, n_samples=100):\n \t\t\n\t\t#how many times should ancestral sampling be run\n\t\t#n_samples\n prior_samples=[]\n for i in range(0,n_samples):\n prior_sample = self.prior.get_samples(\n n_latent_nodes=self.n_latent_nodes,\n n_gibbs_sampling_steps=100, \n sampling_mode=\"gibbs_ancestral\")\n prior_sample = torch.cat(prior_sample)\n prior_samples.append(prior_sample)\n prior_samples=torch.stack(prior_samples)\n # prior_samples = tf.slice(prior_samples, [0, 0], [num_samples, -1])\n output_activations = self.decoder.decode(prior_samples)\n output_activations = output_activations+self._train_bias\n output_distribution = Bernoulli(logit=output_activations)\n output=torch.sigmoid(output_distribution.logits)\n # output_activations[0] = output_activations[0] + self.train_bias\n # output_dist = FactorialBernoulliUtil(output_activations)\n # output_samples = tf.nn.sigmoid(output_dist.logit_mu)\n # print(\"--- \",\"end VAE::generate_samples()\")\n return output","def get_random_samplers(self, n):\n if not self.has_samplers():\n self.draw_samplers()\n \n def get_shuffledcopy(x):\n x_ = x.copy()\n np.random.shuffle(x_)\n return x_\n \n return get_shuffledcopy(self.samplers)[:n]","def sample_gp_prior(self, x_list, n_samp, full_cov=True):\n mu, cov = self.get_gp_prior_mu_cov(x_list, full_cov)\n return self.get_normal_samples(mu, cov, n_samp, full_cov)","def get_posterior_sample(self):\n total_tries = self.prior_success + self.prior_failure\n prob_success = self.prior_success / total_tries\n # np.random.binomial采样出来的是二项分布的均值, 即正面朝上的次数,所以要除以N\n boot_sample = np.random.binomial(total_tries, prob_success) / total_tries\n return boot_sample","def sample(self, n_samples=1):\n\n\t\tsamples = np.random.random((n_samples, self.__ndims))*self.__range + self.__low\n\t\treturn samples","def get_samples(self, nsamples):\n sample_ids = np.random.choice(self.embed.shape[0], nsamples, replace=False)\n return self.embed[sample_ids]","def sample_predict(self, X, n_samples):\n W_samples = self.W_prior.sample(n_samples) ##size: [n_samples, n_in, n_out]\n if self.use_bias:\n bs = W_samples[:, :1, :]\n Ws = W_samples[:, 1:, :]\n if self.scaled_variance:\n Ws = Ws / np.sqrt(self.n_in)\n return torch.matmul(X, Ws) + bs\n else:\n if self.scaled_variance:\n W_samples = W_samples / np.sqrt(self.n_in)\n return torch.matmul(X, W_samples)","def pickTopicFromPrior(self):\r\n return self.priorTopicDistr.sampleIndex()","def fetch_samples(self):\n return torch.cat(self.samples,dim=0).reshape(-1,self.parameters.numel())","def _uniform_number(self, N=None):\n num_frames = self.data.shape[0]\n if not N:\n N = int(np.floor(\n Constants.DEFAULT_SAMPLE_PERCENTAGE * num_frames\n ))\n k = int(np.floor(num_frames / N))\n if k == 0:\n k = 1\n elif N > num_frames:\n k = 1\n else:\n assert type(N) is IntType, 'N is not an integer: {}'.format(N)\n k = int(np.floor(num_frames / N))\n\n # ensure that at most N samples are taken if N does not divide nicely\n return self.data[::k][:N], self.labels[::k][:N]","def sample(self, n_samples, sample_seed):\n self.seed_samples(sample_seed)\n eps = torch.randn(self.batch_size, n_samples, self.Y_dim)\n samples = eps*torch.exp(0.5*self.logvar.unsqueeze(1)) + self.mu.unsqueeze(1)\n samples = self.unwhiten_back(samples)\n samples = samples.data.cpu().numpy()\n return samples","def prior_sample(self, bn):\n x = np.zeros(3)\n\n # first joint prob\n random_choice = np.random.choice(bn[0], 1, bn[0].all(), bn[0])\n x[0] = random_choice[0]\n\n # Second Joint Prob\n if x[0] == 0.1:\n random_choice = np.random.choice(bn[1][0], 1, bn[1][0].all(), bn[1][0])\n x[1] = random_choice\n elif x[0] == 0.9:\n random_choice = np.random.choice(bn[1][1], 1, bn[1][1].all(), bn[1][1])\n x[1] = random_choice\n\n # Third Joint Prob\n if random_choice[0] == 0.8 or random_choice == 0.1:\n random_choice = np.random.choice(bn[2][0], 1, bn[2][0].all(), bn[2][0])\n x[2] = random_choice\n else:\n random_choice = np.random.choice(bn[2][1], 1, bn[2][1].all(), bn[2][1])\n x[2] = random_choice\n return x","def prob1(n):\n#raise NotImplementedError(\"Problem 1 Incomplete\")\n if n == 0 :\n raise ValueError(\"Sampling 0 points is not defined.\")\n total = 0\n for i in xrange(n) :\n if np.random.normal() > 3 :\n total += 1\n return float(total)/n","def sample (self, n):\n y = self.bins\n x = np.r_[0, self.values.cumsum ()] / self.sum\n # interpolate inverse CDF\n out = np.interp (np.random.random (n), x, y)\n if n == 1:\n return out[0]\n else:\n return out.reshape ((n,))","def get_n(self, n):\n \n return [self.get_values() for _ in range(n)]","def getNrSamples(self): \r\n return self.numSamples","def sample(self, n):\n idx = np.random.randint(0, len(self.memory), size=n)\n return [self.memory[i] for i in idx]","def pull_n_samples(dset, n):\n return list(dset[i] for i in random.sample(range(len(dset)), n))","def lnprior(self):\n \n return","def samples(self) -> int:\n return self._samples","def _construct_sample_from_prior(self):\n z_sym = T.matrix()\n x_sym = T.matrix()\n irs = self.ir_steps\n oputs = [self.obs_transform(self.s0)]\n oputs.extend([self.obs_transform(self.si[i]) for i in range(irs)])\n _, hi_zmuv = self._construct_zmuv_samples(x_sym, 1)\n sample_func = theano.function(inputs=[z_sym, x_sym], outputs=oputs, \\\n givens={ self.z: z_sym, \\\n self.x_in: T.zeros_like(x_sym), \\\n self.x_out: T.zeros_like(x_sym), \\\n self.hi_zmuv: hi_zmuv }, \\\n updates=self.scan_updates)\n def prior_sampler(samp_count):\n x_samps = to_fX( np.zeros((samp_count, self.obs_dim)) )\n old_switch = self.train_switch.get_value(borrow=False)\n # set model to generation mode\n self.set_train_switch(switch_val=0.0)\n z_samps = to_fX( npr.randn(samp_count, self.z_dim) )\n model_samps = sample_func(z_samps, x_samps)\n # set model back to either training or generation mode\n self.set_train_switch(switch_val=old_switch)\n return model_samps\n return prior_sampler","def generate_samples(self):\n self.analytic_probability()","def prior_distribution(self):\n out = self.model.forward(self.inducing_points)\n return MultivariateNormal(out.mean, out.lazy_covariance_matrix.evaluate_kernel())","def sample(self, size=1):\n samples = scipy.stats.bernoulli.rvs(self.p, size=size)\n if size == 1:\n return samples[0]\n return samples","def n_remaining_samples(self):\n return -1","def n_remaining_samples(self):\n return -1","def n_remaining_samples(self):\n return -1","def sample(self, nsamples):\n return self.dist.sample(nsamples)","def __resample(self):\n p_resample = []\n w_max = max(self.weights)\n index = int(round(random.uniform(0, self.n - 1)))\n beta = 0\n for i in range(self.n):\n beta += random.uniform(0, 2 * w_max)\n while self.weights[index] < beta:\n beta -= self.weights[index]\n index = (index + 1) % self.n\n p_resample.append(self.particles[index, :])\n return np.array(p_resample)","def burnin_by_first_n(samples, N, step_number=False, logger_level=\"debug\"):\n _samples = copy.deepcopy(samples)\n n_samples = {key: N for key in _samples.keys()}\n if step_number:\n n_samples = {\n key: item + N if item is not None else N for key, item in\n _number_of_negative_steps(_samples, logger_level=logger_level).items()\n }\n getattr(logger, logger_level)(\n \"Removing the first {} as burnin\".format(\n \", \".join(\n [\"{} samples from {}\".format(val, key) for key, val in n_samples.items()]\n )\n )\n )\n return _samples.discard_samples(n_samples)","def take_samples(self, num_samples: int) -> List:\n if num_samples > len(self.samples):\n return random.sample(self.samples, len(self.samples))\n return random.sample(self.samples, num_samples)","def n_samples(self) -> int: # pragma: no cover\n return self.samples.shape[0]","def sample_count(self):","def sample_users(self, n):\n return torch.rand((n, self.num_topics), generator=self.generator) * 2 - 1","def sample_size_with_prior(self, prior=\"small\"):\n if prior == \"small\":\n prior_sizes = list(range(self._min_size, self._max_size))\n elif prior == \"big\":\n prior_sizes = list(range(self._min_size+1, self._max_size+1))\n return random.choice(prior_sizes)","def sample(self,\n n_samples: Optional[int],\n temperatures: Optional[List[float]] = None) -> T.Tensor:\n samples = self.levels[-1].sample(\n self.top_prior_shape, n_samples=n_samples, temperatures=temperatures)\n for level in self.levels[-2::-1]:\n samples = level.sample(context=samples)\n return samples","def prior_predictive(self):\n cfg = self.config\n n = cfg['batch_size'] * cfg['q/n_samples']\n n_samples = cfg['q/n_samples']\n with util.get_or_create_scope('model', reuse=True):\n h_prior = tf.cast(self.p_h_L.sample(n), cfg['dtype'])\n h_prior = tf.reshape(\n h_prior, [cfg['q/n_samples'], cfg['batch_size'], -1])\n h = [None] * cfg['p/n_layers']\n h[cfg['p/n_layers'] - 1] = h_prior\n for n in range(cfg['p/n_layers'] - 1, 0, -1):\n p_h_n = self.build_stochastic_layer(n, h_above=h[n])\n h[n - 1] = tf.cast(p_h_n.sample(), cfg['dtype'])\n return self.likelihood(h[0])","def sample(self, num_samples = 1):\n\n X = np.empty(num_samples)\n for k in range(len(X)):\n\n # starting\n n = 1\n a = 1\n Un = self.UG.sample()\n a = a * Un\n\n # iterate over\n while a >= np.exp(-self.rate):\n n = n + 1\n Un = self.UG.sample()\n a = a * Un\n\n X[k] = n - 1\n\n return X","def sample_pagerank(corpus, damping_factor, n):\n probabilities = dict()\n samples = []\n\n # Random first sample\n page = random.choice(list(corpus.keys()))\n samples.append(page)\n \n # Remaining samples after first\n for i in range(n-1):\n p = transition_model(corpus, page, damping_factor)\n page = random.choices(list(p.keys()), weights=list(p.values()), k=1)[0]\n samples.append(page)\n\n # Count\n for p in corpus.keys():\n probabilities[p] = samples.count(p) / n\n\n return probabilities","def get_prev_samples(self):\n return self.y_p","def main(n_samples):\n uso = usolib.uso.uar(N)\n lst = [usolib.randomfacet.randomfacet_sample(uso, N) for i in range(n_samples)]\n return sum(lst) / float(n_samples)","def sample_data(self, N, pmin=8., pmax=12.):\n ps = [matrix(ra.uniform(pmin, pmax, (self.n, 1))) for k in range(N)]\n xs = [self.compute_demand(p) for p in ps]\n return zip(xs,ps)","def simplex_sampler(self, n):\n\n # edge case: n = 1\n if n == 1:\n return np.array([self.initial_potential])\n\n values = [np.random.uniform() for i in range(n-1)]\n values.extend([0, 1])\n values.sort()\n values_arr = np.array(values)\n\n xs = values_arr[1:] - values_arr[:-1]\n\n # return in decresing order of magnitude, to use for higher levels\n xs = self.initial_potential*np.sort(xs)\n xs = xs[::-1]\n return xs","def _number_of_samples(self):\n return len(self._raw_data.samples)","def get_next_sample(self):","def get_number_samples(self):\n return self.samples.shape[0]","def sample_low_rank(self, n_samples, mu, logvar, F):\n #F = torch.unsqueeze(F, dim=1).repeat(1, n_samples, 1, 1) # [self.batch_size, n_samples, self.Y_dim, self.rank]\n F = F.repeat(n_samples, 1, 1) # [self.batch_size*n_samples, self.Y_dim, self.rank]\n mu = mu.repeat(n_samples, 1) # [self.batch_size*n_samples, self.Y_dim]\n logvar = logvar.repeat(n_samples, 1) # [self.batch_size*n_samples, self.Y_dim]\n eps_low_rank = torch.randn(self.batch_size*n_samples, self.rank, 1)\n eps_diag = torch.randn(self.batch_size*n_samples, self.Y_dim)\n half_var = torch.exp(0.5*logvar) # [self.batch_size*n_samples, self.Y_dim]\n samples = torch.bmm(F, eps_low_rank).squeeze() + mu + half_var*eps_diag\n samples = samples.reshape(n_samples, self.batch_size, self.Y_dim)\n samples = samples.transpose(0, 1)\n samples = self.unwhiten_back(samples)\n samples = samples.data.cpu().numpy()\n return samples","def get_log_prior(samples):\n prior_distribution = tfp.distributions.Normal(loc=0., scale=1.)\n log_prior = tf.reduce_sum(prior_distribution.log_prob(samples), axis=1)\n return log_prior","def sample_from_log_prob(A, n):\n A_cum = log_cum_sum(A)\n C_pos = [ -c for c in reversed(A_cum)]\n sel = np.log(np.random.random(n))\n pos = [len(A) - np.searchsorted(C_pos,-r) for r in sel]\n return pos","def priors_sample(self):\n \n theta_star = np.zeros(self.n_params)\n\n for i in xrange(self.n_params): \n np.random.seed() \n theta_star[i] = self.param_obj.prior()[i].rvs(size=1)[0]\n\n return theta_star","def sample_pagerank(corpus, damping_factor, n):\n all_pages = []\n first_sample_prob = random.randint(0, len(corpus) - 1)\n distribution_count = dict()\n\n for u in corpus:\n distribution_count[u] = 0\n all_pages.append(u)\n\n sample = all_pages[first_sample_prob]\n for i in range(n - 1): # n - 1 because first sample was already calculated\n selection_bucket = dict()\n selection_start = 0.0\n sample_distribution = transition_model(corpus, sample, damping_factor)\n sample_prob = random.random()\n for u in sample_distribution:\n floor = selection_start\n ceiling = selection_start + sample_distribution[u]\n selection_start = ceiling\n selection_bucket[u] = [floor, ceiling]\n for u in selection_bucket:\n v = selection_bucket[u]\n if v[0] < sample_prob < v[1]:\n sample = u\n distribution_count[u] += 1\n distribution = dict()\n for u in distribution_count:\n distribution[u] = float(distribution_count[u]) / n\n\n return distribution","def get_num_samples(self):\n return self._num_samples","def sample(self, n):\n unparameterized = [\n v for v in self._weights.keys() if v not in self._weights_p.keys()\n ]\n if len(unparameterized) > 0:\n raise ValueError(\n \"The following variables must be numerically parameterized before sampling: {0}\".format(\n unparameterized\n )\n )\n mu, cov = self.parameterized_mean_cov()\n cov = np.asarray(cov).astype(np.float64)\n mu = np.asarray(mu).astype(np.float64)\n return np.random.multivariate_normal(mu.ravel(), cov, size=n)","def generate_samples(self,n_samples=100):\n rnd_input=torch.randn((n_samples,self._reparam_nodes[1]))\n zeta=rnd_input \n # rnd_input=torch.where((rnd_input>0.5),torch.ones(rnd_input.size()),torch.zeros(rnd_input.size()))\n # print(rnd_input) \n # output, mu, logvar, zeta=self.forward(rnd_input)\n # mu = self._reparam_layers['mu'](rnd_input)\n # logvar = self._reparam_layers['var'](rnd_input)\n # zeta = self.reparameterize(mu, logvar)\n output = self.decoder.decode(zeta)\n return output","def process_custom_prior(prior) -> Tuple[Distribution, int, bool]:\n\n check_prior_methods(prior)\n\n check_prior_batch_behavior(prior)\n\n prior, is_prior_numpy = maybe_wrap_prior_to_pytorch(prior)\n\n parameter_dim = prior.sample().numel()\n\n return prior, parameter_dim, is_prior_numpy","def __call__(self, *args):\n r = np.random.rand(*args)\n if type(r) is float:\n samples = self.values[(r < self.p).nonzero()[0][0]]\n elif type(r) is np.ndarray:\n samples = np.array(\n [self.values[np.nonzero(x < self.p)[0][0]] \n for x in r.flat]).reshape(r.shape)\n return samples","def get_num_samples(self) -> int:\n # must be implemented in subclass\n raise NotImplementedError","def sample(self, n_samples: int) -> torch.Tensor:\n return self.dist.sample((n_samples,))","def ts_rewards_sample(self):\n exp_rewards_list = [np.random.beta(a, b) for a, b in zip(self.alpha_params, self.beta_params)]\n return np.array(exp_rewards_list)","def prediction_samples(self) -> np.ndarray:\n return self.prediction_samples","def sample(self, n_samples: int, random_state: Optional[int] = None) -> np.ndarray:\n\n if not isinstance(self.prob, np.ndarray):\n samples = stats.bernoulli.rvs(\n p=self.prob, size=n_samples, random_state=random_state\n )\n else:\n n_classes = len(self.prob) # type: ignore\n samples = np.zeros((n_samples, n_classes))\n\n for cls in range(n_classes):\n samples[:, cls] = stats.bernoulli.rvs(p=self.prob[cls], size=n_samples, random_state=random_state) # type: ignore\n\n return samples","def generate_synth_data(n):","def prior_sample_parameter(self, parameter):\n pass","def get_samples_per_signal(self):\n return np.array([self.samples_in_file(chn) for chn in range(self.signals_in_file)])","def sample(self, num):\n state, action, reward, next_state, not_terminal = [], [], [], [], []\n for exp in self.experiences.sample(num):\n state.append(exp.state)\n action.append(exp.action)\n reward.append(exp.reward)\n next_state.append(exp.next_state)\n not_terminal.append(exp.not_terminal)\n\n return np.asarray(state), np.asarray(action), np.asarray(reward), \\\n np.asarray(next_state), np.asarray(not_terminal)","def samples(self):\n pass","def sample_observations(self, n_samples, z):\n lat_dim = self.transition.dim_x\n x = []\n for i in range(self.time_steps):\n z_t = tf.slice(z, [0, i * lat_dim], [-1, lat_dim]) \n x.append(self.emmision.sample(z_t))\n return tf.concat(x, axis=1)","def sample(self, n_samples):\n with torch.no_grad():\n z = torch.randn((n_samples, self.z_dim))\n samples = self.decoder(z)\n im_size = int(np.sqrt(self.input_dim))\n samples = samples.view(-1, 1, im_size, im_size)\n\n return samples","def sample_pagerank(corpus, damping_factor, n):\n # All pages in entire corpus\n all_pages = [key for key in corpus.keys()]\n\n # Dictionary which will map each page to the number of generated samples that were the page\n counts = dict()\n for page in all_pages:\n counts[page] = 0\n\n # Choosing first sample randomly and updating counts dict\n first_sample = random.choice(all_pages)\n counts[first_sample] += 1\n\n for i in range(1, n):\n # If we are on the second sample, do the calculation based on the first\n if i == 1:\n prev_sample = first_sample\n\n distributions = transition_model(corpus, prev_sample, damping_factor)\n\n keys = [key for key in distributions.keys()]\n values = [distributions[key] for key in distributions.keys()]\n\n # Using random.choices to select an option given a list of weights (distribution)\n current_sample = random.choices(keys, weights=values, k=1)[0]\n\n # Updating counts dict given the current sample\n counts[current_sample] += 1\n\n prev_sample = current_sample\n\n # Modifying counts dict to represent the proportion of samples that corresponded to that page instead of the raw amount\n for page in all_pages:\n counts[page] /= n\n\n return counts","def take(n, seq):\n return itertools.islice(seq, n)","def get_samples(self) -> McmcPtResult:","def deterministic_sample(choices, n_to_sample, p): # pylint: disable=invalid-name\n\n sample_counts = np.ceil(n_to_sample * p).astype(int)\n\n n_to_remove = np.sum(sample_counts) - n_to_sample\n\n if n_to_remove == 0:\n return choices[counts_to_vector(sample_counts)]\n\n non_zero_mask = sample_counts > 0\n\n removal_indices = np.floor(np.linspace(0.0,\n np.sum(non_zero_mask),\n n_to_remove,\n endpoint=False)).astype(int)\n\n tmp = sample_counts[non_zero_mask]\n tmp[removal_indices] = tmp[removal_indices] - 1\n\n sample_counts[non_zero_mask] = tmp\n\n assert np.sum(sample_counts) == n_to_sample\n\n samples = choices[counts_to_vector(sample_counts)]\n\n return samples"],"string":"[\n \"def sample_from_prior(self, n_samples):\\n pass\",\n \"def sample_from_prior(self, n_samples):\\n\\n p0 = self.min + self.rng.rand(n_samples) * (self.max - self.min)\\n return p0[:, np.newaxis]\",\n \"def sample_from_prior(self, n_samples):\\n\\n p0 = self.rng.normal(loc=self.mean, scale=self.sigma, size=n_samples)\\n return p0[:, np.newaxis]\",\n \"def sample_from_prior(self, n_samples):\\n\\n p0 = self.rng.lognormal(mean=self.mean, sigma=self.sigma, size=n_samples)\\n return p0[:, np.newaxis]\",\n \"def sample_prior(size):\\n return torch.randn(size)\",\n \"def sample_from_prior(self):\\n raise NotImplementedError\",\n \"def sample_parameters_prior(self, n_samples, random_seed=None):\\n\\n if random_seed is not None:\\n np.random.seed(random_seed)\\n samples = []\\n samples.append(self.var_noise.sample_from_prior(n_samples))\\n samples.append(self.mean.sample_from_prior(n_samples))\\n samples.append(self.kernel.sample_parameters(n_samples))\\n\\n return np.concatenate(samples, 1)\",\n \"def sample(self, n_samples=1):\\n\\n\\t\\tif isinstance(n_samples, int):\\n\\t\\t\\tn_samples = (n_samples,)\\n\\t\\tbeta_sample = self.beta_prior.sample(n_samples)\\n\\t\\tgamma_sample = self.gamma_prior.sample(n_samples)\\n\\t\\tp = torch.stack((beta_sample, gamma_sample)).T\\n\\t\\tif n_samples == (1,):\\n\\t\\t\\tp = p[0]\\n\\t\\tp = p.numpy()\\n\\t\\treturn p\",\n \"def sample_from_prior(self, *args, **kwargs):\\n pass\",\n \"def forward_sample(self, n):\\n return self.flow.sample(n)\",\n \"def prior_sample(self):\\n pass\",\n \"def generate_samples(self, n_samples):\",\n \"def generate_samples(self, n_samples):\",\n \"def sample(self, n):\\n # Est probs from priority weights\\n summed = sum(self.weight) + self.eps\\n self.probs = [w / summed for w in self.priority]\\n\\n # Wieghted sample\\n return np.random.choice(self.memory, size=n, p=self.probs).tolist()\",\n \"def prior_samples(self, nsamples, coords=None):\\n S = self.eval_S(self.kappa, self.sigma_f)\\n if coords is None:\\n coords = slice(self.mesh.num_vertices()) # take all coords\\n weights = np.random.normal(scale=np.sqrt(S),\\n size=(nsamples,) + S.shape) # shape (s, l)\\n prior = np.einsum('sl,nl->sn', weights, self.eigenfunctions[coords])\\n\\n return prior\",\n \"def samples(self):\\n return self._values[:self.nsamples]\",\n \"def generate_samples(self, no=10):\\n observations = []\\n state_sequence = []\\n initial_state = np.random.choice(\\n self.latent_variable_markov_chain.states,\\n p=self.prior_probabilities)\\n state_sequence.append(initial_state)\\n observations.append(self.observation_from_state(initial_state))\\n current_state = initial_state\\n for i in range(2, no):\\n next_state = self.latent_variable_markov_chain.next_state(current_state)\\n state_sequence.append(next_state)\\n observations.append(self.observation_from_state(next_state))\\n current_state = next_state\\n return observations, state_sequence\",\n \"def sample(self, n, include=True):\\n return [self(t / n) for t in range(n + int(include))]\",\n \"def prior(n=10):\\r\\n p = []\\r\\n trials = 0\\r\\n acc = 0\\r\\n while acc < n:\\r\\n trials += 1\\r\\n r = np.random.rand(2) * np.array([4, 2]) + np.array([-2, -1])\\r\\n # print(\\\"r: \\\", r)\\r\\n if r[1] + r[0] >= -1 and r[1] - r[0] >= -1:\\r\\n p.append(r)\\r\\n acc += 1\\r\\n # print(\\\"trials: \\\", trials, \\\", acc: \\\", acc)\\r\\n return p\",\n \"def pull_reads(self, n, prng): \\n if not self.sampling:\\n self.convert_to_array()\\n index = prng.randint(0, self.total, size=n)\\n index = np.sort(index)\\n return self.reads[index,:]\",\n \"def sample(self, n=1):\\n raise NotImplementedError\",\n \"def get_training_n(self, n: Optional[int] = None, random: bool = True) -> Tuple[torch.Tensor, torch.Tensor]:\\n return self.__sample(self.train_data, self.train_los, n, random)\",\n \"def sample(self, n):\\n raise NotImplementedError\",\n \"def _sample(self, n=1):\\n return [self[i] for i in np.random.choice(self.length, n, replace=False)]\",\n \"def get_n_samples(self):\\n return {op.rx.get_n_samples() for op in self.ops}\",\n \"def sample(self, n_samples):\\n\\n z = sample_prior((n_samples,) + self.flow.z_shape)\\n ldj = torch.zeros(z.size(0))\\n\\n z, ldj = self.flow (z, ldj, reverse=True)\\n z, ldj = self.logit_normalize(z, ldj, reverse=True)\\n\\n return z\",\n \"def Sample(n=6):\\n t = [random.normalvariate(0.0, 1.0) for i in range(n)]\\n t.sort()\\n return t\",\n \"def posterior_sampler(self, nsamples, seed=0, verbose=True):\\n\\n import random\\n\\n random.seed(seed)\\n sample = self.get_chain()[-self.get_tune:]\\n sample = sample.reshape(-1, sample.shape[-1])\\n sample = random.choices(sample, k=nsamples)\\n\\n return sample\",\n \"def generate_samples(self, n_samples=100):\\n \\t\\t\\n\\t\\t#how many times should ancestral sampling be run\\n\\t\\t#n_samples\\n prior_samples=[]\\n for i in range(0,n_samples):\\n prior_sample = self.prior.get_samples(\\n n_latent_nodes=self.n_latent_nodes,\\n n_gibbs_sampling_steps=100, \\n sampling_mode=\\\"gibbs_ancestral\\\")\\n prior_sample = torch.cat(prior_sample)\\n prior_samples.append(prior_sample)\\n prior_samples=torch.stack(prior_samples)\\n # prior_samples = tf.slice(prior_samples, [0, 0], [num_samples, -1])\\n output_activations = self.decoder.decode(prior_samples)\\n output_activations = output_activations+self._train_bias\\n output_distribution = Bernoulli(logit=output_activations)\\n output=torch.sigmoid(output_distribution.logits)\\n # output_activations[0] = output_activations[0] + self.train_bias\\n # output_dist = FactorialBernoulliUtil(output_activations)\\n # output_samples = tf.nn.sigmoid(output_dist.logit_mu)\\n # print(\\\"--- \\\",\\\"end VAE::generate_samples()\\\")\\n return output\",\n \"def get_random_samplers(self, n):\\n if not self.has_samplers():\\n self.draw_samplers()\\n \\n def get_shuffledcopy(x):\\n x_ = x.copy()\\n np.random.shuffle(x_)\\n return x_\\n \\n return get_shuffledcopy(self.samplers)[:n]\",\n \"def sample_gp_prior(self, x_list, n_samp, full_cov=True):\\n mu, cov = self.get_gp_prior_mu_cov(x_list, full_cov)\\n return self.get_normal_samples(mu, cov, n_samp, full_cov)\",\n \"def get_posterior_sample(self):\\n total_tries = self.prior_success + self.prior_failure\\n prob_success = self.prior_success / total_tries\\n # np.random.binomial采样出来的是二项分布的均值, 即正面朝上的次数,所以要除以N\\n boot_sample = np.random.binomial(total_tries, prob_success) / total_tries\\n return boot_sample\",\n \"def sample(self, n_samples=1):\\n\\n\\t\\tsamples = np.random.random((n_samples, self.__ndims))*self.__range + self.__low\\n\\t\\treturn samples\",\n \"def get_samples(self, nsamples):\\n sample_ids = np.random.choice(self.embed.shape[0], nsamples, replace=False)\\n return self.embed[sample_ids]\",\n \"def sample_predict(self, X, n_samples):\\n W_samples = self.W_prior.sample(n_samples) ##size: [n_samples, n_in, n_out]\\n if self.use_bias:\\n bs = W_samples[:, :1, :]\\n Ws = W_samples[:, 1:, :]\\n if self.scaled_variance:\\n Ws = Ws / np.sqrt(self.n_in)\\n return torch.matmul(X, Ws) + bs\\n else:\\n if self.scaled_variance:\\n W_samples = W_samples / np.sqrt(self.n_in)\\n return torch.matmul(X, W_samples)\",\n \"def pickTopicFromPrior(self):\\r\\n return self.priorTopicDistr.sampleIndex()\",\n \"def fetch_samples(self):\\n return torch.cat(self.samples,dim=0).reshape(-1,self.parameters.numel())\",\n \"def _uniform_number(self, N=None):\\n num_frames = self.data.shape[0]\\n if not N:\\n N = int(np.floor(\\n Constants.DEFAULT_SAMPLE_PERCENTAGE * num_frames\\n ))\\n k = int(np.floor(num_frames / N))\\n if k == 0:\\n k = 1\\n elif N > num_frames:\\n k = 1\\n else:\\n assert type(N) is IntType, 'N is not an integer: {}'.format(N)\\n k = int(np.floor(num_frames / N))\\n\\n # ensure that at most N samples are taken if N does not divide nicely\\n return self.data[::k][:N], self.labels[::k][:N]\",\n \"def sample(self, n_samples, sample_seed):\\n self.seed_samples(sample_seed)\\n eps = torch.randn(self.batch_size, n_samples, self.Y_dim)\\n samples = eps*torch.exp(0.5*self.logvar.unsqueeze(1)) + self.mu.unsqueeze(1)\\n samples = self.unwhiten_back(samples)\\n samples = samples.data.cpu().numpy()\\n return samples\",\n \"def prior_sample(self, bn):\\n x = np.zeros(3)\\n\\n # first joint prob\\n random_choice = np.random.choice(bn[0], 1, bn[0].all(), bn[0])\\n x[0] = random_choice[0]\\n\\n # Second Joint Prob\\n if x[0] == 0.1:\\n random_choice = np.random.choice(bn[1][0], 1, bn[1][0].all(), bn[1][0])\\n x[1] = random_choice\\n elif x[0] == 0.9:\\n random_choice = np.random.choice(bn[1][1], 1, bn[1][1].all(), bn[1][1])\\n x[1] = random_choice\\n\\n # Third Joint Prob\\n if random_choice[0] == 0.8 or random_choice == 0.1:\\n random_choice = np.random.choice(bn[2][0], 1, bn[2][0].all(), bn[2][0])\\n x[2] = random_choice\\n else:\\n random_choice = np.random.choice(bn[2][1], 1, bn[2][1].all(), bn[2][1])\\n x[2] = random_choice\\n return x\",\n \"def prob1(n):\\n#raise NotImplementedError(\\\"Problem 1 Incomplete\\\")\\n if n == 0 :\\n raise ValueError(\\\"Sampling 0 points is not defined.\\\")\\n total = 0\\n for i in xrange(n) :\\n if np.random.normal() > 3 :\\n total += 1\\n return float(total)/n\",\n \"def sample (self, n):\\n y = self.bins\\n x = np.r_[0, self.values.cumsum ()] / self.sum\\n # interpolate inverse CDF\\n out = np.interp (np.random.random (n), x, y)\\n if n == 1:\\n return out[0]\\n else:\\n return out.reshape ((n,))\",\n \"def get_n(self, n):\\n \\n return [self.get_values() for _ in range(n)]\",\n \"def getNrSamples(self): \\r\\n return self.numSamples\",\n \"def sample(self, n):\\n idx = np.random.randint(0, len(self.memory), size=n)\\n return [self.memory[i] for i in idx]\",\n \"def pull_n_samples(dset, n):\\n return list(dset[i] for i in random.sample(range(len(dset)), n))\",\n \"def lnprior(self):\\n \\n return\",\n \"def samples(self) -> int:\\n return self._samples\",\n \"def _construct_sample_from_prior(self):\\n z_sym = T.matrix()\\n x_sym = T.matrix()\\n irs = self.ir_steps\\n oputs = [self.obs_transform(self.s0)]\\n oputs.extend([self.obs_transform(self.si[i]) for i in range(irs)])\\n _, hi_zmuv = self._construct_zmuv_samples(x_sym, 1)\\n sample_func = theano.function(inputs=[z_sym, x_sym], outputs=oputs, \\\\\\n givens={ self.z: z_sym, \\\\\\n self.x_in: T.zeros_like(x_sym), \\\\\\n self.x_out: T.zeros_like(x_sym), \\\\\\n self.hi_zmuv: hi_zmuv }, \\\\\\n updates=self.scan_updates)\\n def prior_sampler(samp_count):\\n x_samps = to_fX( np.zeros((samp_count, self.obs_dim)) )\\n old_switch = self.train_switch.get_value(borrow=False)\\n # set model to generation mode\\n self.set_train_switch(switch_val=0.0)\\n z_samps = to_fX( npr.randn(samp_count, self.z_dim) )\\n model_samps = sample_func(z_samps, x_samps)\\n # set model back to either training or generation mode\\n self.set_train_switch(switch_val=old_switch)\\n return model_samps\\n return prior_sampler\",\n \"def generate_samples(self):\\n self.analytic_probability()\",\n \"def prior_distribution(self):\\n out = self.model.forward(self.inducing_points)\\n return MultivariateNormal(out.mean, out.lazy_covariance_matrix.evaluate_kernel())\",\n \"def sample(self, size=1):\\n samples = scipy.stats.bernoulli.rvs(self.p, size=size)\\n if size == 1:\\n return samples[0]\\n return samples\",\n \"def n_remaining_samples(self):\\n return -1\",\n \"def n_remaining_samples(self):\\n return -1\",\n \"def n_remaining_samples(self):\\n return -1\",\n \"def sample(self, nsamples):\\n return self.dist.sample(nsamples)\",\n \"def __resample(self):\\n p_resample = []\\n w_max = max(self.weights)\\n index = int(round(random.uniform(0, self.n - 1)))\\n beta = 0\\n for i in range(self.n):\\n beta += random.uniform(0, 2 * w_max)\\n while self.weights[index] < beta:\\n beta -= self.weights[index]\\n index = (index + 1) % self.n\\n p_resample.append(self.particles[index, :])\\n return np.array(p_resample)\",\n \"def burnin_by_first_n(samples, N, step_number=False, logger_level=\\\"debug\\\"):\\n _samples = copy.deepcopy(samples)\\n n_samples = {key: N for key in _samples.keys()}\\n if step_number:\\n n_samples = {\\n key: item + N if item is not None else N for key, item in\\n _number_of_negative_steps(_samples, logger_level=logger_level).items()\\n }\\n getattr(logger, logger_level)(\\n \\\"Removing the first {} as burnin\\\".format(\\n \\\", \\\".join(\\n [\\\"{} samples from {}\\\".format(val, key) for key, val in n_samples.items()]\\n )\\n )\\n )\\n return _samples.discard_samples(n_samples)\",\n \"def take_samples(self, num_samples: int) -> List:\\n if num_samples > len(self.samples):\\n return random.sample(self.samples, len(self.samples))\\n return random.sample(self.samples, num_samples)\",\n \"def n_samples(self) -> int: # pragma: no cover\\n return self.samples.shape[0]\",\n \"def sample_count(self):\",\n \"def sample_users(self, n):\\n return torch.rand((n, self.num_topics), generator=self.generator) * 2 - 1\",\n \"def sample_size_with_prior(self, prior=\\\"small\\\"):\\n if prior == \\\"small\\\":\\n prior_sizes = list(range(self._min_size, self._max_size))\\n elif prior == \\\"big\\\":\\n prior_sizes = list(range(self._min_size+1, self._max_size+1))\\n return random.choice(prior_sizes)\",\n \"def sample(self,\\n n_samples: Optional[int],\\n temperatures: Optional[List[float]] = None) -> T.Tensor:\\n samples = self.levels[-1].sample(\\n self.top_prior_shape, n_samples=n_samples, temperatures=temperatures)\\n for level in self.levels[-2::-1]:\\n samples = level.sample(context=samples)\\n return samples\",\n \"def prior_predictive(self):\\n cfg = self.config\\n n = cfg['batch_size'] * cfg['q/n_samples']\\n n_samples = cfg['q/n_samples']\\n with util.get_or_create_scope('model', reuse=True):\\n h_prior = tf.cast(self.p_h_L.sample(n), cfg['dtype'])\\n h_prior = tf.reshape(\\n h_prior, [cfg['q/n_samples'], cfg['batch_size'], -1])\\n h = [None] * cfg['p/n_layers']\\n h[cfg['p/n_layers'] - 1] = h_prior\\n for n in range(cfg['p/n_layers'] - 1, 0, -1):\\n p_h_n = self.build_stochastic_layer(n, h_above=h[n])\\n h[n - 1] = tf.cast(p_h_n.sample(), cfg['dtype'])\\n return self.likelihood(h[0])\",\n \"def sample(self, num_samples = 1):\\n\\n X = np.empty(num_samples)\\n for k in range(len(X)):\\n\\n # starting\\n n = 1\\n a = 1\\n Un = self.UG.sample()\\n a = a * Un\\n\\n # iterate over\\n while a >= np.exp(-self.rate):\\n n = n + 1\\n Un = self.UG.sample()\\n a = a * Un\\n\\n X[k] = n - 1\\n\\n return X\",\n \"def sample_pagerank(corpus, damping_factor, n):\\n probabilities = dict()\\n samples = []\\n\\n # Random first sample\\n page = random.choice(list(corpus.keys()))\\n samples.append(page)\\n \\n # Remaining samples after first\\n for i in range(n-1):\\n p = transition_model(corpus, page, damping_factor)\\n page = random.choices(list(p.keys()), weights=list(p.values()), k=1)[0]\\n samples.append(page)\\n\\n # Count\\n for p in corpus.keys():\\n probabilities[p] = samples.count(p) / n\\n\\n return probabilities\",\n \"def get_prev_samples(self):\\n return self.y_p\",\n \"def main(n_samples):\\n uso = usolib.uso.uar(N)\\n lst = [usolib.randomfacet.randomfacet_sample(uso, N) for i in range(n_samples)]\\n return sum(lst) / float(n_samples)\",\n \"def sample_data(self, N, pmin=8., pmax=12.):\\n ps = [matrix(ra.uniform(pmin, pmax, (self.n, 1))) for k in range(N)]\\n xs = [self.compute_demand(p) for p in ps]\\n return zip(xs,ps)\",\n \"def simplex_sampler(self, n):\\n\\n # edge case: n = 1\\n if n == 1:\\n return np.array([self.initial_potential])\\n\\n values = [np.random.uniform() for i in range(n-1)]\\n values.extend([0, 1])\\n values.sort()\\n values_arr = np.array(values)\\n\\n xs = values_arr[1:] - values_arr[:-1]\\n\\n # return in decresing order of magnitude, to use for higher levels\\n xs = self.initial_potential*np.sort(xs)\\n xs = xs[::-1]\\n return xs\",\n \"def _number_of_samples(self):\\n return len(self._raw_data.samples)\",\n \"def get_next_sample(self):\",\n \"def get_number_samples(self):\\n return self.samples.shape[0]\",\n \"def sample_low_rank(self, n_samples, mu, logvar, F):\\n #F = torch.unsqueeze(F, dim=1).repeat(1, n_samples, 1, 1) # [self.batch_size, n_samples, self.Y_dim, self.rank]\\n F = F.repeat(n_samples, 1, 1) # [self.batch_size*n_samples, self.Y_dim, self.rank]\\n mu = mu.repeat(n_samples, 1) # [self.batch_size*n_samples, self.Y_dim]\\n logvar = logvar.repeat(n_samples, 1) # [self.batch_size*n_samples, self.Y_dim]\\n eps_low_rank = torch.randn(self.batch_size*n_samples, self.rank, 1)\\n eps_diag = torch.randn(self.batch_size*n_samples, self.Y_dim)\\n half_var = torch.exp(0.5*logvar) # [self.batch_size*n_samples, self.Y_dim]\\n samples = torch.bmm(F, eps_low_rank).squeeze() + mu + half_var*eps_diag\\n samples = samples.reshape(n_samples, self.batch_size, self.Y_dim)\\n samples = samples.transpose(0, 1)\\n samples = self.unwhiten_back(samples)\\n samples = samples.data.cpu().numpy()\\n return samples\",\n \"def get_log_prior(samples):\\n prior_distribution = tfp.distributions.Normal(loc=0., scale=1.)\\n log_prior = tf.reduce_sum(prior_distribution.log_prob(samples), axis=1)\\n return log_prior\",\n \"def sample_from_log_prob(A, n):\\n A_cum = log_cum_sum(A)\\n C_pos = [ -c for c in reversed(A_cum)]\\n sel = np.log(np.random.random(n))\\n pos = [len(A) - np.searchsorted(C_pos,-r) for r in sel]\\n return pos\",\n \"def priors_sample(self):\\n \\n theta_star = np.zeros(self.n_params)\\n\\n for i in xrange(self.n_params): \\n np.random.seed() \\n theta_star[i] = self.param_obj.prior()[i].rvs(size=1)[0]\\n\\n return theta_star\",\n \"def sample_pagerank(corpus, damping_factor, n):\\n all_pages = []\\n first_sample_prob = random.randint(0, len(corpus) - 1)\\n distribution_count = dict()\\n\\n for u in corpus:\\n distribution_count[u] = 0\\n all_pages.append(u)\\n\\n sample = all_pages[first_sample_prob]\\n for i in range(n - 1): # n - 1 because first sample was already calculated\\n selection_bucket = dict()\\n selection_start = 0.0\\n sample_distribution = transition_model(corpus, sample, damping_factor)\\n sample_prob = random.random()\\n for u in sample_distribution:\\n floor = selection_start\\n ceiling = selection_start + sample_distribution[u]\\n selection_start = ceiling\\n selection_bucket[u] = [floor, ceiling]\\n for u in selection_bucket:\\n v = selection_bucket[u]\\n if v[0] < sample_prob < v[1]:\\n sample = u\\n distribution_count[u] += 1\\n distribution = dict()\\n for u in distribution_count:\\n distribution[u] = float(distribution_count[u]) / n\\n\\n return distribution\",\n \"def get_num_samples(self):\\n return self._num_samples\",\n \"def sample(self, n):\\n unparameterized = [\\n v for v in self._weights.keys() if v not in self._weights_p.keys()\\n ]\\n if len(unparameterized) > 0:\\n raise ValueError(\\n \\\"The following variables must be numerically parameterized before sampling: {0}\\\".format(\\n unparameterized\\n )\\n )\\n mu, cov = self.parameterized_mean_cov()\\n cov = np.asarray(cov).astype(np.float64)\\n mu = np.asarray(mu).astype(np.float64)\\n return np.random.multivariate_normal(mu.ravel(), cov, size=n)\",\n \"def generate_samples(self,n_samples=100):\\n rnd_input=torch.randn((n_samples,self._reparam_nodes[1]))\\n zeta=rnd_input \\n # rnd_input=torch.where((rnd_input>0.5),torch.ones(rnd_input.size()),torch.zeros(rnd_input.size()))\\n # print(rnd_input) \\n # output, mu, logvar, zeta=self.forward(rnd_input)\\n # mu = self._reparam_layers['mu'](rnd_input)\\n # logvar = self._reparam_layers['var'](rnd_input)\\n # zeta = self.reparameterize(mu, logvar)\\n output = self.decoder.decode(zeta)\\n return output\",\n \"def process_custom_prior(prior) -> Tuple[Distribution, int, bool]:\\n\\n check_prior_methods(prior)\\n\\n check_prior_batch_behavior(prior)\\n\\n prior, is_prior_numpy = maybe_wrap_prior_to_pytorch(prior)\\n\\n parameter_dim = prior.sample().numel()\\n\\n return prior, parameter_dim, is_prior_numpy\",\n \"def __call__(self, *args):\\n r = np.random.rand(*args)\\n if type(r) is float:\\n samples = self.values[(r < self.p).nonzero()[0][0]]\\n elif type(r) is np.ndarray:\\n samples = np.array(\\n [self.values[np.nonzero(x < self.p)[0][0]] \\n for x in r.flat]).reshape(r.shape)\\n return samples\",\n \"def get_num_samples(self) -> int:\\n # must be implemented in subclass\\n raise NotImplementedError\",\n \"def sample(self, n_samples: int) -> torch.Tensor:\\n return self.dist.sample((n_samples,))\",\n \"def ts_rewards_sample(self):\\n exp_rewards_list = [np.random.beta(a, b) for a, b in zip(self.alpha_params, self.beta_params)]\\n return np.array(exp_rewards_list)\",\n \"def prediction_samples(self) -> np.ndarray:\\n return self.prediction_samples\",\n \"def sample(self, n_samples: int, random_state: Optional[int] = None) -> np.ndarray:\\n\\n if not isinstance(self.prob, np.ndarray):\\n samples = stats.bernoulli.rvs(\\n p=self.prob, size=n_samples, random_state=random_state\\n )\\n else:\\n n_classes = len(self.prob) # type: ignore\\n samples = np.zeros((n_samples, n_classes))\\n\\n for cls in range(n_classes):\\n samples[:, cls] = stats.bernoulli.rvs(p=self.prob[cls], size=n_samples, random_state=random_state) # type: ignore\\n\\n return samples\",\n \"def generate_synth_data(n):\",\n \"def prior_sample_parameter(self, parameter):\\n pass\",\n \"def get_samples_per_signal(self):\\n return np.array([self.samples_in_file(chn) for chn in range(self.signals_in_file)])\",\n \"def sample(self, num):\\n state, action, reward, next_state, not_terminal = [], [], [], [], []\\n for exp in self.experiences.sample(num):\\n state.append(exp.state)\\n action.append(exp.action)\\n reward.append(exp.reward)\\n next_state.append(exp.next_state)\\n not_terminal.append(exp.not_terminal)\\n\\n return np.asarray(state), np.asarray(action), np.asarray(reward), \\\\\\n np.asarray(next_state), np.asarray(not_terminal)\",\n \"def samples(self):\\n pass\",\n \"def sample_observations(self, n_samples, z):\\n lat_dim = self.transition.dim_x\\n x = []\\n for i in range(self.time_steps):\\n z_t = tf.slice(z, [0, i * lat_dim], [-1, lat_dim]) \\n x.append(self.emmision.sample(z_t))\\n return tf.concat(x, axis=1)\",\n \"def sample(self, n_samples):\\n with torch.no_grad():\\n z = torch.randn((n_samples, self.z_dim))\\n samples = self.decoder(z)\\n im_size = int(np.sqrt(self.input_dim))\\n samples = samples.view(-1, 1, im_size, im_size)\\n\\n return samples\",\n \"def sample_pagerank(corpus, damping_factor, n):\\n # All pages in entire corpus\\n all_pages = [key for key in corpus.keys()]\\n\\n # Dictionary which will map each page to the number of generated samples that were the page\\n counts = dict()\\n for page in all_pages:\\n counts[page] = 0\\n\\n # Choosing first sample randomly and updating counts dict\\n first_sample = random.choice(all_pages)\\n counts[first_sample] += 1\\n\\n for i in range(1, n):\\n # If we are on the second sample, do the calculation based on the first\\n if i == 1:\\n prev_sample = first_sample\\n\\n distributions = transition_model(corpus, prev_sample, damping_factor)\\n\\n keys = [key for key in distributions.keys()]\\n values = [distributions[key] for key in distributions.keys()]\\n\\n # Using random.choices to select an option given a list of weights (distribution)\\n current_sample = random.choices(keys, weights=values, k=1)[0]\\n\\n # Updating counts dict given the current sample\\n counts[current_sample] += 1\\n\\n prev_sample = current_sample\\n\\n # Modifying counts dict to represent the proportion of samples that corresponded to that page instead of the raw amount\\n for page in all_pages:\\n counts[page] /= n\\n\\n return counts\",\n \"def take(n, seq):\\n return itertools.islice(seq, n)\",\n \"def get_samples(self) -> McmcPtResult:\",\n \"def deterministic_sample(choices, n_to_sample, p): # pylint: disable=invalid-name\\n\\n sample_counts = np.ceil(n_to_sample * p).astype(int)\\n\\n n_to_remove = np.sum(sample_counts) - n_to_sample\\n\\n if n_to_remove == 0:\\n return choices[counts_to_vector(sample_counts)]\\n\\n non_zero_mask = sample_counts > 0\\n\\n removal_indices = np.floor(np.linspace(0.0,\\n np.sum(non_zero_mask),\\n n_to_remove,\\n endpoint=False)).astype(int)\\n\\n tmp = sample_counts[non_zero_mask]\\n tmp[removal_indices] = tmp[removal_indices] - 1\\n\\n sample_counts[non_zero_mask] = tmp\\n\\n assert np.sum(sample_counts) == n_to_sample\\n\\n samples = choices[counts_to_vector(sample_counts)]\\n\\n return samples\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7954478","0.7308122","0.7254096","0.71898454","0.6979248","0.6961805","0.67606914","0.6745526","0.6690407","0.6622515","0.6562736","0.65446556","0.65446556","0.6425273","0.6419413","0.6395372","0.63613814","0.6323354","0.63091654","0.6305061","0.6287302","0.6239015","0.6221694","0.6207399","0.6197996","0.6184932","0.6165855","0.6153619","0.6153358","0.6140597","0.61257386","0.6109061","0.6078866","0.60286486","0.60100406","0.6004092","0.59905887","0.5970731","0.59330416","0.5920617","0.59058857","0.58966595","0.589106","0.5890634","0.58760834","0.58759904","0.5870663","0.5850432","0.58440155","0.58292997","0.58253235","0.5818603","0.5816317","0.5816317","0.5816317","0.58079565","0.58007497","0.5788163","0.57879496","0.57798713","0.5776126","0.5774034","0.577337","0.5767064","0.5765437","0.5762816","0.5749947","0.5746394","0.5736112","0.5728723","0.57278186","0.5722879","0.5718899","0.5717263","0.5683421","0.56821394","0.56575644","0.5654737","0.5651699","0.5648227","0.56469744","0.5641609","0.5639136","0.5636752","0.56361675","0.56329775","0.56267655","0.5622955","0.5622405","0.5618009","0.5616757","0.5616295","0.5613543","0.56135225","0.5599461","0.55893856","0.55878794","0.55790335","0.5576369","0.5575169"],"string":"[\n \"0.7954478\",\n \"0.7308122\",\n \"0.7254096\",\n \"0.71898454\",\n \"0.6979248\",\n \"0.6961805\",\n \"0.67606914\",\n \"0.6745526\",\n \"0.6690407\",\n \"0.6622515\",\n \"0.6562736\",\n \"0.65446556\",\n \"0.65446556\",\n \"0.6425273\",\n \"0.6419413\",\n \"0.6395372\",\n \"0.63613814\",\n \"0.6323354\",\n \"0.63091654\",\n \"0.6305061\",\n \"0.6287302\",\n \"0.6239015\",\n \"0.6221694\",\n \"0.6207399\",\n \"0.6197996\",\n \"0.6184932\",\n \"0.6165855\",\n \"0.6153619\",\n \"0.6153358\",\n \"0.6140597\",\n \"0.61257386\",\n \"0.6109061\",\n \"0.6078866\",\n \"0.60286486\",\n \"0.60100406\",\n \"0.6004092\",\n \"0.59905887\",\n \"0.5970731\",\n \"0.59330416\",\n \"0.5920617\",\n \"0.59058857\",\n \"0.58966595\",\n \"0.589106\",\n \"0.5890634\",\n \"0.58760834\",\n \"0.58759904\",\n \"0.5870663\",\n \"0.5850432\",\n \"0.58440155\",\n \"0.58292997\",\n \"0.58253235\",\n \"0.5818603\",\n \"0.5816317\",\n \"0.5816317\",\n \"0.5816317\",\n \"0.58079565\",\n \"0.58007497\",\n \"0.5788163\",\n \"0.57879496\",\n \"0.57798713\",\n \"0.5776126\",\n \"0.5774034\",\n \"0.577337\",\n \"0.5767064\",\n \"0.5765437\",\n \"0.5762816\",\n \"0.5749947\",\n \"0.5746394\",\n \"0.5736112\",\n \"0.5728723\",\n \"0.57278186\",\n \"0.5722879\",\n \"0.5718899\",\n \"0.5717263\",\n \"0.5683421\",\n \"0.56821394\",\n \"0.56575644\",\n \"0.5654737\",\n \"0.5651699\",\n \"0.5648227\",\n \"0.56469744\",\n \"0.5641609\",\n \"0.5639136\",\n \"0.5636752\",\n \"0.56361675\",\n \"0.56329775\",\n \"0.56267655\",\n \"0.5622955\",\n \"0.5622405\",\n \"0.5618009\",\n \"0.5616757\",\n \"0.5616295\",\n \"0.5613543\",\n \"0.56135225\",\n \"0.5599461\",\n \"0.55893856\",\n \"0.55878794\",\n \"0.55790335\",\n \"0.5576369\",\n \"0.5575169\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7128596"},"document_rank":{"kind":"string","value":"4"}}},{"rowIdx":14394,"cells":{"query":{"kind":"string","value":"Computes the gradient of the prior with respect to theta."},"document":{"kind":"string","value":"def gradient(self, theta):\n a = -(6 * self.scale ** 2)\n b = 3 * self.scale ** 2 + np.exp(2 * theta)\n b *= np.log(3 * self.scale ** 2 * np.exp(-2 * theta) + 1)\n return a / b"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def gradient(self, theta):\n pass","def gradient(self, theta):\n pass","def gradient(self, theta):\n return (1 / (self.sigma * np.sqrt(2 * np.pi))) * (\n -theta / (self.sigma ** 2) * np.exp(-(theta ** 2) / (2 * self.sigma ** 2))\n )","def gradient(theta, X, y, learning_rate):\n m = len(y)\n\n theta = theta.reshape((-1,1))\n grad = np.zeros(theta.shape)\n h = sigmoid(np.dot(X, theta)) \n \n grad = np.dot((h-y).T, X)/m\n grad = grad.T\n grad[1:] += (learning_rate/m)*theta[1:]\n return grad","def gradient_function(theta, X, y):\n\n grad = None\n #######################################################################\n # TODO: #\n # Compute the gradient for a particular choice of theta. #\n # Compute the partial derivatives and set grad to the partial #\n # derivatives of the cost w.r.t. each parameter in theta #\n # #\n #######################################################################\n \n theta = theta[:, np.newaxis]\n \n thetatrans = theta.T\n Xtrans = X.T\n \n MulThetaX = np.dot(thetatrans, Xtrans)\n \n h = sigmoid(MulThetaX)\n \n grad = (y - h) * Xtrans\n\n #######################################################################\n # END OF YOUR CODE #\n #######################################################################\n return grad","def compute_gradient(theta, X, y):\n m = X.shape[0]\n grad_theta = np.dot(X.transpose(), (np.dot(X, theta) - y)) / m\n #print theta, grad_theta, objective_function(theta, X, y)\n return grad_theta","def gradient(self, theta):\n return np.zeros([theta.shape[0]])","def gradientFunction(theta, X, y):\n y = y[:, 0]\n m = y.shape # number of training samples\n grad = X.T.dot(sigmoid(theta.dot(X.T))-1*y)\n grad /= m\n return grad","def gradient(theta, x, y):\n m = len(y)\n n = len(theta)\n z = theta.dot(x.T)\n grad = np.zeros(n)\n for i in xrange(m):\n grad += (g(z[i]) - y[i]) * x[i]\n return 1. / m * grad","def gradient(theta, X, y, Lambda=0.0):\n m = X.shape[0] # number of samples\n\n h = hypothesis(theta, X)\n\n if Lambda:\n g_0 = (1/m)*(X.T@(h - y))[0]\n g_1 = (1/m)*(X.T@(h - y))[1:] + (Lambda/m)*theta[1:] # skip theta-0\n \n return np.append(g_0, g_1)\n else:\n return (1/m)*(X.T@(h - y))","def gradient(x, y, theta):\n if x.ndim == 1:\n x = x[:, np.newaxis]\n if y.ndim == 2 and y.shape[1] == 1:\n y = y.flatten()\n if theta.ndim == 2 and theta.shape[1] == 1:\n theta = theta.flatten()\n\n if (x.size == 0 or y.size == 0 or theta.size == 0\n or x.ndim != 2 or y.ndim != 1 or theta.ndim != 1\n or x.shape[0] != y.shape[0] or x.shape[1] + 1 != theta.shape[0]):\n return None\n\n x_padded = np.c_[np.ones(x.shape[0]), x]\n\n return x_padded.T.dot(x_padded.dot(theta) - y) / y.shape[0]","def calc_grad(X, Y, theta):\n m, n = X.shape\n\n margins = Y * X.dot(theta)\n probs = 1. / (1 + np.exp(margins))\n grad = -(1./m) * (X.T.dot(probs * Y))\n\n return grad","def gradient_descent(X, y, theta, alpha, total_iterations, hypothesis):\n len_theta = len(theta)\n m = len(y)\n one_over_m = (1.0 / float(m))\n\n for _ in range(0, total_iterations):\n temp_theta = numpy.zeros(len_theta)\n\n X_by_theta_minus_y = numpy.subtract(hypothesis(numpy.matrix(theta), X), y)\n\n for j in range(0, len_theta):\n jth_column_of_X = X[:,j]\n derivative_j = one_over_m * numpy.multiply(X_by_theta_minus_y, jth_column_of_X).sum()\n temp_theta[j] = theta[j] - alpha*derivative_j\n\n theta = temp_theta\n\n return numpy.matrix(theta)","def gradient_descent(self, X, theta, Y, m):\n\n Z = X.dot(theta)\n H = Predict.g(Z)\n gradient = np.dot(X.T, (H - Y)) / m\n return self.alpha * gradient","def gradientFunctionReg(theta, X, y, Lambda):\n m = len(y) # number of training examples\n grad = np.zeros(theta.shape[0])\n theta = np.transpose(theta)\n sum_1 = 0\n X = X.values\n y = y.values\n #calcuate the theta_0 \n# ====================== YOUR CODE HERE ======================\n# Instructions: Compute the gradient of a particular choice of theta.\n# Compute the partial derivatives and set grad to the partial\n# derivatives of the cost w.r.t. each parameter in theta\n for i in range(theta.shape[0]):\n if i == 0:\n for j in range(m):\n sum_1 += (sigmoid(np.dot(X[j],theta)) - y[j]) * X[j,i]\n else:\n for j in range(m):\n sum_1 += (sigmoid(np.dot(X[j],theta)) - y[j]) * X[j,i] + Lambda*theta[i]\n grad[i] = sum_1/m\n sum_1 = 0\n\n# =============================================================\n\n return grad","def gradientFunctionReg(theta, X, y, Lambda): \n y = np.squeeze(y)\n m = y.shape # number of training samples\n grad = X.T.dot(sigmoid(theta.dot(X.T))-1*y)\n grad[1:] = grad[1:] + Lambda*theta[1:]\n grad /= m\n\n return grad","def logit_cost_grad(self, theta, X, y):\n\n grad = np.zeros(len(theta))\n\n ### YOUR CODE HERE\n sig = utils.sigmoid(theta)\n # sig = np.subtract(sig, y)\n sig = sig - y\n grad = np.dot(X.T, sig) + 2 * self.params['lamb'] * self.regularizer[1](self.weights)\n ### END YOUR CODE\n\n return grad","def compute_loss_gradient(theta_vector, *args):\n\n psi = args[0] # feed psi as a parameter\n circ_depth = args[1]\n num_qbits = args[2]\n theta = np.reshape(theta_vector, (circ_depth, num_qbits)) # reshapes the flat theta vector\n fidelity = get_fidelity(theta, psi)\n\n # the derivative of the loss wrt fidelity\n dl_df = -0.5 * fidelity ** (-0.5)\n\n df_dtheta = [] # a list of partial derivatives of the fidelity wrt the theta parameters\n\n for index in range(len(theta_vector)):\n layer_index = index // num_qbits\n qbit_index = index % num_qbits\n\n theta_plus = np.copy(theta)\n theta_plus[layer_index][qbit_index] += np.pi / 2 # added pi/2 to the ith theta parameter\n\n theta_minus = np.copy(theta)\n theta_minus[layer_index][qbit_index] -= np.pi / 2 # subtracted pi/2 to the ith theta parameter\n\n df_dtheta_i = 0.5 * (get_fidelity(theta_plus, psi) - get_fidelity(theta_minus, psi)) # ith derivative\n df_dtheta.append(df_dtheta_i)\n\n df_dtheta = np.array(df_dtheta)\n dl_dtheta = dl_df * df_dtheta # chain rule to get partial derivative of loss wrt theta parameters\n\n return dl_dtheta","def run_gradient_descent_iteration(X, Y, theta, alpha, lambda_factor, temp_parameter):\n delta = sparse.coo_matrix(theta.shape).toarray()\n\n h = compute_probabilities(X, theta, temp_parameter)\n\n for j in range(delta.shape[0]):\n y = Y\n y = np.where(y != j, 0, 1)\n p = y - h[j]\n\n x = X.T * p\n x = x.T\n x = x.sum(axis=0)\n\n grad = -x / (temp_parameter * X.shape[0]) + lambda_factor * theta[j]\n\n delta[j] += grad\n\n theta -= alpha * delta\n\n return theta","def gradient_descent(features, values, theta, alpha, num_iterations):\n \n # number of points\n npoints = len(values)\n \n # intialize cost history\n cost_history = []\n \n # num_interations iterations\n for iiter in range(num_iterations):\n \n # compute and store cost\n cost = compute_cost(features, values, theta)\n cost_history.append(cost)\n \n # update values of theta\n values_predicted = np.dot(features, theta)\n theta = theta + (alpha/npoints)*(np.dot(values - values_predicted,features))\n \n return theta, pandas.Series(cost_history)","def gradient_descent(x, y, theta=[[0], [0]]):\n m = y.size\n j_history = []\n for i in range(ITERATIONS):\n h = x.dot(theta)\n theta = theta - (ALPHA / m) * (x.T.dot(h - y))\n j_history.append(compute_cost(x, y, theta))\n return theta, j_history","def gradient_descent(features, values, theta, alpha, num_iterations):\r\n\r\n m = len(values)\r\n cost_history = []\r\n\r\n for i in range (num_iterations):\r\n \r\n h = numpy.dot(features, theta)\r\n \r\n theta = theta - alpha / m * numpy.dot((h-values),features)\r\n \r\n cost = compute_cost(features, values, theta)\r\n \r\n cost_history.append(cost)\r\n\r\n return theta, pandas.Series(cost_history) # leave this line for the grader\r","def approx_grad(theta, X, y):\n grad_a = np.array([(cost(theta + e, X, y) - cost(theta - e, X, y)) / (2 * 1e-5)\n for e in np.identity(len(theta)) * 1e-5])\n return grad_a","def gradient_descent(features, values, theta, alpha, num_iterations):\n m = len(values)\n cost_history = []\n\n for i in range(num_iterations):\n predicted_values = np.dot(features, theta)\n delta = alpha / m * np.dot((predicted_values - values), features)\n theta = theta - delta\n cost = compute_cost(features, values, theta)\n cost_history.append(cost)\n return theta, pandas.Series(cost_history)","def calculate_gradient(y, tx, w): \n return tx.T@(sigmoid(tx@w)-y)","def lr_cost_function_grad(theta: np.ndarray, X: np.ndarray, y: np.ndarray, l: float) -> np.ndarray:\n # Initialize some useful values\n m = len(y) # number of training examples\n\n # You need to return the following variable correctly.\n grad = np.zeros(theta.shape)\n\n # ====================== YOUR CODE HERE ======================\n # Instructions: Compute the partial derivatives and set grad to the partial\n # derivatives of the cost w.r.t. each parameter in theta.\n\n # =============================================================\n return grad","def gradient_ascent(self, w, X, y, lr):\r\n # INSERT YOUR CODE HERE\r\n #raise Exception('Function not yet implemented!')\r\n # gradient = x_j*(y-σ(wTX))\r\n return np.dot(X.T, y-self.sigmoid(np.dot(X, w)))","def computeNumericalGradient(J, theta):\n numgrad = np.zeros(theta.size)\n perturb = np.zeros(theta.size)\n e = 1e-4\n for p in range(theta.size):\n # Set perturbation vector\n perturb[p] = e\n loss1 = J(theta - perturb)[0]\n loss2 = J(theta + perturb)[0]\n # Compute Numerical Gradient\n numgrad[p] = (loss2 - loss1) / (2*e)\n perturb[p] = 0\n return numgrad","def computeNumericalGradient(J, theta):\n numgrad = np.zeros_like(theta).reshape(-1)\n perturb = np.zeros_like(theta).reshape(-1)\n e = 1e-4\n for p in range(theta.size):\n # Set perturbation vector\n perturb[p] = e\n loss1, _ = J(theta - perturb.reshape(theta.shape))\n loss2, _ = J(theta + perturb.reshape(theta.shape))\n # Compute Numerical Gradient\n numgrad[p] = (loss2 - loss1) / (2 * e)\n perturb[p] = 0\n\n return numgrad.reshape(theta.shape)","def gradientdescent(cost_func, theta, args=(), delta_func = 0):\n step = 1\n old_cost = 0\n while True:\n theta_old = theta.copy()\n cost = cost_func(theta, *args)\n delta = delta_func(theta, *args)\n theta = theta - step * delta\n if cost > old_cost and old_cost != 0:\n step = step*0.7\n if np.allclose(theta_old, theta):\n break\n old_cost = cost\n return theta","def gradient(cls, x):\n y = Sigmoid.apply(x)\n return np.multiply(y, 1 - y)","def gradient_descent(features, values, theta, alpha, num_iterations):\r\n \r\n m = len(values)\r\n cost_history = []\r\n\r\n for i in range(num_iterations):\r\n # your code here\r\n cost = compute_cost(features, values, theta)/(2.0*m)\r\n cost_history.append([cost])\r\n \r\n error = features.dot(theta) - values\r\n error = np.reshape(error,(error.shape[0], 1))\r\n errorWeighted = features*error\r\n errorSum = (np.sum(errorWeighted,0))/(m*1.0)\r\n theta = theta - alpha*errorSum \r\n \r\n return theta, pandas.Series(cost_history)","def compute_gradient(y, tx, w):\n\tN = y.shape[0]\n\te = y - np.dot(tx, w)\n\n\tgradLw = -1/N * np.dot(tx.T, e)\n\treturn gradLw","def compute_square_loss_gradient(X, y, theta):\n #TODO\n P = (np.dot(X, theta)-y)\n m = X.shape[0]\n\n return (2/m)*np.dot(X.T, P)","def learning_by_gradient_descent(y, tx, w, gamma):\n\tgrad = calculate_gradient(y, tx, w)\n\n\tw = w - gamma * grad\n\treturn w","def calculate_gradient(y, tx, w):\n return tx.T.dot(sigmoid(tx.dot(w))-np.reshape(y,(len(y),1)))","def gradientDescent(self,X, y, theta): \n # number of instances\n m = len(y)\n J_history = np.zeros((self.NUM_ITERS,1))\n for i in range(self.NUM_ITERS):\n h = self.sigmoid(X@theta)\n grad = 1 / m * X.T @ (h - y)\n theta = theta - self.ALPHA * grad \n J_history[i] = self.costFunction(theta, X, y)\n \n \n return theta, J_history","def compute_square_loss_gradient(X, y, theta):\n #TODO\n (N,p) = np.shape(X)\n grad = -(1/np.float(N))*np.array([(y - X.dot(theta))*X[:,i] for i in range(p)])\n return np.sum(grad,axis=1)","def gradient_descent(X, y, theta, learning_rate, num_iters, lambda_reg):\n thetas = [theta]\n cost = np.zeros(num_iters)\n\n J = mean_cross_entropy_costs(X, y, lambda_reg)\n cost[0] = J(thetas[0])\n for i in range(1, num_iters):\n thetas.append(compute_new_theta(X, y, thetas[i - 1], learning_rate, lambda_reg))\n cost[i] = J(thetas[i])\n return cost, thetas","def gradient(img):\n G = np.zeros(img.shape)\n theta = np.zeros(img.shape)\n\n #####################################\n # START YOUR CODE HERE #\n #####################################\n Gx = partial_x(img)\n Gy = partial_y(img)\n G = np.sqrt(np.square(Gx) + np.square(Gy))\n theta = np.degrees(np.arctan2(Gy, Gx)) % 360\n ######################################\n # END OF YOUR CODE #\n ######################################\n\n return G, theta","def gradient_descent(initial_theta, X, y, niter, alpha, Lambda=0.0):\n theta_list = []\n cost_list = []\n\n theta = initial_theta\n for i in range(0, niter):\n theta -= alpha*gradient(theta, X, y, Lambda)\n theta_list.append(theta)\n cost_list.append(cost(theta, X, y, Lambda))\n\n return theta_list, cost_list","def linreg_stochastic_grad(X, y, alpha=.01):\n m = X.shape[0]\n n = X.shape[1]\n theta = np.zeros(n)\n for i in range(m):\n delta = alpha * (np.dot(theta.transpose(), X[i,:]) -y[i]) * X[i,:]\n theta = theta - delta\n return theta","def log_likelihood_gradient(y, tx, w):\n return tx.T.dot(sigmoid(tx.dot(w))-y)","def gradient(cls, x):\n return 1 - TanH.apply(x) ** 2","def learning_by_gradient_descent(y, tx, w, gamma):\n loss = calculate_loss(y,tx,w)\n grad = calculate_gradient(y,tx,w)\n w_new = w - gamma*grad\n #grad is for debugging purpose\n return loss, w_new,grad","def simple_gradient(x, y, theta):\n if x.shape[0] * y.shape[0] * theta.shape[0] == 0:\n return None\n if x.shape[0] != y.shape[0] or theta.shape[0] != 2:\n return None\n\n x = add_intercept(x)\n\n result = [\n forumla(x, y, theta, 0),\n forumla(x, y, theta, 1)\n ]\n return result","def calculate_gradient(y, tx, w):\n\n\tret = tx.T.dot(sigmoid(np.dot(tx, w)) - y)\n\treturn ret","def learning_by_gradient_descent(y, tx, w, gamma):\n loss = calculate_loss(y,tx,w)\n grad = calculate_gradient(y,tx,w)\n w = w-gamma*grad\n return w, loss","def gradient(self, x):\n pass","def train_fn_grad(self, theta_value, theta):\n theta.set_value(theta_value, borrow=True)\n\n train_grads = [self.batch_grad(i, self.n_train)\n for i in xrange(self.n_train_batches)]\n train_batch_sizes = [self.get_batch_size(i, self.n_train)\n for i in xrange(self.n_train_batches)]\n\n return np.average(train_grads, weights=train_batch_sizes,\n axis=0)","def approx_grad_reg(theta, X, y, _lambda):\n grad_a = np.array([(cost_reg(theta + e, X, y, _lambda) - cost_reg(theta - e, X, y, _lambda)) / (2 * 1e-5)\n for e in np.identity(len(theta)) * 1e-5])\n return grad_a","def gradient(img):\n G = np.zeros(img.shape)\n theta = np.zeros(img.shape)\n\n ### YOUR CODE HERE\n Gx = partial_x(img)\n Gy = partial_y(img)\n G = np.sqrt(np.power(Gx,2)+np.power(Gy,2))\n theta = (np.arctan2(Gy, Gx) * 180 / np.pi) % 360\n ### END YOUR CODE\n\n return G, theta","def get_gradient(phi, pred, t, dot_product, weight, reg= 1, regression= \"logistic\"):\n if regression == \"logistic\":\n gradient = np.matmul(phi.T, pred - t)\n elif regression == \"probit\":\n R = np.eye(pred.shape[0])\n for i in range(pred.shape[0]):\n y_n = pred[i,0]\n dotp = dot_product[i, 0]\n pdf = norm.pdf(dotp)\n R[i,i] = pdf/(y_n*(1-y_n) + TOLERANCE)\n gradient = np.matmul(np.matmul(phi.T, R), pred-t)\n elif regression == \"multiclass\":\n gradient = np.matmul(phi.T, pred - t)\n\n # Add regularization\n gradient += weight/ reg\n return gradient","def gradient(self, x):\n u = np.asarray([x[0]])\n C = self.C_func(u)\n dC = self.dC_func(u, order=1)\n P = self.P\n numerator = np.sum((C - P) * dC, axis=0)\n denominator = np.sum(np.sum((C - P) ** 2, axis=0) ** (1 / 2))\n if np.abs(denominator) > 0:\n gradient = numerator/denominator\n else:\n gradient = np.asarray(0)[np.newaxis]\n return gradient","def apply_gradient(params: torch.Tensor, grads: torch.Tensor, lr: float) -> torch.Tensor:\n params_prime = params + lr * grads\n return params_prime","def run_steep_gradient_descent(data_x, data_y, len_data, alpha, theta):\n n = len_data\n # WE NEED TO transpose data_x into (p+1) *n ,theta is 1*(p+1)\n prod = np.dot(theta, data_x.transpose())\n\n prod -= data_y\n print(\"pro: data_x\", prod.shape, data_x.shape)\n #prod represent the loss of the hypothesis and true label\n sum_grad = np.dot(prod, data_x)\n print(\"总梯度的值：\",sum_grad.shape)\n\n # batch-gradient descent\n theta = theta -(alpha / n) * sum_grad\n return theta","def GDS(x, theta, y, lamb, alpha):\n\tm = y.shape[0]\n\tdif = (1/m) * x.T @ (sigmoid(x,theta) - y) #classical differentiation term, output is (n,1)\n\tdif_reg = (lamb/m) * theta #regularization term\n\treturn (dif + dif_reg) * alpha","def gradientDescent(X, y, theta, alpha, num_iters):\n\n # Initialize some useful values\n J_history = []\n m = y.size # number of training examples\n\n for i in range(num_iters):\n # ====================== YOUR CODE HERE ======================\n # Instructions: Perform a single gradient step on the parameter vector\n # theta.\n #\n # Hint: While debugging, it can be useful to print out the values\n # of the cost function (computeCost) and gradient here.\n #\n # Calculate the gradient step according to the equation for theta1:\n g_step1 = (alpha / m * np.sum( (np.dot(X,theta) - y) * X[:,1]) )\n # Gradient step for theta knot:\n g_step0 = (alpha / m * np.sum( (np.dot(X,theta) - y) ) )\n \n #update theta\n theta[0] = (theta[0] - g_step0)\n theta[1] = (theta[1] - g_step1)\n \n #print([theta , g_step1, g_step0])\n\n # ============================================================\n\n # Save the cost J in every iteration\n J_history.append(computeCost(X, y, theta))\n\n return theta, J_history","def gradient(w, x, t):\n return 2 * np.dot(x.T, (nn(x, w) - t))","def costFunction(theta,X,y):\n m = X.shape[0]\n J = 0\n h = sigmoid (np.dot(X,theta))\n \n J = (1/m)* ((-np.dot(y.T,(np.log(h)))) - np.dot((1 - y).T,(np.log(1-h))))\n \n #grad = (1/m) * np.dot(X.T,(h-y))\n grad = (1/m) * np.dot((h.T - y), X).T\n \n return J, grad","def stochasticGradientDescent(x,y,theta,alpha):\n m,n = np.shape(x)\n convergence = 0.000000001\n lastCost = 0\n cost = -1 \n recurseCount = 0\n while abs(lastCost - cost) > convergence: # rcurse until converge\n lastCost = cost\n hypothesis = np.dot(x,theta) \n for i in range(m):\n # alpha = 4.0 / (1.0 + i) + 0.01 \n loss = hypothesis[i] - y[i]\n # gradient = np.dot(x[i],loss)\n gradient = x[i,:].transpose() * loss \n theta = theta - alpha * gradient\n cost = np.sum((hypothesis-y)**2)/(2*m)\n recurseCount += 1\n return recurseCount,theta","def gradient(self):\n result = np.zeros(len(self.variables))\n result[self.bivariateGradInd] = (self.shape-1)/self.variable - self.rate\n return result","def gradient(self):\n functional = self\n\n class KLGradient(Operator):\n\n \"\"\"The gradient operator of this functional.\"\"\"\n\n def __init__(self):\n \"\"\"Initialize a new instance.\"\"\"\n super().__init__(functional.domain, functional.domain,\n linear=False)\n\n def _call(self, x):\n \"\"\"Apply the gradient operator to the given point.\n The gradient is not defined in points where one or more\n components are non-positive.\n \"\"\"\n if functional.prior is None:\n return (-1.0) / x + 1\n else:\n return (-functional.prior) / x + 1\n\n return KLGradient()","def batchGradientDescent(x,y,theta,alpha):\n m,n = np.shape(x)\n xTran = x.transpose()\n convergence = 0.000000001\n lastCost = 0\n cost = -1 \n recurseCount = 0\n while abs(lastCost - cost) > convergence: # rcurse until converge\n lastCost = cost\n hypothesis = np.dot(x,theta)\n loss = hypothesis - y\n cost = np.sum(loss**2)/(2*m)\n gradient = np.dot(xTran,loss)/m\n theta = theta - alpha*gradient\n recurseCount += 1\n return recurseCount,theta","def learning_by_penalized_gradient(y, tx, w, gamma, lambda_):\n\tgrad, H = penalized_logistic_regression(y, tx, w, lambda_)\n\n\thgrad = np.linalg.inv(H).dot(grad)\n\n\tw = w - gamma * hgrad\n\n\treturn w","def grad(self, w):\n l1_grad = self.r * np.sign(w)\n l2_grad = np.asarray(1 - self.r) * w \n\n gradient_penalty = self.alpha * (l1_grad + l2_grad)\n\n # Insert 0 for bias term.\n return np.insert(gradient_penalty, 0, 0, axis=0)","def gradient(self, x):\n g = self._grad(\n time_series=self.observed_data,\n a=x[0],\n b=x[1],\n c=x[2],\n sigma=self.sigma\n )\n return g","def compute_gradient(self):\n A = np.dot(self.X, self.w)\n m = self.t.shape[0]\n C = -1 * self.t * (1 / (1 + np.exp(A * self.t)))\n return (1 / m) * np.dot(self.X.T, C)","def reg_autograd(alpha,X,theta,y):\n g=grad(func)\n for i in range(0,20):\n theta=theta- alpha * g(theta)\n print(\"cost\", func(theta))\n print(\"final theta\",theta)","def grad(self, x):\n norm_beta = np.linalg.norm(x)\n if norm_beta > utils.TOLERANCE:\n return (self.l / norm_beta) * x\n else:\n D = x.shape[0]\n u = (self.rng(D, 1) * 2.0) - 1.0 # [-1, 1]^D\n norm_u = np.linalg.norm(u)\n a = self.rng() # [0, 1]\n\n return (self.l * (a / norm_u)) * u","def gradcovfunc(self, theta, d):\n sigmaf, l = theta[:2] \n xxl = np.sum((d/l)**2, axis=1)\n dk_dsigmaf = 2 * sigmaf * np.exp(-xxl/2.)\n dk_dl = sigmaf**2/l * xxl * np.exp(-xxl/2.)\n grad = np.array([dk_dsigmaf, dk_dl])\n return grad","def log_prior_grad(self, inputs):","def gradient(series, **options):\n x = series.index\n y = series.values\n\n a = np.gradient(y, x, **options)\n return series.__class__(a, series.index)","def compute_logistic_gradient(y, tx, w):\n return tx.T.dot(sigmoid(tx.dot(w)) - y) / len(y)","def derivative(self, theta):\n diag_gamma = np.dot(theta.T, self.X.T)\n logistic_term = self.logistic_fn(diag_gamma)\n diag_gamma = logistic_term * (1.0 - logistic_term)\n gamma = np.diag(diag_gamma)\n\n # v computation\n diags_v = 1.0 - 2*self.logistic_fn(np.dot(theta.T, self.X.T))\n diags_v = diags_v.reshape((-1, 1))\n diags_v = diags_v*self.X\n assert diags_v.shape == self.X.shape #N*d shape\n\n XtGamma = np.dot(self.X.T, gamma) # d*N shape\n\n # TODO: Verifier car pas sur de mon coup ... et surtout plus long...\n # id = np.eye(self.n_examples).reshape((self.n_examples, self.n_examples, 1))\n # diags_v = diags_v.reshape((self.n_examples, 1, self.dim))\n # v = id*diags_v # n*n*d tensor\n # left = np.tensordot(XtGamma, v, axes=(1, 0)) # shape d*N*d\n # assert left.shape == (self.dim, self.n_examples, self.dim)\n # dg = np.tensordot(left, self.X, axes=(1, 0))\n # dg = np.swapaxes(dg, axis1=-2, axis2=-1)\n\n dg = np.zeros((self.dim, self.dim, self.dim))\n for idx, v_i_diag in enumerate(diags_v.T):\n v_i = np.diag(v_i_diag)\n dg_di = np.dot(np.dot(XtGamma, v_i), self.X)\n dg[:, :, idx] = dg_di\n return dg","def run_gradient_descent(data,theta,alpha,num_iters):\n population = data[:,0]\n prices = data[:,1]\n x = ones(shape=(len(population),2)) #add ones for theta0 \n x[:,1] = population\n x = transpose(x)\n error_history = zeros(shape=(num_iters,1))\n \n for i in range(num_iters):\n predictions = theta.dot(x)\n errors_x1 = (predictions - prices) * x[0,:]\n errors_x2 = (predictions - prices) * x[1,:]\n theta[0][0] = theta[0][0] - alpha*(1.0/len(population))*errors_x1.sum()\n theta[0][1] = theta[0][1] - alpha*(1.0/len(population))*errors_x2.sum()\n error_history[i,0] = calculate_cost(theta,data)\n \n return theta, error_history","def gradient(self, x):\n return 0.0","def gradient(poly):\n return differential(\n poly, chaospy.poly.collection.basis(1, 1, poly.dim, sort=\"GR\"))","def curves_gradient(theta, m_, LAMBDA=0):\n # X0: column of ones\n theta0_gradient = (1 / m_) * sum((curves_hypothesis(theta, m_) - y[:m_].ravel()) * np.ones(m_))\n # X1: data given as X\n theta1_gradient = ((1 / m_) * sum((curves_hypothesis(theta, m_) - y[:m_].ravel()) * X[:m_].ravel())) \\\n + (LAMBDA / m_) * theta[1]\n return np.array([theta0_gradient, theta1_gradient])","def costFunction(self,theta, X, y): \n m = len(y)\n h = self.sigmoid(X@theta)\n J = 1 / m * (- y.T @ self.log(h) - (1-y).T @ self.log(1-h)) \n # grad = 1/ m * X.T @ (h - y)\n return J","def gradient(self, x_in):\r\n\r\n return 1 - torch.pow(self.forward(x_in), 2)","def compute_gradient(self): # TODO: try to change to square loss since it's hessian is easier to obtain\n A = np.dot(self.X, self.w)\n m = self.t.shape[0]\n C = -1 * self.t * (1 / (1 + np.exp(A * self.t)))\n return (1 / m) * np.dot(self.X.T, C)","def vb_grad_natgrad(self):\r\n\r\n # Gradient for eta\r\n dL_dmmT_S = -0.5*self.Lambda/self.data_prop + 0.5*self.q_u_prec\r\n Kmmipsi1V,_ = dpotrs(self.Lm,self.psi1V,lower=1)\r\n dL_dm = (Kmmipsi1V - np.dot(self.Lambda,self.q_u_mean))/self.data_prop\r\n\r\n # Gradients for theta\r\n S = self.q_u_cov\r\n Si = self.q_u_prec\r\n m = self.q_u_mean\r\n dL_dSi = -mdot(S,dL_dmmT_S, S)\r\n\r\n dL_dmhSi = -2*dL_dSi\r\n dL_dSim = np.dot(dL_dSi,m) + np.dot(Si, dL_dm)\r\n\r\n return np.hstack((dL_dm.flatten(),dL_dmmT_S.flatten())) , np.hstack((dL_dSim.flatten(), dL_dmhSi.flatten()))","def gradient(self):\n functional = self\n\n class KLCCGradient(Operator):\n\n \"\"\"The gradient operator of this functional.\"\"\"\n\n def __init__(self):\n \"\"\"Initialize a new instance.\"\"\"\n super().__init__(functional.domain, functional.domain,\n linear=False)\n\n def _call(self, x):\n \"\"\"Apply the gradient operator to the given point.\n\n The gradient is not defined in points where one or more\n components are larger than or equal to one.\n \"\"\"\n if functional.prior is None:\n return 1.0 / (1 - x)\n else:\n return functional.prior / (1 - x)\n\n return KLCCGradient()","def grad(self, w):\n gradient_penalty = np.asarray(self.alpha) * w\n # Insert 0 for bias term.\n return np.insert(gradient_penalty, 0, 0, axis=0)","def grad(self, w):\n gradient_penalty = np.asarray(self.alpha) * w\n # Insert 0 for bias term.\n return np.insert(gradient_penalty, 0, 0, axis=0)","def learning_by_penalized_gradient(y, tx, w, gamma, lambda_):\n\n #on test avec Newton\n\n loss,gradient,_ = penalized_logistic_regression(y,tx,w,lambda_)\n\n w = w - gamma*gradient\n return loss, w,gradient","def gradient_ascent(f, df, theta_init, step_size, max_iter):\n\n fs = []\n xs = []\n thetas = theta_init\n for i in range(max_iter): #for each data example\n fs.append(f(thetas))\n\n temp = step_size*df(thetas)\n thetas = step_size*df(thetas) #modify that feature by using the derivative of log likelihood\n xs.append(thetas.flatten())\n if i % 10 == 0:\n print(i, thetas)\n\n return thetas, fs, xs","def calculate_maxL_gradient(y, tx, w):\n sig=sigmoid(tx.dot(w))\n grad=tx.T.dot(sig-y)\n #divides with number of samples so that learning rate is not dependant on number of samples\n grad=grad/len(y) \n return grad","def _log_prior_gradients(self):\n if self.priors.size == 0:\n return 0.\n x = self.param_array\n ret = np.zeros(x.size)\n #compute derivate of prior density\n [np.put(ret, ind, p.lnpdf_grad(x[ind])) for p, ind in self.priors.items()]\n #add in jacobian derivatives if transformed\n priored_indexes = np.hstack([i for p, i in self.priors.items()])\n for c,j in self.constraints.items():\n if not isinstance(c, Transformation):continue\n for jj in j:\n if jj in priored_indexes:\n ret[jj] += c.log_jacobian_grad(x[jj])\n return ret","def grad(self, w):\n gradient_penalty = np.asarray(self.alpha) * w\n\n # Insert 0 for bias term.\n return np.insert(gradient_penalty, 0, 0, axis=0)","def grad(theta, X, y, lambda_):\n # ... dopolnite (naloga 1, naloga 2)\n\n l = []\n for i, e in enumerate(theta):\n l.append(1 / len(y) * sum([(h(x, theta) - yi) * x[i] for x, yi in zip(X, y)]) + 2 * lambda_ * e)\n\n return np.array(l)","def lr_cost_function(theta, X, y, learning_rate):\n m = len(y) # number of training examples\n # You need to return the following variables correctly \n J = 0\n grad = np.zeros(theta.shape)\n h = sigmoid(np.dot(X, theta))\n\n #Compute cost:\n first = (1/m)*np.sum(np.dot(-y.T, np.log(h)) - np.dot((1-y).T, np.log(1-h)), axis=0)\n second = (learning_rate/(2*m))*np.sum(theta[1:]**2)\n J = first + second\n \n #Compute gradient\n grad = np.dot((h-y).T, X)/m\n grad = grad.T\n grad[1:] += (learning_rate/m)*theta[1:]\n return float(J), grad","def gradient(self, x, i=None):\n self.set_parameters(x=x)\n gradient = self.gradient_loss()\n if self.add_bias:\n gradient[1:] += self.alpha * self.gradient_regularizer()\n else:\n gradient += self.alpha * self.gradient_regularizer()\n if i is not None:\n return gradient.ravel()[i]\n return gradient.ravel()","def compute_gradient_lasso(y, tx, w, lambda_):\n e = y - tx.dot(w)\n subgrad = lambda_ * np.sign(w)\n\n return -tx.T.dot(e)/len(e) + subgrad","def compute_gradient(self, X, y, weights):\n sigmoid = self.sigmoid(np.dot(X, weights))\n return np.dot(X.T, y - sigmoid)","def batch_grad_descent(X, y, alpha=0.1, num_iter=1000, check_gradient=False):\n num_instances, num_features = X.shape[0], X.shape[1]\n theta_hist = np.zeros((num_iter+1, num_features)) #Initialize theta_hist\n loss_hist = np.zeros(num_iter+1) #initialize loss_hist\n theta = np.ones(num_features) #initialize theta\n\n count = 0\n while count < num_iter+1:\n if check_gradient:\n assert grad_checker(X,y,theta)\n\n grad = compute_square_loss_gradient(X,y,theta)\n theta -= alpha*grad\n theta_hist[count] = theta\n loss_hist[count] = compute_square_loss(X,y,theta)\n count += 1\n \n return theta_hist, loss_hist","def check_gradient(self, x, y):\n x = x.transpose()\n y = y.transpose()\n layers_copy = deepcopy(self.layers)\n epsilon = 10 ** -4\n a, layer = self.forward_propagation(x)\n delta = self.calculate_delta(a, y, layer)\n self.backpropagation(delta=delta, theta=layer.theta)\n previous_layer_output = x\n for layer in self.layers:\n theta_copy = deepcopy(layer.theta)\n real_theta_size = theta_copy.shape\n delta = layer.delta\n dc_dtheta = np.outer(previous_layer_output, delta).transpose()\n previous_layer_output = layer.a\n R, C = theta_copy.shape\n for i in range(R):\n for j in range(C):\n theta_plus = deepcopy(theta_copy)\n theta_plus[i, j] += epsilon\n layer.theta = theta_plus\n a_plus, l_plus = self.forward_propagation(x)\n err_plus = self.calculate_loss(a_plus, y)\n theta_minus = deepcopy(theta_copy)\n theta_minus[i, j] -= epsilon\n layer.theta = theta_minus\n a_minus, l_minus = self.forward_propagation(x)\n err_minus = self.calculate_loss(a_minus, y)\n limit = (err_plus - err_minus)/(2*epsilon)\n grad_diff = abs(dc_dtheta[i,j] - limit)\n assert grad_diff < 10 ** -6, f\"Diff {grad_diff} is too big.\"\n layer.theta = theta_copy","def _compute_func_grad(self, w):\n W = w.reshape((self.X.shape[1], self.Y.shape[1]))\n self.nll_, self.grad_ = calculate_gradient(self.X, self.Y, W, self.prior, self.weighted,0)","def gradient(data_x, data_y, parameters):\n return data_x.T @ (data_x @ parameters - data_y) / data_x.shape[0]"],"string":"[\n \"def gradient(self, theta):\\n pass\",\n \"def gradient(self, theta):\\n pass\",\n \"def gradient(self, theta):\\n return (1 / (self.sigma * np.sqrt(2 * np.pi))) * (\\n -theta / (self.sigma ** 2) * np.exp(-(theta ** 2) / (2 * self.sigma ** 2))\\n )\",\n \"def gradient(theta, X, y, learning_rate):\\n m = len(y)\\n\\n theta = theta.reshape((-1,1))\\n grad = np.zeros(theta.shape)\\n h = sigmoid(np.dot(X, theta)) \\n \\n grad = np.dot((h-y).T, X)/m\\n grad = grad.T\\n grad[1:] += (learning_rate/m)*theta[1:]\\n return grad\",\n \"def gradient_function(theta, X, y):\\n\\n grad = None\\n #######################################################################\\n # TODO: #\\n # Compute the gradient for a particular choice of theta. #\\n # Compute the partial derivatives and set grad to the partial #\\n # derivatives of the cost w.r.t. each parameter in theta #\\n # #\\n #######################################################################\\n \\n theta = theta[:, np.newaxis]\\n \\n thetatrans = theta.T\\n Xtrans = X.T\\n \\n MulThetaX = np.dot(thetatrans, Xtrans)\\n \\n h = sigmoid(MulThetaX)\\n \\n grad = (y - h) * Xtrans\\n\\n #######################################################################\\n # END OF YOUR CODE #\\n #######################################################################\\n return grad\",\n \"def compute_gradient(theta, X, y):\\n m = X.shape[0]\\n grad_theta = np.dot(X.transpose(), (np.dot(X, theta) - y)) / m\\n #print theta, grad_theta, objective_function(theta, X, y)\\n return grad_theta\",\n \"def gradient(self, theta):\\n return np.zeros([theta.shape[0]])\",\n \"def gradientFunction(theta, X, y):\\n y = y[:, 0]\\n m = y.shape # number of training samples\\n grad = X.T.dot(sigmoid(theta.dot(X.T))-1*y)\\n grad /= m\\n return grad\",\n \"def gradient(theta, x, y):\\n m = len(y)\\n n = len(theta)\\n z = theta.dot(x.T)\\n grad = np.zeros(n)\\n for i in xrange(m):\\n grad += (g(z[i]) - y[i]) * x[i]\\n return 1. / m * grad\",\n \"def gradient(theta, X, y, Lambda=0.0):\\n m = X.shape[0] # number of samples\\n\\n h = hypothesis(theta, X)\\n\\n if Lambda:\\n g_0 = (1/m)*(X.T@(h - y))[0]\\n g_1 = (1/m)*(X.T@(h - y))[1:] + (Lambda/m)*theta[1:] # skip theta-0\\n \\n return np.append(g_0, g_1)\\n else:\\n return (1/m)*(X.T@(h - y))\",\n \"def gradient(x, y, theta):\\n if x.ndim == 1:\\n x = x[:, np.newaxis]\\n if y.ndim == 2 and y.shape[1] == 1:\\n y = y.flatten()\\n if theta.ndim == 2 and theta.shape[1] == 1:\\n theta = theta.flatten()\\n\\n if (x.size == 0 or y.size == 0 or theta.size == 0\\n or x.ndim != 2 or y.ndim != 1 or theta.ndim != 1\\n or x.shape[0] != y.shape[0] or x.shape[1] + 1 != theta.shape[0]):\\n return None\\n\\n x_padded = np.c_[np.ones(x.shape[0]), x]\\n\\n return x_padded.T.dot(x_padded.dot(theta) - y) / y.shape[0]\",\n \"def calc_grad(X, Y, theta):\\n m, n = X.shape\\n\\n margins = Y * X.dot(theta)\\n probs = 1. / (1 + np.exp(margins))\\n grad = -(1./m) * (X.T.dot(probs * Y))\\n\\n return grad\",\n \"def gradient_descent(X, y, theta, alpha, total_iterations, hypothesis):\\n len_theta = len(theta)\\n m = len(y)\\n one_over_m = (1.0 / float(m))\\n\\n for _ in range(0, total_iterations):\\n temp_theta = numpy.zeros(len_theta)\\n\\n X_by_theta_minus_y = numpy.subtract(hypothesis(numpy.matrix(theta), X), y)\\n\\n for j in range(0, len_theta):\\n jth_column_of_X = X[:,j]\\n derivative_j = one_over_m * numpy.multiply(X_by_theta_minus_y, jth_column_of_X).sum()\\n temp_theta[j] = theta[j] - alpha*derivative_j\\n\\n theta = temp_theta\\n\\n return numpy.matrix(theta)\",\n \"def gradient_descent(self, X, theta, Y, m):\\n\\n Z = X.dot(theta)\\n H = Predict.g(Z)\\n gradient = np.dot(X.T, (H - Y)) / m\\n return self.alpha * gradient\",\n \"def gradientFunctionReg(theta, X, y, Lambda):\\n m = len(y) # number of training examples\\n grad = np.zeros(theta.shape[0])\\n theta = np.transpose(theta)\\n sum_1 = 0\\n X = X.values\\n y = y.values\\n #calcuate the theta_0 \\n# ====================== YOUR CODE HERE ======================\\n# Instructions: Compute the gradient of a particular choice of theta.\\n# Compute the partial derivatives and set grad to the partial\\n# derivatives of the cost w.r.t. each parameter in theta\\n for i in range(theta.shape[0]):\\n if i == 0:\\n for j in range(m):\\n sum_1 += (sigmoid(np.dot(X[j],theta)) - y[j]) * X[j,i]\\n else:\\n for j in range(m):\\n sum_1 += (sigmoid(np.dot(X[j],theta)) - y[j]) * X[j,i] + Lambda*theta[i]\\n grad[i] = sum_1/m\\n sum_1 = 0\\n\\n# =============================================================\\n\\n return grad\",\n \"def gradientFunctionReg(theta, X, y, Lambda): \\n y = np.squeeze(y)\\n m = y.shape # number of training samples\\n grad = X.T.dot(sigmoid(theta.dot(X.T))-1*y)\\n grad[1:] = grad[1:] + Lambda*theta[1:]\\n grad /= m\\n\\n return grad\",\n \"def logit_cost_grad(self, theta, X, y):\\n\\n grad = np.zeros(len(theta))\\n\\n ### YOUR CODE HERE\\n sig = utils.sigmoid(theta)\\n # sig = np.subtract(sig, y)\\n sig = sig - y\\n grad = np.dot(X.T, sig) + 2 * self.params['lamb'] * self.regularizer[1](self.weights)\\n ### END YOUR CODE\\n\\n return grad\",\n \"def compute_loss_gradient(theta_vector, *args):\\n\\n psi = args[0] # feed psi as a parameter\\n circ_depth = args[1]\\n num_qbits = args[2]\\n theta = np.reshape(theta_vector, (circ_depth, num_qbits)) # reshapes the flat theta vector\\n fidelity = get_fidelity(theta, psi)\\n\\n # the derivative of the loss wrt fidelity\\n dl_df = -0.5 * fidelity ** (-0.5)\\n\\n df_dtheta = [] # a list of partial derivatives of the fidelity wrt the theta parameters\\n\\n for index in range(len(theta_vector)):\\n layer_index = index // num_qbits\\n qbit_index = index % num_qbits\\n\\n theta_plus = np.copy(theta)\\n theta_plus[layer_index][qbit_index] += np.pi / 2 # added pi/2 to the ith theta parameter\\n\\n theta_minus = np.copy(theta)\\n theta_minus[layer_index][qbit_index] -= np.pi / 2 # subtracted pi/2 to the ith theta parameter\\n\\n df_dtheta_i = 0.5 * (get_fidelity(theta_plus, psi) - get_fidelity(theta_minus, psi)) # ith derivative\\n df_dtheta.append(df_dtheta_i)\\n\\n df_dtheta = np.array(df_dtheta)\\n dl_dtheta = dl_df * df_dtheta # chain rule to get partial derivative of loss wrt theta parameters\\n\\n return dl_dtheta\",\n \"def run_gradient_descent_iteration(X, Y, theta, alpha, lambda_factor, temp_parameter):\\n delta = sparse.coo_matrix(theta.shape).toarray()\\n\\n h = compute_probabilities(X, theta, temp_parameter)\\n\\n for j in range(delta.shape[0]):\\n y = Y\\n y = np.where(y != j, 0, 1)\\n p = y - h[j]\\n\\n x = X.T * p\\n x = x.T\\n x = x.sum(axis=0)\\n\\n grad = -x / (temp_parameter * X.shape[0]) + lambda_factor * theta[j]\\n\\n delta[j] += grad\\n\\n theta -= alpha * delta\\n\\n return theta\",\n \"def gradient_descent(features, values, theta, alpha, num_iterations):\\n \\n # number of points\\n npoints = len(values)\\n \\n # intialize cost history\\n cost_history = []\\n \\n # num_interations iterations\\n for iiter in range(num_iterations):\\n \\n # compute and store cost\\n cost = compute_cost(features, values, theta)\\n cost_history.append(cost)\\n \\n # update values of theta\\n values_predicted = np.dot(features, theta)\\n theta = theta + (alpha/npoints)*(np.dot(values - values_predicted,features))\\n \\n return theta, pandas.Series(cost_history)\",\n \"def gradient_descent(x, y, theta=[[0], [0]]):\\n m = y.size\\n j_history = []\\n for i in range(ITERATIONS):\\n h = x.dot(theta)\\n theta = theta - (ALPHA / m) * (x.T.dot(h - y))\\n j_history.append(compute_cost(x, y, theta))\\n return theta, j_history\",\n \"def gradient_descent(features, values, theta, alpha, num_iterations):\\r\\n\\r\\n m = len(values)\\r\\n cost_history = []\\r\\n\\r\\n for i in range (num_iterations):\\r\\n \\r\\n h = numpy.dot(features, theta)\\r\\n \\r\\n theta = theta - alpha / m * numpy.dot((h-values),features)\\r\\n \\r\\n cost = compute_cost(features, values, theta)\\r\\n \\r\\n cost_history.append(cost)\\r\\n\\r\\n return theta, pandas.Series(cost_history) # leave this line for the grader\\r\",\n \"def approx_grad(theta, X, y):\\n grad_a = np.array([(cost(theta + e, X, y) - cost(theta - e, X, y)) / (2 * 1e-5)\\n for e in np.identity(len(theta)) * 1e-5])\\n return grad_a\",\n \"def gradient_descent(features, values, theta, alpha, num_iterations):\\n m = len(values)\\n cost_history = []\\n\\n for i in range(num_iterations):\\n predicted_values = np.dot(features, theta)\\n delta = alpha / m * np.dot((predicted_values - values), features)\\n theta = theta - delta\\n cost = compute_cost(features, values, theta)\\n cost_history.append(cost)\\n return theta, pandas.Series(cost_history)\",\n \"def calculate_gradient(y, tx, w): \\n return tx.T@(sigmoid(tx@w)-y)\",\n \"def lr_cost_function_grad(theta: np.ndarray, X: np.ndarray, y: np.ndarray, l: float) -> np.ndarray:\\n # Initialize some useful values\\n m = len(y) # number of training examples\\n\\n # You need to return the following variable correctly.\\n grad = np.zeros(theta.shape)\\n\\n # ====================== YOUR CODE HERE ======================\\n # Instructions: Compute the partial derivatives and set grad to the partial\\n # derivatives of the cost w.r.t. each parameter in theta.\\n\\n # =============================================================\\n return grad\",\n \"def gradient_ascent(self, w, X, y, lr):\\r\\n # INSERT YOUR CODE HERE\\r\\n #raise Exception('Function not yet implemented!')\\r\\n # gradient = x_j*(y-σ(wTX))\\r\\n return np.dot(X.T, y-self.sigmoid(np.dot(X, w)))\",\n \"def computeNumericalGradient(J, theta):\\n numgrad = np.zeros(theta.size)\\n perturb = np.zeros(theta.size)\\n e = 1e-4\\n for p in range(theta.size):\\n # Set perturbation vector\\n perturb[p] = e\\n loss1 = J(theta - perturb)[0]\\n loss2 = J(theta + perturb)[0]\\n # Compute Numerical Gradient\\n numgrad[p] = (loss2 - loss1) / (2*e)\\n perturb[p] = 0\\n return numgrad\",\n \"def computeNumericalGradient(J, theta):\\n numgrad = np.zeros_like(theta).reshape(-1)\\n perturb = np.zeros_like(theta).reshape(-1)\\n e = 1e-4\\n for p in range(theta.size):\\n # Set perturbation vector\\n perturb[p] = e\\n loss1, _ = J(theta - perturb.reshape(theta.shape))\\n loss2, _ = J(theta + perturb.reshape(theta.shape))\\n # Compute Numerical Gradient\\n numgrad[p] = (loss2 - loss1) / (2 * e)\\n perturb[p] = 0\\n\\n return numgrad.reshape(theta.shape)\",\n \"def gradientdescent(cost_func, theta, args=(), delta_func = 0):\\n step = 1\\n old_cost = 0\\n while True:\\n theta_old = theta.copy()\\n cost = cost_func(theta, *args)\\n delta = delta_func(theta, *args)\\n theta = theta - step * delta\\n if cost > old_cost and old_cost != 0:\\n step = step*0.7\\n if np.allclose(theta_old, theta):\\n break\\n old_cost = cost\\n return theta\",\n \"def gradient(cls, x):\\n y = Sigmoid.apply(x)\\n return np.multiply(y, 1 - y)\",\n \"def gradient_descent(features, values, theta, alpha, num_iterations):\\r\\n \\r\\n m = len(values)\\r\\n cost_history = []\\r\\n\\r\\n for i in range(num_iterations):\\r\\n # your code here\\r\\n cost = compute_cost(features, values, theta)/(2.0*m)\\r\\n cost_history.append([cost])\\r\\n \\r\\n error = features.dot(theta) - values\\r\\n error = np.reshape(error,(error.shape[0], 1))\\r\\n errorWeighted = features*error\\r\\n errorSum = (np.sum(errorWeighted,0))/(m*1.0)\\r\\n theta = theta - alpha*errorSum \\r\\n \\r\\n return theta, pandas.Series(cost_history)\",\n \"def compute_gradient(y, tx, w):\\n\\tN = y.shape[0]\\n\\te = y - np.dot(tx, w)\\n\\n\\tgradLw = -1/N * np.dot(tx.T, e)\\n\\treturn gradLw\",\n \"def compute_square_loss_gradient(X, y, theta):\\n #TODO\\n P = (np.dot(X, theta)-y)\\n m = X.shape[0]\\n\\n return (2/m)*np.dot(X.T, P)\",\n \"def learning_by_gradient_descent(y, tx, w, gamma):\\n\\tgrad = calculate_gradient(y, tx, w)\\n\\n\\tw = w - gamma * grad\\n\\treturn w\",\n \"def calculate_gradient(y, tx, w):\\n return tx.T.dot(sigmoid(tx.dot(w))-np.reshape(y,(len(y),1)))\",\n \"def gradientDescent(self,X, y, theta): \\n # number of instances\\n m = len(y)\\n J_history = np.zeros((self.NUM_ITERS,1))\\n for i in range(self.NUM_ITERS):\\n h = self.sigmoid(X@theta)\\n grad = 1 / m * X.T @ (h - y)\\n theta = theta - self.ALPHA * grad \\n J_history[i] = self.costFunction(theta, X, y)\\n \\n \\n return theta, J_history\",\n \"def compute_square_loss_gradient(X, y, theta):\\n #TODO\\n (N,p) = np.shape(X)\\n grad = -(1/np.float(N))*np.array([(y - X.dot(theta))*X[:,i] for i in range(p)])\\n return np.sum(grad,axis=1)\",\n \"def gradient_descent(X, y, theta, learning_rate, num_iters, lambda_reg):\\n thetas = [theta]\\n cost = np.zeros(num_iters)\\n\\n J = mean_cross_entropy_costs(X, y, lambda_reg)\\n cost[0] = J(thetas[0])\\n for i in range(1, num_iters):\\n thetas.append(compute_new_theta(X, y, thetas[i - 1], learning_rate, lambda_reg))\\n cost[i] = J(thetas[i])\\n return cost, thetas\",\n \"def gradient(img):\\n G = np.zeros(img.shape)\\n theta = np.zeros(img.shape)\\n\\n #####################################\\n # START YOUR CODE HERE #\\n #####################################\\n Gx = partial_x(img)\\n Gy = partial_y(img)\\n G = np.sqrt(np.square(Gx) + np.square(Gy))\\n theta = np.degrees(np.arctan2(Gy, Gx)) % 360\\n ######################################\\n # END OF YOUR CODE #\\n ######################################\\n\\n return G, theta\",\n \"def gradient_descent(initial_theta, X, y, niter, alpha, Lambda=0.0):\\n theta_list = []\\n cost_list = []\\n\\n theta = initial_theta\\n for i in range(0, niter):\\n theta -= alpha*gradient(theta, X, y, Lambda)\\n theta_list.append(theta)\\n cost_list.append(cost(theta, X, y, Lambda))\\n\\n return theta_list, cost_list\",\n \"def linreg_stochastic_grad(X, y, alpha=.01):\\n m = X.shape[0]\\n n = X.shape[1]\\n theta = np.zeros(n)\\n for i in range(m):\\n delta = alpha * (np.dot(theta.transpose(), X[i,:]) -y[i]) * X[i,:]\\n theta = theta - delta\\n return theta\",\n \"def log_likelihood_gradient(y, tx, w):\\n return tx.T.dot(sigmoid(tx.dot(w))-y)\",\n \"def gradient(cls, x):\\n return 1 - TanH.apply(x) ** 2\",\n \"def learning_by_gradient_descent(y, tx, w, gamma):\\n loss = calculate_loss(y,tx,w)\\n grad = calculate_gradient(y,tx,w)\\n w_new = w - gamma*grad\\n #grad is for debugging purpose\\n return loss, w_new,grad\",\n \"def simple_gradient(x, y, theta):\\n if x.shape[0] * y.shape[0] * theta.shape[0] == 0:\\n return None\\n if x.shape[0] != y.shape[0] or theta.shape[0] != 2:\\n return None\\n\\n x = add_intercept(x)\\n\\n result = [\\n forumla(x, y, theta, 0),\\n forumla(x, y, theta, 1)\\n ]\\n return result\",\n \"def calculate_gradient(y, tx, w):\\n\\n\\tret = tx.T.dot(sigmoid(np.dot(tx, w)) - y)\\n\\treturn ret\",\n \"def learning_by_gradient_descent(y, tx, w, gamma):\\n loss = calculate_loss(y,tx,w)\\n grad = calculate_gradient(y,tx,w)\\n w = w-gamma*grad\\n return w, loss\",\n \"def gradient(self, x):\\n pass\",\n \"def train_fn_grad(self, theta_value, theta):\\n theta.set_value(theta_value, borrow=True)\\n\\n train_grads = [self.batch_grad(i, self.n_train)\\n for i in xrange(self.n_train_batches)]\\n train_batch_sizes = [self.get_batch_size(i, self.n_train)\\n for i in xrange(self.n_train_batches)]\\n\\n return np.average(train_grads, weights=train_batch_sizes,\\n axis=0)\",\n \"def approx_grad_reg(theta, X, y, _lambda):\\n grad_a = np.array([(cost_reg(theta + e, X, y, _lambda) - cost_reg(theta - e, X, y, _lambda)) / (2 * 1e-5)\\n for e in np.identity(len(theta)) * 1e-5])\\n return grad_a\",\n \"def gradient(img):\\n G = np.zeros(img.shape)\\n theta = np.zeros(img.shape)\\n\\n ### YOUR CODE HERE\\n Gx = partial_x(img)\\n Gy = partial_y(img)\\n G = np.sqrt(np.power(Gx,2)+np.power(Gy,2))\\n theta = (np.arctan2(Gy, Gx) * 180 / np.pi) % 360\\n ### END YOUR CODE\\n\\n return G, theta\",\n \"def get_gradient(phi, pred, t, dot_product, weight, reg= 1, regression= \\\"logistic\\\"):\\n if regression == \\\"logistic\\\":\\n gradient = np.matmul(phi.T, pred - t)\\n elif regression == \\\"probit\\\":\\n R = np.eye(pred.shape[0])\\n for i in range(pred.shape[0]):\\n y_n = pred[i,0]\\n dotp = dot_product[i, 0]\\n pdf = norm.pdf(dotp)\\n R[i,i] = pdf/(y_n*(1-y_n) + TOLERANCE)\\n gradient = np.matmul(np.matmul(phi.T, R), pred-t)\\n elif regression == \\\"multiclass\\\":\\n gradient = np.matmul(phi.T, pred - t)\\n\\n # Add regularization\\n gradient += weight/ reg\\n return gradient\",\n \"def gradient(self, x):\\n u = np.asarray([x[0]])\\n C = self.C_func(u)\\n dC = self.dC_func(u, order=1)\\n P = self.P\\n numerator = np.sum((C - P) * dC, axis=0)\\n denominator = np.sum(np.sum((C - P) ** 2, axis=0) ** (1 / 2))\\n if np.abs(denominator) > 0:\\n gradient = numerator/denominator\\n else:\\n gradient = np.asarray(0)[np.newaxis]\\n return gradient\",\n \"def apply_gradient(params: torch.Tensor, grads: torch.Tensor, lr: float) -> torch.Tensor:\\n params_prime = params + lr * grads\\n return params_prime\",\n \"def run_steep_gradient_descent(data_x, data_y, len_data, alpha, theta):\\n n = len_data\\n # WE NEED TO transpose data_x into (p+1) *n ,theta is 1*(p+1)\\n prod = np.dot(theta, data_x.transpose())\\n\\n prod -= data_y\\n print(\\\"pro: data_x\\\", prod.shape, data_x.shape)\\n #prod represent the loss of the hypothesis and true label\\n sum_grad = np.dot(prod, data_x)\\n print(\\\"总梯度的值：\\\",sum_grad.shape)\\n\\n # batch-gradient descent\\n theta = theta -(alpha / n) * sum_grad\\n return theta\",\n \"def GDS(x, theta, y, lamb, alpha):\\n\\tm = y.shape[0]\\n\\tdif = (1/m) * x.T @ (sigmoid(x,theta) - y) #classical differentiation term, output is (n,1)\\n\\tdif_reg = (lamb/m) * theta #regularization term\\n\\treturn (dif + dif_reg) * alpha\",\n \"def gradientDescent(X, y, theta, alpha, num_iters):\\n\\n # Initialize some useful values\\n J_history = []\\n m = y.size # number of training examples\\n\\n for i in range(num_iters):\\n # ====================== YOUR CODE HERE ======================\\n # Instructions: Perform a single gradient step on the parameter vector\\n # theta.\\n #\\n # Hint: While debugging, it can be useful to print out the values\\n # of the cost function (computeCost) and gradient here.\\n #\\n # Calculate the gradient step according to the equation for theta1:\\n g_step1 = (alpha / m * np.sum( (np.dot(X,theta) - y) * X[:,1]) )\\n # Gradient step for theta knot:\\n g_step0 = (alpha / m * np.sum( (np.dot(X,theta) - y) ) )\\n \\n #update theta\\n theta[0] = (theta[0] - g_step0)\\n theta[1] = (theta[1] - g_step1)\\n \\n #print([theta , g_step1, g_step0])\\n\\n # ============================================================\\n\\n # Save the cost J in every iteration\\n J_history.append(computeCost(X, y, theta))\\n\\n return theta, J_history\",\n \"def gradient(w, x, t):\\n return 2 * np.dot(x.T, (nn(x, w) - t))\",\n \"def costFunction(theta,X,y):\\n m = X.shape[0]\\n J = 0\\n h = sigmoid (np.dot(X,theta))\\n \\n J = (1/m)* ((-np.dot(y.T,(np.log(h)))) - np.dot((1 - y).T,(np.log(1-h))))\\n \\n #grad = (1/m) * np.dot(X.T,(h-y))\\n grad = (1/m) * np.dot((h.T - y), X).T\\n \\n return J, grad\",\n \"def stochasticGradientDescent(x,y,theta,alpha):\\n m,n = np.shape(x)\\n convergence = 0.000000001\\n lastCost = 0\\n cost = -1 \\n recurseCount = 0\\n while abs(lastCost - cost) > convergence: # rcurse until converge\\n lastCost = cost\\n hypothesis = np.dot(x,theta) \\n for i in range(m):\\n # alpha = 4.0 / (1.0 + i) + 0.01 \\n loss = hypothesis[i] - y[i]\\n # gradient = np.dot(x[i],loss)\\n gradient = x[i,:].transpose() * loss \\n theta = theta - alpha * gradient\\n cost = np.sum((hypothesis-y)**2)/(2*m)\\n recurseCount += 1\\n return recurseCount,theta\",\n \"def gradient(self):\\n result = np.zeros(len(self.variables))\\n result[self.bivariateGradInd] = (self.shape-1)/self.variable - self.rate\\n return result\",\n \"def gradient(self):\\n functional = self\\n\\n class KLGradient(Operator):\\n\\n \\\"\\\"\\\"The gradient operator of this functional.\\\"\\\"\\\"\\n\\n def __init__(self):\\n \\\"\\\"\\\"Initialize a new instance.\\\"\\\"\\\"\\n super().__init__(functional.domain, functional.domain,\\n linear=False)\\n\\n def _call(self, x):\\n \\\"\\\"\\\"Apply the gradient operator to the given point.\\n The gradient is not defined in points where one or more\\n components are non-positive.\\n \\\"\\\"\\\"\\n if functional.prior is None:\\n return (-1.0) / x + 1\\n else:\\n return (-functional.prior) / x + 1\\n\\n return KLGradient()\",\n \"def batchGradientDescent(x,y,theta,alpha):\\n m,n = np.shape(x)\\n xTran = x.transpose()\\n convergence = 0.000000001\\n lastCost = 0\\n cost = -1 \\n recurseCount = 0\\n while abs(lastCost - cost) > convergence: # rcurse until converge\\n lastCost = cost\\n hypothesis = np.dot(x,theta)\\n loss = hypothesis - y\\n cost = np.sum(loss**2)/(2*m)\\n gradient = np.dot(xTran,loss)/m\\n theta = theta - alpha*gradient\\n recurseCount += 1\\n return recurseCount,theta\",\n \"def learning_by_penalized_gradient(y, tx, w, gamma, lambda_):\\n\\tgrad, H = penalized_logistic_regression(y, tx, w, lambda_)\\n\\n\\thgrad = np.linalg.inv(H).dot(grad)\\n\\n\\tw = w - gamma * hgrad\\n\\n\\treturn w\",\n \"def grad(self, w):\\n l1_grad = self.r * np.sign(w)\\n l2_grad = np.asarray(1 - self.r) * w \\n\\n gradient_penalty = self.alpha * (l1_grad + l2_grad)\\n\\n # Insert 0 for bias term.\\n return np.insert(gradient_penalty, 0, 0, axis=0)\",\n \"def gradient(self, x):\\n g = self._grad(\\n time_series=self.observed_data,\\n a=x[0],\\n b=x[1],\\n c=x[2],\\n sigma=self.sigma\\n )\\n return g\",\n \"def compute_gradient(self):\\n A = np.dot(self.X, self.w)\\n m = self.t.shape[0]\\n C = -1 * self.t * (1 / (1 + np.exp(A * self.t)))\\n return (1 / m) * np.dot(self.X.T, C)\",\n \"def reg_autograd(alpha,X,theta,y):\\n g=grad(func)\\n for i in range(0,20):\\n theta=theta- alpha * g(theta)\\n print(\\\"cost\\\", func(theta))\\n print(\\\"final theta\\\",theta)\",\n \"def grad(self, x):\\n norm_beta = np.linalg.norm(x)\\n if norm_beta > utils.TOLERANCE:\\n return (self.l / norm_beta) * x\\n else:\\n D = x.shape[0]\\n u = (self.rng(D, 1) * 2.0) - 1.0 # [-1, 1]^D\\n norm_u = np.linalg.norm(u)\\n a = self.rng() # [0, 1]\\n\\n return (self.l * (a / norm_u)) * u\",\n \"def gradcovfunc(self, theta, d):\\n sigmaf, l = theta[:2] \\n xxl = np.sum((d/l)**2, axis=1)\\n dk_dsigmaf = 2 * sigmaf * np.exp(-xxl/2.)\\n dk_dl = sigmaf**2/l * xxl * np.exp(-xxl/2.)\\n grad = np.array([dk_dsigmaf, dk_dl])\\n return grad\",\n \"def log_prior_grad(self, inputs):\",\n \"def gradient(series, **options):\\n x = series.index\\n y = series.values\\n\\n a = np.gradient(y, x, **options)\\n return series.__class__(a, series.index)\",\n \"def compute_logistic_gradient(y, tx, w):\\n return tx.T.dot(sigmoid(tx.dot(w)) - y) / len(y)\",\n \"def derivative(self, theta):\\n diag_gamma = np.dot(theta.T, self.X.T)\\n logistic_term = self.logistic_fn(diag_gamma)\\n diag_gamma = logistic_term * (1.0 - logistic_term)\\n gamma = np.diag(diag_gamma)\\n\\n # v computation\\n diags_v = 1.0 - 2*self.logistic_fn(np.dot(theta.T, self.X.T))\\n diags_v = diags_v.reshape((-1, 1))\\n diags_v = diags_v*self.X\\n assert diags_v.shape == self.X.shape #N*d shape\\n\\n XtGamma = np.dot(self.X.T, gamma) # d*N shape\\n\\n # TODO: Verifier car pas sur de mon coup ... et surtout plus long...\\n # id = np.eye(self.n_examples).reshape((self.n_examples, self.n_examples, 1))\\n # diags_v = diags_v.reshape((self.n_examples, 1, self.dim))\\n # v = id*diags_v # n*n*d tensor\\n # left = np.tensordot(XtGamma, v, axes=(1, 0)) # shape d*N*d\\n # assert left.shape == (self.dim, self.n_examples, self.dim)\\n # dg = np.tensordot(left, self.X, axes=(1, 0))\\n # dg = np.swapaxes(dg, axis1=-2, axis2=-1)\\n\\n dg = np.zeros((self.dim, self.dim, self.dim))\\n for idx, v_i_diag in enumerate(diags_v.T):\\n v_i = np.diag(v_i_diag)\\n dg_di = np.dot(np.dot(XtGamma, v_i), self.X)\\n dg[:, :, idx] = dg_di\\n return dg\",\n \"def run_gradient_descent(data,theta,alpha,num_iters):\\n population = data[:,0]\\n prices = data[:,1]\\n x = ones(shape=(len(population),2)) #add ones for theta0 \\n x[:,1] = population\\n x = transpose(x)\\n error_history = zeros(shape=(num_iters,1))\\n \\n for i in range(num_iters):\\n predictions = theta.dot(x)\\n errors_x1 = (predictions - prices) * x[0,:]\\n errors_x2 = (predictions - prices) * x[1,:]\\n theta[0][0] = theta[0][0] - alpha*(1.0/len(population))*errors_x1.sum()\\n theta[0][1] = theta[0][1] - alpha*(1.0/len(population))*errors_x2.sum()\\n error_history[i,0] = calculate_cost(theta,data)\\n \\n return theta, error_history\",\n \"def gradient(self, x):\\n return 0.0\",\n \"def gradient(poly):\\n return differential(\\n poly, chaospy.poly.collection.basis(1, 1, poly.dim, sort=\\\"GR\\\"))\",\n \"def curves_gradient(theta, m_, LAMBDA=0):\\n # X0: column of ones\\n theta0_gradient = (1 / m_) * sum((curves_hypothesis(theta, m_) - y[:m_].ravel()) * np.ones(m_))\\n # X1: data given as X\\n theta1_gradient = ((1 / m_) * sum((curves_hypothesis(theta, m_) - y[:m_].ravel()) * X[:m_].ravel())) \\\\\\n + (LAMBDA / m_) * theta[1]\\n return np.array([theta0_gradient, theta1_gradient])\",\n \"def costFunction(self,theta, X, y): \\n m = len(y)\\n h = self.sigmoid(X@theta)\\n J = 1 / m * (- y.T @ self.log(h) - (1-y).T @ self.log(1-h)) \\n # grad = 1/ m * X.T @ (h - y)\\n return J\",\n \"def gradient(self, x_in):\\r\\n\\r\\n return 1 - torch.pow(self.forward(x_in), 2)\",\n \"def compute_gradient(self): # TODO: try to change to square loss since it's hessian is easier to obtain\\n A = np.dot(self.X, self.w)\\n m = self.t.shape[0]\\n C = -1 * self.t * (1 / (1 + np.exp(A * self.t)))\\n return (1 / m) * np.dot(self.X.T, C)\",\n \"def vb_grad_natgrad(self):\\r\\n\\r\\n # Gradient for eta\\r\\n dL_dmmT_S = -0.5*self.Lambda/self.data_prop + 0.5*self.q_u_prec\\r\\n Kmmipsi1V,_ = dpotrs(self.Lm,self.psi1V,lower=1)\\r\\n dL_dm = (Kmmipsi1V - np.dot(self.Lambda,self.q_u_mean))/self.data_prop\\r\\n\\r\\n # Gradients for theta\\r\\n S = self.q_u_cov\\r\\n Si = self.q_u_prec\\r\\n m = self.q_u_mean\\r\\n dL_dSi = -mdot(S,dL_dmmT_S, S)\\r\\n\\r\\n dL_dmhSi = -2*dL_dSi\\r\\n dL_dSim = np.dot(dL_dSi,m) + np.dot(Si, dL_dm)\\r\\n\\r\\n return np.hstack((dL_dm.flatten(),dL_dmmT_S.flatten())) , np.hstack((dL_dSim.flatten(), dL_dmhSi.flatten()))\",\n \"def gradient(self):\\n functional = self\\n\\n class KLCCGradient(Operator):\\n\\n \\\"\\\"\\\"The gradient operator of this functional.\\\"\\\"\\\"\\n\\n def __init__(self):\\n \\\"\\\"\\\"Initialize a new instance.\\\"\\\"\\\"\\n super().__init__(functional.domain, functional.domain,\\n linear=False)\\n\\n def _call(self, x):\\n \\\"\\\"\\\"Apply the gradient operator to the given point.\\n\\n The gradient is not defined in points where one or more\\n components are larger than or equal to one.\\n \\\"\\\"\\\"\\n if functional.prior is None:\\n return 1.0 / (1 - x)\\n else:\\n return functional.prior / (1 - x)\\n\\n return KLCCGradient()\",\n \"def grad(self, w):\\n gradient_penalty = np.asarray(self.alpha) * w\\n # Insert 0 for bias term.\\n return np.insert(gradient_penalty, 0, 0, axis=0)\",\n \"def grad(self, w):\\n gradient_penalty = np.asarray(self.alpha) * w\\n # Insert 0 for bias term.\\n return np.insert(gradient_penalty, 0, 0, axis=0)\",\n \"def learning_by_penalized_gradient(y, tx, w, gamma, lambda_):\\n\\n #on test avec Newton\\n\\n loss,gradient,_ = penalized_logistic_regression(y,tx,w,lambda_)\\n\\n w = w - gamma*gradient\\n return loss, w,gradient\",\n \"def gradient_ascent(f, df, theta_init, step_size, max_iter):\\n\\n fs = []\\n xs = []\\n thetas = theta_init\\n for i in range(max_iter): #for each data example\\n fs.append(f(thetas))\\n\\n temp = step_size*df(thetas)\\n thetas = step_size*df(thetas) #modify that feature by using the derivative of log likelihood\\n xs.append(thetas.flatten())\\n if i % 10 == 0:\\n print(i, thetas)\\n\\n return thetas, fs, xs\",\n \"def calculate_maxL_gradient(y, tx, w):\\n sig=sigmoid(tx.dot(w))\\n grad=tx.T.dot(sig-y)\\n #divides with number of samples so that learning rate is not dependant on number of samples\\n grad=grad/len(y) \\n return grad\",\n \"def _log_prior_gradients(self):\\n if self.priors.size == 0:\\n return 0.\\n x = self.param_array\\n ret = np.zeros(x.size)\\n #compute derivate of prior density\\n [np.put(ret, ind, p.lnpdf_grad(x[ind])) for p, ind in self.priors.items()]\\n #add in jacobian derivatives if transformed\\n priored_indexes = np.hstack([i for p, i in self.priors.items()])\\n for c,j in self.constraints.items():\\n if not isinstance(c, Transformation):continue\\n for jj in j:\\n if jj in priored_indexes:\\n ret[jj] += c.log_jacobian_grad(x[jj])\\n return ret\",\n \"def grad(self, w):\\n gradient_penalty = np.asarray(self.alpha) * w\\n\\n # Insert 0 for bias term.\\n return np.insert(gradient_penalty, 0, 0, axis=0)\",\n \"def grad(theta, X, y, lambda_):\\n # ... dopolnite (naloga 1, naloga 2)\\n\\n l = []\\n for i, e in enumerate(theta):\\n l.append(1 / len(y) * sum([(h(x, theta) - yi) * x[i] for x, yi in zip(X, y)]) + 2 * lambda_ * e)\\n\\n return np.array(l)\",\n \"def lr_cost_function(theta, X, y, learning_rate):\\n m = len(y) # number of training examples\\n # You need to return the following variables correctly \\n J = 0\\n grad = np.zeros(theta.shape)\\n h = sigmoid(np.dot(X, theta))\\n\\n #Compute cost:\\n first = (1/m)*np.sum(np.dot(-y.T, np.log(h)) - np.dot((1-y).T, np.log(1-h)), axis=0)\\n second = (learning_rate/(2*m))*np.sum(theta[1:]**2)\\n J = first + second\\n \\n #Compute gradient\\n grad = np.dot((h-y).T, X)/m\\n grad = grad.T\\n grad[1:] += (learning_rate/m)*theta[1:]\\n return float(J), grad\",\n \"def gradient(self, x, i=None):\\n self.set_parameters(x=x)\\n gradient = self.gradient_loss()\\n if self.add_bias:\\n gradient[1:] += self.alpha * self.gradient_regularizer()\\n else:\\n gradient += self.alpha * self.gradient_regularizer()\\n if i is not None:\\n return gradient.ravel()[i]\\n return gradient.ravel()\",\n \"def compute_gradient_lasso(y, tx, w, lambda_):\\n e = y - tx.dot(w)\\n subgrad = lambda_ * np.sign(w)\\n\\n return -tx.T.dot(e)/len(e) + subgrad\",\n \"def compute_gradient(self, X, y, weights):\\n sigmoid = self.sigmoid(np.dot(X, weights))\\n return np.dot(X.T, y - sigmoid)\",\n \"def batch_grad_descent(X, y, alpha=0.1, num_iter=1000, check_gradient=False):\\n num_instances, num_features = X.shape[0], X.shape[1]\\n theta_hist = np.zeros((num_iter+1, num_features)) #Initialize theta_hist\\n loss_hist = np.zeros(num_iter+1) #initialize loss_hist\\n theta = np.ones(num_features) #initialize theta\\n\\n count = 0\\n while count < num_iter+1:\\n if check_gradient:\\n assert grad_checker(X,y,theta)\\n\\n grad = compute_square_loss_gradient(X,y,theta)\\n theta -= alpha*grad\\n theta_hist[count] = theta\\n loss_hist[count] = compute_square_loss(X,y,theta)\\n count += 1\\n \\n return theta_hist, loss_hist\",\n \"def check_gradient(self, x, y):\\n x = x.transpose()\\n y = y.transpose()\\n layers_copy = deepcopy(self.layers)\\n epsilon = 10 ** -4\\n a, layer = self.forward_propagation(x)\\n delta = self.calculate_delta(a, y, layer)\\n self.backpropagation(delta=delta, theta=layer.theta)\\n previous_layer_output = x\\n for layer in self.layers:\\n theta_copy = deepcopy(layer.theta)\\n real_theta_size = theta_copy.shape\\n delta = layer.delta\\n dc_dtheta = np.outer(previous_layer_output, delta).transpose()\\n previous_layer_output = layer.a\\n R, C = theta_copy.shape\\n for i in range(R):\\n for j in range(C):\\n theta_plus = deepcopy(theta_copy)\\n theta_plus[i, j] += epsilon\\n layer.theta = theta_plus\\n a_plus, l_plus = self.forward_propagation(x)\\n err_plus = self.calculate_loss(a_plus, y)\\n theta_minus = deepcopy(theta_copy)\\n theta_minus[i, j] -= epsilon\\n layer.theta = theta_minus\\n a_minus, l_minus = self.forward_propagation(x)\\n err_minus = self.calculate_loss(a_minus, y)\\n limit = (err_plus - err_minus)/(2*epsilon)\\n grad_diff = abs(dc_dtheta[i,j] - limit)\\n assert grad_diff < 10 ** -6, f\\\"Diff {grad_diff} is too big.\\\"\\n layer.theta = theta_copy\",\n \"def _compute_func_grad(self, w):\\n W = w.reshape((self.X.shape[1], self.Y.shape[1]))\\n self.nll_, self.grad_ = calculate_gradient(self.X, self.Y, W, self.prior, self.weighted,0)\",\n \"def gradient(data_x, data_y, parameters):\\n return data_x.T @ (data_x @ parameters - data_y) / data_x.shape[0]\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8320425","0.8320425","0.8256829","0.794873","0.7868711","0.7830685","0.78282064","0.77438694","0.77200764","0.77105415","0.76834327","0.7621433","0.7562439","0.72585183","0.7218782","0.7218774","0.721298","0.7015742","0.7006995","0.69971156","0.6987514","0.69131017","0.69129544","0.68834984","0.68834686","0.68663037","0.68401706","0.6830559","0.6802646","0.6792539","0.6765618","0.67565787","0.67539907","0.6721832","0.67160386","0.6714549","0.6703719","0.66849947","0.6667146","0.6664239","0.6656793","0.6634402","0.66270006","0.6620344","0.66145825","0.6607552","0.6605948","0.6588644","0.6581453","0.65814304","0.6569963","0.65691054","0.6555958","0.65261567","0.6510347","0.65001076","0.6494862","0.64866775","0.6464623","0.64565086","0.64560163","0.64458627","0.6425783","0.6425727","0.6412266","0.6389222","0.6378101","0.63779145","0.6373901","0.6371549","0.63605356","0.63578737","0.63553464","0.6347787","0.6347673","0.63456696","0.6327963","0.6307092","0.63065755","0.6306372","0.62852186","0.628112","0.6278384","0.6270677","0.62701714","0.62701714","0.62589717","0.6258369","0.6256502","0.6252224","0.6249683","0.62469107","0.6233294","0.6229668","0.62279737","0.61870456","0.6186673","0.6184583","0.61812323","0.6172965"],"string":"[\n \"0.8320425\",\n \"0.8320425\",\n \"0.8256829\",\n \"0.794873\",\n \"0.7868711\",\n \"0.7830685\",\n \"0.78282064\",\n \"0.77438694\",\n \"0.77200764\",\n \"0.77105415\",\n \"0.76834327\",\n \"0.7621433\",\n \"0.7562439\",\n \"0.72585183\",\n \"0.7218782\",\n \"0.7218774\",\n \"0.721298\",\n \"0.7015742\",\n \"0.7006995\",\n \"0.69971156\",\n \"0.6987514\",\n \"0.69131017\",\n \"0.69129544\",\n \"0.68834984\",\n \"0.68834686\",\n \"0.68663037\",\n \"0.68401706\",\n \"0.6830559\",\n \"0.6802646\",\n \"0.6792539\",\n \"0.6765618\",\n \"0.67565787\",\n \"0.67539907\",\n \"0.6721832\",\n \"0.67160386\",\n \"0.6714549\",\n \"0.6703719\",\n \"0.66849947\",\n \"0.6667146\",\n \"0.6664239\",\n \"0.6656793\",\n \"0.6634402\",\n \"0.66270006\",\n \"0.6620344\",\n \"0.66145825\",\n \"0.6607552\",\n \"0.6605948\",\n \"0.6588644\",\n \"0.6581453\",\n \"0.65814304\",\n \"0.6569963\",\n \"0.65691054\",\n \"0.6555958\",\n \"0.65261567\",\n \"0.6510347\",\n \"0.65001076\",\n \"0.6494862\",\n \"0.64866775\",\n \"0.6464623\",\n \"0.64565086\",\n \"0.64560163\",\n \"0.64458627\",\n \"0.6425783\",\n \"0.6425727\",\n \"0.6412266\",\n \"0.6389222\",\n \"0.6378101\",\n \"0.63779145\",\n \"0.6373901\",\n \"0.6371549\",\n \"0.63605356\",\n \"0.63578737\",\n \"0.63553464\",\n \"0.6347787\",\n \"0.6347673\",\n \"0.63456696\",\n \"0.6327963\",\n \"0.6307092\",\n \"0.63065755\",\n \"0.6306372\",\n \"0.62852186\",\n \"0.628112\",\n \"0.6278384\",\n \"0.6270677\",\n \"0.62701714\",\n \"0.62701714\",\n \"0.62589717\",\n \"0.6258369\",\n \"0.6256502\",\n \"0.6252224\",\n \"0.6249683\",\n \"0.62469107\",\n \"0.6233294\",\n \"0.6229668\",\n \"0.62279737\",\n \"0.61870456\",\n \"0.6186673\",\n \"0.6184583\",\n \"0.61812323\",\n \"0.6172965\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.80552995"},"document_rank":{"kind":"string","value":"3"}}},{"rowIdx":14395,"cells":{"query":{"kind":"string","value":"Returns N samples from the prior."},"document":{"kind":"string","value":"def sample_from_prior(self, n_samples):\n\n p0 = self.rng.lognormal(mean=self.mean, sigma=self.sigma, size=n_samples)\n return p0[:, np.newaxis]"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def sample_from_prior(self, n_samples):\n pass","def sample_from_prior(self, n_samples):\n\n p0 = self.min + self.rng.rand(n_samples) * (self.max - self.min)\n return p0[:, np.newaxis]","def sample_from_prior(self, n_samples):\n\n p0 = self.rng.normal(loc=self.mean, scale=self.sigma, size=n_samples)\n return p0[:, np.newaxis]","def sample_from_prior(self, n_samples):\n\n lamda = np.abs(self.rng.standard_cauchy(size=n_samples))\n\n p0 = np.log(np.abs(self.rng.randn() * lamda * self.scale))\n return p0[:, np.newaxis]","def sample_prior(size):\n return torch.randn(size)","def sample_from_prior(self):\n raise NotImplementedError","def sample_parameters_prior(self, n_samples, random_seed=None):\n\n if random_seed is not None:\n np.random.seed(random_seed)\n samples = []\n samples.append(self.var_noise.sample_from_prior(n_samples))\n samples.append(self.mean.sample_from_prior(n_samples))\n samples.append(self.kernel.sample_parameters(n_samples))\n\n return np.concatenate(samples, 1)","def sample(self, n_samples=1):\n\n\t\tif isinstance(n_samples, int):\n\t\t\tn_samples = (n_samples,)\n\t\tbeta_sample = self.beta_prior.sample(n_samples)\n\t\tgamma_sample = self.gamma_prior.sample(n_samples)\n\t\tp = torch.stack((beta_sample, gamma_sample)).T\n\t\tif n_samples == (1,):\n\t\t\tp = p[0]\n\t\tp = p.numpy()\n\t\treturn p","def sample_from_prior(self, *args, **kwargs):\n pass","def forward_sample(self, n):\n return self.flow.sample(n)","def prior_sample(self):\n pass","def generate_samples(self, n_samples):","def generate_samples(self, n_samples):","def sample(self, n):\n # Est probs from priority weights\n summed = sum(self.weight) + self.eps\n self.probs = [w / summed for w in self.priority]\n\n # Wieghted sample\n return np.random.choice(self.memory, size=n, p=self.probs).tolist()","def prior_samples(self, nsamples, coords=None):\n S = self.eval_S(self.kappa, self.sigma_f)\n if coords is None:\n coords = slice(self.mesh.num_vertices()) # take all coords\n weights = np.random.normal(scale=np.sqrt(S),\n size=(nsamples,) + S.shape) # shape (s, l)\n prior = np.einsum('sl,nl->sn', weights, self.eigenfunctions[coords])\n\n return prior","def samples(self):\n return self._values[:self.nsamples]","def generate_samples(self, no=10):\n observations = []\n state_sequence = []\n initial_state = np.random.choice(\n self.latent_variable_markov_chain.states,\n p=self.prior_probabilities)\n state_sequence.append(initial_state)\n observations.append(self.observation_from_state(initial_state))\n current_state = initial_state\n for i in range(2, no):\n next_state = self.latent_variable_markov_chain.next_state(current_state)\n state_sequence.append(next_state)\n observations.append(self.observation_from_state(next_state))\n current_state = next_state\n return observations, state_sequence","def sample(self, n, include=True):\n return [self(t / n) for t in range(n + int(include))]","def prior(n=10):\r\n p = []\r\n trials = 0\r\n acc = 0\r\n while acc < n:\r\n trials += 1\r\n r = np.random.rand(2) * np.array([4, 2]) + np.array([-2, -1])\r\n # print(\"r: \", r)\r\n if r[1] + r[0] >= -1 and r[1] - r[0] >= -1:\r\n p.append(r)\r\n acc += 1\r\n # print(\"trials: \", trials, \", acc: \", acc)\r\n return p","def pull_reads(self, n, prng): \n if not self.sampling:\n self.convert_to_array()\n index = prng.randint(0, self.total, size=n)\n index = np.sort(index)\n return self.reads[index,:]","def sample(self, n=1):\n raise NotImplementedError","def get_training_n(self, n: Optional[int] = None, random: bool = True) -> Tuple[torch.Tensor, torch.Tensor]:\n return self.__sample(self.train_data, self.train_los, n, random)","def sample(self, n):\n raise NotImplementedError","def _sample(self, n=1):\n return [self[i] for i in np.random.choice(self.length, n, replace=False)]","def get_n_samples(self):\n return {op.rx.get_n_samples() for op in self.ops}","def sample(self, n_samples):\n\n z = sample_prior((n_samples,) + self.flow.z_shape)\n ldj = torch.zeros(z.size(0))\n\n z, ldj = self.flow (z, ldj, reverse=True)\n z, ldj = self.logit_normalize(z, ldj, reverse=True)\n\n return z","def Sample(n=6):\n t = [random.normalvariate(0.0, 1.0) for i in range(n)]\n t.sort()\n return t","def posterior_sampler(self, nsamples, seed=0, verbose=True):\n\n import random\n\n random.seed(seed)\n sample = self.get_chain()[-self.get_tune:]\n sample = sample.reshape(-1, sample.shape[-1])\n sample = random.choices(sample, k=nsamples)\n\n return sample","def generate_samples(self, n_samples=100):\n \t\t\n\t\t#how many times should ancestral sampling be run\n\t\t#n_samples\n prior_samples=[]\n for i in range(0,n_samples):\n prior_sample = self.prior.get_samples(\n n_latent_nodes=self.n_latent_nodes,\n n_gibbs_sampling_steps=100, \n sampling_mode=\"gibbs_ancestral\")\n prior_sample = torch.cat(prior_sample)\n prior_samples.append(prior_sample)\n prior_samples=torch.stack(prior_samples)\n # prior_samples = tf.slice(prior_samples, [0, 0], [num_samples, -1])\n output_activations = self.decoder.decode(prior_samples)\n output_activations = output_activations+self._train_bias\n output_distribution = Bernoulli(logit=output_activations)\n output=torch.sigmoid(output_distribution.logits)\n # output_activations[0] = output_activations[0] + self.train_bias\n # output_dist = FactorialBernoulliUtil(output_activations)\n # output_samples = tf.nn.sigmoid(output_dist.logit_mu)\n # print(\"--- \",\"end VAE::generate_samples()\")\n return output","def get_random_samplers(self, n):\n if not self.has_samplers():\n self.draw_samplers()\n \n def get_shuffledcopy(x):\n x_ = x.copy()\n np.random.shuffle(x_)\n return x_\n \n return get_shuffledcopy(self.samplers)[:n]","def sample_gp_prior(self, x_list, n_samp, full_cov=True):\n mu, cov = self.get_gp_prior_mu_cov(x_list, full_cov)\n return self.get_normal_samples(mu, cov, n_samp, full_cov)","def get_posterior_sample(self):\n total_tries = self.prior_success + self.prior_failure\n prob_success = self.prior_success / total_tries\n # np.random.binomial采样出来的是二项分布的均值, 即正面朝上的次数,所以要除以N\n boot_sample = np.random.binomial(total_tries, prob_success) / total_tries\n return boot_sample","def sample(self, n_samples=1):\n\n\t\tsamples = np.random.random((n_samples, self.__ndims))*self.__range + self.__low\n\t\treturn samples","def get_samples(self, nsamples):\n sample_ids = np.random.choice(self.embed.shape[0], nsamples, replace=False)\n return self.embed[sample_ids]","def sample_predict(self, X, n_samples):\n W_samples = self.W_prior.sample(n_samples) ##size: [n_samples, n_in, n_out]\n if self.use_bias:\n bs = W_samples[:, :1, :]\n Ws = W_samples[:, 1:, :]\n if self.scaled_variance:\n Ws = Ws / np.sqrt(self.n_in)\n return torch.matmul(X, Ws) + bs\n else:\n if self.scaled_variance:\n W_samples = W_samples / np.sqrt(self.n_in)\n return torch.matmul(X, W_samples)","def pickTopicFromPrior(self):\r\n return self.priorTopicDistr.sampleIndex()","def fetch_samples(self):\n return torch.cat(self.samples,dim=0).reshape(-1,self.parameters.numel())","def _uniform_number(self, N=None):\n num_frames = self.data.shape[0]\n if not N:\n N = int(np.floor(\n Constants.DEFAULT_SAMPLE_PERCENTAGE * num_frames\n ))\n k = int(np.floor(num_frames / N))\n if k == 0:\n k = 1\n elif N > num_frames:\n k = 1\n else:\n assert type(N) is IntType, 'N is not an integer: {}'.format(N)\n k = int(np.floor(num_frames / N))\n\n # ensure that at most N samples are taken if N does not divide nicely\n return self.data[::k][:N], self.labels[::k][:N]","def sample(self, n_samples, sample_seed):\n self.seed_samples(sample_seed)\n eps = torch.randn(self.batch_size, n_samples, self.Y_dim)\n samples = eps*torch.exp(0.5*self.logvar.unsqueeze(1)) + self.mu.unsqueeze(1)\n samples = self.unwhiten_back(samples)\n samples = samples.data.cpu().numpy()\n return samples","def prior_sample(self, bn):\n x = np.zeros(3)\n\n # first joint prob\n random_choice = np.random.choice(bn[0], 1, bn[0].all(), bn[0])\n x[0] = random_choice[0]\n\n # Second Joint Prob\n if x[0] == 0.1:\n random_choice = np.random.choice(bn[1][0], 1, bn[1][0].all(), bn[1][0])\n x[1] = random_choice\n elif x[0] == 0.9:\n random_choice = np.random.choice(bn[1][1], 1, bn[1][1].all(), bn[1][1])\n x[1] = random_choice\n\n # Third Joint Prob\n if random_choice[0] == 0.8 or random_choice == 0.1:\n random_choice = np.random.choice(bn[2][0], 1, bn[2][0].all(), bn[2][0])\n x[2] = random_choice\n else:\n random_choice = np.random.choice(bn[2][1], 1, bn[2][1].all(), bn[2][1])\n x[2] = random_choice\n return x","def prob1(n):\n#raise NotImplementedError(\"Problem 1 Incomplete\")\n if n == 0 :\n raise ValueError(\"Sampling 0 points is not defined.\")\n total = 0\n for i in xrange(n) :\n if np.random.normal() > 3 :\n total += 1\n return float(total)/n","def sample (self, n):\n y = self.bins\n x = np.r_[0, self.values.cumsum ()] / self.sum\n # interpolate inverse CDF\n out = np.interp (np.random.random (n), x, y)\n if n == 1:\n return out[0]\n else:\n return out.reshape ((n,))","def get_n(self, n):\n \n return [self.get_values() for _ in range(n)]","def getNrSamples(self): \r\n return self.numSamples","def sample(self, n):\n idx = np.random.randint(0, len(self.memory), size=n)\n return [self.memory[i] for i in idx]","def pull_n_samples(dset, n):\n return list(dset[i] for i in random.sample(range(len(dset)), n))","def lnprior(self):\n \n return","def samples(self) -> int:\n return self._samples","def _construct_sample_from_prior(self):\n z_sym = T.matrix()\n x_sym = T.matrix()\n irs = self.ir_steps\n oputs = [self.obs_transform(self.s0)]\n oputs.extend([self.obs_transform(self.si[i]) for i in range(irs)])\n _, hi_zmuv = self._construct_zmuv_samples(x_sym, 1)\n sample_func = theano.function(inputs=[z_sym, x_sym], outputs=oputs, \\\n givens={ self.z: z_sym, \\\n self.x_in: T.zeros_like(x_sym), \\\n self.x_out: T.zeros_like(x_sym), \\\n self.hi_zmuv: hi_zmuv }, \\\n updates=self.scan_updates)\n def prior_sampler(samp_count):\n x_samps = to_fX( np.zeros((samp_count, self.obs_dim)) )\n old_switch = self.train_switch.get_value(borrow=False)\n # set model to generation mode\n self.set_train_switch(switch_val=0.0)\n z_samps = to_fX( npr.randn(samp_count, self.z_dim) )\n model_samps = sample_func(z_samps, x_samps)\n # set model back to either training or generation mode\n self.set_train_switch(switch_val=old_switch)\n return model_samps\n return prior_sampler","def generate_samples(self):\n self.analytic_probability()","def prior_distribution(self):\n out = self.model.forward(self.inducing_points)\n return MultivariateNormal(out.mean, out.lazy_covariance_matrix.evaluate_kernel())","def sample(self, size=1):\n samples = scipy.stats.bernoulli.rvs(self.p, size=size)\n if size == 1:\n return samples[0]\n return samples","def n_remaining_samples(self):\n return -1","def n_remaining_samples(self):\n return -1","def n_remaining_samples(self):\n return -1","def sample(self, nsamples):\n return self.dist.sample(nsamples)","def __resample(self):\n p_resample = []\n w_max = max(self.weights)\n index = int(round(random.uniform(0, self.n - 1)))\n beta = 0\n for i in range(self.n):\n beta += random.uniform(0, 2 * w_max)\n while self.weights[index] < beta:\n beta -= self.weights[index]\n index = (index + 1) % self.n\n p_resample.append(self.particles[index, :])\n return np.array(p_resample)","def burnin_by_first_n(samples, N, step_number=False, logger_level=\"debug\"):\n _samples = copy.deepcopy(samples)\n n_samples = {key: N for key in _samples.keys()}\n if step_number:\n n_samples = {\n key: item + N if item is not None else N for key, item in\n _number_of_negative_steps(_samples, logger_level=logger_level).items()\n }\n getattr(logger, logger_level)(\n \"Removing the first {} as burnin\".format(\n \", \".join(\n [\"{} samples from {}\".format(val, key) for key, val in n_samples.items()]\n )\n )\n )\n return _samples.discard_samples(n_samples)","def take_samples(self, num_samples: int) -> List:\n if num_samples > len(self.samples):\n return random.sample(self.samples, len(self.samples))\n return random.sample(self.samples, num_samples)","def n_samples(self) -> int: # pragma: no cover\n return self.samples.shape[0]","def sample_count(self):","def sample_users(self, n):\n return torch.rand((n, self.num_topics), generator=self.generator) * 2 - 1","def sample_size_with_prior(self, prior=\"small\"):\n if prior == \"small\":\n prior_sizes = list(range(self._min_size, self._max_size))\n elif prior == \"big\":\n prior_sizes = list(range(self._min_size+1, self._max_size+1))\n return random.choice(prior_sizes)","def sample(self,\n n_samples: Optional[int],\n temperatures: Optional[List[float]] = None) -> T.Tensor:\n samples = self.levels[-1].sample(\n self.top_prior_shape, n_samples=n_samples, temperatures=temperatures)\n for level in self.levels[-2::-1]:\n samples = level.sample(context=samples)\n return samples","def prior_predictive(self):\n cfg = self.config\n n = cfg['batch_size'] * cfg['q/n_samples']\n n_samples = cfg['q/n_samples']\n with util.get_or_create_scope('model', reuse=True):\n h_prior = tf.cast(self.p_h_L.sample(n), cfg['dtype'])\n h_prior = tf.reshape(\n h_prior, [cfg['q/n_samples'], cfg['batch_size'], -1])\n h = [None] * cfg['p/n_layers']\n h[cfg['p/n_layers'] - 1] = h_prior\n for n in range(cfg['p/n_layers'] - 1, 0, -1):\n p_h_n = self.build_stochastic_layer(n, h_above=h[n])\n h[n - 1] = tf.cast(p_h_n.sample(), cfg['dtype'])\n return self.likelihood(h[0])","def sample(self, num_samples = 1):\n\n X = np.empty(num_samples)\n for k in range(len(X)):\n\n # starting\n n = 1\n a = 1\n Un = self.UG.sample()\n a = a * Un\n\n # iterate over\n while a >= np.exp(-self.rate):\n n = n + 1\n Un = self.UG.sample()\n a = a * Un\n\n X[k] = n - 1\n\n return X","def sample_pagerank(corpus, damping_factor, n):\n probabilities = dict()\n samples = []\n\n # Random first sample\n page = random.choice(list(corpus.keys()))\n samples.append(page)\n \n # Remaining samples after first\n for i in range(n-1):\n p = transition_model(corpus, page, damping_factor)\n page = random.choices(list(p.keys()), weights=list(p.values()), k=1)[0]\n samples.append(page)\n\n # Count\n for p in corpus.keys():\n probabilities[p] = samples.count(p) / n\n\n return probabilities","def get_prev_samples(self):\n return self.y_p","def main(n_samples):\n uso = usolib.uso.uar(N)\n lst = [usolib.randomfacet.randomfacet_sample(uso, N) for i in range(n_samples)]\n return sum(lst) / float(n_samples)","def sample_data(self, N, pmin=8., pmax=12.):\n ps = [matrix(ra.uniform(pmin, pmax, (self.n, 1))) for k in range(N)]\n xs = [self.compute_demand(p) for p in ps]\n return zip(xs,ps)","def simplex_sampler(self, n):\n\n # edge case: n = 1\n if n == 1:\n return np.array([self.initial_potential])\n\n values = [np.random.uniform() for i in range(n-1)]\n values.extend([0, 1])\n values.sort()\n values_arr = np.array(values)\n\n xs = values_arr[1:] - values_arr[:-1]\n\n # return in decresing order of magnitude, to use for higher levels\n xs = self.initial_potential*np.sort(xs)\n xs = xs[::-1]\n return xs","def _number_of_samples(self):\n return len(self._raw_data.samples)","def get_next_sample(self):","def get_number_samples(self):\n return self.samples.shape[0]","def sample_low_rank(self, n_samples, mu, logvar, F):\n #F = torch.unsqueeze(F, dim=1).repeat(1, n_samples, 1, 1) # [self.batch_size, n_samples, self.Y_dim, self.rank]\n F = F.repeat(n_samples, 1, 1) # [self.batch_size*n_samples, self.Y_dim, self.rank]\n mu = mu.repeat(n_samples, 1) # [self.batch_size*n_samples, self.Y_dim]\n logvar = logvar.repeat(n_samples, 1) # [self.batch_size*n_samples, self.Y_dim]\n eps_low_rank = torch.randn(self.batch_size*n_samples, self.rank, 1)\n eps_diag = torch.randn(self.batch_size*n_samples, self.Y_dim)\n half_var = torch.exp(0.5*logvar) # [self.batch_size*n_samples, self.Y_dim]\n samples = torch.bmm(F, eps_low_rank).squeeze() + mu + half_var*eps_diag\n samples = samples.reshape(n_samples, self.batch_size, self.Y_dim)\n samples = samples.transpose(0, 1)\n samples = self.unwhiten_back(samples)\n samples = samples.data.cpu().numpy()\n return samples","def get_log_prior(samples):\n prior_distribution = tfp.distributions.Normal(loc=0., scale=1.)\n log_prior = tf.reduce_sum(prior_distribution.log_prob(samples), axis=1)\n return log_prior","def sample_from_log_prob(A, n):\n A_cum = log_cum_sum(A)\n C_pos = [ -c for c in reversed(A_cum)]\n sel = np.log(np.random.random(n))\n pos = [len(A) - np.searchsorted(C_pos,-r) for r in sel]\n return pos","def priors_sample(self):\n \n theta_star = np.zeros(self.n_params)\n\n for i in xrange(self.n_params): \n np.random.seed() \n theta_star[i] = self.param_obj.prior()[i].rvs(size=1)[0]\n\n return theta_star","def sample_pagerank(corpus, damping_factor, n):\n all_pages = []\n first_sample_prob = random.randint(0, len(corpus) - 1)\n distribution_count = dict()\n\n for u in corpus:\n distribution_count[u] = 0\n all_pages.append(u)\n\n sample = all_pages[first_sample_prob]\n for i in range(n - 1): # n - 1 because first sample was already calculated\n selection_bucket = dict()\n selection_start = 0.0\n sample_distribution = transition_model(corpus, sample, damping_factor)\n sample_prob = random.random()\n for u in sample_distribution:\n floor = selection_start\n ceiling = selection_start + sample_distribution[u]\n selection_start = ceiling\n selection_bucket[u] = [floor, ceiling]\n for u in selection_bucket:\n v = selection_bucket[u]\n if v[0] < sample_prob < v[1]:\n sample = u\n distribution_count[u] += 1\n distribution = dict()\n for u in distribution_count:\n distribution[u] = float(distribution_count[u]) / n\n\n return distribution","def get_num_samples(self):\n return self._num_samples","def sample(self, n):\n unparameterized = [\n v for v in self._weights.keys() if v not in self._weights_p.keys()\n ]\n if len(unparameterized) > 0:\n raise ValueError(\n \"The following variables must be numerically parameterized before sampling: {0}\".format(\n unparameterized\n )\n )\n mu, cov = self.parameterized_mean_cov()\n cov = np.asarray(cov).astype(np.float64)\n mu = np.asarray(mu).astype(np.float64)\n return np.random.multivariate_normal(mu.ravel(), cov, size=n)","def generate_samples(self,n_samples=100):\n rnd_input=torch.randn((n_samples,self._reparam_nodes[1]))\n zeta=rnd_input \n # rnd_input=torch.where((rnd_input>0.5),torch.ones(rnd_input.size()),torch.zeros(rnd_input.size()))\n # print(rnd_input) \n # output, mu, logvar, zeta=self.forward(rnd_input)\n # mu = self._reparam_layers['mu'](rnd_input)\n # logvar = self._reparam_layers['var'](rnd_input)\n # zeta = self.reparameterize(mu, logvar)\n output = self.decoder.decode(zeta)\n return output","def process_custom_prior(prior) -> Tuple[Distribution, int, bool]:\n\n check_prior_methods(prior)\n\n check_prior_batch_behavior(prior)\n\n prior, is_prior_numpy = maybe_wrap_prior_to_pytorch(prior)\n\n parameter_dim = prior.sample().numel()\n\n return prior, parameter_dim, is_prior_numpy","def __call__(self, *args):\n r = np.random.rand(*args)\n if type(r) is float:\n samples = self.values[(r < self.p).nonzero()[0][0]]\n elif type(r) is np.ndarray:\n samples = np.array(\n [self.values[np.nonzero(x < self.p)[0][0]] \n for x in r.flat]).reshape(r.shape)\n return samples","def get_num_samples(self) -> int:\n # must be implemented in subclass\n raise NotImplementedError","def sample(self, n_samples: int) -> torch.Tensor:\n return self.dist.sample((n_samples,))","def ts_rewards_sample(self):\n exp_rewards_list = [np.random.beta(a, b) for a, b in zip(self.alpha_params, self.beta_params)]\n return np.array(exp_rewards_list)","def prediction_samples(self) -> np.ndarray:\n return self.prediction_samples","def sample(self, n_samples: int, random_state: Optional[int] = None) -> np.ndarray:\n\n if not isinstance(self.prob, np.ndarray):\n samples = stats.bernoulli.rvs(\n p=self.prob, size=n_samples, random_state=random_state\n )\n else:\n n_classes = len(self.prob) # type: ignore\n samples = np.zeros((n_samples, n_classes))\n\n for cls in range(n_classes):\n samples[:, cls] = stats.bernoulli.rvs(p=self.prob[cls], size=n_samples, random_state=random_state) # type: ignore\n\n return samples","def generate_synth_data(n):","def prior_sample_parameter(self, parameter):\n pass","def get_samples_per_signal(self):\n return np.array([self.samples_in_file(chn) for chn in range(self.signals_in_file)])","def sample(self, num):\n state, action, reward, next_state, not_terminal = [], [], [], [], []\n for exp in self.experiences.sample(num):\n state.append(exp.state)\n action.append(exp.action)\n reward.append(exp.reward)\n next_state.append(exp.next_state)\n not_terminal.append(exp.not_terminal)\n\n return np.asarray(state), np.asarray(action), np.asarray(reward), \\\n np.asarray(next_state), np.asarray(not_terminal)","def samples(self):\n pass","def sample_observations(self, n_samples, z):\n lat_dim = self.transition.dim_x\n x = []\n for i in range(self.time_steps):\n z_t = tf.slice(z, [0, i * lat_dim], [-1, lat_dim]) \n x.append(self.emmision.sample(z_t))\n return tf.concat(x, axis=1)","def sample(self, n_samples):\n with torch.no_grad():\n z = torch.randn((n_samples, self.z_dim))\n samples = self.decoder(z)\n im_size = int(np.sqrt(self.input_dim))\n samples = samples.view(-1, 1, im_size, im_size)\n\n return samples","def sample_pagerank(corpus, damping_factor, n):\n # All pages in entire corpus\n all_pages = [key for key in corpus.keys()]\n\n # Dictionary which will map each page to the number of generated samples that were the page\n counts = dict()\n for page in all_pages:\n counts[page] = 0\n\n # Choosing first sample randomly and updating counts dict\n first_sample = random.choice(all_pages)\n counts[first_sample] += 1\n\n for i in range(1, n):\n # If we are on the second sample, do the calculation based on the first\n if i == 1:\n prev_sample = first_sample\n\n distributions = transition_model(corpus, prev_sample, damping_factor)\n\n keys = [key for key in distributions.keys()]\n values = [distributions[key] for key in distributions.keys()]\n\n # Using random.choices to select an option given a list of weights (distribution)\n current_sample = random.choices(keys, weights=values, k=1)[0]\n\n # Updating counts dict given the current sample\n counts[current_sample] += 1\n\n prev_sample = current_sample\n\n # Modifying counts dict to represent the proportion of samples that corresponded to that page instead of the raw amount\n for page in all_pages:\n counts[page] /= n\n\n return counts","def take(n, seq):\n return itertools.islice(seq, n)","def get_samples(self) -> McmcPtResult:","def deterministic_sample(choices, n_to_sample, p): # pylint: disable=invalid-name\n\n sample_counts = np.ceil(n_to_sample * p).astype(int)\n\n n_to_remove = np.sum(sample_counts) - n_to_sample\n\n if n_to_remove == 0:\n return choices[counts_to_vector(sample_counts)]\n\n non_zero_mask = sample_counts > 0\n\n removal_indices = np.floor(np.linspace(0.0,\n np.sum(non_zero_mask),\n n_to_remove,\n endpoint=False)).astype(int)\n\n tmp = sample_counts[non_zero_mask]\n tmp[removal_indices] = tmp[removal_indices] - 1\n\n sample_counts[non_zero_mask] = tmp\n\n assert np.sum(sample_counts) == n_to_sample\n\n samples = choices[counts_to_vector(sample_counts)]\n\n return samples"],"string":"[\n \"def sample_from_prior(self, n_samples):\\n pass\",\n \"def sample_from_prior(self, n_samples):\\n\\n p0 = self.min + self.rng.rand(n_samples) * (self.max - self.min)\\n return p0[:, np.newaxis]\",\n \"def sample_from_prior(self, n_samples):\\n\\n p0 = self.rng.normal(loc=self.mean, scale=self.sigma, size=n_samples)\\n return p0[:, np.newaxis]\",\n \"def sample_from_prior(self, n_samples):\\n\\n lamda = np.abs(self.rng.standard_cauchy(size=n_samples))\\n\\n p0 = np.log(np.abs(self.rng.randn() * lamda * self.scale))\\n return p0[:, np.newaxis]\",\n \"def sample_prior(size):\\n return torch.randn(size)\",\n \"def sample_from_prior(self):\\n raise NotImplementedError\",\n \"def sample_parameters_prior(self, n_samples, random_seed=None):\\n\\n if random_seed is not None:\\n np.random.seed(random_seed)\\n samples = []\\n samples.append(self.var_noise.sample_from_prior(n_samples))\\n samples.append(self.mean.sample_from_prior(n_samples))\\n samples.append(self.kernel.sample_parameters(n_samples))\\n\\n return np.concatenate(samples, 1)\",\n \"def sample(self, n_samples=1):\\n\\n\\t\\tif isinstance(n_samples, int):\\n\\t\\t\\tn_samples = (n_samples,)\\n\\t\\tbeta_sample = self.beta_prior.sample(n_samples)\\n\\t\\tgamma_sample = self.gamma_prior.sample(n_samples)\\n\\t\\tp = torch.stack((beta_sample, gamma_sample)).T\\n\\t\\tif n_samples == (1,):\\n\\t\\t\\tp = p[0]\\n\\t\\tp = p.numpy()\\n\\t\\treturn p\",\n \"def sample_from_prior(self, *args, **kwargs):\\n pass\",\n \"def forward_sample(self, n):\\n return self.flow.sample(n)\",\n \"def prior_sample(self):\\n pass\",\n \"def generate_samples(self, n_samples):\",\n \"def generate_samples(self, n_samples):\",\n \"def sample(self, n):\\n # Est probs from priority weights\\n summed = sum(self.weight) + self.eps\\n self.probs = [w / summed for w in self.priority]\\n\\n # Wieghted sample\\n return np.random.choice(self.memory, size=n, p=self.probs).tolist()\",\n \"def prior_samples(self, nsamples, coords=None):\\n S = self.eval_S(self.kappa, self.sigma_f)\\n if coords is None:\\n coords = slice(self.mesh.num_vertices()) # take all coords\\n weights = np.random.normal(scale=np.sqrt(S),\\n size=(nsamples,) + S.shape) # shape (s, l)\\n prior = np.einsum('sl,nl->sn', weights, self.eigenfunctions[coords])\\n\\n return prior\",\n \"def samples(self):\\n return self._values[:self.nsamples]\",\n \"def generate_samples(self, no=10):\\n observations = []\\n state_sequence = []\\n initial_state = np.random.choice(\\n self.latent_variable_markov_chain.states,\\n p=self.prior_probabilities)\\n state_sequence.append(initial_state)\\n observations.append(self.observation_from_state(initial_state))\\n current_state = initial_state\\n for i in range(2, no):\\n next_state = self.latent_variable_markov_chain.next_state(current_state)\\n state_sequence.append(next_state)\\n observations.append(self.observation_from_state(next_state))\\n current_state = next_state\\n return observations, state_sequence\",\n \"def sample(self, n, include=True):\\n return [self(t / n) for t in range(n + int(include))]\",\n \"def prior(n=10):\\r\\n p = []\\r\\n trials = 0\\r\\n acc = 0\\r\\n while acc < n:\\r\\n trials += 1\\r\\n r = np.random.rand(2) * np.array([4, 2]) + np.array([-2, -1])\\r\\n # print(\\\"r: \\\", r)\\r\\n if r[1] + r[0] >= -1 and r[1] - r[0] >= -1:\\r\\n p.append(r)\\r\\n acc += 1\\r\\n # print(\\\"trials: \\\", trials, \\\", acc: \\\", acc)\\r\\n return p\",\n \"def pull_reads(self, n, prng): \\n if not self.sampling:\\n self.convert_to_array()\\n index = prng.randint(0, self.total, size=n)\\n index = np.sort(index)\\n return self.reads[index,:]\",\n \"def sample(self, n=1):\\n raise NotImplementedError\",\n \"def get_training_n(self, n: Optional[int] = None, random: bool = True) -> Tuple[torch.Tensor, torch.Tensor]:\\n return self.__sample(self.train_data, self.train_los, n, random)\",\n \"def sample(self, n):\\n raise NotImplementedError\",\n \"def _sample(self, n=1):\\n return [self[i] for i in np.random.choice(self.length, n, replace=False)]\",\n \"def get_n_samples(self):\\n return {op.rx.get_n_samples() for op in self.ops}\",\n \"def sample(self, n_samples):\\n\\n z = sample_prior((n_samples,) + self.flow.z_shape)\\n ldj = torch.zeros(z.size(0))\\n\\n z, ldj = self.flow (z, ldj, reverse=True)\\n z, ldj = self.logit_normalize(z, ldj, reverse=True)\\n\\n return z\",\n \"def Sample(n=6):\\n t = [random.normalvariate(0.0, 1.0) for i in range(n)]\\n t.sort()\\n return t\",\n \"def posterior_sampler(self, nsamples, seed=0, verbose=True):\\n\\n import random\\n\\n random.seed(seed)\\n sample = self.get_chain()[-self.get_tune:]\\n sample = sample.reshape(-1, sample.shape[-1])\\n sample = random.choices(sample, k=nsamples)\\n\\n return sample\",\n \"def generate_samples(self, n_samples=100):\\n \\t\\t\\n\\t\\t#how many times should ancestral sampling be run\\n\\t\\t#n_samples\\n prior_samples=[]\\n for i in range(0,n_samples):\\n prior_sample = self.prior.get_samples(\\n n_latent_nodes=self.n_latent_nodes,\\n n_gibbs_sampling_steps=100, \\n sampling_mode=\\\"gibbs_ancestral\\\")\\n prior_sample = torch.cat(prior_sample)\\n prior_samples.append(prior_sample)\\n prior_samples=torch.stack(prior_samples)\\n # prior_samples = tf.slice(prior_samples, [0, 0], [num_samples, -1])\\n output_activations = self.decoder.decode(prior_samples)\\n output_activations = output_activations+self._train_bias\\n output_distribution = Bernoulli(logit=output_activations)\\n output=torch.sigmoid(output_distribution.logits)\\n # output_activations[0] = output_activations[0] + self.train_bias\\n # output_dist = FactorialBernoulliUtil(output_activations)\\n # output_samples = tf.nn.sigmoid(output_dist.logit_mu)\\n # print(\\\"--- \\\",\\\"end VAE::generate_samples()\\\")\\n return output\",\n \"def get_random_samplers(self, n):\\n if not self.has_samplers():\\n self.draw_samplers()\\n \\n def get_shuffledcopy(x):\\n x_ = x.copy()\\n np.random.shuffle(x_)\\n return x_\\n \\n return get_shuffledcopy(self.samplers)[:n]\",\n \"def sample_gp_prior(self, x_list, n_samp, full_cov=True):\\n mu, cov = self.get_gp_prior_mu_cov(x_list, full_cov)\\n return self.get_normal_samples(mu, cov, n_samp, full_cov)\",\n \"def get_posterior_sample(self):\\n total_tries = self.prior_success + self.prior_failure\\n prob_success = self.prior_success / total_tries\\n # np.random.binomial采样出来的是二项分布的均值, 即正面朝上的次数,所以要除以N\\n boot_sample = np.random.binomial(total_tries, prob_success) / total_tries\\n return boot_sample\",\n \"def sample(self, n_samples=1):\\n\\n\\t\\tsamples = np.random.random((n_samples, self.__ndims))*self.__range + self.__low\\n\\t\\treturn samples\",\n \"def get_samples(self, nsamples):\\n sample_ids = np.random.choice(self.embed.shape[0], nsamples, replace=False)\\n return self.embed[sample_ids]\",\n \"def sample_predict(self, X, n_samples):\\n W_samples = self.W_prior.sample(n_samples) ##size: [n_samples, n_in, n_out]\\n if self.use_bias:\\n bs = W_samples[:, :1, :]\\n Ws = W_samples[:, 1:, :]\\n if self.scaled_variance:\\n Ws = Ws / np.sqrt(self.n_in)\\n return torch.matmul(X, Ws) + bs\\n else:\\n if self.scaled_variance:\\n W_samples = W_samples / np.sqrt(self.n_in)\\n return torch.matmul(X, W_samples)\",\n \"def pickTopicFromPrior(self):\\r\\n return self.priorTopicDistr.sampleIndex()\",\n \"def fetch_samples(self):\\n return torch.cat(self.samples,dim=0).reshape(-1,self.parameters.numel())\",\n \"def _uniform_number(self, N=None):\\n num_frames = self.data.shape[0]\\n if not N:\\n N = int(np.floor(\\n Constants.DEFAULT_SAMPLE_PERCENTAGE * num_frames\\n ))\\n k = int(np.floor(num_frames / N))\\n if k == 0:\\n k = 1\\n elif N > num_frames:\\n k = 1\\n else:\\n assert type(N) is IntType, 'N is not an integer: {}'.format(N)\\n k = int(np.floor(num_frames / N))\\n\\n # ensure that at most N samples are taken if N does not divide nicely\\n return self.data[::k][:N], self.labels[::k][:N]\",\n \"def sample(self, n_samples, sample_seed):\\n self.seed_samples(sample_seed)\\n eps = torch.randn(self.batch_size, n_samples, self.Y_dim)\\n samples = eps*torch.exp(0.5*self.logvar.unsqueeze(1)) + self.mu.unsqueeze(1)\\n samples = self.unwhiten_back(samples)\\n samples = samples.data.cpu().numpy()\\n return samples\",\n \"def prior_sample(self, bn):\\n x = np.zeros(3)\\n\\n # first joint prob\\n random_choice = np.random.choice(bn[0], 1, bn[0].all(), bn[0])\\n x[0] = random_choice[0]\\n\\n # Second Joint Prob\\n if x[0] == 0.1:\\n random_choice = np.random.choice(bn[1][0], 1, bn[1][0].all(), bn[1][0])\\n x[1] = random_choice\\n elif x[0] == 0.9:\\n random_choice = np.random.choice(bn[1][1], 1, bn[1][1].all(), bn[1][1])\\n x[1] = random_choice\\n\\n # Third Joint Prob\\n if random_choice[0] == 0.8 or random_choice == 0.1:\\n random_choice = np.random.choice(bn[2][0], 1, bn[2][0].all(), bn[2][0])\\n x[2] = random_choice\\n else:\\n random_choice = np.random.choice(bn[2][1], 1, bn[2][1].all(), bn[2][1])\\n x[2] = random_choice\\n return x\",\n \"def prob1(n):\\n#raise NotImplementedError(\\\"Problem 1 Incomplete\\\")\\n if n == 0 :\\n raise ValueError(\\\"Sampling 0 points is not defined.\\\")\\n total = 0\\n for i in xrange(n) :\\n if np.random.normal() > 3 :\\n total += 1\\n return float(total)/n\",\n \"def sample (self, n):\\n y = self.bins\\n x = np.r_[0, self.values.cumsum ()] / self.sum\\n # interpolate inverse CDF\\n out = np.interp (np.random.random (n), x, y)\\n if n == 1:\\n return out[0]\\n else:\\n return out.reshape ((n,))\",\n \"def get_n(self, n):\\n \\n return [self.get_values() for _ in range(n)]\",\n \"def getNrSamples(self): \\r\\n return self.numSamples\",\n \"def sample(self, n):\\n idx = np.random.randint(0, len(self.memory), size=n)\\n return [self.memory[i] for i in idx]\",\n \"def pull_n_samples(dset, n):\\n return list(dset[i] for i in random.sample(range(len(dset)), n))\",\n \"def lnprior(self):\\n \\n return\",\n \"def samples(self) -> int:\\n return self._samples\",\n \"def _construct_sample_from_prior(self):\\n z_sym = T.matrix()\\n x_sym = T.matrix()\\n irs = self.ir_steps\\n oputs = [self.obs_transform(self.s0)]\\n oputs.extend([self.obs_transform(self.si[i]) for i in range(irs)])\\n _, hi_zmuv = self._construct_zmuv_samples(x_sym, 1)\\n sample_func = theano.function(inputs=[z_sym, x_sym], outputs=oputs, \\\\\\n givens={ self.z: z_sym, \\\\\\n self.x_in: T.zeros_like(x_sym), \\\\\\n self.x_out: T.zeros_like(x_sym), \\\\\\n self.hi_zmuv: hi_zmuv }, \\\\\\n updates=self.scan_updates)\\n def prior_sampler(samp_count):\\n x_samps = to_fX( np.zeros((samp_count, self.obs_dim)) )\\n old_switch = self.train_switch.get_value(borrow=False)\\n # set model to generation mode\\n self.set_train_switch(switch_val=0.0)\\n z_samps = to_fX( npr.randn(samp_count, self.z_dim) )\\n model_samps = sample_func(z_samps, x_samps)\\n # set model back to either training or generation mode\\n self.set_train_switch(switch_val=old_switch)\\n return model_samps\\n return prior_sampler\",\n \"def generate_samples(self):\\n self.analytic_probability()\",\n \"def prior_distribution(self):\\n out = self.model.forward(self.inducing_points)\\n return MultivariateNormal(out.mean, out.lazy_covariance_matrix.evaluate_kernel())\",\n \"def sample(self, size=1):\\n samples = scipy.stats.bernoulli.rvs(self.p, size=size)\\n if size == 1:\\n return samples[0]\\n return samples\",\n \"def n_remaining_samples(self):\\n return -1\",\n \"def n_remaining_samples(self):\\n return -1\",\n \"def n_remaining_samples(self):\\n return -1\",\n \"def sample(self, nsamples):\\n return self.dist.sample(nsamples)\",\n \"def __resample(self):\\n p_resample = []\\n w_max = max(self.weights)\\n index = int(round(random.uniform(0, self.n - 1)))\\n beta = 0\\n for i in range(self.n):\\n beta += random.uniform(0, 2 * w_max)\\n while self.weights[index] < beta:\\n beta -= self.weights[index]\\n index = (index + 1) % self.n\\n p_resample.append(self.particles[index, :])\\n return np.array(p_resample)\",\n \"def burnin_by_first_n(samples, N, step_number=False, logger_level=\\\"debug\\\"):\\n _samples = copy.deepcopy(samples)\\n n_samples = {key: N for key in _samples.keys()}\\n if step_number:\\n n_samples = {\\n key: item + N if item is not None else N for key, item in\\n _number_of_negative_steps(_samples, logger_level=logger_level).items()\\n }\\n getattr(logger, logger_level)(\\n \\\"Removing the first {} as burnin\\\".format(\\n \\\", \\\".join(\\n [\\\"{} samples from {}\\\".format(val, key) for key, val in n_samples.items()]\\n )\\n )\\n )\\n return _samples.discard_samples(n_samples)\",\n \"def take_samples(self, num_samples: int) -> List:\\n if num_samples > len(self.samples):\\n return random.sample(self.samples, len(self.samples))\\n return random.sample(self.samples, num_samples)\",\n \"def n_samples(self) -> int: # pragma: no cover\\n return self.samples.shape[0]\",\n \"def sample_count(self):\",\n \"def sample_users(self, n):\\n return torch.rand((n, self.num_topics), generator=self.generator) * 2 - 1\",\n \"def sample_size_with_prior(self, prior=\\\"small\\\"):\\n if prior == \\\"small\\\":\\n prior_sizes = list(range(self._min_size, self._max_size))\\n elif prior == \\\"big\\\":\\n prior_sizes = list(range(self._min_size+1, self._max_size+1))\\n return random.choice(prior_sizes)\",\n \"def sample(self,\\n n_samples: Optional[int],\\n temperatures: Optional[List[float]] = None) -> T.Tensor:\\n samples = self.levels[-1].sample(\\n self.top_prior_shape, n_samples=n_samples, temperatures=temperatures)\\n for level in self.levels[-2::-1]:\\n samples = level.sample(context=samples)\\n return samples\",\n \"def prior_predictive(self):\\n cfg = self.config\\n n = cfg['batch_size'] * cfg['q/n_samples']\\n n_samples = cfg['q/n_samples']\\n with util.get_or_create_scope('model', reuse=True):\\n h_prior = tf.cast(self.p_h_L.sample(n), cfg['dtype'])\\n h_prior = tf.reshape(\\n h_prior, [cfg['q/n_samples'], cfg['batch_size'], -1])\\n h = [None] * cfg['p/n_layers']\\n h[cfg['p/n_layers'] - 1] = h_prior\\n for n in range(cfg['p/n_layers'] - 1, 0, -1):\\n p_h_n = self.build_stochastic_layer(n, h_above=h[n])\\n h[n - 1] = tf.cast(p_h_n.sample(), cfg['dtype'])\\n return self.likelihood(h[0])\",\n \"def sample(self, num_samples = 1):\\n\\n X = np.empty(num_samples)\\n for k in range(len(X)):\\n\\n # starting\\n n = 1\\n a = 1\\n Un = self.UG.sample()\\n a = a * Un\\n\\n # iterate over\\n while a >= np.exp(-self.rate):\\n n = n + 1\\n Un = self.UG.sample()\\n a = a * Un\\n\\n X[k] = n - 1\\n\\n return X\",\n \"def sample_pagerank(corpus, damping_factor, n):\\n probabilities = dict()\\n samples = []\\n\\n # Random first sample\\n page = random.choice(list(corpus.keys()))\\n samples.append(page)\\n \\n # Remaining samples after first\\n for i in range(n-1):\\n p = transition_model(corpus, page, damping_factor)\\n page = random.choices(list(p.keys()), weights=list(p.values()), k=1)[0]\\n samples.append(page)\\n\\n # Count\\n for p in corpus.keys():\\n probabilities[p] = samples.count(p) / n\\n\\n return probabilities\",\n \"def get_prev_samples(self):\\n return self.y_p\",\n \"def main(n_samples):\\n uso = usolib.uso.uar(N)\\n lst = [usolib.randomfacet.randomfacet_sample(uso, N) for i in range(n_samples)]\\n return sum(lst) / float(n_samples)\",\n \"def sample_data(self, N, pmin=8., pmax=12.):\\n ps = [matrix(ra.uniform(pmin, pmax, (self.n, 1))) for k in range(N)]\\n xs = [self.compute_demand(p) for p in ps]\\n return zip(xs,ps)\",\n \"def simplex_sampler(self, n):\\n\\n # edge case: n = 1\\n if n == 1:\\n return np.array([self.initial_potential])\\n\\n values = [np.random.uniform() for i in range(n-1)]\\n values.extend([0, 1])\\n values.sort()\\n values_arr = np.array(values)\\n\\n xs = values_arr[1:] - values_arr[:-1]\\n\\n # return in decresing order of magnitude, to use for higher levels\\n xs = self.initial_potential*np.sort(xs)\\n xs = xs[::-1]\\n return xs\",\n \"def _number_of_samples(self):\\n return len(self._raw_data.samples)\",\n \"def get_next_sample(self):\",\n \"def get_number_samples(self):\\n return self.samples.shape[0]\",\n \"def sample_low_rank(self, n_samples, mu, logvar, F):\\n #F = torch.unsqueeze(F, dim=1).repeat(1, n_samples, 1, 1) # [self.batch_size, n_samples, self.Y_dim, self.rank]\\n F = F.repeat(n_samples, 1, 1) # [self.batch_size*n_samples, self.Y_dim, self.rank]\\n mu = mu.repeat(n_samples, 1) # [self.batch_size*n_samples, self.Y_dim]\\n logvar = logvar.repeat(n_samples, 1) # [self.batch_size*n_samples, self.Y_dim]\\n eps_low_rank = torch.randn(self.batch_size*n_samples, self.rank, 1)\\n eps_diag = torch.randn(self.batch_size*n_samples, self.Y_dim)\\n half_var = torch.exp(0.5*logvar) # [self.batch_size*n_samples, self.Y_dim]\\n samples = torch.bmm(F, eps_low_rank).squeeze() + mu + half_var*eps_diag\\n samples = samples.reshape(n_samples, self.batch_size, self.Y_dim)\\n samples = samples.transpose(0, 1)\\n samples = self.unwhiten_back(samples)\\n samples = samples.data.cpu().numpy()\\n return samples\",\n \"def get_log_prior(samples):\\n prior_distribution = tfp.distributions.Normal(loc=0., scale=1.)\\n log_prior = tf.reduce_sum(prior_distribution.log_prob(samples), axis=1)\\n return log_prior\",\n \"def sample_from_log_prob(A, n):\\n A_cum = log_cum_sum(A)\\n C_pos = [ -c for c in reversed(A_cum)]\\n sel = np.log(np.random.random(n))\\n pos = [len(A) - np.searchsorted(C_pos,-r) for r in sel]\\n return pos\",\n \"def priors_sample(self):\\n \\n theta_star = np.zeros(self.n_params)\\n\\n for i in xrange(self.n_params): \\n np.random.seed() \\n theta_star[i] = self.param_obj.prior()[i].rvs(size=1)[0]\\n\\n return theta_star\",\n \"def sample_pagerank(corpus, damping_factor, n):\\n all_pages = []\\n first_sample_prob = random.randint(0, len(corpus) - 1)\\n distribution_count = dict()\\n\\n for u in corpus:\\n distribution_count[u] = 0\\n all_pages.append(u)\\n\\n sample = all_pages[first_sample_prob]\\n for i in range(n - 1): # n - 1 because first sample was already calculated\\n selection_bucket = dict()\\n selection_start = 0.0\\n sample_distribution = transition_model(corpus, sample, damping_factor)\\n sample_prob = random.random()\\n for u in sample_distribution:\\n floor = selection_start\\n ceiling = selection_start + sample_distribution[u]\\n selection_start = ceiling\\n selection_bucket[u] = [floor, ceiling]\\n for u in selection_bucket:\\n v = selection_bucket[u]\\n if v[0] < sample_prob < v[1]:\\n sample = u\\n distribution_count[u] += 1\\n distribution = dict()\\n for u in distribution_count:\\n distribution[u] = float(distribution_count[u]) / n\\n\\n return distribution\",\n \"def get_num_samples(self):\\n return self._num_samples\",\n \"def sample(self, n):\\n unparameterized = [\\n v for v in self._weights.keys() if v not in self._weights_p.keys()\\n ]\\n if len(unparameterized) > 0:\\n raise ValueError(\\n \\\"The following variables must be numerically parameterized before sampling: {0}\\\".format(\\n unparameterized\\n )\\n )\\n mu, cov = self.parameterized_mean_cov()\\n cov = np.asarray(cov).astype(np.float64)\\n mu = np.asarray(mu).astype(np.float64)\\n return np.random.multivariate_normal(mu.ravel(), cov, size=n)\",\n \"def generate_samples(self,n_samples=100):\\n rnd_input=torch.randn((n_samples,self._reparam_nodes[1]))\\n zeta=rnd_input \\n # rnd_input=torch.where((rnd_input>0.5),torch.ones(rnd_input.size()),torch.zeros(rnd_input.size()))\\n # print(rnd_input) \\n # output, mu, logvar, zeta=self.forward(rnd_input)\\n # mu = self._reparam_layers['mu'](rnd_input)\\n # logvar = self._reparam_layers['var'](rnd_input)\\n # zeta = self.reparameterize(mu, logvar)\\n output = self.decoder.decode(zeta)\\n return output\",\n \"def process_custom_prior(prior) -> Tuple[Distribution, int, bool]:\\n\\n check_prior_methods(prior)\\n\\n check_prior_batch_behavior(prior)\\n\\n prior, is_prior_numpy = maybe_wrap_prior_to_pytorch(prior)\\n\\n parameter_dim = prior.sample().numel()\\n\\n return prior, parameter_dim, is_prior_numpy\",\n \"def __call__(self, *args):\\n r = np.random.rand(*args)\\n if type(r) is float:\\n samples = self.values[(r < self.p).nonzero()[0][0]]\\n elif type(r) is np.ndarray:\\n samples = np.array(\\n [self.values[np.nonzero(x < self.p)[0][0]] \\n for x in r.flat]).reshape(r.shape)\\n return samples\",\n \"def get_num_samples(self) -> int:\\n # must be implemented in subclass\\n raise NotImplementedError\",\n \"def sample(self, n_samples: int) -> torch.Tensor:\\n return self.dist.sample((n_samples,))\",\n \"def ts_rewards_sample(self):\\n exp_rewards_list = [np.random.beta(a, b) for a, b in zip(self.alpha_params, self.beta_params)]\\n return np.array(exp_rewards_list)\",\n \"def prediction_samples(self) -> np.ndarray:\\n return self.prediction_samples\",\n \"def sample(self, n_samples: int, random_state: Optional[int] = None) -> np.ndarray:\\n\\n if not isinstance(self.prob, np.ndarray):\\n samples = stats.bernoulli.rvs(\\n p=self.prob, size=n_samples, random_state=random_state\\n )\\n else:\\n n_classes = len(self.prob) # type: ignore\\n samples = np.zeros((n_samples, n_classes))\\n\\n for cls in range(n_classes):\\n samples[:, cls] = stats.bernoulli.rvs(p=self.prob[cls], size=n_samples, random_state=random_state) # type: ignore\\n\\n return samples\",\n \"def generate_synth_data(n):\",\n \"def prior_sample_parameter(self, parameter):\\n pass\",\n \"def get_samples_per_signal(self):\\n return np.array([self.samples_in_file(chn) for chn in range(self.signals_in_file)])\",\n \"def sample(self, num):\\n state, action, reward, next_state, not_terminal = [], [], [], [], []\\n for exp in self.experiences.sample(num):\\n state.append(exp.state)\\n action.append(exp.action)\\n reward.append(exp.reward)\\n next_state.append(exp.next_state)\\n not_terminal.append(exp.not_terminal)\\n\\n return np.asarray(state), np.asarray(action), np.asarray(reward), \\\\\\n np.asarray(next_state), np.asarray(not_terminal)\",\n \"def samples(self):\\n pass\",\n \"def sample_observations(self, n_samples, z):\\n lat_dim = self.transition.dim_x\\n x = []\\n for i in range(self.time_steps):\\n z_t = tf.slice(z, [0, i * lat_dim], [-1, lat_dim]) \\n x.append(self.emmision.sample(z_t))\\n return tf.concat(x, axis=1)\",\n \"def sample(self, n_samples):\\n with torch.no_grad():\\n z = torch.randn((n_samples, self.z_dim))\\n samples = self.decoder(z)\\n im_size = int(np.sqrt(self.input_dim))\\n samples = samples.view(-1, 1, im_size, im_size)\\n\\n return samples\",\n \"def sample_pagerank(corpus, damping_factor, n):\\n # All pages in entire corpus\\n all_pages = [key for key in corpus.keys()]\\n\\n # Dictionary which will map each page to the number of generated samples that were the page\\n counts = dict()\\n for page in all_pages:\\n counts[page] = 0\\n\\n # Choosing first sample randomly and updating counts dict\\n first_sample = random.choice(all_pages)\\n counts[first_sample] += 1\\n\\n for i in range(1, n):\\n # If we are on the second sample, do the calculation based on the first\\n if i == 1:\\n prev_sample = first_sample\\n\\n distributions = transition_model(corpus, prev_sample, damping_factor)\\n\\n keys = [key for key in distributions.keys()]\\n values = [distributions[key] for key in distributions.keys()]\\n\\n # Using random.choices to select an option given a list of weights (distribution)\\n current_sample = random.choices(keys, weights=values, k=1)[0]\\n\\n # Updating counts dict given the current sample\\n counts[current_sample] += 1\\n\\n prev_sample = current_sample\\n\\n # Modifying counts dict to represent the proportion of samples that corresponded to that page instead of the raw amount\\n for page in all_pages:\\n counts[page] /= n\\n\\n return counts\",\n \"def take(n, seq):\\n return itertools.islice(seq, n)\",\n \"def get_samples(self) -> McmcPtResult:\",\n \"def deterministic_sample(choices, n_to_sample, p): # pylint: disable=invalid-name\\n\\n sample_counts = np.ceil(n_to_sample * p).astype(int)\\n\\n n_to_remove = np.sum(sample_counts) - n_to_sample\\n\\n if n_to_remove == 0:\\n return choices[counts_to_vector(sample_counts)]\\n\\n non_zero_mask = sample_counts > 0\\n\\n removal_indices = np.floor(np.linspace(0.0,\\n np.sum(non_zero_mask),\\n n_to_remove,\\n endpoint=False)).astype(int)\\n\\n tmp = sample_counts[non_zero_mask]\\n tmp[removal_indices] = tmp[removal_indices] - 1\\n\\n sample_counts[non_zero_mask] = tmp\\n\\n assert np.sum(sample_counts) == n_to_sample\\n\\n samples = choices[counts_to_vector(sample_counts)]\\n\\n return samples\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7954478","0.7308122","0.7254096","0.7128596","0.6979248","0.6961805","0.67606914","0.6745526","0.6690407","0.6622515","0.6562736","0.65446556","0.65446556","0.6425273","0.6419413","0.6395372","0.63613814","0.6323354","0.63091654","0.6305061","0.6287302","0.6239015","0.6221694","0.6207399","0.6197996","0.6184932","0.6165855","0.6153619","0.6153358","0.6140597","0.61257386","0.6109061","0.6078866","0.60286486","0.60100406","0.6004092","0.59905887","0.5970731","0.59330416","0.5920617","0.59058857","0.58966595","0.589106","0.5890634","0.58760834","0.58759904","0.5870663","0.5850432","0.58440155","0.58292997","0.58253235","0.5818603","0.5816317","0.5816317","0.5816317","0.58079565","0.58007497","0.5788163","0.57879496","0.57798713","0.5776126","0.5774034","0.577337","0.5767064","0.5765437","0.5762816","0.5749947","0.5746394","0.5736112","0.5728723","0.57278186","0.5722879","0.5718899","0.5717263","0.5683421","0.56821394","0.56575644","0.5654737","0.5651699","0.5648227","0.56469744","0.5641609","0.5639136","0.5636752","0.56361675","0.56329775","0.56267655","0.5622955","0.5622405","0.5618009","0.5616757","0.5616295","0.5613543","0.56135225","0.5599461","0.55893856","0.55878794","0.55790335","0.5576369","0.5575169"],"string":"[\n \"0.7954478\",\n \"0.7308122\",\n \"0.7254096\",\n \"0.7128596\",\n \"0.6979248\",\n \"0.6961805\",\n \"0.67606914\",\n \"0.6745526\",\n \"0.6690407\",\n \"0.6622515\",\n \"0.6562736\",\n \"0.65446556\",\n \"0.65446556\",\n \"0.6425273\",\n \"0.6419413\",\n \"0.6395372\",\n \"0.63613814\",\n \"0.6323354\",\n \"0.63091654\",\n \"0.6305061\",\n \"0.6287302\",\n \"0.6239015\",\n \"0.6221694\",\n \"0.6207399\",\n \"0.6197996\",\n \"0.6184932\",\n \"0.6165855\",\n \"0.6153619\",\n \"0.6153358\",\n \"0.6140597\",\n \"0.61257386\",\n \"0.6109061\",\n \"0.6078866\",\n \"0.60286486\",\n \"0.60100406\",\n \"0.6004092\",\n \"0.59905887\",\n \"0.5970731\",\n \"0.59330416\",\n \"0.5920617\",\n \"0.59058857\",\n \"0.58966595\",\n \"0.589106\",\n \"0.5890634\",\n \"0.58760834\",\n \"0.58759904\",\n \"0.5870663\",\n \"0.5850432\",\n \"0.58440155\",\n \"0.58292997\",\n \"0.58253235\",\n \"0.5818603\",\n \"0.5816317\",\n \"0.5816317\",\n \"0.5816317\",\n \"0.58079565\",\n \"0.58007497\",\n \"0.5788163\",\n \"0.57879496\",\n \"0.57798713\",\n \"0.5776126\",\n \"0.5774034\",\n \"0.577337\",\n \"0.5767064\",\n \"0.5765437\",\n \"0.5762816\",\n \"0.5749947\",\n \"0.5746394\",\n \"0.5736112\",\n \"0.5728723\",\n \"0.57278186\",\n \"0.5722879\",\n \"0.5718899\",\n \"0.5717263\",\n \"0.5683421\",\n \"0.56821394\",\n \"0.56575644\",\n \"0.5654737\",\n \"0.5651699\",\n \"0.5648227\",\n \"0.56469744\",\n \"0.5641609\",\n \"0.5639136\",\n \"0.5636752\",\n \"0.56361675\",\n \"0.56329775\",\n \"0.56267655\",\n \"0.5622955\",\n \"0.5622405\",\n \"0.5618009\",\n \"0.5616757\",\n \"0.5616295\",\n \"0.5613543\",\n \"0.56135225\",\n \"0.5599461\",\n \"0.55893856\",\n \"0.55878794\",\n \"0.55790335\",\n \"0.5576369\",\n \"0.5575169\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.71898454"},"document_rank":{"kind":"string","value":"3"}}},{"rowIdx":14396,"cells":{"query":{"kind":"string","value":"Computes the gradient of the prior with respect to theta."},"document":{"kind":"string","value":"def gradient(self, theta):\n pass"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def gradient(self, theta):\n return (1 / (self.sigma * np.sqrt(2 * np.pi))) * (\n -theta / (self.sigma ** 2) * np.exp(-(theta ** 2) / (2 * self.sigma ** 2))\n )","def gradient(self, theta):\n a = -(6 * self.scale ** 2)\n b = 3 * self.scale ** 2 + np.exp(2 * theta)\n b *= np.log(3 * self.scale ** 2 * np.exp(-2 * theta) + 1)\n return a / b","def gradient(theta, X, y, learning_rate):\n m = len(y)\n\n theta = theta.reshape((-1,1))\n grad = np.zeros(theta.shape)\n h = sigmoid(np.dot(X, theta)) \n \n grad = np.dot((h-y).T, X)/m\n grad = grad.T\n grad[1:] += (learning_rate/m)*theta[1:]\n return grad","def gradient_function(theta, X, y):\n\n grad = None\n #######################################################################\n # TODO: #\n # Compute the gradient for a particular choice of theta. #\n # Compute the partial derivatives and set grad to the partial #\n # derivatives of the cost w.r.t. each parameter in theta #\n # #\n #######################################################################\n \n theta = theta[:, np.newaxis]\n \n thetatrans = theta.T\n Xtrans = X.T\n \n MulThetaX = np.dot(thetatrans, Xtrans)\n \n h = sigmoid(MulThetaX)\n \n grad = (y - h) * Xtrans\n\n #######################################################################\n # END OF YOUR CODE #\n #######################################################################\n return grad","def compute_gradient(theta, X, y):\n m = X.shape[0]\n grad_theta = np.dot(X.transpose(), (np.dot(X, theta) - y)) / m\n #print theta, grad_theta, objective_function(theta, X, y)\n return grad_theta","def gradient(self, theta):\n return np.zeros([theta.shape[0]])","def gradientFunction(theta, X, y):\n y = y[:, 0]\n m = y.shape # number of training samples\n grad = X.T.dot(sigmoid(theta.dot(X.T))-1*y)\n grad /= m\n return grad","def gradient(theta, x, y):\n m = len(y)\n n = len(theta)\n z = theta.dot(x.T)\n grad = np.zeros(n)\n for i in xrange(m):\n grad += (g(z[i]) - y[i]) * x[i]\n return 1. / m * grad","def gradient(theta, X, y, Lambda=0.0):\n m = X.shape[0] # number of samples\n\n h = hypothesis(theta, X)\n\n if Lambda:\n g_0 = (1/m)*(X.T@(h - y))[0]\n g_1 = (1/m)*(X.T@(h - y))[1:] + (Lambda/m)*theta[1:] # skip theta-0\n \n return np.append(g_0, g_1)\n else:\n return (1/m)*(X.T@(h - y))","def gradient(x, y, theta):\n if x.ndim == 1:\n x = x[:, np.newaxis]\n if y.ndim == 2 and y.shape[1] == 1:\n y = y.flatten()\n if theta.ndim == 2 and theta.shape[1] == 1:\n theta = theta.flatten()\n\n if (x.size == 0 or y.size == 0 or theta.size == 0\n or x.ndim != 2 or y.ndim != 1 or theta.ndim != 1\n or x.shape[0] != y.shape[0] or x.shape[1] + 1 != theta.shape[0]):\n return None\n\n x_padded = np.c_[np.ones(x.shape[0]), x]\n\n return x_padded.T.dot(x_padded.dot(theta) - y) / y.shape[0]","def calc_grad(X, Y, theta):\n m, n = X.shape\n\n margins = Y * X.dot(theta)\n probs = 1. / (1 + np.exp(margins))\n grad = -(1./m) * (X.T.dot(probs * Y))\n\n return grad","def gradient_descent(X, y, theta, alpha, total_iterations, hypothesis):\n len_theta = len(theta)\n m = len(y)\n one_over_m = (1.0 / float(m))\n\n for _ in range(0, total_iterations):\n temp_theta = numpy.zeros(len_theta)\n\n X_by_theta_minus_y = numpy.subtract(hypothesis(numpy.matrix(theta), X), y)\n\n for j in range(0, len_theta):\n jth_column_of_X = X[:,j]\n derivative_j = one_over_m * numpy.multiply(X_by_theta_minus_y, jth_column_of_X).sum()\n temp_theta[j] = theta[j] - alpha*derivative_j\n\n theta = temp_theta\n\n return numpy.matrix(theta)","def gradient_descent(self, X, theta, Y, m):\n\n Z = X.dot(theta)\n H = Predict.g(Z)\n gradient = np.dot(X.T, (H - Y)) / m\n return self.alpha * gradient","def gradientFunctionReg(theta, X, y, Lambda): \n y = np.squeeze(y)\n m = y.shape # number of training samples\n grad = X.T.dot(sigmoid(theta.dot(X.T))-1*y)\n grad[1:] = grad[1:] + Lambda*theta[1:]\n grad /= m\n\n return grad","def gradientFunctionReg(theta, X, y, Lambda):\n m = len(y) # number of training examples\n grad = np.zeros(theta.shape[0])\n theta = np.transpose(theta)\n sum_1 = 0\n X = X.values\n y = y.values\n #calcuate the theta_0 \n# ====================== YOUR CODE HERE ======================\n# Instructions: Compute the gradient of a particular choice of theta.\n# Compute the partial derivatives and set grad to the partial\n# derivatives of the cost w.r.t. each parameter in theta\n for i in range(theta.shape[0]):\n if i == 0:\n for j in range(m):\n sum_1 += (sigmoid(np.dot(X[j],theta)) - y[j]) * X[j,i]\n else:\n for j in range(m):\n sum_1 += (sigmoid(np.dot(X[j],theta)) - y[j]) * X[j,i] + Lambda*theta[i]\n grad[i] = sum_1/m\n sum_1 = 0\n\n# =============================================================\n\n return grad","def logit_cost_grad(self, theta, X, y):\n\n grad = np.zeros(len(theta))\n\n ### YOUR CODE HERE\n sig = utils.sigmoid(theta)\n # sig = np.subtract(sig, y)\n sig = sig - y\n grad = np.dot(X.T, sig) + 2 * self.params['lamb'] * self.regularizer[1](self.weights)\n ### END YOUR CODE\n\n return grad","def compute_loss_gradient(theta_vector, *args):\n\n psi = args[0] # feed psi as a parameter\n circ_depth = args[1]\n num_qbits = args[2]\n theta = np.reshape(theta_vector, (circ_depth, num_qbits)) # reshapes the flat theta vector\n fidelity = get_fidelity(theta, psi)\n\n # the derivative of the loss wrt fidelity\n dl_df = -0.5 * fidelity ** (-0.5)\n\n df_dtheta = [] # a list of partial derivatives of the fidelity wrt the theta parameters\n\n for index in range(len(theta_vector)):\n layer_index = index // num_qbits\n qbit_index = index % num_qbits\n\n theta_plus = np.copy(theta)\n theta_plus[layer_index][qbit_index] += np.pi / 2 # added pi/2 to the ith theta parameter\n\n theta_minus = np.copy(theta)\n theta_minus[layer_index][qbit_index] -= np.pi / 2 # subtracted pi/2 to the ith theta parameter\n\n df_dtheta_i = 0.5 * (get_fidelity(theta_plus, psi) - get_fidelity(theta_minus, psi)) # ith derivative\n df_dtheta.append(df_dtheta_i)\n\n df_dtheta = np.array(df_dtheta)\n dl_dtheta = dl_df * df_dtheta # chain rule to get partial derivative of loss wrt theta parameters\n\n return dl_dtheta","def run_gradient_descent_iteration(X, Y, theta, alpha, lambda_factor, temp_parameter):\n delta = sparse.coo_matrix(theta.shape).toarray()\n\n h = compute_probabilities(X, theta, temp_parameter)\n\n for j in range(delta.shape[0]):\n y = Y\n y = np.where(y != j, 0, 1)\n p = y - h[j]\n\n x = X.T * p\n x = x.T\n x = x.sum(axis=0)\n\n grad = -x / (temp_parameter * X.shape[0]) + lambda_factor * theta[j]\n\n delta[j] += grad\n\n theta -= alpha * delta\n\n return theta","def gradient_descent(features, values, theta, alpha, num_iterations):\n \n # number of points\n npoints = len(values)\n \n # intialize cost history\n cost_history = []\n \n # num_interations iterations\n for iiter in range(num_iterations):\n \n # compute and store cost\n cost = compute_cost(features, values, theta)\n cost_history.append(cost)\n \n # update values of theta\n values_predicted = np.dot(features, theta)\n theta = theta + (alpha/npoints)*(np.dot(values - values_predicted,features))\n \n return theta, pandas.Series(cost_history)","def gradient_descent(x, y, theta=[[0], [0]]):\n m = y.size\n j_history = []\n for i in range(ITERATIONS):\n h = x.dot(theta)\n theta = theta - (ALPHA / m) * (x.T.dot(h - y))\n j_history.append(compute_cost(x, y, theta))\n return theta, j_history","def approx_grad(theta, X, y):\n grad_a = np.array([(cost(theta + e, X, y) - cost(theta - e, X, y)) / (2 * 1e-5)\n for e in np.identity(len(theta)) * 1e-5])\n return grad_a","def gradient_descent(features, values, theta, alpha, num_iterations):\r\n\r\n m = len(values)\r\n cost_history = []\r\n\r\n for i in range (num_iterations):\r\n \r\n h = numpy.dot(features, theta)\r\n \r\n theta = theta - alpha / m * numpy.dot((h-values),features)\r\n \r\n cost = compute_cost(features, values, theta)\r\n \r\n cost_history.append(cost)\r\n\r\n return theta, pandas.Series(cost_history) # leave this line for the grader\r","def calculate_gradient(y, tx, w): \n return tx.T@(sigmoid(tx@w)-y)","def gradient_descent(features, values, theta, alpha, num_iterations):\n m = len(values)\n cost_history = []\n\n for i in range(num_iterations):\n predicted_values = np.dot(features, theta)\n delta = alpha / m * np.dot((predicted_values - values), features)\n theta = theta - delta\n cost = compute_cost(features, values, theta)\n cost_history.append(cost)\n return theta, pandas.Series(cost_history)","def lr_cost_function_grad(theta: np.ndarray, X: np.ndarray, y: np.ndarray, l: float) -> np.ndarray:\n # Initialize some useful values\n m = len(y) # number of training examples\n\n # You need to return the following variable correctly.\n grad = np.zeros(theta.shape)\n\n # ====================== YOUR CODE HERE ======================\n # Instructions: Compute the partial derivatives and set grad to the partial\n # derivatives of the cost w.r.t. each parameter in theta.\n\n # =============================================================\n return grad","def gradient_ascent(self, w, X, y, lr):\r\n # INSERT YOUR CODE HERE\r\n #raise Exception('Function not yet implemented!')\r\n # gradient = x_j*(y-σ(wTX))\r\n return np.dot(X.T, y-self.sigmoid(np.dot(X, w)))","def computeNumericalGradient(J, theta):\n numgrad = np.zeros(theta.size)\n perturb = np.zeros(theta.size)\n e = 1e-4\n for p in range(theta.size):\n # Set perturbation vector\n perturb[p] = e\n loss1 = J(theta - perturb)[0]\n loss2 = J(theta + perturb)[0]\n # Compute Numerical Gradient\n numgrad[p] = (loss2 - loss1) / (2*e)\n perturb[p] = 0\n return numgrad","def computeNumericalGradient(J, theta):\n numgrad = np.zeros_like(theta).reshape(-1)\n perturb = np.zeros_like(theta).reshape(-1)\n e = 1e-4\n for p in range(theta.size):\n # Set perturbation vector\n perturb[p] = e\n loss1, _ = J(theta - perturb.reshape(theta.shape))\n loss2, _ = J(theta + perturb.reshape(theta.shape))\n # Compute Numerical Gradient\n numgrad[p] = (loss2 - loss1) / (2 * e)\n perturb[p] = 0\n\n return numgrad.reshape(theta.shape)","def gradientdescent(cost_func, theta, args=(), delta_func = 0):\n step = 1\n old_cost = 0\n while True:\n theta_old = theta.copy()\n cost = cost_func(theta, *args)\n delta = delta_func(theta, *args)\n theta = theta - step * delta\n if cost > old_cost and old_cost != 0:\n step = step*0.7\n if np.allclose(theta_old, theta):\n break\n old_cost = cost\n return theta","def gradient(cls, x):\n y = Sigmoid.apply(x)\n return np.multiply(y, 1 - y)","def gradient_descent(features, values, theta, alpha, num_iterations):\r\n \r\n m = len(values)\r\n cost_history = []\r\n\r\n for i in range(num_iterations):\r\n # your code here\r\n cost = compute_cost(features, values, theta)/(2.0*m)\r\n cost_history.append([cost])\r\n \r\n error = features.dot(theta) - values\r\n error = np.reshape(error,(error.shape[0], 1))\r\n errorWeighted = features*error\r\n errorSum = (np.sum(errorWeighted,0))/(m*1.0)\r\n theta = theta - alpha*errorSum \r\n \r\n return theta, pandas.Series(cost_history)","def compute_gradient(y, tx, w):\n\tN = y.shape[0]\n\te = y - np.dot(tx, w)\n\n\tgradLw = -1/N * np.dot(tx.T, e)\n\treturn gradLw","def compute_square_loss_gradient(X, y, theta):\n #TODO\n P = (np.dot(X, theta)-y)\n m = X.shape[0]\n\n return (2/m)*np.dot(X.T, P)","def learning_by_gradient_descent(y, tx, w, gamma):\n\tgrad = calculate_gradient(y, tx, w)\n\n\tw = w - gamma * grad\n\treturn w","def calculate_gradient(y, tx, w):\n return tx.T.dot(sigmoid(tx.dot(w))-np.reshape(y,(len(y),1)))","def gradientDescent(self,X, y, theta): \n # number of instances\n m = len(y)\n J_history = np.zeros((self.NUM_ITERS,1))\n for i in range(self.NUM_ITERS):\n h = self.sigmoid(X@theta)\n grad = 1 / m * X.T @ (h - y)\n theta = theta - self.ALPHA * grad \n J_history[i] = self.costFunction(theta, X, y)\n \n \n return theta, J_history","def compute_square_loss_gradient(X, y, theta):\n #TODO\n (N,p) = np.shape(X)\n grad = -(1/np.float(N))*np.array([(y - X.dot(theta))*X[:,i] for i in range(p)])\n return np.sum(grad,axis=1)","def gradient_descent(X, y, theta, learning_rate, num_iters, lambda_reg):\n thetas = [theta]\n cost = np.zeros(num_iters)\n\n J = mean_cross_entropy_costs(X, y, lambda_reg)\n cost[0] = J(thetas[0])\n for i in range(1, num_iters):\n thetas.append(compute_new_theta(X, y, thetas[i - 1], learning_rate, lambda_reg))\n cost[i] = J(thetas[i])\n return cost, thetas","def gradient(img):\n G = np.zeros(img.shape)\n theta = np.zeros(img.shape)\n\n #####################################\n # START YOUR CODE HERE #\n #####################################\n Gx = partial_x(img)\n Gy = partial_y(img)\n G = np.sqrt(np.square(Gx) + np.square(Gy))\n theta = np.degrees(np.arctan2(Gy, Gx)) % 360\n ######################################\n # END OF YOUR CODE #\n ######################################\n\n return G, theta","def gradient_descent(initial_theta, X, y, niter, alpha, Lambda=0.0):\n theta_list = []\n cost_list = []\n\n theta = initial_theta\n for i in range(0, niter):\n theta -= alpha*gradient(theta, X, y, Lambda)\n theta_list.append(theta)\n cost_list.append(cost(theta, X, y, Lambda))\n\n return theta_list, cost_list","def linreg_stochastic_grad(X, y, alpha=.01):\n m = X.shape[0]\n n = X.shape[1]\n theta = np.zeros(n)\n for i in range(m):\n delta = alpha * (np.dot(theta.transpose(), X[i,:]) -y[i]) * X[i,:]\n theta = theta - delta\n return theta","def log_likelihood_gradient(y, tx, w):\n return tx.T.dot(sigmoid(tx.dot(w))-y)","def gradient(cls, x):\n return 1 - TanH.apply(x) ** 2","def learning_by_gradient_descent(y, tx, w, gamma):\n loss = calculate_loss(y,tx,w)\n grad = calculate_gradient(y,tx,w)\n w_new = w - gamma*grad\n #grad is for debugging purpose\n return loss, w_new,grad","def simple_gradient(x, y, theta):\n if x.shape[0] * y.shape[0] * theta.shape[0] == 0:\n return None\n if x.shape[0] != y.shape[0] or theta.shape[0] != 2:\n return None\n\n x = add_intercept(x)\n\n result = [\n forumla(x, y, theta, 0),\n forumla(x, y, theta, 1)\n ]\n return result","def calculate_gradient(y, tx, w):\n\n\tret = tx.T.dot(sigmoid(np.dot(tx, w)) - y)\n\treturn ret","def learning_by_gradient_descent(y, tx, w, gamma):\n loss = calculate_loss(y,tx,w)\n grad = calculate_gradient(y,tx,w)\n w = w-gamma*grad\n return w, loss","def train_fn_grad(self, theta_value, theta):\n theta.set_value(theta_value, borrow=True)\n\n train_grads = [self.batch_grad(i, self.n_train)\n for i in xrange(self.n_train_batches)]\n train_batch_sizes = [self.get_batch_size(i, self.n_train)\n for i in xrange(self.n_train_batches)]\n\n return np.average(train_grads, weights=train_batch_sizes,\n axis=0)","def gradient(self, x):\n pass","def gradient(img):\n G = np.zeros(img.shape)\n theta = np.zeros(img.shape)\n\n ### YOUR CODE HERE\n Gx = partial_x(img)\n Gy = partial_y(img)\n G = np.sqrt(np.power(Gx,2)+np.power(Gy,2))\n theta = (np.arctan2(Gy, Gx) * 180 / np.pi) % 360\n ### END YOUR CODE\n\n return G, theta","def approx_grad_reg(theta, X, y, _lambda):\n grad_a = np.array([(cost_reg(theta + e, X, y, _lambda) - cost_reg(theta - e, X, y, _lambda)) / (2 * 1e-5)\n for e in np.identity(len(theta)) * 1e-5])\n return grad_a","def get_gradient(phi, pred, t, dot_product, weight, reg= 1, regression= \"logistic\"):\n if regression == \"logistic\":\n gradient = np.matmul(phi.T, pred - t)\n elif regression == \"probit\":\n R = np.eye(pred.shape[0])\n for i in range(pred.shape[0]):\n y_n = pred[i,0]\n dotp = dot_product[i, 0]\n pdf = norm.pdf(dotp)\n R[i,i] = pdf/(y_n*(1-y_n) + TOLERANCE)\n gradient = np.matmul(np.matmul(phi.T, R), pred-t)\n elif regression == \"multiclass\":\n gradient = np.matmul(phi.T, pred - t)\n\n # Add regularization\n gradient += weight/ reg\n return gradient","def gradient(self, x):\n u = np.asarray([x[0]])\n C = self.C_func(u)\n dC = self.dC_func(u, order=1)\n P = self.P\n numerator = np.sum((C - P) * dC, axis=0)\n denominator = np.sum(np.sum((C - P) ** 2, axis=0) ** (1 / 2))\n if np.abs(denominator) > 0:\n gradient = numerator/denominator\n else:\n gradient = np.asarray(0)[np.newaxis]\n return gradient","def apply_gradient(params: torch.Tensor, grads: torch.Tensor, lr: float) -> torch.Tensor:\n params_prime = params + lr * grads\n return params_prime","def run_steep_gradient_descent(data_x, data_y, len_data, alpha, theta):\n n = len_data\n # WE NEED TO transpose data_x into (p+1) *n ,theta is 1*(p+1)\n prod = np.dot(theta, data_x.transpose())\n\n prod -= data_y\n print(\"pro: data_x\", prod.shape, data_x.shape)\n #prod represent the loss of the hypothesis and true label\n sum_grad = np.dot(prod, data_x)\n print(\"总梯度的值：\",sum_grad.shape)\n\n # batch-gradient descent\n theta = theta -(alpha / n) * sum_grad\n return theta","def GDS(x, theta, y, lamb, alpha):\n\tm = y.shape[0]\n\tdif = (1/m) * x.T @ (sigmoid(x,theta) - y) #classical differentiation term, output is (n,1)\n\tdif_reg = (lamb/m) * theta #regularization term\n\treturn (dif + dif_reg) * alpha","def gradientDescent(X, y, theta, alpha, num_iters):\n\n # Initialize some useful values\n J_history = []\n m = y.size # number of training examples\n\n for i in range(num_iters):\n # ====================== YOUR CODE HERE ======================\n # Instructions: Perform a single gradient step on the parameter vector\n # theta.\n #\n # Hint: While debugging, it can be useful to print out the values\n # of the cost function (computeCost) and gradient here.\n #\n # Calculate the gradient step according to the equation for theta1:\n g_step1 = (alpha / m * np.sum( (np.dot(X,theta) - y) * X[:,1]) )\n # Gradient step for theta knot:\n g_step0 = (alpha / m * np.sum( (np.dot(X,theta) - y) ) )\n \n #update theta\n theta[0] = (theta[0] - g_step0)\n theta[1] = (theta[1] - g_step1)\n \n #print([theta , g_step1, g_step0])\n\n # ============================================================\n\n # Save the cost J in every iteration\n J_history.append(computeCost(X, y, theta))\n\n return theta, J_history","def gradient(w, x, t):\n return 2 * np.dot(x.T, (nn(x, w) - t))","def costFunction(theta,X,y):\n m = X.shape[0]\n J = 0\n h = sigmoid (np.dot(X,theta))\n \n J = (1/m)* ((-np.dot(y.T,(np.log(h)))) - np.dot((1 - y).T,(np.log(1-h))))\n \n #grad = (1/m) * np.dot(X.T,(h-y))\n grad = (1/m) * np.dot((h.T - y), X).T\n \n return J, grad","def stochasticGradientDescent(x,y,theta,alpha):\n m,n = np.shape(x)\n convergence = 0.000000001\n lastCost = 0\n cost = -1 \n recurseCount = 0\n while abs(lastCost - cost) > convergence: # rcurse until converge\n lastCost = cost\n hypothesis = np.dot(x,theta) \n for i in range(m):\n # alpha = 4.0 / (1.0 + i) + 0.01 \n loss = hypothesis[i] - y[i]\n # gradient = np.dot(x[i],loss)\n gradient = x[i,:].transpose() * loss \n theta = theta - alpha * gradient\n cost = np.sum((hypothesis-y)**2)/(2*m)\n recurseCount += 1\n return recurseCount,theta","def gradient(self):\n result = np.zeros(len(self.variables))\n result[self.bivariateGradInd] = (self.shape-1)/self.variable - self.rate\n return result","def gradient(self):\n functional = self\n\n class KLGradient(Operator):\n\n \"\"\"The gradient operator of this functional.\"\"\"\n\n def __init__(self):\n \"\"\"Initialize a new instance.\"\"\"\n super().__init__(functional.domain, functional.domain,\n linear=False)\n\n def _call(self, x):\n \"\"\"Apply the gradient operator to the given point.\n The gradient is not defined in points where one or more\n components are non-positive.\n \"\"\"\n if functional.prior is None:\n return (-1.0) / x + 1\n else:\n return (-functional.prior) / x + 1\n\n return KLGradient()","def batchGradientDescent(x,y,theta,alpha):\n m,n = np.shape(x)\n xTran = x.transpose()\n convergence = 0.000000001\n lastCost = 0\n cost = -1 \n recurseCount = 0\n while abs(lastCost - cost) > convergence: # rcurse until converge\n lastCost = cost\n hypothesis = np.dot(x,theta)\n loss = hypothesis - y\n cost = np.sum(loss**2)/(2*m)\n gradient = np.dot(xTran,loss)/m\n theta = theta - alpha*gradient\n recurseCount += 1\n return recurseCount,theta","def learning_by_penalized_gradient(y, tx, w, gamma, lambda_):\n\tgrad, H = penalized_logistic_regression(y, tx, w, lambda_)\n\n\thgrad = np.linalg.inv(H).dot(grad)\n\n\tw = w - gamma * hgrad\n\n\treturn w","def grad(self, w):\n l1_grad = self.r * np.sign(w)\n l2_grad = np.asarray(1 - self.r) * w \n\n gradient_penalty = self.alpha * (l1_grad + l2_grad)\n\n # Insert 0 for bias term.\n return np.insert(gradient_penalty, 0, 0, axis=0)","def compute_gradient(self):\n A = np.dot(self.X, self.w)\n m = self.t.shape[0]\n C = -1 * self.t * (1 / (1 + np.exp(A * self.t)))\n return (1 / m) * np.dot(self.X.T, C)","def gradient(self, x):\n g = self._grad(\n time_series=self.observed_data,\n a=x[0],\n b=x[1],\n c=x[2],\n sigma=self.sigma\n )\n return g","def reg_autograd(alpha,X,theta,y):\n g=grad(func)\n for i in range(0,20):\n theta=theta- alpha * g(theta)\n print(\"cost\", func(theta))\n print(\"final theta\",theta)","def grad(self, x):\n norm_beta = np.linalg.norm(x)\n if norm_beta > utils.TOLERANCE:\n return (self.l / norm_beta) * x\n else:\n D = x.shape[0]\n u = (self.rng(D, 1) * 2.0) - 1.0 # [-1, 1]^D\n norm_u = np.linalg.norm(u)\n a = self.rng() # [0, 1]\n\n return (self.l * (a / norm_u)) * u","def gradcovfunc(self, theta, d):\n sigmaf, l = theta[:2] \n xxl = np.sum((d/l)**2, axis=1)\n dk_dsigmaf = 2 * sigmaf * np.exp(-xxl/2.)\n dk_dl = sigmaf**2/l * xxl * np.exp(-xxl/2.)\n grad = np.array([dk_dsigmaf, dk_dl])\n return grad","def log_prior_grad(self, inputs):","def gradient(series, **options):\n x = series.index\n y = series.values\n\n a = np.gradient(y, x, **options)\n return series.__class__(a, series.index)","def derivative(self, theta):\n diag_gamma = np.dot(theta.T, self.X.T)\n logistic_term = self.logistic_fn(diag_gamma)\n diag_gamma = logistic_term * (1.0 - logistic_term)\n gamma = np.diag(diag_gamma)\n\n # v computation\n diags_v = 1.0 - 2*self.logistic_fn(np.dot(theta.T, self.X.T))\n diags_v = diags_v.reshape((-1, 1))\n diags_v = diags_v*self.X\n assert diags_v.shape == self.X.shape #N*d shape\n\n XtGamma = np.dot(self.X.T, gamma) # d*N shape\n\n # TODO: Verifier car pas sur de mon coup ... et surtout plus long...\n # id = np.eye(self.n_examples).reshape((self.n_examples, self.n_examples, 1))\n # diags_v = diags_v.reshape((self.n_examples, 1, self.dim))\n # v = id*diags_v # n*n*d tensor\n # left = np.tensordot(XtGamma, v, axes=(1, 0)) # shape d*N*d\n # assert left.shape == (self.dim, self.n_examples, self.dim)\n # dg = np.tensordot(left, self.X, axes=(1, 0))\n # dg = np.swapaxes(dg, axis1=-2, axis2=-1)\n\n dg = np.zeros((self.dim, self.dim, self.dim))\n for idx, v_i_diag in enumerate(diags_v.T):\n v_i = np.diag(v_i_diag)\n dg_di = np.dot(np.dot(XtGamma, v_i), self.X)\n dg[:, :, idx] = dg_di\n return dg","def compute_logistic_gradient(y, tx, w):\n return tx.T.dot(sigmoid(tx.dot(w)) - y) / len(y)","def run_gradient_descent(data,theta,alpha,num_iters):\n population = data[:,0]\n prices = data[:,1]\n x = ones(shape=(len(population),2)) #add ones for theta0 \n x[:,1] = population\n x = transpose(x)\n error_history = zeros(shape=(num_iters,1))\n \n for i in range(num_iters):\n predictions = theta.dot(x)\n errors_x1 = (predictions - prices) * x[0,:]\n errors_x2 = (predictions - prices) * x[1,:]\n theta[0][0] = theta[0][0] - alpha*(1.0/len(population))*errors_x1.sum()\n theta[0][1] = theta[0][1] - alpha*(1.0/len(population))*errors_x2.sum()\n error_history[i,0] = calculate_cost(theta,data)\n \n return theta, error_history","def gradient(self, x):\n return 0.0","def costFunction(self,theta, X, y): \n m = len(y)\n h = self.sigmoid(X@theta)\n J = 1 / m * (- y.T @ self.log(h) - (1-y).T @ self.log(1-h)) \n # grad = 1/ m * X.T @ (h - y)\n return J","def gradient(poly):\n return differential(\n poly, chaospy.poly.collection.basis(1, 1, poly.dim, sort=\"GR\"))","def curves_gradient(theta, m_, LAMBDA=0):\n # X0: column of ones\n theta0_gradient = (1 / m_) * sum((curves_hypothesis(theta, m_) - y[:m_].ravel()) * np.ones(m_))\n # X1: data given as X\n theta1_gradient = ((1 / m_) * sum((curves_hypothesis(theta, m_) - y[:m_].ravel()) * X[:m_].ravel())) \\\n + (LAMBDA / m_) * theta[1]\n return np.array([theta0_gradient, theta1_gradient])","def gradient(self, x_in):\r\n\r\n return 1 - torch.pow(self.forward(x_in), 2)","def compute_gradient(self): # TODO: try to change to square loss since it's hessian is easier to obtain\n A = np.dot(self.X, self.w)\n m = self.t.shape[0]\n C = -1 * self.t * (1 / (1 + np.exp(A * self.t)))\n return (1 / m) * np.dot(self.X.T, C)","def vb_grad_natgrad(self):\r\n\r\n # Gradient for eta\r\n dL_dmmT_S = -0.5*self.Lambda/self.data_prop + 0.5*self.q_u_prec\r\n Kmmipsi1V,_ = dpotrs(self.Lm,self.psi1V,lower=1)\r\n dL_dm = (Kmmipsi1V - np.dot(self.Lambda,self.q_u_mean))/self.data_prop\r\n\r\n # Gradients for theta\r\n S = self.q_u_cov\r\n Si = self.q_u_prec\r\n m = self.q_u_mean\r\n dL_dSi = -mdot(S,dL_dmmT_S, S)\r\n\r\n dL_dmhSi = -2*dL_dSi\r\n dL_dSim = np.dot(dL_dSi,m) + np.dot(Si, dL_dm)\r\n\r\n return np.hstack((dL_dm.flatten(),dL_dmmT_S.flatten())) , np.hstack((dL_dSim.flatten(), dL_dmhSi.flatten()))","def grad(self, w):\n gradient_penalty = np.asarray(self.alpha) * w\n # Insert 0 for bias term.\n return np.insert(gradient_penalty, 0, 0, axis=0)","def grad(self, w):\n gradient_penalty = np.asarray(self.alpha) * w\n # Insert 0 for bias term.\n return np.insert(gradient_penalty, 0, 0, axis=0)","def gradient(self):\n functional = self\n\n class KLCCGradient(Operator):\n\n \"\"\"The gradient operator of this functional.\"\"\"\n\n def __init__(self):\n \"\"\"Initialize a new instance.\"\"\"\n super().__init__(functional.domain, functional.domain,\n linear=False)\n\n def _call(self, x):\n \"\"\"Apply the gradient operator to the given point.\n\n The gradient is not defined in points where one or more\n components are larger than or equal to one.\n \"\"\"\n if functional.prior is None:\n return 1.0 / (1 - x)\n else:\n return functional.prior / (1 - x)\n\n return KLCCGradient()","def learning_by_penalized_gradient(y, tx, w, gamma, lambda_):\n\n #on test avec Newton\n\n loss,gradient,_ = penalized_logistic_regression(y,tx,w,lambda_)\n\n w = w - gamma*gradient\n return loss, w,gradient","def gradient_ascent(f, df, theta_init, step_size, max_iter):\n\n fs = []\n xs = []\n thetas = theta_init\n for i in range(max_iter): #for each data example\n fs.append(f(thetas))\n\n temp = step_size*df(thetas)\n thetas = step_size*df(thetas) #modify that feature by using the derivative of log likelihood\n xs.append(thetas.flatten())\n if i % 10 == 0:\n print(i, thetas)\n\n return thetas, fs, xs","def calculate_maxL_gradient(y, tx, w):\n sig=sigmoid(tx.dot(w))\n grad=tx.T.dot(sig-y)\n #divides with number of samples so that learning rate is not dependant on number of samples\n grad=grad/len(y) \n return grad","def _log_prior_gradients(self):\n if self.priors.size == 0:\n return 0.\n x = self.param_array\n ret = np.zeros(x.size)\n #compute derivate of prior density\n [np.put(ret, ind, p.lnpdf_grad(x[ind])) for p, ind in self.priors.items()]\n #add in jacobian derivatives if transformed\n priored_indexes = np.hstack([i for p, i in self.priors.items()])\n for c,j in self.constraints.items():\n if not isinstance(c, Transformation):continue\n for jj in j:\n if jj in priored_indexes:\n ret[jj] += c.log_jacobian_grad(x[jj])\n return ret","def grad(self, w):\n gradient_penalty = np.asarray(self.alpha) * w\n\n # Insert 0 for bias term.\n return np.insert(gradient_penalty, 0, 0, axis=0)","def grad(theta, X, y, lambda_):\n # ... dopolnite (naloga 1, naloga 2)\n\n l = []\n for i, e in enumerate(theta):\n l.append(1 / len(y) * sum([(h(x, theta) - yi) * x[i] for x, yi in zip(X, y)]) + 2 * lambda_ * e)\n\n return np.array(l)","def lr_cost_function(theta, X, y, learning_rate):\n m = len(y) # number of training examples\n # You need to return the following variables correctly \n J = 0\n grad = np.zeros(theta.shape)\n h = sigmoid(np.dot(X, theta))\n\n #Compute cost:\n first = (1/m)*np.sum(np.dot(-y.T, np.log(h)) - np.dot((1-y).T, np.log(1-h)), axis=0)\n second = (learning_rate/(2*m))*np.sum(theta[1:]**2)\n J = first + second\n \n #Compute gradient\n grad = np.dot((h-y).T, X)/m\n grad = grad.T\n grad[1:] += (learning_rate/m)*theta[1:]\n return float(J), grad","def gradient(self, x, i=None):\n self.set_parameters(x=x)\n gradient = self.gradient_loss()\n if self.add_bias:\n gradient[1:] += self.alpha * self.gradient_regularizer()\n else:\n gradient += self.alpha * self.gradient_regularizer()\n if i is not None:\n return gradient.ravel()[i]\n return gradient.ravel()","def compute_gradient_lasso(y, tx, w, lambda_):\n e = y - tx.dot(w)\n subgrad = lambda_ * np.sign(w)\n\n return -tx.T.dot(e)/len(e) + subgrad","def compute_gradient(self, X, y, weights):\n sigmoid = self.sigmoid(np.dot(X, weights))\n return np.dot(X.T, y - sigmoid)","def batch_grad_descent(X, y, alpha=0.1, num_iter=1000, check_gradient=False):\n num_instances, num_features = X.shape[0], X.shape[1]\n theta_hist = np.zeros((num_iter+1, num_features)) #Initialize theta_hist\n loss_hist = np.zeros(num_iter+1) #initialize loss_hist\n theta = np.ones(num_features) #initialize theta\n\n count = 0\n while count < num_iter+1:\n if check_gradient:\n assert grad_checker(X,y,theta)\n\n grad = compute_square_loss_gradient(X,y,theta)\n theta -= alpha*grad\n theta_hist[count] = theta\n loss_hist[count] = compute_square_loss(X,y,theta)\n count += 1\n \n return theta_hist, loss_hist","def check_gradient(self, x, y):\n x = x.transpose()\n y = y.transpose()\n layers_copy = deepcopy(self.layers)\n epsilon = 10 ** -4\n a, layer = self.forward_propagation(x)\n delta = self.calculate_delta(a, y, layer)\n self.backpropagation(delta=delta, theta=layer.theta)\n previous_layer_output = x\n for layer in self.layers:\n theta_copy = deepcopy(layer.theta)\n real_theta_size = theta_copy.shape\n delta = layer.delta\n dc_dtheta = np.outer(previous_layer_output, delta).transpose()\n previous_layer_output = layer.a\n R, C = theta_copy.shape\n for i in range(R):\n for j in range(C):\n theta_plus = deepcopy(theta_copy)\n theta_plus[i, j] += epsilon\n layer.theta = theta_plus\n a_plus, l_plus = self.forward_propagation(x)\n err_plus = self.calculate_loss(a_plus, y)\n theta_minus = deepcopy(theta_copy)\n theta_minus[i, j] -= epsilon\n layer.theta = theta_minus\n a_minus, l_minus = self.forward_propagation(x)\n err_minus = self.calculate_loss(a_minus, y)\n limit = (err_plus - err_minus)/(2*epsilon)\n grad_diff = abs(dc_dtheta[i,j] - limit)\n assert grad_diff < 10 ** -6, f\"Diff {grad_diff} is too big.\"\n layer.theta = theta_copy","def _compute_func_grad(self, w):\n W = w.reshape((self.X.shape[1], self.Y.shape[1]))\n self.nll_, self.grad_ = calculate_gradient(self.X, self.Y, W, self.prior, self.weighted,0)","def gradient(data_x, data_y, parameters):\n return data_x.T @ (data_x @ parameters - data_y) / data_x.shape[0]"],"string":"[\n \"def gradient(self, theta):\\n return (1 / (self.sigma * np.sqrt(2 * np.pi))) * (\\n -theta / (self.sigma ** 2) * np.exp(-(theta ** 2) / (2 * self.sigma ** 2))\\n )\",\n \"def gradient(self, theta):\\n a = -(6 * self.scale ** 2)\\n b = 3 * self.scale ** 2 + np.exp(2 * theta)\\n b *= np.log(3 * self.scale ** 2 * np.exp(-2 * theta) + 1)\\n return a / b\",\n \"def gradient(theta, X, y, learning_rate):\\n m = len(y)\\n\\n theta = theta.reshape((-1,1))\\n grad = np.zeros(theta.shape)\\n h = sigmoid(np.dot(X, theta)) \\n \\n grad = np.dot((h-y).T, X)/m\\n grad = grad.T\\n grad[1:] += (learning_rate/m)*theta[1:]\\n return grad\",\n \"def gradient_function(theta, X, y):\\n\\n grad = None\\n #######################################################################\\n # TODO: #\\n # Compute the gradient for a particular choice of theta. #\\n # Compute the partial derivatives and set grad to the partial #\\n # derivatives of the cost w.r.t. each parameter in theta #\\n # #\\n #######################################################################\\n \\n theta = theta[:, np.newaxis]\\n \\n thetatrans = theta.T\\n Xtrans = X.T\\n \\n MulThetaX = np.dot(thetatrans, Xtrans)\\n \\n h = sigmoid(MulThetaX)\\n \\n grad = (y - h) * Xtrans\\n\\n #######################################################################\\n # END OF YOUR CODE #\\n #######################################################################\\n return grad\",\n \"def compute_gradient(theta, X, y):\\n m = X.shape[0]\\n grad_theta = np.dot(X.transpose(), (np.dot(X, theta) - y)) / m\\n #print theta, grad_theta, objective_function(theta, X, y)\\n return grad_theta\",\n \"def gradient(self, theta):\\n return np.zeros([theta.shape[0]])\",\n \"def gradientFunction(theta, X, y):\\n y = y[:, 0]\\n m = y.shape # number of training samples\\n grad = X.T.dot(sigmoid(theta.dot(X.T))-1*y)\\n grad /= m\\n return grad\",\n \"def gradient(theta, x, y):\\n m = len(y)\\n n = len(theta)\\n z = theta.dot(x.T)\\n grad = np.zeros(n)\\n for i in xrange(m):\\n grad += (g(z[i]) - y[i]) * x[i]\\n return 1. / m * grad\",\n \"def gradient(theta, X, y, Lambda=0.0):\\n m = X.shape[0] # number of samples\\n\\n h = hypothesis(theta, X)\\n\\n if Lambda:\\n g_0 = (1/m)*(X.T@(h - y))[0]\\n g_1 = (1/m)*(X.T@(h - y))[1:] + (Lambda/m)*theta[1:] # skip theta-0\\n \\n return np.append(g_0, g_1)\\n else:\\n return (1/m)*(X.T@(h - y))\",\n \"def gradient(x, y, theta):\\n if x.ndim == 1:\\n x = x[:, np.newaxis]\\n if y.ndim == 2 and y.shape[1] == 1:\\n y = y.flatten()\\n if theta.ndim == 2 and theta.shape[1] == 1:\\n theta = theta.flatten()\\n\\n if (x.size == 0 or y.size == 0 or theta.size == 0\\n or x.ndim != 2 or y.ndim != 1 or theta.ndim != 1\\n or x.shape[0] != y.shape[0] or x.shape[1] + 1 != theta.shape[0]):\\n return None\\n\\n x_padded = np.c_[np.ones(x.shape[0]), x]\\n\\n return x_padded.T.dot(x_padded.dot(theta) - y) / y.shape[0]\",\n \"def calc_grad(X, Y, theta):\\n m, n = X.shape\\n\\n margins = Y * X.dot(theta)\\n probs = 1. / (1 + np.exp(margins))\\n grad = -(1./m) * (X.T.dot(probs * Y))\\n\\n return grad\",\n \"def gradient_descent(X, y, theta, alpha, total_iterations, hypothesis):\\n len_theta = len(theta)\\n m = len(y)\\n one_over_m = (1.0 / float(m))\\n\\n for _ in range(0, total_iterations):\\n temp_theta = numpy.zeros(len_theta)\\n\\n X_by_theta_minus_y = numpy.subtract(hypothesis(numpy.matrix(theta), X), y)\\n\\n for j in range(0, len_theta):\\n jth_column_of_X = X[:,j]\\n derivative_j = one_over_m * numpy.multiply(X_by_theta_minus_y, jth_column_of_X).sum()\\n temp_theta[j] = theta[j] - alpha*derivative_j\\n\\n theta = temp_theta\\n\\n return numpy.matrix(theta)\",\n \"def gradient_descent(self, X, theta, Y, m):\\n\\n Z = X.dot(theta)\\n H = Predict.g(Z)\\n gradient = np.dot(X.T, (H - Y)) / m\\n return self.alpha * gradient\",\n \"def gradientFunctionReg(theta, X, y, Lambda): \\n y = np.squeeze(y)\\n m = y.shape # number of training samples\\n grad = X.T.dot(sigmoid(theta.dot(X.T))-1*y)\\n grad[1:] = grad[1:] + Lambda*theta[1:]\\n grad /= m\\n\\n return grad\",\n \"def gradientFunctionReg(theta, X, y, Lambda):\\n m = len(y) # number of training examples\\n grad = np.zeros(theta.shape[0])\\n theta = np.transpose(theta)\\n sum_1 = 0\\n X = X.values\\n y = y.values\\n #calcuate the theta_0 \\n# ====================== YOUR CODE HERE ======================\\n# Instructions: Compute the gradient of a particular choice of theta.\\n# Compute the partial derivatives and set grad to the partial\\n# derivatives of the cost w.r.t. each parameter in theta\\n for i in range(theta.shape[0]):\\n if i == 0:\\n for j in range(m):\\n sum_1 += (sigmoid(np.dot(X[j],theta)) - y[j]) * X[j,i]\\n else:\\n for j in range(m):\\n sum_1 += (sigmoid(np.dot(X[j],theta)) - y[j]) * X[j,i] + Lambda*theta[i]\\n grad[i] = sum_1/m\\n sum_1 = 0\\n\\n# =============================================================\\n\\n return grad\",\n \"def logit_cost_grad(self, theta, X, y):\\n\\n grad = np.zeros(len(theta))\\n\\n ### YOUR CODE HERE\\n sig = utils.sigmoid(theta)\\n # sig = np.subtract(sig, y)\\n sig = sig - y\\n grad = np.dot(X.T, sig) + 2 * self.params['lamb'] * self.regularizer[1](self.weights)\\n ### END YOUR CODE\\n\\n return grad\",\n \"def compute_loss_gradient(theta_vector, *args):\\n\\n psi = args[0] # feed psi as a parameter\\n circ_depth = args[1]\\n num_qbits = args[2]\\n theta = np.reshape(theta_vector, (circ_depth, num_qbits)) # reshapes the flat theta vector\\n fidelity = get_fidelity(theta, psi)\\n\\n # the derivative of the loss wrt fidelity\\n dl_df = -0.5 * fidelity ** (-0.5)\\n\\n df_dtheta = [] # a list of partial derivatives of the fidelity wrt the theta parameters\\n\\n for index in range(len(theta_vector)):\\n layer_index = index // num_qbits\\n qbit_index = index % num_qbits\\n\\n theta_plus = np.copy(theta)\\n theta_plus[layer_index][qbit_index] += np.pi / 2 # added pi/2 to the ith theta parameter\\n\\n theta_minus = np.copy(theta)\\n theta_minus[layer_index][qbit_index] -= np.pi / 2 # subtracted pi/2 to the ith theta parameter\\n\\n df_dtheta_i = 0.5 * (get_fidelity(theta_plus, psi) - get_fidelity(theta_minus, psi)) # ith derivative\\n df_dtheta.append(df_dtheta_i)\\n\\n df_dtheta = np.array(df_dtheta)\\n dl_dtheta = dl_df * df_dtheta # chain rule to get partial derivative of loss wrt theta parameters\\n\\n return dl_dtheta\",\n \"def run_gradient_descent_iteration(X, Y, theta, alpha, lambda_factor, temp_parameter):\\n delta = sparse.coo_matrix(theta.shape).toarray()\\n\\n h = compute_probabilities(X, theta, temp_parameter)\\n\\n for j in range(delta.shape[0]):\\n y = Y\\n y = np.where(y != j, 0, 1)\\n p = y - h[j]\\n\\n x = X.T * p\\n x = x.T\\n x = x.sum(axis=0)\\n\\n grad = -x / (temp_parameter * X.shape[0]) + lambda_factor * theta[j]\\n\\n delta[j] += grad\\n\\n theta -= alpha * delta\\n\\n return theta\",\n \"def gradient_descent(features, values, theta, alpha, num_iterations):\\n \\n # number of points\\n npoints = len(values)\\n \\n # intialize cost history\\n cost_history = []\\n \\n # num_interations iterations\\n for iiter in range(num_iterations):\\n \\n # compute and store cost\\n cost = compute_cost(features, values, theta)\\n cost_history.append(cost)\\n \\n # update values of theta\\n values_predicted = np.dot(features, theta)\\n theta = theta + (alpha/npoints)*(np.dot(values - values_predicted,features))\\n \\n return theta, pandas.Series(cost_history)\",\n \"def gradient_descent(x, y, theta=[[0], [0]]):\\n m = y.size\\n j_history = []\\n for i in range(ITERATIONS):\\n h = x.dot(theta)\\n theta = theta - (ALPHA / m) * (x.T.dot(h - y))\\n j_history.append(compute_cost(x, y, theta))\\n return theta, j_history\",\n \"def approx_grad(theta, X, y):\\n grad_a = np.array([(cost(theta + e, X, y) - cost(theta - e, X, y)) / (2 * 1e-5)\\n for e in np.identity(len(theta)) * 1e-5])\\n return grad_a\",\n \"def gradient_descent(features, values, theta, alpha, num_iterations):\\r\\n\\r\\n m = len(values)\\r\\n cost_history = []\\r\\n\\r\\n for i in range (num_iterations):\\r\\n \\r\\n h = numpy.dot(features, theta)\\r\\n \\r\\n theta = theta - alpha / m * numpy.dot((h-values),features)\\r\\n \\r\\n cost = compute_cost(features, values, theta)\\r\\n \\r\\n cost_history.append(cost)\\r\\n\\r\\n return theta, pandas.Series(cost_history) # leave this line for the grader\\r\",\n \"def calculate_gradient(y, tx, w): \\n return tx.T@(sigmoid(tx@w)-y)\",\n \"def gradient_descent(features, values, theta, alpha, num_iterations):\\n m = len(values)\\n cost_history = []\\n\\n for i in range(num_iterations):\\n predicted_values = np.dot(features, theta)\\n delta = alpha / m * np.dot((predicted_values - values), features)\\n theta = theta - delta\\n cost = compute_cost(features, values, theta)\\n cost_history.append(cost)\\n return theta, pandas.Series(cost_history)\",\n \"def lr_cost_function_grad(theta: np.ndarray, X: np.ndarray, y: np.ndarray, l: float) -> np.ndarray:\\n # Initialize some useful values\\n m = len(y) # number of training examples\\n\\n # You need to return the following variable correctly.\\n grad = np.zeros(theta.shape)\\n\\n # ====================== YOUR CODE HERE ======================\\n # Instructions: Compute the partial derivatives and set grad to the partial\\n # derivatives of the cost w.r.t. each parameter in theta.\\n\\n # =============================================================\\n return grad\",\n \"def gradient_ascent(self, w, X, y, lr):\\r\\n # INSERT YOUR CODE HERE\\r\\n #raise Exception('Function not yet implemented!')\\r\\n # gradient = x_j*(y-σ(wTX))\\r\\n return np.dot(X.T, y-self.sigmoid(np.dot(X, w)))\",\n \"def computeNumericalGradient(J, theta):\\n numgrad = np.zeros(theta.size)\\n perturb = np.zeros(theta.size)\\n e = 1e-4\\n for p in range(theta.size):\\n # Set perturbation vector\\n perturb[p] = e\\n loss1 = J(theta - perturb)[0]\\n loss2 = J(theta + perturb)[0]\\n # Compute Numerical Gradient\\n numgrad[p] = (loss2 - loss1) / (2*e)\\n perturb[p] = 0\\n return numgrad\",\n \"def computeNumericalGradient(J, theta):\\n numgrad = np.zeros_like(theta).reshape(-1)\\n perturb = np.zeros_like(theta).reshape(-1)\\n e = 1e-4\\n for p in range(theta.size):\\n # Set perturbation vector\\n perturb[p] = e\\n loss1, _ = J(theta - perturb.reshape(theta.shape))\\n loss2, _ = J(theta + perturb.reshape(theta.shape))\\n # Compute Numerical Gradient\\n numgrad[p] = (loss2 - loss1) / (2 * e)\\n perturb[p] = 0\\n\\n return numgrad.reshape(theta.shape)\",\n \"def gradientdescent(cost_func, theta, args=(), delta_func = 0):\\n step = 1\\n old_cost = 0\\n while True:\\n theta_old = theta.copy()\\n cost = cost_func(theta, *args)\\n delta = delta_func(theta, *args)\\n theta = theta - step * delta\\n if cost > old_cost and old_cost != 0:\\n step = step*0.7\\n if np.allclose(theta_old, theta):\\n break\\n old_cost = cost\\n return theta\",\n \"def gradient(cls, x):\\n y = Sigmoid.apply(x)\\n return np.multiply(y, 1 - y)\",\n \"def gradient_descent(features, values, theta, alpha, num_iterations):\\r\\n \\r\\n m = len(values)\\r\\n cost_history = []\\r\\n\\r\\n for i in range(num_iterations):\\r\\n # your code here\\r\\n cost = compute_cost(features, values, theta)/(2.0*m)\\r\\n cost_history.append([cost])\\r\\n \\r\\n error = features.dot(theta) - values\\r\\n error = np.reshape(error,(error.shape[0], 1))\\r\\n errorWeighted = features*error\\r\\n errorSum = (np.sum(errorWeighted,0))/(m*1.0)\\r\\n theta = theta - alpha*errorSum \\r\\n \\r\\n return theta, pandas.Series(cost_history)\",\n \"def compute_gradient(y, tx, w):\\n\\tN = y.shape[0]\\n\\te = y - np.dot(tx, w)\\n\\n\\tgradLw = -1/N * np.dot(tx.T, e)\\n\\treturn gradLw\",\n \"def compute_square_loss_gradient(X, y, theta):\\n #TODO\\n P = (np.dot(X, theta)-y)\\n m = X.shape[0]\\n\\n return (2/m)*np.dot(X.T, P)\",\n \"def learning_by_gradient_descent(y, tx, w, gamma):\\n\\tgrad = calculate_gradient(y, tx, w)\\n\\n\\tw = w - gamma * grad\\n\\treturn w\",\n \"def calculate_gradient(y, tx, w):\\n return tx.T.dot(sigmoid(tx.dot(w))-np.reshape(y,(len(y),1)))\",\n \"def gradientDescent(self,X, y, theta): \\n # number of instances\\n m = len(y)\\n J_history = np.zeros((self.NUM_ITERS,1))\\n for i in range(self.NUM_ITERS):\\n h = self.sigmoid(X@theta)\\n grad = 1 / m * X.T @ (h - y)\\n theta = theta - self.ALPHA * grad \\n J_history[i] = self.costFunction(theta, X, y)\\n \\n \\n return theta, J_history\",\n \"def compute_square_loss_gradient(X, y, theta):\\n #TODO\\n (N,p) = np.shape(X)\\n grad = -(1/np.float(N))*np.array([(y - X.dot(theta))*X[:,i] for i in range(p)])\\n return np.sum(grad,axis=1)\",\n \"def gradient_descent(X, y, theta, learning_rate, num_iters, lambda_reg):\\n thetas = [theta]\\n cost = np.zeros(num_iters)\\n\\n J = mean_cross_entropy_costs(X, y, lambda_reg)\\n cost[0] = J(thetas[0])\\n for i in range(1, num_iters):\\n thetas.append(compute_new_theta(X, y, thetas[i - 1], learning_rate, lambda_reg))\\n cost[i] = J(thetas[i])\\n return cost, thetas\",\n \"def gradient(img):\\n G = np.zeros(img.shape)\\n theta = np.zeros(img.shape)\\n\\n #####################################\\n # START YOUR CODE HERE #\\n #####################################\\n Gx = partial_x(img)\\n Gy = partial_y(img)\\n G = np.sqrt(np.square(Gx) + np.square(Gy))\\n theta = np.degrees(np.arctan2(Gy, Gx)) % 360\\n ######################################\\n # END OF YOUR CODE #\\n ######################################\\n\\n return G, theta\",\n \"def gradient_descent(initial_theta, X, y, niter, alpha, Lambda=0.0):\\n theta_list = []\\n cost_list = []\\n\\n theta = initial_theta\\n for i in range(0, niter):\\n theta -= alpha*gradient(theta, X, y, Lambda)\\n theta_list.append(theta)\\n cost_list.append(cost(theta, X, y, Lambda))\\n\\n return theta_list, cost_list\",\n \"def linreg_stochastic_grad(X, y, alpha=.01):\\n m = X.shape[0]\\n n = X.shape[1]\\n theta = np.zeros(n)\\n for i in range(m):\\n delta = alpha * (np.dot(theta.transpose(), X[i,:]) -y[i]) * X[i,:]\\n theta = theta - delta\\n return theta\",\n \"def log_likelihood_gradient(y, tx, w):\\n return tx.T.dot(sigmoid(tx.dot(w))-y)\",\n \"def gradient(cls, x):\\n return 1 - TanH.apply(x) ** 2\",\n \"def learning_by_gradient_descent(y, tx, w, gamma):\\n loss = calculate_loss(y,tx,w)\\n grad = calculate_gradient(y,tx,w)\\n w_new = w - gamma*grad\\n #grad is for debugging purpose\\n return loss, w_new,grad\",\n \"def simple_gradient(x, y, theta):\\n if x.shape[0] * y.shape[0] * theta.shape[0] == 0:\\n return None\\n if x.shape[0] != y.shape[0] or theta.shape[0] != 2:\\n return None\\n\\n x = add_intercept(x)\\n\\n result = [\\n forumla(x, y, theta, 0),\\n forumla(x, y, theta, 1)\\n ]\\n return result\",\n \"def calculate_gradient(y, tx, w):\\n\\n\\tret = tx.T.dot(sigmoid(np.dot(tx, w)) - y)\\n\\treturn ret\",\n \"def learning_by_gradient_descent(y, tx, w, gamma):\\n loss = calculate_loss(y,tx,w)\\n grad = calculate_gradient(y,tx,w)\\n w = w-gamma*grad\\n return w, loss\",\n \"def train_fn_grad(self, theta_value, theta):\\n theta.set_value(theta_value, borrow=True)\\n\\n train_grads = [self.batch_grad(i, self.n_train)\\n for i in xrange(self.n_train_batches)]\\n train_batch_sizes = [self.get_batch_size(i, self.n_train)\\n for i in xrange(self.n_train_batches)]\\n\\n return np.average(train_grads, weights=train_batch_sizes,\\n axis=0)\",\n \"def gradient(self, x):\\n pass\",\n \"def gradient(img):\\n G = np.zeros(img.shape)\\n theta = np.zeros(img.shape)\\n\\n ### YOUR CODE HERE\\n Gx = partial_x(img)\\n Gy = partial_y(img)\\n G = np.sqrt(np.power(Gx,2)+np.power(Gy,2))\\n theta = (np.arctan2(Gy, Gx) * 180 / np.pi) % 360\\n ### END YOUR CODE\\n\\n return G, theta\",\n \"def approx_grad_reg(theta, X, y, _lambda):\\n grad_a = np.array([(cost_reg(theta + e, X, y, _lambda) - cost_reg(theta - e, X, y, _lambda)) / (2 * 1e-5)\\n for e in np.identity(len(theta)) * 1e-5])\\n return grad_a\",\n \"def get_gradient(phi, pred, t, dot_product, weight, reg= 1, regression= \\\"logistic\\\"):\\n if regression == \\\"logistic\\\":\\n gradient = np.matmul(phi.T, pred - t)\\n elif regression == \\\"probit\\\":\\n R = np.eye(pred.shape[0])\\n for i in range(pred.shape[0]):\\n y_n = pred[i,0]\\n dotp = dot_product[i, 0]\\n pdf = norm.pdf(dotp)\\n R[i,i] = pdf/(y_n*(1-y_n) + TOLERANCE)\\n gradient = np.matmul(np.matmul(phi.T, R), pred-t)\\n elif regression == \\\"multiclass\\\":\\n gradient = np.matmul(phi.T, pred - t)\\n\\n # Add regularization\\n gradient += weight/ reg\\n return gradient\",\n \"def gradient(self, x):\\n u = np.asarray([x[0]])\\n C = self.C_func(u)\\n dC = self.dC_func(u, order=1)\\n P = self.P\\n numerator = np.sum((C - P) * dC, axis=0)\\n denominator = np.sum(np.sum((C - P) ** 2, axis=0) ** (1 / 2))\\n if np.abs(denominator) > 0:\\n gradient = numerator/denominator\\n else:\\n gradient = np.asarray(0)[np.newaxis]\\n return gradient\",\n \"def apply_gradient(params: torch.Tensor, grads: torch.Tensor, lr: float) -> torch.Tensor:\\n params_prime = params + lr * grads\\n return params_prime\",\n \"def run_steep_gradient_descent(data_x, data_y, len_data, alpha, theta):\\n n = len_data\\n # WE NEED TO transpose data_x into (p+1) *n ,theta is 1*(p+1)\\n prod = np.dot(theta, data_x.transpose())\\n\\n prod -= data_y\\n print(\\\"pro: data_x\\\", prod.shape, data_x.shape)\\n #prod represent the loss of the hypothesis and true label\\n sum_grad = np.dot(prod, data_x)\\n print(\\\"总梯度的值：\\\",sum_grad.shape)\\n\\n # batch-gradient descent\\n theta = theta -(alpha / n) * sum_grad\\n return theta\",\n \"def GDS(x, theta, y, lamb, alpha):\\n\\tm = y.shape[0]\\n\\tdif = (1/m) * x.T @ (sigmoid(x,theta) - y) #classical differentiation term, output is (n,1)\\n\\tdif_reg = (lamb/m) * theta #regularization term\\n\\treturn (dif + dif_reg) * alpha\",\n \"def gradientDescent(X, y, theta, alpha, num_iters):\\n\\n # Initialize some useful values\\n J_history = []\\n m = y.size # number of training examples\\n\\n for i in range(num_iters):\\n # ====================== YOUR CODE HERE ======================\\n # Instructions: Perform a single gradient step on the parameter vector\\n # theta.\\n #\\n # Hint: While debugging, it can be useful to print out the values\\n # of the cost function (computeCost) and gradient here.\\n #\\n # Calculate the gradient step according to the equation for theta1:\\n g_step1 = (alpha / m * np.sum( (np.dot(X,theta) - y) * X[:,1]) )\\n # Gradient step for theta knot:\\n g_step0 = (alpha / m * np.sum( (np.dot(X,theta) - y) ) )\\n \\n #update theta\\n theta[0] = (theta[0] - g_step0)\\n theta[1] = (theta[1] - g_step1)\\n \\n #print([theta , g_step1, g_step0])\\n\\n # ============================================================\\n\\n # Save the cost J in every iteration\\n J_history.append(computeCost(X, y, theta))\\n\\n return theta, J_history\",\n \"def gradient(w, x, t):\\n return 2 * np.dot(x.T, (nn(x, w) - t))\",\n \"def costFunction(theta,X,y):\\n m = X.shape[0]\\n J = 0\\n h = sigmoid (np.dot(X,theta))\\n \\n J = (1/m)* ((-np.dot(y.T,(np.log(h)))) - np.dot((1 - y).T,(np.log(1-h))))\\n \\n #grad = (1/m) * np.dot(X.T,(h-y))\\n grad = (1/m) * np.dot((h.T - y), X).T\\n \\n return J, grad\",\n \"def stochasticGradientDescent(x,y,theta,alpha):\\n m,n = np.shape(x)\\n convergence = 0.000000001\\n lastCost = 0\\n cost = -1 \\n recurseCount = 0\\n while abs(lastCost - cost) > convergence: # rcurse until converge\\n lastCost = cost\\n hypothesis = np.dot(x,theta) \\n for i in range(m):\\n # alpha = 4.0 / (1.0 + i) + 0.01 \\n loss = hypothesis[i] - y[i]\\n # gradient = np.dot(x[i],loss)\\n gradient = x[i,:].transpose() * loss \\n theta = theta - alpha * gradient\\n cost = np.sum((hypothesis-y)**2)/(2*m)\\n recurseCount += 1\\n return recurseCount,theta\",\n \"def gradient(self):\\n result = np.zeros(len(self.variables))\\n result[self.bivariateGradInd] = (self.shape-1)/self.variable - self.rate\\n return result\",\n \"def gradient(self):\\n functional = self\\n\\n class KLGradient(Operator):\\n\\n \\\"\\\"\\\"The gradient operator of this functional.\\\"\\\"\\\"\\n\\n def __init__(self):\\n \\\"\\\"\\\"Initialize a new instance.\\\"\\\"\\\"\\n super().__init__(functional.domain, functional.domain,\\n linear=False)\\n\\n def _call(self, x):\\n \\\"\\\"\\\"Apply the gradient operator to the given point.\\n The gradient is not defined in points where one or more\\n components are non-positive.\\n \\\"\\\"\\\"\\n if functional.prior is None:\\n return (-1.0) / x + 1\\n else:\\n return (-functional.prior) / x + 1\\n\\n return KLGradient()\",\n \"def batchGradientDescent(x,y,theta,alpha):\\n m,n = np.shape(x)\\n xTran = x.transpose()\\n convergence = 0.000000001\\n lastCost = 0\\n cost = -1 \\n recurseCount = 0\\n while abs(lastCost - cost) > convergence: # rcurse until converge\\n lastCost = cost\\n hypothesis = np.dot(x,theta)\\n loss = hypothesis - y\\n cost = np.sum(loss**2)/(2*m)\\n gradient = np.dot(xTran,loss)/m\\n theta = theta - alpha*gradient\\n recurseCount += 1\\n return recurseCount,theta\",\n \"def learning_by_penalized_gradient(y, tx, w, gamma, lambda_):\\n\\tgrad, H = penalized_logistic_regression(y, tx, w, lambda_)\\n\\n\\thgrad = np.linalg.inv(H).dot(grad)\\n\\n\\tw = w - gamma * hgrad\\n\\n\\treturn w\",\n \"def grad(self, w):\\n l1_grad = self.r * np.sign(w)\\n l2_grad = np.asarray(1 - self.r) * w \\n\\n gradient_penalty = self.alpha * (l1_grad + l2_grad)\\n\\n # Insert 0 for bias term.\\n return np.insert(gradient_penalty, 0, 0, axis=0)\",\n \"def compute_gradient(self):\\n A = np.dot(self.X, self.w)\\n m = self.t.shape[0]\\n C = -1 * self.t * (1 / (1 + np.exp(A * self.t)))\\n return (1 / m) * np.dot(self.X.T, C)\",\n \"def gradient(self, x):\\n g = self._grad(\\n time_series=self.observed_data,\\n a=x[0],\\n b=x[1],\\n c=x[2],\\n sigma=self.sigma\\n )\\n return g\",\n \"def reg_autograd(alpha,X,theta,y):\\n g=grad(func)\\n for i in range(0,20):\\n theta=theta- alpha * g(theta)\\n print(\\\"cost\\\", func(theta))\\n print(\\\"final theta\\\",theta)\",\n \"def grad(self, x):\\n norm_beta = np.linalg.norm(x)\\n if norm_beta > utils.TOLERANCE:\\n return (self.l / norm_beta) * x\\n else:\\n D = x.shape[0]\\n u = (self.rng(D, 1) * 2.0) - 1.0 # [-1, 1]^D\\n norm_u = np.linalg.norm(u)\\n a = self.rng() # [0, 1]\\n\\n return (self.l * (a / norm_u)) * u\",\n \"def gradcovfunc(self, theta, d):\\n sigmaf, l = theta[:2] \\n xxl = np.sum((d/l)**2, axis=1)\\n dk_dsigmaf = 2 * sigmaf * np.exp(-xxl/2.)\\n dk_dl = sigmaf**2/l * xxl * np.exp(-xxl/2.)\\n grad = np.array([dk_dsigmaf, dk_dl])\\n return grad\",\n \"def log_prior_grad(self, inputs):\",\n \"def gradient(series, **options):\\n x = series.index\\n y = series.values\\n\\n a = np.gradient(y, x, **options)\\n return series.__class__(a, series.index)\",\n \"def derivative(self, theta):\\n diag_gamma = np.dot(theta.T, self.X.T)\\n logistic_term = self.logistic_fn(diag_gamma)\\n diag_gamma = logistic_term * (1.0 - logistic_term)\\n gamma = np.diag(diag_gamma)\\n\\n # v computation\\n diags_v = 1.0 - 2*self.logistic_fn(np.dot(theta.T, self.X.T))\\n diags_v = diags_v.reshape((-1, 1))\\n diags_v = diags_v*self.X\\n assert diags_v.shape == self.X.shape #N*d shape\\n\\n XtGamma = np.dot(self.X.T, gamma) # d*N shape\\n\\n # TODO: Verifier car pas sur de mon coup ... et surtout plus long...\\n # id = np.eye(self.n_examples).reshape((self.n_examples, self.n_examples, 1))\\n # diags_v = diags_v.reshape((self.n_examples, 1, self.dim))\\n # v = id*diags_v # n*n*d tensor\\n # left = np.tensordot(XtGamma, v, axes=(1, 0)) # shape d*N*d\\n # assert left.shape == (self.dim, self.n_examples, self.dim)\\n # dg = np.tensordot(left, self.X, axes=(1, 0))\\n # dg = np.swapaxes(dg, axis1=-2, axis2=-1)\\n\\n dg = np.zeros((self.dim, self.dim, self.dim))\\n for idx, v_i_diag in enumerate(diags_v.T):\\n v_i = np.diag(v_i_diag)\\n dg_di = np.dot(np.dot(XtGamma, v_i), self.X)\\n dg[:, :, idx] = dg_di\\n return dg\",\n \"def compute_logistic_gradient(y, tx, w):\\n return tx.T.dot(sigmoid(tx.dot(w)) - y) / len(y)\",\n \"def run_gradient_descent(data,theta,alpha,num_iters):\\n population = data[:,0]\\n prices = data[:,1]\\n x = ones(shape=(len(population),2)) #add ones for theta0 \\n x[:,1] = population\\n x = transpose(x)\\n error_history = zeros(shape=(num_iters,1))\\n \\n for i in range(num_iters):\\n predictions = theta.dot(x)\\n errors_x1 = (predictions - prices) * x[0,:]\\n errors_x2 = (predictions - prices) * x[1,:]\\n theta[0][0] = theta[0][0] - alpha*(1.0/len(population))*errors_x1.sum()\\n theta[0][1] = theta[0][1] - alpha*(1.0/len(population))*errors_x2.sum()\\n error_history[i,0] = calculate_cost(theta,data)\\n \\n return theta, error_history\",\n \"def gradient(self, x):\\n return 0.0\",\n \"def costFunction(self,theta, X, y): \\n m = len(y)\\n h = self.sigmoid(X@theta)\\n J = 1 / m * (- y.T @ self.log(h) - (1-y).T @ self.log(1-h)) \\n # grad = 1/ m * X.T @ (h - y)\\n return J\",\n \"def gradient(poly):\\n return differential(\\n poly, chaospy.poly.collection.basis(1, 1, poly.dim, sort=\\\"GR\\\"))\",\n \"def curves_gradient(theta, m_, LAMBDA=0):\\n # X0: column of ones\\n theta0_gradient = (1 / m_) * sum((curves_hypothesis(theta, m_) - y[:m_].ravel()) * np.ones(m_))\\n # X1: data given as X\\n theta1_gradient = ((1 / m_) * sum((curves_hypothesis(theta, m_) - y[:m_].ravel()) * X[:m_].ravel())) \\\\\\n + (LAMBDA / m_) * theta[1]\\n return np.array([theta0_gradient, theta1_gradient])\",\n \"def gradient(self, x_in):\\r\\n\\r\\n return 1 - torch.pow(self.forward(x_in), 2)\",\n \"def compute_gradient(self): # TODO: try to change to square loss since it's hessian is easier to obtain\\n A = np.dot(self.X, self.w)\\n m = self.t.shape[0]\\n C = -1 * self.t * (1 / (1 + np.exp(A * self.t)))\\n return (1 / m) * np.dot(self.X.T, C)\",\n \"def vb_grad_natgrad(self):\\r\\n\\r\\n # Gradient for eta\\r\\n dL_dmmT_S = -0.5*self.Lambda/self.data_prop + 0.5*self.q_u_prec\\r\\n Kmmipsi1V,_ = dpotrs(self.Lm,self.psi1V,lower=1)\\r\\n dL_dm = (Kmmipsi1V - np.dot(self.Lambda,self.q_u_mean))/self.data_prop\\r\\n\\r\\n # Gradients for theta\\r\\n S = self.q_u_cov\\r\\n Si = self.q_u_prec\\r\\n m = self.q_u_mean\\r\\n dL_dSi = -mdot(S,dL_dmmT_S, S)\\r\\n\\r\\n dL_dmhSi = -2*dL_dSi\\r\\n dL_dSim = np.dot(dL_dSi,m) + np.dot(Si, dL_dm)\\r\\n\\r\\n return np.hstack((dL_dm.flatten(),dL_dmmT_S.flatten())) , np.hstack((dL_dSim.flatten(), dL_dmhSi.flatten()))\",\n \"def grad(self, w):\\n gradient_penalty = np.asarray(self.alpha) * w\\n # Insert 0 for bias term.\\n return np.insert(gradient_penalty, 0, 0, axis=0)\",\n \"def grad(self, w):\\n gradient_penalty = np.asarray(self.alpha) * w\\n # Insert 0 for bias term.\\n return np.insert(gradient_penalty, 0, 0, axis=0)\",\n \"def gradient(self):\\n functional = self\\n\\n class KLCCGradient(Operator):\\n\\n \\\"\\\"\\\"The gradient operator of this functional.\\\"\\\"\\\"\\n\\n def __init__(self):\\n \\\"\\\"\\\"Initialize a new instance.\\\"\\\"\\\"\\n super().__init__(functional.domain, functional.domain,\\n linear=False)\\n\\n def _call(self, x):\\n \\\"\\\"\\\"Apply the gradient operator to the given point.\\n\\n The gradient is not defined in points where one or more\\n components are larger than or equal to one.\\n \\\"\\\"\\\"\\n if functional.prior is None:\\n return 1.0 / (1 - x)\\n else:\\n return functional.prior / (1 - x)\\n\\n return KLCCGradient()\",\n \"def learning_by_penalized_gradient(y, tx, w, gamma, lambda_):\\n\\n #on test avec Newton\\n\\n loss,gradient,_ = penalized_logistic_regression(y,tx,w,lambda_)\\n\\n w = w - gamma*gradient\\n return loss, w,gradient\",\n \"def gradient_ascent(f, df, theta_init, step_size, max_iter):\\n\\n fs = []\\n xs = []\\n thetas = theta_init\\n for i in range(max_iter): #for each data example\\n fs.append(f(thetas))\\n\\n temp = step_size*df(thetas)\\n thetas = step_size*df(thetas) #modify that feature by using the derivative of log likelihood\\n xs.append(thetas.flatten())\\n if i % 10 == 0:\\n print(i, thetas)\\n\\n return thetas, fs, xs\",\n \"def calculate_maxL_gradient(y, tx, w):\\n sig=sigmoid(tx.dot(w))\\n grad=tx.T.dot(sig-y)\\n #divides with number of samples so that learning rate is not dependant on number of samples\\n grad=grad/len(y) \\n return grad\",\n \"def _log_prior_gradients(self):\\n if self.priors.size == 0:\\n return 0.\\n x = self.param_array\\n ret = np.zeros(x.size)\\n #compute derivate of prior density\\n [np.put(ret, ind, p.lnpdf_grad(x[ind])) for p, ind in self.priors.items()]\\n #add in jacobian derivatives if transformed\\n priored_indexes = np.hstack([i for p, i in self.priors.items()])\\n for c,j in self.constraints.items():\\n if not isinstance(c, Transformation):continue\\n for jj in j:\\n if jj in priored_indexes:\\n ret[jj] += c.log_jacobian_grad(x[jj])\\n return ret\",\n \"def grad(self, w):\\n gradient_penalty = np.asarray(self.alpha) * w\\n\\n # Insert 0 for bias term.\\n return np.insert(gradient_penalty, 0, 0, axis=0)\",\n \"def grad(theta, X, y, lambda_):\\n # ... dopolnite (naloga 1, naloga 2)\\n\\n l = []\\n for i, e in enumerate(theta):\\n l.append(1 / len(y) * sum([(h(x, theta) - yi) * x[i] for x, yi in zip(X, y)]) + 2 * lambda_ * e)\\n\\n return np.array(l)\",\n \"def lr_cost_function(theta, X, y, learning_rate):\\n m = len(y) # number of training examples\\n # You need to return the following variables correctly \\n J = 0\\n grad = np.zeros(theta.shape)\\n h = sigmoid(np.dot(X, theta))\\n\\n #Compute cost:\\n first = (1/m)*np.sum(np.dot(-y.T, np.log(h)) - np.dot((1-y).T, np.log(1-h)), axis=0)\\n second = (learning_rate/(2*m))*np.sum(theta[1:]**2)\\n J = first + second\\n \\n #Compute gradient\\n grad = np.dot((h-y).T, X)/m\\n grad = grad.T\\n grad[1:] += (learning_rate/m)*theta[1:]\\n return float(J), grad\",\n \"def gradient(self, x, i=None):\\n self.set_parameters(x=x)\\n gradient = self.gradient_loss()\\n if self.add_bias:\\n gradient[1:] += self.alpha * self.gradient_regularizer()\\n else:\\n gradient += self.alpha * self.gradient_regularizer()\\n if i is not None:\\n return gradient.ravel()[i]\\n return gradient.ravel()\",\n \"def compute_gradient_lasso(y, tx, w, lambda_):\\n e = y - tx.dot(w)\\n subgrad = lambda_ * np.sign(w)\\n\\n return -tx.T.dot(e)/len(e) + subgrad\",\n \"def compute_gradient(self, X, y, weights):\\n sigmoid = self.sigmoid(np.dot(X, weights))\\n return np.dot(X.T, y - sigmoid)\",\n \"def batch_grad_descent(X, y, alpha=0.1, num_iter=1000, check_gradient=False):\\n num_instances, num_features = X.shape[0], X.shape[1]\\n theta_hist = np.zeros((num_iter+1, num_features)) #Initialize theta_hist\\n loss_hist = np.zeros(num_iter+1) #initialize loss_hist\\n theta = np.ones(num_features) #initialize theta\\n\\n count = 0\\n while count < num_iter+1:\\n if check_gradient:\\n assert grad_checker(X,y,theta)\\n\\n grad = compute_square_loss_gradient(X,y,theta)\\n theta -= alpha*grad\\n theta_hist[count] = theta\\n loss_hist[count] = compute_square_loss(X,y,theta)\\n count += 1\\n \\n return theta_hist, loss_hist\",\n \"def check_gradient(self, x, y):\\n x = x.transpose()\\n y = y.transpose()\\n layers_copy = deepcopy(self.layers)\\n epsilon = 10 ** -4\\n a, layer = self.forward_propagation(x)\\n delta = self.calculate_delta(a, y, layer)\\n self.backpropagation(delta=delta, theta=layer.theta)\\n previous_layer_output = x\\n for layer in self.layers:\\n theta_copy = deepcopy(layer.theta)\\n real_theta_size = theta_copy.shape\\n delta = layer.delta\\n dc_dtheta = np.outer(previous_layer_output, delta).transpose()\\n previous_layer_output = layer.a\\n R, C = theta_copy.shape\\n for i in range(R):\\n for j in range(C):\\n theta_plus = deepcopy(theta_copy)\\n theta_plus[i, j] += epsilon\\n layer.theta = theta_plus\\n a_plus, l_plus = self.forward_propagation(x)\\n err_plus = self.calculate_loss(a_plus, y)\\n theta_minus = deepcopy(theta_copy)\\n theta_minus[i, j] -= epsilon\\n layer.theta = theta_minus\\n a_minus, l_minus = self.forward_propagation(x)\\n err_minus = self.calculate_loss(a_minus, y)\\n limit = (err_plus - err_minus)/(2*epsilon)\\n grad_diff = abs(dc_dtheta[i,j] - limit)\\n assert grad_diff < 10 ** -6, f\\\"Diff {grad_diff} is too big.\\\"\\n layer.theta = theta_copy\",\n \"def _compute_func_grad(self, w):\\n W = w.reshape((self.X.shape[1], self.Y.shape[1]))\\n self.nll_, self.grad_ = calculate_gradient(self.X, self.Y, W, self.prior, self.weighted,0)\",\n \"def gradient(data_x, data_y, parameters):\\n return data_x.T @ (data_x @ parameters - data_y) / data_x.shape[0]\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8256445","0.8055304","0.7947039","0.7867961","0.78287417","0.78280646","0.7741965","0.77182055","0.77098596","0.76828635","0.76208895","0.75614756","0.72576725","0.72168595","0.7216574","0.7211889","0.70136374","0.70051277","0.6994997","0.69862175","0.69123644","0.6910823","0.68812525","0.6881161","0.6863728","0.6838628","0.6829457","0.68013173","0.67898685","0.67635065","0.6753912","0.6751456","0.67212635","0.67147833","0.6712214","0.67026615","0.6683733","0.6665269","0.6664574","0.66554016","0.6632431","0.66249985","0.6618367","0.66131556","0.6605929","0.66040915","0.658713","0.6579545","0.65779126","0.65694577","0.65687287","0.65535915","0.65233195","0.6507123","0.64980435","0.6492682","0.64849585","0.64616925","0.6455795","0.64541864","0.64431417","0.64234275","0.64233476","0.64115876","0.6388624","0.6375172","0.63749987","0.63732344","0.63698816","0.6358501","0.6354826","0.6352325","0.63463557","0.6346066","0.63423747","0.6325621","0.6305958","0.63044894","0.6303761","0.6282822","0.6278642","0.62777394","0.62690866","0.62690866","0.6268236","0.6257674","0.6254688","0.62541944","0.625029","0.624863","0.6246089","0.62321514","0.6226891","0.6226241","0.6183781","0.61833704","0.6182383","0.61771554","0.6168317"],"string":"[\n \"0.8256445\",\n \"0.8055304\",\n \"0.7947039\",\n \"0.7867961\",\n \"0.78287417\",\n \"0.78280646\",\n \"0.7741965\",\n \"0.77182055\",\n \"0.77098596\",\n \"0.76828635\",\n \"0.76208895\",\n \"0.75614756\",\n \"0.72576725\",\n \"0.72168595\",\n \"0.7216574\",\n \"0.7211889\",\n \"0.70136374\",\n \"0.70051277\",\n \"0.6994997\",\n \"0.69862175\",\n \"0.69123644\",\n \"0.6910823\",\n \"0.68812525\",\n \"0.6881161\",\n \"0.6863728\",\n \"0.6838628\",\n \"0.6829457\",\n \"0.68013173\",\n \"0.67898685\",\n \"0.67635065\",\n \"0.6753912\",\n \"0.6751456\",\n \"0.67212635\",\n \"0.67147833\",\n \"0.6712214\",\n \"0.67026615\",\n \"0.6683733\",\n \"0.6665269\",\n \"0.6664574\",\n \"0.66554016\",\n \"0.6632431\",\n \"0.66249985\",\n \"0.6618367\",\n \"0.66131556\",\n \"0.6605929\",\n \"0.66040915\",\n \"0.658713\",\n \"0.6579545\",\n \"0.65779126\",\n \"0.65694577\",\n \"0.65687287\",\n \"0.65535915\",\n \"0.65233195\",\n \"0.6507123\",\n \"0.64980435\",\n \"0.6492682\",\n \"0.64849585\",\n \"0.64616925\",\n \"0.6455795\",\n \"0.64541864\",\n \"0.64431417\",\n \"0.64234275\",\n \"0.64233476\",\n \"0.64115876\",\n \"0.6388624\",\n \"0.6375172\",\n \"0.63749987\",\n \"0.63732344\",\n \"0.63698816\",\n \"0.6358501\",\n \"0.6354826\",\n \"0.6352325\",\n \"0.63463557\",\n \"0.6346066\",\n \"0.63423747\",\n \"0.6325621\",\n \"0.6305958\",\n \"0.63044894\",\n \"0.6303761\",\n \"0.6282822\",\n \"0.6278642\",\n \"0.62777394\",\n \"0.62690866\",\n \"0.62690866\",\n \"0.6268236\",\n \"0.6257674\",\n \"0.6254688\",\n \"0.62541944\",\n \"0.625029\",\n \"0.624863\",\n \"0.6246089\",\n \"0.62321514\",\n \"0.6226891\",\n \"0.6226241\",\n \"0.6183781\",\n \"0.61833704\",\n \"0.6182383\",\n \"0.61771554\",\n \"0.6168317\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.83195007"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":14397,"cells":{"query":{"kind":"string","value":"Returns N samples from the prior."},"document":{"kind":"string","value":"def sample_from_prior(self, n_samples):\n\n p0 = self.rng.normal(loc=self.mean, scale=self.sigma, size=n_samples)\n return p0[:, np.newaxis]"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def sample_from_prior(self, n_samples):\n pass","def sample_from_prior(self, n_samples):\n\n p0 = self.min + self.rng.rand(n_samples) * (self.max - self.min)\n return p0[:, np.newaxis]","def sample_from_prior(self, n_samples):\n\n p0 = self.rng.lognormal(mean=self.mean, sigma=self.sigma, size=n_samples)\n return p0[:, np.newaxis]","def sample_from_prior(self, n_samples):\n\n lamda = np.abs(self.rng.standard_cauchy(size=n_samples))\n\n p0 = np.log(np.abs(self.rng.randn() * lamda * self.scale))\n return p0[:, np.newaxis]","def sample_prior(size):\n return torch.randn(size)","def sample_from_prior(self):\n raise NotImplementedError","def sample_parameters_prior(self, n_samples, random_seed=None):\n\n if random_seed is not None:\n np.random.seed(random_seed)\n samples = []\n samples.append(self.var_noise.sample_from_prior(n_samples))\n samples.append(self.mean.sample_from_prior(n_samples))\n samples.append(self.kernel.sample_parameters(n_samples))\n\n return np.concatenate(samples, 1)","def sample(self, n_samples=1):\n\n\t\tif isinstance(n_samples, int):\n\t\t\tn_samples = (n_samples,)\n\t\tbeta_sample = self.beta_prior.sample(n_samples)\n\t\tgamma_sample = self.gamma_prior.sample(n_samples)\n\t\tp = torch.stack((beta_sample, gamma_sample)).T\n\t\tif n_samples == (1,):\n\t\t\tp = p[0]\n\t\tp = p.numpy()\n\t\treturn p","def sample_from_prior(self, *args, **kwargs):\n pass","def forward_sample(self, n):\n return self.flow.sample(n)","def prior_sample(self):\n pass","def generate_samples(self, n_samples):","def generate_samples(self, n_samples):","def sample(self, n):\n # Est probs from priority weights\n summed = sum(self.weight) + self.eps\n self.probs = [w / summed for w in self.priority]\n\n # Wieghted sample\n return np.random.choice(self.memory, size=n, p=self.probs).tolist()","def prior_samples(self, nsamples, coords=None):\n S = self.eval_S(self.kappa, self.sigma_f)\n if coords is None:\n coords = slice(self.mesh.num_vertices()) # take all coords\n weights = np.random.normal(scale=np.sqrt(S),\n size=(nsamples,) + S.shape) # shape (s, l)\n prior = np.einsum('sl,nl->sn', weights, self.eigenfunctions[coords])\n\n return prior","def samples(self):\n return self._values[:self.nsamples]","def generate_samples(self, no=10):\n observations = []\n state_sequence = []\n initial_state = np.random.choice(\n self.latent_variable_markov_chain.states,\n p=self.prior_probabilities)\n state_sequence.append(initial_state)\n observations.append(self.observation_from_state(initial_state))\n current_state = initial_state\n for i in range(2, no):\n next_state = self.latent_variable_markov_chain.next_state(current_state)\n state_sequence.append(next_state)\n observations.append(self.observation_from_state(next_state))\n current_state = next_state\n return observations, state_sequence","def sample(self, n, include=True):\n return [self(t / n) for t in range(n + int(include))]","def prior(n=10):\r\n p = []\r\n trials = 0\r\n acc = 0\r\n while acc < n:\r\n trials += 1\r\n r = np.random.rand(2) * np.array([4, 2]) + np.array([-2, -1])\r\n # print(\"r: \", r)\r\n if r[1] + r[0] >= -1 and r[1] - r[0] >= -1:\r\n p.append(r)\r\n acc += 1\r\n # print(\"trials: \", trials, \", acc: \", acc)\r\n return p","def pull_reads(self, n, prng): \n if not self.sampling:\n self.convert_to_array()\n index = prng.randint(0, self.total, size=n)\n index = np.sort(index)\n return self.reads[index,:]","def sample(self, n=1):\n raise NotImplementedError","def get_training_n(self, n: Optional[int] = None, random: bool = True) -> Tuple[torch.Tensor, torch.Tensor]:\n return self.__sample(self.train_data, self.train_los, n, random)","def sample(self, n):\n raise NotImplementedError","def _sample(self, n=1):\n return [self[i] for i in np.random.choice(self.length, n, replace=False)]","def get_n_samples(self):\n return {op.rx.get_n_samples() for op in self.ops}","def sample(self, n_samples):\n\n z = sample_prior((n_samples,) + self.flow.z_shape)\n ldj = torch.zeros(z.size(0))\n\n z, ldj = self.flow (z, ldj, reverse=True)\n z, ldj = self.logit_normalize(z, ldj, reverse=True)\n\n return z","def Sample(n=6):\n t = [random.normalvariate(0.0, 1.0) for i in range(n)]\n t.sort()\n return t","def posterior_sampler(self, nsamples, seed=0, verbose=True):\n\n import random\n\n random.seed(seed)\n sample = self.get_chain()[-self.get_tune:]\n sample = sample.reshape(-1, sample.shape[-1])\n sample = random.choices(sample, k=nsamples)\n\n return sample","def generate_samples(self, n_samples=100):\n \t\t\n\t\t#how many times should ancestral sampling be run\n\t\t#n_samples\n prior_samples=[]\n for i in range(0,n_samples):\n prior_sample = self.prior.get_samples(\n n_latent_nodes=self.n_latent_nodes,\n n_gibbs_sampling_steps=100, \n sampling_mode=\"gibbs_ancestral\")\n prior_sample = torch.cat(prior_sample)\n prior_samples.append(prior_sample)\n prior_samples=torch.stack(prior_samples)\n # prior_samples = tf.slice(prior_samples, [0, 0], [num_samples, -1])\n output_activations = self.decoder.decode(prior_samples)\n output_activations = output_activations+self._train_bias\n output_distribution = Bernoulli(logit=output_activations)\n output=torch.sigmoid(output_distribution.logits)\n # output_activations[0] = output_activations[0] + self.train_bias\n # output_dist = FactorialBernoulliUtil(output_activations)\n # output_samples = tf.nn.sigmoid(output_dist.logit_mu)\n # print(\"--- \",\"end VAE::generate_samples()\")\n return output","def get_random_samplers(self, n):\n if not self.has_samplers():\n self.draw_samplers()\n \n def get_shuffledcopy(x):\n x_ = x.copy()\n np.random.shuffle(x_)\n return x_\n \n return get_shuffledcopy(self.samplers)[:n]","def sample_gp_prior(self, x_list, n_samp, full_cov=True):\n mu, cov = self.get_gp_prior_mu_cov(x_list, full_cov)\n return self.get_normal_samples(mu, cov, n_samp, full_cov)","def get_posterior_sample(self):\n total_tries = self.prior_success + self.prior_failure\n prob_success = self.prior_success / total_tries\n # np.random.binomial采样出来的是二项分布的均值, 即正面朝上的次数,所以要除以N\n boot_sample = np.random.binomial(total_tries, prob_success) / total_tries\n return boot_sample","def sample(self, n_samples=1):\n\n\t\tsamples = np.random.random((n_samples, self.__ndims))*self.__range + self.__low\n\t\treturn samples","def get_samples(self, nsamples):\n sample_ids = np.random.choice(self.embed.shape[0], nsamples, replace=False)\n return self.embed[sample_ids]","def sample_predict(self, X, n_samples):\n W_samples = self.W_prior.sample(n_samples) ##size: [n_samples, n_in, n_out]\n if self.use_bias:\n bs = W_samples[:, :1, :]\n Ws = W_samples[:, 1:, :]\n if self.scaled_variance:\n Ws = Ws / np.sqrt(self.n_in)\n return torch.matmul(X, Ws) + bs\n else:\n if self.scaled_variance:\n W_samples = W_samples / np.sqrt(self.n_in)\n return torch.matmul(X, W_samples)","def pickTopicFromPrior(self):\r\n return self.priorTopicDistr.sampleIndex()","def fetch_samples(self):\n return torch.cat(self.samples,dim=0).reshape(-1,self.parameters.numel())","def _uniform_number(self, N=None):\n num_frames = self.data.shape[0]\n if not N:\n N = int(np.floor(\n Constants.DEFAULT_SAMPLE_PERCENTAGE * num_frames\n ))\n k = int(np.floor(num_frames / N))\n if k == 0:\n k = 1\n elif N > num_frames:\n k = 1\n else:\n assert type(N) is IntType, 'N is not an integer: {}'.format(N)\n k = int(np.floor(num_frames / N))\n\n # ensure that at most N samples are taken if N does not divide nicely\n return self.data[::k][:N], self.labels[::k][:N]","def sample(self, n_samples, sample_seed):\n self.seed_samples(sample_seed)\n eps = torch.randn(self.batch_size, n_samples, self.Y_dim)\n samples = eps*torch.exp(0.5*self.logvar.unsqueeze(1)) + self.mu.unsqueeze(1)\n samples = self.unwhiten_back(samples)\n samples = samples.data.cpu().numpy()\n return samples","def prior_sample(self, bn):\n x = np.zeros(3)\n\n # first joint prob\n random_choice = np.random.choice(bn[0], 1, bn[0].all(), bn[0])\n x[0] = random_choice[0]\n\n # Second Joint Prob\n if x[0] == 0.1:\n random_choice = np.random.choice(bn[1][0], 1, bn[1][0].all(), bn[1][0])\n x[1] = random_choice\n elif x[0] == 0.9:\n random_choice = np.random.choice(bn[1][1], 1, bn[1][1].all(), bn[1][1])\n x[1] = random_choice\n\n # Third Joint Prob\n if random_choice[0] == 0.8 or random_choice == 0.1:\n random_choice = np.random.choice(bn[2][0], 1, bn[2][0].all(), bn[2][0])\n x[2] = random_choice\n else:\n random_choice = np.random.choice(bn[2][1], 1, bn[2][1].all(), bn[2][1])\n x[2] = random_choice\n return x","def prob1(n):\n#raise NotImplementedError(\"Problem 1 Incomplete\")\n if n == 0 :\n raise ValueError(\"Sampling 0 points is not defined.\")\n total = 0\n for i in xrange(n) :\n if np.random.normal() > 3 :\n total += 1\n return float(total)/n","def sample (self, n):\n y = self.bins\n x = np.r_[0, self.values.cumsum ()] / self.sum\n # interpolate inverse CDF\n out = np.interp (np.random.random (n), x, y)\n if n == 1:\n return out[0]\n else:\n return out.reshape ((n,))","def get_n(self, n):\n \n return [self.get_values() for _ in range(n)]","def getNrSamples(self): \r\n return self.numSamples","def sample(self, n):\n idx = np.random.randint(0, len(self.memory), size=n)\n return [self.memory[i] for i in idx]","def pull_n_samples(dset, n):\n return list(dset[i] for i in random.sample(range(len(dset)), n))","def lnprior(self):\n \n return","def samples(self) -> int:\n return self._samples","def _construct_sample_from_prior(self):\n z_sym = T.matrix()\n x_sym = T.matrix()\n irs = self.ir_steps\n oputs = [self.obs_transform(self.s0)]\n oputs.extend([self.obs_transform(self.si[i]) for i in range(irs)])\n _, hi_zmuv = self._construct_zmuv_samples(x_sym, 1)\n sample_func = theano.function(inputs=[z_sym, x_sym], outputs=oputs, \\\n givens={ self.z: z_sym, \\\n self.x_in: T.zeros_like(x_sym), \\\n self.x_out: T.zeros_like(x_sym), \\\n self.hi_zmuv: hi_zmuv }, \\\n updates=self.scan_updates)\n def prior_sampler(samp_count):\n x_samps = to_fX( np.zeros((samp_count, self.obs_dim)) )\n old_switch = self.train_switch.get_value(borrow=False)\n # set model to generation mode\n self.set_train_switch(switch_val=0.0)\n z_samps = to_fX( npr.randn(samp_count, self.z_dim) )\n model_samps = sample_func(z_samps, x_samps)\n # set model back to either training or generation mode\n self.set_train_switch(switch_val=old_switch)\n return model_samps\n return prior_sampler","def generate_samples(self):\n self.analytic_probability()","def prior_distribution(self):\n out = self.model.forward(self.inducing_points)\n return MultivariateNormal(out.mean, out.lazy_covariance_matrix.evaluate_kernel())","def sample(self, size=1):\n samples = scipy.stats.bernoulli.rvs(self.p, size=size)\n if size == 1:\n return samples[0]\n return samples","def n_remaining_samples(self):\n return -1","def n_remaining_samples(self):\n return -1","def n_remaining_samples(self):\n return -1","def sample(self, nsamples):\n return self.dist.sample(nsamples)","def __resample(self):\n p_resample = []\n w_max = max(self.weights)\n index = int(round(random.uniform(0, self.n - 1)))\n beta = 0\n for i in range(self.n):\n beta += random.uniform(0, 2 * w_max)\n while self.weights[index] < beta:\n beta -= self.weights[index]\n index = (index + 1) % self.n\n p_resample.append(self.particles[index, :])\n return np.array(p_resample)","def burnin_by_first_n(samples, N, step_number=False, logger_level=\"debug\"):\n _samples = copy.deepcopy(samples)\n n_samples = {key: N for key in _samples.keys()}\n if step_number:\n n_samples = {\n key: item + N if item is not None else N for key, item in\n _number_of_negative_steps(_samples, logger_level=logger_level).items()\n }\n getattr(logger, logger_level)(\n \"Removing the first {} as burnin\".format(\n \", \".join(\n [\"{} samples from {}\".format(val, key) for key, val in n_samples.items()]\n )\n )\n )\n return _samples.discard_samples(n_samples)","def take_samples(self, num_samples: int) -> List:\n if num_samples > len(self.samples):\n return random.sample(self.samples, len(self.samples))\n return random.sample(self.samples, num_samples)","def n_samples(self) -> int: # pragma: no cover\n return self.samples.shape[0]","def sample_count(self):","def sample_users(self, n):\n return torch.rand((n, self.num_topics), generator=self.generator) * 2 - 1","def sample_size_with_prior(self, prior=\"small\"):\n if prior == \"small\":\n prior_sizes = list(range(self._min_size, self._max_size))\n elif prior == \"big\":\n prior_sizes = list(range(self._min_size+1, self._max_size+1))\n return random.choice(prior_sizes)","def sample(self,\n n_samples: Optional[int],\n temperatures: Optional[List[float]] = None) -> T.Tensor:\n samples = self.levels[-1].sample(\n self.top_prior_shape, n_samples=n_samples, temperatures=temperatures)\n for level in self.levels[-2::-1]:\n samples = level.sample(context=samples)\n return samples","def prior_predictive(self):\n cfg = self.config\n n = cfg['batch_size'] * cfg['q/n_samples']\n n_samples = cfg['q/n_samples']\n with util.get_or_create_scope('model', reuse=True):\n h_prior = tf.cast(self.p_h_L.sample(n), cfg['dtype'])\n h_prior = tf.reshape(\n h_prior, [cfg['q/n_samples'], cfg['batch_size'], -1])\n h = [None] * cfg['p/n_layers']\n h[cfg['p/n_layers'] - 1] = h_prior\n for n in range(cfg['p/n_layers'] - 1, 0, -1):\n p_h_n = self.build_stochastic_layer(n, h_above=h[n])\n h[n - 1] = tf.cast(p_h_n.sample(), cfg['dtype'])\n return self.likelihood(h[0])","def sample(self, num_samples = 1):\n\n X = np.empty(num_samples)\n for k in range(len(X)):\n\n # starting\n n = 1\n a = 1\n Un = self.UG.sample()\n a = a * Un\n\n # iterate over\n while a >= np.exp(-self.rate):\n n = n + 1\n Un = self.UG.sample()\n a = a * Un\n\n X[k] = n - 1\n\n return X","def sample_pagerank(corpus, damping_factor, n):\n probabilities = dict()\n samples = []\n\n # Random first sample\n page = random.choice(list(corpus.keys()))\n samples.append(page)\n \n # Remaining samples after first\n for i in range(n-1):\n p = transition_model(corpus, page, damping_factor)\n page = random.choices(list(p.keys()), weights=list(p.values()), k=1)[0]\n samples.append(page)\n\n # Count\n for p in corpus.keys():\n probabilities[p] = samples.count(p) / n\n\n return probabilities","def get_prev_samples(self):\n return self.y_p","def main(n_samples):\n uso = usolib.uso.uar(N)\n lst = [usolib.randomfacet.randomfacet_sample(uso, N) for i in range(n_samples)]\n return sum(lst) / float(n_samples)","def sample_data(self, N, pmin=8., pmax=12.):\n ps = [matrix(ra.uniform(pmin, pmax, (self.n, 1))) for k in range(N)]\n xs = [self.compute_demand(p) for p in ps]\n return zip(xs,ps)","def simplex_sampler(self, n):\n\n # edge case: n = 1\n if n == 1:\n return np.array([self.initial_potential])\n\n values = [np.random.uniform() for i in range(n-1)]\n values.extend([0, 1])\n values.sort()\n values_arr = np.array(values)\n\n xs = values_arr[1:] - values_arr[:-1]\n\n # return in decresing order of magnitude, to use for higher levels\n xs = self.initial_potential*np.sort(xs)\n xs = xs[::-1]\n return xs","def _number_of_samples(self):\n return len(self._raw_data.samples)","def get_next_sample(self):","def get_number_samples(self):\n return self.samples.shape[0]","def sample_low_rank(self, n_samples, mu, logvar, F):\n #F = torch.unsqueeze(F, dim=1).repeat(1, n_samples, 1, 1) # [self.batch_size, n_samples, self.Y_dim, self.rank]\n F = F.repeat(n_samples, 1, 1) # [self.batch_size*n_samples, self.Y_dim, self.rank]\n mu = mu.repeat(n_samples, 1) # [self.batch_size*n_samples, self.Y_dim]\n logvar = logvar.repeat(n_samples, 1) # [self.batch_size*n_samples, self.Y_dim]\n eps_low_rank = torch.randn(self.batch_size*n_samples, self.rank, 1)\n eps_diag = torch.randn(self.batch_size*n_samples, self.Y_dim)\n half_var = torch.exp(0.5*logvar) # [self.batch_size*n_samples, self.Y_dim]\n samples = torch.bmm(F, eps_low_rank).squeeze() + mu + half_var*eps_diag\n samples = samples.reshape(n_samples, self.batch_size, self.Y_dim)\n samples = samples.transpose(0, 1)\n samples = self.unwhiten_back(samples)\n samples = samples.data.cpu().numpy()\n return samples","def get_log_prior(samples):\n prior_distribution = tfp.distributions.Normal(loc=0., scale=1.)\n log_prior = tf.reduce_sum(prior_distribution.log_prob(samples), axis=1)\n return log_prior","def sample_from_log_prob(A, n):\n A_cum = log_cum_sum(A)\n C_pos = [ -c for c in reversed(A_cum)]\n sel = np.log(np.random.random(n))\n pos = [len(A) - np.searchsorted(C_pos,-r) for r in sel]\n return pos","def priors_sample(self):\n \n theta_star = np.zeros(self.n_params)\n\n for i in xrange(self.n_params): \n np.random.seed() \n theta_star[i] = self.param_obj.prior()[i].rvs(size=1)[0]\n\n return theta_star","def sample_pagerank(corpus, damping_factor, n):\n all_pages = []\n first_sample_prob = random.randint(0, len(corpus) - 1)\n distribution_count = dict()\n\n for u in corpus:\n distribution_count[u] = 0\n all_pages.append(u)\n\n sample = all_pages[first_sample_prob]\n for i in range(n - 1): # n - 1 because first sample was already calculated\n selection_bucket = dict()\n selection_start = 0.0\n sample_distribution = transition_model(corpus, sample, damping_factor)\n sample_prob = random.random()\n for u in sample_distribution:\n floor = selection_start\n ceiling = selection_start + sample_distribution[u]\n selection_start = ceiling\n selection_bucket[u] = [floor, ceiling]\n for u in selection_bucket:\n v = selection_bucket[u]\n if v[0] < sample_prob < v[1]:\n sample = u\n distribution_count[u] += 1\n distribution = dict()\n for u in distribution_count:\n distribution[u] = float(distribution_count[u]) / n\n\n return distribution","def get_num_samples(self):\n return self._num_samples","def sample(self, n):\n unparameterized = [\n v for v in self._weights.keys() if v not in self._weights_p.keys()\n ]\n if len(unparameterized) > 0:\n raise ValueError(\n \"The following variables must be numerically parameterized before sampling: {0}\".format(\n unparameterized\n )\n )\n mu, cov = self.parameterized_mean_cov()\n cov = np.asarray(cov).astype(np.float64)\n mu = np.asarray(mu).astype(np.float64)\n return np.random.multivariate_normal(mu.ravel(), cov, size=n)","def generate_samples(self,n_samples=100):\n rnd_input=torch.randn((n_samples,self._reparam_nodes[1]))\n zeta=rnd_input \n # rnd_input=torch.where((rnd_input>0.5),torch.ones(rnd_input.size()),torch.zeros(rnd_input.size()))\n # print(rnd_input) \n # output, mu, logvar, zeta=self.forward(rnd_input)\n # mu = self._reparam_layers['mu'](rnd_input)\n # logvar = self._reparam_layers['var'](rnd_input)\n # zeta = self.reparameterize(mu, logvar)\n output = self.decoder.decode(zeta)\n return output","def process_custom_prior(prior) -> Tuple[Distribution, int, bool]:\n\n check_prior_methods(prior)\n\n check_prior_batch_behavior(prior)\n\n prior, is_prior_numpy = maybe_wrap_prior_to_pytorch(prior)\n\n parameter_dim = prior.sample().numel()\n\n return prior, parameter_dim, is_prior_numpy","def __call__(self, *args):\n r = np.random.rand(*args)\n if type(r) is float:\n samples = self.values[(r < self.p).nonzero()[0][0]]\n elif type(r) is np.ndarray:\n samples = np.array(\n [self.values[np.nonzero(x < self.p)[0][0]] \n for x in r.flat]).reshape(r.shape)\n return samples","def get_num_samples(self) -> int:\n # must be implemented in subclass\n raise NotImplementedError","def sample(self, n_samples: int) -> torch.Tensor:\n return self.dist.sample((n_samples,))","def ts_rewards_sample(self):\n exp_rewards_list = [np.random.beta(a, b) for a, b in zip(self.alpha_params, self.beta_params)]\n return np.array(exp_rewards_list)","def prediction_samples(self) -> np.ndarray:\n return self.prediction_samples","def sample(self, n_samples: int, random_state: Optional[int] = None) -> np.ndarray:\n\n if not isinstance(self.prob, np.ndarray):\n samples = stats.bernoulli.rvs(\n p=self.prob, size=n_samples, random_state=random_state\n )\n else:\n n_classes = len(self.prob) # type: ignore\n samples = np.zeros((n_samples, n_classes))\n\n for cls in range(n_classes):\n samples[:, cls] = stats.bernoulli.rvs(p=self.prob[cls], size=n_samples, random_state=random_state) # type: ignore\n\n return samples","def generate_synth_data(n):","def prior_sample_parameter(self, parameter):\n pass","def get_samples_per_signal(self):\n return np.array([self.samples_in_file(chn) for chn in range(self.signals_in_file)])","def sample(self, num):\n state, action, reward, next_state, not_terminal = [], [], [], [], []\n for exp in self.experiences.sample(num):\n state.append(exp.state)\n action.append(exp.action)\n reward.append(exp.reward)\n next_state.append(exp.next_state)\n not_terminal.append(exp.not_terminal)\n\n return np.asarray(state), np.asarray(action), np.asarray(reward), \\\n np.asarray(next_state), np.asarray(not_terminal)","def samples(self):\n pass","def sample_observations(self, n_samples, z):\n lat_dim = self.transition.dim_x\n x = []\n for i in range(self.time_steps):\n z_t = tf.slice(z, [0, i * lat_dim], [-1, lat_dim]) \n x.append(self.emmision.sample(z_t))\n return tf.concat(x, axis=1)","def sample(self, n_samples):\n with torch.no_grad():\n z = torch.randn((n_samples, self.z_dim))\n samples = self.decoder(z)\n im_size = int(np.sqrt(self.input_dim))\n samples = samples.view(-1, 1, im_size, im_size)\n\n return samples","def sample_pagerank(corpus, damping_factor, n):\n # All pages in entire corpus\n all_pages = [key for key in corpus.keys()]\n\n # Dictionary which will map each page to the number of generated samples that were the page\n counts = dict()\n for page in all_pages:\n counts[page] = 0\n\n # Choosing first sample randomly and updating counts dict\n first_sample = random.choice(all_pages)\n counts[first_sample] += 1\n\n for i in range(1, n):\n # If we are on the second sample, do the calculation based on the first\n if i == 1:\n prev_sample = first_sample\n\n distributions = transition_model(corpus, prev_sample, damping_factor)\n\n keys = [key for key in distributions.keys()]\n values = [distributions[key] for key in distributions.keys()]\n\n # Using random.choices to select an option given a list of weights (distribution)\n current_sample = random.choices(keys, weights=values, k=1)[0]\n\n # Updating counts dict given the current sample\n counts[current_sample] += 1\n\n prev_sample = current_sample\n\n # Modifying counts dict to represent the proportion of samples that corresponded to that page instead of the raw amount\n for page in all_pages:\n counts[page] /= n\n\n return counts","def take(n, seq):\n return itertools.islice(seq, n)","def get_samples(self) -> McmcPtResult:","def deterministic_sample(choices, n_to_sample, p): # pylint: disable=invalid-name\n\n sample_counts = np.ceil(n_to_sample * p).astype(int)\n\n n_to_remove = np.sum(sample_counts) - n_to_sample\n\n if n_to_remove == 0:\n return choices[counts_to_vector(sample_counts)]\n\n non_zero_mask = sample_counts > 0\n\n removal_indices = np.floor(np.linspace(0.0,\n np.sum(non_zero_mask),\n n_to_remove,\n endpoint=False)).astype(int)\n\n tmp = sample_counts[non_zero_mask]\n tmp[removal_indices] = tmp[removal_indices] - 1\n\n sample_counts[non_zero_mask] = tmp\n\n assert np.sum(sample_counts) == n_to_sample\n\n samples = choices[counts_to_vector(sample_counts)]\n\n return samples"],"string":"[\n \"def sample_from_prior(self, n_samples):\\n pass\",\n \"def sample_from_prior(self, n_samples):\\n\\n p0 = self.min + self.rng.rand(n_samples) * (self.max - self.min)\\n return p0[:, np.newaxis]\",\n \"def sample_from_prior(self, n_samples):\\n\\n p0 = self.rng.lognormal(mean=self.mean, sigma=self.sigma, size=n_samples)\\n return p0[:, np.newaxis]\",\n \"def sample_from_prior(self, n_samples):\\n\\n lamda = np.abs(self.rng.standard_cauchy(size=n_samples))\\n\\n p0 = np.log(np.abs(self.rng.randn() * lamda * self.scale))\\n return p0[:, np.newaxis]\",\n \"def sample_prior(size):\\n return torch.randn(size)\",\n \"def sample_from_prior(self):\\n raise NotImplementedError\",\n \"def sample_parameters_prior(self, n_samples, random_seed=None):\\n\\n if random_seed is not None:\\n np.random.seed(random_seed)\\n samples = []\\n samples.append(self.var_noise.sample_from_prior(n_samples))\\n samples.append(self.mean.sample_from_prior(n_samples))\\n samples.append(self.kernel.sample_parameters(n_samples))\\n\\n return np.concatenate(samples, 1)\",\n \"def sample(self, n_samples=1):\\n\\n\\t\\tif isinstance(n_samples, int):\\n\\t\\t\\tn_samples = (n_samples,)\\n\\t\\tbeta_sample = self.beta_prior.sample(n_samples)\\n\\t\\tgamma_sample = self.gamma_prior.sample(n_samples)\\n\\t\\tp = torch.stack((beta_sample, gamma_sample)).T\\n\\t\\tif n_samples == (1,):\\n\\t\\t\\tp = p[0]\\n\\t\\tp = p.numpy()\\n\\t\\treturn p\",\n \"def sample_from_prior(self, *args, **kwargs):\\n pass\",\n \"def forward_sample(self, n):\\n return self.flow.sample(n)\",\n \"def prior_sample(self):\\n pass\",\n \"def generate_samples(self, n_samples):\",\n \"def generate_samples(self, n_samples):\",\n \"def sample(self, n):\\n # Est probs from priority weights\\n summed = sum(self.weight) + self.eps\\n self.probs = [w / summed for w in self.priority]\\n\\n # Wieghted sample\\n return np.random.choice(self.memory, size=n, p=self.probs).tolist()\",\n \"def prior_samples(self, nsamples, coords=None):\\n S = self.eval_S(self.kappa, self.sigma_f)\\n if coords is None:\\n coords = slice(self.mesh.num_vertices()) # take all coords\\n weights = np.random.normal(scale=np.sqrt(S),\\n size=(nsamples,) + S.shape) # shape (s, l)\\n prior = np.einsum('sl,nl->sn', weights, self.eigenfunctions[coords])\\n\\n return prior\",\n \"def samples(self):\\n return self._values[:self.nsamples]\",\n \"def generate_samples(self, no=10):\\n observations = []\\n state_sequence = []\\n initial_state = np.random.choice(\\n self.latent_variable_markov_chain.states,\\n p=self.prior_probabilities)\\n state_sequence.append(initial_state)\\n observations.append(self.observation_from_state(initial_state))\\n current_state = initial_state\\n for i in range(2, no):\\n next_state = self.latent_variable_markov_chain.next_state(current_state)\\n state_sequence.append(next_state)\\n observations.append(self.observation_from_state(next_state))\\n current_state = next_state\\n return observations, state_sequence\",\n \"def sample(self, n, include=True):\\n return [self(t / n) for t in range(n + int(include))]\",\n \"def prior(n=10):\\r\\n p = []\\r\\n trials = 0\\r\\n acc = 0\\r\\n while acc < n:\\r\\n trials += 1\\r\\n r = np.random.rand(2) * np.array([4, 2]) + np.array([-2, -1])\\r\\n # print(\\\"r: \\\", r)\\r\\n if r[1] + r[0] >= -1 and r[1] - r[0] >= -1:\\r\\n p.append(r)\\r\\n acc += 1\\r\\n # print(\\\"trials: \\\", trials, \\\", acc: \\\", acc)\\r\\n return p\",\n \"def pull_reads(self, n, prng): \\n if not self.sampling:\\n self.convert_to_array()\\n index = prng.randint(0, self.total, size=n)\\n index = np.sort(index)\\n return self.reads[index,:]\",\n \"def sample(self, n=1):\\n raise NotImplementedError\",\n \"def get_training_n(self, n: Optional[int] = None, random: bool = True) -> Tuple[torch.Tensor, torch.Tensor]:\\n return self.__sample(self.train_data, self.train_los, n, random)\",\n \"def sample(self, n):\\n raise NotImplementedError\",\n \"def _sample(self, n=1):\\n return [self[i] for i in np.random.choice(self.length, n, replace=False)]\",\n \"def get_n_samples(self):\\n return {op.rx.get_n_samples() for op in self.ops}\",\n \"def sample(self, n_samples):\\n\\n z = sample_prior((n_samples,) + self.flow.z_shape)\\n ldj = torch.zeros(z.size(0))\\n\\n z, ldj = self.flow (z, ldj, reverse=True)\\n z, ldj = self.logit_normalize(z, ldj, reverse=True)\\n\\n return z\",\n \"def Sample(n=6):\\n t = [random.normalvariate(0.0, 1.0) for i in range(n)]\\n t.sort()\\n return t\",\n \"def posterior_sampler(self, nsamples, seed=0, verbose=True):\\n\\n import random\\n\\n random.seed(seed)\\n sample = self.get_chain()[-self.get_tune:]\\n sample = sample.reshape(-1, sample.shape[-1])\\n sample = random.choices(sample, k=nsamples)\\n\\n return sample\",\n \"def generate_samples(self, n_samples=100):\\n \\t\\t\\n\\t\\t#how many times should ancestral sampling be run\\n\\t\\t#n_samples\\n prior_samples=[]\\n for i in range(0,n_samples):\\n prior_sample = self.prior.get_samples(\\n n_latent_nodes=self.n_latent_nodes,\\n n_gibbs_sampling_steps=100, \\n sampling_mode=\\\"gibbs_ancestral\\\")\\n prior_sample = torch.cat(prior_sample)\\n prior_samples.append(prior_sample)\\n prior_samples=torch.stack(prior_samples)\\n # prior_samples = tf.slice(prior_samples, [0, 0], [num_samples, -1])\\n output_activations = self.decoder.decode(prior_samples)\\n output_activations = output_activations+self._train_bias\\n output_distribution = Bernoulli(logit=output_activations)\\n output=torch.sigmoid(output_distribution.logits)\\n # output_activations[0] = output_activations[0] + self.train_bias\\n # output_dist = FactorialBernoulliUtil(output_activations)\\n # output_samples = tf.nn.sigmoid(output_dist.logit_mu)\\n # print(\\\"--- \\\",\\\"end VAE::generate_samples()\\\")\\n return output\",\n \"def get_random_samplers(self, n):\\n if not self.has_samplers():\\n self.draw_samplers()\\n \\n def get_shuffledcopy(x):\\n x_ = x.copy()\\n np.random.shuffle(x_)\\n return x_\\n \\n return get_shuffledcopy(self.samplers)[:n]\",\n \"def sample_gp_prior(self, x_list, n_samp, full_cov=True):\\n mu, cov = self.get_gp_prior_mu_cov(x_list, full_cov)\\n return self.get_normal_samples(mu, cov, n_samp, full_cov)\",\n \"def get_posterior_sample(self):\\n total_tries = self.prior_success + self.prior_failure\\n prob_success = self.prior_success / total_tries\\n # np.random.binomial采样出来的是二项分布的均值, 即正面朝上的次数,所以要除以N\\n boot_sample = np.random.binomial(total_tries, prob_success) / total_tries\\n return boot_sample\",\n \"def sample(self, n_samples=1):\\n\\n\\t\\tsamples = np.random.random((n_samples, self.__ndims))*self.__range + self.__low\\n\\t\\treturn samples\",\n \"def get_samples(self, nsamples):\\n sample_ids = np.random.choice(self.embed.shape[0], nsamples, replace=False)\\n return self.embed[sample_ids]\",\n \"def sample_predict(self, X, n_samples):\\n W_samples = self.W_prior.sample(n_samples) ##size: [n_samples, n_in, n_out]\\n if self.use_bias:\\n bs = W_samples[:, :1, :]\\n Ws = W_samples[:, 1:, :]\\n if self.scaled_variance:\\n Ws = Ws / np.sqrt(self.n_in)\\n return torch.matmul(X, Ws) + bs\\n else:\\n if self.scaled_variance:\\n W_samples = W_samples / np.sqrt(self.n_in)\\n return torch.matmul(X, W_samples)\",\n \"def pickTopicFromPrior(self):\\r\\n return self.priorTopicDistr.sampleIndex()\",\n \"def fetch_samples(self):\\n return torch.cat(self.samples,dim=0).reshape(-1,self.parameters.numel())\",\n \"def _uniform_number(self, N=None):\\n num_frames = self.data.shape[0]\\n if not N:\\n N = int(np.floor(\\n Constants.DEFAULT_SAMPLE_PERCENTAGE * num_frames\\n ))\\n k = int(np.floor(num_frames / N))\\n if k == 0:\\n k = 1\\n elif N > num_frames:\\n k = 1\\n else:\\n assert type(N) is IntType, 'N is not an integer: {}'.format(N)\\n k = int(np.floor(num_frames / N))\\n\\n # ensure that at most N samples are taken if N does not divide nicely\\n return self.data[::k][:N], self.labels[::k][:N]\",\n \"def sample(self, n_samples, sample_seed):\\n self.seed_samples(sample_seed)\\n eps = torch.randn(self.batch_size, n_samples, self.Y_dim)\\n samples = eps*torch.exp(0.5*self.logvar.unsqueeze(1)) + self.mu.unsqueeze(1)\\n samples = self.unwhiten_back(samples)\\n samples = samples.data.cpu().numpy()\\n return samples\",\n \"def prior_sample(self, bn):\\n x = np.zeros(3)\\n\\n # first joint prob\\n random_choice = np.random.choice(bn[0], 1, bn[0].all(), bn[0])\\n x[0] = random_choice[0]\\n\\n # Second Joint Prob\\n if x[0] == 0.1:\\n random_choice = np.random.choice(bn[1][0], 1, bn[1][0].all(), bn[1][0])\\n x[1] = random_choice\\n elif x[0] == 0.9:\\n random_choice = np.random.choice(bn[1][1], 1, bn[1][1].all(), bn[1][1])\\n x[1] = random_choice\\n\\n # Third Joint Prob\\n if random_choice[0] == 0.8 or random_choice == 0.1:\\n random_choice = np.random.choice(bn[2][0], 1, bn[2][0].all(), bn[2][0])\\n x[2] = random_choice\\n else:\\n random_choice = np.random.choice(bn[2][1], 1, bn[2][1].all(), bn[2][1])\\n x[2] = random_choice\\n return x\",\n \"def prob1(n):\\n#raise NotImplementedError(\\\"Problem 1 Incomplete\\\")\\n if n == 0 :\\n raise ValueError(\\\"Sampling 0 points is not defined.\\\")\\n total = 0\\n for i in xrange(n) :\\n if np.random.normal() > 3 :\\n total += 1\\n return float(total)/n\",\n \"def sample (self, n):\\n y = self.bins\\n x = np.r_[0, self.values.cumsum ()] / self.sum\\n # interpolate inverse CDF\\n out = np.interp (np.random.random (n), x, y)\\n if n == 1:\\n return out[0]\\n else:\\n return out.reshape ((n,))\",\n \"def get_n(self, n):\\n \\n return [self.get_values() for _ in range(n)]\",\n \"def getNrSamples(self): \\r\\n return self.numSamples\",\n \"def sample(self, n):\\n idx = np.random.randint(0, len(self.memory), size=n)\\n return [self.memory[i] for i in idx]\",\n \"def pull_n_samples(dset, n):\\n return list(dset[i] for i in random.sample(range(len(dset)), n))\",\n \"def lnprior(self):\\n \\n return\",\n \"def samples(self) -> int:\\n return self._samples\",\n \"def _construct_sample_from_prior(self):\\n z_sym = T.matrix()\\n x_sym = T.matrix()\\n irs = self.ir_steps\\n oputs = [self.obs_transform(self.s0)]\\n oputs.extend([self.obs_transform(self.si[i]) for i in range(irs)])\\n _, hi_zmuv = self._construct_zmuv_samples(x_sym, 1)\\n sample_func = theano.function(inputs=[z_sym, x_sym], outputs=oputs, \\\\\\n givens={ self.z: z_sym, \\\\\\n self.x_in: T.zeros_like(x_sym), \\\\\\n self.x_out: T.zeros_like(x_sym), \\\\\\n self.hi_zmuv: hi_zmuv }, \\\\\\n updates=self.scan_updates)\\n def prior_sampler(samp_count):\\n x_samps = to_fX( np.zeros((samp_count, self.obs_dim)) )\\n old_switch = self.train_switch.get_value(borrow=False)\\n # set model to generation mode\\n self.set_train_switch(switch_val=0.0)\\n z_samps = to_fX( npr.randn(samp_count, self.z_dim) )\\n model_samps = sample_func(z_samps, x_samps)\\n # set model back to either training or generation mode\\n self.set_train_switch(switch_val=old_switch)\\n return model_samps\\n return prior_sampler\",\n \"def generate_samples(self):\\n self.analytic_probability()\",\n \"def prior_distribution(self):\\n out = self.model.forward(self.inducing_points)\\n return MultivariateNormal(out.mean, out.lazy_covariance_matrix.evaluate_kernel())\",\n \"def sample(self, size=1):\\n samples = scipy.stats.bernoulli.rvs(self.p, size=size)\\n if size == 1:\\n return samples[0]\\n return samples\",\n \"def n_remaining_samples(self):\\n return -1\",\n \"def n_remaining_samples(self):\\n return -1\",\n \"def n_remaining_samples(self):\\n return -1\",\n \"def sample(self, nsamples):\\n return self.dist.sample(nsamples)\",\n \"def __resample(self):\\n p_resample = []\\n w_max = max(self.weights)\\n index = int(round(random.uniform(0, self.n - 1)))\\n beta = 0\\n for i in range(self.n):\\n beta += random.uniform(0, 2 * w_max)\\n while self.weights[index] < beta:\\n beta -= self.weights[index]\\n index = (index + 1) % self.n\\n p_resample.append(self.particles[index, :])\\n return np.array(p_resample)\",\n \"def burnin_by_first_n(samples, N, step_number=False, logger_level=\\\"debug\\\"):\\n _samples = copy.deepcopy(samples)\\n n_samples = {key: N for key in _samples.keys()}\\n if step_number:\\n n_samples = {\\n key: item + N if item is not None else N for key, item in\\n _number_of_negative_steps(_samples, logger_level=logger_level).items()\\n }\\n getattr(logger, logger_level)(\\n \\\"Removing the first {} as burnin\\\".format(\\n \\\", \\\".join(\\n [\\\"{} samples from {}\\\".format(val, key) for key, val in n_samples.items()]\\n )\\n )\\n )\\n return _samples.discard_samples(n_samples)\",\n \"def take_samples(self, num_samples: int) -> List:\\n if num_samples > len(self.samples):\\n return random.sample(self.samples, len(self.samples))\\n return random.sample(self.samples, num_samples)\",\n \"def n_samples(self) -> int: # pragma: no cover\\n return self.samples.shape[0]\",\n \"def sample_count(self):\",\n \"def sample_users(self, n):\\n return torch.rand((n, self.num_topics), generator=self.generator) * 2 - 1\",\n \"def sample_size_with_prior(self, prior=\\\"small\\\"):\\n if prior == \\\"small\\\":\\n prior_sizes = list(range(self._min_size, self._max_size))\\n elif prior == \\\"big\\\":\\n prior_sizes = list(range(self._min_size+1, self._max_size+1))\\n return random.choice(prior_sizes)\",\n \"def sample(self,\\n n_samples: Optional[int],\\n temperatures: Optional[List[float]] = None) -> T.Tensor:\\n samples = self.levels[-1].sample(\\n self.top_prior_shape, n_samples=n_samples, temperatures=temperatures)\\n for level in self.levels[-2::-1]:\\n samples = level.sample(context=samples)\\n return samples\",\n \"def prior_predictive(self):\\n cfg = self.config\\n n = cfg['batch_size'] * cfg['q/n_samples']\\n n_samples = cfg['q/n_samples']\\n with util.get_or_create_scope('model', reuse=True):\\n h_prior = tf.cast(self.p_h_L.sample(n), cfg['dtype'])\\n h_prior = tf.reshape(\\n h_prior, [cfg['q/n_samples'], cfg['batch_size'], -1])\\n h = [None] * cfg['p/n_layers']\\n h[cfg['p/n_layers'] - 1] = h_prior\\n for n in range(cfg['p/n_layers'] - 1, 0, -1):\\n p_h_n = self.build_stochastic_layer(n, h_above=h[n])\\n h[n - 1] = tf.cast(p_h_n.sample(), cfg['dtype'])\\n return self.likelihood(h[0])\",\n \"def sample(self, num_samples = 1):\\n\\n X = np.empty(num_samples)\\n for k in range(len(X)):\\n\\n # starting\\n n = 1\\n a = 1\\n Un = self.UG.sample()\\n a = a * Un\\n\\n # iterate over\\n while a >= np.exp(-self.rate):\\n n = n + 1\\n Un = self.UG.sample()\\n a = a * Un\\n\\n X[k] = n - 1\\n\\n return X\",\n \"def sample_pagerank(corpus, damping_factor, n):\\n probabilities = dict()\\n samples = []\\n\\n # Random first sample\\n page = random.choice(list(corpus.keys()))\\n samples.append(page)\\n \\n # Remaining samples after first\\n for i in range(n-1):\\n p = transition_model(corpus, page, damping_factor)\\n page = random.choices(list(p.keys()), weights=list(p.values()), k=1)[0]\\n samples.append(page)\\n\\n # Count\\n for p in corpus.keys():\\n probabilities[p] = samples.count(p) / n\\n\\n return probabilities\",\n \"def get_prev_samples(self):\\n return self.y_p\",\n \"def main(n_samples):\\n uso = usolib.uso.uar(N)\\n lst = [usolib.randomfacet.randomfacet_sample(uso, N) for i in range(n_samples)]\\n return sum(lst) / float(n_samples)\",\n \"def sample_data(self, N, pmin=8., pmax=12.):\\n ps = [matrix(ra.uniform(pmin, pmax, (self.n, 1))) for k in range(N)]\\n xs = [self.compute_demand(p) for p in ps]\\n return zip(xs,ps)\",\n \"def simplex_sampler(self, n):\\n\\n # edge case: n = 1\\n if n == 1:\\n return np.array([self.initial_potential])\\n\\n values = [np.random.uniform() for i in range(n-1)]\\n values.extend([0, 1])\\n values.sort()\\n values_arr = np.array(values)\\n\\n xs = values_arr[1:] - values_arr[:-1]\\n\\n # return in decresing order of magnitude, to use for higher levels\\n xs = self.initial_potential*np.sort(xs)\\n xs = xs[::-1]\\n return xs\",\n \"def _number_of_samples(self):\\n return len(self._raw_data.samples)\",\n \"def get_next_sample(self):\",\n \"def get_number_samples(self):\\n return self.samples.shape[0]\",\n \"def sample_low_rank(self, n_samples, mu, logvar, F):\\n #F = torch.unsqueeze(F, dim=1).repeat(1, n_samples, 1, 1) # [self.batch_size, n_samples, self.Y_dim, self.rank]\\n F = F.repeat(n_samples, 1, 1) # [self.batch_size*n_samples, self.Y_dim, self.rank]\\n mu = mu.repeat(n_samples, 1) # [self.batch_size*n_samples, self.Y_dim]\\n logvar = logvar.repeat(n_samples, 1) # [self.batch_size*n_samples, self.Y_dim]\\n eps_low_rank = torch.randn(self.batch_size*n_samples, self.rank, 1)\\n eps_diag = torch.randn(self.batch_size*n_samples, self.Y_dim)\\n half_var = torch.exp(0.5*logvar) # [self.batch_size*n_samples, self.Y_dim]\\n samples = torch.bmm(F, eps_low_rank).squeeze() + mu + half_var*eps_diag\\n samples = samples.reshape(n_samples, self.batch_size, self.Y_dim)\\n samples = samples.transpose(0, 1)\\n samples = self.unwhiten_back(samples)\\n samples = samples.data.cpu().numpy()\\n return samples\",\n \"def get_log_prior(samples):\\n prior_distribution = tfp.distributions.Normal(loc=0., scale=1.)\\n log_prior = tf.reduce_sum(prior_distribution.log_prob(samples), axis=1)\\n return log_prior\",\n \"def sample_from_log_prob(A, n):\\n A_cum = log_cum_sum(A)\\n C_pos = [ -c for c in reversed(A_cum)]\\n sel = np.log(np.random.random(n))\\n pos = [len(A) - np.searchsorted(C_pos,-r) for r in sel]\\n return pos\",\n \"def priors_sample(self):\\n \\n theta_star = np.zeros(self.n_params)\\n\\n for i in xrange(self.n_params): \\n np.random.seed() \\n theta_star[i] = self.param_obj.prior()[i].rvs(size=1)[0]\\n\\n return theta_star\",\n \"def sample_pagerank(corpus, damping_factor, n):\\n all_pages = []\\n first_sample_prob = random.randint(0, len(corpus) - 1)\\n distribution_count = dict()\\n\\n for u in corpus:\\n distribution_count[u] = 0\\n all_pages.append(u)\\n\\n sample = all_pages[first_sample_prob]\\n for i in range(n - 1): # n - 1 because first sample was already calculated\\n selection_bucket = dict()\\n selection_start = 0.0\\n sample_distribution = transition_model(corpus, sample, damping_factor)\\n sample_prob = random.random()\\n for u in sample_distribution:\\n floor = selection_start\\n ceiling = selection_start + sample_distribution[u]\\n selection_start = ceiling\\n selection_bucket[u] = [floor, ceiling]\\n for u in selection_bucket:\\n v = selection_bucket[u]\\n if v[0] < sample_prob < v[1]:\\n sample = u\\n distribution_count[u] += 1\\n distribution = dict()\\n for u in distribution_count:\\n distribution[u] = float(distribution_count[u]) / n\\n\\n return distribution\",\n \"def get_num_samples(self):\\n return self._num_samples\",\n \"def sample(self, n):\\n unparameterized = [\\n v for v in self._weights.keys() if v not in self._weights_p.keys()\\n ]\\n if len(unparameterized) > 0:\\n raise ValueError(\\n \\\"The following variables must be numerically parameterized before sampling: {0}\\\".format(\\n unparameterized\\n )\\n )\\n mu, cov = self.parameterized_mean_cov()\\n cov = np.asarray(cov).astype(np.float64)\\n mu = np.asarray(mu).astype(np.float64)\\n return np.random.multivariate_normal(mu.ravel(), cov, size=n)\",\n \"def generate_samples(self,n_samples=100):\\n rnd_input=torch.randn((n_samples,self._reparam_nodes[1]))\\n zeta=rnd_input \\n # rnd_input=torch.where((rnd_input>0.5),torch.ones(rnd_input.size()),torch.zeros(rnd_input.size()))\\n # print(rnd_input) \\n # output, mu, logvar, zeta=self.forward(rnd_input)\\n # mu = self._reparam_layers['mu'](rnd_input)\\n # logvar = self._reparam_layers['var'](rnd_input)\\n # zeta = self.reparameterize(mu, logvar)\\n output = self.decoder.decode(zeta)\\n return output\",\n \"def process_custom_prior(prior) -> Tuple[Distribution, int, bool]:\\n\\n check_prior_methods(prior)\\n\\n check_prior_batch_behavior(prior)\\n\\n prior, is_prior_numpy = maybe_wrap_prior_to_pytorch(prior)\\n\\n parameter_dim = prior.sample().numel()\\n\\n return prior, parameter_dim, is_prior_numpy\",\n \"def __call__(self, *args):\\n r = np.random.rand(*args)\\n if type(r) is float:\\n samples = self.values[(r < self.p).nonzero()[0][0]]\\n elif type(r) is np.ndarray:\\n samples = np.array(\\n [self.values[np.nonzero(x < self.p)[0][0]] \\n for x in r.flat]).reshape(r.shape)\\n return samples\",\n \"def get_num_samples(self) -> int:\\n # must be implemented in subclass\\n raise NotImplementedError\",\n \"def sample(self, n_samples: int) -> torch.Tensor:\\n return self.dist.sample((n_samples,))\",\n \"def ts_rewards_sample(self):\\n exp_rewards_list = [np.random.beta(a, b) for a, b in zip(self.alpha_params, self.beta_params)]\\n return np.array(exp_rewards_list)\",\n \"def prediction_samples(self) -> np.ndarray:\\n return self.prediction_samples\",\n \"def sample(self, n_samples: int, random_state: Optional[int] = None) -> np.ndarray:\\n\\n if not isinstance(self.prob, np.ndarray):\\n samples = stats.bernoulli.rvs(\\n p=self.prob, size=n_samples, random_state=random_state\\n )\\n else:\\n n_classes = len(self.prob) # type: ignore\\n samples = np.zeros((n_samples, n_classes))\\n\\n for cls in range(n_classes):\\n samples[:, cls] = stats.bernoulli.rvs(p=self.prob[cls], size=n_samples, random_state=random_state) # type: ignore\\n\\n return samples\",\n \"def generate_synth_data(n):\",\n \"def prior_sample_parameter(self, parameter):\\n pass\",\n \"def get_samples_per_signal(self):\\n return np.array([self.samples_in_file(chn) for chn in range(self.signals_in_file)])\",\n \"def sample(self, num):\\n state, action, reward, next_state, not_terminal = [], [], [], [], []\\n for exp in self.experiences.sample(num):\\n state.append(exp.state)\\n action.append(exp.action)\\n reward.append(exp.reward)\\n next_state.append(exp.next_state)\\n not_terminal.append(exp.not_terminal)\\n\\n return np.asarray(state), np.asarray(action), np.asarray(reward), \\\\\\n np.asarray(next_state), np.asarray(not_terminal)\",\n \"def samples(self):\\n pass\",\n \"def sample_observations(self, n_samples, z):\\n lat_dim = self.transition.dim_x\\n x = []\\n for i in range(self.time_steps):\\n z_t = tf.slice(z, [0, i * lat_dim], [-1, lat_dim]) \\n x.append(self.emmision.sample(z_t))\\n return tf.concat(x, axis=1)\",\n \"def sample(self, n_samples):\\n with torch.no_grad():\\n z = torch.randn((n_samples, self.z_dim))\\n samples = self.decoder(z)\\n im_size = int(np.sqrt(self.input_dim))\\n samples = samples.view(-1, 1, im_size, im_size)\\n\\n return samples\",\n \"def sample_pagerank(corpus, damping_factor, n):\\n # All pages in entire corpus\\n all_pages = [key for key in corpus.keys()]\\n\\n # Dictionary which will map each page to the number of generated samples that were the page\\n counts = dict()\\n for page in all_pages:\\n counts[page] = 0\\n\\n # Choosing first sample randomly and updating counts dict\\n first_sample = random.choice(all_pages)\\n counts[first_sample] += 1\\n\\n for i in range(1, n):\\n # If we are on the second sample, do the calculation based on the first\\n if i == 1:\\n prev_sample = first_sample\\n\\n distributions = transition_model(corpus, prev_sample, damping_factor)\\n\\n keys = [key for key in distributions.keys()]\\n values = [distributions[key] for key in distributions.keys()]\\n\\n # Using random.choices to select an option given a list of weights (distribution)\\n current_sample = random.choices(keys, weights=values, k=1)[0]\\n\\n # Updating counts dict given the current sample\\n counts[current_sample] += 1\\n\\n prev_sample = current_sample\\n\\n # Modifying counts dict to represent the proportion of samples that corresponded to that page instead of the raw amount\\n for page in all_pages:\\n counts[page] /= n\\n\\n return counts\",\n \"def take(n, seq):\\n return itertools.islice(seq, n)\",\n \"def get_samples(self) -> McmcPtResult:\",\n \"def deterministic_sample(choices, n_to_sample, p): # pylint: disable=invalid-name\\n\\n sample_counts = np.ceil(n_to_sample * p).astype(int)\\n\\n n_to_remove = np.sum(sample_counts) - n_to_sample\\n\\n if n_to_remove == 0:\\n return choices[counts_to_vector(sample_counts)]\\n\\n non_zero_mask = sample_counts > 0\\n\\n removal_indices = np.floor(np.linspace(0.0,\\n np.sum(non_zero_mask),\\n n_to_remove,\\n endpoint=False)).astype(int)\\n\\n tmp = sample_counts[non_zero_mask]\\n tmp[removal_indices] = tmp[removal_indices] - 1\\n\\n sample_counts[non_zero_mask] = tmp\\n\\n assert np.sum(sample_counts) == n_to_sample\\n\\n samples = choices[counts_to_vector(sample_counts)]\\n\\n return samples\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7954478","0.7308122","0.71898454","0.7128596","0.6979248","0.6961805","0.67606914","0.6745526","0.6690407","0.6622515","0.6562736","0.65446556","0.65446556","0.6425273","0.6419413","0.6395372","0.63613814","0.6323354","0.63091654","0.6305061","0.6287302","0.6239015","0.6221694","0.6207399","0.6197996","0.6184932","0.6165855","0.6153619","0.6153358","0.6140597","0.61257386","0.6109061","0.6078866","0.60286486","0.60100406","0.6004092","0.59905887","0.5970731","0.59330416","0.5920617","0.59058857","0.58966595","0.589106","0.5890634","0.58760834","0.58759904","0.5870663","0.5850432","0.58440155","0.58292997","0.58253235","0.5818603","0.5816317","0.5816317","0.5816317","0.58079565","0.58007497","0.5788163","0.57879496","0.57798713","0.5776126","0.5774034","0.577337","0.5767064","0.5765437","0.5762816","0.5749947","0.5746394","0.5736112","0.5728723","0.57278186","0.5722879","0.5718899","0.5717263","0.5683421","0.56821394","0.56575644","0.5654737","0.5651699","0.5648227","0.56469744","0.5641609","0.5639136","0.5636752","0.56361675","0.56329775","0.56267655","0.5622955","0.5622405","0.5618009","0.5616757","0.5616295","0.5613543","0.56135225","0.5599461","0.55893856","0.55878794","0.55790335","0.5576369","0.5575169"],"string":"[\n \"0.7954478\",\n \"0.7308122\",\n \"0.71898454\",\n \"0.7128596\",\n \"0.6979248\",\n \"0.6961805\",\n \"0.67606914\",\n \"0.6745526\",\n \"0.6690407\",\n \"0.6622515\",\n \"0.6562736\",\n \"0.65446556\",\n \"0.65446556\",\n \"0.6425273\",\n \"0.6419413\",\n \"0.6395372\",\n \"0.63613814\",\n \"0.6323354\",\n \"0.63091654\",\n \"0.6305061\",\n \"0.6287302\",\n \"0.6239015\",\n \"0.6221694\",\n \"0.6207399\",\n \"0.6197996\",\n \"0.6184932\",\n \"0.6165855\",\n \"0.6153619\",\n \"0.6153358\",\n \"0.6140597\",\n \"0.61257386\",\n \"0.6109061\",\n \"0.6078866\",\n \"0.60286486\",\n \"0.60100406\",\n \"0.6004092\",\n \"0.59905887\",\n \"0.5970731\",\n \"0.59330416\",\n \"0.5920617\",\n \"0.59058857\",\n \"0.58966595\",\n \"0.589106\",\n \"0.5890634\",\n \"0.58760834\",\n \"0.58759904\",\n \"0.5870663\",\n \"0.5850432\",\n \"0.58440155\",\n \"0.58292997\",\n \"0.58253235\",\n \"0.5818603\",\n \"0.5816317\",\n \"0.5816317\",\n \"0.5816317\",\n \"0.58079565\",\n \"0.58007497\",\n \"0.5788163\",\n \"0.57879496\",\n \"0.57798713\",\n \"0.5776126\",\n \"0.5774034\",\n \"0.577337\",\n \"0.5767064\",\n \"0.5765437\",\n \"0.5762816\",\n \"0.5749947\",\n \"0.5746394\",\n \"0.5736112\",\n \"0.5728723\",\n \"0.57278186\",\n \"0.5722879\",\n \"0.5718899\",\n \"0.5717263\",\n \"0.5683421\",\n \"0.56821394\",\n \"0.56575644\",\n \"0.5654737\",\n \"0.5651699\",\n \"0.5648227\",\n \"0.56469744\",\n \"0.5641609\",\n \"0.5639136\",\n \"0.5636752\",\n \"0.56361675\",\n \"0.56329775\",\n \"0.56267655\",\n \"0.5622955\",\n \"0.5622405\",\n \"0.5618009\",\n \"0.5616757\",\n \"0.5616295\",\n \"0.5613543\",\n \"0.56135225\",\n \"0.5599461\",\n \"0.55893856\",\n \"0.55878794\",\n \"0.55790335\",\n \"0.5576369\",\n \"0.5575169\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7254096"},"document_rank":{"kind":"string","value":"2"}}},{"rowIdx":14398,"cells":{"query":{"kind":"string","value":"Computes the gradient of the prior with respect to theta."},"document":{"kind":"string","value":"def gradient(self, theta):\n return (1 / (self.sigma * np.sqrt(2 * np.pi))) * (\n -theta / (self.sigma ** 2) * np.exp(-(theta ** 2) / (2 * self.sigma ** 2))\n )"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def gradient(self, theta):\n pass","def gradient(self, theta):\n pass","def gradient(self, theta):\n a = -(6 * self.scale ** 2)\n b = 3 * self.scale ** 2 + np.exp(2 * theta)\n b *= np.log(3 * self.scale ** 2 * np.exp(-2 * theta) + 1)\n return a / b","def gradient(theta, X, y, learning_rate):\n m = len(y)\n\n theta = theta.reshape((-1,1))\n grad = np.zeros(theta.shape)\n h = sigmoid(np.dot(X, theta)) \n \n grad = np.dot((h-y).T, X)/m\n grad = grad.T\n grad[1:] += (learning_rate/m)*theta[1:]\n return grad","def gradient_function(theta, X, y):\n\n grad = None\n #######################################################################\n # TODO: #\n # Compute the gradient for a particular choice of theta. #\n # Compute the partial derivatives and set grad to the partial #\n # derivatives of the cost w.r.t. each parameter in theta #\n # #\n #######################################################################\n \n theta = theta[:, np.newaxis]\n \n thetatrans = theta.T\n Xtrans = X.T\n \n MulThetaX = np.dot(thetatrans, Xtrans)\n \n h = sigmoid(MulThetaX)\n \n grad = (y - h) * Xtrans\n\n #######################################################################\n # END OF YOUR CODE #\n #######################################################################\n return grad","def compute_gradient(theta, X, y):\n m = X.shape[0]\n grad_theta = np.dot(X.transpose(), (np.dot(X, theta) - y)) / m\n #print theta, grad_theta, objective_function(theta, X, y)\n return grad_theta","def gradient(self, theta):\n return np.zeros([theta.shape[0]])","def gradientFunction(theta, X, y):\n y = y[:, 0]\n m = y.shape # number of training samples\n grad = X.T.dot(sigmoid(theta.dot(X.T))-1*y)\n grad /= m\n return grad","def gradient(theta, x, y):\n m = len(y)\n n = len(theta)\n z = theta.dot(x.T)\n grad = np.zeros(n)\n for i in xrange(m):\n grad += (g(z[i]) - y[i]) * x[i]\n return 1. / m * grad","def gradient(theta, X, y, Lambda=0.0):\n m = X.shape[0] # number of samples\n\n h = hypothesis(theta, X)\n\n if Lambda:\n g_0 = (1/m)*(X.T@(h - y))[0]\n g_1 = (1/m)*(X.T@(h - y))[1:] + (Lambda/m)*theta[1:] # skip theta-0\n \n return np.append(g_0, g_1)\n else:\n return (1/m)*(X.T@(h - y))","def gradient(x, y, theta):\n if x.ndim == 1:\n x = x[:, np.newaxis]\n if y.ndim == 2 and y.shape[1] == 1:\n y = y.flatten()\n if theta.ndim == 2 and theta.shape[1] == 1:\n theta = theta.flatten()\n\n if (x.size == 0 or y.size == 0 or theta.size == 0\n or x.ndim != 2 or y.ndim != 1 or theta.ndim != 1\n or x.shape[0] != y.shape[0] or x.shape[1] + 1 != theta.shape[0]):\n return None\n\n x_padded = np.c_[np.ones(x.shape[0]), x]\n\n return x_padded.T.dot(x_padded.dot(theta) - y) / y.shape[0]","def calc_grad(X, Y, theta):\n m, n = X.shape\n\n margins = Y * X.dot(theta)\n probs = 1. / (1 + np.exp(margins))\n grad = -(1./m) * (X.T.dot(probs * Y))\n\n return grad","def gradient_descent(X, y, theta, alpha, total_iterations, hypothesis):\n len_theta = len(theta)\n m = len(y)\n one_over_m = (1.0 / float(m))\n\n for _ in range(0, total_iterations):\n temp_theta = numpy.zeros(len_theta)\n\n X_by_theta_minus_y = numpy.subtract(hypothesis(numpy.matrix(theta), X), y)\n\n for j in range(0, len_theta):\n jth_column_of_X = X[:,j]\n derivative_j = one_over_m * numpy.multiply(X_by_theta_minus_y, jth_column_of_X).sum()\n temp_theta[j] = theta[j] - alpha*derivative_j\n\n theta = temp_theta\n\n return numpy.matrix(theta)","def gradient_descent(self, X, theta, Y, m):\n\n Z = X.dot(theta)\n H = Predict.g(Z)\n gradient = np.dot(X.T, (H - Y)) / m\n return self.alpha * gradient","def gradientFunctionReg(theta, X, y, Lambda):\n m = len(y) # number of training examples\n grad = np.zeros(theta.shape[0])\n theta = np.transpose(theta)\n sum_1 = 0\n X = X.values\n y = y.values\n #calcuate the theta_0 \n# ====================== YOUR CODE HERE ======================\n# Instructions: Compute the gradient of a particular choice of theta.\n# Compute the partial derivatives and set grad to the partial\n# derivatives of the cost w.r.t. each parameter in theta\n for i in range(theta.shape[0]):\n if i == 0:\n for j in range(m):\n sum_1 += (sigmoid(np.dot(X[j],theta)) - y[j]) * X[j,i]\n else:\n for j in range(m):\n sum_1 += (sigmoid(np.dot(X[j],theta)) - y[j]) * X[j,i] + Lambda*theta[i]\n grad[i] = sum_1/m\n sum_1 = 0\n\n# =============================================================\n\n return grad","def gradientFunctionReg(theta, X, y, Lambda): \n y = np.squeeze(y)\n m = y.shape # number of training samples\n grad = X.T.dot(sigmoid(theta.dot(X.T))-1*y)\n grad[1:] = grad[1:] + Lambda*theta[1:]\n grad /= m\n\n return grad","def logit_cost_grad(self, theta, X, y):\n\n grad = np.zeros(len(theta))\n\n ### YOUR CODE HERE\n sig = utils.sigmoid(theta)\n # sig = np.subtract(sig, y)\n sig = sig - y\n grad = np.dot(X.T, sig) + 2 * self.params['lamb'] * self.regularizer[1](self.weights)\n ### END YOUR CODE\n\n return grad","def compute_loss_gradient(theta_vector, *args):\n\n psi = args[0] # feed psi as a parameter\n circ_depth = args[1]\n num_qbits = args[2]\n theta = np.reshape(theta_vector, (circ_depth, num_qbits)) # reshapes the flat theta vector\n fidelity = get_fidelity(theta, psi)\n\n # the derivative of the loss wrt fidelity\n dl_df = -0.5 * fidelity ** (-0.5)\n\n df_dtheta = [] # a list of partial derivatives of the fidelity wrt the theta parameters\n\n for index in range(len(theta_vector)):\n layer_index = index // num_qbits\n qbit_index = index % num_qbits\n\n theta_plus = np.copy(theta)\n theta_plus[layer_index][qbit_index] += np.pi / 2 # added pi/2 to the ith theta parameter\n\n theta_minus = np.copy(theta)\n theta_minus[layer_index][qbit_index] -= np.pi / 2 # subtracted pi/2 to the ith theta parameter\n\n df_dtheta_i = 0.5 * (get_fidelity(theta_plus, psi) - get_fidelity(theta_minus, psi)) # ith derivative\n df_dtheta.append(df_dtheta_i)\n\n df_dtheta = np.array(df_dtheta)\n dl_dtheta = dl_df * df_dtheta # chain rule to get partial derivative of loss wrt theta parameters\n\n return dl_dtheta","def run_gradient_descent_iteration(X, Y, theta, alpha, lambda_factor, temp_parameter):\n delta = sparse.coo_matrix(theta.shape).toarray()\n\n h = compute_probabilities(X, theta, temp_parameter)\n\n for j in range(delta.shape[0]):\n y = Y\n y = np.where(y != j, 0, 1)\n p = y - h[j]\n\n x = X.T * p\n x = x.T\n x = x.sum(axis=0)\n\n grad = -x / (temp_parameter * X.shape[0]) + lambda_factor * theta[j]\n\n delta[j] += grad\n\n theta -= alpha * delta\n\n return theta","def gradient_descent(features, values, theta, alpha, num_iterations):\n \n # number of points\n npoints = len(values)\n \n # intialize cost history\n cost_history = []\n \n # num_interations iterations\n for iiter in range(num_iterations):\n \n # compute and store cost\n cost = compute_cost(features, values, theta)\n cost_history.append(cost)\n \n # update values of theta\n values_predicted = np.dot(features, theta)\n theta = theta + (alpha/npoints)*(np.dot(values - values_predicted,features))\n \n return theta, pandas.Series(cost_history)","def gradient_descent(x, y, theta=[[0], [0]]):\n m = y.size\n j_history = []\n for i in range(ITERATIONS):\n h = x.dot(theta)\n theta = theta - (ALPHA / m) * (x.T.dot(h - y))\n j_history.append(compute_cost(x, y, theta))\n return theta, j_history","def gradient_descent(features, values, theta, alpha, num_iterations):\r\n\r\n m = len(values)\r\n cost_history = []\r\n\r\n for i in range (num_iterations):\r\n \r\n h = numpy.dot(features, theta)\r\n \r\n theta = theta - alpha / m * numpy.dot((h-values),features)\r\n \r\n cost = compute_cost(features, values, theta)\r\n \r\n cost_history.append(cost)\r\n\r\n return theta, pandas.Series(cost_history) # leave this line for the grader\r","def approx_grad(theta, X, y):\n grad_a = np.array([(cost(theta + e, X, y) - cost(theta - e, X, y)) / (2 * 1e-5)\n for e in np.identity(len(theta)) * 1e-5])\n return grad_a","def gradient_descent(features, values, theta, alpha, num_iterations):\n m = len(values)\n cost_history = []\n\n for i in range(num_iterations):\n predicted_values = np.dot(features, theta)\n delta = alpha / m * np.dot((predicted_values - values), features)\n theta = theta - delta\n cost = compute_cost(features, values, theta)\n cost_history.append(cost)\n return theta, pandas.Series(cost_history)","def calculate_gradient(y, tx, w): \n return tx.T@(sigmoid(tx@w)-y)","def lr_cost_function_grad(theta: np.ndarray, X: np.ndarray, y: np.ndarray, l: float) -> np.ndarray:\n # Initialize some useful values\n m = len(y) # number of training examples\n\n # You need to return the following variable correctly.\n grad = np.zeros(theta.shape)\n\n # ====================== YOUR CODE HERE ======================\n # Instructions: Compute the partial derivatives and set grad to the partial\n # derivatives of the cost w.r.t. each parameter in theta.\n\n # =============================================================\n return grad","def gradient_ascent(self, w, X, y, lr):\r\n # INSERT YOUR CODE HERE\r\n #raise Exception('Function not yet implemented!')\r\n # gradient = x_j*(y-σ(wTX))\r\n return np.dot(X.T, y-self.sigmoid(np.dot(X, w)))","def computeNumericalGradient(J, theta):\n numgrad = np.zeros(theta.size)\n perturb = np.zeros(theta.size)\n e = 1e-4\n for p in range(theta.size):\n # Set perturbation vector\n perturb[p] = e\n loss1 = J(theta - perturb)[0]\n loss2 = J(theta + perturb)[0]\n # Compute Numerical Gradient\n numgrad[p] = (loss2 - loss1) / (2*e)\n perturb[p] = 0\n return numgrad","def computeNumericalGradient(J, theta):\n numgrad = np.zeros_like(theta).reshape(-1)\n perturb = np.zeros_like(theta).reshape(-1)\n e = 1e-4\n for p in range(theta.size):\n # Set perturbation vector\n perturb[p] = e\n loss1, _ = J(theta - perturb.reshape(theta.shape))\n loss2, _ = J(theta + perturb.reshape(theta.shape))\n # Compute Numerical Gradient\n numgrad[p] = (loss2 - loss1) / (2 * e)\n perturb[p] = 0\n\n return numgrad.reshape(theta.shape)","def gradientdescent(cost_func, theta, args=(), delta_func = 0):\n step = 1\n old_cost = 0\n while True:\n theta_old = theta.copy()\n cost = cost_func(theta, *args)\n delta = delta_func(theta, *args)\n theta = theta - step * delta\n if cost > old_cost and old_cost != 0:\n step = step*0.7\n if np.allclose(theta_old, theta):\n break\n old_cost = cost\n return theta","def gradient(cls, x):\n y = Sigmoid.apply(x)\n return np.multiply(y, 1 - y)","def gradient_descent(features, values, theta, alpha, num_iterations):\r\n \r\n m = len(values)\r\n cost_history = []\r\n\r\n for i in range(num_iterations):\r\n # your code here\r\n cost = compute_cost(features, values, theta)/(2.0*m)\r\n cost_history.append([cost])\r\n \r\n error = features.dot(theta) - values\r\n error = np.reshape(error,(error.shape[0], 1))\r\n errorWeighted = features*error\r\n errorSum = (np.sum(errorWeighted,0))/(m*1.0)\r\n theta = theta - alpha*errorSum \r\n \r\n return theta, pandas.Series(cost_history)","def compute_gradient(y, tx, w):\n\tN = y.shape[0]\n\te = y - np.dot(tx, w)\n\n\tgradLw = -1/N * np.dot(tx.T, e)\n\treturn gradLw","def compute_square_loss_gradient(X, y, theta):\n #TODO\n P = (np.dot(X, theta)-y)\n m = X.shape[0]\n\n return (2/m)*np.dot(X.T, P)","def learning_by_gradient_descent(y, tx, w, gamma):\n\tgrad = calculate_gradient(y, tx, w)\n\n\tw = w - gamma * grad\n\treturn w","def calculate_gradient(y, tx, w):\n return tx.T.dot(sigmoid(tx.dot(w))-np.reshape(y,(len(y),1)))","def gradientDescent(self,X, y, theta): \n # number of instances\n m = len(y)\n J_history = np.zeros((self.NUM_ITERS,1))\n for i in range(self.NUM_ITERS):\n h = self.sigmoid(X@theta)\n grad = 1 / m * X.T @ (h - y)\n theta = theta - self.ALPHA * grad \n J_history[i] = self.costFunction(theta, X, y)\n \n \n return theta, J_history","def compute_square_loss_gradient(X, y, theta):\n #TODO\n (N,p) = np.shape(X)\n grad = -(1/np.float(N))*np.array([(y - X.dot(theta))*X[:,i] for i in range(p)])\n return np.sum(grad,axis=1)","def gradient_descent(X, y, theta, learning_rate, num_iters, lambda_reg):\n thetas = [theta]\n cost = np.zeros(num_iters)\n\n J = mean_cross_entropy_costs(X, y, lambda_reg)\n cost[0] = J(thetas[0])\n for i in range(1, num_iters):\n thetas.append(compute_new_theta(X, y, thetas[i - 1], learning_rate, lambda_reg))\n cost[i] = J(thetas[i])\n return cost, thetas","def gradient(img):\n G = np.zeros(img.shape)\n theta = np.zeros(img.shape)\n\n #####################################\n # START YOUR CODE HERE #\n #####################################\n Gx = partial_x(img)\n Gy = partial_y(img)\n G = np.sqrt(np.square(Gx) + np.square(Gy))\n theta = np.degrees(np.arctan2(Gy, Gx)) % 360\n ######################################\n # END OF YOUR CODE #\n ######################################\n\n return G, theta","def gradient_descent(initial_theta, X, y, niter, alpha, Lambda=0.0):\n theta_list = []\n cost_list = []\n\n theta = initial_theta\n for i in range(0, niter):\n theta -= alpha*gradient(theta, X, y, Lambda)\n theta_list.append(theta)\n cost_list.append(cost(theta, X, y, Lambda))\n\n return theta_list, cost_list","def linreg_stochastic_grad(X, y, alpha=.01):\n m = X.shape[0]\n n = X.shape[1]\n theta = np.zeros(n)\n for i in range(m):\n delta = alpha * (np.dot(theta.transpose(), X[i,:]) -y[i]) * X[i,:]\n theta = theta - delta\n return theta","def log_likelihood_gradient(y, tx, w):\n return tx.T.dot(sigmoid(tx.dot(w))-y)","def gradient(cls, x):\n return 1 - TanH.apply(x) ** 2","def learning_by_gradient_descent(y, tx, w, gamma):\n loss = calculate_loss(y,tx,w)\n grad = calculate_gradient(y,tx,w)\n w_new = w - gamma*grad\n #grad is for debugging purpose\n return loss, w_new,grad","def simple_gradient(x, y, theta):\n if x.shape[0] * y.shape[0] * theta.shape[0] == 0:\n return None\n if x.shape[0] != y.shape[0] or theta.shape[0] != 2:\n return None\n\n x = add_intercept(x)\n\n result = [\n forumla(x, y, theta, 0),\n forumla(x, y, theta, 1)\n ]\n return result","def calculate_gradient(y, tx, w):\n\n\tret = tx.T.dot(sigmoid(np.dot(tx, w)) - y)\n\treturn ret","def learning_by_gradient_descent(y, tx, w, gamma):\n loss = calculate_loss(y,tx,w)\n grad = calculate_gradient(y,tx,w)\n w = w-gamma*grad\n return w, loss","def gradient(self, x):\n pass","def train_fn_grad(self, theta_value, theta):\n theta.set_value(theta_value, borrow=True)\n\n train_grads = [self.batch_grad(i, self.n_train)\n for i in xrange(self.n_train_batches)]\n train_batch_sizes = [self.get_batch_size(i, self.n_train)\n for i in xrange(self.n_train_batches)]\n\n return np.average(train_grads, weights=train_batch_sizes,\n axis=0)","def approx_grad_reg(theta, X, y, _lambda):\n grad_a = np.array([(cost_reg(theta + e, X, y, _lambda) - cost_reg(theta - e, X, y, _lambda)) / (2 * 1e-5)\n for e in np.identity(len(theta)) * 1e-5])\n return grad_a","def gradient(img):\n G = np.zeros(img.shape)\n theta = np.zeros(img.shape)\n\n ### YOUR CODE HERE\n Gx = partial_x(img)\n Gy = partial_y(img)\n G = np.sqrt(np.power(Gx,2)+np.power(Gy,2))\n theta = (np.arctan2(Gy, Gx) * 180 / np.pi) % 360\n ### END YOUR CODE\n\n return G, theta","def get_gradient(phi, pred, t, dot_product, weight, reg= 1, regression= \"logistic\"):\n if regression == \"logistic\":\n gradient = np.matmul(phi.T, pred - t)\n elif regression == \"probit\":\n R = np.eye(pred.shape[0])\n for i in range(pred.shape[0]):\n y_n = pred[i,0]\n dotp = dot_product[i, 0]\n pdf = norm.pdf(dotp)\n R[i,i] = pdf/(y_n*(1-y_n) + TOLERANCE)\n gradient = np.matmul(np.matmul(phi.T, R), pred-t)\n elif regression == \"multiclass\":\n gradient = np.matmul(phi.T, pred - t)\n\n # Add regularization\n gradient += weight/ reg\n return gradient","def gradient(self, x):\n u = np.asarray([x[0]])\n C = self.C_func(u)\n dC = self.dC_func(u, order=1)\n P = self.P\n numerator = np.sum((C - P) * dC, axis=0)\n denominator = np.sum(np.sum((C - P) ** 2, axis=0) ** (1 / 2))\n if np.abs(denominator) > 0:\n gradient = numerator/denominator\n else:\n gradient = np.asarray(0)[np.newaxis]\n return gradient","def apply_gradient(params: torch.Tensor, grads: torch.Tensor, lr: float) -> torch.Tensor:\n params_prime = params + lr * grads\n return params_prime","def run_steep_gradient_descent(data_x, data_y, len_data, alpha, theta):\n n = len_data\n # WE NEED TO transpose data_x into (p+1) *n ,theta is 1*(p+1)\n prod = np.dot(theta, data_x.transpose())\n\n prod -= data_y\n print(\"pro: data_x\", prod.shape, data_x.shape)\n #prod represent the loss of the hypothesis and true label\n sum_grad = np.dot(prod, data_x)\n print(\"总梯度的值：\",sum_grad.shape)\n\n # batch-gradient descent\n theta = theta -(alpha / n) * sum_grad\n return theta","def GDS(x, theta, y, lamb, alpha):\n\tm = y.shape[0]\n\tdif = (1/m) * x.T @ (sigmoid(x,theta) - y) #classical differentiation term, output is (n,1)\n\tdif_reg = (lamb/m) * theta #regularization term\n\treturn (dif + dif_reg) * alpha","def gradientDescent(X, y, theta, alpha, num_iters):\n\n # Initialize some useful values\n J_history = []\n m = y.size # number of training examples\n\n for i in range(num_iters):\n # ====================== YOUR CODE HERE ======================\n # Instructions: Perform a single gradient step on the parameter vector\n # theta.\n #\n # Hint: While debugging, it can be useful to print out the values\n # of the cost function (computeCost) and gradient here.\n #\n # Calculate the gradient step according to the equation for theta1:\n g_step1 = (alpha / m * np.sum( (np.dot(X,theta) - y) * X[:,1]) )\n # Gradient step for theta knot:\n g_step0 = (alpha / m * np.sum( (np.dot(X,theta) - y) ) )\n \n #update theta\n theta[0] = (theta[0] - g_step0)\n theta[1] = (theta[1] - g_step1)\n \n #print([theta , g_step1, g_step0])\n\n # ============================================================\n\n # Save the cost J in every iteration\n J_history.append(computeCost(X, y, theta))\n\n return theta, J_history","def gradient(w, x, t):\n return 2 * np.dot(x.T, (nn(x, w) - t))","def costFunction(theta,X,y):\n m = X.shape[0]\n J = 0\n h = sigmoid (np.dot(X,theta))\n \n J = (1/m)* ((-np.dot(y.T,(np.log(h)))) - np.dot((1 - y).T,(np.log(1-h))))\n \n #grad = (1/m) * np.dot(X.T,(h-y))\n grad = (1/m) * np.dot((h.T - y), X).T\n \n return J, grad","def stochasticGradientDescent(x,y,theta,alpha):\n m,n = np.shape(x)\n convergence = 0.000000001\n lastCost = 0\n cost = -1 \n recurseCount = 0\n while abs(lastCost - cost) > convergence: # rcurse until converge\n lastCost = cost\n hypothesis = np.dot(x,theta) \n for i in range(m):\n # alpha = 4.0 / (1.0 + i) + 0.01 \n loss = hypothesis[i] - y[i]\n # gradient = np.dot(x[i],loss)\n gradient = x[i,:].transpose() * loss \n theta = theta - alpha * gradient\n cost = np.sum((hypothesis-y)**2)/(2*m)\n recurseCount += 1\n return recurseCount,theta","def gradient(self):\n result = np.zeros(len(self.variables))\n result[self.bivariateGradInd] = (self.shape-1)/self.variable - self.rate\n return result","def gradient(self):\n functional = self\n\n class KLGradient(Operator):\n\n \"\"\"The gradient operator of this functional.\"\"\"\n\n def __init__(self):\n \"\"\"Initialize a new instance.\"\"\"\n super().__init__(functional.domain, functional.domain,\n linear=False)\n\n def _call(self, x):\n \"\"\"Apply the gradient operator to the given point.\n The gradient is not defined in points where one or more\n components are non-positive.\n \"\"\"\n if functional.prior is None:\n return (-1.0) / x + 1\n else:\n return (-functional.prior) / x + 1\n\n return KLGradient()","def batchGradientDescent(x,y,theta,alpha):\n m,n = np.shape(x)\n xTran = x.transpose()\n convergence = 0.000000001\n lastCost = 0\n cost = -1 \n recurseCount = 0\n while abs(lastCost - cost) > convergence: # rcurse until converge\n lastCost = cost\n hypothesis = np.dot(x,theta)\n loss = hypothesis - y\n cost = np.sum(loss**2)/(2*m)\n gradient = np.dot(xTran,loss)/m\n theta = theta - alpha*gradient\n recurseCount += 1\n return recurseCount,theta","def learning_by_penalized_gradient(y, tx, w, gamma, lambda_):\n\tgrad, H = penalized_logistic_regression(y, tx, w, lambda_)\n\n\thgrad = np.linalg.inv(H).dot(grad)\n\n\tw = w - gamma * hgrad\n\n\treturn w","def grad(self, w):\n l1_grad = self.r * np.sign(w)\n l2_grad = np.asarray(1 - self.r) * w \n\n gradient_penalty = self.alpha * (l1_grad + l2_grad)\n\n # Insert 0 for bias term.\n return np.insert(gradient_penalty, 0, 0, axis=0)","def gradient(self, x):\n g = self._grad(\n time_series=self.observed_data,\n a=x[0],\n b=x[1],\n c=x[2],\n sigma=self.sigma\n )\n return g","def compute_gradient(self):\n A = np.dot(self.X, self.w)\n m = self.t.shape[0]\n C = -1 * self.t * (1 / (1 + np.exp(A * self.t)))\n return (1 / m) * np.dot(self.X.T, C)","def reg_autograd(alpha,X,theta,y):\n g=grad(func)\n for i in range(0,20):\n theta=theta- alpha * g(theta)\n print(\"cost\", func(theta))\n print(\"final theta\",theta)","def grad(self, x):\n norm_beta = np.linalg.norm(x)\n if norm_beta > utils.TOLERANCE:\n return (self.l / norm_beta) * x\n else:\n D = x.shape[0]\n u = (self.rng(D, 1) * 2.0) - 1.0 # [-1, 1]^D\n norm_u = np.linalg.norm(u)\n a = self.rng() # [0, 1]\n\n return (self.l * (a / norm_u)) * u","def gradcovfunc(self, theta, d):\n sigmaf, l = theta[:2] \n xxl = np.sum((d/l)**2, axis=1)\n dk_dsigmaf = 2 * sigmaf * np.exp(-xxl/2.)\n dk_dl = sigmaf**2/l * xxl * np.exp(-xxl/2.)\n grad = np.array([dk_dsigmaf, dk_dl])\n return grad","def log_prior_grad(self, inputs):","def gradient(series, **options):\n x = series.index\n y = series.values\n\n a = np.gradient(y, x, **options)\n return series.__class__(a, series.index)","def compute_logistic_gradient(y, tx, w):\n return tx.T.dot(sigmoid(tx.dot(w)) - y) / len(y)","def derivative(self, theta):\n diag_gamma = np.dot(theta.T, self.X.T)\n logistic_term = self.logistic_fn(diag_gamma)\n diag_gamma = logistic_term * (1.0 - logistic_term)\n gamma = np.diag(diag_gamma)\n\n # v computation\n diags_v = 1.0 - 2*self.logistic_fn(np.dot(theta.T, self.X.T))\n diags_v = diags_v.reshape((-1, 1))\n diags_v = diags_v*self.X\n assert diags_v.shape == self.X.shape #N*d shape\n\n XtGamma = np.dot(self.X.T, gamma) # d*N shape\n\n # TODO: Verifier car pas sur de mon coup ... et surtout plus long...\n # id = np.eye(self.n_examples).reshape((self.n_examples, self.n_examples, 1))\n # diags_v = diags_v.reshape((self.n_examples, 1, self.dim))\n # v = id*diags_v # n*n*d tensor\n # left = np.tensordot(XtGamma, v, axes=(1, 0)) # shape d*N*d\n # assert left.shape == (self.dim, self.n_examples, self.dim)\n # dg = np.tensordot(left, self.X, axes=(1, 0))\n # dg = np.swapaxes(dg, axis1=-2, axis2=-1)\n\n dg = np.zeros((self.dim, self.dim, self.dim))\n for idx, v_i_diag in enumerate(diags_v.T):\n v_i = np.diag(v_i_diag)\n dg_di = np.dot(np.dot(XtGamma, v_i), self.X)\n dg[:, :, idx] = dg_di\n return dg","def run_gradient_descent(data,theta,alpha,num_iters):\n population = data[:,0]\n prices = data[:,1]\n x = ones(shape=(len(population),2)) #add ones for theta0 \n x[:,1] = population\n x = transpose(x)\n error_history = zeros(shape=(num_iters,1))\n \n for i in range(num_iters):\n predictions = theta.dot(x)\n errors_x1 = (predictions - prices) * x[0,:]\n errors_x2 = (predictions - prices) * x[1,:]\n theta[0][0] = theta[0][0] - alpha*(1.0/len(population))*errors_x1.sum()\n theta[0][1] = theta[0][1] - alpha*(1.0/len(population))*errors_x2.sum()\n error_history[i,0] = calculate_cost(theta,data)\n \n return theta, error_history","def gradient(self, x):\n return 0.0","def gradient(poly):\n return differential(\n poly, chaospy.poly.collection.basis(1, 1, poly.dim, sort=\"GR\"))","def curves_gradient(theta, m_, LAMBDA=0):\n # X0: column of ones\n theta0_gradient = (1 / m_) * sum((curves_hypothesis(theta, m_) - y[:m_].ravel()) * np.ones(m_))\n # X1: data given as X\n theta1_gradient = ((1 / m_) * sum((curves_hypothesis(theta, m_) - y[:m_].ravel()) * X[:m_].ravel())) \\\n + (LAMBDA / m_) * theta[1]\n return np.array([theta0_gradient, theta1_gradient])","def costFunction(self,theta, X, y): \n m = len(y)\n h = self.sigmoid(X@theta)\n J = 1 / m * (- y.T @ self.log(h) - (1-y).T @ self.log(1-h)) \n # grad = 1/ m * X.T @ (h - y)\n return J","def gradient(self, x_in):\r\n\r\n return 1 - torch.pow(self.forward(x_in), 2)","def compute_gradient(self): # TODO: try to change to square loss since it's hessian is easier to obtain\n A = np.dot(self.X, self.w)\n m = self.t.shape[0]\n C = -1 * self.t * (1 / (1 + np.exp(A * self.t)))\n return (1 / m) * np.dot(self.X.T, C)","def vb_grad_natgrad(self):\r\n\r\n # Gradient for eta\r\n dL_dmmT_S = -0.5*self.Lambda/self.data_prop + 0.5*self.q_u_prec\r\n Kmmipsi1V,_ = dpotrs(self.Lm,self.psi1V,lower=1)\r\n dL_dm = (Kmmipsi1V - np.dot(self.Lambda,self.q_u_mean))/self.data_prop\r\n\r\n # Gradients for theta\r\n S = self.q_u_cov\r\n Si = self.q_u_prec\r\n m = self.q_u_mean\r\n dL_dSi = -mdot(S,dL_dmmT_S, S)\r\n\r\n dL_dmhSi = -2*dL_dSi\r\n dL_dSim = np.dot(dL_dSi,m) + np.dot(Si, dL_dm)\r\n\r\n return np.hstack((dL_dm.flatten(),dL_dmmT_S.flatten())) , np.hstack((dL_dSim.flatten(), dL_dmhSi.flatten()))","def gradient(self):\n functional = self\n\n class KLCCGradient(Operator):\n\n \"\"\"The gradient operator of this functional.\"\"\"\n\n def __init__(self):\n \"\"\"Initialize a new instance.\"\"\"\n super().__init__(functional.domain, functional.domain,\n linear=False)\n\n def _call(self, x):\n \"\"\"Apply the gradient operator to the given point.\n\n The gradient is not defined in points where one or more\n components are larger than or equal to one.\n \"\"\"\n if functional.prior is None:\n return 1.0 / (1 - x)\n else:\n return functional.prior / (1 - x)\n\n return KLCCGradient()","def grad(self, w):\n gradient_penalty = np.asarray(self.alpha) * w\n # Insert 0 for bias term.\n return np.insert(gradient_penalty, 0, 0, axis=0)","def grad(self, w):\n gradient_penalty = np.asarray(self.alpha) * w\n # Insert 0 for bias term.\n return np.insert(gradient_penalty, 0, 0, axis=0)","def learning_by_penalized_gradient(y, tx, w, gamma, lambda_):\n\n #on test avec Newton\n\n loss,gradient,_ = penalized_logistic_regression(y,tx,w,lambda_)\n\n w = w - gamma*gradient\n return loss, w,gradient","def gradient_ascent(f, df, theta_init, step_size, max_iter):\n\n fs = []\n xs = []\n thetas = theta_init\n for i in range(max_iter): #for each data example\n fs.append(f(thetas))\n\n temp = step_size*df(thetas)\n thetas = step_size*df(thetas) #modify that feature by using the derivative of log likelihood\n xs.append(thetas.flatten())\n if i % 10 == 0:\n print(i, thetas)\n\n return thetas, fs, xs","def calculate_maxL_gradient(y, tx, w):\n sig=sigmoid(tx.dot(w))\n grad=tx.T.dot(sig-y)\n #divides with number of samples so that learning rate is not dependant on number of samples\n grad=grad/len(y) \n return grad","def _log_prior_gradients(self):\n if self.priors.size == 0:\n return 0.\n x = self.param_array\n ret = np.zeros(x.size)\n #compute derivate of prior density\n [np.put(ret, ind, p.lnpdf_grad(x[ind])) for p, ind in self.priors.items()]\n #add in jacobian derivatives if transformed\n priored_indexes = np.hstack([i for p, i in self.priors.items()])\n for c,j in self.constraints.items():\n if not isinstance(c, Transformation):continue\n for jj in j:\n if jj in priored_indexes:\n ret[jj] += c.log_jacobian_grad(x[jj])\n return ret","def grad(self, w):\n gradient_penalty = np.asarray(self.alpha) * w\n\n # Insert 0 for bias term.\n return np.insert(gradient_penalty, 0, 0, axis=0)","def grad(theta, X, y, lambda_):\n # ... dopolnite (naloga 1, naloga 2)\n\n l = []\n for i, e in enumerate(theta):\n l.append(1 / len(y) * sum([(h(x, theta) - yi) * x[i] for x, yi in zip(X, y)]) + 2 * lambda_ * e)\n\n return np.array(l)","def lr_cost_function(theta, X, y, learning_rate):\n m = len(y) # number of training examples\n # You need to return the following variables correctly \n J = 0\n grad = np.zeros(theta.shape)\n h = sigmoid(np.dot(X, theta))\n\n #Compute cost:\n first = (1/m)*np.sum(np.dot(-y.T, np.log(h)) - np.dot((1-y).T, np.log(1-h)), axis=0)\n second = (learning_rate/(2*m))*np.sum(theta[1:]**2)\n J = first + second\n \n #Compute gradient\n grad = np.dot((h-y).T, X)/m\n grad = grad.T\n grad[1:] += (learning_rate/m)*theta[1:]\n return float(J), grad","def gradient(self, x, i=None):\n self.set_parameters(x=x)\n gradient = self.gradient_loss()\n if self.add_bias:\n gradient[1:] += self.alpha * self.gradient_regularizer()\n else:\n gradient += self.alpha * self.gradient_regularizer()\n if i is not None:\n return gradient.ravel()[i]\n return gradient.ravel()","def compute_gradient_lasso(y, tx, w, lambda_):\n e = y - tx.dot(w)\n subgrad = lambda_ * np.sign(w)\n\n return -tx.T.dot(e)/len(e) + subgrad","def compute_gradient(self, X, y, weights):\n sigmoid = self.sigmoid(np.dot(X, weights))\n return np.dot(X.T, y - sigmoid)","def batch_grad_descent(X, y, alpha=0.1, num_iter=1000, check_gradient=False):\n num_instances, num_features = X.shape[0], X.shape[1]\n theta_hist = np.zeros((num_iter+1, num_features)) #Initialize theta_hist\n loss_hist = np.zeros(num_iter+1) #initialize loss_hist\n theta = np.ones(num_features) #initialize theta\n\n count = 0\n while count < num_iter+1:\n if check_gradient:\n assert grad_checker(X,y,theta)\n\n grad = compute_square_loss_gradient(X,y,theta)\n theta -= alpha*grad\n theta_hist[count] = theta\n loss_hist[count] = compute_square_loss(X,y,theta)\n count += 1\n \n return theta_hist, loss_hist","def check_gradient(self, x, y):\n x = x.transpose()\n y = y.transpose()\n layers_copy = deepcopy(self.layers)\n epsilon = 10 ** -4\n a, layer = self.forward_propagation(x)\n delta = self.calculate_delta(a, y, layer)\n self.backpropagation(delta=delta, theta=layer.theta)\n previous_layer_output = x\n for layer in self.layers:\n theta_copy = deepcopy(layer.theta)\n real_theta_size = theta_copy.shape\n delta = layer.delta\n dc_dtheta = np.outer(previous_layer_output, delta).transpose()\n previous_layer_output = layer.a\n R, C = theta_copy.shape\n for i in range(R):\n for j in range(C):\n theta_plus = deepcopy(theta_copy)\n theta_plus[i, j] += epsilon\n layer.theta = theta_plus\n a_plus, l_plus = self.forward_propagation(x)\n err_plus = self.calculate_loss(a_plus, y)\n theta_minus = deepcopy(theta_copy)\n theta_minus[i, j] -= epsilon\n layer.theta = theta_minus\n a_minus, l_minus = self.forward_propagation(x)\n err_minus = self.calculate_loss(a_minus, y)\n limit = (err_plus - err_minus)/(2*epsilon)\n grad_diff = abs(dc_dtheta[i,j] - limit)\n assert grad_diff < 10 ** -6, f\"Diff {grad_diff} is too big.\"\n layer.theta = theta_copy","def _compute_func_grad(self, w):\n W = w.reshape((self.X.shape[1], self.Y.shape[1]))\n self.nll_, self.grad_ = calculate_gradient(self.X, self.Y, W, self.prior, self.weighted,0)","def gradient(data_x, data_y, parameters):\n return data_x.T @ (data_x @ parameters - data_y) / data_x.shape[0]"],"string":"[\n \"def gradient(self, theta):\\n pass\",\n \"def gradient(self, theta):\\n pass\",\n \"def gradient(self, theta):\\n a = -(6 * self.scale ** 2)\\n b = 3 * self.scale ** 2 + np.exp(2 * theta)\\n b *= np.log(3 * self.scale ** 2 * np.exp(-2 * theta) + 1)\\n return a / b\",\n \"def gradient(theta, X, y, learning_rate):\\n m = len(y)\\n\\n theta = theta.reshape((-1,1))\\n grad = np.zeros(theta.shape)\\n h = sigmoid(np.dot(X, theta)) \\n \\n grad = np.dot((h-y).T, X)/m\\n grad = grad.T\\n grad[1:] += (learning_rate/m)*theta[1:]\\n return grad\",\n \"def gradient_function(theta, X, y):\\n\\n grad = None\\n #######################################################################\\n # TODO: #\\n # Compute the gradient for a particular choice of theta. #\\n # Compute the partial derivatives and set grad to the partial #\\n # derivatives of the cost w.r.t. each parameter in theta #\\n # #\\n #######################################################################\\n \\n theta = theta[:, np.newaxis]\\n \\n thetatrans = theta.T\\n Xtrans = X.T\\n \\n MulThetaX = np.dot(thetatrans, Xtrans)\\n \\n h = sigmoid(MulThetaX)\\n \\n grad = (y - h) * Xtrans\\n\\n #######################################################################\\n # END OF YOUR CODE #\\n #######################################################################\\n return grad\",\n \"def compute_gradient(theta, X, y):\\n m = X.shape[0]\\n grad_theta = np.dot(X.transpose(), (np.dot(X, theta) - y)) / m\\n #print theta, grad_theta, objective_function(theta, X, y)\\n return grad_theta\",\n \"def gradient(self, theta):\\n return np.zeros([theta.shape[0]])\",\n \"def gradientFunction(theta, X, y):\\n y = y[:, 0]\\n m = y.shape # number of training samples\\n grad = X.T.dot(sigmoid(theta.dot(X.T))-1*y)\\n grad /= m\\n return grad\",\n \"def gradient(theta, x, y):\\n m = len(y)\\n n = len(theta)\\n z = theta.dot(x.T)\\n grad = np.zeros(n)\\n for i in xrange(m):\\n grad += (g(z[i]) - y[i]) * x[i]\\n return 1. / m * grad\",\n \"def gradient(theta, X, y, Lambda=0.0):\\n m = X.shape[0] # number of samples\\n\\n h = hypothesis(theta, X)\\n\\n if Lambda:\\n g_0 = (1/m)*(X.T@(h - y))[0]\\n g_1 = (1/m)*(X.T@(h - y))[1:] + (Lambda/m)*theta[1:] # skip theta-0\\n \\n return np.append(g_0, g_1)\\n else:\\n return (1/m)*(X.T@(h - y))\",\n \"def gradient(x, y, theta):\\n if x.ndim == 1:\\n x = x[:, np.newaxis]\\n if y.ndim == 2 and y.shape[1] == 1:\\n y = y.flatten()\\n if theta.ndim == 2 and theta.shape[1] == 1:\\n theta = theta.flatten()\\n\\n if (x.size == 0 or y.size == 0 or theta.size == 0\\n or x.ndim != 2 or y.ndim != 1 or theta.ndim != 1\\n or x.shape[0] != y.shape[0] or x.shape[1] + 1 != theta.shape[0]):\\n return None\\n\\n x_padded = np.c_[np.ones(x.shape[0]), x]\\n\\n return x_padded.T.dot(x_padded.dot(theta) - y) / y.shape[0]\",\n \"def calc_grad(X, Y, theta):\\n m, n = X.shape\\n\\n margins = Y * X.dot(theta)\\n probs = 1. / (1 + np.exp(margins))\\n grad = -(1./m) * (X.T.dot(probs * Y))\\n\\n return grad\",\n \"def gradient_descent(X, y, theta, alpha, total_iterations, hypothesis):\\n len_theta = len(theta)\\n m = len(y)\\n one_over_m = (1.0 / float(m))\\n\\n for _ in range(0, total_iterations):\\n temp_theta = numpy.zeros(len_theta)\\n\\n X_by_theta_minus_y = numpy.subtract(hypothesis(numpy.matrix(theta), X), y)\\n\\n for j in range(0, len_theta):\\n jth_column_of_X = X[:,j]\\n derivative_j = one_over_m * numpy.multiply(X_by_theta_minus_y, jth_column_of_X).sum()\\n temp_theta[j] = theta[j] - alpha*derivative_j\\n\\n theta = temp_theta\\n\\n return numpy.matrix(theta)\",\n \"def gradient_descent(self, X, theta, Y, m):\\n\\n Z = X.dot(theta)\\n H = Predict.g(Z)\\n gradient = np.dot(X.T, (H - Y)) / m\\n return self.alpha * gradient\",\n \"def gradientFunctionReg(theta, X, y, Lambda):\\n m = len(y) # number of training examples\\n grad = np.zeros(theta.shape[0])\\n theta = np.transpose(theta)\\n sum_1 = 0\\n X = X.values\\n y = y.values\\n #calcuate the theta_0 \\n# ====================== YOUR CODE HERE ======================\\n# Instructions: Compute the gradient of a particular choice of theta.\\n# Compute the partial derivatives and set grad to the partial\\n# derivatives of the cost w.r.t. each parameter in theta\\n for i in range(theta.shape[0]):\\n if i == 0:\\n for j in range(m):\\n sum_1 += (sigmoid(np.dot(X[j],theta)) - y[j]) * X[j,i]\\n else:\\n for j in range(m):\\n sum_1 += (sigmoid(np.dot(X[j],theta)) - y[j]) * X[j,i] + Lambda*theta[i]\\n grad[i] = sum_1/m\\n sum_1 = 0\\n\\n# =============================================================\\n\\n return grad\",\n \"def gradientFunctionReg(theta, X, y, Lambda): \\n y = np.squeeze(y)\\n m = y.shape # number of training samples\\n grad = X.T.dot(sigmoid(theta.dot(X.T))-1*y)\\n grad[1:] = grad[1:] + Lambda*theta[1:]\\n grad /= m\\n\\n return grad\",\n \"def logit_cost_grad(self, theta, X, y):\\n\\n grad = np.zeros(len(theta))\\n\\n ### YOUR CODE HERE\\n sig = utils.sigmoid(theta)\\n # sig = np.subtract(sig, y)\\n sig = sig - y\\n grad = np.dot(X.T, sig) + 2 * self.params['lamb'] * self.regularizer[1](self.weights)\\n ### END YOUR CODE\\n\\n return grad\",\n \"def compute_loss_gradient(theta_vector, *args):\\n\\n psi = args[0] # feed psi as a parameter\\n circ_depth = args[1]\\n num_qbits = args[2]\\n theta = np.reshape(theta_vector, (circ_depth, num_qbits)) # reshapes the flat theta vector\\n fidelity = get_fidelity(theta, psi)\\n\\n # the derivative of the loss wrt fidelity\\n dl_df = -0.5 * fidelity ** (-0.5)\\n\\n df_dtheta = [] # a list of partial derivatives of the fidelity wrt the theta parameters\\n\\n for index in range(len(theta_vector)):\\n layer_index = index // num_qbits\\n qbit_index = index % num_qbits\\n\\n theta_plus = np.copy(theta)\\n theta_plus[layer_index][qbit_index] += np.pi / 2 # added pi/2 to the ith theta parameter\\n\\n theta_minus = np.copy(theta)\\n theta_minus[layer_index][qbit_index] -= np.pi / 2 # subtracted pi/2 to the ith theta parameter\\n\\n df_dtheta_i = 0.5 * (get_fidelity(theta_plus, psi) - get_fidelity(theta_minus, psi)) # ith derivative\\n df_dtheta.append(df_dtheta_i)\\n\\n df_dtheta = np.array(df_dtheta)\\n dl_dtheta = dl_df * df_dtheta # chain rule to get partial derivative of loss wrt theta parameters\\n\\n return dl_dtheta\",\n \"def run_gradient_descent_iteration(X, Y, theta, alpha, lambda_factor, temp_parameter):\\n delta = sparse.coo_matrix(theta.shape).toarray()\\n\\n h = compute_probabilities(X, theta, temp_parameter)\\n\\n for j in range(delta.shape[0]):\\n y = Y\\n y = np.where(y != j, 0, 1)\\n p = y - h[j]\\n\\n x = X.T * p\\n x = x.T\\n x = x.sum(axis=0)\\n\\n grad = -x / (temp_parameter * X.shape[0]) + lambda_factor * theta[j]\\n\\n delta[j] += grad\\n\\n theta -= alpha * delta\\n\\n return theta\",\n \"def gradient_descent(features, values, theta, alpha, num_iterations):\\n \\n # number of points\\n npoints = len(values)\\n \\n # intialize cost history\\n cost_history = []\\n \\n # num_interations iterations\\n for iiter in range(num_iterations):\\n \\n # compute and store cost\\n cost = compute_cost(features, values, theta)\\n cost_history.append(cost)\\n \\n # update values of theta\\n values_predicted = np.dot(features, theta)\\n theta = theta + (alpha/npoints)*(np.dot(values - values_predicted,features))\\n \\n return theta, pandas.Series(cost_history)\",\n \"def gradient_descent(x, y, theta=[[0], [0]]):\\n m = y.size\\n j_history = []\\n for i in range(ITERATIONS):\\n h = x.dot(theta)\\n theta = theta - (ALPHA / m) * (x.T.dot(h - y))\\n j_history.append(compute_cost(x, y, theta))\\n return theta, j_history\",\n \"def gradient_descent(features, values, theta, alpha, num_iterations):\\r\\n\\r\\n m = len(values)\\r\\n cost_history = []\\r\\n\\r\\n for i in range (num_iterations):\\r\\n \\r\\n h = numpy.dot(features, theta)\\r\\n \\r\\n theta = theta - alpha / m * numpy.dot((h-values),features)\\r\\n \\r\\n cost = compute_cost(features, values, theta)\\r\\n \\r\\n cost_history.append(cost)\\r\\n\\r\\n return theta, pandas.Series(cost_history) # leave this line for the grader\\r\",\n \"def approx_grad(theta, X, y):\\n grad_a = np.array([(cost(theta + e, X, y) - cost(theta - e, X, y)) / (2 * 1e-5)\\n for e in np.identity(len(theta)) * 1e-5])\\n return grad_a\",\n \"def gradient_descent(features, values, theta, alpha, num_iterations):\\n m = len(values)\\n cost_history = []\\n\\n for i in range(num_iterations):\\n predicted_values = np.dot(features, theta)\\n delta = alpha / m * np.dot((predicted_values - values), features)\\n theta = theta - delta\\n cost = compute_cost(features, values, theta)\\n cost_history.append(cost)\\n return theta, pandas.Series(cost_history)\",\n \"def calculate_gradient(y, tx, w): \\n return tx.T@(sigmoid(tx@w)-y)\",\n \"def lr_cost_function_grad(theta: np.ndarray, X: np.ndarray, y: np.ndarray, l: float) -> np.ndarray:\\n # Initialize some useful values\\n m = len(y) # number of training examples\\n\\n # You need to return the following variable correctly.\\n grad = np.zeros(theta.shape)\\n\\n # ====================== YOUR CODE HERE ======================\\n # Instructions: Compute the partial derivatives and set grad to the partial\\n # derivatives of the cost w.r.t. each parameter in theta.\\n\\n # =============================================================\\n return grad\",\n \"def gradient_ascent(self, w, X, y, lr):\\r\\n # INSERT YOUR CODE HERE\\r\\n #raise Exception('Function not yet implemented!')\\r\\n # gradient = x_j*(y-σ(wTX))\\r\\n return np.dot(X.T, y-self.sigmoid(np.dot(X, w)))\",\n \"def computeNumericalGradient(J, theta):\\n numgrad = np.zeros(theta.size)\\n perturb = np.zeros(theta.size)\\n e = 1e-4\\n for p in range(theta.size):\\n # Set perturbation vector\\n perturb[p] = e\\n loss1 = J(theta - perturb)[0]\\n loss2 = J(theta + perturb)[0]\\n # Compute Numerical Gradient\\n numgrad[p] = (loss2 - loss1) / (2*e)\\n perturb[p] = 0\\n return numgrad\",\n \"def computeNumericalGradient(J, theta):\\n numgrad = np.zeros_like(theta).reshape(-1)\\n perturb = np.zeros_like(theta).reshape(-1)\\n e = 1e-4\\n for p in range(theta.size):\\n # Set perturbation vector\\n perturb[p] = e\\n loss1, _ = J(theta - perturb.reshape(theta.shape))\\n loss2, _ = J(theta + perturb.reshape(theta.shape))\\n # Compute Numerical Gradient\\n numgrad[p] = (loss2 - loss1) / (2 * e)\\n perturb[p] = 0\\n\\n return numgrad.reshape(theta.shape)\",\n \"def gradientdescent(cost_func, theta, args=(), delta_func = 0):\\n step = 1\\n old_cost = 0\\n while True:\\n theta_old = theta.copy()\\n cost = cost_func(theta, *args)\\n delta = delta_func(theta, *args)\\n theta = theta - step * delta\\n if cost > old_cost and old_cost != 0:\\n step = step*0.7\\n if np.allclose(theta_old, theta):\\n break\\n old_cost = cost\\n return theta\",\n \"def gradient(cls, x):\\n y = Sigmoid.apply(x)\\n return np.multiply(y, 1 - y)\",\n \"def gradient_descent(features, values, theta, alpha, num_iterations):\\r\\n \\r\\n m = len(values)\\r\\n cost_history = []\\r\\n\\r\\n for i in range(num_iterations):\\r\\n # your code here\\r\\n cost = compute_cost(features, values, theta)/(2.0*m)\\r\\n cost_history.append([cost])\\r\\n \\r\\n error = features.dot(theta) - values\\r\\n error = np.reshape(error,(error.shape[0], 1))\\r\\n errorWeighted = features*error\\r\\n errorSum = (np.sum(errorWeighted,0))/(m*1.0)\\r\\n theta = theta - alpha*errorSum \\r\\n \\r\\n return theta, pandas.Series(cost_history)\",\n \"def compute_gradient(y, tx, w):\\n\\tN = y.shape[0]\\n\\te = y - np.dot(tx, w)\\n\\n\\tgradLw = -1/N * np.dot(tx.T, e)\\n\\treturn gradLw\",\n \"def compute_square_loss_gradient(X, y, theta):\\n #TODO\\n P = (np.dot(X, theta)-y)\\n m = X.shape[0]\\n\\n return (2/m)*np.dot(X.T, P)\",\n \"def learning_by_gradient_descent(y, tx, w, gamma):\\n\\tgrad = calculate_gradient(y, tx, w)\\n\\n\\tw = w - gamma * grad\\n\\treturn w\",\n \"def calculate_gradient(y, tx, w):\\n return tx.T.dot(sigmoid(tx.dot(w))-np.reshape(y,(len(y),1)))\",\n \"def gradientDescent(self,X, y, theta): \\n # number of instances\\n m = len(y)\\n J_history = np.zeros((self.NUM_ITERS,1))\\n for i in range(self.NUM_ITERS):\\n h = self.sigmoid(X@theta)\\n grad = 1 / m * X.T @ (h - y)\\n theta = theta - self.ALPHA * grad \\n J_history[i] = self.costFunction(theta, X, y)\\n \\n \\n return theta, J_history\",\n \"def compute_square_loss_gradient(X, y, theta):\\n #TODO\\n (N,p) = np.shape(X)\\n grad = -(1/np.float(N))*np.array([(y - X.dot(theta))*X[:,i] for i in range(p)])\\n return np.sum(grad,axis=1)\",\n \"def gradient_descent(X, y, theta, learning_rate, num_iters, lambda_reg):\\n thetas = [theta]\\n cost = np.zeros(num_iters)\\n\\n J = mean_cross_entropy_costs(X, y, lambda_reg)\\n cost[0] = J(thetas[0])\\n for i in range(1, num_iters):\\n thetas.append(compute_new_theta(X, y, thetas[i - 1], learning_rate, lambda_reg))\\n cost[i] = J(thetas[i])\\n return cost, thetas\",\n \"def gradient(img):\\n G = np.zeros(img.shape)\\n theta = np.zeros(img.shape)\\n\\n #####################################\\n # START YOUR CODE HERE #\\n #####################################\\n Gx = partial_x(img)\\n Gy = partial_y(img)\\n G = np.sqrt(np.square(Gx) + np.square(Gy))\\n theta = np.degrees(np.arctan2(Gy, Gx)) % 360\\n ######################################\\n # END OF YOUR CODE #\\n ######################################\\n\\n return G, theta\",\n \"def gradient_descent(initial_theta, X, y, niter, alpha, Lambda=0.0):\\n theta_list = []\\n cost_list = []\\n\\n theta = initial_theta\\n for i in range(0, niter):\\n theta -= alpha*gradient(theta, X, y, Lambda)\\n theta_list.append(theta)\\n cost_list.append(cost(theta, X, y, Lambda))\\n\\n return theta_list, cost_list\",\n \"def linreg_stochastic_grad(X, y, alpha=.01):\\n m = X.shape[0]\\n n = X.shape[1]\\n theta = np.zeros(n)\\n for i in range(m):\\n delta = alpha * (np.dot(theta.transpose(), X[i,:]) -y[i]) * X[i,:]\\n theta = theta - delta\\n return theta\",\n \"def log_likelihood_gradient(y, tx, w):\\n return tx.T.dot(sigmoid(tx.dot(w))-y)\",\n \"def gradient(cls, x):\\n return 1 - TanH.apply(x) ** 2\",\n \"def learning_by_gradient_descent(y, tx, w, gamma):\\n loss = calculate_loss(y,tx,w)\\n grad = calculate_gradient(y,tx,w)\\n w_new = w - gamma*grad\\n #grad is for debugging purpose\\n return loss, w_new,grad\",\n \"def simple_gradient(x, y, theta):\\n if x.shape[0] * y.shape[0] * theta.shape[0] == 0:\\n return None\\n if x.shape[0] != y.shape[0] or theta.shape[0] != 2:\\n return None\\n\\n x = add_intercept(x)\\n\\n result = [\\n forumla(x, y, theta, 0),\\n forumla(x, y, theta, 1)\\n ]\\n return result\",\n \"def calculate_gradient(y, tx, w):\\n\\n\\tret = tx.T.dot(sigmoid(np.dot(tx, w)) - y)\\n\\treturn ret\",\n \"def learning_by_gradient_descent(y, tx, w, gamma):\\n loss = calculate_loss(y,tx,w)\\n grad = calculate_gradient(y,tx,w)\\n w = w-gamma*grad\\n return w, loss\",\n \"def gradient(self, x):\\n pass\",\n \"def train_fn_grad(self, theta_value, theta):\\n theta.set_value(theta_value, borrow=True)\\n\\n train_grads = [self.batch_grad(i, self.n_train)\\n for i in xrange(self.n_train_batches)]\\n train_batch_sizes = [self.get_batch_size(i, self.n_train)\\n for i in xrange(self.n_train_batches)]\\n\\n return np.average(train_grads, weights=train_batch_sizes,\\n axis=0)\",\n \"def approx_grad_reg(theta, X, y, _lambda):\\n grad_a = np.array([(cost_reg(theta + e, X, y, _lambda) - cost_reg(theta - e, X, y, _lambda)) / (2 * 1e-5)\\n for e in np.identity(len(theta)) * 1e-5])\\n return grad_a\",\n \"def gradient(img):\\n G = np.zeros(img.shape)\\n theta = np.zeros(img.shape)\\n\\n ### YOUR CODE HERE\\n Gx = partial_x(img)\\n Gy = partial_y(img)\\n G = np.sqrt(np.power(Gx,2)+np.power(Gy,2))\\n theta = (np.arctan2(Gy, Gx) * 180 / np.pi) % 360\\n ### END YOUR CODE\\n\\n return G, theta\",\n \"def get_gradient(phi, pred, t, dot_product, weight, reg= 1, regression= \\\"logistic\\\"):\\n if regression == \\\"logistic\\\":\\n gradient = np.matmul(phi.T, pred - t)\\n elif regression == \\\"probit\\\":\\n R = np.eye(pred.shape[0])\\n for i in range(pred.shape[0]):\\n y_n = pred[i,0]\\n dotp = dot_product[i, 0]\\n pdf = norm.pdf(dotp)\\n R[i,i] = pdf/(y_n*(1-y_n) + TOLERANCE)\\n gradient = np.matmul(np.matmul(phi.T, R), pred-t)\\n elif regression == \\\"multiclass\\\":\\n gradient = np.matmul(phi.T, pred - t)\\n\\n # Add regularization\\n gradient += weight/ reg\\n return gradient\",\n \"def gradient(self, x):\\n u = np.asarray([x[0]])\\n C = self.C_func(u)\\n dC = self.dC_func(u, order=1)\\n P = self.P\\n numerator = np.sum((C - P) * dC, axis=0)\\n denominator = np.sum(np.sum((C - P) ** 2, axis=0) ** (1 / 2))\\n if np.abs(denominator) > 0:\\n gradient = numerator/denominator\\n else:\\n gradient = np.asarray(0)[np.newaxis]\\n return gradient\",\n \"def apply_gradient(params: torch.Tensor, grads: torch.Tensor, lr: float) -> torch.Tensor:\\n params_prime = params + lr * grads\\n return params_prime\",\n \"def run_steep_gradient_descent(data_x, data_y, len_data, alpha, theta):\\n n = len_data\\n # WE NEED TO transpose data_x into (p+1) *n ,theta is 1*(p+1)\\n prod = np.dot(theta, data_x.transpose())\\n\\n prod -= data_y\\n print(\\\"pro: data_x\\\", prod.shape, data_x.shape)\\n #prod represent the loss of the hypothesis and true label\\n sum_grad = np.dot(prod, data_x)\\n print(\\\"总梯度的值：\\\",sum_grad.shape)\\n\\n # batch-gradient descent\\n theta = theta -(alpha / n) * sum_grad\\n return theta\",\n \"def GDS(x, theta, y, lamb, alpha):\\n\\tm = y.shape[0]\\n\\tdif = (1/m) * x.T @ (sigmoid(x,theta) - y) #classical differentiation term, output is (n,1)\\n\\tdif_reg = (lamb/m) * theta #regularization term\\n\\treturn (dif + dif_reg) * alpha\",\n \"def gradientDescent(X, y, theta, alpha, num_iters):\\n\\n # Initialize some useful values\\n J_history = []\\n m = y.size # number of training examples\\n\\n for i in range(num_iters):\\n # ====================== YOUR CODE HERE ======================\\n # Instructions: Perform a single gradient step on the parameter vector\\n # theta.\\n #\\n # Hint: While debugging, it can be useful to print out the values\\n # of the cost function (computeCost) and gradient here.\\n #\\n # Calculate the gradient step according to the equation for theta1:\\n g_step1 = (alpha / m * np.sum( (np.dot(X,theta) - y) * X[:,1]) )\\n # Gradient step for theta knot:\\n g_step0 = (alpha / m * np.sum( (np.dot(X,theta) - y) ) )\\n \\n #update theta\\n theta[0] = (theta[0] - g_step0)\\n theta[1] = (theta[1] - g_step1)\\n \\n #print([theta , g_step1, g_step0])\\n\\n # ============================================================\\n\\n # Save the cost J in every iteration\\n J_history.append(computeCost(X, y, theta))\\n\\n return theta, J_history\",\n \"def gradient(w, x, t):\\n return 2 * np.dot(x.T, (nn(x, w) - t))\",\n \"def costFunction(theta,X,y):\\n m = X.shape[0]\\n J = 0\\n h = sigmoid (np.dot(X,theta))\\n \\n J = (1/m)* ((-np.dot(y.T,(np.log(h)))) - np.dot((1 - y).T,(np.log(1-h))))\\n \\n #grad = (1/m) * np.dot(X.T,(h-y))\\n grad = (1/m) * np.dot((h.T - y), X).T\\n \\n return J, grad\",\n \"def stochasticGradientDescent(x,y,theta,alpha):\\n m,n = np.shape(x)\\n convergence = 0.000000001\\n lastCost = 0\\n cost = -1 \\n recurseCount = 0\\n while abs(lastCost - cost) > convergence: # rcurse until converge\\n lastCost = cost\\n hypothesis = np.dot(x,theta) \\n for i in range(m):\\n # alpha = 4.0 / (1.0 + i) + 0.01 \\n loss = hypothesis[i] - y[i]\\n # gradient = np.dot(x[i],loss)\\n gradient = x[i,:].transpose() * loss \\n theta = theta - alpha * gradient\\n cost = np.sum((hypothesis-y)**2)/(2*m)\\n recurseCount += 1\\n return recurseCount,theta\",\n \"def gradient(self):\\n result = np.zeros(len(self.variables))\\n result[self.bivariateGradInd] = (self.shape-1)/self.variable - self.rate\\n return result\",\n \"def gradient(self):\\n functional = self\\n\\n class KLGradient(Operator):\\n\\n \\\"\\\"\\\"The gradient operator of this functional.\\\"\\\"\\\"\\n\\n def __init__(self):\\n \\\"\\\"\\\"Initialize a new instance.\\\"\\\"\\\"\\n super().__init__(functional.domain, functional.domain,\\n linear=False)\\n\\n def _call(self, x):\\n \\\"\\\"\\\"Apply the gradient operator to the given point.\\n The gradient is not defined in points where one or more\\n components are non-positive.\\n \\\"\\\"\\\"\\n if functional.prior is None:\\n return (-1.0) / x + 1\\n else:\\n return (-functional.prior) / x + 1\\n\\n return KLGradient()\",\n \"def batchGradientDescent(x,y,theta,alpha):\\n m,n = np.shape(x)\\n xTran = x.transpose()\\n convergence = 0.000000001\\n lastCost = 0\\n cost = -1 \\n recurseCount = 0\\n while abs(lastCost - cost) > convergence: # rcurse until converge\\n lastCost = cost\\n hypothesis = np.dot(x,theta)\\n loss = hypothesis - y\\n cost = np.sum(loss**2)/(2*m)\\n gradient = np.dot(xTran,loss)/m\\n theta = theta - alpha*gradient\\n recurseCount += 1\\n return recurseCount,theta\",\n \"def learning_by_penalized_gradient(y, tx, w, gamma, lambda_):\\n\\tgrad, H = penalized_logistic_regression(y, tx, w, lambda_)\\n\\n\\thgrad = np.linalg.inv(H).dot(grad)\\n\\n\\tw = w - gamma * hgrad\\n\\n\\treturn w\",\n \"def grad(self, w):\\n l1_grad = self.r * np.sign(w)\\n l2_grad = np.asarray(1 - self.r) * w \\n\\n gradient_penalty = self.alpha * (l1_grad + l2_grad)\\n\\n # Insert 0 for bias term.\\n return np.insert(gradient_penalty, 0, 0, axis=0)\",\n \"def gradient(self, x):\\n g = self._grad(\\n time_series=self.observed_data,\\n a=x[0],\\n b=x[1],\\n c=x[2],\\n sigma=self.sigma\\n )\\n return g\",\n \"def compute_gradient(self):\\n A = np.dot(self.X, self.w)\\n m = self.t.shape[0]\\n C = -1 * self.t * (1 / (1 + np.exp(A * self.t)))\\n return (1 / m) * np.dot(self.X.T, C)\",\n \"def reg_autograd(alpha,X,theta,y):\\n g=grad(func)\\n for i in range(0,20):\\n theta=theta- alpha * g(theta)\\n print(\\\"cost\\\", func(theta))\\n print(\\\"final theta\\\",theta)\",\n \"def grad(self, x):\\n norm_beta = np.linalg.norm(x)\\n if norm_beta > utils.TOLERANCE:\\n return (self.l / norm_beta) * x\\n else:\\n D = x.shape[0]\\n u = (self.rng(D, 1) * 2.0) - 1.0 # [-1, 1]^D\\n norm_u = np.linalg.norm(u)\\n a = self.rng() # [0, 1]\\n\\n return (self.l * (a / norm_u)) * u\",\n \"def gradcovfunc(self, theta, d):\\n sigmaf, l = theta[:2] \\n xxl = np.sum((d/l)**2, axis=1)\\n dk_dsigmaf = 2 * sigmaf * np.exp(-xxl/2.)\\n dk_dl = sigmaf**2/l * xxl * np.exp(-xxl/2.)\\n grad = np.array([dk_dsigmaf, dk_dl])\\n return grad\",\n \"def log_prior_grad(self, inputs):\",\n \"def gradient(series, **options):\\n x = series.index\\n y = series.values\\n\\n a = np.gradient(y, x, **options)\\n return series.__class__(a, series.index)\",\n \"def compute_logistic_gradient(y, tx, w):\\n return tx.T.dot(sigmoid(tx.dot(w)) - y) / len(y)\",\n \"def derivative(self, theta):\\n diag_gamma = np.dot(theta.T, self.X.T)\\n logistic_term = self.logistic_fn(diag_gamma)\\n diag_gamma = logistic_term * (1.0 - logistic_term)\\n gamma = np.diag(diag_gamma)\\n\\n # v computation\\n diags_v = 1.0 - 2*self.logistic_fn(np.dot(theta.T, self.X.T))\\n diags_v = diags_v.reshape((-1, 1))\\n diags_v = diags_v*self.X\\n assert diags_v.shape == self.X.shape #N*d shape\\n\\n XtGamma = np.dot(self.X.T, gamma) # d*N shape\\n\\n # TODO: Verifier car pas sur de mon coup ... et surtout plus long...\\n # id = np.eye(self.n_examples).reshape((self.n_examples, self.n_examples, 1))\\n # diags_v = diags_v.reshape((self.n_examples, 1, self.dim))\\n # v = id*diags_v # n*n*d tensor\\n # left = np.tensordot(XtGamma, v, axes=(1, 0)) # shape d*N*d\\n # assert left.shape == (self.dim, self.n_examples, self.dim)\\n # dg = np.tensordot(left, self.X, axes=(1, 0))\\n # dg = np.swapaxes(dg, axis1=-2, axis2=-1)\\n\\n dg = np.zeros((self.dim, self.dim, self.dim))\\n for idx, v_i_diag in enumerate(diags_v.T):\\n v_i = np.diag(v_i_diag)\\n dg_di = np.dot(np.dot(XtGamma, v_i), self.X)\\n dg[:, :, idx] = dg_di\\n return dg\",\n \"def run_gradient_descent(data,theta,alpha,num_iters):\\n population = data[:,0]\\n prices = data[:,1]\\n x = ones(shape=(len(population),2)) #add ones for theta0 \\n x[:,1] = population\\n x = transpose(x)\\n error_history = zeros(shape=(num_iters,1))\\n \\n for i in range(num_iters):\\n predictions = theta.dot(x)\\n errors_x1 = (predictions - prices) * x[0,:]\\n errors_x2 = (predictions - prices) * x[1,:]\\n theta[0][0] = theta[0][0] - alpha*(1.0/len(population))*errors_x1.sum()\\n theta[0][1] = theta[0][1] - alpha*(1.0/len(population))*errors_x2.sum()\\n error_history[i,0] = calculate_cost(theta,data)\\n \\n return theta, error_history\",\n \"def gradient(self, x):\\n return 0.0\",\n \"def gradient(poly):\\n return differential(\\n poly, chaospy.poly.collection.basis(1, 1, poly.dim, sort=\\\"GR\\\"))\",\n \"def curves_gradient(theta, m_, LAMBDA=0):\\n # X0: column of ones\\n theta0_gradient = (1 / m_) * sum((curves_hypothesis(theta, m_) - y[:m_].ravel()) * np.ones(m_))\\n # X1: data given as X\\n theta1_gradient = ((1 / m_) * sum((curves_hypothesis(theta, m_) - y[:m_].ravel()) * X[:m_].ravel())) \\\\\\n + (LAMBDA / m_) * theta[1]\\n return np.array([theta0_gradient, theta1_gradient])\",\n \"def costFunction(self,theta, X, y): \\n m = len(y)\\n h = self.sigmoid(X@theta)\\n J = 1 / m * (- y.T @ self.log(h) - (1-y).T @ self.log(1-h)) \\n # grad = 1/ m * X.T @ (h - y)\\n return J\",\n \"def gradient(self, x_in):\\r\\n\\r\\n return 1 - torch.pow(self.forward(x_in), 2)\",\n \"def compute_gradient(self): # TODO: try to change to square loss since it's hessian is easier to obtain\\n A = np.dot(self.X, self.w)\\n m = self.t.shape[0]\\n C = -1 * self.t * (1 / (1 + np.exp(A * self.t)))\\n return (1 / m) * np.dot(self.X.T, C)\",\n \"def vb_grad_natgrad(self):\\r\\n\\r\\n # Gradient for eta\\r\\n dL_dmmT_S = -0.5*self.Lambda/self.data_prop + 0.5*self.q_u_prec\\r\\n Kmmipsi1V,_ = dpotrs(self.Lm,self.psi1V,lower=1)\\r\\n dL_dm = (Kmmipsi1V - np.dot(self.Lambda,self.q_u_mean))/self.data_prop\\r\\n\\r\\n # Gradients for theta\\r\\n S = self.q_u_cov\\r\\n Si = self.q_u_prec\\r\\n m = self.q_u_mean\\r\\n dL_dSi = -mdot(S,dL_dmmT_S, S)\\r\\n\\r\\n dL_dmhSi = -2*dL_dSi\\r\\n dL_dSim = np.dot(dL_dSi,m) + np.dot(Si, dL_dm)\\r\\n\\r\\n return np.hstack((dL_dm.flatten(),dL_dmmT_S.flatten())) , np.hstack((dL_dSim.flatten(), dL_dmhSi.flatten()))\",\n \"def gradient(self):\\n functional = self\\n\\n class KLCCGradient(Operator):\\n\\n \\\"\\\"\\\"The gradient operator of this functional.\\\"\\\"\\\"\\n\\n def __init__(self):\\n \\\"\\\"\\\"Initialize a new instance.\\\"\\\"\\\"\\n super().__init__(functional.domain, functional.domain,\\n linear=False)\\n\\n def _call(self, x):\\n \\\"\\\"\\\"Apply the gradient operator to the given point.\\n\\n The gradient is not defined in points where one or more\\n components are larger than or equal to one.\\n \\\"\\\"\\\"\\n if functional.prior is None:\\n return 1.0 / (1 - x)\\n else:\\n return functional.prior / (1 - x)\\n\\n return KLCCGradient()\",\n \"def grad(self, w):\\n gradient_penalty = np.asarray(self.alpha) * w\\n # Insert 0 for bias term.\\n return np.insert(gradient_penalty, 0, 0, axis=0)\",\n \"def grad(self, w):\\n gradient_penalty = np.asarray(self.alpha) * w\\n # Insert 0 for bias term.\\n return np.insert(gradient_penalty, 0, 0, axis=0)\",\n \"def learning_by_penalized_gradient(y, tx, w, gamma, lambda_):\\n\\n #on test avec Newton\\n\\n loss,gradient,_ = penalized_logistic_regression(y,tx,w,lambda_)\\n\\n w = w - gamma*gradient\\n return loss, w,gradient\",\n \"def gradient_ascent(f, df, theta_init, step_size, max_iter):\\n\\n fs = []\\n xs = []\\n thetas = theta_init\\n for i in range(max_iter): #for each data example\\n fs.append(f(thetas))\\n\\n temp = step_size*df(thetas)\\n thetas = step_size*df(thetas) #modify that feature by using the derivative of log likelihood\\n xs.append(thetas.flatten())\\n if i % 10 == 0:\\n print(i, thetas)\\n\\n return thetas, fs, xs\",\n \"def calculate_maxL_gradient(y, tx, w):\\n sig=sigmoid(tx.dot(w))\\n grad=tx.T.dot(sig-y)\\n #divides with number of samples so that learning rate is not dependant on number of samples\\n grad=grad/len(y) \\n return grad\",\n \"def _log_prior_gradients(self):\\n if self.priors.size == 0:\\n return 0.\\n x = self.param_array\\n ret = np.zeros(x.size)\\n #compute derivate of prior density\\n [np.put(ret, ind, p.lnpdf_grad(x[ind])) for p, ind in self.priors.items()]\\n #add in jacobian derivatives if transformed\\n priored_indexes = np.hstack([i for p, i in self.priors.items()])\\n for c,j in self.constraints.items():\\n if not isinstance(c, Transformation):continue\\n for jj in j:\\n if jj in priored_indexes:\\n ret[jj] += c.log_jacobian_grad(x[jj])\\n return ret\",\n \"def grad(self, w):\\n gradient_penalty = np.asarray(self.alpha) * w\\n\\n # Insert 0 for bias term.\\n return np.insert(gradient_penalty, 0, 0, axis=0)\",\n \"def grad(theta, X, y, lambda_):\\n # ... dopolnite (naloga 1, naloga 2)\\n\\n l = []\\n for i, e in enumerate(theta):\\n l.append(1 / len(y) * sum([(h(x, theta) - yi) * x[i] for x, yi in zip(X, y)]) + 2 * lambda_ * e)\\n\\n return np.array(l)\",\n \"def lr_cost_function(theta, X, y, learning_rate):\\n m = len(y) # number of training examples\\n # You need to return the following variables correctly \\n J = 0\\n grad = np.zeros(theta.shape)\\n h = sigmoid(np.dot(X, theta))\\n\\n #Compute cost:\\n first = (1/m)*np.sum(np.dot(-y.T, np.log(h)) - np.dot((1-y).T, np.log(1-h)), axis=0)\\n second = (learning_rate/(2*m))*np.sum(theta[1:]**2)\\n J = first + second\\n \\n #Compute gradient\\n grad = np.dot((h-y).T, X)/m\\n grad = grad.T\\n grad[1:] += (learning_rate/m)*theta[1:]\\n return float(J), grad\",\n \"def gradient(self, x, i=None):\\n self.set_parameters(x=x)\\n gradient = self.gradient_loss()\\n if self.add_bias:\\n gradient[1:] += self.alpha * self.gradient_regularizer()\\n else:\\n gradient += self.alpha * self.gradient_regularizer()\\n if i is not None:\\n return gradient.ravel()[i]\\n return gradient.ravel()\",\n \"def compute_gradient_lasso(y, tx, w, lambda_):\\n e = y - tx.dot(w)\\n subgrad = lambda_ * np.sign(w)\\n\\n return -tx.T.dot(e)/len(e) + subgrad\",\n \"def compute_gradient(self, X, y, weights):\\n sigmoid = self.sigmoid(np.dot(X, weights))\\n return np.dot(X.T, y - sigmoid)\",\n \"def batch_grad_descent(X, y, alpha=0.1, num_iter=1000, check_gradient=False):\\n num_instances, num_features = X.shape[0], X.shape[1]\\n theta_hist = np.zeros((num_iter+1, num_features)) #Initialize theta_hist\\n loss_hist = np.zeros(num_iter+1) #initialize loss_hist\\n theta = np.ones(num_features) #initialize theta\\n\\n count = 0\\n while count < num_iter+1:\\n if check_gradient:\\n assert grad_checker(X,y,theta)\\n\\n grad = compute_square_loss_gradient(X,y,theta)\\n theta -= alpha*grad\\n theta_hist[count] = theta\\n loss_hist[count] = compute_square_loss(X,y,theta)\\n count += 1\\n \\n return theta_hist, loss_hist\",\n \"def check_gradient(self, x, y):\\n x = x.transpose()\\n y = y.transpose()\\n layers_copy = deepcopy(self.layers)\\n epsilon = 10 ** -4\\n a, layer = self.forward_propagation(x)\\n delta = self.calculate_delta(a, y, layer)\\n self.backpropagation(delta=delta, theta=layer.theta)\\n previous_layer_output = x\\n for layer in self.layers:\\n theta_copy = deepcopy(layer.theta)\\n real_theta_size = theta_copy.shape\\n delta = layer.delta\\n dc_dtheta = np.outer(previous_layer_output, delta).transpose()\\n previous_layer_output = layer.a\\n R, C = theta_copy.shape\\n for i in range(R):\\n for j in range(C):\\n theta_plus = deepcopy(theta_copy)\\n theta_plus[i, j] += epsilon\\n layer.theta = theta_plus\\n a_plus, l_plus = self.forward_propagation(x)\\n err_plus = self.calculate_loss(a_plus, y)\\n theta_minus = deepcopy(theta_copy)\\n theta_minus[i, j] -= epsilon\\n layer.theta = theta_minus\\n a_minus, l_minus = self.forward_propagation(x)\\n err_minus = self.calculate_loss(a_minus, y)\\n limit = (err_plus - err_minus)/(2*epsilon)\\n grad_diff = abs(dc_dtheta[i,j] - limit)\\n assert grad_diff < 10 ** -6, f\\\"Diff {grad_diff} is too big.\\\"\\n layer.theta = theta_copy\",\n \"def _compute_func_grad(self, w):\\n W = w.reshape((self.X.shape[1], self.Y.shape[1]))\\n self.nll_, self.grad_ = calculate_gradient(self.X, self.Y, W, self.prior, self.weighted,0)\",\n \"def gradient(data_x, data_y, parameters):\\n return data_x.T @ (data_x @ parameters - data_y) / data_x.shape[0]\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8320425","0.8320425","0.80552995","0.794873","0.7868711","0.7830685","0.78282064","0.77438694","0.77200764","0.77105415","0.76834327","0.7621433","0.7562439","0.72585183","0.7218782","0.7218774","0.721298","0.7015742","0.7006995","0.69971156","0.6987514","0.69131017","0.69129544","0.68834984","0.68834686","0.68663037","0.68401706","0.6830559","0.6802646","0.6792539","0.6765618","0.67565787","0.67539907","0.6721832","0.67160386","0.6714549","0.6703719","0.66849947","0.6667146","0.6664239","0.6656793","0.6634402","0.66270006","0.6620344","0.66145825","0.6607552","0.6605948","0.6588644","0.6581453","0.65814304","0.6569963","0.65691054","0.6555958","0.65261567","0.6510347","0.65001076","0.6494862","0.64866775","0.6464623","0.64565086","0.64560163","0.64458627","0.6425783","0.6425727","0.6412266","0.6389222","0.6378101","0.63779145","0.6373901","0.6371549","0.63605356","0.63578737","0.63553464","0.6347787","0.6347673","0.63456696","0.6327963","0.6307092","0.63065755","0.6306372","0.62852186","0.628112","0.6278384","0.6270677","0.62701714","0.62701714","0.62589717","0.6258369","0.6256502","0.6252224","0.6249683","0.62469107","0.6233294","0.6229668","0.62279737","0.61870456","0.6186673","0.6184583","0.61812323","0.6172965"],"string":"[\n \"0.8320425\",\n \"0.8320425\",\n \"0.80552995\",\n \"0.794873\",\n \"0.7868711\",\n \"0.7830685\",\n \"0.78282064\",\n \"0.77438694\",\n \"0.77200764\",\n \"0.77105415\",\n \"0.76834327\",\n \"0.7621433\",\n \"0.7562439\",\n \"0.72585183\",\n \"0.7218782\",\n \"0.7218774\",\n \"0.721298\",\n \"0.7015742\",\n \"0.7006995\",\n \"0.69971156\",\n \"0.6987514\",\n \"0.69131017\",\n \"0.69129544\",\n \"0.68834984\",\n \"0.68834686\",\n \"0.68663037\",\n \"0.68401706\",\n \"0.6830559\",\n \"0.6802646\",\n \"0.6792539\",\n \"0.6765618\",\n \"0.67565787\",\n \"0.67539907\",\n \"0.6721832\",\n \"0.67160386\",\n \"0.6714549\",\n \"0.6703719\",\n \"0.66849947\",\n \"0.6667146\",\n \"0.6664239\",\n \"0.6656793\",\n \"0.6634402\",\n \"0.66270006\",\n \"0.6620344\",\n \"0.66145825\",\n \"0.6607552\",\n \"0.6605948\",\n \"0.6588644\",\n \"0.6581453\",\n \"0.65814304\",\n \"0.6569963\",\n \"0.65691054\",\n \"0.6555958\",\n \"0.65261567\",\n \"0.6510347\",\n \"0.65001076\",\n \"0.6494862\",\n \"0.64866775\",\n \"0.6464623\",\n \"0.64565086\",\n \"0.64560163\",\n \"0.64458627\",\n \"0.6425783\",\n \"0.6425727\",\n \"0.6412266\",\n \"0.6389222\",\n \"0.6378101\",\n \"0.63779145\",\n \"0.6373901\",\n \"0.6371549\",\n \"0.63605356\",\n \"0.63578737\",\n \"0.63553464\",\n \"0.6347787\",\n \"0.6347673\",\n \"0.63456696\",\n \"0.6327963\",\n \"0.6307092\",\n \"0.63065755\",\n \"0.6306372\",\n \"0.62852186\",\n \"0.628112\",\n \"0.6278384\",\n \"0.6270677\",\n \"0.62701714\",\n \"0.62701714\",\n \"0.62589717\",\n \"0.6258369\",\n \"0.6256502\",\n \"0.6252224\",\n \"0.6249683\",\n \"0.62469107\",\n \"0.6233294\",\n \"0.6229668\",\n \"0.62279737\",\n \"0.61870456\",\n \"0.6186673\",\n \"0.6184583\",\n \"0.61812323\",\n \"0.6172965\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8256829"},"document_rank":{"kind":"string","value":"2"}}},{"rowIdx":14399,"cells":{"query":{"kind":"string","value":"returns the best possible move"},"document":{"kind":"string","value":"def alpha_beta_search(self, maxDepth, board, player, best_move):\r\n \"\"\"j refers to the list where j[0] = score and j[1] = move\"\"\"\r\n start = time()\r\n #playerPiece = core.BLACK\r\n #if player == \"BLACK\":\r\n #print(\"hi\")\r\n #playerPiece = core.WHITE\r\n self.max_value(board, -1*float(\"inf\"), float(\"inf\"), player, 0, maxDepth, best_move)\r\n endTime = time()\r\n #print(endTime - start)\r\n #sort by square weights\r\n #how many flips each move will make\r\n #change the scoring matrix\r\n #dictionary\r\n #time limits\r\n #return j[1]\r"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def find_best_move(state: GameState) -> None:","def best_move(self):\n if self._move is not None:\n return self._move\n else:\n return self.pass_move","def get_optimal_move(self):\n # create the root state\n root = State(self.current_board, True, self.__machine_token, self.__human_token)\n # alpha-beta-pruning algorithm\n best_move = max_value_a_b(root, depth(root), -1000, 1000)\n # obtain the direct children.\n direct_children = get_direct_children(root, all_states_generated)\n # obtain the coordinates of the movement.\n for direct_child in direct_children:\n if direct_child.value == best_move:\n return get_coordinates(root, direct_child)","def get_best_move(self):\n moves1 = self.get_next_moves1() # moves1 represents all legal moves.\n moves2 = self.get_next_moves2() # moves2 represents the moves that allow the AI to score a box.\n moves3 = self.get_next_moves3() # moves3 represents the moves that will allow the player to score a box.\n\n\n if len(moves1) == 0: # the siuation that there is no legal move\n return self\n if len(moves2) != 0:\n return moves2[len(moves2) // 2] # the siuation that there is(are) move(s) to score\n\n elif len(moves3) != 0:\n return moves3[len(moves3) // 2] # the siuation that there is(are) moves(s) to allow the player to score\n\n else:\n return moves1[len(moves1) // 2] # if there is no better moves, the AI will play sequentially, starting from the top left.","def getBestMove(listOfPossibleMoves, gamestate):\n\n if listOfPossibleMoves:\n maximum = -math.inf\n posisi = Posisi.Posisi(-999, -999)\n\n # Kalau currentPlayer adalah player 1, titik acuan adalah (boardSize-1, boardSize-1)\n if (gamestate.currentPlayer.noPlayer == 1):\n M = Posisi.Posisi(gamestate.board.size - 1, gamestate.board.size - 1)\n\n # Kalau currentPlayer adalah player 2, titik acuan adalah (0, 0)\n else:\n M = Posisi.Posisi(0, 0)\n\n # Mengambil aksi tunggal terbaik berdasarkan heuristic function\n for i in listOfPossibleMoves:\n value = -i.euclidean(M)\n if value > maximum:\n posisi = i\n maximum = value\n return posisi","def __get_next_greedy_move(self, game_state): \n best_move = None\n best_score = None\n for free_seat in self.__get_free_seats(game_state):\n next_game_state_score = self.__get_score(game_state, free_seat)\n if best_score is None:\n best_score = next_game_state_score\n best_move = free_seat\n continue\n if next_game_state_score > best_score:\n best_score = next_game_state_score\n best_move = free_seat\n return best_move","def compute_best_move(board):\n best_node = alphabeta([0, []], depth_tree, float(\"-inf\"), float(\"inf\"), True, board)\n\n if (best_node[0] == float(\"inf\")) and (len(best_node[1]) == 1):\n return best_node[1][0], True\n return best_node[1][0], False","def best_move(self) -> tuple:\n if self.root_state.winner != GameMeta.PLAYERS['none']:\n return GameMeta.GAME_OVER\n\n # choose the move of the most simulated node breaking ties randomly\n max_value = max(self.root.children.values(), key=lambda n: n.N).N\n max_nodes = [n for n in self.root.children.values() if n.N == max_value]\n bestchild = choice(max_nodes)\n return bestchild.move","def find_best_move(state) -> None:\n C: float = 2 ** 0.5 # Start with sqrt(2)\n idx: int = -1\n win_ratio: float = 0\n\n # First create the root node for the game\n root = State(None, None, state)\n root = expand(root)\n root = run_each_move(root, state)\n print(\"New Move UCBs: \")\n for m in range(len(root.move_ucbs)):\n print(\" \", root.move_ucbs[m])\n \n #while True:\n for _ in range(10000):\n _, win_ratio = MCTS(root, state, True, win_ratio) \n #print(\"\\n\\n\\nRoot Attempts: \", root.attempts)\n #print(\"Back in main\\n\\n\\n\")\n \n # Pick one of the highest ranked by UCB's","def get_move(self, game, time_left):\n self.time_left = time_left\n self.best_move = (-1, -1)\n\n try:\n i = 1\n while True:\n self.best_move = self.alphabeta(game, i)\n i = i + 1\n return self.best_move\n except SearchTimeout:\n return self.best_move\n\n return self.best_move","def get_best_moves(self):\n return self.best_moves","def move(self, board):\r\n self.start_time = time.time()\r\n disk_total = self.get_disk_count(self.my_color, board) + self.get_disk_count(self.opponent_color, board)\r\n\r\n if disk_total < 15:\r\n # In early-game, we can allow a deeper minimax search since there's not too many possible moves.\r\n self.minimax_max_depth = 7\r\n\r\n elif disk_total < 45:\r\n # In mid-game, minimax tree has the most branches. Therefore, we must give it space to breathe.\r\n self.minimax_max_depth = 5\r\n else:\r\n # In the very end-game, minimax tree has the least branches, so we can allow a full search.\r\n self.minimax_max_depth = 8\r\n\r\n possible_moves = self.find_possible_moves(board, self.my_color)\r\n\r\n # If there's only one move available, return it\r\n if len(possible_moves) == 1:\r\n return possible_moves[0]\r\n\r\n # If we can take a corner, take it and don't consider any other options.\r\n # This rarely backfires and allows to save a tiny bit of time\r\n corners = [(0,0), (0,7), (7,0), (7,7)]\r\n for corner in corners:\r\n if corner in possible_moves:\r\n return corner\r\n\r\n # Grow a minimax tree to find the best available move\r\n alpha_init = -10000000\r\n beta_init = 10000000\r\n\r\n available_moves = self.minimax(board, 0, self.my_color, alpha_init, beta_init)\r\n print(available_moves)\r\n if available_moves != 0:\r\n best_value = max(available_moves.values())\r\n for move in available_moves:\r\n if available_moves[move] == best_value:\r\n return move\r\n\r\n return None","def next_move(self, board):\n \n return self.best_move(self.score_columns(board))","def get_move(self, game, time_left):\n self.time_left = time_left\n \n max_depth = game.height * game.width\n \n \n # FIRST, check initial conditions wrt legal_moves\n legal_moves = game.get_legal_moves()\n # If there are no legal_moves return no move\n if len(legal_moves) == 0:\n return (-1,-1)\n # If there's only one legal_move return it, the only choice\n elif len(legal_moves) == 1:\n return legal_moves[0]\n # Otherwise, initialize best_choice at first legal_move\n else:\n best_move = legal_moves[0]\n try:\n for node_number in range(1, max_depth + 1):\n # This exception handing returns the best_move found\n # thus far in the event of a timeout\n \n best_move = self.alphabeta(game, node_number)\n return best_move\n except SearchTimeout:\n pass\n \n # Return the best_move found thus far (or ultimately in the event\n # of exhaustive search completion or timeout)\n return best_move","def __find_best(self):\n # First look for offensive moves\n for i in range(0, 3):\n col = self.__get_col(i)\n if len(col.get('empty')) == 1:\n if col.get(self.opponent_char) == 2:\n return col.get('empty')[0]\n for i in range(0, 3):\n row = self.__get_row(i)\n if len(row.get('empty')) == 1:\n if row.get(self.opponent_char) == 2:\n return row.get('empty')[0]\n for i in range(0, 2):\n diag = self.__get_diag(i)\n if len(diag.get('empty')) == 1:\n if diag.get(self.opponent_char) == 2:\n return diag.get('empty')[0]\n\n # Then check again looking for defensive moves\n for i in range(0, 3):\n col = self.__get_col(i)\n if len(col.get('empty')) == 1:\n if col.get(self.player_char) == 2:\n return col.get('empty')[0]\n for i in range(0, 3):\n row = self.__get_row(i)\n if len(row.get('empty')) == 1:\n if row.get(self.player_char) == 2:\n return row.get('empty')[0]\n for i in range(0, 2):\n diag = self.__get_diag(i)\n if len(diag.get('empty')) == 1:\n if diag.get(self.player_char) == 2:\n return diag.get('empty')[0]\n\n ##### CLEAN THIS METHOD UP LATER #####\n return None","def minimax(board):\n if (terminal(board)):\n return None\n if (player(board) == X):\n best_so_far = -2\n else:\n best_so_far = 2\n best_move = ()\n for move in actions(board):\n new_board = [row[:] for row in board]\n res = recursion(result(new_board, move))\n if (player(board) == X):\n if (res > best_so_far):\n best_so_far = res\n best_move = move\n if (res == 1):\n break\n else:\n if (res < best_so_far):\n best_so_far = res\n best_move = move\n if (res == -1):\n break\n \n return best_move","def search(self, is_max, possible_moves, state, depth, alpha, beta):\n temp_state = state.deepcopy()\n best_move = None\n best_move_val = float('-inf') if is_max else float('inf')\n \n for move in possible_moves:\n for to in move['to']:\n \n if time() > self.thinking_time:\n return best_move, best_move_val\n \n temp_state.board.move_pawn(move['from'], to)\n temp_state.next_turn()\n _, val = self.minimax(temp_state, not(is_max), depth+1, alpha, beta)\n \n temp_state.board.move_pawn(to, move['from'])\n temp_state.undo_turn()\n \n if is_max and val > best_move_val:\n alpha = max(val, alpha)\n best_move_val = val\n best_move = (move['from'], to)\n \n if not(is_max) and val < best_move_val:\n beta = min(val, beta)\n best_move_val = val\n best_move = (move['from'], to)\n \n if beta <= alpha: #pruning\n return best_move, best_move_val\n \n return best_move, best_move_val","def get_best_move(board, scores):\n if board.check_win()==None:\n maximum=-999\n ret_location=(0,0)\n empty=board.get_empty_squares()\n #print empty\n for position in empty:\n if scores[position[0]][position[1]]>maximum:\n maximum=scores[position[0]][position[1]]\n #print max\n ret_location=(position[0],position[1])\n return ret_location\n else:\n return None","def get_move(self, board, possible_moves):\n next_move = None\n max_score = -float('Inf')\n self.start_time = datetime.now()\n for depth in range(2,3): # iterative deepening\n try:\n for move in possible_moves:\n board_copy = deepcopy(board)\n self.man.play_move(board_copy, move, self.color)\n score = self.minimaxm(depth, board, False)\n if score > max_score:\n max_score = score\n next_move = move\n\n except TimeoutError:\n print(\"ran out of time\")\n break\n return next_move","def get_move(self, game, time_left):\n self.time_left = time_left\n # check if new game, reset if it is\n self.check_reset(game)\n best_move = (-1, -1)\n depth = 1\n\n try:\n while True:\n # each completed iteration will be more accurate than previous, so update best move with each iteration's\n # root node (max) result\n best_move = self.alphabeta(game=game, depth=depth)\n\n # return best move if no result\n if best_move == (-1, -1):\n # DEBUG_PRINT\n # print('D={} MOVING TO=> {} from TERM\\n'.format(depth, best_move))\n return best_move\n\n # global stats\n # stats['max_search_depth'] = max(depth, stats['max_search_depth'])\n depth += 1\n\n\n except SearchTimeout:\n # DEBUG_PRINT\n # print('D={} MOVING TO=> {}\\n\\n\\n'.format(stats['max_search_depth'], best_move))\n pass\n\n return best_move","def get_move(self, game, legal_moves, time_left):\n\n self.time_left = time_left\n\n if not legal_moves:\n return (-1, -1)\n\n move = None\n try:\n algorithm_name = getattr(self, self.method) # Method selected to get move (minimax, alphabeta)\n if self.iterative:\n depth = 1 # Depth used for iterative deepening\n while True:\n _, move = algorithm_name(game, depth)\n depth += 1\n else:\n _, move = algorithm_name(game, self.search_depth)\n except Timeout:\n return move # A timeout has occurred, return best move so far\n return move","def getbestMove(board, player, computer):\n bestScore = -100\n bestMove = -1\n\n for i in range(1, len(board) + 1):\n if board[i] == ' ':\n # make a move for the computer\n board[i] = computer\n\n # find value for potential move\n curScore = valueOfBoard(board, False, 0, player, computer)\n # clear move\n board[i] = ' '\n\n if curScore > bestScore:\n bestMove = i\n bestScore = curScore\n\n return bestMove","def get_move(self, game, time_left):\n self.time_left = time_left\n\n # Initialize the best move so that this function returns something\n # in case the search fails due to timeout\n best_move = (-1, -1)\n\n # Opening Book\n # Check if my agent is first to move\n # If yes, use opening book\n if (game._board_state[-1] == None):\n if (not game.get_legal_moves()):\n return best_move\n else:\n best_move = (4,4)\n return best_move\n\n try:\n # The try/except block will automatically catch the exception\n # raised when the timer is about to expire.\n\n # Iterative Deepning, stop when timeout\n depth = 0\n while (True):\n depth += 1\n best_move = self.alphabeta(game, depth)\n\n except SearchTimeout:\n pass # Handle any actions required after timeout as needed\n\n # Return the best move from the last completed search iteration\n return best_move","def choose_move(self):\n return 0","def get_best_move(position, max_its=None, return_positions=False):\n max_its = max_its or MAX_ITS\n\n # init the positions list\n positions = [position]\n original_to_move = position.to_move\n moves_made = len(position.moves)\n to_move = original_to_move\n\n i = 1\n while True:\n LOG.info(f'getting positions for it {i}, with {original_to_move} to move')\n positions = extend_positions(positions, to_move)\n\n # filter the positions\n positions = filter_positions(positions, i, to_move)\n to_move = invert_color(to_move)\n\n if i == max_its:\n break\n\n i += 1\n\n\n# if best_position.mate and best_position.checked:\n# # if the best move is mate then its over\n# print(f'It is mate, returning None best move for {to_move}')\n# best_move = None\n# else:\n# best_move = best_position.moves[0]\n\n if not positions:\n return 'checkmate', None\n\n # need to re-sort, as will be ordered by the last to_move\n positions.sort(key=lambda x: x.score, reverse=original_to_move=='white')\n\n # because of zero indexing, the first NEXT move is at moves_made in list\n best_move = positions[0].moves[moves_made]\n\n if return_positions:\n return best_move, positions\n\n return best_move","def search_best_next_move(self, model, initial_tree_node):\n\n # EDIT THIS METHOD TO RETURN BEST NEXT POSSIBLE MODE FROM MINIMAX MODEL ###\n\n # NOTE: Don't forget to initialize the children of the current node\n # with its compute_and_get_children() method!\n\n alpha = float('-inf')\n beta = float('inf')\n depth = 4\n player = 0\n ##send root node and these parameters to minimax\n start_time = time()\n move = self.alpha_beta(initial_tree_node, player, alpha, beta, start_time)\n #random_move = random.randrange(5)\n return ACTION_TO_STR[move[1]]","def minimax(board):\n if terminal(board):\n return None\n \n if player(board) == O:\n v = math.inf\n for action in actions(board):\n move = max_value(result(board, action))\n if move < v:\n v = move\n bestMove = action\n\n else:\n v = -math.inf\n for action in actions(board):\n move = min_value(result(board, action))\n if move > v:\n v = move\n bestMove = action\n \n return bestMove","def get_move(self, game, time_left):\n self.time_left = time_left\n\n # Initialize the best move so that this function returns something\n # in case the search fails due to timeout\n\n best_move = (-1, -1)\n\n while self.time_left() > self.TIMER_THRESHOLD:\n self.update(game)\n\n if not self.is_terminal(game):\n moves = game.get_legal_moves()\n scores = [(self._plays[game.forecast_move(m)], m) for m in moves]\n _, best_move = max(scores, key=lambda s: s[0])\n\n return best_move","def find_best_move(board, score):\n\n global UP, DOWN, LEFT, RIGHT\n global move_args\n\n max_depth = 1\n if score <= 10000:\n max_depth += 0\n elif score <= 20000:\n max_depth += 1\n else:\n max_depth += 2\n\n pool = mp.Pool(mp.cpu_count())\n\n result = [pool.apply(score_toplevel_move, args=(i, board, max_depth)) for i in range(len(move_args))]\n best_move = result.index(max(result))\n\n pool.close()\n\n for m in move_args:\n print(\"move: %d score: %.4f\" % (m, result[m]))\n\n return best_move","def minimax(board):\n current_player = player(board)\n if(terminal(board)):\n return None\n\n if (board == initial_state()):\n x = random.choice([0, 2])\n y = random.choice([0, 2])\n return(x, y)\n\n if(current_player == X):\n v = -math.inf\n for action in actions(board):\n mini = min_value(result(board, action))\n if mini > v:\n v = mini\n best_move = action\n else:\n v = math.inf\n for action in actions(board):\n maxi = max_value(result(board, action))\n if maxi < v:\n v = maxi\n best_move = action\n return best_move","def bestMove(self, depth, state, curr_player):\n\n # determine opponent's color\n if curr_player == self.colors[0]:\n opp_player = self.colors[1]\n else:\n opp_player = self.colors[0]\n\n # enumerate all legal moves\n legal_moves = {} # will map legal move states to their alpha values\n for col in range(COLUMN):\n # if column i is a legal move...\n if self.isLegalMove(col, state):\n # make the move in column 'col' for curr_player\n temp = self.makeMove(state, col, curr_player)\n legal_moves[col] = -self.search(depth - 1, temp, opp_player)\n\n best_alpha = -float(\"inf\")\n best_move = None\n moves = legal_moves.items()\n random.shuffle(list(moves))\n for move, alpha in moves:\n if alpha >= best_alpha:\n best_alpha = alpha\n best_move = move\n\n return best_move, best_alpha","def move(self, gstate: gamestate.Gamestate) -> util.Move:\n moves = gstate.legal_moves_vector(gstate.agents[self.id])\n scores = {move: self.evaluate(gstate.copy, move) for move in moves}\n max_score = max(scores.values())\n max_moves = [move for move in moves if scores[move] == max_score]\n return random.choice(max_moves)","def minimax(self, game, depth):\n self.__check_time()\n\n legal_moves = game.get_legal_moves()\n\n if len(legal_moves) == 0:\n return (-1, -1)\n else:\n # ensure we return something\n best_move = legal_moves[0]\n best_move_score = float('-inf')\n try:\n for move in legal_moves:\n # search with depth limit decreasing as each ply is visited\n move_score = self.__min_value(game.forecast_move(move), depth - 1)\n if move_score > best_move_score:\n best_move_score = move_score\n best_move = move\n\n except SearchTimeout:\n pass\n\n return best_move","def minimax(self, game, depth):\n if self.time_left() < self.TIMER_THRESHOLD:\n raise SearchTimeout()\n\n moves = game.get_legal_moves(game.active_player)\n if not moves:\n return -1, -1\n\n scores = [(self.minimax_value(game.forecast_move(m), depth - 1), m) for m in moves]\n _, move = max(scores, key=lambda s: s[0]) # Only order on score, not move tuple.\n\n return move","def determineNextMove(playerLocation, opponentLocation, coins):\n global packages, route_table, best_path, best_weight, route\n if len(best_path) == 0:\n current_package = packages.pop(0)\n exhaustive(current_package, playerLocation, [], 0, (route_table,dists))\n api.debug(best_path)\n return u.direction(playerLocation, best_path.pop(0))","def bestMove(self, depth, state, curr_player):\n \n # determine opponent's color\n if curr_player == self.colors[0]:\n opp_player = self.colors[1]\n else:\n opp_player = self.colors[0]\n \n # enumerate all legal moves\n legal_moves = {} # will map legal move states to their alpha values\n for col in range(7):\n # if column i is a legal move...\n if self.isLegalMove(col, state):\n # make the move in column 'col' for curr_player\n temp = self.makeMove(state, col, curr_player)\n legal_moves[col] = -self.search(depth-1, temp, opp_player)\n \n best_alpha = -99999999\n best_move = None\n moves = legal_moves.items()\n random.shuffle(list(moves))\n for move, alpha in moves:\n if alpha >= best_alpha:\n best_alpha = alpha\n best_move = move\n \n return best_move, best_alpha","def minimax(board):\n #This function will return the best move. \n #If Ai is playing as X, I can reduce the processing time by creating a random first move. \n if (board == initial_state()):\n coord1 = randint(0,2)\n coord2 = randint(0,2)\n return ((coord1,coord2))\n #first I determine which player's turn it is\n player_to_move = player(board)\n best_action = None\n #If I am X\n if(player_to_move == \"X\"):\n current_max = float('-inf')\n #for every possible action I have right now, I'll call my \"future\" Min_Value since I will asume what will happen if I take this move.\n for action in actions(board):\n #peak on the future if I take that move\n curr_score = Min_Value(result(board,action))\n #if my future is favorable, I will store it as my current best option.\n if curr_score>= current_max:\n current_max = curr_score\n best_action = action\n else:\n #If I am O, I do something similar. \n current_max = float('inf')\n #for every action I peak on the future for favorable results\n for action in actions(board):\n #this time, however, it would be X's turn so I need to start with Max_Value\n curr_score = Max_Value(result(board,action))\n #if my future is favorable, I store it\n if curr_score<= current_max:\n current_max = curr_score\n best_action = action\n #I return the best move.\n return best_action","def find_best_move(board):\n new_board = board.get_board()\n\n # X | X | X <-- Check for win on this row\n # ---------\n # 3 | 4 | 5\n # ---------\n # 6 | 7 | 9\n if new_board[0] == new_board[1] and new_board[2] == \"2\":\n return 2\n elif new_board[0] == new_board[2] and new_board[1] == \"1\":\n return 1\n elif new_board[1] == new_board[2] and new_board[0] == \"0\":\n return 0\n\n # 0 | 1 | 2\n # ---------\n # X | X | X <-- Check for win on this row\n # ---------\n # 6 | 7 | 9\n elif new_board[3] == new_board[4] and new_board[5] == \"5\":\n return 5\n elif new_board[3] == new_board[5] and new_board[4] == \"4\":\n return 4\n elif new_board[4] == new_board[5] and new_board[3] == \"3\":\n return 3\n\n # 0 | 1 | 2\n # ---------\n # 3 | 4 | 5\n # ---------\n # X | X | X <-- Check for win on this row\n elif new_board[6] == new_board[7] and new_board[8] == \"8\":\n return 8\n elif new_board[6] == new_board[8] and new_board[7] == \"7\":\n return 7\n elif new_board[7] == new_board[8] and new_board[6] == \"6\":\n return 6\n\n # X | 1 | 2 Check for win on column one\n # ---------\n # X | 4 | 5\n # ---------\n # X | 7 | 9\n elif new_board[0] == new_board[3] and new_board[6] == \"6\":\n return 6\n elif new_board[0] == new_board[6] and new_board[3] == \"3\":\n return 3\n elif new_board[6] == new_board[3] and new_board[0] == \"0\":\n return 0\n\n # 0 | X | 2 Checks for win on column two\n # ---------\n # 3 | X | 5\n # ---------\n # 6 | X | 9\n elif new_board[1] == new_board[4] and new_board[7] == \"7\":\n return 7\n elif new_board[1] == new_board[7] and new_board[4] == \"4\":\n return 4\n elif new_board[7] == new_board[4] and new_board[0] == \"0\":\n return 0\n\n # 0 | 1 | X\n # ---------\n # 3 | 4 | X\n # ---------\n # 6 | 7 | X\n elif new_board[2] == new_board[5] and new_board[8] == \"8\":\n return 8\n elif new_board[2] == new_board[8] and new_board[5] == \"5\":\n return 5\n elif new_board[8] == new_board[5] and new_board[2] == \"2\":\n return 2\n\n # X | 1 | 2\n # ---------\n # 3 | X | 5\n # ---------\n # 6 | 7 | X\n elif new_board[0] == new_board[4] and new_board[8] == \"8\":\n return 8\n elif new_board[0] == new_board[8] and new_board[4] == \"4\":\n return 4\n elif new_board[8] == new_board[4] and new_board[0] == \"0\":\n return 0\n\n # 0 | 1 | X\n # ---------\n # 3 | X | 5\n # ---------\n # X | 7 | 9\n elif new_board[2] == new_board[4] and new_board[6] == \"6\":\n return 6\n elif new_board[2] == new_board[6] and new_board[4] == \"4\":\n return 4\n elif new_board[6] == new_board[4] and new_board[2] == \"2\":\n return 2\n\n # If corners are empty, play there\n elif new_board[0] == \"0\" or new_board[2] == \"2\" or new_board[6] == \"6\" or new_board[8] == \"8\":\n try_spot = 0\n while True:\n if new_board[try_spot] != \"X\" and new_board[try_spot] != \"O\":\n return try_spot\n else:\n try_spot = try_spot + 2\n\n # If middle is empty, play there\n elif new_board[4] == \"4\":\n return 4\n\n # Finally if edges are empty try there\n elif new_board[1] == \"1\" or new_board[3] == \"3\" or new_board[5] == \"5\" or new_board[7] == \"7\":\n try_spot = 1\n while True:\n if new_board[try_spot] != \"X\" and new_board[try_spot] != \"O\":\n return try_spot\n else:\n try_spot = try_spot + 2","def get_move(self, game, time_left):\n self.time_left = time_left\n\n # Initialize the best move so that this function returns something\n # in case the search fails due to timeout\n best_move = (-1, -1)\n\n if self._iterative:\n depth = 0\n try:\n while depth < len(game.get_blank_spaces()):\n depth += 1\n best_move = self.alphabeta(game, depth)\n except SearchTimeout:\n if depth == 1:\n print(\"Warning: An iterative AlphaBetaPlayer timed out on \"\n \"the first level. Match time_limit too short?\")\n if depth >= 1:\n self._depths.append(depth)\n else:\n try:\n best_move = self.alphabeta(game, self.search_depth)\n except SearchTimeout:\n print(\"Warning: A fixed-depth AlphaBetaPlayer timed out. \"\n \"Match time_limit too short or search_depth too high.\")\n\n return best_move","def get_move(self, board_state, letter):\n board_hash = hash_value(board_state)\n qvals = self.get_q(board_hash)\n freeMoves = TicTacToeGame.getFreePositions(board_state) \n \n while True:\n bestMove = np.argmax(qvals) # type: int\n if bestMove in freeMoves:\n self.q[board_hash] = qvals\n self.move_history.append((board_hash, bestMove))\n return bestMove, qvals\n else:\n qvals[bestMove] = -1.0","def get_move(self):\n if self._difficulty == 0:\n return self._get_easy_move()\n else:\n # Different stategies/difficulties can be attached here\n return","def next_move(self):\n\n # Calculate all paths to destination from current location and time.\n solution = self.calculate_best_solution((None, None), self.currentTurn, [self.character.path[-1]],\n self.character.spent)\n\n # Add travel weight to spent.\n if solution[1] is not None and solution[1][0] != solution[1][1]:\n self.character.spent += self.pekingMap.get_vertex(solution[1][0]).weight(solution[1][1])\n\n # Return next point in shortest path to location.\n if solution[1] is not None:\n return solution[1][1]\n\n return None","def get_move(self, game, time_left):\n self.time_left = time_left\n\n # Initialize the best move so that this function returns something\n # in case the search fails due to timeout\n if not game.get_legal_moves():\n return (-1,-1)\n best_move = game.get_legal_moves()[0]\n search_depth = 1\n\t\n try:\n while(self.time_left()>self.TIMER_THRESHOLD):\n last_move = best_move\n best_move = self.alphabeta(game,search_depth)\n search_depth += 1\n\n except SearchTimeout:\n best_move = last_move\n return best_move\n\t\n # Return the best move from the last completed search iteration\n return best_move","def best_move(self, game:'Game', real_game=False, max_iters=900, # noqa: E0602, F821\n ai_move=True, verbose=False) -> str:\n best_move = '00000' # Null move\n if real_game:\n policy = self.predict_policy(game)\n best_move = game.get_legal_moves()[np.argmax(policy)]\n else:\n if game.get_result() is None:\n current_tree = mctree.SelfPlayTree(\n game,\n threads=self.num_threads)\n best_move = current_tree.search_move(self, max_iters=max_iters,\n verbose=verbose,\n ai_move=ai_move)\n\n return best_move","def bestMove(self, g, p): # Super Minimax specially adapted for morpions\n choices = self.getChoices(g)\n if len(choices) == 1:\n w = self.outcome(self.choose(g, choices[0], p))\n return (choices[0], w)\n else:\n moves = []\n for choice in choices:\n ng = self.choose(g, choice, p)\n w = self.outcome(ng)\n if w == p:\n # We already won, so it's needless to compute further\n return (choice, w)\n moves.append(self.bestMove(ng, -p))\n # We use the fact that p equals 1 or -1\n m = max(moves, key=lambda x: p * x[1])\n return [choices[moves.index(m)], m[1]]","def best_move(self, depth, board, current_player, opposite_player):\n legal_moves = {}\n for column in range(7):\n if board.get_hole(column, 0) is 0:\n temp = deepcopy(board)\n temp.set_column(column, current_player)\n legal_moves[column] = -self.search(depth-1, temp, opposite_player, current_player)\n \n best_alpha = -99999999\n best_move = None\n moves = legal_moves.items()\n random.shuffle(list(moves))\n for move, alpha in moves:\n if alpha > best_alpha:\n best_alpha = alpha\n best_move = [move]\n elif alpha == best_alpha and best_alpha is not -99999999:\n if (best_move is not None):\n best_move.append(move)\n \n return best_move, best_alpha","def get_move(self, board, possible_moves, player_1_or_2):\n\n # Given a Tic-Tac-Toe 3x3 board position where 1 => current player's square,\n # -1 => opponent's square, 0 => blank square,\n # this will return the current player's best move [as the x and y indexes into \n # the board array.]\n # The second input parameter, player_1_or_2, is 1 or -1 to indicate which player's\n # move it is. \n \n print('RL ~ Current player 1 or 2 (= -1):', player_1_or_2)\n \n print('RL ~ Current board: ')\n print(board)\n \n print('RL ~ possible_moves:', possible_moves)\n\n next_move = () \n\n # This will be the best move i.e. the move with the current\n # value of highest winning probability except when it is making exploratory\n # (as opposed to greedy) moves.\n\n next_move = self.board_position_states.get_next_move(board, possible_moves, self.current_player)\n\n next_move_location_tuple = possible_moves[next_move]\n board[next_move_location_tuple] = self.current_player\n\n self.list_board_positions_moved_to.append(board.copy()) # This board that we are\n # appending here could be changed by the next line of code, for example.\n # Hence we need to make a copy\n\n board[next_move_location_tuple] = 0 # undo the move in case it affects the calling method.\n\n return next_move","def minimax(self, board: str):\n \"\"\" Your Code Here \"\"\"\n\n # ----------------------------BEGIN CODE----------------------------\n # disclaimer: I did only minimax (as said on the homework description https://drive.google.com/file/d/1JXBi_5JB8fwTWX34j0ZwN5YwjoIexh-Z/view)\n # my minimax does NOT try to prolong the game. It always goes for the best move!\n\n # initializing default values\n moveToMake = validMoves(board)[0]\n bestValue = -100\n\n # this will find the best move to go to, since value only returns the best score\n # util's setMove creates a copy, so we can use that as the successor\n for validmove in validMoves(board):\n currentValue = self.value(\n setMove(board, validmove, whoseMove(board)), 0)\n if currentValue > bestValue:\n bestValue = currentValue\n moveToMake = validmove\n\n # currently the val and game_length tuple is arbitrary because i have not implemented depth!\n return (moveToMake)","def get_best_move(board, scores): \n empty_squares = board.get_empty_squares()\n highest_score = None\n best_pos = []\n \n for empty in range(len(empty_squares)):\n pos = empty_squares[empty] \n if highest_score == None:\n highest_score = scores[pos[0]][pos[1]]\n if scores[pos[0]][pos[1]] >= highest_score:\n highest_score = scores[pos[0]][pos[1]]\n \n for empty in range(len(empty_squares)):\n pos = empty_squares[empty]\n if scores[pos[0]][pos[1]] == highest_score:\n best_pos.append(pos) \n return random.choice(best_pos)","def get_move(self, game, time_left):\n self.time_left = time_left\n\n # Initialize the best move so that this function returns something\n # in case the search fails due to timeout\n best_move = (-1, -1)\n\n try:\n # The try/except block will automatically catch the exception\n # raised when the timer is about to expire.\n best_move = self.minimax(game, self.search_depth)\n except SearchTimeout:\n pass # Handle any actions required after timeout as needed\n\n # Return the best move from the last completed search iteration\n return best_move","def mm_move(board, player):\n result = board.check_win() # get result of the current board\n if result == None:\n move_list = board.get_empty_squares() # get the tree branches and possible next moves\n best = (None, (-1, -1))\n for step in move_list:\n bd_clone = board.clone()\n bd_clone.move(step[0], step[1], player) #make a move on a cloned board\n next_player = provided.switch_player(player)\n next_score = mm_move(bd_clone, next_player) #make a recursive call to mm_move() pasing the cloned board and the 'other' player\n if player == 3: #if it is oppo O--min\n if best[0] == None or (next_score[0] < best[0]):\n best = (next_score[0], step)\n #print best\n elif player ==2: #if it is X--max\n if best[0] == None or (next_score[0] > best[0]):\n best = (next_score[0], step)\n return best\n else:\n return SCORES[result], (-1, -1)","def find_best_move(self, board, factor=2.0):\n o = Node(board)\n return MTCS(MAX_DEPTH, o, factor, self.id)","def select_best_move(self, stats, depth, board, color):\n\n bestscore = 0\n bestmove = None\n total_n = 0\n\n for action in SP.available_actions(board):\n next_board = board[:]\n SP.play(next_board, action, color)\n n, w = stats[MCTSRandomPlayer.to_board_id(next_board)]\n if n == 0:\n continue\n total_n += n\n if self.DEBUG: print('Move %d score: %d/%d (%0.1f%%)' % (action, w, n, w/n*100))\n if n > bestscore or (n == bestscore and random.random() <= 0.5): # 가장 많이 방문해 본 길을 따라 간다. WHY???\n bestmove = action\n bestscore = n\n\n assert bestmove is not None\n\n if self.DEBUG: print('Maximum depth: %d, Total simulations: %d on %d' % (depth, total_n, MCTSRandomPlayer.to_board_id(board)))\n\n return bestmove","def minimax_decision(gameState):\n value = -sys.maxsize\n best_value = -sys.maxsize\n best_move = None\n legal_moves = gameState.get_legal_moves()\n for move in legal_moves:\n game = gameState.forecast_move(move)\n value = max(value, min_value(game))\n if value > best_value:\n best_value = value\n best_move = move\n return best_move","def get_move(self, game, time_left):\n\n self.time_left = time_left\n legal_moves = game.get_legal_moves()\n if not legal_moves:\n return (-1, -1)\n\n\n # Initialize the best move so that this function returns something\n # in case the search fails due to timeout\n best_score = float(\"-inf\")\n best_move = legal_moves[0]\n\n try:\n # The try/except block will automatically catch the exception\n # raised when the timer is about to expire.\n self.search_depth = 1\n while best_score is not float(\"inf\"):\n best_move = self.alphabeta(game, self.search_depth, alpha=float(\"-inf\"), beta=float(\"inf\"))\n self.search_depth += 1\n except SearchTimeout:\n return best_move\n pass # Handle any actions required after timeout as needed\n\n # Return the best move from the last completed search iteration\n return best_move","def get_move(self, game, time_left):\n self.time_left = time_left\n\n # Initialize the best move so that this function returns something\n # in case the search fails due to timeout\n self.best_move = (-1, -1)\n\n try:\n # The try/except block will automatically catch the exception\n # raised when the timer is about to expire.\n move = self.minimax(game, self.search_depth)\n return move\n\n except SearchTimeout:\n pass # Handle any actions required after timeout as needed\n\n # Return the best move from the last completed search iteration\n return self.best_move","def get_move(self, game, time_left):\n self.time_left = time_left\n\n # Initialize the best move so that this function returns something\n # in case the search fails due to timeout\n best_move = (-1, -1)\n\n try:\n # The try/except block will automatically catch the exception\n # raised when the timer is about to expire.\n return self.minimax(game, self.search_depth)\n\n except SearchTimeout:\n pass # Handle any actions required after timeout as needed\n\n # Return the best move from the last completed search iteration\n return best_move","def get_move(self, game, time_left):\n self.time_left = time_left\n\n # Initialize the best move so that this function returns something\n # in case the search fails due to timeout\n best_move = (-1, -1)\n\n try:\n # The try/except block will automatically catch the exception\n # raised when the timer is about to expire.\n return self.minimax(game, self.search_depth)\n\n except SearchTimeout:\n pass # Handle any actions required after timeout as needed\n\n # Return the best move from the last completed search iteration\n return best_move","def get_move(self, game, time_left):\n self.time_left = time_left\n\n # Initialize the best move so that this function returns something\n # in case the search fails due to timeout\n best_move = (-1, -1)\n\n try:\n # The try/except block will automatically catch the exception\n # raised when the timer is about to expire.\n return self.minimax(game, self.search_depth)\n\n except SearchTimeout:\n pass # Handle any actions required after timeout as needed\n\n # Return the best move from the last completed search iteration\n return best_move","def get_move(self, game, time_left):\n self.time_left = time_left\n\n # Initialize the best move so that this function returns something\n # in case the search fails due to timeout\n best_move = (-1, -1)\n\n try:\n # The try/except block will automatically catch the exception\n # raised when the timer is about to expire.\n return self.minimax(game, self.search_depth)\n\n except SearchTimeout:\n pass # Handle any actions required after timeout as needed\n\n # Return the best move from the last completed search iteration\n return best_move","def get_move(self, game, time_left):\n self.time_left = time_left\n \n # Initialize the best move so that this function returns something\n # in case the search fails due to timeout\n best_move = (-1, -1)\n\n try:\n # The try/except block will automatically catch the exception\n # raised when the timer is about to expire.\n return self.minimax(game, self.search_depth)\n\n except SearchTimeout:\n pass # Handle any actions required after timeout as needed\n\n # Return the best move from the last completed search iteration\n return best_move","def findNextMove(curHVal):\n minHCalc = curHVal; #Initializing to curHVal\n minNewPosition = (0,0); #Initializing it\n\n tempBoardPositions = [x for x in positions]\n \n for move in findPlacesToMove():\n #Move number to blank\n tempBoardPositions[curBlank[0]][curBlank[1]] = tempBoardPositions[move[0]][move[1]];\n #Move blank to new move position\n tempBoardPositions[move[0]][move[1]] = 0;\n \n hVal = heuristicValueOfPosition(tempBoardPositions);\n if(hVal < minHCalc):\n minHCalc = hVal;\n minNewPosition = move;\n \n #Resetting board to what it was before this move\n tempBoardPositions[move[0]][move[1]] = tempBoardPositions[curBlank[0]][curBlank[1]]\n #tempBoardPositions[curBlank[0]][curBlank[1]] = 0;\n\n print(\"After calc -- {}\".format(positions))\n \n if minHCalc < curHVal:\n return (minNewPosition, minHCalc)\n else:\n return -1; #That we have reached local minima","def minimax(board):\n\n if len(actions(board)) == 9:\n action = actions(board)\n return action[random.randint(0, len(actions(board)) - 1)]\n\n if terminal(board):\n return None\n\n best_move = None\n\n if player(board) == X:\n best_score = -1\n else:\n best_score = 1\n\n for action in actions(board):\n if player(board) == X:\n score = minimize(result(board, action))\n\n if score >= best_score:\n best_move = action\n best_score = score\n else:\n score = maximize(result(board, action))\n\n if score <= best_score:\n best_move = action\n best_score = score\n\n return best_move","def make_move(self, time_limit, players_score):\n finish_time = time.time() + time_limit\n depth = 1\n best_move = (-np.inf, (-1, 0))\n while True:\n for direction in self.directions:\n initial_state = utils.State(self.board, direction, self.pos, self.current_turn,\n self.fruits_on_board_dict,\n finish_time)\n try:\n outcome = self.minimax_algo.search(initial_state, depth, True)\n if outcome[0] > best_move[0]:\n best_move = outcome\n except TimeoutError:\n self.board[self.pos[0]][self.pos[1]] = -1\n self.pos = (self.pos[0] + best_move[1][0], self.pos[1] + best_move[1][1])\n self.board[self.pos[0]][self.pos[1]] = 1\n\n return best_move[1]\n depth += 1\n # print('bigger_depth : {} '.format(depth))","def choose_move(self):\r\n \r\n return None","def get_move(board, player):\n #optimization to always pick the top-left corner on an empty board\n if set(board) == set([config.NO_PLAYER]):\n return 0\n result = minimax(board, player, 2, config.NEG_INF, config.INF)\n return result.index","def next_move(board):\n\tcalculate_material(board)\n\n\tif board.turn:\n\t\tmax(board, DEPTH, -MAXVALUE, MAXVALUE)\n\telse:\n\t\tmin(board, DEPTH, -MAXVALUE, MAXVALUE)\n\tif SAVED_MOVE is None:\n\t\tprint(\"No moves found\")\n\telse:\n\t\tprint(\"Saved move\", SAVED_MOVE)\n\treturn SAVED_MOVE","def get_move(self, game, legal_moves, time_left):\n logging.debug(\"get_move - legal moves: %s\", str(legal_moves))\n \n self.time_left = time_left\n\n\n # Check if we have any legal moves\n if not legal_moves:\n return (-1, -1)\n\n # Let's set best move so far to be the first legal move so we always \n # have something to return in case of timeout\n self.best_move_so_far = legal_moves[0]\n \n \n # Perform any required initializations, including selecting an initial\n # move from the game board (i.e., an opening book), or returning\n # immediately if there are no legal moves\n\n try:\n # The search method call (alpha beta or minimax) should happen in\n # here in order to avoid timeout. The try/except block will\n # automatically catch the exception raised by the search method\n # when the timer gets close to expiring\n if self.iterative:\n it = 1\n while True:\n if self.method == 'minimax':\n _, self.best_move_so_far = self.minimax(game, it)\n else:\n _, self.best_move_so_far = self.alphabeta(game, it)\n it += 1\n else: \n if self.method == 'minimax':\n _, self.best_move_so_far = self.minimax(game, self.search_depth)\n else:\n _, self.best_move_so_far = self.alphabeta(game, self.search_depth)\n\n except Timeout:\n # Handle any actions required at timeout, if necessary\n logging.debug(\"Time is up - get_move returning: %s\", str(self.best_move_so_far))\n return self.best_move_so_far\n\n # Return the best move from the last completed search iteration\n logging.debug(\"get_move returning: %s\", str(self.best_move_so_far))\n\n return self.best_move_so_far","def get_move(self, game, time_left):\n\n self.time_left = time_left\n\n # Perform any required initializations, including selecting an initial\n # move from the game board (i.e., an opening book), or returning\n # immediately if there are no legal moves\n\n remaining_legal_moves = game.get_legal_moves(game.active_player)\n no_legal_moves = (-1, -1)\n best_move = no_legal_moves\n if not remaining_legal_moves:\n return no_legal_moves\n\n # Use random IDS depth between 9 and 12\n depth = random.randint(9, 12)\n\n try:\n # Perform IDS\n while True:\n depth += 1\n _, best_move = self.alphabeta(game, depth)\n\n if self.time_left() <= 0.001:\n return best_move\n\n except Timeout:\n # Handle any actions required at timeout, if necessary\n return best_move\n\n # Return the best move from the last completed search iteration\n return best_move","def best_move(self, state, curr_player):\n\t\t# determine opponent's color\n\t\tif curr_player == self.colors[0]:\n\t\t\topp_player = self.colors[1]\n\t\telse:\n\t\t\topp_player = self.colors[0]\n\n\t\treturn self.value(state, curr_player)","def get_move(self, game, legal_moves, time_left):\n\n self.time_left = time_left\n\n # TODO: finish this function!\n\n # Perform any required initializations, including selecting an initial\n # move from the game board (i.e., an opening book), or returning\n # immediately if there are no legal moves\n moves = game.get_legal_moves()\n best_move = (-1, -1)\n best_score = float(\"-inf\")\n depth = 1\n if moves:\n best_move = moves[0]\n else :\n return best_move\n \n\n try:\n # The search method call (alpha beta or minimax) should happen in\n # here in order to avoid timeout. The try/except block will\n # automatically catch the exception raised by the search method\n # when the timer gets close to expiring\n if not self.iterative:\n if self.method == 'minimax':\n _, best_move = self.minimax(game, self.search_depth)\n elif self.method == 'alphabeta':\n _, best_move = self.alphabeta(game, self.search_depth) \n else:\n while self.time_left() > self.TIMER_THRESHOLD:\n if self.method == 'minimax':\n score, move = self.minimax(game, depth)\n elif self.method == 'alphabeta':\n score, move = self.alphabeta(game, depth)\n if score > best_score:\n best_move = move\n depth += 1 \n\n\n except Timeout:\n # Handle any actions required at timeout, if necessary\n pass\n\n # Return the best move from the last completed search iteration\n return best_move","def get_move(self, game, time_left):\n legal_moves = game.get_legal_moves()\n if not legal_moves:\n return (-1, -1)\n _, move = max([(self.score(game.forecast_move(m), self), m) for m in legal_moves])\n return move","def minimax(self, game, depth):\n if self.time_left() < self.TIMER_THRESHOLD:\n raise SearchTimeout()\n # TODO: finish this function!\n # raise NotImplementedError\n\n legal_moves = game.get_legal_moves() # obtain legal moves available to the board\n best_move = (-1,-1) # initialisation of best move\n best_score = -math.inf # abstraction of infinity\n\n for m in legal_moves: # for each ACTION, create a new state for its outcome, RESULT\n new_state = game.forecast_move(m)\n score = self.min_value(new_state, depth - 1) # recursion to calculate the score of that state\n if score > best_score:\n best_move = m\n best_score = score\n return best_move","def get_move(self, game, time_left):\n self.time_left = time_left\n\n # TODO: finish this function!\n # Initialize the best move so that this function returns something\n # in case the search fails due to timeout\n best_move = (-1, -1)\n # The try/except block will automatically catch the exception# raised when the timer is about to expire.\n # Implementation of Iterative Deepening Search\n\n try:\n depth = 0 # initialisation of depth\n while True:\n # runs along as game is still active\n depth += 1 # increment depth after each search\n best_move = self.alphabeta(game, depth) # apply alpha beta to search\n\n except SearchTimeout: # cutoff - when timer runs out\n pass # Handle any actions required after timeout as needed\n # Failure\n\n # Return the best move from the last completed search iteration\n return best_move # Solution","def get_best_move(self, curr_player, orig_player, depth=0, alpha=-100, beta=100):\n\n best_move, best_score = None, 0\n is_over, winner = self.is_over()\n\n current_board_state = self.get_board_state()\n current_board_state_best_move_score = self.state_best_move_map.get(current_board_state, {}).get(curr_player)\n\n if current_board_state_best_move_score:\n return current_board_state_best_move_score\n\n # If game is over return the score\n if is_over:\n self.LEAF_COUNT += 1\n if winner:\n best_score = (10 - depth) * (1 if winner == orig_player else -1)\n return best_move, best_score\n\n opponent = self.human if curr_player == self.comp else self.comp\n\n # Else find the move with best score.\n for move in list(self.available_moves):\n\n self.play_move(move, curr_player)\n _, score = self.get_best_move(opponent, orig_player, depth=depth+1, alpha=alpha, beta=beta)\n\n # If move is for orig-player, max score move will be best move.\n if best_move is None or (curr_player == orig_player and score > best_score):\n best_move, best_score = move, score\n alpha = max(alpha, best_score)\n # If move is not for orig-player, min score move will be best move.\n elif best_move is None or (curr_player != orig_player and score < best_score):\n best_move, best_score = move, score\n beta = min(beta, best_score)\n\n self.undo_move(move)\n\n # If the maximum harm (-ve score) the opponent can do is less than the minimum benefit (+ve score) the\n # current player has, no need to check other moves as the opponent can not do better.\n if beta <= alpha:\n break\n\n self.state_best_move_map[current_board_state] = self.state_best_move_map.get(current_board_state) or {}\n self.state_best_move_map[current_board_state][curr_player] = best_move, best_score\n return best_move, best_score","def determineNextMove(player_location, opponentLocation, coins):\n global route, currentcoin, meta_route, best_weight, best_path, coins_to_search, index\n if opponentLocation in coins_to_search:\n coins_to_search, meta_route, route = change_way(coins, opponentLocation, player_location)[:3]\n index = 0\n elif currentcoin == player_location: \n if len(route) != 0:\n old_dist = algo.dijkstra(mazeMap, player_location)[1][meta_route[index+1]]\n coins_to_search2, meta_route2, route2, new_dist = change_way(coins, opponentLocation, player_location)\n\n #dist_matrix, route_matrix = u.update_dists_from_each(dists_matrix, routes_matrix, player_location, mazeMap, coins)\n #coins_to_search = get_n_shortest(3, coins, player_location, dists_matrix)\n \t\n #ennemy_dists = algo.dijkstra(mazeMap, opponentLocation)\n #for c in coins_to_search:\n #if len(coins_to_search) >= 2 and ennemy_dists[1][c] < dists_matrix[player_location][c]:\n # coins_to_search.remove(c)\n #break\n \t\t\n #best_weight = float(\"inf\")\n #best_path = []\n #exhaustive(coins_to_search, player_location, [], 0, dist_matrix)\n #meta_route2 = [player_location] + best_path\n #route2 = u.location_list_to_route(meta_route2, route_matrix)\n #new_dist = dist_matrix[player_location][meta_route2[1]]\n\t\t\n if len(route) == 0 or old_dist - new_dist > 3:\n route = route2\n meta_route = meta_route2 \n index = 0\n index += 1\n currentcoin = meta_route[index]\n #api.debug(route)\n return u.direction(player_location, route.pop(0))","def movee(self):\n\n #return the initial state if he cant move and he's in the initial state\n if not self.move and self.index == 0:\n return self.path[self.index]\n\n #return the goal state if he's at the goal state\n if self.index == len(self.path):\n return self.path[-1]\n\n #return the next move and increments the index attribute\n nextMove = self.path[self.index]\n self.index += 1\n\n return nextMove","def recursive_minimax_strategy(game: Game) -> Any:\n candidate = game.current_state.get_possible_moves()\n canditate_prop = [\n -1 * get_state_score(game, game.current_state.make_move(\n move\n ))\n # get_state_score() provides the STATE SCORE for the OPPO player\n # in each resultent state.\n # -1 * ... to calculate the STATE SCORE for player taking action now.\n for move in candidate\n ]\n max_index = canditate_prop.index(max(canditate_prop))\n assert len(canditate_prop) == len(candidate), \"Inconsistent length.\"\n return candidate[max_index]","def get_move(self, game, time_left):\n self.time_left = time_left\n\n # Initialize the best move so that this function returns something\n # in case the search fails due to timeout\n if not game.get_legal_moves():\n return (-1,-1)\n best_move = game.get_legal_moves()[0]\n\n try:\n # The try/except block will automatically catch the exception\n # raised when the timer is about to expire.\n return self.minimax(game, self.search_depth)\n\n except SearchTimeout:\n pass # Handle any actions required after timeout as needed\n\n # Return the best move from the last completed search iteration\n return best_move","def best_move(game, method='score'):\n board, score = game\n\n if method == 'score':\n def val(g):\n return g[1]\n elif method == 'empty':\n val = empty_squares\n elif method == 'gradient':\n val = gradient_value\n else:\n print('Invalid method given to best_move function')\n exit(1)\n\n if score < 10000:\n depth = 4\n elif score < 20000:\n depth = 5\n elif score < 30000:\n depth = 7\n else:\n depth = 7\n\n moves = possible_moves(board)\n\n # dict with score for each move\n move_score = dict(zip(moves, [0] * len(moves)))\n\n # search for max score for each move\n for move in moves:\n move_score[move] += max_score_search(move_made(game, move), depth, val)\n\n move = max(move_score, key=move_score.get)\n return move","def get_move(self, game, time_left):\n self.time_left = time_left\n\n # Initialize the best move so that this function returns something\n # in case the search fails due to timeout\n best_move = (-1, -1)\n\n try:\n # The try/except block will automatically catch the exception\n # raised when the timer is about to expire.\n self.search_depth = 0\n\n while self.time_left() > self.TIMER_THRESHOLD:\n self.search_depth += 1\n best_move = self.alphabeta(game, self.search_depth)\n\n except SearchTimeout:\n pass # Handle any actions required after timeout as needed\n\n # Return the best move from the last completed search iteration\n return best_move","def mm_move(board, player): \r\n if board.check_win() != None:\r\n score = SCORES[board.check_win()]\r\n return score, (-1,-1)\r\n else:\r\n best_score = -2\r\n score_list = []\r\n move_list = []\r\n for each_cell in board.get_empty_squares():\r\n passboard = board.clone()\r\n passboard.move(each_cell[0], each_cell[1], player) \r\n other_player = provided.switch_player(player)\r\n nextmove = mm_move(passboard, other_player)\r\n score_list.append(nextmove[0])\r\n move_list.append(nextmove[1])\r\n if nextmove[0] == SCORES[player]:\r\n return nextmove[0], each_cell\r\n #print score_list\r\n #print move_list\r\n #print \"\"\r\n if player == provided.PLAYERX:\r\n best_score = max(score_list)\r\n else:\r\n best_score = min (score_list)\r\n best_move = move_list[score_list.index(best_score)]\r\n return best_score, best_move","def get_best_move(board, scores):\r\n blankies = board.get_empty_squares()\r\n max_score = scores[blankies[0][0]][blankies[0][1]]\r\n result = []\r\n for current in blankies:\r\n if scores[current[0]][current[1]] > max_score:\r\n max_score = scores[current[0]][current[1]]\r\n for current in blankies:\r\n if scores[current[0]][current[1]] == max_score:\r\n result.append(current)\r\n return random.choice(result)","def get_move(self, game, legal_moves, time_left):\n\n self.time_left = time_left\n move = (-1, -1) #Default\n\n # Perform any required initializations, including selecting an initial\n # move from the game board (i.e., an opening book), or returning\n # immediately if there are no legal moves\n\n max_depth = 0\n try:\n # The search method call (alpha beta or minimax) should happen in\n # here in order to avoid timeout. The try/except block will\n # automatically catch the exception raised by the search method\n # when the timer gets close to expiring\n if self.iterative:\n #Perform iterative search\n num_of_remaining_moves = len(game.get_blank_spaces())\n for depth in range(1,num_of_remaining_moves):\n if self.time_left() <= self.TIMER_THRESHOLD:\n return move\n\n if self.method == 'alphabeta':\n iterative_best_score, iterative_best_move = self.alphabeta(game, depth)\n else:\n iterative_best_score, iterative_best_move = self.minimax(game, depth)\n\n #Stores score and move of the deepest search\n score = iterative_best_score\n move = iterative_best_move\n max_depth = depth\n else:\n #Perform fixed-depth search\n if self.method == 'alphabeta':\n score, move = self.alphabeta(game, self.search_depth)\n else:\n score, move = self.minimax(game, self.search_depth)\n\n except Timeout:\n # Handle any actions required at timeout, if necessary\n pass\n\n return move","def minimax_maximize_play(self, game, legal_moves, depth):\n highest_score, selected_move = (float('-inf'), (-1, -1))\n for move in legal_moves:\n score, _ = self.minimax(game.forecast_move(move), depth - 1, False)\n highest_score, selected_move = max((highest_score, selected_move), (score, move))\n return (highest_score, selected_move)","def get_move(self, board):\n # First, check if we can win in the next move\n winning_move = self.get_winning_move(board, self.letter)\n if winning_move is not None:\n return winning_move\n # Check if the player could win on their next move, and block them.\n blocking_move = self.get_winning_move(board, self.opponent_letter)\n if blocking_move is not None:\n return blocking_move\n # Try to take one of the corners, if they are free.\n corner_move = self.move_in_a_corner(board)\n if corner_move is not None:\n return corner_move\n # Try to take the center, if it is free.\n if board.size % 2 == 1:\n if board.is_position_availible(board.letters[board.size // 2]\n + board.numbers[board.size // 2]):\n return board.letters[board.size // 2] + board.numbers[board.size // 2]\n # Move on one of the sides.\n return self.choose_random_move_from_list(board, list(board.positions.keys()))","def get_best_move(board, scores):\n empty = board.get_empty_squares()\n if len(empty) == 0:\n return\n best_move = None\n best_score = None\n for square in empty:\n if best_move == None or scores[square[0]][square[1]] > best_score:\n best_move = square\n best_score = scores[square[0]][square[1]]\n return best_move","def takeStrategicMove():\r\n\tglobal move1, move2\r\n\r\n\tif move1==0 or move2==0:\r\n\t\tif validMove(1):\r\n\t\t\treturn 1\r\n\t\telif validMove(5):\r\n\t\t\treturn 5\r\n\telif winningMove():\r\n\t\treturn winningMove()\t\t\r\n\telif blockingMove():\r\n\t\treturn blockingMove()\r\n\telse:\r\n\t\treturn takeNaiveMove()","def minimax(self, game, depth):\n if self.time_left() < self.TIMER_THRESHOLD:\n raise SearchTimeout()\n\n \"\"\"\n From AIMA psuedocode:\n\n function MINIMAX-DECISION(state) returns an action\n return arg max a is in ACTIONS(s) MIN-VALUE(RESULT(state, a))\n \"\"\"\n\n best_move = (-1,-1)\n best_score = float(\"-inf\")\n actions = game.get_legal_moves()\n\n if not actions:\n return best_move\n else:\n best_move = actions[randint(0, len(actions) - 1)]\n\n try:\n # The try/except block will automatically catch the exception\n # raised when the timer is about to expire.\n # return max(actions, key=lambda action: self._min_value(game.forecast_move(action), 1))\n for action in actions:\n score = self._min_value(game.forecast_move(action), 1)\n if score > best_score:\n best_score = score\n best_move = action\n\n except SearchTimeout:\n pass\n\n return best_move","def get_best_move(board, scores):\n all_scores = {}\n for row_num in range(board.get_dim()):\n for col_num in range(board.get_dim()):\n if board.square(row_num, col_num) == provided.EMPTY:\n all_scores [(row_num, col_num)] = scores[row_num][col_num]\n max_score = max(all_scores.values())\n max_sqrs = []\n for sqr in all_scores:\n if all_scores[sqr] == max_score:\n max_sqrs.append(sqr)\n return random.choice(max_sqrs)","def minimax(board):\n if terminal(board):\n return None\n # return the optimal move for the player\n # possible outcomes 1(X wins) 0(No winner) -1(O wins)\n if player(board) == X:\n _ , action = max_value(board)\n return action\n elif player(board) == O:\n _ , action = min_value(board)\n return action","def computeActionFromValues(self, state):\n best_move = None\n best_Q = float(\"-inf\")\n\n for a in self.mdp.getPossibleActions(state):\n q = self.computeQValueFromValues(state,a)\n if q > best_Q:\n best_Q = q\n best_move = a\n\n return best_move","def ai_move():\n\tinitial_state = map(get_filled_edges, rects)\n\tpossible_moves = []\n\tfor index, filled_edges in enumerate(initial_state):\n\t\tif filled_edges == 0:\n\t\t\tpossible_moves.extend([(index, i) for i in 'ltrb'])\n\t\telif filled_edges == 1:\n\t\t\tpossible_moves.extend(one_filled_edge(index))\n\t\telif filled_edges == 2:\n\t\t\tpossible_moves.extend(two_filled_edge(index))\n\t\telif filled_edges == 3:\n\t\t\tpossible_moves.extend(three_filled_edge(index))\n\tprint possible_moves\n\tpossible_decisions = []\n\tfor move in possible_moves:\n\t\tfinal_state = apply_move(move)\n\t\tpossible_decisions.append(is_feasible(initial_state, final_state))\n\tprint possible_decisions\n\t# randomizing when some decisions have the same weight\n\tmax_weight = max(possible_decisions)\n\t# list of indices which have the same weight\n\tmax_indices = []\n\tfor index, weight in enumerate(possible_decisions):\n\t\tif weight == max_weight:\n\t\t\tmax_indices.append(index)\n\tx = choice(max_indices)\n\tprint x\n\treturn possible_moves[x]\n\t# return possible_moves[possible_decisions.index(max(possible_decisions))]","def minimax(board):\n if terminal(board):\n return None\n if player(board)==X:\n value, move = max_value(board)\n return move\n if player(board)==O:\n value, move = min_value(board)\n return move","def utility(state:State,maximizing_player):\n best_move_score = -1\n #######################[Goal]#########################\n is_current_player_stuck = is_stuck(state,state.player_type)\n other_player = RIVAL if state.player_type == PLAYER else PLAYER\n # Check if stuck\n if is_current_player_stuck:\n if state.player_type == PLAYER:\n state.players_score[state.player_type] -= state.penalty_score\n else:\n state.players_score[state.player_type] += state.penalty_score\n return state.players_score[state.player_type] - state.players_score[other_player] \n ######################################################\n # Else\n #--------------------------------------------------\n ################# Available Steps #################\n #--------------------------------------------------\n player_available_steps = availables(state.board, state.locations[PLAYER])\n h1 = 4-player_available_steps\n h4 = player_available_steps\n #--------------------------------------------------\n ################# Fruits Distance #################\n #--------------------------------------------------\n h2 = -1\n if state.fruits_ttl > 0 and len(state.fruits_dict) > 0:\n min_fruit_dist = float('inf')\n for fruit_loc in state.fruits_dict:\n curr_fruit_dist = Manhattan(state.locations[state.player_type], fruit_loc)\n # Check what is the closest fruit reachable\n if curr_fruit_dist < min_fruit_dist and curr_fruit_dist <= state.fruits_ttl:\n other_player_fruit_dist = Manhattan(state.locations[other_player], fruit_loc)\n if curr_fruit_dist < other_player_fruit_dist:\n min_fruit_dist = curr_fruit_dist\n max_dist = len(state.board)+len(state.board[0])\n h2 = (max_dist*10.0/min_fruit_dist)+1 if min_fruit_dist < float('inf') else -1\n #--------------------------------------------------\n ################# Reachable Squrs #################\n #--------------------------------------------------\n reachables_player = reachables(state.board,state.locations[PLAYER])\n reachables_rival = reachables(state.board,state.locations[RIVAL])\n h3 = reachables_player - reachables_rival # We want more for us\n #--------------------------------------------------\n ################# Combine it all. #################\n #--------------------------------------------------\n if not state.half_game():\n w = 0.8 if h2 > 0 else 1\n best_move_score = w*(h1-h3) + (1-w)*h2 \n else:\n w = 0.7 if h2 > 0 else 1\n best_move_score = w*(h4+h3) + (1-w)*h2 \n\n best_move_score += state.players_score[state.player_type]\n return best_move_score","def find_closest_move(self, position, valid_moves):\n closest = None\n closest_value = 100\n for move in valid_moves:\n if closest:\n x_dist = abs(position[0] - move[0])\n y_dist = abs(position[1] - move[1])\n if x_dist + y_dist < closest_value:\n closest = move\n closest_value = x_dist + y_dist\n else:\n closest = move\n\n return closest","def optimal_max(board):\n # Board full?\n if terminal(board):\n return [None, utility(board)]\n\n available_actions = list(actions(board))\n\n # Naive baseline comparison is negative infinity\n global_optimum = [None, -math.inf]\n\n # For each move, what would opponent do next? Update best move.\n for action in available_actions:\n # Anticipates optimal adversarial moves\n local_optimum = optimal_min(result(board, action))\n\n # Compares local vs global optima\n if global_optimum[1] <= local_optimum[1]:\n global_optimum = [action, local_optimum[1]]\n\n return global_optimum","def game_move(self):\n\t\t# make a note of the player who isn't playing\n\t\tfor x in self.players.keys():\n\t\t\tif x != self.nextPlayer:\n\t\t\t\totherPlayer = x\n\t\t\t\tbreak\n\t\t\n\t\t\n\t\t# If there are no remaining moves for this player, either the other\n\t\t# player has won or it's a draw\n\t\t# self.expansions = 1\n\t\tself.expansionCounter.count = 1\n\t\tif len(self.state.successors()) == 0:\n\t\t\tif self.state.is_win(otherPlayer):\n\t\t\t\treturn (None, otherPlayer)\n\t\t\telse:\n\t\t\t\t# None, None for a draw\n\t\t\t\treturn (None, None)\n\t\t\t\n\t\t# allow the player max_expansions for this turn\n\t\t# self.expansions = self.max_expansions\n\t\tself.expansionCounter.count = self.max_expansions\n\t\t\n\t\tnextPlayer = self.players[self.nextPlayer]\n\t\tlastPlayer = None\n\t\t\n\t\t# player may throw an exception\n\t\ttry:\n\t\t\t# get player's move, make sure we don't modify the current state\n\t\t\tmove = nextPlayer.move(self.state.get_player_state(self.nextPlayer), \n\t\t\t\t\t self.visitedStates)\n\t\t\t# player may give up\n\t\t\tif move.is_forfeit():\n\t\t\t\tprint \"Player\", self.nextPlayer, \"forfeits.\"\n\t\t\t\treturn (None, otherPlayer)\n\t\t\t# player may return illegal move\n\t\t\tif not self.state.is_valid_move(move):\n\t\t\t\tprint \"Illegal move returned by player\", self.nextPlayer, \\\n\t\t\t\t\t\t\"(\", self.players[self.nextPlayer].get_name(), \")\"\n\t\t\t\treturn (move, otherPlayer)\n\t\t\t# this player is now last player\n\t\t\tlastPlayer = self.nextPlayer\n\t\t\t# get the new next player and make the indicated move\n\t\t\tself.nextPlayer, clear = self.state.move(move, True)\n\t\t\tif clear:\n\t\t\t\tself.clear_repeat()\n\t\texcept:\n\t\t\tprint \"Exception thrown by player\", self.nextPlayer, \\\n\t\t\t\t\t\t\"(\", self.players[self.nextPlayer].get_name(), \")\"\n\t\t\tprint\n\t\t\ttraceback.print_exc()\n\t\t\tprint\n\t\t\treturn (None, otherPlayer)\n\t\t\n\t\tos.chdir(self.wd)\n\t\t\n\t\t# may be a repeated state IF the game cycles\n\t\tif self.is_repeat(self.state):\n\t\t\tself.state.handle_cycle()\n\t\t# otherwise, if the game cycles, note that we've been here\n\t\telif self.state.repeats():\n\t\t\tself.visitedStates.add(self.state.repeated_rep())\n\t\t\t\n\t\t# player may have sacrificed the game\n\t\tif self.state.is_win(otherPlayer):\n\t\t\treturn (move, otherPlayer)\n\t\t\n\t\t# player may have won\n\t\tif self.state.is_win(lastPlayer):\n\t\t\treturn (move, lastPlayer)\n\t\t\n\t\t# nobody's won or lost yet\n\t\treturn (move, None)","def mm_move(board, player):\n moves = []\n results = []\n best_score = None\n best_move = None\n \n opponet = op_player(player)\n \n if board.check_win() != None:\n \n if board.check_win() == provided.PLAYERX:\n return SCORES[provided.PLAYERX] , (-1, -1)\n \n if board.check_win() == provided.PLAYERO:\n return SCORES[provided.PLAYERO] , (-1, -1)\n \n if board.check_win() == provided.DRAW:\n return SCORES[provided.DRAW] , (-1, -1)\n \n free_steps = board.get_empty_squares()\n \n for step in free_steps:\n clone = board.clone() \n clone.move(step[0],step[1],player)\n temp = mm_move(clone,opponet)\n \n if temp != None:\n if temp[0] == SCORES[player]: \n return temp[0] , step \n else: \n results.append(temp)\n moves.append(step)\n \n for result, move in zip(results, moves): \n if result[0] * SCORES[player] > best_score:\n best_score = result[0]\n best_move = move\n return best_score, best_move","def search_my_move(self, env:Chess, is_root_node=False) -> float:\n if env.over():\n if env.victor == Victor.draw:\n return 0\n # assert env.whitewon != env.white_to_move # side to move can't be winner!\n return -1\n\n state = board_state_key(env)\n\n with self.node_lock[state]:\n if state not in self.tree:\n leaf_p, leaf_v = self.expand_and_evaluate(env)\n self.tree[state].p = leaf_p\n return leaf_v # I'm returning everything from the POV of side to move\n\n # SELECT STEP\n action_t = self.select_action_q_and_u(env, is_root_node)\n\n virtual_loss = self.play_conf.virtual_loss\n\n my_visit_stats = self.tree[state]\n my_stats = my_visit_stats.a[action_t]\n\n my_visit_stats.sum_n += virtual_loss\n my_stats.n += virtual_loss\n my_stats.w += -virtual_loss\n my_stats.q = my_stats.w / my_stats.n\n\n env.make_move(action_t.uci())\n leaf_v = self.search_my_move(env) # next move from enemy POV\n leaf_v = -leaf_v\n\n # BACKUP STEP\n # on returning search path\n # update: N, W, Q\n with self.node_lock[state]:\n my_visit_stats.sum_n += -virtual_loss + 1\n my_stats.n += -virtual_loss + 1\n my_stats.w += virtual_loss + leaf_v\n my_stats.q = my_stats.w / my_stats.n\n\n return leaf_v","def best_move(self, scores):\n \n max_val = max(scores)\n \n tie_list = []\n for i in range(len(scores)):\n if scores[i] == max_val:\n tie_list.append(i)\n if self.is_random:\n return random.choice(tie_list)\n else:\n return tie_list[0]"],"string":"[\n \"def find_best_move(state: GameState) -> None:\",\n \"def best_move(self):\\n if self._move is not None:\\n return self._move\\n else:\\n return self.pass_move\",\n \"def get_optimal_move(self):\\n # create the root state\\n root = State(self.current_board, True, self.__machine_token, self.__human_token)\\n # alpha-beta-pruning algorithm\\n best_move = max_value_a_b(root, depth(root), -1000, 1000)\\n # obtain the direct children.\\n direct_children = get_direct_children(root, all_states_generated)\\n # obtain the coordinates of the movement.\\n for direct_child in direct_children:\\n if direct_child.value == best_move:\\n return get_coordinates(root, direct_child)\",\n \"def get_best_move(self):\\n moves1 = self.get_next_moves1() # moves1 represents all legal moves.\\n moves2 = self.get_next_moves2() # moves2 represents the moves that allow the AI to score a box.\\n moves3 = self.get_next_moves3() # moves3 represents the moves that will allow the player to score a box.\\n\\n\\n if len(moves1) == 0: # the siuation that there is no legal move\\n return self\\n if len(moves2) != 0:\\n return moves2[len(moves2) // 2] # the siuation that there is(are) move(s) to score\\n\\n elif len(moves3) != 0:\\n return moves3[len(moves3) // 2] # the siuation that there is(are) moves(s) to allow the player to score\\n\\n else:\\n return moves1[len(moves1) // 2] # if there is no better moves, the AI will play sequentially, starting from the top left.\",\n \"def getBestMove(listOfPossibleMoves, gamestate):\\n\\n if listOfPossibleMoves:\\n maximum = -math.inf\\n posisi = Posisi.Posisi(-999, -999)\\n\\n # Kalau currentPlayer adalah player 1, titik acuan adalah (boardSize-1, boardSize-1)\\n if (gamestate.currentPlayer.noPlayer == 1):\\n M = Posisi.Posisi(gamestate.board.size - 1, gamestate.board.size - 1)\\n\\n # Kalau currentPlayer adalah player 2, titik acuan adalah (0, 0)\\n else:\\n M = Posisi.Posisi(0, 0)\\n\\n # Mengambil aksi tunggal terbaik berdasarkan heuristic function\\n for i in listOfPossibleMoves:\\n value = -i.euclidean(M)\\n if value > maximum:\\n posisi = i\\n maximum = value\\n return posisi\",\n \"def __get_next_greedy_move(self, game_state): \\n best_move = None\\n best_score = None\\n for free_seat in self.__get_free_seats(game_state):\\n next_game_state_score = self.__get_score(game_state, free_seat)\\n if best_score is None:\\n best_score = next_game_state_score\\n best_move = free_seat\\n continue\\n if next_game_state_score > best_score:\\n best_score = next_game_state_score\\n best_move = free_seat\\n return best_move\",\n \"def compute_best_move(board):\\n best_node = alphabeta([0, []], depth_tree, float(\\\"-inf\\\"), float(\\\"inf\\\"), True, board)\\n\\n if (best_node[0] == float(\\\"inf\\\")) and (len(best_node[1]) == 1):\\n return best_node[1][0], True\\n return best_node[1][0], False\",\n \"def best_move(self) -> tuple:\\n if self.root_state.winner != GameMeta.PLAYERS['none']:\\n return GameMeta.GAME_OVER\\n\\n # choose the move of the most simulated node breaking ties randomly\\n max_value = max(self.root.children.values(), key=lambda n: n.N).N\\n max_nodes = [n for n in self.root.children.values() if n.N == max_value]\\n bestchild = choice(max_nodes)\\n return bestchild.move\",\n \"def find_best_move(state) -> None:\\n C: float = 2 ** 0.5 # Start with sqrt(2)\\n idx: int = -1\\n win_ratio: float = 0\\n\\n # First create the root node for the game\\n root = State(None, None, state)\\n root = expand(root)\\n root = run_each_move(root, state)\\n print(\\\"New Move UCBs: \\\")\\n for m in range(len(root.move_ucbs)):\\n print(\\\" \\\", root.move_ucbs[m])\\n \\n #while True:\\n for _ in range(10000):\\n _, win_ratio = MCTS(root, state, True, win_ratio) \\n #print(\\\"\\\\n\\\\n\\\\nRoot Attempts: \\\", root.attempts)\\n #print(\\\"Back in main\\\\n\\\\n\\\\n\\\")\\n \\n # Pick one of the highest ranked by UCB's\",\n \"def get_move(self, game, time_left):\\n self.time_left = time_left\\n self.best_move = (-1, -1)\\n\\n try:\\n i = 1\\n while True:\\n self.best_move = self.alphabeta(game, i)\\n i = i + 1\\n return self.best_move\\n except SearchTimeout:\\n return self.best_move\\n\\n return self.best_move\",\n \"def get_best_moves(self):\\n return self.best_moves\",\n \"def move(self, board):\\r\\n self.start_time = time.time()\\r\\n disk_total = self.get_disk_count(self.my_color, board) + self.get_disk_count(self.opponent_color, board)\\r\\n\\r\\n if disk_total < 15:\\r\\n # In early-game, we can allow a deeper minimax search since there's not too many possible moves.\\r\\n self.minimax_max_depth = 7\\r\\n\\r\\n elif disk_total < 45:\\r\\n # In mid-game, minimax tree has the most branches. Therefore, we must give it space to breathe.\\r\\n self.minimax_max_depth = 5\\r\\n else:\\r\\n # In the very end-game, minimax tree has the least branches, so we can allow a full search.\\r\\n self.minimax_max_depth = 8\\r\\n\\r\\n possible_moves = self.find_possible_moves(board, self.my_color)\\r\\n\\r\\n # If there's only one move available, return it\\r\\n if len(possible_moves) == 1:\\r\\n return possible_moves[0]\\r\\n\\r\\n # If we can take a corner, take it and don't consider any other options.\\r\\n # This rarely backfires and allows to save a tiny bit of time\\r\\n corners = [(0,0), (0,7), (7,0), (7,7)]\\r\\n for corner in corners:\\r\\n if corner in possible_moves:\\r\\n return corner\\r\\n\\r\\n # Grow a minimax tree to find the best available move\\r\\n alpha_init = -10000000\\r\\n beta_init = 10000000\\r\\n\\r\\n available_moves = self.minimax(board, 0, self.my_color, alpha_init, beta_init)\\r\\n print(available_moves)\\r\\n if available_moves != 0:\\r\\n best_value = max(available_moves.values())\\r\\n for move in available_moves:\\r\\n if available_moves[move] == best_value:\\r\\n return move\\r\\n\\r\\n return None\",\n \"def next_move(self, board):\\n \\n return self.best_move(self.score_columns(board))\",\n \"def get_move(self, game, time_left):\\n self.time_left = time_left\\n \\n max_depth = game.height * game.width\\n \\n \\n # FIRST, check initial conditions wrt legal_moves\\n legal_moves = game.get_legal_moves()\\n # If there are no legal_moves return no move\\n if len(legal_moves) == 0:\\n return (-1,-1)\\n # If there's only one legal_move return it, the only choice\\n elif len(legal_moves) == 1:\\n return legal_moves[0]\\n # Otherwise, initialize best_choice at first legal_move\\n else:\\n best_move = legal_moves[0]\\n try:\\n for node_number in range(1, max_depth + 1):\\n # This exception handing returns the best_move found\\n # thus far in the event of a timeout\\n \\n best_move = self.alphabeta(game, node_number)\\n return best_move\\n except SearchTimeout:\\n pass\\n \\n # Return the best_move found thus far (or ultimately in the event\\n # of exhaustive search completion or timeout)\\n return best_move\",\n \"def __find_best(self):\\n # First look for offensive moves\\n for i in range(0, 3):\\n col = self.__get_col(i)\\n if len(col.get('empty')) == 1:\\n if col.get(self.opponent_char) == 2:\\n return col.get('empty')[0]\\n for i in range(0, 3):\\n row = self.__get_row(i)\\n if len(row.get('empty')) == 1:\\n if row.get(self.opponent_char) == 2:\\n return row.get('empty')[0]\\n for i in range(0, 2):\\n diag = self.__get_diag(i)\\n if len(diag.get('empty')) == 1:\\n if diag.get(self.opponent_char) == 2:\\n return diag.get('empty')[0]\\n\\n # Then check again looking for defensive moves\\n for i in range(0, 3):\\n col = self.__get_col(i)\\n if len(col.get('empty')) == 1:\\n if col.get(self.player_char) == 2:\\n return col.get('empty')[0]\\n for i in range(0, 3):\\n row = self.__get_row(i)\\n if len(row.get('empty')) == 1:\\n if row.get(self.player_char) == 2:\\n return row.get('empty')[0]\\n for i in range(0, 2):\\n diag = self.__get_diag(i)\\n if len(diag.get('empty')) == 1:\\n if diag.get(self.player_char) == 2:\\n return diag.get('empty')[0]\\n\\n ##### CLEAN THIS METHOD UP LATER #####\\n return None\",\n \"def minimax(board):\\n if (terminal(board)):\\n return None\\n if (player(board) == X):\\n best_so_far = -2\\n else:\\n best_so_far = 2\\n best_move = ()\\n for move in actions(board):\\n new_board = [row[:] for row in board]\\n res = recursion(result(new_board, move))\\n if (player(board) == X):\\n if (res > best_so_far):\\n best_so_far = res\\n best_move = move\\n if (res == 1):\\n break\\n else:\\n if (res < best_so_far):\\n best_so_far = res\\n best_move = move\\n if (res == -1):\\n break\\n \\n return best_move\",\n \"def search(self, is_max, possible_moves, state, depth, alpha, beta):\\n temp_state = state.deepcopy()\\n best_move = None\\n best_move_val = float('-inf') if is_max else float('inf')\\n \\n for move in possible_moves:\\n for to in move['to']:\\n \\n if time() > self.thinking_time:\\n return best_move, best_move_val\\n \\n temp_state.board.move_pawn(move['from'], to)\\n temp_state.next_turn()\\n _, val = self.minimax(temp_state, not(is_max), depth+1, alpha, beta)\\n \\n temp_state.board.move_pawn(to, move['from'])\\n temp_state.undo_turn()\\n \\n if is_max and val > best_move_val:\\n alpha = max(val, alpha)\\n best_move_val = val\\n best_move = (move['from'], to)\\n \\n if not(is_max) and val < best_move_val:\\n beta = min(val, beta)\\n best_move_val = val\\n best_move = (move['from'], to)\\n \\n if beta <= alpha: #pruning\\n return best_move, best_move_val\\n \\n return best_move, best_move_val\",\n \"def get_best_move(board, scores):\\n if board.check_win()==None:\\n maximum=-999\\n ret_location=(0,0)\\n empty=board.get_empty_squares()\\n #print empty\\n for position in empty:\\n if scores[position[0]][position[1]]>maximum:\\n maximum=scores[position[0]][position[1]]\\n #print max\\n ret_location=(position[0],position[1])\\n return ret_location\\n else:\\n return None\",\n \"def get_move(self, board, possible_moves):\\n next_move = None\\n max_score = -float('Inf')\\n self.start_time = datetime.now()\\n for depth in range(2,3): # iterative deepening\\n try:\\n for move in possible_moves:\\n board_copy = deepcopy(board)\\n self.man.play_move(board_copy, move, self.color)\\n score = self.minimaxm(depth, board, False)\\n if score > max_score:\\n max_score = score\\n next_move = move\\n\\n except TimeoutError:\\n print(\\\"ran out of time\\\")\\n break\\n return next_move\",\n \"def get_move(self, game, time_left):\\n self.time_left = time_left\\n # check if new game, reset if it is\\n self.check_reset(game)\\n best_move = (-1, -1)\\n depth = 1\\n\\n try:\\n while True:\\n # each completed iteration will be more accurate than previous, so update best move with each iteration's\\n # root node (max) result\\n best_move = self.alphabeta(game=game, depth=depth)\\n\\n # return best move if no result\\n if best_move == (-1, -1):\\n # DEBUG_PRINT\\n # print('D={} MOVING TO=> {} from TERM\\\\n'.format(depth, best_move))\\n return best_move\\n\\n # global stats\\n # stats['max_search_depth'] = max(depth, stats['max_search_depth'])\\n depth += 1\\n\\n\\n except SearchTimeout:\\n # DEBUG_PRINT\\n # print('D={} MOVING TO=> {}\\\\n\\\\n\\\\n'.format(stats['max_search_depth'], best_move))\\n pass\\n\\n return best_move\",\n \"def get_move(self, game, legal_moves, time_left):\\n\\n self.time_left = time_left\\n\\n if not legal_moves:\\n return (-1, -1)\\n\\n move = None\\n try:\\n algorithm_name = getattr(self, self.method) # Method selected to get move (minimax, alphabeta)\\n if self.iterative:\\n depth = 1 # Depth used for iterative deepening\\n while True:\\n _, move = algorithm_name(game, depth)\\n depth += 1\\n else:\\n _, move = algorithm_name(game, self.search_depth)\\n except Timeout:\\n return move # A timeout has occurred, return best move so far\\n return move\",\n \"def getbestMove(board, player, computer):\\n bestScore = -100\\n bestMove = -1\\n\\n for i in range(1, len(board) + 1):\\n if board[i] == ' ':\\n # make a move for the computer\\n board[i] = computer\\n\\n # find value for potential move\\n curScore = valueOfBoard(board, False, 0, player, computer)\\n # clear move\\n board[i] = ' '\\n\\n if curScore > bestScore:\\n bestMove = i\\n bestScore = curScore\\n\\n return bestMove\",\n \"def get_move(self, game, time_left):\\n self.time_left = time_left\\n\\n # Initialize the best move so that this function returns something\\n # in case the search fails due to timeout\\n best_move = (-1, -1)\\n\\n # Opening Book\\n # Check if my agent is first to move\\n # If yes, use opening book\\n if (game._board_state[-1] == None):\\n if (not game.get_legal_moves()):\\n return best_move\\n else:\\n best_move = (4,4)\\n return best_move\\n\\n try:\\n # The try/except block will automatically catch the exception\\n # raised when the timer is about to expire.\\n\\n # Iterative Deepning, stop when timeout\\n depth = 0\\n while (True):\\n depth += 1\\n best_move = self.alphabeta(game, depth)\\n\\n except SearchTimeout:\\n pass # Handle any actions required after timeout as needed\\n\\n # Return the best move from the last completed search iteration\\n return best_move\",\n \"def choose_move(self):\\n return 0\",\n \"def get_best_move(position, max_its=None, return_positions=False):\\n max_its = max_its or MAX_ITS\\n\\n # init the positions list\\n positions = [position]\\n original_to_move = position.to_move\\n moves_made = len(position.moves)\\n to_move = original_to_move\\n\\n i = 1\\n while True:\\n LOG.info(f'getting positions for it {i}, with {original_to_move} to move')\\n positions = extend_positions(positions, to_move)\\n\\n # filter the positions\\n positions = filter_positions(positions, i, to_move)\\n to_move = invert_color(to_move)\\n\\n if i == max_its:\\n break\\n\\n i += 1\\n\\n\\n# if best_position.mate and best_position.checked:\\n# # if the best move is mate then its over\\n# print(f'It is mate, returning None best move for {to_move}')\\n# best_move = None\\n# else:\\n# best_move = best_position.moves[0]\\n\\n if not positions:\\n return 'checkmate', None\\n\\n # need to re-sort, as will be ordered by the last to_move\\n positions.sort(key=lambda x: x.score, reverse=original_to_move=='white')\\n\\n # because of zero indexing, the first NEXT move is at moves_made in list\\n best_move = positions[0].moves[moves_made]\\n\\n if return_positions:\\n return best_move, positions\\n\\n return best_move\",\n \"def search_best_next_move(self, model, initial_tree_node):\\n\\n # EDIT THIS METHOD TO RETURN BEST NEXT POSSIBLE MODE FROM MINIMAX MODEL ###\\n\\n # NOTE: Don't forget to initialize the children of the current node\\n # with its compute_and_get_children() method!\\n\\n alpha = float('-inf')\\n beta = float('inf')\\n depth = 4\\n player = 0\\n ##send root node and these parameters to minimax\\n start_time = time()\\n move = self.alpha_beta(initial_tree_node, player, alpha, beta, start_time)\\n #random_move = random.randrange(5)\\n return ACTION_TO_STR[move[1]]\",\n \"def minimax(board):\\n if terminal(board):\\n return None\\n \\n if player(board) == O:\\n v = math.inf\\n for action in actions(board):\\n move = max_value(result(board, action))\\n if move < v:\\n v = move\\n bestMove = action\\n\\n else:\\n v = -math.inf\\n for action in actions(board):\\n move = min_value(result(board, action))\\n if move > v:\\n v = move\\n bestMove = action\\n \\n return bestMove\",\n \"def get_move(self, game, time_left):\\n self.time_left = time_left\\n\\n # Initialize the best move so that this function returns something\\n # in case the search fails due to timeout\\n\\n best_move = (-1, -1)\\n\\n while self.time_left() > self.TIMER_THRESHOLD:\\n self.update(game)\\n\\n if not self.is_terminal(game):\\n moves = game.get_legal_moves()\\n scores = [(self._plays[game.forecast_move(m)], m) for m in moves]\\n _, best_move = max(scores, key=lambda s: s[0])\\n\\n return best_move\",\n \"def find_best_move(board, score):\\n\\n global UP, DOWN, LEFT, RIGHT\\n global move_args\\n\\n max_depth = 1\\n if score <= 10000:\\n max_depth += 0\\n elif score <= 20000:\\n max_depth += 1\\n else:\\n max_depth += 2\\n\\n pool = mp.Pool(mp.cpu_count())\\n\\n result = [pool.apply(score_toplevel_move, args=(i, board, max_depth)) for i in range(len(move_args))]\\n best_move = result.index(max(result))\\n\\n pool.close()\\n\\n for m in move_args:\\n print(\\\"move: %d score: %.4f\\\" % (m, result[m]))\\n\\n return best_move\",\n \"def minimax(board):\\n current_player = player(board)\\n if(terminal(board)):\\n return None\\n\\n if (board == initial_state()):\\n x = random.choice([0, 2])\\n y = random.choice([0, 2])\\n return(x, y)\\n\\n if(current_player == X):\\n v = -math.inf\\n for action in actions(board):\\n mini = min_value(result(board, action))\\n if mini > v:\\n v = mini\\n best_move = action\\n else:\\n v = math.inf\\n for action in actions(board):\\n maxi = max_value(result(board, action))\\n if maxi < v:\\n v = maxi\\n best_move = action\\n return best_move\",\n \"def bestMove(self, depth, state, curr_player):\\n\\n # determine opponent's color\\n if curr_player == self.colors[0]:\\n opp_player = self.colors[1]\\n else:\\n opp_player = self.colors[0]\\n\\n # enumerate all legal moves\\n legal_moves = {} # will map legal move states to their alpha values\\n for col in range(COLUMN):\\n # if column i is a legal move...\\n if self.isLegalMove(col, state):\\n # make the move in column 'col' for curr_player\\n temp = self.makeMove(state, col, curr_player)\\n legal_moves[col] = -self.search(depth - 1, temp, opp_player)\\n\\n best_alpha = -float(\\\"inf\\\")\\n best_move = None\\n moves = legal_moves.items()\\n random.shuffle(list(moves))\\n for move, alpha in moves:\\n if alpha >= best_alpha:\\n best_alpha = alpha\\n best_move = move\\n\\n return best_move, best_alpha\",\n \"def move(self, gstate: gamestate.Gamestate) -> util.Move:\\n moves = gstate.legal_moves_vector(gstate.agents[self.id])\\n scores = {move: self.evaluate(gstate.copy, move) for move in moves}\\n max_score = max(scores.values())\\n max_moves = [move for move in moves if scores[move] == max_score]\\n return random.choice(max_moves)\",\n \"def minimax(self, game, depth):\\n self.__check_time()\\n\\n legal_moves = game.get_legal_moves()\\n\\n if len(legal_moves) == 0:\\n return (-1, -1)\\n else:\\n # ensure we return something\\n best_move = legal_moves[0]\\n best_move_score = float('-inf')\\n try:\\n for move in legal_moves:\\n # search with depth limit decreasing as each ply is visited\\n move_score = self.__min_value(game.forecast_move(move), depth - 1)\\n if move_score > best_move_score:\\n best_move_score = move_score\\n best_move = move\\n\\n except SearchTimeout:\\n pass\\n\\n return best_move\",\n \"def minimax(self, game, depth):\\n if self.time_left() < self.TIMER_THRESHOLD:\\n raise SearchTimeout()\\n\\n moves = game.get_legal_moves(game.active_player)\\n if not moves:\\n return -1, -1\\n\\n scores = [(self.minimax_value(game.forecast_move(m), depth - 1), m) for m in moves]\\n _, move = max(scores, key=lambda s: s[0]) # Only order on score, not move tuple.\\n\\n return move\",\n \"def determineNextMove(playerLocation, opponentLocation, coins):\\n global packages, route_table, best_path, best_weight, route\\n if len(best_path) == 0:\\n current_package = packages.pop(0)\\n exhaustive(current_package, playerLocation, [], 0, (route_table,dists))\\n api.debug(best_path)\\n return u.direction(playerLocation, best_path.pop(0))\",\n \"def bestMove(self, depth, state, curr_player):\\n \\n # determine opponent's color\\n if curr_player == self.colors[0]:\\n opp_player = self.colors[1]\\n else:\\n opp_player = self.colors[0]\\n \\n # enumerate all legal moves\\n legal_moves = {} # will map legal move states to their alpha values\\n for col in range(7):\\n # if column i is a legal move...\\n if self.isLegalMove(col, state):\\n # make the move in column 'col' for curr_player\\n temp = self.makeMove(state, col, curr_player)\\n legal_moves[col] = -self.search(depth-1, temp, opp_player)\\n \\n best_alpha = -99999999\\n best_move = None\\n moves = legal_moves.items()\\n random.shuffle(list(moves))\\n for move, alpha in moves:\\n if alpha >= best_alpha:\\n best_alpha = alpha\\n best_move = move\\n \\n return best_move, best_alpha\",\n \"def minimax(board):\\n #This function will return the best move. \\n #If Ai is playing as X, I can reduce the processing time by creating a random first move. \\n if (board == initial_state()):\\n coord1 = randint(0,2)\\n coord2 = randint(0,2)\\n return ((coord1,coord2))\\n #first I determine which player's turn it is\\n player_to_move = player(board)\\n best_action = None\\n #If I am X\\n if(player_to_move == \\\"X\\\"):\\n current_max = float('-inf')\\n #for every possible action I have right now, I'll call my \\\"future\\\" Min_Value since I will asume what will happen if I take this move.\\n for action in actions(board):\\n #peak on the future if I take that move\\n curr_score = Min_Value(result(board,action))\\n #if my future is favorable, I will store it as my current best option.\\n if curr_score>= current_max:\\n current_max = curr_score\\n best_action = action\\n else:\\n #If I am O, I do something similar. \\n current_max = float('inf')\\n #for every action I peak on the future for favorable results\\n for action in actions(board):\\n #this time, however, it would be X's turn so I need to start with Max_Value\\n curr_score = Max_Value(result(board,action))\\n #if my future is favorable, I store it\\n if curr_score<= current_max:\\n current_max = curr_score\\n best_action = action\\n #I return the best move.\\n return best_action\",\n \"def find_best_move(board):\\n new_board = board.get_board()\\n\\n # X | X | X <-- Check for win on this row\\n # ---------\\n # 3 | 4 | 5\\n # ---------\\n # 6 | 7 | 9\\n if new_board[0] == new_board[1] and new_board[2] == \\\"2\\\":\\n return 2\\n elif new_board[0] == new_board[2] and new_board[1] == \\\"1\\\":\\n return 1\\n elif new_board[1] == new_board[2] and new_board[0] == \\\"0\\\":\\n return 0\\n\\n # 0 | 1 | 2\\n # ---------\\n # X | X | X <-- Check for win on this row\\n # ---------\\n # 6 | 7 | 9\\n elif new_board[3] == new_board[4] and new_board[5] == \\\"5\\\":\\n return 5\\n elif new_board[3] == new_board[5] and new_board[4] == \\\"4\\\":\\n return 4\\n elif new_board[4] == new_board[5] and new_board[3] == \\\"3\\\":\\n return 3\\n\\n # 0 | 1 | 2\\n # ---------\\n # 3 | 4 | 5\\n # ---------\\n # X | X | X <-- Check for win on this row\\n elif new_board[6] == new_board[7] and new_board[8] == \\\"8\\\":\\n return 8\\n elif new_board[6] == new_board[8] and new_board[7] == \\\"7\\\":\\n return 7\\n elif new_board[7] == new_board[8] and new_board[6] == \\\"6\\\":\\n return 6\\n\\n # X | 1 | 2 Check for win on column one\\n # ---------\\n # X | 4 | 5\\n # ---------\\n # X | 7 | 9\\n elif new_board[0] == new_board[3] and new_board[6] == \\\"6\\\":\\n return 6\\n elif new_board[0] == new_board[6] and new_board[3] == \\\"3\\\":\\n return 3\\n elif new_board[6] == new_board[3] and new_board[0] == \\\"0\\\":\\n return 0\\n\\n # 0 | X | 2 Checks for win on column two\\n # ---------\\n # 3 | X | 5\\n # ---------\\n # 6 | X | 9\\n elif new_board[1] == new_board[4] and new_board[7] == \\\"7\\\":\\n return 7\\n elif new_board[1] == new_board[7] and new_board[4] == \\\"4\\\":\\n return 4\\n elif new_board[7] == new_board[4] and new_board[0] == \\\"0\\\":\\n return 0\\n\\n # 0 | 1 | X\\n # ---------\\n # 3 | 4 | X\\n # ---------\\n # 6 | 7 | X\\n elif new_board[2] == new_board[5] and new_board[8] == \\\"8\\\":\\n return 8\\n elif new_board[2] == new_board[8] and new_board[5] == \\\"5\\\":\\n return 5\\n elif new_board[8] == new_board[5] and new_board[2] == \\\"2\\\":\\n return 2\\n\\n # X | 1 | 2\\n # ---------\\n # 3 | X | 5\\n # ---------\\n # 6 | 7 | X\\n elif new_board[0] == new_board[4] and new_board[8] == \\\"8\\\":\\n return 8\\n elif new_board[0] == new_board[8] and new_board[4] == \\\"4\\\":\\n return 4\\n elif new_board[8] == new_board[4] and new_board[0] == \\\"0\\\":\\n return 0\\n\\n # 0 | 1 | X\\n # ---------\\n # 3 | X | 5\\n # ---------\\n # X | 7 | 9\\n elif new_board[2] == new_board[4] and new_board[6] == \\\"6\\\":\\n return 6\\n elif new_board[2] == new_board[6] and new_board[4] == \\\"4\\\":\\n return 4\\n elif new_board[6] == new_board[4] and new_board[2] == \\\"2\\\":\\n return 2\\n\\n # If corners are empty, play there\\n elif new_board[0] == \\\"0\\\" or new_board[2] == \\\"2\\\" or new_board[6] == \\\"6\\\" or new_board[8] == \\\"8\\\":\\n try_spot = 0\\n while True:\\n if new_board[try_spot] != \\\"X\\\" and new_board[try_spot] != \\\"O\\\":\\n return try_spot\\n else:\\n try_spot = try_spot + 2\\n\\n # If middle is empty, play there\\n elif new_board[4] == \\\"4\\\":\\n return 4\\n\\n # Finally if edges are empty try there\\n elif new_board[1] == \\\"1\\\" or new_board[3] == \\\"3\\\" or new_board[5] == \\\"5\\\" or new_board[7] == \\\"7\\\":\\n try_spot = 1\\n while True:\\n if new_board[try_spot] != \\\"X\\\" and new_board[try_spot] != \\\"O\\\":\\n return try_spot\\n else:\\n try_spot = try_spot + 2\",\n \"def get_move(self, game, time_left):\\n self.time_left = time_left\\n\\n # Initialize the best move so that this function returns something\\n # in case the search fails due to timeout\\n best_move = (-1, -1)\\n\\n if self._iterative:\\n depth = 0\\n try:\\n while depth < len(game.get_blank_spaces()):\\n depth += 1\\n best_move = self.alphabeta(game, depth)\\n except SearchTimeout:\\n if depth == 1:\\n print(\\\"Warning: An iterative AlphaBetaPlayer timed out on \\\"\\n \\\"the first level. Match time_limit too short?\\\")\\n if depth >= 1:\\n self._depths.append(depth)\\n else:\\n try:\\n best_move = self.alphabeta(game, self.search_depth)\\n except SearchTimeout:\\n print(\\\"Warning: A fixed-depth AlphaBetaPlayer timed out. \\\"\\n \\\"Match time_limit too short or search_depth too high.\\\")\\n\\n return best_move\",\n \"def get_move(self, board_state, letter):\\n board_hash = hash_value(board_state)\\n qvals = self.get_q(board_hash)\\n freeMoves = TicTacToeGame.getFreePositions(board_state) \\n \\n while True:\\n bestMove = np.argmax(qvals) # type: int\\n if bestMove in freeMoves:\\n self.q[board_hash] = qvals\\n self.move_history.append((board_hash, bestMove))\\n return bestMove, qvals\\n else:\\n qvals[bestMove] = -1.0\",\n \"def get_move(self):\\n if self._difficulty == 0:\\n return self._get_easy_move()\\n else:\\n # Different stategies/difficulties can be attached here\\n return\",\n \"def next_move(self):\\n\\n # Calculate all paths to destination from current location and time.\\n solution = self.calculate_best_solution((None, None), self.currentTurn, [self.character.path[-1]],\\n self.character.spent)\\n\\n # Add travel weight to spent.\\n if solution[1] is not None and solution[1][0] != solution[1][1]:\\n self.character.spent += self.pekingMap.get_vertex(solution[1][0]).weight(solution[1][1])\\n\\n # Return next point in shortest path to location.\\n if solution[1] is not None:\\n return solution[1][1]\\n\\n return None\",\n \"def get_move(self, game, time_left):\\n self.time_left = time_left\\n\\n # Initialize the best move so that this function returns something\\n # in case the search fails due to timeout\\n if not game.get_legal_moves():\\n return (-1,-1)\\n best_move = game.get_legal_moves()[0]\\n search_depth = 1\\n\\t\\n try:\\n while(self.time_left()>self.TIMER_THRESHOLD):\\n last_move = best_move\\n best_move = self.alphabeta(game,search_depth)\\n search_depth += 1\\n\\n except SearchTimeout:\\n best_move = last_move\\n return best_move\\n\\t\\n # Return the best move from the last completed search iteration\\n return best_move\",\n \"def best_move(self, game:'Game', real_game=False, max_iters=900, # noqa: E0602, F821\\n ai_move=True, verbose=False) -> str:\\n best_move = '00000' # Null move\\n if real_game:\\n policy = self.predict_policy(game)\\n best_move = game.get_legal_moves()[np.argmax(policy)]\\n else:\\n if game.get_result() is None:\\n current_tree = mctree.SelfPlayTree(\\n game,\\n threads=self.num_threads)\\n best_move = current_tree.search_move(self, max_iters=max_iters,\\n verbose=verbose,\\n ai_move=ai_move)\\n\\n return best_move\",\n \"def bestMove(self, g, p): # Super Minimax specially adapted for morpions\\n choices = self.getChoices(g)\\n if len(choices) == 1:\\n w = self.outcome(self.choose(g, choices[0], p))\\n return (choices[0], w)\\n else:\\n moves = []\\n for choice in choices:\\n ng = self.choose(g, choice, p)\\n w = self.outcome(ng)\\n if w == p:\\n # We already won, so it's needless to compute further\\n return (choice, w)\\n moves.append(self.bestMove(ng, -p))\\n # We use the fact that p equals 1 or -1\\n m = max(moves, key=lambda x: p * x[1])\\n return [choices[moves.index(m)], m[1]]\",\n \"def best_move(self, depth, board, current_player, opposite_player):\\n legal_moves = {}\\n for column in range(7):\\n if board.get_hole(column, 0) is 0:\\n temp = deepcopy(board)\\n temp.set_column(column, current_player)\\n legal_moves[column] = -self.search(depth-1, temp, opposite_player, current_player)\\n \\n best_alpha = -99999999\\n best_move = None\\n moves = legal_moves.items()\\n random.shuffle(list(moves))\\n for move, alpha in moves:\\n if alpha > best_alpha:\\n best_alpha = alpha\\n best_move = [move]\\n elif alpha == best_alpha and best_alpha is not -99999999:\\n if (best_move is not None):\\n best_move.append(move)\\n \\n return best_move, best_alpha\",\n \"def get_move(self, board, possible_moves, player_1_or_2):\\n\\n # Given a Tic-Tac-Toe 3x3 board position where 1 => current player's square,\\n # -1 => opponent's square, 0 => blank square,\\n # this will return the current player's best move [as the x and y indexes into \\n # the board array.]\\n # The second input parameter, player_1_or_2, is 1 or -1 to indicate which player's\\n # move it is. \\n \\n print('RL ~ Current player 1 or 2 (= -1):', player_1_or_2)\\n \\n print('RL ~ Current board: ')\\n print(board)\\n \\n print('RL ~ possible_moves:', possible_moves)\\n\\n next_move = () \\n\\n # This will be the best move i.e. the move with the current\\n # value of highest winning probability except when it is making exploratory\\n # (as opposed to greedy) moves.\\n\\n next_move = self.board_position_states.get_next_move(board, possible_moves, self.current_player)\\n\\n next_move_location_tuple = possible_moves[next_move]\\n board[next_move_location_tuple] = self.current_player\\n\\n self.list_board_positions_moved_to.append(board.copy()) # This board that we are\\n # appending here could be changed by the next line of code, for example.\\n # Hence we need to make a copy\\n\\n board[next_move_location_tuple] = 0 # undo the move in case it affects the calling method.\\n\\n return next_move\",\n \"def minimax(self, board: str):\\n \\\"\\\"\\\" Your Code Here \\\"\\\"\\\"\\n\\n # ----------------------------BEGIN CODE----------------------------\\n # disclaimer: I did only minimax (as said on the homework description https://drive.google.com/file/d/1JXBi_5JB8fwTWX34j0ZwN5YwjoIexh-Z/view)\\n # my minimax does NOT try to prolong the game. It always goes for the best move!\\n\\n # initializing default values\\n moveToMake = validMoves(board)[0]\\n bestValue = -100\\n\\n # this will find the best move to go to, since value only returns the best score\\n # util's setMove creates a copy, so we can use that as the successor\\n for validmove in validMoves(board):\\n currentValue = self.value(\\n setMove(board, validmove, whoseMove(board)), 0)\\n if currentValue > bestValue:\\n bestValue = currentValue\\n moveToMake = validmove\\n\\n # currently the val and game_length tuple is arbitrary because i have not implemented depth!\\n return (moveToMake)\",\n \"def get_best_move(board, scores): \\n empty_squares = board.get_empty_squares()\\n highest_score = None\\n best_pos = []\\n \\n for empty in range(len(empty_squares)):\\n pos = empty_squares[empty] \\n if highest_score == None:\\n highest_score = scores[pos[0]][pos[1]]\\n if scores[pos[0]][pos[1]] >= highest_score:\\n highest_score = scores[pos[0]][pos[1]]\\n \\n for empty in range(len(empty_squares)):\\n pos = empty_squares[empty]\\n if scores[pos[0]][pos[1]] == highest_score:\\n best_pos.append(pos) \\n return random.choice(best_pos)\",\n \"def get_move(self, game, time_left):\\n self.time_left = time_left\\n\\n # Initialize the best move so that this function returns something\\n # in case the search fails due to timeout\\n best_move = (-1, -1)\\n\\n try:\\n # The try/except block will automatically catch the exception\\n # raised when the timer is about to expire.\\n best_move = self.minimax(game, self.search_depth)\\n except SearchTimeout:\\n pass # Handle any actions required after timeout as needed\\n\\n # Return the best move from the last completed search iteration\\n return best_move\",\n \"def mm_move(board, player):\\n result = board.check_win() # get result of the current board\\n if result == None:\\n move_list = board.get_empty_squares() # get the tree branches and possible next moves\\n best = (None, (-1, -1))\\n for step in move_list:\\n bd_clone = board.clone()\\n bd_clone.move(step[0], step[1], player) #make a move on a cloned board\\n next_player = provided.switch_player(player)\\n next_score = mm_move(bd_clone, next_player) #make a recursive call to mm_move() pasing the cloned board and the 'other' player\\n if player == 3: #if it is oppo O--min\\n if best[0] == None or (next_score[0] < best[0]):\\n best = (next_score[0], step)\\n #print best\\n elif player ==2: #if it is X--max\\n if best[0] == None or (next_score[0] > best[0]):\\n best = (next_score[0], step)\\n return best\\n else:\\n return SCORES[result], (-1, -1)\",\n \"def find_best_move(self, board, factor=2.0):\\n o = Node(board)\\n return MTCS(MAX_DEPTH, o, factor, self.id)\",\n \"def select_best_move(self, stats, depth, board, color):\\n\\n bestscore = 0\\n bestmove = None\\n total_n = 0\\n\\n for action in SP.available_actions(board):\\n next_board = board[:]\\n SP.play(next_board, action, color)\\n n, w = stats[MCTSRandomPlayer.to_board_id(next_board)]\\n if n == 0:\\n continue\\n total_n += n\\n if self.DEBUG: print('Move %d score: %d/%d (%0.1f%%)' % (action, w, n, w/n*100))\\n if n > bestscore or (n == bestscore and random.random() <= 0.5): # 가장 많이 방문해 본 길을 따라 간다. WHY???\\n bestmove = action\\n bestscore = n\\n\\n assert bestmove is not None\\n\\n if self.DEBUG: print('Maximum depth: %d, Total simulations: %d on %d' % (depth, total_n, MCTSRandomPlayer.to_board_id(board)))\\n\\n return bestmove\",\n \"def minimax_decision(gameState):\\n value = -sys.maxsize\\n best_value = -sys.maxsize\\n best_move = None\\n legal_moves = gameState.get_legal_moves()\\n for move in legal_moves:\\n game = gameState.forecast_move(move)\\n value = max(value, min_value(game))\\n if value > best_value:\\n best_value = value\\n best_move = move\\n return best_move\",\n \"def get_move(self, game, time_left):\\n\\n self.time_left = time_left\\n legal_moves = game.get_legal_moves()\\n if not legal_moves:\\n return (-1, -1)\\n\\n\\n # Initialize the best move so that this function returns something\\n # in case the search fails due to timeout\\n best_score = float(\\\"-inf\\\")\\n best_move = legal_moves[0]\\n\\n try:\\n # The try/except block will automatically catch the exception\\n # raised when the timer is about to expire.\\n self.search_depth = 1\\n while best_score is not float(\\\"inf\\\"):\\n best_move = self.alphabeta(game, self.search_depth, alpha=float(\\\"-inf\\\"), beta=float(\\\"inf\\\"))\\n self.search_depth += 1\\n except SearchTimeout:\\n return best_move\\n pass # Handle any actions required after timeout as needed\\n\\n # Return the best move from the last completed search iteration\\n return best_move\",\n \"def get_move(self, game, time_left):\\n self.time_left = time_left\\n\\n # Initialize the best move so that this function returns something\\n # in case the search fails due to timeout\\n self.best_move = (-1, -1)\\n\\n try:\\n # The try/except block will automatically catch the exception\\n # raised when the timer is about to expire.\\n move = self.minimax(game, self.search_depth)\\n return move\\n\\n except SearchTimeout:\\n pass # Handle any actions required after timeout as needed\\n\\n # Return the best move from the last completed search iteration\\n return self.best_move\",\n \"def get_move(self, game, time_left):\\n self.time_left = time_left\\n\\n # Initialize the best move so that this function returns something\\n # in case the search fails due to timeout\\n best_move = (-1, -1)\\n\\n try:\\n # The try/except block will automatically catch the exception\\n # raised when the timer is about to expire.\\n return self.minimax(game, self.search_depth)\\n\\n except SearchTimeout:\\n pass # Handle any actions required after timeout as needed\\n\\n # Return the best move from the last completed search iteration\\n return best_move\",\n \"def get_move(self, game, time_left):\\n self.time_left = time_left\\n\\n # Initialize the best move so that this function returns something\\n # in case the search fails due to timeout\\n best_move = (-1, -1)\\n\\n try:\\n # The try/except block will automatically catch the exception\\n # raised when the timer is about to expire.\\n return self.minimax(game, self.search_depth)\\n\\n except SearchTimeout:\\n pass # Handle any actions required after timeout as needed\\n\\n # Return the best move from the last completed search iteration\\n return best_move\",\n \"def get_move(self, game, time_left):\\n self.time_left = time_left\\n\\n # Initialize the best move so that this function returns something\\n # in case the search fails due to timeout\\n best_move = (-1, -1)\\n\\n try:\\n # The try/except block will automatically catch the exception\\n # raised when the timer is about to expire.\\n return self.minimax(game, self.search_depth)\\n\\n except SearchTimeout:\\n pass # Handle any actions required after timeout as needed\\n\\n # Return the best move from the last completed search iteration\\n return best_move\",\n \"def get_move(self, game, time_left):\\n self.time_left = time_left\\n\\n # Initialize the best move so that this function returns something\\n # in case the search fails due to timeout\\n best_move = (-1, -1)\\n\\n try:\\n # The try/except block will automatically catch the exception\\n # raised when the timer is about to expire.\\n return self.minimax(game, self.search_depth)\\n\\n except SearchTimeout:\\n pass # Handle any actions required after timeout as needed\\n\\n # Return the best move from the last completed search iteration\\n return best_move\",\n \"def get_move(self, game, time_left):\\n self.time_left = time_left\\n \\n # Initialize the best move so that this function returns something\\n # in case the search fails due to timeout\\n best_move = (-1, -1)\\n\\n try:\\n # The try/except block will automatically catch the exception\\n # raised when the timer is about to expire.\\n return self.minimax(game, self.search_depth)\\n\\n except SearchTimeout:\\n pass # Handle any actions required after timeout as needed\\n\\n # Return the best move from the last completed search iteration\\n return best_move\",\n \"def findNextMove(curHVal):\\n minHCalc = curHVal; #Initializing to curHVal\\n minNewPosition = (0,0); #Initializing it\\n\\n tempBoardPositions = [x for x in positions]\\n \\n for move in findPlacesToMove():\\n #Move number to blank\\n tempBoardPositions[curBlank[0]][curBlank[1]] = tempBoardPositions[move[0]][move[1]];\\n #Move blank to new move position\\n tempBoardPositions[move[0]][move[1]] = 0;\\n \\n hVal = heuristicValueOfPosition(tempBoardPositions);\\n if(hVal < minHCalc):\\n minHCalc = hVal;\\n minNewPosition = move;\\n \\n #Resetting board to what it was before this move\\n tempBoardPositions[move[0]][move[1]] = tempBoardPositions[curBlank[0]][curBlank[1]]\\n #tempBoardPositions[curBlank[0]][curBlank[1]] = 0;\\n\\n print(\\\"After calc -- {}\\\".format(positions))\\n \\n if minHCalc < curHVal:\\n return (minNewPosition, minHCalc)\\n else:\\n return -1; #That we have reached local minima\",\n \"def minimax(board):\\n\\n if len(actions(board)) == 9:\\n action = actions(board)\\n return action[random.randint(0, len(actions(board)) - 1)]\\n\\n if terminal(board):\\n return None\\n\\n best_move = None\\n\\n if player(board) == X:\\n best_score = -1\\n else:\\n best_score = 1\\n\\n for action in actions(board):\\n if player(board) == X:\\n score = minimize(result(board, action))\\n\\n if score >= best_score:\\n best_move = action\\n best_score = score\\n else:\\n score = maximize(result(board, action))\\n\\n if score <= best_score:\\n best_move = action\\n best_score = score\\n\\n return best_move\",\n \"def make_move(self, time_limit, players_score):\\n finish_time = time.time() + time_limit\\n depth = 1\\n best_move = (-np.inf, (-1, 0))\\n while True:\\n for direction in self.directions:\\n initial_state = utils.State(self.board, direction, self.pos, self.current_turn,\\n self.fruits_on_board_dict,\\n finish_time)\\n try:\\n outcome = self.minimax_algo.search(initial_state, depth, True)\\n if outcome[0] > best_move[0]:\\n best_move = outcome\\n except TimeoutError:\\n self.board[self.pos[0]][self.pos[1]] = -1\\n self.pos = (self.pos[0] + best_move[1][0], self.pos[1] + best_move[1][1])\\n self.board[self.pos[0]][self.pos[1]] = 1\\n\\n return best_move[1]\\n depth += 1\\n # print('bigger_depth : {} '.format(depth))\",\n \"def choose_move(self):\\r\\n \\r\\n return None\",\n \"def get_move(board, player):\\n #optimization to always pick the top-left corner on an empty board\\n if set(board) == set([config.NO_PLAYER]):\\n return 0\\n result = minimax(board, player, 2, config.NEG_INF, config.INF)\\n return result.index\",\n \"def next_move(board):\\n\\tcalculate_material(board)\\n\\n\\tif board.turn:\\n\\t\\tmax(board, DEPTH, -MAXVALUE, MAXVALUE)\\n\\telse:\\n\\t\\tmin(board, DEPTH, -MAXVALUE, MAXVALUE)\\n\\tif SAVED_MOVE is None:\\n\\t\\tprint(\\\"No moves found\\\")\\n\\telse:\\n\\t\\tprint(\\\"Saved move\\\", SAVED_MOVE)\\n\\treturn SAVED_MOVE\",\n \"def get_move(self, game, legal_moves, time_left):\\n logging.debug(\\\"get_move - legal moves: %s\\\", str(legal_moves))\\n \\n self.time_left = time_left\\n\\n\\n # Check if we have any legal moves\\n if not legal_moves:\\n return (-1, -1)\\n\\n # Let's set best move so far to be the first legal move so we always \\n # have something to return in case of timeout\\n self.best_move_so_far = legal_moves[0]\\n \\n \\n # Perform any required initializations, including selecting an initial\\n # move from the game board (i.e., an opening book), or returning\\n # immediately if there are no legal moves\\n\\n try:\\n # The search method call (alpha beta or minimax) should happen in\\n # here in order to avoid timeout. The try/except block will\\n # automatically catch the exception raised by the search method\\n # when the timer gets close to expiring\\n if self.iterative:\\n it = 1\\n while True:\\n if self.method == 'minimax':\\n _, self.best_move_so_far = self.minimax(game, it)\\n else:\\n _, self.best_move_so_far = self.alphabeta(game, it)\\n it += 1\\n else: \\n if self.method == 'minimax':\\n _, self.best_move_so_far = self.minimax(game, self.search_depth)\\n else:\\n _, self.best_move_so_far = self.alphabeta(game, self.search_depth)\\n\\n except Timeout:\\n # Handle any actions required at timeout, if necessary\\n logging.debug(\\\"Time is up - get_move returning: %s\\\", str(self.best_move_so_far))\\n return self.best_move_so_far\\n\\n # Return the best move from the last completed search iteration\\n logging.debug(\\\"get_move returning: %s\\\", str(self.best_move_so_far))\\n\\n return self.best_move_so_far\",\n \"def get_move(self, game, time_left):\\n\\n self.time_left = time_left\\n\\n # Perform any required initializations, including selecting an initial\\n # move from the game board (i.e., an opening book), or returning\\n # immediately if there are no legal moves\\n\\n remaining_legal_moves = game.get_legal_moves(game.active_player)\\n no_legal_moves = (-1, -1)\\n best_move = no_legal_moves\\n if not remaining_legal_moves:\\n return no_legal_moves\\n\\n # Use random IDS depth between 9 and 12\\n depth = random.randint(9, 12)\\n\\n try:\\n # Perform IDS\\n while True:\\n depth += 1\\n _, best_move = self.alphabeta(game, depth)\\n\\n if self.time_left() <= 0.001:\\n return best_move\\n\\n except Timeout:\\n # Handle any actions required at timeout, if necessary\\n return best_move\\n\\n # Return the best move from the last completed search iteration\\n return best_move\",\n \"def best_move(self, state, curr_player):\\n\\t\\t# determine opponent's color\\n\\t\\tif curr_player == self.colors[0]:\\n\\t\\t\\topp_player = self.colors[1]\\n\\t\\telse:\\n\\t\\t\\topp_player = self.colors[0]\\n\\n\\t\\treturn self.value(state, curr_player)\",\n \"def get_move(self, game, legal_moves, time_left):\\n\\n self.time_left = time_left\\n\\n # TODO: finish this function!\\n\\n # Perform any required initializations, including selecting an initial\\n # move from the game board (i.e., an opening book), or returning\\n # immediately if there are no legal moves\\n moves = game.get_legal_moves()\\n best_move = (-1, -1)\\n best_score = float(\\\"-inf\\\")\\n depth = 1\\n if moves:\\n best_move = moves[0]\\n else :\\n return best_move\\n \\n\\n try:\\n # The search method call (alpha beta or minimax) should happen in\\n # here in order to avoid timeout. The try/except block will\\n # automatically catch the exception raised by the search method\\n # when the timer gets close to expiring\\n if not self.iterative:\\n if self.method == 'minimax':\\n _, best_move = self.minimax(game, self.search_depth)\\n elif self.method == 'alphabeta':\\n _, best_move = self.alphabeta(game, self.search_depth) \\n else:\\n while self.time_left() > self.TIMER_THRESHOLD:\\n if self.method == 'minimax':\\n score, move = self.minimax(game, depth)\\n elif self.method == 'alphabeta':\\n score, move = self.alphabeta(game, depth)\\n if score > best_score:\\n best_move = move\\n depth += 1 \\n\\n\\n except Timeout:\\n # Handle any actions required at timeout, if necessary\\n pass\\n\\n # Return the best move from the last completed search iteration\\n return best_move\",\n \"def get_move(self, game, time_left):\\n legal_moves = game.get_legal_moves()\\n if not legal_moves:\\n return (-1, -1)\\n _, move = max([(self.score(game.forecast_move(m), self), m) for m in legal_moves])\\n return move\",\n \"def minimax(self, game, depth):\\n if self.time_left() < self.TIMER_THRESHOLD:\\n raise SearchTimeout()\\n # TODO: finish this function!\\n # raise NotImplementedError\\n\\n legal_moves = game.get_legal_moves() # obtain legal moves available to the board\\n best_move = (-1,-1) # initialisation of best move\\n best_score = -math.inf # abstraction of infinity\\n\\n for m in legal_moves: # for each ACTION, create a new state for its outcome, RESULT\\n new_state = game.forecast_move(m)\\n score = self.min_value(new_state, depth - 1) # recursion to calculate the score of that state\\n if score > best_score:\\n best_move = m\\n best_score = score\\n return best_move\",\n \"def get_move(self, game, time_left):\\n self.time_left = time_left\\n\\n # TODO: finish this function!\\n # Initialize the best move so that this function returns something\\n # in case the search fails due to timeout\\n best_move = (-1, -1)\\n # The try/except block will automatically catch the exception# raised when the timer is about to expire.\\n # Implementation of Iterative Deepening Search\\n\\n try:\\n depth = 0 # initialisation of depth\\n while True:\\n # runs along as game is still active\\n depth += 1 # increment depth after each search\\n best_move = self.alphabeta(game, depth) # apply alpha beta to search\\n\\n except SearchTimeout: # cutoff - when timer runs out\\n pass # Handle any actions required after timeout as needed\\n # Failure\\n\\n # Return the best move from the last completed search iteration\\n return best_move # Solution\",\n \"def get_best_move(self, curr_player, orig_player, depth=0, alpha=-100, beta=100):\\n\\n best_move, best_score = None, 0\\n is_over, winner = self.is_over()\\n\\n current_board_state = self.get_board_state()\\n current_board_state_best_move_score = self.state_best_move_map.get(current_board_state, {}).get(curr_player)\\n\\n if current_board_state_best_move_score:\\n return current_board_state_best_move_score\\n\\n # If game is over return the score\\n if is_over:\\n self.LEAF_COUNT += 1\\n if winner:\\n best_score = (10 - depth) * (1 if winner == orig_player else -1)\\n return best_move, best_score\\n\\n opponent = self.human if curr_player == self.comp else self.comp\\n\\n # Else find the move with best score.\\n for move in list(self.available_moves):\\n\\n self.play_move(move, curr_player)\\n _, score = self.get_best_move(opponent, orig_player, depth=depth+1, alpha=alpha, beta=beta)\\n\\n # If move is for orig-player, max score move will be best move.\\n if best_move is None or (curr_player == orig_player and score > best_score):\\n best_move, best_score = move, score\\n alpha = max(alpha, best_score)\\n # If move is not for orig-player, min score move will be best move.\\n elif best_move is None or (curr_player != orig_player and score < best_score):\\n best_move, best_score = move, score\\n beta = min(beta, best_score)\\n\\n self.undo_move(move)\\n\\n # If the maximum harm (-ve score) the opponent can do is less than the minimum benefit (+ve score) the\\n # current player has, no need to check other moves as the opponent can not do better.\\n if beta <= alpha:\\n break\\n\\n self.state_best_move_map[current_board_state] = self.state_best_move_map.get(current_board_state) or {}\\n self.state_best_move_map[current_board_state][curr_player] = best_move, best_score\\n return best_move, best_score\",\n \"def determineNextMove(player_location, opponentLocation, coins):\\n global route, currentcoin, meta_route, best_weight, best_path, coins_to_search, index\\n if opponentLocation in coins_to_search:\\n coins_to_search, meta_route, route = change_way(coins, opponentLocation, player_location)[:3]\\n index = 0\\n elif currentcoin == player_location: \\n if len(route) != 0:\\n old_dist = algo.dijkstra(mazeMap, player_location)[1][meta_route[index+1]]\\n coins_to_search2, meta_route2, route2, new_dist = change_way(coins, opponentLocation, player_location)\\n\\n #dist_matrix, route_matrix = u.update_dists_from_each(dists_matrix, routes_matrix, player_location, mazeMap, coins)\\n #coins_to_search = get_n_shortest(3, coins, player_location, dists_matrix)\\n \\t\\n #ennemy_dists = algo.dijkstra(mazeMap, opponentLocation)\\n #for c in coins_to_search:\\n #if len(coins_to_search) >= 2 and ennemy_dists[1][c] < dists_matrix[player_location][c]:\\n # coins_to_search.remove(c)\\n #break\\n \\t\\t\\n #best_weight = float(\\\"inf\\\")\\n #best_path = []\\n #exhaustive(coins_to_search, player_location, [], 0, dist_matrix)\\n #meta_route2 = [player_location] + best_path\\n #route2 = u.location_list_to_route(meta_route2, route_matrix)\\n #new_dist = dist_matrix[player_location][meta_route2[1]]\\n\\t\\t\\n if len(route) == 0 or old_dist - new_dist > 3:\\n route = route2\\n meta_route = meta_route2 \\n index = 0\\n index += 1\\n currentcoin = meta_route[index]\\n #api.debug(route)\\n return u.direction(player_location, route.pop(0))\",\n \"def movee(self):\\n\\n #return the initial state if he cant move and he's in the initial state\\n if not self.move and self.index == 0:\\n return self.path[self.index]\\n\\n #return the goal state if he's at the goal state\\n if self.index == len(self.path):\\n return self.path[-1]\\n\\n #return the next move and increments the index attribute\\n nextMove = self.path[self.index]\\n self.index += 1\\n\\n return nextMove\",\n \"def recursive_minimax_strategy(game: Game) -> Any:\\n candidate = game.current_state.get_possible_moves()\\n canditate_prop = [\\n -1 * get_state_score(game, game.current_state.make_move(\\n move\\n ))\\n # get_state_score() provides the STATE SCORE for the OPPO player\\n # in each resultent state.\\n # -1 * ... to calculate the STATE SCORE for player taking action now.\\n for move in candidate\\n ]\\n max_index = canditate_prop.index(max(canditate_prop))\\n assert len(canditate_prop) == len(candidate), \\\"Inconsistent length.\\\"\\n return candidate[max_index]\",\n \"def get_move(self, game, time_left):\\n self.time_left = time_left\\n\\n # Initialize the best move so that this function returns something\\n # in case the search fails due to timeout\\n if not game.get_legal_moves():\\n return (-1,-1)\\n best_move = game.get_legal_moves()[0]\\n\\n try:\\n # The try/except block will automatically catch the exception\\n # raised when the timer is about to expire.\\n return self.minimax(game, self.search_depth)\\n\\n except SearchTimeout:\\n pass # Handle any actions required after timeout as needed\\n\\n # Return the best move from the last completed search iteration\\n return best_move\",\n \"def best_move(game, method='score'):\\n board, score = game\\n\\n if method == 'score':\\n def val(g):\\n return g[1]\\n elif method == 'empty':\\n val = empty_squares\\n elif method == 'gradient':\\n val = gradient_value\\n else:\\n print('Invalid method given to best_move function')\\n exit(1)\\n\\n if score < 10000:\\n depth = 4\\n elif score < 20000:\\n depth = 5\\n elif score < 30000:\\n depth = 7\\n else:\\n depth = 7\\n\\n moves = possible_moves(board)\\n\\n # dict with score for each move\\n move_score = dict(zip(moves, [0] * len(moves)))\\n\\n # search for max score for each move\\n for move in moves:\\n move_score[move] += max_score_search(move_made(game, move), depth, val)\\n\\n move = max(move_score, key=move_score.get)\\n return move\",\n \"def get_move(self, game, time_left):\\n self.time_left = time_left\\n\\n # Initialize the best move so that this function returns something\\n # in case the search fails due to timeout\\n best_move = (-1, -1)\\n\\n try:\\n # The try/except block will automatically catch the exception\\n # raised when the timer is about to expire.\\n self.search_depth = 0\\n\\n while self.time_left() > self.TIMER_THRESHOLD:\\n self.search_depth += 1\\n best_move = self.alphabeta(game, self.search_depth)\\n\\n except SearchTimeout:\\n pass # Handle any actions required after timeout as needed\\n\\n # Return the best move from the last completed search iteration\\n return best_move\",\n \"def mm_move(board, player): \\r\\n if board.check_win() != None:\\r\\n score = SCORES[board.check_win()]\\r\\n return score, (-1,-1)\\r\\n else:\\r\\n best_score = -2\\r\\n score_list = []\\r\\n move_list = []\\r\\n for each_cell in board.get_empty_squares():\\r\\n passboard = board.clone()\\r\\n passboard.move(each_cell[0], each_cell[1], player) \\r\\n other_player = provided.switch_player(player)\\r\\n nextmove = mm_move(passboard, other_player)\\r\\n score_list.append(nextmove[0])\\r\\n move_list.append(nextmove[1])\\r\\n if nextmove[0] == SCORES[player]:\\r\\n return nextmove[0], each_cell\\r\\n #print score_list\\r\\n #print move_list\\r\\n #print \\\"\\\"\\r\\n if player == provided.PLAYERX:\\r\\n best_score = max(score_list)\\r\\n else:\\r\\n best_score = min (score_list)\\r\\n best_move = move_list[score_list.index(best_score)]\\r\\n return best_score, best_move\",\n \"def get_best_move(board, scores):\\r\\n blankies = board.get_empty_squares()\\r\\n max_score = scores[blankies[0][0]][blankies[0][1]]\\r\\n result = []\\r\\n for current in blankies:\\r\\n if scores[current[0]][current[1]] > max_score:\\r\\n max_score = scores[current[0]][current[1]]\\r\\n for current in blankies:\\r\\n if scores[current[0]][current[1]] == max_score:\\r\\n result.append(current)\\r\\n return random.choice(result)\",\n \"def get_move(self, game, legal_moves, time_left):\\n\\n self.time_left = time_left\\n move = (-1, -1) #Default\\n\\n # Perform any required initializations, including selecting an initial\\n # move from the game board (i.e., an opening book), or returning\\n # immediately if there are no legal moves\\n\\n max_depth = 0\\n try:\\n # The search method call (alpha beta or minimax) should happen in\\n # here in order to avoid timeout. The try/except block will\\n # automatically catch the exception raised by the search method\\n # when the timer gets close to expiring\\n if self.iterative:\\n #Perform iterative search\\n num_of_remaining_moves = len(game.get_blank_spaces())\\n for depth in range(1,num_of_remaining_moves):\\n if self.time_left() <= self.TIMER_THRESHOLD:\\n return move\\n\\n if self.method == 'alphabeta':\\n iterative_best_score, iterative_best_move = self.alphabeta(game, depth)\\n else:\\n iterative_best_score, iterative_best_move = self.minimax(game, depth)\\n\\n #Stores score and move of the deepest search\\n score = iterative_best_score\\n move = iterative_best_move\\n max_depth = depth\\n else:\\n #Perform fixed-depth search\\n if self.method == 'alphabeta':\\n score, move = self.alphabeta(game, self.search_depth)\\n else:\\n score, move = self.minimax(game, self.search_depth)\\n\\n except Timeout:\\n # Handle any actions required at timeout, if necessary\\n pass\\n\\n return move\",\n \"def minimax_maximize_play(self, game, legal_moves, depth):\\n highest_score, selected_move = (float('-inf'), (-1, -1))\\n for move in legal_moves:\\n score, _ = self.minimax(game.forecast_move(move), depth - 1, False)\\n highest_score, selected_move = max((highest_score, selected_move), (score, move))\\n return (highest_score, selected_move)\",\n \"def get_move(self, board):\\n # First, check if we can win in the next move\\n winning_move = self.get_winning_move(board, self.letter)\\n if winning_move is not None:\\n return winning_move\\n # Check if the player could win on their next move, and block them.\\n blocking_move = self.get_winning_move(board, self.opponent_letter)\\n if blocking_move is not None:\\n return blocking_move\\n # Try to take one of the corners, if they are free.\\n corner_move = self.move_in_a_corner(board)\\n if corner_move is not None:\\n return corner_move\\n # Try to take the center, if it is free.\\n if board.size % 2 == 1:\\n if board.is_position_availible(board.letters[board.size // 2]\\n + board.numbers[board.size // 2]):\\n return board.letters[board.size // 2] + board.numbers[board.size // 2]\\n # Move on one of the sides.\\n return self.choose_random_move_from_list(board, list(board.positions.keys()))\",\n \"def get_best_move(board, scores):\\n empty = board.get_empty_squares()\\n if len(empty) == 0:\\n return\\n best_move = None\\n best_score = None\\n for square in empty:\\n if best_move == None or scores[square[0]][square[1]] > best_score:\\n best_move = square\\n best_score = scores[square[0]][square[1]]\\n return best_move\",\n \"def takeStrategicMove():\\r\\n\\tglobal move1, move2\\r\\n\\r\\n\\tif move1==0 or move2==0:\\r\\n\\t\\tif validMove(1):\\r\\n\\t\\t\\treturn 1\\r\\n\\t\\telif validMove(5):\\r\\n\\t\\t\\treturn 5\\r\\n\\telif winningMove():\\r\\n\\t\\treturn winningMove()\\t\\t\\r\\n\\telif blockingMove():\\r\\n\\t\\treturn blockingMove()\\r\\n\\telse:\\r\\n\\t\\treturn takeNaiveMove()\",\n \"def minimax(self, game, depth):\\n if self.time_left() < self.TIMER_THRESHOLD:\\n raise SearchTimeout()\\n\\n \\\"\\\"\\\"\\n From AIMA psuedocode:\\n\\n function MINIMAX-DECISION(state) returns an action\\n return arg max a is in ACTIONS(s) MIN-VALUE(RESULT(state, a))\\n \\\"\\\"\\\"\\n\\n best_move = (-1,-1)\\n best_score = float(\\\"-inf\\\")\\n actions = game.get_legal_moves()\\n\\n if not actions:\\n return best_move\\n else:\\n best_move = actions[randint(0, len(actions) - 1)]\\n\\n try:\\n # The try/except block will automatically catch the exception\\n # raised when the timer is about to expire.\\n # return max(actions, key=lambda action: self._min_value(game.forecast_move(action), 1))\\n for action in actions:\\n score = self._min_value(game.forecast_move(action), 1)\\n if score > best_score:\\n best_score = score\\n best_move = action\\n\\n except SearchTimeout:\\n pass\\n\\n return best_move\",\n \"def get_best_move(board, scores):\\n all_scores = {}\\n for row_num in range(board.get_dim()):\\n for col_num in range(board.get_dim()):\\n if board.square(row_num, col_num) == provided.EMPTY:\\n all_scores [(row_num, col_num)] = scores[row_num][col_num]\\n max_score = max(all_scores.values())\\n max_sqrs = []\\n for sqr in all_scores:\\n if all_scores[sqr] == max_score:\\n max_sqrs.append(sqr)\\n return random.choice(max_sqrs)\",\n \"def minimax(board):\\n if terminal(board):\\n return None\\n # return the optimal move for the player\\n # possible outcomes 1(X wins) 0(No winner) -1(O wins)\\n if player(board) == X:\\n _ , action = max_value(board)\\n return action\\n elif player(board) == O:\\n _ , action = min_value(board)\\n return action\",\n \"def computeActionFromValues(self, state):\\n best_move = None\\n best_Q = float(\\\"-inf\\\")\\n\\n for a in self.mdp.getPossibleActions(state):\\n q = self.computeQValueFromValues(state,a)\\n if q > best_Q:\\n best_Q = q\\n best_move = a\\n\\n return best_move\",\n \"def ai_move():\\n\\tinitial_state = map(get_filled_edges, rects)\\n\\tpossible_moves = []\\n\\tfor index, filled_edges in enumerate(initial_state):\\n\\t\\tif filled_edges == 0:\\n\\t\\t\\tpossible_moves.extend([(index, i) for i in 'ltrb'])\\n\\t\\telif filled_edges == 1:\\n\\t\\t\\tpossible_moves.extend(one_filled_edge(index))\\n\\t\\telif filled_edges == 2:\\n\\t\\t\\tpossible_moves.extend(two_filled_edge(index))\\n\\t\\telif filled_edges == 3:\\n\\t\\t\\tpossible_moves.extend(three_filled_edge(index))\\n\\tprint possible_moves\\n\\tpossible_decisions = []\\n\\tfor move in possible_moves:\\n\\t\\tfinal_state = apply_move(move)\\n\\t\\tpossible_decisions.append(is_feasible(initial_state, final_state))\\n\\tprint possible_decisions\\n\\t# randomizing when some decisions have the same weight\\n\\tmax_weight = max(possible_decisions)\\n\\t# list of indices which have the same weight\\n\\tmax_indices = []\\n\\tfor index, weight in enumerate(possible_decisions):\\n\\t\\tif weight == max_weight:\\n\\t\\t\\tmax_indices.append(index)\\n\\tx = choice(max_indices)\\n\\tprint x\\n\\treturn possible_moves[x]\\n\\t# return possible_moves[possible_decisions.index(max(possible_decisions))]\",\n \"def minimax(board):\\n if terminal(board):\\n return None\\n if player(board)==X:\\n value, move = max_value(board)\\n return move\\n if player(board)==O:\\n value, move = min_value(board)\\n return move\",\n \"def utility(state:State,maximizing_player):\\n best_move_score = -1\\n #######################[Goal]#########################\\n is_current_player_stuck = is_stuck(state,state.player_type)\\n other_player = RIVAL if state.player_type == PLAYER else PLAYER\\n # Check if stuck\\n if is_current_player_stuck:\\n if state.player_type == PLAYER:\\n state.players_score[state.player_type] -= state.penalty_score\\n else:\\n state.players_score[state.player_type] += state.penalty_score\\n return state.players_score[state.player_type] - state.players_score[other_player] \\n ######################################################\\n # Else\\n #--------------------------------------------------\\n ################# Available Steps #################\\n #--------------------------------------------------\\n player_available_steps = availables(state.board, state.locations[PLAYER])\\n h1 = 4-player_available_steps\\n h4 = player_available_steps\\n #--------------------------------------------------\\n ################# Fruits Distance #################\\n #--------------------------------------------------\\n h2 = -1\\n if state.fruits_ttl > 0 and len(state.fruits_dict) > 0:\\n min_fruit_dist = float('inf')\\n for fruit_loc in state.fruits_dict:\\n curr_fruit_dist = Manhattan(state.locations[state.player_type], fruit_loc)\\n # Check what is the closest fruit reachable\\n if curr_fruit_dist < min_fruit_dist and curr_fruit_dist <= state.fruits_ttl:\\n other_player_fruit_dist = Manhattan(state.locations[other_player], fruit_loc)\\n if curr_fruit_dist < other_player_fruit_dist:\\n min_fruit_dist = curr_fruit_dist\\n max_dist = len(state.board)+len(state.board[0])\\n h2 = (max_dist*10.0/min_fruit_dist)+1 if min_fruit_dist < float('inf') else -1\\n #--------------------------------------------------\\n ################# Reachable Squrs #################\\n #--------------------------------------------------\\n reachables_player = reachables(state.board,state.locations[PLAYER])\\n reachables_rival = reachables(state.board,state.locations[RIVAL])\\n h3 = reachables_player - reachables_rival # We want more for us\\n #--------------------------------------------------\\n ################# Combine it all. #################\\n #--------------------------------------------------\\n if not state.half_game():\\n w = 0.8 if h2 > 0 else 1\\n best_move_score = w*(h1-h3) + (1-w)*h2 \\n else:\\n w = 0.7 if h2 > 0 else 1\\n best_move_score = w*(h4+h3) + (1-w)*h2 \\n\\n best_move_score += state.players_score[state.player_type]\\n return best_move_score\",\n \"def find_closest_move(self, position, valid_moves):\\n closest = None\\n closest_value = 100\\n for move in valid_moves:\\n if closest:\\n x_dist = abs(position[0] - move[0])\\n y_dist = abs(position[1] - move[1])\\n if x_dist + y_dist < closest_value:\\n closest = move\\n closest_value = x_dist + y_dist\\n else:\\n closest = move\\n\\n return closest\",\n \"def optimal_max(board):\\n # Board full?\\n if terminal(board):\\n return [None, utility(board)]\\n\\n available_actions = list(actions(board))\\n\\n # Naive baseline comparison is negative infinity\\n global_optimum = [None, -math.inf]\\n\\n # For each move, what would opponent do next? Update best move.\\n for action in available_actions:\\n # Anticipates optimal adversarial moves\\n local_optimum = optimal_min(result(board, action))\\n\\n # Compares local vs global optima\\n if global_optimum[1] <= local_optimum[1]:\\n global_optimum = [action, local_optimum[1]]\\n\\n return global_optimum\",\n \"def game_move(self):\\n\\t\\t# make a note of the player who isn't playing\\n\\t\\tfor x in self.players.keys():\\n\\t\\t\\tif x != self.nextPlayer:\\n\\t\\t\\t\\totherPlayer = x\\n\\t\\t\\t\\tbreak\\n\\t\\t\\n\\t\\t\\n\\t\\t# If there are no remaining moves for this player, either the other\\n\\t\\t# player has won or it's a draw\\n\\t\\t# self.expansions = 1\\n\\t\\tself.expansionCounter.count = 1\\n\\t\\tif len(self.state.successors()) == 0:\\n\\t\\t\\tif self.state.is_win(otherPlayer):\\n\\t\\t\\t\\treturn (None, otherPlayer)\\n\\t\\t\\telse:\\n\\t\\t\\t\\t# None, None for a draw\\n\\t\\t\\t\\treturn (None, None)\\n\\t\\t\\t\\n\\t\\t# allow the player max_expansions for this turn\\n\\t\\t# self.expansions = self.max_expansions\\n\\t\\tself.expansionCounter.count = self.max_expansions\\n\\t\\t\\n\\t\\tnextPlayer = self.players[self.nextPlayer]\\n\\t\\tlastPlayer = None\\n\\t\\t\\n\\t\\t# player may throw an exception\\n\\t\\ttry:\\n\\t\\t\\t# get player's move, make sure we don't modify the current state\\n\\t\\t\\tmove = nextPlayer.move(self.state.get_player_state(self.nextPlayer), \\n\\t\\t\\t\\t\\t self.visitedStates)\\n\\t\\t\\t# player may give up\\n\\t\\t\\tif move.is_forfeit():\\n\\t\\t\\t\\tprint \\\"Player\\\", self.nextPlayer, \\\"forfeits.\\\"\\n\\t\\t\\t\\treturn (None, otherPlayer)\\n\\t\\t\\t# player may return illegal move\\n\\t\\t\\tif not self.state.is_valid_move(move):\\n\\t\\t\\t\\tprint \\\"Illegal move returned by player\\\", self.nextPlayer, \\\\\\n\\t\\t\\t\\t\\t\\t\\\"(\\\", self.players[self.nextPlayer].get_name(), \\\")\\\"\\n\\t\\t\\t\\treturn (move, otherPlayer)\\n\\t\\t\\t# this player is now last player\\n\\t\\t\\tlastPlayer = self.nextPlayer\\n\\t\\t\\t# get the new next player and make the indicated move\\n\\t\\t\\tself.nextPlayer, clear = self.state.move(move, True)\\n\\t\\t\\tif clear:\\n\\t\\t\\t\\tself.clear_repeat()\\n\\t\\texcept:\\n\\t\\t\\tprint \\\"Exception thrown by player\\\", self.nextPlayer, \\\\\\n\\t\\t\\t\\t\\t\\t\\\"(\\\", self.players[self.nextPlayer].get_name(), \\\")\\\"\\n\\t\\t\\tprint\\n\\t\\t\\ttraceback.print_exc()\\n\\t\\t\\tprint\\n\\t\\t\\treturn (None, otherPlayer)\\n\\t\\t\\n\\t\\tos.chdir(self.wd)\\n\\t\\t\\n\\t\\t# may be a repeated state IF the game cycles\\n\\t\\tif self.is_repeat(self.state):\\n\\t\\t\\tself.state.handle_cycle()\\n\\t\\t# otherwise, if the game cycles, note that we've been here\\n\\t\\telif self.state.repeats():\\n\\t\\t\\tself.visitedStates.add(self.state.repeated_rep())\\n\\t\\t\\t\\n\\t\\t# player may have sacrificed the game\\n\\t\\tif self.state.is_win(otherPlayer):\\n\\t\\t\\treturn (move, otherPlayer)\\n\\t\\t\\n\\t\\t# player may have won\\n\\t\\tif self.state.is_win(lastPlayer):\\n\\t\\t\\treturn (move, lastPlayer)\\n\\t\\t\\n\\t\\t# nobody's won or lost yet\\n\\t\\treturn (move, None)\",\n \"def mm_move(board, player):\\n moves = []\\n results = []\\n best_score = None\\n best_move = None\\n \\n opponet = op_player(player)\\n \\n if board.check_win() != None:\\n \\n if board.check_win() == provided.PLAYERX:\\n return SCORES[provided.PLAYERX] , (-1, -1)\\n \\n if board.check_win() == provided.PLAYERO:\\n return SCORES[provided.PLAYERO] , (-1, -1)\\n \\n if board.check_win() == provided.DRAW:\\n return SCORES[provided.DRAW] , (-1, -1)\\n \\n free_steps = board.get_empty_squares()\\n \\n for step in free_steps:\\n clone = board.clone() \\n clone.move(step[0],step[1],player)\\n temp = mm_move(clone,opponet)\\n \\n if temp != None:\\n if temp[0] == SCORES[player]: \\n return temp[0] , step \\n else: \\n results.append(temp)\\n moves.append(step)\\n \\n for result, move in zip(results, moves): \\n if result[0] * SCORES[player] > best_score:\\n best_score = result[0]\\n best_move = move\\n return best_score, best_move\",\n \"def search_my_move(self, env:Chess, is_root_node=False) -> float:\\n if env.over():\\n if env.victor == Victor.draw:\\n return 0\\n # assert env.whitewon != env.white_to_move # side to move can't be winner!\\n return -1\\n\\n state = board_state_key(env)\\n\\n with self.node_lock[state]:\\n if state not in self.tree:\\n leaf_p, leaf_v = self.expand_and_evaluate(env)\\n self.tree[state].p = leaf_p\\n return leaf_v # I'm returning everything from the POV of side to move\\n\\n # SELECT STEP\\n action_t = self.select_action_q_and_u(env, is_root_node)\\n\\n virtual_loss = self.play_conf.virtual_loss\\n\\n my_visit_stats = self.tree[state]\\n my_stats = my_visit_stats.a[action_t]\\n\\n my_visit_stats.sum_n += virtual_loss\\n my_stats.n += virtual_loss\\n my_stats.w += -virtual_loss\\n my_stats.q = my_stats.w / my_stats.n\\n\\n env.make_move(action_t.uci())\\n leaf_v = self.search_my_move(env) # next move from enemy POV\\n leaf_v = -leaf_v\\n\\n # BACKUP STEP\\n # on returning search path\\n # update: N, W, Q\\n with self.node_lock[state]:\\n my_visit_stats.sum_n += -virtual_loss + 1\\n my_stats.n += -virtual_loss + 1\\n my_stats.w += virtual_loss + leaf_v\\n my_stats.q = my_stats.w / my_stats.n\\n\\n return leaf_v\",\n \"def best_move(self, scores):\\n \\n max_val = max(scores)\\n \\n tie_list = []\\n for i in range(len(scores)):\\n if scores[i] == max_val:\\n tie_list.append(i)\\n if self.is_random:\\n return random.choice(tie_list)\\n else:\\n return tie_list[0]\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8980694","0.85602146","0.8383858","0.8242038","0.8026913","0.7937195","0.79176444","0.7797753","0.77027047","0.7662692","0.7647024","0.7636651","0.76326895","0.7620526","0.75877464","0.75660557","0.7504784","0.7500295","0.7482623","0.7458627","0.745814","0.74458337","0.741608","0.7412292","0.7411595","0.74101806","0.7401392","0.7397986","0.7394182","0.7386535","0.73853415","0.7379668","0.73747647","0.73704505","0.7368207","0.73676527","0.7363532","0.7355053","0.7349878","0.73435795","0.73349994","0.7321281","0.73154366","0.7302574","0.73008776","0.7275933","0.726277","0.7246977","0.72433925","0.72389877","0.7238767","0.723809","0.7232085","0.7205521","0.72012925","0.7199848","0.71975756","0.71975756","0.71975756","0.71975756","0.717846","0.7175947","0.7173361","0.71731603","0.71704143","0.7167717","0.716489","0.71604294","0.7153889","0.7152653","0.71500903","0.71497506","0.7143184","0.7135491","0.7131586","0.71304226","0.7102468","0.71012884","0.70993763","0.7078137","0.7076766","0.70691115","0.70583236","0.7053048","0.70483637","0.7043191","0.70284545","0.70267385","0.70140666","0.700807","0.6997626","0.69946706","0.69932306","0.69900054","0.6982532","0.6982329","0.6958324","0.6951102","0.69441307","0.6926092","0.692204"],"string":"[\n \"0.8980694\",\n \"0.85602146\",\n \"0.8383858\",\n \"0.8242038\",\n \"0.8026913\",\n \"0.7937195\",\n \"0.79176444\",\n \"0.7797753\",\n \"0.77027047\",\n \"0.7662692\",\n \"0.7647024\",\n \"0.7636651\",\n \"0.76326895\",\n \"0.7620526\",\n \"0.75877464\",\n \"0.75660557\",\n \"0.7504784\",\n \"0.7500295\",\n \"0.7482623\",\n \"0.7458627\",\n \"0.745814\",\n \"0.74458337\",\n \"0.741608\",\n \"0.7412292\",\n \"0.7411595\",\n \"0.74101806\",\n \"0.7401392\",\n \"0.7397986\",\n \"0.7394182\",\n \"0.7386535\",\n \"0.73853415\",\n \"0.7379668\",\n \"0.73747647\",\n \"0.73704505\",\n \"0.7368207\",\n \"0.73676527\",\n \"0.7363532\",\n \"0.7355053\",\n \"0.7349878\",\n \"0.73435795\",\n \"0.73349994\",\n \"0.7321281\",\n \"0.73154366\",\n \"0.7302574\",\n \"0.73008776\",\n \"0.7275933\",\n \"0.726277\",\n \"0.7246977\",\n \"0.72433925\",\n \"0.72389877\",\n \"0.7238767\",\n \"0.723809\",\n \"0.7232085\",\n \"0.7205521\",\n \"0.72012925\",\n \"0.7199848\",\n \"0.71975756\",\n \"0.71975756\",\n \"0.71975756\",\n \"0.71975756\",\n \"0.717846\",\n \"0.7175947\",\n \"0.7173361\",\n \"0.71731603\",\n \"0.71704143\",\n \"0.7167717\",\n \"0.716489\",\n \"0.71604294\",\n \"0.7153889\",\n \"0.7152653\",\n \"0.71500903\",\n \"0.71497506\",\n \"0.7143184\",\n \"0.7135491\",\n \"0.7131586\",\n \"0.71304226\",\n \"0.7102468\",\n \"0.71012884\",\n \"0.70993763\",\n \"0.7078137\",\n \"0.7076766\",\n \"0.70691115\",\n \"0.70583236\",\n \"0.7053048\",\n \"0.70483637\",\n \"0.7043191\",\n \"0.70284545\",\n \"0.70267385\",\n \"0.70140666\",\n \"0.700807\",\n \"0.6997626\",\n \"0.69946706\",\n \"0.69932306\",\n \"0.69900054\",\n \"0.6982532\",\n \"0.6982329\",\n \"0.6958324\",\n \"0.6951102\",\n \"0.69441307\",\n \"0.6926092\",\n \"0.692204\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":143,"numItemsPerPage":100,"numTotalItems":95772,"offset":14300,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6IjVHZDBvd0g5MTM2eDZjc1FvbE1zcktNWUZoRVFoUm5PVVVybEpjOUhaUEEifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc4NjIwNTYyMCwianRpIjoiNTQ0N2E2YmMtNDNhYi00YjQyLWE0ZDQtM2EwZWE2OGVhNmRkIiwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3ODYyMDkyMjAsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.1FDEUX8WLrRQOo4CTEa3024YCknxeomgH59poKlXTr3EKOK9VjuUb4UYQSoABTtv8KZaU8OMpEaZgTe4QWuXBA","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":95772,"numBytesParquet":1928375891}]},"discussionsStats":{"closed":1,"open":1,"total":2},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
query stringlengths 9 3.4k | document stringlengths 9 87.4k | metadata dict | negatives listlengths 4 101 | negative_scores listlengths 4 101 | document_score stringlengths 3 10 | document_rank stringclasses 102
values |
|---|---|---|---|---|---|---|
This func calc the number of words in one song | def get_len(song, album):
length = 0
words = dbase()[album][0][song]
words = words[2]
words = words.split()
for word in words:
length += 1
return str(length) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def song_length(ans):\r\n length = 0\r\n flag = 1\r\n albums = simple_album_list()\r\n for album in albums:\r\n songs = simple_songs_list(album)\r\n for song in songs:\r\n if ans == song:\r\n words = dbase()[album][0][song]\r\n words = words[2]\r\n... | [
"0.7641895",
"0.74189013",
"0.7375139",
"0.7181407",
"0.71441495",
"0.70609474",
"0.70113283",
"0.6958899",
"0.6921988",
"0.6921822",
"0.6886437",
"0.6875009",
"0.6845606",
"0.68437195",
"0.68406963",
"0.6803877",
"0.67994726",
"0.67805064",
"0.6773257",
"0.67365235",
"0.6724... | 0.764421 | 0 |
This func calc how many words there is in all of the songs, albums. using "get_len" function | def song_length(ans):
length = 0
flag = 1
albums = simple_album_list()
for album in albums:
songs = simple_songs_list(album)
for song in songs:
if ans == song:
words = dbase()[album][0][song]
words = words[2]
words = w... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_len(song, album):\r\n length = 0\r\n words = dbase()[album][0][song]\r\n words = words[2]\r\n words = words.split()\r\n for word in words:\r\n length += 1\r\n return str(length)",
"def common():\r\n full_song = \"\"\r\n albums = simple_album_list()\r\n for album in album... | [
"0.8198582",
"0.66658145",
"0.64683807",
"0.6397444",
"0.6326289",
"0.6298644",
"0.623144",
"0.62150586",
"0.62072754",
"0.61455053",
"0.6131659",
"0.61273545",
"0.6073577",
"0.60692155",
"0.6067708",
"0.60635406",
"0.60574627",
"0.59762967",
"0.5966263",
"0.59660995",
"0.595... | 0.81630695 | 1 |
This function returns the lyrics of specific song | def song_lyrics(ans):
albums = simple_album_list()
for album in albums:
songs = simple_songs_list(album)
for song in songs:
if ans == song:
words = dbase()[album][0][song]
words = words[2]
return words | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def lyrics(self):\n return get_lyrics(self.artist, self.title,'')",
"def get_lyrics(artist, song, language='', linesep='\\n', timeout=None):\n return get_all_lyrics(artist, song, language, linesep, timeout)[0]",
"def get_lyrics(self):\n url = 'http://api.lyricsnmusic.com/songs?api_key=[5358b25... | [
"0.82025915",
"0.7692463",
"0.760172",
"0.72425085",
"0.71974313",
"0.71780723",
"0.7152949",
"0.71083266",
"0.70352906",
"0.7034073",
"0.7004775",
"0.6972529",
"0.69618744",
"0.6934143",
"0.6913037",
"0.68426704",
"0.68249315",
"0.6794879",
"0.6770472",
"0.67184097",
"0.6686... | 0.81326777 | 1 |
This function finds what album the song in | def song_album(ans):
albums = simple_album_list()
for album in albums:
songs = simple_songs_list(album)
for song in songs:
if ans == song:
return album | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def find_album_playlist(data):\n\n return data['album'].lower() + '.m3u'",
"def search_for_album(album_name):\n\n print(f'Searching for album: {album_name}')\n\n search_result = spotifyObject.search(q=f'\"{album_name}\"', limit=20, type='album')\n\n items = search_result['albums']['items']\n\n res... | [
"0.7356128",
"0.72707754",
"0.7060411",
"0.7050987",
"0.69334686",
"0.6912387",
"0.67691773",
"0.66861814",
"0.66782176",
"0.66549325",
"0.66268027",
"0.6598319",
"0.65646094",
"0.6564256",
"0.6559331",
"0.6554879",
"0.65491766",
"0.65131474",
"0.6505864",
"0.64808756",
"0.64... | 0.83801645 | 0 |
This func finds a song by a string from the user(by name) | def song_by_word(ans):
songs_list = ""
ans = ans.lower()
albums = simple_album_list()
for album in albums:
songs = simple_songs_list(album)
for song in songs:
song = str(song)
if ans in song.lower():
songs_list += song + ", "
return s... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def search_song(self, name):\n self.logger.debug('Searched for Song: {}'.format(name))\n results = self.sp.search(q='track:' + name, type='track')\n songs = [song for song in results['tracks']['items']]\n i = 1\n songs_ls = []\n table_ls = []\n for song in songs:\n ... | [
"0.6947447",
"0.6876963",
"0.6818424",
"0.6807256",
"0.67954665",
"0.67806184",
"0.6759367",
"0.667911",
"0.65942836",
"0.65542203",
"0.64625835",
"0.64612514",
"0.6326261",
"0.6276666",
"0.6270919",
"0.62137413",
"0.620946",
"0.60927653",
"0.6086777",
"0.6085682",
"0.6072206... | 0.6074408 | 20 |
This func finds a song by a string from the user(by lyrics) | def lyrics_by_word(ans):
songs_list = ""
ans = ans.lower()
albums = simple_album_list()
for album in albums:
songs = simple_songs_list(album)
for song in songs:
x = song_lyrics(song)
song = str(song)
if ans in x:
songs_list +=... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def search_song(title, artist):\n\ttitle = quote(title, safe='')\n\tartist = quote(artist, safe='')\n\tbase_url = SPOTIFY_API_HOST + 'search/' + '?q=track:{0}+artist:{1}&type=track&limit=1'\n\turl = base_url.format(title, artist)\n\tresults = requests.get(url).json()\n\n\ttry:\n\t\tif results['tracks']['total'] ==... | [
"0.6918285",
"0.687244",
"0.66271144",
"0.6582607",
"0.6552037",
"0.65269274",
"0.64055306",
"0.6378039",
"0.63699645",
"0.63548654",
"0.63060015",
"0.63043284",
"0.62030554",
"0.61645436",
"0.61451346",
"0.61190856",
"0.6113265",
"0.60822713",
"0.60463125",
"0.6037531",
"0.6... | 0.6374961 | 8 |
This function makes list of the top 50 commonest words of all songs | def common():
full_song = ""
albums = simple_album_list()
for album in albums:
songs = simple_songs_list(album)
for song in songs:
full_song += str(song_lyrics(song))
split_lyrics = full_song.lower().split()
counter = collections.Counter(split_lyrics)
most_wo... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def top_k_frequent(top_k, words, list_of_texts):\n dict_top_freq = {}\n for word in words:\n dict_top_freq[word.lower()] = 0\n for string in list_of_texts:\n if word.lower() in string.lower():\n counter = string.lower().count(word.lower())\n dict_top_fre... | [
"0.738273",
"0.7270854",
"0.72609174",
"0.6993908",
"0.6986561",
"0.69415885",
"0.6875806",
"0.6855121",
"0.684043",
"0.6821874",
"0.6817871",
"0.68166554",
"0.67563236",
"0.6738581",
"0.6699482",
"0.6695034",
"0.6693428",
"0.66674244",
"0.6641728",
"0.66324925",
"0.66190416"... | 0.7426751 | 0 |
This function makes list of the top 50 commonest words of all songs | def stats():
times_lst = []
time_dict = {}
for album, details in dbase().items():
time_m = 0
time_s = 0
for songs, details_s in details[0].items():
time = details_s[1].split(":")
min = int(time[0])
sec = int(time[1])
time_m +=... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def common():\r\n full_song = \"\"\r\n albums = simple_album_list()\r\n for album in albums:\r\n songs = simple_songs_list(album)\r\n for song in songs:\r\n full_song += str(song_lyrics(song))\r\n split_lyrics = full_song.lower().split()\r\n counter = collections.Counter(spl... | [
"0.7426751",
"0.738273",
"0.7270854",
"0.72609174",
"0.6993908",
"0.6986561",
"0.69415885",
"0.6875806",
"0.6855121",
"0.684043",
"0.6821874",
"0.6817871",
"0.68166554",
"0.67563236",
"0.6738581",
"0.6699482",
"0.6695034",
"0.6693428",
"0.66674244",
"0.6641728",
"0.66324925",... | 0.0 | -1 |
This function organise the two answer to one answers | def organise(times, words):
length_lst = ""
comm_lst = ""
for item in times:
length_lst += str(item[0]) + " - " + str(item[1]) + "\n"
for item in words:
comm_lst += str(item[0]) + " - " + str(item[1]) + "\n"
return length_lst, comm_lst | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def putTogetherAnAnswer(self):\n currentLayer = self.frames[\"layer \" + str(self.currentFrame)]\n possibleTopics = []\n subject = \"\"\n\n # Authentication\n if self.authenticateQuestions.authStepInProgress != 0:\n return self.authenticateQuestions.continueAuth(self.i... | [
"0.5920408",
"0.5828135",
"0.5653084",
"0.5597326",
"0.55670136",
"0.55494255",
"0.5470206",
"0.54302776",
"0.54012376",
"0.5351897",
"0.53424776",
"0.5341781",
"0.5333092",
"0.53298074",
"0.5328876",
"0.52590394",
"0.52549297",
"0.52336955",
"0.52108526",
"0.5204357",
"0.519... | 0.0 | -1 |
line Tuple containing the (rho, theta) parameters of the line point Tuple containing the (x,y) coods of the line | def get_normal_dist(line, point):
# Rotate:
x_rot = np.cos(line[1])*point[0] + np.sin(line[1])*point[1]
# Normal distance: x_rot - rho:
return x_rot - line[0] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def make_line_points(y1, y2, line):\n if line is None:\n return None\n\n slope, intercept = line\n\n # make sure everything is integer as cv2.line requires it\n x1 = int((y1 - intercept) / slope)\n x2 = int((y2 - intercept) / slope)\n y1 = int(y1)\n y2 = int(y2)\n\n return ((x1, y1),... | [
"0.67866886",
"0.62087274",
"0.62049174",
"0.61395484",
"0.60201013",
"0.6018803",
"0.60153675",
"0.59591883",
"0.5937377",
"0.5927099",
"0.5927099",
"0.5925086",
"0.59203935",
"0.59140366",
"0.591017",
"0.59039605",
"0.59010077",
"0.5860332",
"0.5824119",
"0.5807715",
"0.580... | 0.54883045 | 59 |
Points List of tuples, where each tuple has (x,y) coods of the points. numLines Number of pairs of points to be randomly sampled numIter Number of ietrations for which estimates of Prob should be refined e_tilde Critical distance for 50% probability of memebership in the line gamma_tilde Critical fraction of valid poin... | def iterative_function(points, numLines, numIter, e_tilde, gamma_tilde, beta_tilde):
numPoints = len(points)
# Randomly sample pairs and get the corresponding rho and theta parameters for a line fitted to the pair:
# Returns a list of tuples - Each tuple has the rho and theta parameters for the l... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def generateData(numPoints,x,y):\n\tfor i in range(0,numPoints):\n\t\tif (i % 2 == 0):\n\t\t\tx.append(random.normalvariate(25, 15))\n\t\t\ty.append(random.normalvariate(25, 15))\n\t\t\t \n\t\t\t\n\t\telse:\n\t\t\tx.append(random.normalvariate(75, 15))\n\t\t\ty.append(random.normalvariate(75, 15))",
"def generat... | [
"0.6171076",
"0.61004645",
"0.60166216",
"0.5971953",
"0.5899086",
"0.5896352",
"0.5832605",
"0.58060277",
"0.5772896",
"0.57310075",
"0.57118297",
"0.5706741",
"0.5691438",
"0.5668014",
"0.566632",
"0.564936",
"0.5631067",
"0.56161624",
"0.56137055",
"0.55902743",
"0.5562805... | 0.6206707 | 0 |
Points List of tuples, where each tuple has (x,y) coods of the points. numLines Number of pairs of points to be randomly sampled numIter Number of ietrations for which estimates of Prob should be refined e_tilde Critical distance for 50% probability of memebership in the line gamma_tilde Critical fraction of valid poin... | def iterative_function_vect(points, numLines, numIter, e_tilde, gamma_tilde, beta_tilde):
numPoints = len(points)
# Randomly sample pairs and get the corresponding rho and theta parameters for a line fitted to the pair:
# Returns a list of tuples - Each tuple has the rho and theta parameters for ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def iterative_function(points, numLines, numIter, e_tilde, gamma_tilde, beta_tilde):\n \n numPoints = len(points)\n \n # Randomly sample pairs and get the corresponding rho and theta parameters for a line fitted to the pair: \n # Returns a list of tuples - Each tuple has the rho and theta parameters... | [
"0.6206707",
"0.6171076",
"0.61004645",
"0.60166216",
"0.5971953",
"0.5899086",
"0.5896352",
"0.5832605",
"0.58060277",
"0.5772896",
"0.57310075",
"0.5706741",
"0.5691438",
"0.5668014",
"0.566632",
"0.564936",
"0.5631067",
"0.56161624",
"0.56137055",
"0.55902743",
"0.55628055... | 0.57118297 | 11 |
`tokens` = a POStagged sentence [(w1, t1), ...] `index` = the index of the token we want to extract features for `history` = the previous predicted IOB tags | def features(self, tokens, index, history):
# print history
# print tokens
# Pad the sequence with placeholders
tokens = [('[START2]', '[START2]'), ('[START1]', '[START1]')] + list(tokens) + [('[END1]', '[END1]'), ('[END2]', '[END2]')]
history = ['[START2]', '[START1]'] + list(history)
# shift the index w... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def features(self, tokens, index, history):\r\n # for more details see: http://nlpforhackers.io/named-entity-extraction/\r\n \r\n # init the stemmer\r\n stemmer = SnowballStemmer('english')\r\n\r\n # Pad the sequence with placeholders\r\n tokens = [('[START2]', '[START2]')... | [
"0.7563386",
"0.75412214",
"0.7117187",
"0.7014282",
"0.6412167",
"0.6303176",
"0.6270073",
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"0.5908066",
"0.5878046",
"0.58631885",
"0.58319926",
"0.58020437",
"0.57797873... | 0.7014282 | 4 |
This function call from contact. | def call_from_contact(self):
log_test_case(self.name, 'call_from_contact')
#lick_textview_by_text(SC.PRIVATE_CONTACT_NUMBER)
click_textview_by_id('primary_action_view')
sleep(1)
goback()
sleep(3)
return | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def call(self):",
"def force_contact(self, *args, **kwargs) -> Any:\n pass",
"def call(self, callee: \"SIPPhoneTemplate\") -> None:",
"def call(self) -> global___Snippet.ClientCall:",
"def call(self) -> global___Snippet.ClientCall:",
"def receiveContactList(self, contactList):",
"def moment_cont... | [
"0.68409646",
"0.660355",
"0.6514977",
"0.6372869",
"0.6372869",
"0.6274491",
"0.6248815",
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"0.58698475",
"0.585964",
"0.58154577",
"0.580931",
"0.58043754",
"0.579605... | 0.7567253 | 0 |
main function ,entry search and show contact record | def test_case_main(self, case_results):
log_test_case(self.name, 'drag up and down')
drag_by_param(95, 50, 95, 85, 1)
drag_by_param(95, 85, 95, 50, 1)
sleep(1)
drag_by_param(95, 50, 95, 85, 1)
drag_by_param(95, 85, 95, 50, 1)
sleep(1)
self.ime = IME()
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def search_contact_list(self):\n\n search_db = Database()\n result = search_db.contact_search(self.name)\n if not result:\n print Fore.YELLOW + ' No such contact'\n return None\n if result > 1:\n print ' Which contact ??'\n for items in result:\n... | [
"0.7752023",
"0.69799906",
"0.6894951",
"0.65923977",
"0.65287244",
"0.65113544",
"0.6378645",
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"0.60771734",
"0.6071167",
"0.6057014",
"0.6046095",
"0.60439444",
"0.6032808... | 0.0 | -1 |
Creates a .yaml file in $2 for each image in $1 | def create_comments_files(images_dir, comments_dir):
# where to look for the images
join = os.path.join
# report_dir = join(data_dir, 'report')
# where to put the comments
# comments_dir = join(data_dir, 'comments')
# obtain files in data_dir/report ending in .eps
eps_files = g... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def do_stage(self, images):\n\n for i, image in enumerate(images):\n pass\n # logging_tags = logs.image_config_to_tags(image, self.group_by_keywords)",
"def transform_images(img1,img2):",
"def build_images(prefix, images, tag=None, commit_range=None, push=False, chart_version=None,... | [
"0.64322525",
"0.58754206",
"0.5844284",
"0.57787067",
"0.57245845",
"0.5698436",
"0.56889397",
"0.566494",
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"0.5499594",
"0.54623115",
"0.54603213",
"0.54489523",
"0.54113674",
"0.541... | 0.0 | -1 |
Returns the URL patterns for the tasks in this module | def getDjangoURLPatterns():
patterns = [
(r'gsoc/tasks/assignslots/assign$',
r'soc.modules.gsoc.tasks.slot_assignment.assignSlots'),
(r'gsoc/tasks/assignslots/program$',
r'soc.modules.gsoc.tasks.slot_assignment.assignProgramSlots'),
]
return patterns | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_urls(self):\n return patterns('')",
"def urlpatterns(self) -> list:\n raise NotImplementedError()",
"def getDjangoURLPatterns():\n\n patterns = [\n (r'^tasks/gci/task/bulk_create_tasks$',\n 'soc.modules.gci.tasks.bulk_create.bulkCreateTasks'),]\n\n return patterns",
"def ur... | [
"0.8020168",
"0.79488254",
"0.7764272",
"0.76677144",
"0.76162356",
"0.7482352",
"0.73301244",
"0.7311515",
"0.72021514",
"0.71234316",
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"0.7022449",
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"0.68901694",
"0.67814434",
"0.66251045",
"0.65685457",
"0.6550831",
"0.65432143",
"0.6... | 0.728069 | 8 |
Assign slots for organizations within a program Gets the slot assignment data as a JSON string from the program and enqueues a task to process the slot assignments | def assignProgramSlots(request, *args, **kwargs):
program = None
params = request.REQUEST
# Query the program entity
try:
program = program_logic.getFromKeyName(params["programkey"])
except KeyError:
logging.error("programkey not in params")
return responses.terminateTask()
if not program:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def assignSlots(request, *args, **kwargs):\n\n # Setup an artifical request deadline\n timelimit = int(request.REQUEST.get(\"timelimit\", 20000))\n timekeeper = Timekeeper(timelimit)\n\n program_key = request.REQUEST.get(\"programkey\")\n last_key = request.REQUEST.get(\"lastkey\", \"\")\n program = program_... | [
"0.8256426",
"0.5762987",
"0.56316525",
"0.54743224",
"0.53757113",
"0.5314603",
"0.5236988",
"0.52270603",
"0.52103895",
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"0.49842697",
"0.49663857",
"0.49040845",
"0.4877993",
"0.48507854",
"0.4843296",
"0.481... | 0.7803846 | 1 |
Sets the slots attribute for each organization entity | def assignSlots(request, *args, **kwargs):
# Setup an artifical request deadline
timelimit = int(request.REQUEST.get("timelimit", 20000))
timekeeper = Timekeeper(timelimit)
program_key = request.REQUEST.get("programkey")
last_key = request.REQUEST.get("lastkey", "")
program = program_logic.getFromKeyName(... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def occupy_slot(self, slot, vehicle):\n self.__occupied_slots__[slot.slot_number] = vehicle.registration_number, vehicle.color\n self.__vehicle_slot_mapping__[vehicle.registration_number] = slot.slot_number\n self.__available_slots__.remove(slot)",
"def extend_slots(self, prediction, item):\... | [
"0.5510804",
"0.5312831",
"0.52753824",
"0.52753824",
"0.5273821",
"0.52403814",
"0.5212397",
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"0.49991065",
"0.4996006",
"0.4996006",
"0.4996006",
"0.49839744",
"0.494... | 0.6378653 | 0 |
receive batch from replay and transfer batch from cpu to gpu | def sample_batch(pid, args, batch_queue, port_dict, device, actor_id_to_ip_dataport, local_size, cache_array):
def recv_data(k, data_stream, actor_set, real_data_tasks_i):
for real_data in data_stream:
tmp = []
tmp.append(real_data.state)
tmp.append(real_data.action)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def process(self, sess):\n global send_counter\n \n #sess.run(self.sync) # copy weights from shared to local\n rollout = self.pull_batch_from_queue()\n batch = process_rollout(rollout, gamma=0.99, lambda_=1.0)\n\n should_compute_summary = self.task == 0 and self.local_ste... | [
"0.6505807",
"0.63949424",
"0.63113874",
"0.61615974",
"0.6134468",
"0.6134468",
"0.61168593",
"0.6034089",
"0.5956476",
"0.5939015",
"0.58440447",
"0.5803942",
"0.5793098",
"0.57923245",
"0.57748014",
"0.5772822",
"0.57639885",
"0.5756384",
"0.5732913",
"0.57300854",
"0.5729... | 0.7087414 | 0 |
initial connect replay server update priority | def update_prios(prios_queue, port_dict):
conn = grpc.insecure_channel(port_dict['replay_ip'] + ':' + port_dict['updatePrioriPort'])
client = apex_data_pb2_grpc.UpdateBatchPrioriStub(channel=conn)
while True:
idxes, prios = prios_queue.get()
response = client.Send(apex_data_pb2.UpdatePrioriR... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def pre_irc_client(self):\n pass",
"def server_activate(self):\n\t\tpass",
"def connect(self):\n return 1",
"def before_rpc(self, cmd, counter, xpath, kind=''):\n index = counter + xpath\n replay_type = kind[kind.find('basic ') + 6:]\n\n if not cmd:\n cmd = 'show... | [
"0.5780111",
"0.57402444",
"0.56140244",
"0.5552766",
"0.5545041",
"0.54940057",
"0.5485339",
"0.542677",
"0.54266566",
"0.5413707",
"0.5408652",
"0.54038936",
"0.5394475",
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"0.53664225",
"0.5342196",
"0.5314109",
"0.527661",
"0.5275035",
"0.52600473",
"0.5251919... | 0.0 | -1 |
thread to fill parameter queue | def train(args, n_actors, batch_queue, prios_queue, param_queue):
def _fill_param():
while True:
model_dict = {}
state_dict = model.state_dict()
for k, v in state_dict.items():
model_dict[k] = v.cpu().numpy()
param_queue.put(model_dict)
en... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def process_thread(self):",
"def main(self,q,controlQueue):\n\n self.dataQueue=q\n\n t1=threading.Thread(target=self.updater,args=(controlQueue,))\n #t2=threading.Thread(target=self.xMotor,args=(controlQueue,))\n #t3=threading.Thread(target=self.yMotor,args=(controlQueue,))\n t... | [
"0.6220907",
"0.6174841",
"0.6161662",
"0.6155145",
"0.61485934",
"0.61485934",
"0.6116193",
"0.6083776",
"0.6083776",
"0.6083776",
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"0.6027521",
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"0.5845421",
"0.58248895",
"0.5790226",
"0.5786648",
"0.5782508",
"0.57810104",
"0.57805663"... | 0.0 | -1 |
Set some data reader parameters for reading SPAM data | def setParameters(self) -> None:
# get a list of the header and data files in the folder
self.headerF = glob.glob(os.path.join(self.dataPath, "*.XTR"))
if len(self.headerF) == 0:
self.headerF = glob.glob(os.path.join(self.dataPath, "*.XTRX"))
self.dataF = glob.glob(os.path.jo... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _read_custom_pars(self):\n if self.use_defaults:\n param_set = \"default_values\"\n else:\n param_set = \"parameters\"\n self.outpars = self.input_cfg_json_data[param_set][self.step_title]",
"def set_parameters(cls):\r\n \"\"\" EXECUTE THIS FUNCTION IN THE FA... | [
"0.60721964",
"0.58910024",
"0.5845442",
"0.5730312",
"0.56539893",
"0.553149",
"0.54175955",
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"0.5407873",
"0.5407873",
"0.54072744",
"0.53905016",
"0.53378683",
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"0.5336671",
"0.53322905",
"0.5329099",
"0.53075695",
"0.5299745",
"0.5289717",
"0.5258... | 0.5704668 | 4 |
Get raw data from data file, returned in mV SPAM raw data is single precision float with unit Volts. Calling this applies the ts_lsb calculated when the headers are read. This is because when a recording consists of multiple data files, each channel of each data file might have a different scaling. The only way to make... | def getUnscaledSamples(self, **kwargs) -> TimeData:
# initialise chans, startSample and endSample with the whole dataset
options = self.parseGetDataKeywords(kwargs)
# get the files to read and the samples to take from them, in the correct order
dataFilesToRead, samplesToRead, scalings =... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def read_magnetometer(self):\n data = self.mag.read_bytes(Register.OUT_X_L_M, 6)\n return lsm9ds1.to_vector(data)",
"def read_raw_data(self):\n dat_file = os.path.join(DATA_DIR, self.patient_number + '.txt')\n if not os.path.exists(dat_file):\n raise AssertionError(\"{} doe... | [
"0.5571445",
"0.5542686",
"0.55078024",
"0.5504112",
"0.54853296",
"0.5461196",
"0.5426089",
"0.539896",
"0.5366386",
"0.53356355",
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"0.5283426",
"0.527169",
"0.52697086",
"0.5268657",
"0.5241611",
"0.5232119",
"0.5224715",... | 0.55713576 | 1 |
Get the data files that have to be read to cover the sample range | def getDataFilesForSamples(
self, startSample: int, endSample: int
) -> Tuple[List[str], List[List[int]], List[float]]:
# have the datafiles saved in sample order beginning with the earliest first
# go through each datafile and find the range to be read
dataFilesToRead = []
s... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_samples_file(foldername, filter=None):\n samples = []\n for file in os.listdir(foldername):\n if filter and file.find(filter) == -1:\n continue\n for sample in sfile(foldername + '/' + file, None).get_samples():\n samples.append(sample)\n return samples",
"def... | [
"0.61742574",
"0.60562795",
"0.5931317",
"0.5895991",
"0.5861483",
"0.58041275",
"0.5802405",
"0.57899123",
"0.5788426",
"0.57653975",
"0.5759746",
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"0.5728617",
"0.5701655",
"0.5679718",
"0.56450206",
"0.5641442",
"0.5636241",
"0.56325316... | 0.73086745 | 0 |
Get data scaled to physical values | def getPhysicalSamples(self, **kwargs):
# initialise chans, startSample and endSample with the whole dataset
options = self.parseGetDataKeywords(kwargs)
# get data
timeData = self.getUnscaledSamples(
chans=options["chans"],
startSample=options["startSample"],
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _process(self, data: np.ndarray) -> np.ndarray:\n return data[..., 1] * self.scale",
"def scale(self, data: np.ndarray):\n if self.scale_type == \"min_max\":\n scaled_data = (data - self.predictor_min) / (\n self.predictor_max - self.predictor_mean\n )\n ... | [
"0.7218168",
"0.71448",
"0.7138502",
"0.69515485",
"0.6725166",
"0.66584325",
"0.6623131",
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"0.65810096",
"0.6528474",
"0.64878947",
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"0.6423392",
"0.6414994",
"0.6407963",
"0.6400517",
"0.6399533",
"0.6392617",
... | 0.0 | -1 |
Get the sections in SPAM header files (XTR and XTRX) Returns | def spamHeaders(self) -> Tuple[List[str], Dict[str, str]]:
sections = ["STATUS", "TITLE", "PROJECT", "FILE", "SITE", "CHANNAME", "DATA"]
sectionHeaders = {}
sectionHeaders["STATUS"] = ["STATUS"]
sectionHeaders["TITLE"] = ["AUTHOR", "VERSION", "DATE", "COMMENT"]
sectionHeaders["FI... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_header_and_sequence_lists(fh_in):\n temp = ''\n isSeq = isSecStr = False\n sequence_header = secstr_header = sequence = secstr = []\n for line in fh_in:\n if (isSeq | isSecStr) & (line[0] != \">\"):\n temp += line.strip()\n elif line[0] == \">\" & line.strip().endswith(... | [
"0.66375244",
"0.6181933",
"0.6116806",
"0.6039351",
"0.6034466",
"0.60314673",
"0.6021332",
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"0.5954007",
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"0.58664584",
"0.5863298",
"0.58631116",
"0.58611786",
"0.58450514",
"0.5824852"... | 0.5751077 | 27 |
Get defaults for channel headers Returns Dict[str, Any] Dictionary of headers for channels and default values | def chanDefaults(self) -> Dict[str, Any]:
chanH = {}
chanH["gain_stage1"] = 1
chanH["gain_stage2"] = 1
chanH["hchopper"] = 0 # this depends on sample frequency
chanH["echopper"] = 0
# channel output information (sensor_type, channel_type, ts_lsb, pos_x1, pos_x2, pos_y1, ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def defaults():\n return {}",
"def default_channel_response_data(channel):\n channel_record = Channel.objects.get(name=channel.name)\n return {\n \"title\": channel.title,\n \"name\": channel.name,\n \"description\": channel.description,\n \"public_description\": channel.... | [
"0.62941504",
"0.6244179",
"0.620184",
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"0.5954738",
"0.58055735",
"0.5720258",
"0.57148266",
"0.56928277",
"0.5690497",
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"0.55866164",
"0.55399114",
"0.55186236",
"0.5510632",
"0.54901797",
"0.5486276",
"0.5461099",
"0.544... | 0.70106703 | 0 |
Read header files For SPAM data, the may be more than one header file as data can be split up into smaller files as it is recorded. In that case, the header information should be somehow merged. All sampling frequencies should be the same | def readHeader(self) -> None:
# read header files
self.headersList = []
self.chanHeadersList = []
for headerFile in self.headerF:
if "xtrx" in headerFile.lower():
headers, chanHeaders = self.readHeaderXTRX(headerFile)
else:
headers,... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def read_header(self):\n # Read entire header into memory in one read to minimize Disk I/O.\n self.fh.seek(0)\n hdr = self.fh.read(self.header['header size'])\n\n # Find several markers in the byte-string\n # Each of these may occur more than once, find last.\n polylist_po... | [
"0.68931645",
"0.68798554",
"0.68419707",
"0.6750417",
"0.67503536",
"0.6726142",
"0.64970535",
"0.64856166",
"0.6483004",
"0.6462398",
"0.642394",
"0.63903534",
"0.6343897",
"0.63411504",
"0.62974936",
"0.6291137",
"0.62813884",
"0.6280724",
"0.62803566",
"0.62442863",
"0.62... | 0.7617964 | 0 |
Read a XTR header file The raw data for SPAM is in single precision Volts. However, if there are multiple data files for a single recording, each one may have a different gain. Therefore, a scaling has to be calculated for each data file and channel. This scaling will convert all channels to mV. For the most part, this... | def readHeaderXTR(self, headerFile: str) -> None:
with open(headerFile, "r") as f:
lines = f.readlines()
sectionLines = {}
# let's get data
for line in lines:
line = line.strip()
line = line.replace("'", " ")
# continue if line is empty
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def readHead(self):\n filesize = self.rhd.tell()\n \n #the order in which all of this is called is critcal\n self.header_identifier = hex(np.uint32(struct.unpack('<I', self.rhd.read(4))))\n v = np.int8(struct.unpack('BBBB', self.rhd.read(4)))\n\n #read each property of the... | [
"0.6155055",
"0.5993816",
"0.5884923",
"0.58070946",
"0.55780053",
"0.5524579",
"0.55149055",
"0.5444751",
"0.5440984",
"0.5410213",
"0.5406023",
"0.5403349",
"0.540114",
"0.53573203",
"0.52880806",
"0.52805924",
"0.52777493",
"0.5273233",
"0.5264056",
"0.52473116",
"0.523383... | 0.66186225 | 0 |
Read a XTRX header files XTRX are newer header files and will supercede XTR | def readHeaderXTRX(self, headerFile):
raise NotImplementedError("Support for XTRX files has not yet been implemented") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def readHeader(self) -> None:\n # read header files\n self.headersList = []\n self.chanHeadersList = []\n for headerFile in self.headerF:\n if \"xtrx\" in headerFile.lower():\n headers, chanHeaders = self.readHeaderXTRX(headerFile)\n else:\n ... | [
"0.6655318",
"0.6545926",
"0.6512777",
"0.5899843",
"0.58021617",
"0.5711781",
"0.5699319",
"0.568349",
"0.5606272",
"0.5598395",
"0.5573075",
"0.5572416",
"0.556946",
"0.5562428",
"0.5521986",
"0.55197376",
"0.5510907",
"0.5490756",
"0.54724866",
"0.54674906",
"0.5455055",
... | 0.7716968 | 0 |
Read headers from the raw data files Read the headers from the raw file and figure out the data byte offset. | def headersFromRawFile(self, rawFile: str, headers: Dict) -> None:
dFile = open(os.path.join(self.dataPath, rawFile), "r", encoding="ISO-8859-1")
generalHeaderString = dFile.read(1000) # this should be long enough
generalSplit = generalHeaderString.split()
# read GENERAL HEADER
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _read_header(self):\n f = self._open(self.filename, 'rb')\n idx = 0\n header = b''\n # reading the header \n while idx < 13: \n header += f.readline().rstrip() # removes the \"\\n\\r\" at the end\n idx += 1\n # \"magically\" compute the data offse... | [
"0.732197",
"0.70453995",
"0.70345956",
"0.6827632",
"0.67381376",
"0.67219263",
"0.6580198",
"0.6544923",
"0.64966327",
"0.64756894",
"0.6393467",
"0.6361028",
"0.6350547",
"0.63097715",
"0.6288819",
"0.62671334",
"0.62456226",
"0.62452",
"0.6240933",
"0.61506295",
"0.611367... | 0.70389855 | 2 |
Merge headers from all the header files Checks all the header files to see if there are any gaps and calculates the sample ranges for each file together with the total number of samples. Sets the start and end time of the recording and class variables datetimeStart and datetimeStop. | def mergeHeaders(self, headersList: List, chanHeadersList: List) -> None:
# take the first header as an example
self.headers = headersList[0]
self.chanHeaders = chanHeadersList[0]
if len(headersList) == 1:
# just fill in the data file list and data ranges
self.dat... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def readHeaderXTR(self, headerFile: str) -> None:\n with open(headerFile, \"r\") as f:\n lines = f.readlines()\n sectionLines = {}\n # let's get data\n for line in lines:\n line = line.strip()\n line = line.replace(\"'\", \" \")\n # continue i... | [
"0.6403937",
"0.61514765",
"0.6100263",
"0.5859067",
"0.58415174",
"0.58295155",
"0.57660097",
"0.56070304",
"0.55764234",
"0.5484562",
"0.5480894",
"0.5478244",
"0.545221",
"0.5397874",
"0.53785336",
"0.5354963",
"0.51996344",
"0.5187501",
"0.5178293",
"0.5173099",
"0.516023... | 0.71078265 | 0 |
Information about the data files as a list of strings Returns List[str] List of information about the data files | def printDataFileList(self) -> List[str]:
textLst: List[str] = []
textLst.append("Data File\t\tSample Ranges")
for dFile, sRanges in zip(self.dataFileList, self.dataRanges):
textLst.append("{}\t\t{} - {}".format(dFile, sRanges[0], sRanges[1]))
textLst.append("Total samples = ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def find_data(self):\n data_list = []\n for root, dirs, files in os.walk(pathfinder.data_path()):\n for name in files:\n data_list.append(os.path.join(root, name))\n return data_list",
"def getDataFiles(directoryName):\r\n \r\n return listdir(directoryName)",
... | [
"0.7393555",
"0.7135669",
"0.69985956",
"0.6970242",
"0.6864774",
"0.6864221",
"0.68211174",
"0.67915004",
"0.678704",
"0.6742841",
"0.6741322",
"0.6736098",
"0.67299247",
"0.6709108",
"0.6708198",
"0.6699793",
"0.6695189",
"0.66744316",
"0.6654875",
"0.6652273",
"0.66320395"... | 0.7159409 | 1 |
Print a list of the data files | def printDataFileInfo(self) -> None:
blockPrint(
"{} Data File List".format(self.__class__.__name__),
self.printDataFileList(),
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def printDataFileList(self) -> List[str]:\n textLst: List[str] = []\n textLst.append(\"Data File\\t\\tSample Ranges\")\n for dFile, sRanges in zip(self.dataFileList, self.dataRanges):\n textLst.append(\"{}\\t\\t{} - {}\".format(dFile, sRanges[0], sRanges[1]))\n textLst.append... | [
"0.7421527",
"0.7151215",
"0.7037491",
"0.68719506",
"0.67474115",
"0.6731406",
"0.67165095",
"0.66810274",
"0.65934056",
"0.65144914",
"0.647909",
"0.64371884",
"0.64266837",
"0.6414693",
"0.64125454",
"0.63596314",
"0.63500434",
"0.63232774",
"0.6245325",
"0.623468",
"0.619... | 0.6734756 | 5 |
Given a set of results, return a list of LDAPSearchResult objects. | def get_search_results(results):
if len(results) == 0:
return []
if type(results) == tuple and len(results) == 2:
(code, arr) = results
elif type(results) == list:
arr = results
res = []
for item in arr:
res.append(LDAPSearchResult(item))
return res | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parse_search_results(fields, results):\n my_results = []\n for result in results:\n my_results.append(SearchAnimeResult(fields, result))\n return my_results",
"def list_results(cls, output_dir, **kwargs):\n results = cls.load(output_dir, **kwargs)\n return results.get_results()"... | [
"0.5853295",
"0.5794549",
"0.5777972",
"0.5770905",
"0.5710312",
"0.57052636",
"0.56758606",
"0.56412905",
"0.5539216",
"0.5504885",
"0.5490016",
"0.54144686",
"0.53994405",
"0.5384345",
"0.53460926",
"0.53145885",
"0.53108484",
"0.5308784",
"0.5290834",
"0.52842665",
"0.5256... | 0.7374579 | 0 |
In Ms we must remove the quotes. | def test_set_ms_filename(self):
self.expect(self.request.get('HTTP_USER_AGENT', ANY)).result('MSIE')
self.replay()
set_attachment_content_disposition(self.request, 'MS Name')
self.assertEquals(
self.header[0],
'attachment; filename=%s' % quote('MS Name')) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def dequote(self, in_str):\n in_str = in_str.replace(\"'\", \"\")\n in_str = in_str.replace('\"', \"\")\n return in_str",
"def embeded_triple_quotes():\n pass",
"def _strip_quotes(self, val):\n logger.debug('Strip quotes')\n val = val.strip()\n if val.startswith('\"... | [
"0.697623",
"0.6833524",
"0.6796816",
"0.6723186",
"0.65923566",
"0.6580481",
"0.65477896",
"0.65477896",
"0.64594156",
"0.64291596",
"0.6404944",
"0.63892174",
"0.6367405",
"0.6345285",
"0.6312037",
"0.62679034",
"0.6257897",
"0.6241633",
"0.6211071",
"0.61968356",
"0.617006... | 0.0 | -1 |
Normaly we have the filename in quotes | def test_filename(self):
self.expect(self.request.get('HTTP_USER_AGENT', ANY)).result('DEF')
self.replay()
set_attachment_content_disposition(self.request, 'Default Name')
self.assertEquals(
self.header[0],
'attachment; filename="%s"' % 'Default Name') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def quoted_filename(self):\n if \" \" in self._filename:\n return f'\"{self._filename}\"'\n return self._filename",
"def _escape_filename(filename):\n #Is adding the following helpful\n #if os.path.isfile(filename):\n # #On Windows, if the file exists, we can ask for\n # ... | [
"0.8299621",
"0.74909776",
"0.7401939",
"0.73211366",
"0.7225268",
"0.7137683",
"0.70245075",
"0.6966879",
"0.6926033",
"0.684065",
"0.6761801",
"0.6725638",
"0.6718539",
"0.66415536",
"0.6637449",
"0.6604877",
"0.66015303",
"0.6574798",
"0.6571564",
"0.6570204",
"0.65622014"... | 0.0 | -1 |
Look for transaction receipt, only raise not found error if they are missing for longer than two minutes. | async def _check_transaction_receipt(self, tx_hash: str, timestamp: int):
async_scheduler: AsyncCallScheduler = AsyncCallScheduler.shared_instance()
try:
return await async_scheduler.call_async(self._w3.eth.getTransactionReceipt, tx_hash)
except TransactionNotFound as e:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def wait_for_receipt(tx_hash, duration=C.EVM_TIMEOUT):\n slept = 0\n tx_rcpt = None\n\n while slept < duration:\n # because web3 throws if not present vs returning None (like the docs say)\n try:\n tx_rcpt = g.w3.eth.getTransactionReceipt(tx_hash)\n except TransactionNotFou... | [
"0.6613937",
"0.6224734",
"0.5850289",
"0.56903857",
"0.5246986",
"0.52447987",
"0.51985824",
"0.51980007",
"0.5125841",
"0.5107872",
"0.50937873",
"0.50544405",
"0.4955215",
"0.49193367",
"0.49190685",
"0.48734447",
"0.48641413",
"0.48391086",
"0.48348594",
"0.483187",
"0.48... | 0.7078107 | 0 |
Look for failed transactions, and emit transaction fail event if any are found. | async def check_transaction_receipts(self):
async_scheduler: AsyncCallScheduler = AsyncCallScheduler.shared_instance()
tasks = [self._check_transaction_receipt(tx_hash, self._pending_tx_dict[tx_hash]['timestamp'])
for tx_hash in self._pending_tx_dict.keys()]
transaction_receipts... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def transaction_failed(self):",
"def transaction_failed_before_processing(self):",
"def test_fail_transaction(self):\n source_wallet = self.source_user.wallets.last()\n target_wallet = self.target_user.wallets.last()\n\n source_balance_init = source_wallet.balance\n target_balance_i... | [
"0.6845539",
"0.6508842",
"0.6043803",
"0.59724367",
"0.58863974",
"0.5845787",
"0.5680421",
"0.5539082",
"0.5481254",
"0.5476299",
"0.5471279",
"0.5429954",
"0.5421628",
"0.54185337",
"0.5392355",
"0.5377968",
"0.53582096",
"0.5348797",
"0.5295418",
"0.52938634",
"0.52896744... | 0.51061434 | 28 |
This function WILL result in immediate network calls (e.g. to get the gas price, nonce and gas cost), even though it is written in sync manner. | def execute_transaction(self, contract_function: ContractFunction, **kwargs) -> str:
if self._network_status is not NetworkStatus.CONNECTED:
raise EnvironmentError("Cannot send transactions when network status is not connected.")
gas_price: int = self.gas_price
transaction_args: Dic... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def sync() -> None:",
"def do_sync(self):\n raise NotImplementedError() # pragma: no cover",
"def lock(self):\n self.words = None\n self.keys = {}\n self.passphrase = b''\n self.language = ''\n self.unspent_txs = {}\n self.spent_txs = []\n self.balanc... | [
"0.5842357",
"0.5409376",
"0.5385483",
"0.518371",
"0.5168875",
"0.51402295",
"0.51202995",
"0.50815713",
"0.50815713",
"0.5059374",
"0.5057339",
"0.5057225",
"0.5049696",
"0.5042864",
"0.502704",
"0.5019603",
"0.5010587",
"0.49878943",
"0.49797568",
"0.49772134",
"0.4973907"... | 0.0 | -1 |
Maintain the approve amounts for a token. This function will be used to ensure trade execution using exchange protocols such as 0x, but should be defined in child classes | async def check_and_fix_approval_amounts(self, spender: str) -> List[str]:
min_approve_amount: int = int(Decimal("1e35"))
target_approve_amount: int = int(Decimal("1e36"))
async_scheduler: AsyncCallScheduler = AsyncCallScheduler.shared_instance()
# Get currently approved amounts
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def approve(self, approver: str, to: str, amount, key: bytes):\n raw_tx = self.approve_build_transaction(approver, to, amount)\n signed_tx = self._sign(raw_tx, key)\n self.send_and_wait(signed_tx)",
"def approve(_spender: address, _amount: uint256) -> bool:\n\n self.allowed[msg.sender][_s... | [
"0.7216647",
"0.6862428",
"0.64983207",
"0.63403976",
"0.62941015",
"0.6252941",
"0.6056391",
"0.6046018",
"0.60323536",
"0.57856506",
"0.5753322",
"0.5654326",
"0.56351185",
"0.5621246",
"0.5617158",
"0.5562989",
"0.55543655",
"0.5552366",
"0.5540227",
"0.55319315",
"0.55084... | 0.668693 | 2 |
Test case for add_asset_share_feed | def test_add_asset_share_feed(self):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_add_assets_signal(self):\n story = create_story(title=\"Test Story\", summary=\"Test Summary\",\n byline=\"Test Byline\", status='published')\n asset = create_html_asset(type='text', title='Test Asset', \n body='Test content')\n ... | [
"0.65638447",
"0.6394304",
"0.63698304",
"0.6273951",
"0.613335",
"0.6046205",
"0.60451096",
"0.60319424",
"0.599211",
"0.5991656",
"0.5964404",
"0.59383875",
"0.5885912",
"0.58813864",
"0.5863835",
"0.57720274",
"0.57403314",
"0.5740057",
"0.57348704",
"0.5733369",
"0.572277... | 0.9495953 | 0 |
Return user details from Facebook account | def get_user_details(self, response):
email = response.get("email")
return {"email": email, "username": email.split("@")[0]} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_facebook_user_info(access_token):\n required_data_list = []\n for per in settings.FACEBOOK_EXTENDED_PERMISSIONS:\n required_data_list.append(per.replace(\"user_\",\"\"))\n \n required_data_list.append(\"picture.type(large)\")\n required_data = (\", \").join([data for data in required_... | [
"0.75915205",
"0.7549937",
"0.75489026",
"0.7247428",
"0.7169986",
"0.71510726",
"0.71399206",
"0.7130518",
"0.6965519",
"0.6933262",
"0.6878169",
"0.68746084",
"0.6857427",
"0.6842357",
"0.6841119",
"0.68202627",
"0.680102",
"0.677454",
"0.6769051",
"0.67508054",
"0.67205805... | 0.66149896 | 32 |
Loads user data from service | def user_data(self, access_token, *args, **kwargs):
params = self.setting("PROFILE_EXTRA_PARAMS", {})
response = kwargs.get('response') or {}
params["access_token"] = access_token
headers = {
"Authorization": "%s %s" % (
response.get("token_type", "Bearer").ca... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load(self):\n\n self.refresh_token()\n\n endpoint = app.config['API']['url'] + 'user/get/' + self._id\n response = requests.get(\n endpoint,\n verify = app.config['API']['verify_ssl'],\n headers = {\n 'Authorization': self.token,\n ... | [
"0.7237133",
"0.71659297",
"0.6840109",
"0.68110734",
"0.6732239",
"0.6647199",
"0.6626025",
"0.6564652",
"0.6559671",
"0.655537",
"0.655537",
"0.655537",
"0.655537",
"0.65010494",
"0.65010494",
"0.6469503",
"0.64684176",
"0.6422018",
"0.640237",
"0.6358974",
"0.63496375",
... | 0.0 | -1 |
Build redirect with redirect_state parameter. | def get_redirect_uri(self, state=None):
regex = re.compile(r"\:(80|443)\/")
uri = regex.sub("/", self.redirect_uri)
if self.REDIRECT_STATE and state:
uri = url_add_parameters(uri, {'redirect_state': state})
return uri | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_redirect_uri(self, state=None):\n if state is not None:\n uri = self.blank_redirect_uri\n if self.REDIRECT_STATE and state:\n uri = url_add_parameters(uri, {'redirect_state': state})\n else:\n uri = self.redirect_uri\n return uri",
"def... | [
"0.6220852",
"0.58699924",
"0.5820168",
"0.5787547",
"0.568278",
"0.560986",
"0.5485404",
"0.54818624",
"0.54510164",
"0.5417507",
"0.54143995",
"0.54086787",
"0.5376258",
"0.53444564",
"0.53163904",
"0.52998877",
"0.5273541",
"0.525929",
"0.5201142",
"0.51966715",
"0.5181374... | 0.5998109 | 1 |
Finish the auth process once the access_token was retrieved | def do_auth(self, access_token, *args, **kwargs):
data = self.user_data(access_token, *args, **kwargs)
response = kwargs.get('response') or {}
response.update(data or {})
if 'access_token' not in response:
response['access_token'] = access_token
kwargs.update({'respon... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def auth_complete(self, *args, **kwargs):\n self.process_error(self.data)\n params = self.auth_complete_params(self.validate_state())\n\n response = requests.post(self.ACCESS_TOKEN_URL, data=params,\n headers=self.auth_headers())\n if response.status_code... | [
"0.77423286",
"0.6978665",
"0.6935192",
"0.6700793",
"0.6664119",
"0.65909183",
"0.65890825",
"0.6557191",
"0.65195364",
"0.6448075",
"0.64399666",
"0.6421506",
"0.6376332",
"0.63507086",
"0.63101214",
"0.6293467",
"0.6244578",
"0.6234269",
"0.6204466",
"0.6197578",
"0.615388... | 0.61004966 | 23 |
Compute softmax probabilities for all actions. | def softmax(x1, x2, x3, beta):
xs = np.array((x1, x2, x3))
num = np.exp(xs * beta)
den = np.exp(xs * beta).sum()
return num / den | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def softmax(inputs):\n probs = np.exp(inputs)\n # print(probs.shape)\n # t = np.sum(probs, axis=0)\n # print(t.shape)\n\n probs /= np.sum(probs, axis=0)[np.newaxis,:]\n return probs",
"def softmax(x):\n shape = x.shape\n probs = np.exp(x - np.max(x, axis=len(shape) - 1, keepdims=True))\n ... | [
"0.7465903",
"0.73197216",
"0.71343404",
"0.71002626",
"0.70893115",
"0.70893115",
"0.6964765",
"0.6963534",
"0.69254",
"0.6904407",
"0.6894728",
"0.6893124",
"0.68816084",
"0.68766975",
"0.68731517",
"0.6869365",
"0.6868904",
"0.6868904",
"0.6868904",
"0.6868904",
"0.6868904... | 0.0 | -1 |
This is probably inefficient, but a useful way to find e.g. the scene position under the mouse cursor. | def read_pixel(self, name: str, x: int, y: int, gl_type=gl.GL_FLOAT, gl_format=gl.GL_RGBA):
c_type = GLTYPE_TO_CTYPE[gl_type]
texture = self.textures[name]
position_value = (c_type * 4)()
with self:
gl.glReadBuffer(gl.GL_COLOR_ATTACHMENT0 + texture.unit)
gl.glRead... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def mousePos():\n data = display.Display().screen().root.query_pointer()._data\n return data[\"root_x\"], data[\"root_y\"]",
"def cursorPosGL(self):\n globalPos = QtGui.QCursor.pos()\n pos = self.mapFromGlobal(globalPos)\n y = self.size().height() - pos.y()\n return pos.x(), y",... | [
"0.76065445",
"0.7346294",
"0.7346294",
"0.7276934",
"0.72699213",
"0.72340405",
"0.70662713",
"0.70662713",
"0.70391846",
"0.68502015",
"0.6846693",
"0.68412995",
"0.68412995",
"0.6807147",
"0.6766796",
"0.6728611",
"0.6693625",
"0.6693625",
"0.6693625",
"0.6686737",
"0.6631... | 0.0 | -1 |
Loads surface mesh using meshio. Not meant for mixed shape meshes. | def load_mesh(fname):
fname = abs_fname_(fname)
m = meshio.read(fname)
mesh = Mesh()
mesh.vertices = m.points
for i, c in enumerate(m.cells):
if i == 0:
faces = c.data
else:
faces = np.vstack((faces, c.data))
mesh.faces = faces
return mesh | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load_mesh(self, script_to_apply=None): \n # convert to an obj file using meshlab\n if script_to_apply is None:\n meshlabserver_cmd = 'meshlabserver -i \\\"%s\\\" -o \\\"%s\\\"' %(self.filename, self.obj_filename)\n else:\n meshlabserver_cmd = 'meshlabserver -i ... | [
"0.7057171",
"0.70245314",
"0.67579937",
"0.67061126",
"0.6692487",
"0.6679398",
"0.6569063",
"0.6533282",
"0.6514972",
"0.6505622",
"0.6436628",
"0.6435029",
"0.6251096",
"0.62127954",
"0.62039167",
"0.6150478",
"0.61312926",
"0.611122",
"0.6106273",
"0.6066741",
"0.6060872"... | 0.7224944 | 0 |
Loads volume mesh using meshio. Not meant for mixed shape meshes. | def load_volume_mesh(fname):
fname = abs_fname_(fname)
m = meshio.read(fname)
mesh = Mesh()
mesh.vertices = m.points
for i, c in enumerate(m.cells):
if i == 0:
elements = c.data
else:
elements = np.vstack((elements, c.data))
mesh.elements = elements
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def read(self, mesh_path: str) -> None:\n\n reader = VtuReader(mesh_path)\n self.set_mesh_data(mesh=reader.mesh, bc=reader.bc, mpc=reader.mpc)",
"def load_volume_mixd(dim, fname=None, mxyz=None, mien=None, hexa=False):\n vertices, elements = mixd_load_(fname, mxyz, mien)\n\n mesh = Mesh()\n ... | [
"0.70592856",
"0.6962647",
"0.69257474",
"0.6415933",
"0.6392293",
"0.6390476",
"0.6343733",
"0.6273749",
"0.6219491",
"0.620209",
"0.609297",
"0.60837203",
"0.6062914",
"0.6002005",
"0.5998866",
"0.5905394",
"0.586846",
"0.585365",
"0.5845951",
"0.580518",
"0.57808256",
"0... | 0.7867217 | 0 |
Raw loading function that can be used from `load_mixd` and `load_volume_mixd`. Meant for internal use. | def mixd_load_(fname=None, mxyz=None, mien=None):
fname = abs_fname_(fname)
if fname is None and (mxyz is None and mien is None):
raise ValueError(
"Either `fname` or (`mxyz` and `mien`) needs to be defined."
)
if fname is None:
if (
(mxyz is None and mien i... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load(self):\n\n raise NotImplementedError",
"def _load_disk(self):",
"def _load_disk(self):",
"def loadRaw(self, path, preprocfunc=None):\n # Only for 8 and 32 bit images\n depth = self.getDepth()\n if depth==1:\n mamba.raiseExceptionOnError(mambaCore.ERR_BAD_DEPTH)\n ... | [
"0.6166569",
"0.61044806",
"0.61044806",
"0.6067018",
"0.6023306",
"0.6013337",
"0.59904814",
"0.59666723",
"0.5963483",
"0.5954268",
"0.59243995",
"0.5867219",
"0.58107245",
"0.5805746",
"0.5797998",
"0.57825094",
"0.5765734",
"0.57507634",
"0.5726716",
"0.56981766",
"0.5690... | 0.0 | -1 |
Loads mixd volume meshes. | def load_volume_mixd(dim, fname=None, mxyz=None, mien=None, hexa=False):
vertices, elements = mixd_load_(fname, mxyz, mien)
mesh = Mesh()
mesh.vertices = vertices.reshape(-1, dim)
if hexa:
mesh.elements = elements.reshape(-1, 8)
else:
mesh.elements = elements.reshape(-1, 4)
re... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load_volume_mesh(fname):\n fname = abs_fname_(fname)\n\n m = meshio.read(fname)\n mesh = Mesh()\n mesh.vertices = m.points\n\n for i, c in enumerate(m.cells):\n if i == 0:\n elements = c.data\n else:\n elements = np.vstack((elements, c.data))\n\n mesh.eleme... | [
"0.6619064",
"0.6575824",
"0.5888349",
"0.5782488",
"0.5687946",
"0.56595165",
"0.55847037",
"0.5549612",
"0.5535285",
"0.54974794",
"0.5464577",
"0.54594773",
"0.54511726",
"0.5433815",
"0.5415109",
"0.53885984",
"0.53758585",
"0.5352629",
"0.5302441",
"0.53019255",
"0.53004... | 0.7286509 | 0 |
Loads spline files of extension `.iges` `.xml` `.itd` | def load_splines(fname):
fname = str(fname)
fname = abs_fname_(fname)
sr = splinelibpy.Reader()
ext = os.path.splitext(fname)[1]
if ext == ".iges":
loaded_splines = sr.read_iges(fname)
elif ext == ".xml":
loaded_splines = sr.read_xml(fname)
elif ext == ".itd":
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load(self):\n if self.__fname == '':\n print('You must pass in a file name to load!')\n return []\n\n ext = os.path.splitext(self.__fname)[1]\n first_pt = None\n if len(self.__fea.points) > 0:\n first_pt = self.__fea.points[0]\n if ext == '.dx... | [
"0.598407",
"0.5837179",
"0.56815714",
"0.5675505",
"0.5656372",
"0.56464905",
"0.56287754",
"0.55418855",
"0.5446652",
"0.54027593",
"0.5383158",
"0.5353577",
"0.5353577",
"0.53077507",
"0.5288568",
"0.5288151",
"0.5274512",
"0.52620083",
"0.5251903",
"0.52356094",
"0.523395... | 0.71475726 | 0 |
Checks if fname is absolute. If not, turns it into an abspath. Tilde safe. | def abs_fname_(fname):
if os.path.isabs(fname):
pass
elif '~' in fname:
fname = os.path.expanduser(fname)
else:
fname = os.path.abspath(fname)
return fname | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _makeAbsolute(fname):\n if fname[0] != '/':\n return os.path.join(os.getcwd(), fname)\n else:\n return fname",
"def getAbsFileName(fname):\n\tfileAbsPath=os.path.abspath(fname)\n\treturn fileAbsPath",
"def abspath(filename, relative_to = None):\n # Create filename relative to the refer... | [
"0.82105744",
"0.7471553",
"0.69280857",
"0.69023055",
"0.6898607",
"0.6897593",
"0.6879507",
"0.6845178",
"0.68191797",
"0.6804619",
"0.67667115",
"0.67245203",
"0.67103535",
"0.6708995",
"0.66932917",
"0.66385156",
"0.6597944",
"0.6584722",
"0.65428245",
"0.6516904",
"0.650... | 0.83377725 | 0 |
Checks to see if the user is a librarian for certain routes | def librarian(f):
@wraps(f)
def decorated_function(*args, **kwargs):
if current_user.lflag == 0:
flash("You are not a librarian! Please sign in to a librarian account")
return redirect(url_for('main'))
return f(*args, **kwargs)
return decorated_function | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def librarian_list(request):\n librarian = User.objects.filter(is_student=False, is_lecturer=False, is_parent=False, is_superuser=False)\n user_type = \"Librarian\"\n context = {\n \"librarian\": librarian,\n \"user_type\": user_type,\n }\n return render(request, 'library/librarians_li... | [
"0.62187976",
"0.55780613",
"0.5519553",
"0.54693735",
"0.5451162",
"0.5360155",
"0.53441876",
"0.53240615",
"0.53006685",
"0.5292587",
"0.5280629",
"0.5270738",
"0.5257878",
"0.52497697",
"0.5249029",
"0.52322376",
"0.5221979",
"0.521075",
"0.52094173",
"0.5189399",
"0.51697... | 0.60173243 | 1 |
Run the forward pass for a model. | def forward(self, *args, **kwargs) -> Dict[str, Any]:
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def on_iter_forward(self, runner):\n # unpack features into features and targets\n *features, target = runner.batch\n # Forward features\n runner.output = runner.model(*features)\n # Ensure `targetL` and `outputL` are always in a list format.\n targetL = [target] if not is... | [
"0.71058494",
"0.6935403",
"0.6893536",
"0.6876209",
"0.68726456",
"0.67623425",
"0.67323256",
"0.67323256",
"0.6713972",
"0.67027366",
"0.6692563",
"0.6682018",
"0.6682018",
"0.66558874",
"0.6654495",
"0.6642389",
"0.6582641",
"0.6569321",
"0.6529306",
"0.6527974",
"0.651852... | 0.0 | -1 |
Model specific postprocess and convert model output to standard model outputs. | def postprocess(self, inputs: Dict[str, Any], **kwargs) -> Dict[str, Any]:
return inputs | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def postprocess(m):\n logger.info(\"Postprocessing the model...\")\n while len(m.graph.value_info) > 0:\n m.graph.value_info.pop()\n m = other.polish_model(m)\n eliminating.eliminate_single_input_Concat(m.graph)\n eliminating.eliminate_nop_Maxpool_and_AveragePool(m.graph)\n eliminating.eli... | [
"0.65986824",
"0.6418254",
"0.6402641",
"0.6136343",
"0.61138946",
"0.6085182",
"0.6063578",
"0.6061771",
"0.6020394",
"0.5969992",
"0.59575593",
"0.5906806",
"0.5903955",
"0.5900462",
"0.5887015",
"0.58500457",
"0.58485085",
"0.58485085",
"0.58485085",
"0.58485085",
"0.58485... | 0.0 | -1 |
Define the instantiation method of a model,default method is by calling the constructor. Note that in the case of no loading model process in constructor of a task model, a load_model method is added, and thus this method is overloaded | def _instantiate(cls, **kwargs):
return cls(**kwargs) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, model: str, **kwargs):\n\n super().__init__(model=model, **kwargs)\n logger.info('load model done')",
"def __init__(self, model: str, **kwargs):\n super().__init__(model=model)",
"def __call__(self,setup_options=True, instantiate_options=True, verbose=False):\n mo... | [
"0.6906803",
"0.661834",
"0.6585854",
"0.65579903",
"0.6555102",
"0.6471729",
"0.6459663",
"0.64521134",
"0.64382696",
"0.64331865",
"0.6394191",
"0.6394191",
"0.6394191",
"0.6394191",
"0.63880175",
"0.63559264",
"0.6355466",
"0.63522595",
"0.63522595",
"0.63354486",
"0.63008... | 0.0 | -1 |
Instantiate a model from local directory or remote model repo. Note that when loading from remote, the model revision can be specified. | def from_pretrained(cls,
model_name_or_path: str,
revision: Optional[str] = DEFAULT_MODEL_REVISION,
cfg_dict: Config = None,
device: str = None,
**kwargs):
prefetched = kwargs.get('model_prefe... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load_model(self, model_path: str):",
"def load_model(fname: os.PathLike) -> Model:\n return Model.load(fname)",
"def _load_from(cls, model_state: dict) -> AbstractModel:\n return cls(model=model_state.get('model'), **model_state.get('kwargs'))",
"def load_model_from_file(path, as_builder=False)... | [
"0.67073345",
"0.6637043",
"0.65116197",
"0.6482459",
"0.64491147",
"0.64228743",
"0.6422444",
"0.64197296",
"0.63858056",
"0.6369337",
"0.6368034",
"0.6346652",
"0.6345023",
"0.62734",
"0.62559134",
"0.62544537",
"0.6245681",
"0.6237426",
"0.6231261",
"0.6220943",
"0.6198383... | 0.7069769 | 0 |
save the pretrained model, its configuration and other related files to a directory, so that it can be reloaded | def save_pretrained(self,
target_folder: Union[str, os.PathLike],
save_checkpoint_names: Union[str, List[str]] = None,
save_function: Callable = save_checkpoint,
config: Optional[dict] = None,
**kwarg... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def save_pretrained(self, save_directory):\n assert os.path.isdir(save_directory), \"Saving path should be a directory where the model and configuration can be saved\"\n\n # Only save the model it-self if we are using distributed training\n model_to_save = self.module if hasattr(self, \"module... | [
"0.7750904",
"0.7590839",
"0.75015235",
"0.74963295",
"0.74457264",
"0.73923844",
"0.7381427",
"0.7379934",
"0.73715615",
"0.7362207",
"0.7362207",
"0.7362207",
"0.73486555",
"0.7346712",
"0.73033834",
"0.7298549",
"0.7298549",
"0.7298549",
"0.7298549",
"0.7298549",
"0.729822... | 0.0 | -1 |
Initializes the Backtest. A Queue is used to hold the Events. The Signals, Orders, and Fills are counted. | def __init__(self, csv_dir, symbol_list, initial_capital, heartbeat, start_date, end_date,
data_handler, execution_handler, portfolio, strategy, strat_params_list=None):
self.csv_dir = csv_dir
self.symbol_list = symbol_list
self.initial_capital = initial_capital
self.hea... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self):\n self._events = collections.deque()\n self._alarms = 0\n self._alarm_active = True\n self._kq = select.kqueue()\n self._kq_events = {}\n self.traceback = ()\n self.event_count = 0\n self._no_tb = False",
"def on_init(self, queue=None, *... | [
"0.7210904",
"0.7131795",
"0.7033969",
"0.6756145",
"0.673009",
"0.67163557",
"0.6671295",
"0.6656317",
"0.6647535",
"0.6618624",
"0.6594603",
"0.6594603",
"0.6594603",
"0.6594603",
"0.6594603",
"0.65499914",
"0.6525102",
"0.6469497",
"0.6469497",
"0.6403421",
"0.63916034",
... | 0.0 | -1 |
Generates the trading instance objects from their class types. This method attaches all of the trading objects (DataHandler, Strategy, Portfolio, and ExecutionHandler) to various internal members. This ties together all the other classes to the Backtester object. | def _generate_trading_instances(self):
print("Creating DataHandler, Strategy, Portfolio, and ExecutionHandler for")
# Set internal data members equal to the classes we passed in earlier, along with necessary parameters.
# https://softwareengineering.stackexchange.com/questions/131403/what-is-th... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _generate_trading_instances(self, strategy_params_dict):\n print(\"Creating DataHandler, Strategy, Portfolio, and ExecutionHandler for\")\n print(\"strategy parameter list: %s...\" % strategy_params_dict)\n\n # Set internal data members equal to the classes we passed in earlier, along with... | [
"0.71847904",
"0.7100767",
"0.6478167",
"0.6361291",
"0.56882477",
"0.56326246",
"0.5572381",
"0.5539028",
"0.55039656",
"0.54438514",
"0.5403827",
"0.5379698",
"0.53721094",
"0.5297526",
"0.5287005",
"0.52309954",
"0.52309954",
"0.51850206",
"0.51665777",
"0.5139257",
"0.511... | 0.7943233 | 0 |
Executes the backtest. This is where the signal handling of the Backtesting engine is carried out. There are two while loops, the outerloop (heartbeat) and the nested innerloop, which checks if there is an event in the Event Queue object. The inner loop acts on the Event by calling the appropriate method | def _run_backtest(self):
i = 0
while True:
i += 1
print(i)
# Update the market bars
if self.data_handler.continue_backtest == True:
self.data_handler.update_bars()
else:
break
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _run_backtest(self):\n i = 0\n\n while True:\n i += 1\n print(i)\n\n # Update the market bars\n if self.data_handler.continue_backtest == True:\n self.data_handler.update_bars()\n else:\n break\n\n # H... | [
"0.7295389",
"0.7248762",
"0.6957512",
"0.67003644",
"0.66377956",
"0.6541956",
"0.64540994",
"0.64049834",
"0.62294203",
"0.62108946",
"0.61638576",
"0.61331415",
"0.6129716",
"0.60833454",
"0.60628366",
"0.6045858",
"0.6027141",
"0.6023538",
"0.5971657",
"0.59704083",
"0.59... | 0.7251965 | 1 |
Outputs the strategy performance and other metrics from the backtest. | def _output_performance(self):
self.portfolio.create_equity_curve_dataframe()
print("Creating summary statistics...")
stats = self.portfolio.output_summary_stats()
print("Creating equity curve...")
print(self.portfolio.equity_curve.tail(10))
pprint.pprin... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def report_performance(self):\n performance = self.amygdala.visualize(self.timestep, \n self.name, \n self.log_dir)\n print('Final performance is {0:.3}'.format(performance))\n self.backup()\n retu... | [
"0.63392967",
"0.63388044",
"0.62344825",
"0.62191135",
"0.61377233",
"0.61345345",
"0.5990634",
"0.597709",
"0.59057546",
"0.5875184",
"0.58620185",
"0.5844489",
"0.5844022",
"0.5822778",
"0.5800217",
"0.58001125",
"0.57988024",
"0.57855344",
"0.57675505",
"0.5763641",
"0.57... | 0.6249993 | 2 |
Simulates the backtest and outputs portfolio performance. | def simulate_trading(self):
self._run_backtest()
self._output_performance() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def backtest_portfolio(self):\n self.rank=dict()\n self.accuracy=dict()\n portfolio = dict()\n \n for algo in self.algos:\n portfolio[algo]=pd.DataFrame(index=self.positions.index)\n self.pos_diff=dict()\n self.pos_diff[algo] = self.positi... | [
"0.7306378",
"0.7142306",
"0.7061594",
"0.7044508",
"0.6963376",
"0.6885208",
"0.6866841",
"0.68580073",
"0.65411276",
"0.6384601",
"0.63703954",
"0.63657266",
"0.6328172",
"0.63091695",
"0.6296223",
"0.629528",
"0.629528",
"0.6279004",
"0.62647766",
"0.61990505",
"0.6168383"... | 0.68904 | 5 |
Initializes the Backtest. A Queue is used to hold the Events. The Signals, Orders, and Fills are counted. | def __init__(self, csv_dir, symbol_list, initial_capital, heartbeat, start_date,
data_handler, execution_handler, portfolio, strategy, strat_params_list=None):
self.csv_dir = csv_dir
self.symbol_list = symbol_list
self.initial_capital = initial_capital
self.heartbeat = h... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self):\n self._events = collections.deque()\n self._alarms = 0\n self._alarm_active = True\n self._kq = select.kqueue()\n self._kq_events = {}\n self.traceback = ()\n self.event_count = 0\n self._no_tb = False",
"def on_init(self, queue=None, *... | [
"0.72095853",
"0.71329",
"0.70346403",
"0.6754693",
"0.67315227",
"0.6717928",
"0.6671912",
"0.66584927",
"0.6648384",
"0.662001",
"0.65959483",
"0.65959483",
"0.65959483",
"0.65959483",
"0.65959483",
"0.6550823",
"0.652473",
"0.6470759",
"0.6470759",
"0.64041936",
"0.639307"... | 0.0 | -1 |
Generates the trading instance objects from their class types. This method attaches all of the trading objects (DataHandler, Strategy, Portfolio, and ExecutionHandler) to various internal members. This ties together all the other classes to the Backtester object. | def _generate_trading_instances(self, strategy_params_dict):
print("Creating DataHandler, Strategy, Portfolio, and ExecutionHandler for")
print("strategy parameter list: %s..." % strategy_params_dict)
# Set internal data members equal to the classes we passed in earlier, along with necessary pa... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _generate_trading_instances(self):\n print(\"Creating DataHandler, Strategy, Portfolio, and ExecutionHandler for\")\n\n # Set internal data members equal to the classes we passed in earlier, along with necessary parameters.\n # https://softwareengineering.stackexchange.com/questions/131403... | [
"0.7944597",
"0.71021026",
"0.6479502",
"0.6362634",
"0.56881684",
"0.56321007",
"0.5572392",
"0.55376",
"0.5502691",
"0.5442817",
"0.5403161",
"0.5379905",
"0.5372698",
"0.5296769",
"0.52877635",
"0.5230109",
"0.5230109",
"0.5185296",
"0.5165134",
"0.5138819",
"0.5119774",
... | 0.71861804 | 1 |
Executes the backtest. This is where the signal handling of the Backtesting engine is carried out. There are two while loops, the outerloop (heartbeat) and the nested innerloop, which checks if there is an event in the Event Queue object. The inner loop acts on the Event by calling the appropriate method | def _run_backtest(self):
i = 0
while True:
i += 1
print(i)
# Update the market bars
if self.data_handler.continue_backtest == True:
self.data_handler.update_bars()
else:
break
# Handle the Events
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _run_backtest(self):\n i = 0\n \n while True:\n i += 1\n print(i)\n \n # Update the market bars\n if self.data_handler.continue_backtest == True:\n self.data_handler.update_bars()\n else:\n brea... | [
"0.7250805",
"0.7247759",
"0.6956706",
"0.6701028",
"0.6637288",
"0.6541509",
"0.64544195",
"0.64051026",
"0.62292856",
"0.621116",
"0.6163216",
"0.6132048",
"0.61309826",
"0.60833305",
"0.60645205",
"0.60457534",
"0.6026709",
"0.6022797",
"0.5971004",
"0.59705234",
"0.593822... | 0.72942674 | 0 |
Outputs the strategy performance and other metrics from the backtest. | def _output_performance(self):
self.portfolio.create_equity_curve_dataframe()
print("Creating summary statistics...")
stats = self.portfolio.output_summary_stats()
print("Creating equity curve...")
print(self.portfolio.equity_curve.tail(10))
pprint.pprint(stats)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def report_performance(self):\n performance = self.amygdala.visualize(self.timestep, \n self.name, \n self.log_dir)\n print('Final performance is {0:.3}'.format(performance))\n self.backup()\n retu... | [
"0.6337549",
"0.6247714",
"0.62319505",
"0.62179327",
"0.6136802",
"0.61355233",
"0.5989832",
"0.5975646",
"0.59036446",
"0.5874888",
"0.5862709",
"0.584473",
"0.58438706",
"0.5822467",
"0.57994264",
"0.5798621",
"0.5797651",
"0.5784963",
"0.5765707",
"0.576413",
"0.5757759",... | 0.6336608 | 1 |
Simulates the backtest and outputs portfolio performance. Loops over all variants of strategy parameters of a space generated by a cartesian product of hyperparameter values. Generates new instances of all the data handlers, event queues, and portfolio objects upon each iteration, in order to ensure a "clean slate" for... | def simulate_trading(self):
# Create the file output stream
posix_now = datetime.datetime.timestamp(datetime.datetime.now())
out_path = os.getcwd() + "/OutputResults/backtest_{}".format(posix_now)[:-7:] + ".csv"
out = open(out_path, "w+")
spl = len(self.strat_params_list)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parameter_optimization(self):\n out = open(self.csv_dir + self.strategy_id + '_gridsearch.csv', \"w\")\n spl = len(self.para_list)\n for i, sp in enumerate(self.para_list):\n print(\"Strategy %s out of %s...\" % (i + 1, spl))\n self._generate_trading_instances(sp)\n ... | [
"0.6824583",
"0.65352273",
"0.6505157",
"0.63680285",
"0.6147844",
"0.6038669",
"0.59978664",
"0.59736603",
"0.58891684",
"0.58687943",
"0.58315384",
"0.58308274",
"0.5772297",
"0.5758246",
"0.57433945",
"0.57423234",
"0.5734684",
"0.5721028",
"0.5683199",
"0.5653934",
"0.565... | 0.6787927 | 1 |
initialize your data structure here. | def __init__(self):
self.heap = []
self.stack = [] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _init_empty(self):\n self._data = []",
"def __init__(self):\n self.data = []\n self.record = {}",
"def __init__(self):\n self.structure = {}",
"def __init__(self):\n self._data = []",
"def __init__(self):\n self._data = []",
"def __init__(self):\n self... | [
"0.7761043",
"0.76102185",
"0.7555967",
"0.7549892",
"0.7549892",
"0.7549892",
"0.7549892",
"0.7549892",
"0.7549892",
"0.752797",
"0.7446006",
"0.7446006",
"0.7446006",
"0.7446006",
"0.7446006",
"0.743338",
"0.743338",
"0.7408609",
"0.7385719",
"0.737986",
"0.737986",
"0.73... | 0.0 | -1 |
Function to allow the new name of the file to contain the number at an arbitrary position or the old name of the file at an arbitrary position in the string of the new file name. | def stitch(toName, oldName, num, zfill = -1, mult=1):
if zfill != -1:
tempNum = str(num).zfill(zfill)
if mult > 1:
for i in range(num+1, num + mult):
tempNum += ' & ' + str(i).zfill(zfill)
num = tempNum
if len(toName) > 0:
# We have 5 potential parts ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def fileRename(current_file,num,digits):\n # Key, value pairs of what to replace.\n dictobj = {\n '<num>': get_numbering_format(digits, num),\n '<datetaken>': date_to_string(get_date_taken(current_file),'%Y%m%d__%H_%M'),\n '<dname>': dirname\n }\n # Rename\n new_filename = multi... | [
"0.7255178",
"0.71344984",
"0.7102937",
"0.6988627",
"0.6971764",
"0.6734604",
"0.67219466",
"0.671219",
"0.6661564",
"0.6661564",
"0.6653499",
"0.663018",
"0.66124356",
"0.6611557",
"0.6605207",
"0.655132",
"0.6525605",
"0.64201343",
"0.63903785",
"0.6386055",
"0.636861",
... | 0.0 | -1 |
Anonymous users can make `whoami` requests. They receive a 401 response confirming they are not logged in. | def test_whoami_by_anonymous_user(self):
response = self.client.get("/api/users/whoami/")
self.assertEqual(response.status_code, 401) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def whoami():\n g.data['authenticated_user'] = g.current_user",
"def whoami():\n try:\n\n token = request.headers['token']\n username, uid, wid = read_auth_token(token)\n return dict(username=username, uid=uid, wid=wid)\n\n except SignatureExpired as e:\n return dict(error=st... | [
"0.71340424",
"0.7014765",
"0.6927591",
"0.6695747",
"0.6655952",
"0.6655952",
"0.6635073",
"0.65991694",
"0.65672773",
"0.6493297",
"0.64832705",
"0.64729846",
"0.6437622",
"0.6405885",
"0.64006865",
"0.6381664",
"0.6346517",
"0.6344521",
"0.6308586",
"0.6299756",
"0.6297887... | 0.8336867 | 0 |
Loggedin users can make `whoami` requests. They receive their own user object. | def test_whoami_by_logged_in_user(self):
user = factories.UserFactory(
first_name="Jane", last_name="Doe", email="jane.doe@example.com"
)
org_1 = factories.OrganizationFactory()
org_access_1 = factories.OrganizationAccessFactory(
user=user, organization=org_1
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def whoami():\n g.data['authenticated_user'] = g.current_user",
"def whoami():\n return current_user._get_current_object()",
"def whoami(self):",
"def whoami(self):\n response = requests.get(self.ENDPOINT + '/user-resource/user', headers={'apikey':self.apikey})\n\n return response.json()"... | [
"0.7910836",
"0.74055797",
"0.730647",
"0.72178155",
"0.71626765",
"0.7057506",
"0.6882682",
"0.6858402",
"0.6839091",
"0.6702892",
"0.6689809",
"0.6612816",
"0.6563662",
"0.65627337",
"0.6543319",
"0.65190023",
"0.6462778",
"0.6462778",
"0.63894653",
"0.62800586",
"0.6200437... | 0.0 | -1 |
Fetch node data using k8s API | def get_node_data(cluster_id):
try:
# fetching the token from secret of the namespace 'dashboard'
_TOKEN = [base64.b64decode(secret_item.data['token']).decode('UTF-8') for secret_item in client.CoreV1Api(
).list_namespaced_secret('dashboard').items if base64.b64decode(secret_item.data['names... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get(node_instance_id, logger, client, tenant_name):\n if tenant_name:\n logger.info('Explicitly using tenant `{0}`'.format(tenant_name))\n logger.info('Retrieving node instance {0}'.format(node_instance_id))\n try:\n node_instance = client.node_instances.get(node_instance_id)\n except... | [
"0.60346806",
"0.59235954",
"0.578973",
"0.57561266",
"0.5736296",
"0.5608303",
"0.5593132",
"0.5583009",
"0.556707",
"0.5539366",
"0.5538481",
"0.55283237",
"0.5513567",
"0.54998785",
"0.5490597",
"0.5488156",
"0.5446512",
"0.5438874",
"0.5410196",
"0.53977907",
"0.5386005",... | 0.6105052 | 0 |
Fetch namespace listing and detail | def get_namespace_data(cluster_id, namespace_id=None):
# namespace detail
if namespace_id:
resource_count = get_resource_count(cluster_id, namespace_id)
resource_info = get_resource_info(cluster_id, 'pods', namespace_id)
return {'resources': resource_count, 'resource_info': resource_info... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def namespace(self, namespace):\n return self.client.call('GET',\n self.name, params={'namespace': namespace})",
"def _fetch_all_namespaces():\n response = _fetch_herd_session() \\\n .get('{}://{}/{}/{}'.format(HERD_REST_PROTOCOL, HERD_BASE_URL,\n ... | [
"0.66033626",
"0.65595657",
"0.6453324",
"0.632987",
"0.63076866",
"0.62413985",
"0.61975986",
"0.61743927",
"0.6086349",
"0.604603",
"0.60328054",
"0.6023155",
"0.6000177",
"0.59959286",
"0.59717184",
"0.59505755",
"0.5948675",
"0.5923967",
"0.5918093",
"0.5893192",
"0.58521... | 0.57417464 | 24 |
Fetch deployment listing and detail | def get_deployment_data(cluster_id, namespace_id=None, deployment_id=None):
# deployment detail
if deployment_id and namespace_id is not None:
# creating cell-pod mapping for getting cell details
cell_pod_map = get_cell_pod_map(cluster_id)
# getting pod count
pods_data = [pod for... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def list_deployments() -> JSONResponse:\n\n deploy_manager = DeployManager()\n deployments = deploy_manager.list()\n return JSONResponse(deployments)",
"def test_get_deployments(self):\n pass",
"def test_get_deployments(self):\n pass",
"def detail(request, deployment_id):\n if reque... | [
"0.6772889",
"0.66647077",
"0.66647077",
"0.6597974",
"0.6530962",
"0.6526921",
"0.6526921",
"0.6485451",
"0.64644516",
"0.63073653",
"0.623222",
"0.621316",
"0.61912525",
"0.6129055",
"0.6088292",
"0.60881466",
"0.6058188",
"0.60260034",
"0.6011649",
"0.5992773",
"0.5969929"... | 0.6200603 | 12 |
Fetch compute cell data | def get_compute_cell_data(cluster_id=None, namespace_id=None):
cells_info = client.CustomObjectsApi().list_cluster_custom_object('kiyot.elotl.co', 'v1beta1', 'cells')
return cells_info | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _fetch_data(self):\n pass",
"def getdata(\n self,\n col: str,\n exp: str,\n chan: str,\n res: int,\n xs: Tuple[int, int],\n ys: Tuple[int, int],\n zs: Tuple[int, int],\n ):\n if self.hasdata(col, exp, chan, res, xs, ys, zs):\n ... | [
"0.6227691",
"0.6165168",
"0.61336154",
"0.6130058",
"0.6112285",
"0.60044104",
"0.5895254",
"0.57669675",
"0.57077354",
"0.5702612",
"0.56423634",
"0.5611894",
"0.56060374",
"0.559",
"0.5576882",
"0.5543976",
"0.552921",
"0.5527102",
"0.5463397",
"0.54594564",
"0.5458928",
... | 0.6701156 | 0 |
Get count of resources for requested cluster and namespace | def get_resource_count(cluster_id, namespace_id=None):
# fetching namespaced resource count
if namespace_id:
# Deployment count
deployment_count = len(client.AppsV1beta2Api().list_namespaced_deployment(namespace_id).items)
# Pod count
pod_items = client.CoreV1Api().list_namespace... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_cluster_count(self) -> int:\n return len(self.get_all_cluster_ids())",
"def count(self, resource):\n return len(self.all(resource))",
"def get_count_all(cls, context, cluster_id):\n return cls.dbapi.get_cluster_nodegroup_count(context, cluster_id)",
"def test_get_resource_license... | [
"0.66817355",
"0.65607",
"0.6554999",
"0.6523906",
"0.65174896",
"0.64166987",
"0.6349572",
"0.6297506",
"0.6249817",
"0.6204213",
"0.6179697",
"0.6140397",
"0.6123831",
"0.6111716",
"0.61096334",
"0.6105647",
"0.6091557",
"0.6071596",
"0.6053722",
"0.6047418",
"0.6045339",
... | 0.7759584 | 0 |
Get the resource details for the namespace to be excluded | def get_hidden_namespace_resources(cluster_id, namespace_id):
# Deployment count
deployment_count = len(client.AppsV1beta2Api().list_namespaced_deployment(namespace_id).items)
# Pod count
pod_items = client.CoreV1Api().list_namespaced_pod(namespace_id).items
pod_count = len(pod_items)
# Cell cou... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def exclude_resource_rules(self) -> Optional[Sequence['outputs.NamedRuleWithOperations']]:\n return pulumi.get(self, \"exclude_resource_rules\")",
"def exclude_resource_ids_scope(self) -> str:\n return pulumi.get(self, \"exclude_resource_ids_scope\")",
"def exclude_resource_ids_scope(self) -> str... | [
"0.60753304",
"0.6006292",
"0.6006292",
"0.57762307",
"0.5728827",
"0.54888046",
"0.53958565",
"0.5395732",
"0.5374372",
"0.53347665",
"0.53347665",
"0.5316922",
"0.52620876",
"0.5223665",
"0.5212365",
"0.5199779",
"0.5170026",
"0.5170026",
"0.51522565",
"0.51229185",
"0.5110... | 0.53204346 | 11 |
calculating capacity and usage for requested resources | def get_resource_info(cluster_id, kind, namespace_id=None, pods_list=None):
if pods_list is None:
pods_list = []
capacity = get_cluster_capacity_info(cluster_id),
usage = get_cluster_usage_info(cluster_id, kind, namespace_id, pods_list)
if capacity[0]['cpu'] != 0 and capacity[0]['memory'] != 0:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_capacity():\n fs.get_capacity()",
"def capacity_used(self):\n raise NotImplementedError()",
"def capacity(self):\n capacity = {}\n resources = self.nodes[0].capacity.keys()\n for r in resources:\n values = [n.capacity[r] for n in self.nodes]\n capaci... | [
"0.76844007",
"0.7094635",
"0.6953356",
"0.6724627",
"0.6691189",
"0.6664577",
"0.6574017",
"0.6568174",
"0.6502811",
"0.646026",
"0.64514846",
"0.64033794",
"0.6387363",
"0.6367259",
"0.6345023",
"0.6332639",
"0.6331895",
"0.6319946",
"0.6291326",
"0.62830526",
"0.62749445",... | 0.0 | -1 |
Get cluster capacity from node detail | def get_cluster_capacity_info(cluster_id):
cpu_capacity_info = get_node_data(cluster_id)
cpu_capacity_in_cores = round(unit_conversion(sum([int(''.join(filter(
str.isdigit, str(item['status']['allocatable']['cpu'])))) for item in cpu_capacity_info]), 'm'), 2)
memory_capacity_in_gib = round(sum(
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_capacity():\n fs.get_capacity()",
"def capacity(self):\n capacity = {}\n resources = self.nodes[0].capacity.keys()\n for r in resources:\n values = [n.capacity[r] for n in self.nodes]\n capacity[r] = mean(values) if len(values) > 0 else 0.0\n return ca... | [
"0.7165189",
"0.67714345",
"0.67460614",
"0.66856956",
"0.6549141",
"0.6534968",
"0.65045047",
"0.64937866",
"0.648811",
"0.64549166",
"0.64503264",
"0.63680506",
"0.6324158",
"0.6307951",
"0.6224518",
"0.6187836",
"0.61583894",
"0.6155459",
"0.6146542",
"0.6124433",
"0.61244... | 0.7078413 | 1 |
get resource usage information from pods usage | def get_cluster_usage_info(cluster_id, kind, namespace_id=None, pods_list=None):
if pods_list is None:
pods_list = []
else:
logger.info('pod list not none')
if pods_list == 'no_pod_resource':
return {'cpu': 0,
'memory': 0}
else:
logger.info('resources no 0')
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_resource_info(cluster_id, kind, namespace_id=None, pods_list=None):\n if pods_list is None:\n pods_list = []\n capacity = get_cluster_capacity_info(cluster_id),\n usage = get_cluster_usage_info(cluster_id, kind, namespace_id, pods_list)\n if capacity[0]['cpu'] != 0 and capacity[0]['memor... | [
"0.684437",
"0.6337999",
"0.6151427",
"0.6085964",
"0.6063859",
"0.6056686",
"0.60112286",
"0.5989251",
"0.5980302",
"0.5904598",
"0.5870229",
"0.58482635",
"0.58432084",
"0.5836446",
"0.5833565",
"0.582723",
"0.57628006",
"0.57566756",
"0.5720972",
"0.5699034",
"0.5695292",
... | 0.730197 | 0 |
Creating a cellpod mapping | def get_cell_pod_map(cluster_id):
# creating a pod-cell mapping
cell_pod_map = dict()
for cell_info in get_compute_cell_data(cluster_id)['items']:
cell_pod_map[cell_info['status']['podName']] = {'cell_name': cell_info['metadata']['name'],
'podN... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def makeMapping(globalMap):\n \n from memops.xml.Implementation import bool2str, str2bool\n\n # Set up top level dictionaries\n loadMaps = globalMap.get('loadMaps')\n mapsByGuid = globalMap.get('mapsByGuid')\n\n abstractTypes = globalMap.get('CCLB').get('abstractTypes')\n exolinks = globalMap.get('CCLB').ge... | [
"0.65705234",
"0.6363869",
"0.6342318",
"0.6297688",
"0.6223787",
"0.61639667",
"0.6126557",
"0.60234666",
"0.5995557",
"0.5985049",
"0.5910921",
"0.5882835",
"0.5853043",
"0.58468926",
"0.58390236",
"0.5810125",
"0.58074224",
"0.5806761",
"0.5770566",
"0.5770005",
"0.5767886... | 0.6313567 | 3 |
Providing random mock values for resource capacity and usage. | def randomise(mock_info):
mock_info["resource_info"]["usage"]["cpu"] = round(random.uniform(0, 1), 2)
mock_info["resource_info"]["usage"]["cpu_percentage"] = round(random.uniform(0, 1), 2)
mock_info["resource_info"]["usage"]["memory"] = round(random.uniform(0, 1), 2)
mock_info["resource_info"]["usage"][... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_glass_capacity__has_expected_default_value():\n glass = moet.create_glass(\"A\")\n assert glass.capacity == 250",
"def _get_random_returns(self): \n return self.asset_process.distrib.random()",
"def test_sdram(self):\n sdram = SDRAMResource(128 * (2**20))\n self.assertEqual(... | [
"0.631372",
"0.61010695",
"0.60488284",
"0.6046735",
"0.5982311",
"0.5956989",
"0.59451956",
"0.5942304",
"0.59133613",
"0.589804",
"0.58927816",
"0.5846016",
"0.57881594",
"0.5747037",
"0.57254654",
"0.57179344",
"0.57009256",
"0.5674274",
"0.5642999",
"0.56372076",
"0.56222... | 0.7475767 | 0 |
Abstract base class to define the interface for priors of GP hyperparameter. | def __init__(self, rng=None):
if rng is None:
self.rng = np.random.RandomState(np.random.randint(0, 10000))
else:
self.rng = rng | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def prim_method(self):",
"def prim_method(self):",
"def __init__(self, p1_proba=0.5):\n self.p1_proba = p1_proba",
"def get_hyperparams(self):",
"def Params(cls):\n return hyperparams.InstantiableParams(cls)",
"def __init__(self, prim):\n self.actual = prim",
"def gen_parameter(self, g... | [
"0.6436486",
"0.6436486",
"0.6161557",
"0.60954475",
"0.6026573",
"0.5960026",
"0.59499425",
"0.59067774",
"0.5904224",
"0.5884801",
"0.58277154",
"0.5816997",
"0.56939894",
"0.56818277",
"0.5675402",
"0.56377643",
"0.5627205",
"0.56166977",
"0.56117594",
"0.5608884",
"0.5592... | 0.0 | -1 |
Returns N samples from the prior. | def sample_from_prior(self, n_samples):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def sample_from_prior(self, n_samples):\n\n p0 = self.min + self.rng.rand(n_samples) * (self.max - self.min)\n return p0[:, np.newaxis]",
"def sample_from_prior(self, n_samples):\n\n p0 = self.rng.normal(loc=self.mean, scale=self.sigma, size=n_samples)\n return p0[:, np.newaxis]",
"... | [
"0.7308122",
"0.7254096",
"0.71898454",
"0.7128596",
"0.6979248",
"0.6961805",
"0.67606914",
"0.6745526",
"0.6690407",
"0.6622515",
"0.6562736",
"0.65446556",
"0.65446556",
"0.6425273",
"0.6419413",
"0.6395372",
"0.63613814",
"0.6323354",
"0.63091654",
"0.6305061",
"0.6287302... | 0.7954478 | 0 |
Computes the gradient of the prior with respect to theta. | def gradient(self, theta):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def gradient(self, theta):\n return (1 / (self.sigma * np.sqrt(2 * np.pi))) * (\n -theta / (self.sigma ** 2) * np.exp(-(theta ** 2) / (2 * self.sigma ** 2))\n )",
"def gradient(self, theta):\n a = -(6 * self.scale ** 2)\n b = 3 * self.scale ** 2 + np.exp(2 * theta)\n ... | [
"0.8256445",
"0.8055304",
"0.7947039",
"0.7867961",
"0.78287417",
"0.78280646",
"0.7741965",
"0.77182055",
"0.77098596",
"0.76828635",
"0.76208895",
"0.75614756",
"0.72576725",
"0.72168595",
"0.7216574",
"0.7211889",
"0.70136374",
"0.70051277",
"0.6994997",
"0.69862175",
"0.6... | 0.83195007 | 0 |
Tophat prior as it used in the original spearmint code. | def __init__(self, l_bound, u_bound, rng=None):
if rng is None:
self.rng = np.random.RandomState(np.random.randint(0, 10000))
else:
self.rng = rng
self.min = l_bound
self.max = u_bound
if not (self.max > self.min):
raise Exception(
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def lnprior(self):\n \n return",
"def prior(self):\n return self.__prior",
"def prior(self):\n return self.__prior",
"def prior(self):\n return self.__prior",
"def prior(self):\n return self.__prior",
"def get_prior(self):\n return self.prior",
"def prio... | [
"0.74466044",
"0.68139035",
"0.68139035",
"0.68139035",
"0.68139035",
"0.6795314",
"0.678799",
"0.660923",
"0.66089433",
"0.65597916",
"0.6525363",
"0.6508171",
"0.6482998",
"0.64318466",
"0.6383781",
"0.6362514",
"0.62887466",
"0.6265354",
"0.6228101",
"0.61609983",
"0.60756... | 0.0 | -1 |
Returns N samples from the prior. | def sample_from_prior(self, n_samples):
p0 = self.min + self.rng.rand(n_samples) * (self.max - self.min)
return p0[:, np.newaxis] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def sample_from_prior(self, n_samples):\n pass",
"def sample_from_prior(self, n_samples):\n\n p0 = self.rng.normal(loc=self.mean, scale=self.sigma, size=n_samples)\n return p0[:, np.newaxis]",
"def sample_from_prior(self, n_samples):\n\n p0 = self.rng.lognormal(mean=self.mean, sigma... | [
"0.7953035",
"0.72522676",
"0.718797",
"0.71269745",
"0.6977341",
"0.6960459",
"0.6759681",
"0.6744323",
"0.66894495",
"0.6622551",
"0.6560197",
"0.65461904",
"0.65461904",
"0.64246196",
"0.641773",
"0.63970256",
"0.6361261",
"0.6323558",
"0.6306985",
"0.63066787",
"0.6286872... | 0.73065966 | 1 |
Computes the gradient of the prior with respect to theta. | def gradient(self, theta):
return np.zeros([theta.shape[0]]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def gradient(self, theta):\n pass",
"def gradient(self, theta):\n pass",
"def gradient(self, theta):\n return (1 / (self.sigma * np.sqrt(2 * np.pi))) * (\n -theta / (self.sigma ** 2) * np.exp(-(theta ** 2) / (2 * self.sigma ** 2))\n )",
"def gradient(self, theta):\n ... | [
"0.83195007",
"0.83195007",
"0.8256445",
"0.8055304",
"0.7947039",
"0.7867961",
"0.78287417",
"0.7741965",
"0.77182055",
"0.77098596",
"0.76828635",
"0.76208895",
"0.75614756",
"0.72576725",
"0.72168595",
"0.7216574",
"0.7211889",
"0.70136374",
"0.70051277",
"0.6994997",
"0.6... | 0.78280646 | 7 |
Horseshoe Prior as it is used in spearmint | def __init__(self, scale=0.1, rng=None):
if rng is None:
self.rng = np.random.RandomState(np.random.randint(0, 10000))
else:
self.rng = rng
self.scale = scale | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def prior_sample(self):\n pass",
"def horde_step(self, observation):",
"def lnprior(self):\n \n return",
"def brain_weight_oz(self):\r\n return Heart.heart_weight_oz(self) # Used method from Heart Class\r",
"def RankineHugoniot(P):\n # prevP = P+1\n # while(abs(P-prevP)>t... | [
"0.58937436",
"0.5873536",
"0.5848326",
"0.58077574",
"0.579661",
"0.570863",
"0.5638334",
"0.5638334",
"0.5638334",
"0.5638334",
"0.56263655",
"0.559631",
"0.55625165",
"0.55474657",
"0.55131316",
"0.5500774",
"0.54708487",
"0.54665077",
"0.54455525",
"0.5444476",
"0.5435669... | 0.0 | -1 |
Returns N samples from the prior. | def sample_from_prior(self, n_samples):
lamda = np.abs(self.rng.standard_cauchy(size=n_samples))
p0 = np.log(np.abs(self.rng.randn() * lamda * self.scale))
return p0[:, np.newaxis] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def sample_from_prior(self, n_samples):\n pass",
"def sample_from_prior(self, n_samples):\n\n p0 = self.min + self.rng.rand(n_samples) * (self.max - self.min)\n return p0[:, np.newaxis]",
"def sample_from_prior(self, n_samples):\n\n p0 = self.rng.normal(loc=self.mean, scale=self.sig... | [
"0.7954478",
"0.7308122",
"0.7254096",
"0.71898454",
"0.6979248",
"0.6961805",
"0.67606914",
"0.6745526",
"0.6690407",
"0.6622515",
"0.6562736",
"0.65446556",
"0.65446556",
"0.6425273",
"0.6419413",
"0.6395372",
"0.63613814",
"0.6323354",
"0.63091654",
"0.6305061",
"0.6287302... | 0.7128596 | 4 |
Computes the gradient of the prior with respect to theta. | def gradient(self, theta):
a = -(6 * self.scale ** 2)
b = 3 * self.scale ** 2 + np.exp(2 * theta)
b *= np.log(3 * self.scale ** 2 * np.exp(-2 * theta) + 1)
return a / b | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def gradient(self, theta):\n pass",
"def gradient(self, theta):\n pass",
"def gradient(self, theta):\n return (1 / (self.sigma * np.sqrt(2 * np.pi))) * (\n -theta / (self.sigma ** 2) * np.exp(-(theta ** 2) / (2 * self.sigma ** 2))\n )",
"def gradient(theta, X, y, learni... | [
"0.8320425",
"0.8320425",
"0.8256829",
"0.794873",
"0.7868711",
"0.7830685",
"0.78282064",
"0.77438694",
"0.77200764",
"0.77105415",
"0.76834327",
"0.7621433",
"0.7562439",
"0.72585183",
"0.7218782",
"0.7218774",
"0.721298",
"0.7015742",
"0.7006995",
"0.69971156",
"0.6987514"... | 0.80552995 | 3 |
Returns N samples from the prior. | def sample_from_prior(self, n_samples):
p0 = self.rng.lognormal(mean=self.mean, sigma=self.sigma, size=n_samples)
return p0[:, np.newaxis] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def sample_from_prior(self, n_samples):\n pass",
"def sample_from_prior(self, n_samples):\n\n p0 = self.min + self.rng.rand(n_samples) * (self.max - self.min)\n return p0[:, np.newaxis]",
"def sample_from_prior(self, n_samples):\n\n p0 = self.rng.normal(loc=self.mean, scale=self.sig... | [
"0.7954478",
"0.7308122",
"0.7254096",
"0.7128596",
"0.6979248",
"0.6961805",
"0.67606914",
"0.6745526",
"0.6690407",
"0.6622515",
"0.6562736",
"0.65446556",
"0.65446556",
"0.6425273",
"0.6419413",
"0.6395372",
"0.63613814",
"0.6323354",
"0.63091654",
"0.6305061",
"0.6287302"... | 0.71898454 | 3 |
Computes the gradient of the prior with respect to theta. | def gradient(self, theta):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def gradient(self, theta):\n return (1 / (self.sigma * np.sqrt(2 * np.pi))) * (\n -theta / (self.sigma ** 2) * np.exp(-(theta ** 2) / (2 * self.sigma ** 2))\n )",
"def gradient(self, theta):\n a = -(6 * self.scale ** 2)\n b = 3 * self.scale ** 2 + np.exp(2 * theta)\n ... | [
"0.8256445",
"0.8055304",
"0.7947039",
"0.7867961",
"0.78287417",
"0.78280646",
"0.7741965",
"0.77182055",
"0.77098596",
"0.76828635",
"0.76208895",
"0.75614756",
"0.72576725",
"0.72168595",
"0.7216574",
"0.7211889",
"0.70136374",
"0.70051277",
"0.6994997",
"0.69862175",
"0.6... | 0.83195007 | 1 |
Returns N samples from the prior. | def sample_from_prior(self, n_samples):
p0 = self.rng.normal(loc=self.mean, scale=self.sigma, size=n_samples)
return p0[:, np.newaxis] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def sample_from_prior(self, n_samples):\n pass",
"def sample_from_prior(self, n_samples):\n\n p0 = self.min + self.rng.rand(n_samples) * (self.max - self.min)\n return p0[:, np.newaxis]",
"def sample_from_prior(self, n_samples):\n\n p0 = self.rng.lognormal(mean=self.mean, sigma=self... | [
"0.7954478",
"0.7308122",
"0.71898454",
"0.7128596",
"0.6979248",
"0.6961805",
"0.67606914",
"0.6745526",
"0.6690407",
"0.6622515",
"0.6562736",
"0.65446556",
"0.65446556",
"0.6425273",
"0.6419413",
"0.6395372",
"0.63613814",
"0.6323354",
"0.63091654",
"0.6305061",
"0.6287302... | 0.7254096 | 2 |
Computes the gradient of the prior with respect to theta. | def gradient(self, theta):
return (1 / (self.sigma * np.sqrt(2 * np.pi))) * (
-theta / (self.sigma ** 2) * np.exp(-(theta ** 2) / (2 * self.sigma ** 2))
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def gradient(self, theta):\n pass",
"def gradient(self, theta):\n pass",
"def gradient(self, theta):\n a = -(6 * self.scale ** 2)\n b = 3 * self.scale ** 2 + np.exp(2 * theta)\n b *= np.log(3 * self.scale ** 2 * np.exp(-2 * theta) + 1)\n return a / b",
"def gradient(... | [
"0.8320425",
"0.8320425",
"0.80552995",
"0.794873",
"0.7868711",
"0.7830685",
"0.78282064",
"0.77438694",
"0.77200764",
"0.77105415",
"0.76834327",
"0.7621433",
"0.7562439",
"0.72585183",
"0.7218782",
"0.7218774",
"0.721298",
"0.7015742",
"0.7006995",
"0.69971156",
"0.6987514... | 0.8256829 | 2 |
returns the best possible move | def alpha_beta_search(self, maxDepth, board, player, best_move):
"""j refers to the list where j[0] = score and j[1] = move"""
start = time()
#playerPiece = core.BLACK
#if player == "BLACK":
#print("hi")
#playerPiece = core.WHITE
self.max_value(boar... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def find_best_move(state: GameState) -> None:",
"def best_move(self):\n if self._move is not None:\n return self._move\n else:\n return self.pass_move",
"def get_optimal_move(self):\n # create the root state\n root = State(self.current_board, True, self.__machi... | [
"0.8980694",
"0.85602146",
"0.8383858",
"0.8242038",
"0.8026913",
"0.7937195",
"0.79176444",
"0.7797753",
"0.77027047",
"0.7662692",
"0.7647024",
"0.7636651",
"0.76326895",
"0.7620526",
"0.75877464",
"0.75660557",
"0.7504784",
"0.7500295",
"0.7482623",
"0.7458627",
"0.745814"... | 0.0 | -1 |
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